diff --git a/.gitattributes b/.gitattributes
index 95b5adf40fb870300095291b7e0cca11b782b31f..e603e35554ab59ef965d0fb30fb1a3fd9fa2e7a3 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1,3 +1,4 @@
 /web/assets/** linguist-generated
 /web/** linguist-vendored
 comfy_api_nodes/apis/__init__.py linguist-generated
+comfy/text_encoders/t5_pile_tokenizer/tokenizer.model filter=lfs diff=lfs merge=lfs -text
diff --git a/comfy/text_encoders/sa_t5.py b/comfy/text_encoders/sa_t5.py
new file mode 100644
index 0000000000000000000000000000000000000000..f81ca341dcf6d96dc27793a0567e61b0d11db994
--- /dev/null
+++ b/comfy/text_encoders/sa_t5.py
@@ -0,0 +1,22 @@
+from comfy import sd1_clip
+from transformers import T5TokenizerFast
+import comfy.text_encoders.t5
+import os
+
+class T5BaseModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+
+class T5BaseTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data)
+
+class SAT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5base", tokenizer=T5BaseTokenizer)
+
+class SAT5Model(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+        super().__init__(device=device, dtype=dtype, model_options=model_options, name="t5base", clip_model=T5BaseModel, **kwargs)
diff --git a/comfy/text_encoders/sam3_clip.py b/comfy/text_encoders/sam3_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..48926187de2fc77341760986acdb2cb6fa6e9c18
--- /dev/null
+++ b/comfy/text_encoders/sam3_clip.py
@@ -0,0 +1,97 @@
+import re
+from comfy import sd1_clip
+
+SAM3_CLIP_CONFIG = {
+    "architectures": ["CLIPTextModel"],
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "max_position_embeddings": 32,
+    "projection_dim": 512,
+    "vocab_size": 49408,
+    "layer_norm_eps": 1e-5,
+    "eos_token_id": 49407,
+}
+
+
+class SAM3ClipModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, max_length=32, layer="last", textmodel_json_config=SAM3_CLIP_CONFIG, special_tokens={"start": 49406, "end": 49407, "pad": 0}, return_projected_pooled=False, return_attention_masks=True, enable_attention_masks=True, model_options=model_options)
+
+
+class SAM3Tokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(max_length=32, pad_with_end=False, pad_token=0, embedding_directory=embedding_directory, embedding_size=1024, embedding_key="sam3_clip", tokenizer_data=tokenizer_data)
+        self.disable_weights = True
+
+
+def _parse_prompts(text):
+    """Split comma-separated prompts with optional :N max detections per category"""
+    text = text.replace("(", "").replace(")", "")
+    parts = [p.strip() for p in text.split(",") if p.strip()]
+    result = []
+    for part in parts:
+        m = re.match(r'^(.+?)\s*:\s*([\d.]+)\s*$', part)
+        if m:
+            text_part = m.group(1).strip()
+            val = m.group(2)
+            max_det = max(1, round(float(val)))
+            result.append((text_part, max_det))
+        else:
+            result.append((part, 1))
+    return result
+
+
+class SAM3TokenizerWrapper(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="l", tokenizer=SAM3Tokenizer, name="sam3_clip")
+
+    def tokenize_with_weights(self, text: str, return_word_ids=False, **kwargs):
+        parsed = _parse_prompts(text)
+        if len(parsed) <= 1 and (not parsed or parsed[0][1] == 1):
+            return super().tokenize_with_weights(text, return_word_ids, **kwargs)
+        # Tokenize each prompt part separately, store per-part batches and metadata
+        inner = getattr(self, self.clip)
+        per_prompt = []
+        for prompt_text, max_det in parsed:
+            batches = inner.tokenize_with_weights(prompt_text, return_word_ids, **kwargs)
+            per_prompt.append((batches, max_det))
+        # Main output uses first prompt's tokens (for compatibility)
+        out = {self.clip_name: per_prompt[0][0], "sam3_per_prompt": per_prompt}
+        return out
+
+
+class SAM3ClipModelWrapper(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+        super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name="l", clip_model=SAM3ClipModel, name="sam3_clip")
+
+    def encode_token_weights(self, token_weight_pairs):
+        per_prompt = token_weight_pairs.pop("sam3_per_prompt", None)
+        if per_prompt is None:
+            return super().encode_token_weights(token_weight_pairs)
+
+        # Encode each prompt separately, pack into extra dict
+        inner = getattr(self, self.clip)
+        multi_cond = []
+        first_pooled = None
+        for batches, max_det in per_prompt:
+            out = inner.encode_token_weights(batches)
+            cond, pooled = out[0], out[1]
+            extra = out[2] if len(out) > 2 else {}
+            if first_pooled is None:
+                first_pooled = pooled
+            multi_cond.append({
+                "cond": cond,
+                "attention_mask": extra.get("attention_mask"),
+                "max_detections": max_det,
+            })
+
+        # Return first prompt as main (for non-SAM3 consumers), all prompts in metadata
+        main = multi_cond[0]
+        main_extra = {}
+        if main["attention_mask"] is not None:
+            main_extra["attention_mask"] = main["attention_mask"]
+        main_extra["sam3_multi_cond"] = multi_cond
+        return (main["cond"], first_pooled, main_extra)
diff --git a/comfy/text_encoders/sd2_clip.py b/comfy/text_encoders/sd2_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e491965e6275c857c282a06954bc8c6833ba562
--- /dev/null
+++ b/comfy/text_encoders/sd2_clip.py
@@ -0,0 +1,23 @@
+from comfy import sd1_clip
+import os
+
+class SD2ClipHModel(sd1_clip.SDClipModel):
+    def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}):
+        if layer == "penultimate":
+            layer="hidden"
+            layer_idx=-2
+
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json")
+        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, return_projected_pooled=True, model_options=model_options)
+
+class SD2ClipHTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024, embedding_key='clip_h', tokenizer_data=tokenizer_data)
+
+class SD2Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="h", tokenizer=SD2ClipHTokenizer)
+
+class SD2ClipModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+        super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name="h", clip_model=SD2ClipHModel, **kwargs)
diff --git a/comfy/text_encoders/sd2_clip_config.json b/comfy/text_encoders/sd2_clip_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..bd31995f443dbabd12ecd4a84d676d8fd6f01e68
--- /dev/null
+++ b/comfy/text_encoders/sd2_clip_config.json
@@ -0,0 +1,23 @@
+{
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 49407,
+  "hidden_act": "gelu",
+  "hidden_size": 1024,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "projection_dim": 1024,
+  "torch_dtype": "float32",
+  "vocab_size": 49408
+}
diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..ceeab18c72a4d01e416c2e5a35e0395a6ccc8260
--- /dev/null
+++ b/comfy/text_encoders/sd3_clip.py
@@ -0,0 +1,167 @@
+from comfy import sd1_clip
+from comfy import sdxl_clip
+from transformers import T5TokenizerFast
+import comfy.text_encoders.t5
+import torch
+import os
+import comfy.model_management
+import logging
+import comfy.utils
+
+class T5XXLModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
+        t5xxl_quantization_metadata = model_options.get("t5xxl_quantization_metadata", None)
+        if t5xxl_quantization_metadata is not None:
+            model_options = model_options.copy()
+            model_options["quantization_metadata"] = t5xxl_quantization_metadata
+
+        model_options = {**model_options, "model_name": "t5xxl"}
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+def t5_xxl_detect(state_dict, prefix=""):
+    out = {}
+    t5_key = "{}encoder.final_layer_norm.weight".format(prefix)
+    if t5_key in state_dict:
+        out["dtype_t5"] = state_dict[t5_key].dtype
+
+    quant = comfy.utils.detect_layer_quantization(state_dict, prefix)
+    if quant is not None:
+        out["t5_quantization_metadata"] = quant
+
+    return out
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77, max_length=99999999):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=max_length, min_length=min_length, tokenizer_data=tokenizer_data)
+
+
+class SD3Tokenizer:
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+        self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+        self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+        out = {}
+        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
+        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return self.clip_g.untokenize(token_weight_pair)
+
+    def state_dict(self):
+        return {}
+
+class SD3ClipModel(torch.nn.Module):
+    def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}):
+        super().__init__()
+        self.dtypes = set()
+        if clip_l:
+            self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options)
+            self.dtypes.add(dtype)
+        else:
+            self.clip_l = None
+
+        if clip_g:
+            self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options)
+            self.dtypes.add(dtype)
+        else:
+            self.clip_g = None
+
+        if t5:
+            dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
+            self.t5_attention_mask = t5_attention_mask
+            self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask)
+            self.dtypes.add(dtype_t5)
+        else:
+            self.t5xxl = None
+
+        logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5))
+
+    def set_clip_options(self, options):
+        if self.clip_l is not None:
+            self.clip_l.set_clip_options(options)
+        if self.clip_g is not None:
+            self.clip_g.set_clip_options(options)
+        if self.t5xxl is not None:
+            self.t5xxl.set_clip_options(options)
+
+    def reset_clip_options(self):
+        if self.clip_l is not None:
+            self.clip_l.reset_clip_options()
+        if self.clip_g is not None:
+            self.clip_g.reset_clip_options()
+        if self.t5xxl is not None:
+            self.t5xxl.reset_clip_options()
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs_l = token_weight_pairs["l"]
+        token_weight_pairs_g = token_weight_pairs["g"]
+        token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
+        lg_out = None
+        pooled = None
+        out = None
+        extra = {}
+
+        if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
+            if self.clip_l is not None:
+                lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+            else:
+                l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())
+
+            if self.clip_g is not None:
+                g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
+                if lg_out is not None:
+                    cut_to = min(lg_out.shape[1], g_out.shape[1])
+                    lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1)
+                else:
+                    lg_out = torch.nn.functional.pad(g_out, (768, 0))
+            else:
+                g_out = None
+                g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
+
+            if lg_out is not None:
+                lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
+                out = lg_out
+            pooled = torch.cat((l_pooled, g_pooled), dim=-1)
+
+        if self.t5xxl is not None:
+            t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+            t5_out, t5_pooled = t5_output[:2]
+            if self.t5_attention_mask:
+                extra["attention_mask"] = t5_output[2]["attention_mask"]
+
+            if lg_out is not None:
+                out = torch.cat([lg_out, t5_out], dim=-2)
+            else:
+                out = t5_out
+
+        if out is None:
+            out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device())
+
+        if pooled is None:
+            pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
+
+        return out, pooled, extra
+
+    def load_sd(self, sd):
+        if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+            return self.clip_g.load_sd(sd)
+        elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+            return self.clip_l.load_sd(sd)
+        else:
+            return self.t5xxl.load_sd(sd)
+
+def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_quantization_metadata=None, t5_attention_mask=False):
+    class SD3ClipModel_(SD3ClipModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if t5_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
+            super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options)
+    return SD3ClipModel_
diff --git a/comfy/text_encoders/sensenova.py b/comfy/text_encoders/sensenova.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a94310cefce2e8738c62cebd2e7f5fa5068db5a
--- /dev/null
+++ b/comfy/text_encoders/sensenova.py
@@ -0,0 +1,149 @@
+"""Tokenizer-only conditioning for SenseNova U1.5.
+
+The language model is part of the diffusion checkpoint, so CLIP only needs to
+produce token ids. SenseNova extends the Qwen vocabulary with image-control
+tokens; their order is significant because the checkpoint embeds them by id.
+"""
+
+import os
+
+import torch
+from transformers import Qwen2Tokenizer
+
+from comfy import sd1_clip
+
+
+SYSTEM_MESSAGE = (
+    "You are an image generation and editing assistant that accurately understands and executes "
+    "user intent.\n\nYou support two modes:\n\n1. Think Mode:\nIf the task requires reasoning, you "
+    "MUST start with a <think></think> block. Put all reasoning inside the block using plain text. "
+    "DO NOT include any image tags. Keep it reasonable and directly useful for producing the final "
+    "image.\n\n2. Non-Think Mode:\nIf no reasoning is needed, directly produce the final image.\n\n"
+    "Task Types:\n\nA. Text-to-Image Generation:\n"
+    "- Generate a high-quality image based on the user's description.\n"
+    "- Ensure visual clarity, semantic consistency, and completeness.\n"
+    "- DO NOT introduce elements that contradict or override the user's intent.\n\n"
+    "B. Image Editing:\n"
+    "- Use the provided image(s) as input or reference for modification or transformation.\n"
+    "- The result can be an edited image or a new image based on the reference(s).\n"
+    "- Preserve all unspecified attributes unless explicitly changed.\n\n"
+    "General Rules:\n"
+    "- For any visible text in the image, follow the language specified for the rendered text in "
+    "the user's description, not the language of the prompt. If no language is specified, use the "
+    "user's input language."
+)
+
+
+def build_generation_prompt(text):
+    return (
+        f"<|im_start|>system\n{SYSTEM_MESSAGE}<|im_end|>\n"
+        f"<|im_start|>user\n{text}<|im_end|>\n"
+        "<|im_start|>assistant\n<think>\n\n</think>\n\n<img>"
+    )
+
+
+def build_unconditional_prompt():
+    return "<|im_start|>user\n<|im_end|>\n<|im_start|>assistant\n<img>"
+
+
+class SenseNovaQwen2Tokenizer:
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        tokenizer = Qwen2Tokenizer.from_pretrained(*args, **kwargs)
+        existing_special_tokens = [
+            token
+            for _, token in sorted(tokenizer.added_tokens_decoder.items())
+            if token.special
+        ]
+        extra_tokens = [
+            "<IMG_CONTEXT>",
+            "<img>",
+            "</img>",
+            "<quad>",
+            "</quad>",
+            "<ref>",
+            "</ref>",
+            "<box>",
+            "</box>",
+            "<|action_start|>",
+            "<|action_end|>",
+            "<|plugin|>",
+            "<|interpreter|>",
+        ]
+        extra_tokens.extend(f"<FAKE_PAD_{index}>" for index in range(254))
+        tokenizer.add_special_tokens(
+            {"additional_special_tokens": existing_special_tokens + extra_tokens}
+        )
+        return tokenizer
+
+
+class SenseNovaQwenTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(
+            os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer"
+        )
+        super().__init__(
+            tokenizer_path,
+            pad_with_end=False,
+            embedding_size=4096,
+            embedding_key="sensenova_u15",
+            tokenizer_class=SenseNovaQwen2Tokenizer,
+            has_start_token=False,
+            has_end_token=False,
+            pad_to_max_length=False,
+            max_length=99999999,
+            min_length=1,
+            pad_token=151643,
+            tokenizer_data=tokenizer_data,
+        )
+
+
+class SenseNovaTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(
+            embedding_directory=embedding_directory,
+            tokenizer_data=tokenizer_data,
+            name="sensenova_u15",
+            tokenizer=SenseNovaQwenTokenizer,
+        )
+
+    def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
+        prompt = build_generation_prompt(text) if text else build_unconditional_prompt()
+        tokens = super().tokenize_with_weights(
+            prompt,
+            return_word_ids=return_word_ids,
+            disable_weights=True,
+            **kwargs,
+        )
+        values = tokens["sensenova_u15"][0]
+        values = [value for value in values if int(value[0]) != 151643]
+        return {"sensenova_u15": [values]}
+
+
+class SenseNovaTextEncoder(torch.nn.Module):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__()
+        self.dtypes = {torch.float32}
+        self.disable_offload = True
+        self.device = torch.device("cpu") if device is None else torch.device(device)
+
+    def encode_token_weights(self, token_weight_pairs):
+        pairs = token_weight_pairs["sensenova_u15"][0]
+        input_ids = torch.tensor([[int(value[0]) for value in pairs]], dtype=torch.long)
+        return (
+            input_ids.unsqueeze(-1).to(torch.float32),
+            None,
+            {"text_input_ids": input_ids},
+        )
+
+    def load_sd(self, sd):
+        return []
+
+    def get_sd(self):
+        return {}
+
+    def reset_clip_options(self):
+        pass
+
+    def set_clip_options(self, options):
+        pass
diff --git a/comfy/text_encoders/spiece_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a544095592151e7a8d3efdd4788c86a325003fa
--- /dev/null
+++ b/comfy/text_encoders/spiece_tokenizer.py
@@ -0,0 +1,59 @@
+import torch
+import os
+
+class SPieceTokenizer:
+    @staticmethod
+    def from_pretrained(path, **kwargs):
+        return SPieceTokenizer(path, **kwargs)
+
+    def __init__(self, tokenizer_path, add_bos=False, add_eos=True, special_tokens=None):
+        self.add_bos = add_bos
+        self.add_eos = add_eos
+        self.special_tokens = special_tokens
+        import sentencepiece
+        if torch.is_tensor(tokenizer_path):
+            tokenizer_path = tokenizer_path.numpy().tobytes()
+
+        if isinstance(tokenizer_path, bytes):
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
+        else:
+            if not os.path.isfile(tokenizer_path):
+                raise ValueError("invalid tokenizer")
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
+
+    def get_vocab(self):
+        out = {}
+        for i in range(self.tokenizer.get_piece_size()):
+            out[self.tokenizer.id_to_piece(i)] = i
+        return out
+
+    def __call__(self, string):
+        if self.special_tokens is not None:
+            import re
+            special_tokens_pattern = '|'.join(re.escape(token) for token in self.special_tokens.keys())
+            if special_tokens_pattern and re.search(special_tokens_pattern, string):
+                parts = re.split(f'({special_tokens_pattern})', string)
+                result = []
+                for part in parts:
+                    if not part:
+                        continue
+                    if part in self.special_tokens:
+                        result.append(self.special_tokens[part])
+                    else:
+                        encoded = self.tokenizer.encode(part, add_bos=False, add_eos=False)
+                        result.extend(encoded)
+                return {"input_ids": result}
+
+        out = self.tokenizer.encode(string)
+        return {"input_ids": out}
+
+    def decode(self, token_ids, skip_special_tokens=False):
+
+        if skip_special_tokens and self.special_tokens:
+            special_token_ids = set(self.special_tokens.values())
+            token_ids = [tid for tid in token_ids if tid not in special_token_ids]
+
+        return self.tokenizer.decode(token_ids)
+
+    def serialize_model(self):
+        return torch.ByteTensor(list(self.tokenizer.serialized_model_proto()))
diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6c24653d5a65769d9532f8084cc2f371258bfb2
--- /dev/null
+++ b/comfy/text_encoders/t5.py
@@ -0,0 +1,249 @@
+import torch
+import math
+from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
+
+class T5LayerNorm(torch.nn.Module):
+    def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
+        self.variance_epsilon = eps
+
+    def forward(self, x):
+        variance = x.pow(2).mean(-1, keepdim=True)
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+        return comfy.ops.cast_to_input(self.weight, x) * x
+
+activations = {
+    "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
+    "relu": torch.nn.functional.relu,
+}
+
+class T5DenseActDense(torch.nn.Module):
+    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+        super().__init__()
+        self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+        self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = activations[ff_activation]
+
+    def forward(self, x):
+        x = self.act(self.wi(x))
+        # x = self.dropout(x)
+        x = self.wo(x)
+        return x
+
+class T5DenseGatedActDense(torch.nn.Module):
+    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+        super().__init__()
+        self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+        self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+        self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = activations[ff_activation]
+
+    def forward(self, x):
+        hidden_gelu = self.act(self.wi_0(x))
+        hidden_linear = self.wi_1(x)
+        x = hidden_gelu * hidden_linear
+        # x = self.dropout(x)
+        x = self.wo(x)
+        return x
+
+class T5LayerFF(torch.nn.Module):
+    def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations):
+        super().__init__()
+        if gated_act:
+            self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
+        else:
+            self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
+
+        self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x):
+        forwarded_states = self.layer_norm(x)
+        forwarded_states = self.DenseReluDense(forwarded_states)
+        # x = x + self.dropout(forwarded_states)
+        x += forwarded_states
+        return x
+
+class T5Attention(torch.nn.Module):
+    def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations):
+        super().__init__()
+
+        # Mesh TensorFlow initialization to avoid scaling before softmax
+        self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
+        self.num_heads = num_heads
+
+        self.relative_attention_bias = None
+        if relative_attention_bias:
+            self.relative_attention_num_buckets = 32
+            self.relative_attention_max_distance = 128
+            self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype)
+
+    @staticmethod
+    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+        """
+        Adapted from Mesh Tensorflow:
+        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+        Translate relative position to a bucket number for relative attention. The relative position is defined as
+        memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+        position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+        small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+        positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+        This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+        Args:
+            relative_position: an int32 Tensor
+            bidirectional: a boolean - whether the attention is bidirectional
+            num_buckets: an integer
+            max_distance: an integer
+
+        Returns:
+            a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+        """
+        relative_buckets = 0
+        if bidirectional:
+            num_buckets //= 2
+            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+            relative_position = torch.abs(relative_position)
+        else:
+            relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+        # now relative_position is in the range [0, inf)
+
+        # half of the buckets are for exact increments in positions
+        max_exact = num_buckets // 2
+        is_small = relative_position < max_exact
+
+        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+        relative_position_if_large = max_exact + (
+            torch.log(relative_position.float() / max_exact)
+            / math.log(max_distance / max_exact)
+            * (num_buckets - max_exact)
+        ).to(torch.long)
+        relative_position_if_large = torch.min(
+            relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
+        )
+
+        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+        return relative_buckets
+
+    def compute_bias(self, query_length, key_length, device, dtype):
+        """Compute binned relative position bias"""
+        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
+        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
+        relative_position = memory_position - context_position  # shape (query_length, key_length)
+        relative_position_bucket = self._relative_position_bucket(
+            relative_position,  # shape (query_length, key_length)
+            bidirectional=True,
+            num_buckets=self.relative_attention_num_buckets,
+            max_distance=self.relative_attention_max_distance,
+        )
+        values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype)  # shape (query_length, key_length, num_heads)
+        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
+        return values.contiguous()
+
+    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+        q = self.q(x)
+        k = self.k(x)
+        v = self.v(x)
+        if self.relative_attention_bias is not None:
+            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype)
+
+        if past_bias is not None:
+            if mask is not None:
+                mask = mask + past_bias
+            else:
+                mask = past_bias
+
+        out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)
+        return self.o(out), past_bias
+
+class T5LayerSelfAttention(torch.nn.Module):
+    def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations):
+        super().__init__()
+        self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations)
+        self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+        output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention)
+        # x = x + self.dropout(attention_output)
+        x += output
+        return x, past_bias
+
+class T5Block(torch.nn.Module):
+    def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations):
+        super().__init__()
+        self.layer = torch.nn.ModuleList()
+        self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))
+        self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations))
+
+    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+        x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)
+        x = self.layer[-1](x)
+        return x, past_bias
+
+class T5Stack(torch.nn.Module):
+    def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations):
+        super().__init__()
+
+        self.block = torch.nn.ModuleList(
+            [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
+        )
+        self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]):
+        mask = None
+        if attention_mask is not None:
+            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+
+        intermediate = None
+        optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)
+        past_bias = None
+
+        if intermediate_output is not None:
+            if intermediate_output < 0:
+                intermediate_output = len(self.block) + intermediate_output
+
+        for i, l in enumerate(self.block):
+            x, past_bias = l(x, mask, past_bias, optimized_attention)
+            if i == intermediate_output:
+                intermediate = x.clone()
+        x = self.final_layer_norm(x)
+        if intermediate is not None and final_layer_norm_intermediate:
+            intermediate = self.final_layer_norm(intermediate)
+        return x, intermediate
+
+class T5(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        self.num_layers = config_dict["num_layers"]
+        model_dim = config_dict["d_model"]
+        inner_dim = config_dict["d_kv"] * config_dict["num_heads"]
+
+        self.encoder = T5Stack(self.num_layers, model_dim, inner_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations)
+        self.dtype = dtype
+        self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype)
+
+    def get_input_embeddings(self):
+        return self.shared
+
+    def set_input_embeddings(self, embeddings):
+        self.shared = embeddings
+
+    def forward(self, input_ids, attention_mask, embeds=None, num_tokens=None, **kwargs):
+        if input_ids is None:
+            x = embeds
+        else:
+            x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32))
+        if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]:
+            x = torch.nan_to_num(x) #Fix for fp8 T5 base
+        return self.encoder(x, attention_mask=attention_mask, **kwargs)
diff --git a/comfy/text_encoders/t5_config_base.json b/comfy/text_encoders/t5_config_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..5f280e958b5aafd921f3f9f9d2c6e0e01852dd63
--- /dev/null
+++ b/comfy/text_encoders/t5_config_base.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}
diff --git a/comfy/text_encoders/t5_config_xxl.json b/comfy/text_encoders/t5_config_xxl.json
new file mode 100644
index 0000000000000000000000000000000000000000..cfdbe43f0300a3f0a3308f56139686088e45ebbf
--- /dev/null
+++ b/comfy/text_encoders/t5_config_xxl.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "gelu_pytorch_tanh",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}
diff --git a/comfy/text_encoders/t5_old_config_xxl.json b/comfy/text_encoders/t5_old_config_xxl.json
new file mode 100644
index 0000000000000000000000000000000000000000..e65979ea30bcd2244a7f493c16a54637786f7503
--- /dev/null
+++ b/comfy/text_encoders/t5_old_config_xxl.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 65536,
+  "d_kv": 128,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 128,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}
diff --git a/comfy/text_encoders/t5_pile_config_xl.json b/comfy/text_encoders/t5_pile_config_xl.json
new file mode 100644
index 0000000000000000000000000000000000000000..68c6d9606668644099000683b6e181cdcec24044
--- /dev/null
+++ b/comfy/text_encoders/t5_pile_config_xl.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 5120,
+  "d_kv": 64,
+  "d_model": 2048,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 2,
+  "dense_act_fn": "gelu_pytorch_tanh",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "umt5",
+  "num_decoder_layers": 24,
+  "num_heads": 32,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}
diff --git a/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model b/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model
new file mode 100644
index 0000000000000000000000000000000000000000..6c00c742ce03c627d6cd5b795984876fa49fa899
--- /dev/null
+++ b/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723
diff --git a/comfy/text_encoders/t5_tokenizer/special_tokens_map.json b/comfy/text_encoders/t5_tokenizer/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..fa0964ac27925e153d40d8afdc49f5fadb9b0a91
--- /dev/null
+++ b/comfy/text_encoders/t5_tokenizer/special_tokens_map.json
@@ -0,0 +1,125 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
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diff --git a/comfy/text_encoders/t5_tokenizer/tokenizer.json b/comfy/text_encoders/t5_tokenizer/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..7e7f15f754477bdca14793cb13ffe9dd3a2c155e
--- /dev/null
+++ b/comfy/text_encoders/t5_tokenizer/tokenizer.json
@@ -0,0 +1,129428 @@
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+      "special": true
+    },
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+      "content": "<extra_id_19>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
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+      "content": "<extra_id_18>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32082,
+      "content": "<extra_id_17>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32083,
+      "content": "<extra_id_16>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
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+      "content": "<extra_id_15>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
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+      "content": "<extra_id_14>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
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+      "content": "<extra_id_13>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32087,
+      "content": "<extra_id_12>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
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+      "content": "<extra_id_11>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32089,
+      "content": "<extra_id_10>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32090,
+      "content": "<extra_id_9>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32091,
+      "content": "<extra_id_8>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32092,
+      "content": "<extra_id_7>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32093,
+      "content": "<extra_id_6>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32094,
+      "content": "<extra_id_5>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32095,
+      "content": "<extra_id_4>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32096,
+      "content": "<extra_id_3>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32097,
+      "content": "<extra_id_2>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32098,
+      "content": "<extra_id_1>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32099,
+      "content": "<extra_id_0>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  ],
+  "normalizer": {
+    "type": "Sequence",
+    "normalizers": [
+      {
+        "type": "Precompiled",
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+      [
+        "<extra_id_0>",
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+      ]
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+  }
+}
\ No newline at end of file
diff --git a/comfy/text_encoders/t5_tokenizer/tokenizer_config.json b/comfy/text_encoders/t5_tokenizer/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..381e0ea9996100a77aedcefda60bd4970795cb15
--- /dev/null
+++ b/comfy/text_encoders/t5_tokenizer/tokenizer_config.json
@@ -0,0 +1,939 @@
+{
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32098": {
+      "content": "<extra_id_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32099": {
+      "content": "<extra_id_0>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "legacy": false,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}
diff --git a/comfy/text_encoders/umt5_config_base.json b/comfy/text_encoders/umt5_config_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..e5d1a4e2bb39b111d3ce0574db3b332a93cec6df
--- /dev/null
+++ b/comfy/text_encoders/umt5_config_base.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "gelu_pytorch_tanh",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "umt5",
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 256384
+}
diff --git a/comfy/text_encoders/umt5_config_xxl.json b/comfy/text_encoders/umt5_config_xxl.json
new file mode 100644
index 0000000000000000000000000000000000000000..9bf9e9fa1ed8eb63f6db6fce819319a53e918ab3
--- /dev/null
+++ b/comfy/text_encoders/umt5_config_xxl.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "gelu_pytorch_tanh",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "umt5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 256384
+}
diff --git a/comfy/text_encoders/wan.py b/comfy/text_encoders/wan.py
new file mode 100644
index 0000000000000000000000000000000000000000..441f55ba3b19e5b46cd3c6b240734d1e88f174a5
--- /dev/null
+++ b/comfy/text_encoders/wan.py
@@ -0,0 +1,37 @@
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+import comfy.text_encoders.t5
+import os
+
+class UMT5XXlModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_xxl.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options)
+
+class UMT5XXlTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=4096, embedding_key='umt5xxl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=0, tokenizer_data=tokenizer_data)
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
+
+
+class WanT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="umt5xxl", tokenizer=UMT5XXlTokenizer)
+
+class WanT5Model(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+        super().__init__(device=device, dtype=dtype, model_options=model_options, name="umt5xxl", clip_model=UMT5XXlModel, **kwargs)
+
+def te(dtype_t5=None, t5_quantization_metadata=None):
+    class WanTEModel(WanT5Model):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if t5_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = t5_quantization_metadata
+            if dtype_t5 is not None:
+                dtype = dtype_t5
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return WanTEModel
diff --git a/comfy/text_encoders/z_image.py b/comfy/text_encoders/z_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..18ce0e383da9fcce6e9620438d803e5ffcd08216
--- /dev/null
+++ b/comfy/text_encoders/z_image.py
@@ -0,0 +1,46 @@
+from transformers import Qwen2Tokenizer
+import comfy.text_encoders.llama
+from comfy import sd1_clip
+import os
+
+class Qwen3Tokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=2560, embedding_key='qwen3_4b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
+
+
+class ZImageTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_4b", tokenizer=Qwen3Tokenizer)
+        self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
+        if llama_template is None:
+            llama_text = self.llama_template.format(text)
+        else:
+            llama_text = llama_template.format(text)
+
+        tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
+        return tokens
+
+
+class Qwen3_4BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class ZImageTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="qwen3_4b", clip_model=Qwen3_4BModel, model_options=model_options)
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class ZImageTEModel_(ZImageTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return ZImageTEModel_
diff --git a/comfy/utils.py b/comfy/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..38686a57e6c13af543ca58a5687d1c7232f0f0a6
--- /dev/null
+++ b/comfy/utils.py
@@ -0,0 +1,1535 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Comfy
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+
+import torch
+import math
+import struct
+import ctypes
+import os
+import comfy.memory_management
+import safetensors.torch
+import numpy as np
+from PIL import Image
+import logging
+import itertools
+from torch.nn.functional import interpolate
+from tqdm.auto import trange
+from einops import rearrange
+from comfy.cli_args import args
+import json
+import time
+import threading
+import warnings
+
+MMAP_TORCH_FILES = args.mmap_torch_files
+DISABLE_MMAP = args.disable_mmap
+
+
+if True:  # ckpt/pt file whitelist for safe loading of old sd files
+    class ModelCheckpoint:
+        pass
+    ModelCheckpoint.__module__ = "pytorch_lightning.callbacks.model_checkpoint"
+
+    def scalar(*args, **kwargs):
+        return None
+    scalar.__module__ = "numpy.core.multiarray"
+
+    from numpy import dtype
+    from numpy.dtypes import Float64DType
+
+    def encode(*args, **kwargs):  # no longer necessary on newer torch
+        return None
+    encode.__module__ = "_codecs"
+
+    torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode])
+    logging.info("Checkpoint files will always be loaded safely.")
+
+
+# Current as of safetensors 0.7.0
+_TYPES = {
+    "F64": torch.float64,
+    "F32": torch.float32,
+    "F16": torch.float16,
+    "BF16": torch.bfloat16,
+    "I64": torch.int64,
+    "I32": torch.int32,
+    "I16": torch.int16,
+    "I8": torch.int8,
+    "U8": torch.uint8,
+    "BOOL": torch.bool,
+    "F8_E4M3": torch.float8_e4m3fn,
+    "F8_E5M2": torch.float8_e5m2,
+    "C64": torch.complex64,
+
+    "U64": torch.uint64,
+    "U32": torch.uint32,
+    "U16": torch.uint16,
+}
+
+_SAFETENSORS_MAX_HEADER_SIZE = 100_000_000
+
+
+def _invalid_safetensors_error(message, ckpt):
+    return ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.".format(message, ckpt))
+
+
+def _incomplete_safetensors_error(message, ckpt):
+    return ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt))
+
+
+def load_safetensors(ckpt):
+    import comfy_aimdo.model_mmap
+
+    file_size = os.path.getsize(ckpt)
+    if file_size < 8:
+        raise _incomplete_safetensors_error("The safetensors header is incomplete.", ckpt)
+
+    file_lock = threading.Lock()
+    model_mmap = comfy_aimdo.model_mmap.ModelMMAP(ckpt)
+    f = model_mmap.get_file_handle()
+    mv = memoryview((ctypes.c_uint8 * file_size).from_address(model_mmap.get()))
+
+    header_size = struct.unpack("<Q", mv[:8])[0]
+    if header_size > _SAFETENSORS_MAX_HEADER_SIZE:
+        raise _invalid_safetensors_error("The safetensors header is too large.", ckpt)
+
+    data_base_offset = 8 + header_size
+    if data_base_offset > file_size:
+        raise _incomplete_safetensors_error("The safetensors header is incomplete.", ckpt)
+
+    try:
+        header = json.loads(mv[8:data_base_offset].tobytes().decode("utf-8"))
+    except (UnicodeDecodeError, json.JSONDecodeError) as e:
+        raise _invalid_safetensors_error(str(e), ckpt) from e
+
+    if not isinstance(header, dict):
+        raise _invalid_safetensors_error("The safetensors header is invalid.", ckpt)
+
+    mv = mv[data_base_offset:]
+    data_size = len(mv)
+
+    sd = {}
+    for name, info in header.items():
+        if name == "__metadata__":
+            continue
+
+        start, end = info["data_offsets"]
+        dtype = _TYPES[info["dtype"]]
+        if start < 0 or end < start:
+            raise _invalid_safetensors_error("Tensor '{}' has invalid data offsets.".format(name), ckpt)
+        if end > data_size:
+            raise _incomplete_safetensors_error("Tensor '{}' extends past the end of the file.".format(name), ckpt)
+        if math.prod(info["shape"]) * dtype.itemsize != end - start:
+            raise _invalid_safetensors_error("Tensor '{}' does not match its declared shape and dtype.".format(name), ckpt)
+
+        if start == end:
+            sd[name] = torch.empty(info["shape"], dtype=dtype)
+        else:
+            with warnings.catch_warnings():
+                #We are working with read-only RAM by design
+                warnings.filterwarnings("ignore", message="The given buffer is not writable")
+                tensor = torch.frombuffer(mv[start:end], dtype=dtype).view(info["shape"])
+                storage = tensor.untyped_storage()
+                setattr(storage,
+                        "_comfy_tensor_file_slice",
+                        comfy.memory_management.TensorFileSlice(f, file_lock, data_base_offset + start, end - start))
+                setattr(storage, "_comfy_tensor_mmap_refs", (model_mmap, mv))
+                sd[name] = tensor
+
+    return sd, header.get("__metadata__", {}),
+
+
+def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
+    if device is None:
+        device = torch.device("cpu")
+    metadata = None
+    if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
+        try:
+            if comfy.memory_management.aimdo_enabled:
+                sd, metadata = load_safetensors(ckpt)
+                if not return_metadata:
+                    metadata = None
+            else:
+                with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f:
+                    sd = {}
+                    for k in f.keys():
+                        tensor = f.get_tensor(k)
+                        if DISABLE_MMAP:  # TODO: Not sure if this is the best way to bypass the mmap issues
+                            tensor = tensor.to(device=device, copy=True)
+                        sd[k] = tensor
+                    if return_metadata:
+                        metadata = f.metadata()
+        except Exception as e:
+            if len(e.args) > 0:
+                message = e.args[0]
+                if "HeaderTooLarge" in message:
+                    raise _invalid_safetensors_error(message, ckpt)
+                if "MetadataIncompleteBuffer" in message:
+                    raise _incomplete_safetensors_error(message, ckpt)
+            raise e
+    else:
+        torch_args = {}
+        if MMAP_TORCH_FILES:
+            torch_args["mmap"] = True
+
+        pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args)
+
+        if "state_dict" in pl_sd:
+            sd = pl_sd["state_dict"]
+        else:
+            if len(pl_sd) == 1:
+                key = list(pl_sd.keys())[0]
+                sd = pl_sd[key]
+                if not isinstance(sd, dict):
+                    sd = pl_sd
+            else:
+                sd = pl_sd
+    return (sd, metadata) if return_metadata else sd
+
+def save_torch_file(sd, ckpt, metadata=None):
+    if metadata is not None:
+        safetensors.torch.save_file(sd, ckpt, metadata=metadata)
+    else:
+        safetensors.torch.save_file(sd, ckpt)
+
+def calculate_parameters(sd, prefix=""):
+    params = 0
+    for k in sd.keys():
+        if k.startswith(prefix):
+            w = sd[k]
+            params += w.nelement()
+    return params
+
+def weight_dtype(sd, prefix=""):
+    dtypes = {}
+    for k in sd.keys():
+        if k.startswith(prefix):
+            w = sd[k]
+            dtypes[w.dtype] = dtypes.get(w.dtype, 0) + w.numel()
+
+    if len(dtypes) == 0:
+        return None
+
+    return max(dtypes, key=dtypes.get)
+
+def state_dict_key_replace(state_dict, keys_to_replace):
+    for x in keys_to_replace:
+        if x in state_dict:
+            state_dict[keys_to_replace[x]] = state_dict.pop(x)
+    return state_dict
+
+def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False):
+    if filter_keys:
+        out = {}
+    else:
+        out = state_dict
+    for rp in replace_prefix:
+        replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys())))
+        for x in replace:
+            w = state_dict.pop(x[0])
+            out[x[1]] = w
+    return out
+
+
+def transformers_convert(sd, prefix_from, prefix_to, number):
+    keys_to_replace = {
+        "{}positional_embedding": "{}embeddings.position_embedding.weight",
+        "{}token_embedding.weight": "{}embeddings.token_embedding.weight",
+        "{}ln_final.weight": "{}final_layer_norm.weight",
+        "{}ln_final.bias": "{}final_layer_norm.bias",
+    }
+
+    for k in keys_to_replace:
+        x = k.format(prefix_from)
+        if x in sd:
+            sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x)
+
+    resblock_to_replace = {
+        "ln_1": "layer_norm1",
+        "ln_2": "layer_norm2",
+        "mlp.c_fc": "mlp.fc1",
+        "mlp.c_proj": "mlp.fc2",
+        "attn.out_proj": "self_attn.out_proj",
+    }
+
+    for resblock in range(number):
+        for x in resblock_to_replace:
+            for y in ["weight", "bias"]:
+                k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y)
+                k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y)
+                if k in sd:
+                    sd[k_to] = sd.pop(k)
+
+        for y in ["weight", "bias"]:
+            k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y)
+            if k_from in sd:
+                weights = sd.pop(k_from)
+                shape_from = weights.shape[0] // 3
+                for x in range(3):
+                    p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
+                    k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y)
+                    sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
+
+    return sd
+
+def clip_text_transformers_convert(sd, prefix_from, prefix_to):
+    sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32)
+
+    tp = "{}text_projection.weight".format(prefix_from)
+    if tp in sd:
+        sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp)
+
+    tp = "{}text_projection".format(prefix_from)
+    if tp in sd:
+        sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous()
+    return sd
+
+
+UNET_MAP_ATTENTIONS = {
+    "proj_in.weight",
+    "proj_in.bias",
+    "proj_out.weight",
+    "proj_out.bias",
+    "norm.weight",
+    "norm.bias",
+}
+
+TRANSFORMER_BLOCKS = {
+    "norm1.weight",
+    "norm1.bias",
+    "norm2.weight",
+    "norm2.bias",
+    "norm3.weight",
+    "norm3.bias",
+    "attn1.to_q.weight",
+    "attn1.to_k.weight",
+    "attn1.to_v.weight",
+    "attn1.to_out.0.weight",
+    "attn1.to_out.0.bias",
+    "attn2.to_q.weight",
+    "attn2.to_k.weight",
+    "attn2.to_v.weight",
+    "attn2.to_out.0.weight",
+    "attn2.to_out.0.bias",
+    "ff.net.0.proj.weight",
+    "ff.net.0.proj.bias",
+    "ff.net.2.weight",
+    "ff.net.2.bias",
+}
+
+UNET_MAP_RESNET = {
+    "in_layers.2.weight": "conv1.weight",
+    "in_layers.2.bias": "conv1.bias",
+    "emb_layers.1.weight": "time_emb_proj.weight",
+    "emb_layers.1.bias": "time_emb_proj.bias",
+    "out_layers.3.weight": "conv2.weight",
+    "out_layers.3.bias": "conv2.bias",
+    "skip_connection.weight": "conv_shortcut.weight",
+    "skip_connection.bias": "conv_shortcut.bias",
+    "in_layers.0.weight": "norm1.weight",
+    "in_layers.0.bias": "norm1.bias",
+    "out_layers.0.weight": "norm2.weight",
+    "out_layers.0.bias": "norm2.bias",
+}
+
+UNET_MAP_BASIC = {
+    ("label_emb.0.0.weight", "class_embedding.linear_1.weight"),
+    ("label_emb.0.0.bias", "class_embedding.linear_1.bias"),
+    ("label_emb.0.2.weight", "class_embedding.linear_2.weight"),
+    ("label_emb.0.2.bias", "class_embedding.linear_2.bias"),
+    ("label_emb.0.0.weight", "add_embedding.linear_1.weight"),
+    ("label_emb.0.0.bias", "add_embedding.linear_1.bias"),
+    ("label_emb.0.2.weight", "add_embedding.linear_2.weight"),
+    ("label_emb.0.2.bias", "add_embedding.linear_2.bias"),
+    ("input_blocks.0.0.weight", "conv_in.weight"),
+    ("input_blocks.0.0.bias", "conv_in.bias"),
+    ("out.0.weight", "conv_norm_out.weight"),
+    ("out.0.bias", "conv_norm_out.bias"),
+    ("out.2.weight", "conv_out.weight"),
+    ("out.2.bias", "conv_out.bias"),
+    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
+    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
+    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
+    ("time_embed.2.bias", "time_embedding.linear_2.bias")
+}
+
+def unet_to_diffusers(unet_config):
+    if "num_res_blocks" not in unet_config:
+        return {}
+    num_res_blocks = unet_config["num_res_blocks"]
+    channel_mult = unet_config["channel_mult"]
+    transformer_depth = unet_config["transformer_depth"][:]
+    transformer_depth_output = unet_config["transformer_depth_output"][:]
+    num_blocks = len(channel_mult)
+
+    transformers_mid = unet_config.get("transformer_depth_middle", None)
+
+    diffusers_unet_map = {}
+    for x in range(num_blocks):
+        n = 1 + (num_res_blocks[x] + 1) * x
+        for i in range(num_res_blocks[x]):
+            for b in UNET_MAP_RESNET:
+                diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b)
+            num_transformers = transformer_depth.pop(0)
+            if num_transformers > 0:
+                for b in UNET_MAP_ATTENTIONS:
+                    diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b)
+                for t in range(num_transformers):
+                    for b in TRANSFORMER_BLOCKS:
+                        diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
+            n += 1
+        for k in ["weight", "bias"]:
+            diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k)
+
+    i = 0
+    for b in UNET_MAP_ATTENTIONS:
+        diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b)
+    for t in range(transformers_mid):
+        for b in TRANSFORMER_BLOCKS:
+            diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b)
+
+    for i, n in enumerate([0, 2]):
+        for b in UNET_MAP_RESNET:
+            diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b)
+
+    num_res_blocks = list(reversed(num_res_blocks))
+    for x in range(num_blocks):
+        n = (num_res_blocks[x] + 1) * x
+        l = num_res_blocks[x] + 1
+        for i in range(l):
+            c = 0
+            for b in UNET_MAP_RESNET:
+                diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b)
+            c += 1
+            num_transformers = transformer_depth_output.pop()
+            if num_transformers > 0:
+                c += 1
+                for b in UNET_MAP_ATTENTIONS:
+                    diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b)
+                for t in range(num_transformers):
+                    for b in TRANSFORMER_BLOCKS:
+                        diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
+            if i == l - 1:
+                for k in ["weight", "bias"]:
+                    diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k)
+            n += 1
+
+    for k in UNET_MAP_BASIC:
+        diffusers_unet_map[k[1]] = k[0]
+
+    return diffusers_unet_map
+
+def swap_scale_shift(weight):
+    shift, scale = weight.chunk(2, dim=0)
+    new_weight = torch.cat([scale, shift], dim=0)
+    return new_weight
+
+MMDIT_MAP_BASIC = {
+    ("context_embedder.bias", "context_embedder.bias"),
+    ("context_embedder.weight", "context_embedder.weight"),
+    ("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
+    ("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
+    ("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
+    ("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
+    ("x_embedder.proj.bias", "pos_embed.proj.bias"),
+    ("x_embedder.proj.weight", "pos_embed.proj.weight"),
+    ("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"),
+    ("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"),
+    ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"),
+    ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"),
+    ("pos_embed", "pos_embed.pos_embed"),
+    ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
+    ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
+    ("final_layer.linear.bias", "proj_out.bias"),
+    ("final_layer.linear.weight", "proj_out.weight"),
+}
+
+MMDIT_MAP_BLOCK = {
+    ("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"),
+    ("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"),
+    ("context_block.attn.proj.bias", "attn.to_add_out.bias"),
+    ("context_block.attn.proj.weight", "attn.to_add_out.weight"),
+    ("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"),
+    ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"),
+    ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"),
+    ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"),
+    ("context_block.attn.ln_q.weight", "attn.norm_added_q.weight"),
+    ("context_block.attn.ln_k.weight", "attn.norm_added_k.weight"),
+    ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"),
+    ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"),
+    ("x_block.attn.proj.bias", "attn.to_out.0.bias"),
+    ("x_block.attn.proj.weight", "attn.to_out.0.weight"),
+    ("x_block.attn.ln_q.weight", "attn.norm_q.weight"),
+    ("x_block.attn.ln_k.weight", "attn.norm_k.weight"),
+    ("x_block.attn2.proj.bias", "attn2.to_out.0.bias"),
+    ("x_block.attn2.proj.weight", "attn2.to_out.0.weight"),
+    ("x_block.attn2.ln_q.weight", "attn2.norm_q.weight"),
+    ("x_block.attn2.ln_k.weight", "attn2.norm_k.weight"),
+    ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"),
+    ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
+    ("x_block.mlp.fc2.bias", "ff.net.2.bias"),
+    ("x_block.mlp.fc2.weight", "ff.net.2.weight"),
+}
+
+def mmdit_to_diffusers(mmdit_config, output_prefix=""):
+    key_map = {}
+
+    depth = mmdit_config.get("depth", 0)
+    num_blocks = mmdit_config.get("num_blocks", depth)
+    for i in range(num_blocks):
+        block_from = "transformer_blocks.{}".format(i)
+        block_to = "{}joint_blocks.{}".format(output_prefix, i)
+
+        offset = depth * 64
+
+        for end in ("weight", "bias"):
+            k = "{}.attn.".format(block_from)
+            qkv = "{}.x_block.attn.qkv.{}".format(block_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
+            qkv = "{}.context_block.attn.qkv.{}".format(block_to, end)
+            key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset))
+            key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset))
+            key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
+            k = "{}.attn2.".format(block_from)
+            qkv = "{}.x_block.attn2.qkv.{}".format(block_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
+        for k in MMDIT_MAP_BLOCK:
+            key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
+
+    map_basic = MMDIT_MAP_BASIC.copy()
+    map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift))
+    map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift))
+
+    for k in map_basic:
+        if len(k) > 2:
+            key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+        else:
+            key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
+
+PIXART_MAP_BASIC = {
+    ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
+    ("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"),
+    ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
+    ("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"),
+    ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
+    ("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
+    ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
+    ("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
+    ("x_embedder.proj.weight", "pos_embed.proj.weight"),
+    ("x_embedder.proj.bias", "pos_embed.proj.bias"),
+    ("y_embedder.y_embedding", "caption_projection.y_embedding"),
+    ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"),
+    ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"),
+    ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"),
+    ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"),
+    ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"),
+    ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"),
+    ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"),
+    ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"),
+    ("t_block.1.weight", "adaln_single.linear.weight"),
+    ("t_block.1.bias", "adaln_single.linear.bias"),
+    ("final_layer.linear.weight", "proj_out.weight"),
+    ("final_layer.linear.bias", "proj_out.bias"),
+    ("final_layer.scale_shift_table", "scale_shift_table"),
+}
+
+PIXART_MAP_BLOCK = {
+    ("scale_shift_table", "scale_shift_table"),
+    ("attn.proj.weight", "attn1.to_out.0.weight"),
+    ("attn.proj.bias", "attn1.to_out.0.bias"),
+    ("mlp.fc1.weight", "ff.net.0.proj.weight"),
+    ("mlp.fc1.bias", "ff.net.0.proj.bias"),
+    ("mlp.fc2.weight", "ff.net.2.weight"),
+    ("mlp.fc2.bias", "ff.net.2.bias"),
+    ("cross_attn.proj.weight" ,"attn2.to_out.0.weight"),
+    ("cross_attn.proj.bias"   ,"attn2.to_out.0.bias"),
+}
+
+def pixart_to_diffusers(mmdit_config, output_prefix=""):
+    key_map = {}
+
+    depth = mmdit_config.get("depth", 0)
+    offset = mmdit_config.get("hidden_size", 1152)
+
+    for i in range(depth):
+        block_from = "transformer_blocks.{}".format(i)
+        block_to = "{}blocks.{}".format(output_prefix, i)
+
+        for end in ("weight", "bias"):
+            s = "{}.attn1.".format(block_from)
+            qkv = "{}.attn.qkv.{}".format(block_to, end)
+            key_map["{}to_q.{}".format(s, end)] = (qkv, (0, 0, offset))
+            key_map["{}to_k.{}".format(s, end)] = (qkv, (0, offset, offset))
+            key_map["{}to_v.{}".format(s, end)] = (qkv, (0, offset * 2, offset))
+
+            s = "{}.attn2.".format(block_from)
+            q = "{}.cross_attn.q_linear.{}".format(block_to, end)
+            kv = "{}.cross_attn.kv_linear.{}".format(block_to, end)
+
+            key_map["{}to_q.{}".format(s, end)] = q
+            key_map["{}to_k.{}".format(s, end)] = (kv, (0, 0, offset))
+            key_map["{}to_v.{}".format(s, end)] = (kv, (0, offset, offset))
+
+        for k in PIXART_MAP_BLOCK:
+            key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
+
+    for k in PIXART_MAP_BASIC:
+        key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
+
+def auraflow_to_diffusers(mmdit_config, output_prefix=""):
+    n_double_layers = mmdit_config.get("n_double_layers", 0)
+    n_layers = mmdit_config.get("n_layers", 0)
+
+    key_map = {}
+    for i in range(n_layers):
+        if i < n_double_layers:
+            index = i
+            prefix_from = "joint_transformer_blocks"
+            prefix_to = "{}double_layers".format(output_prefix)
+            block_map = {
+                            "attn.to_q.weight": "attn.w2q.weight",
+                            "attn.to_k.weight": "attn.w2k.weight",
+                            "attn.to_v.weight": "attn.w2v.weight",
+                            "attn.to_out.0.weight": "attn.w2o.weight",
+                            "attn.add_q_proj.weight": "attn.w1q.weight",
+                            "attn.add_k_proj.weight": "attn.w1k.weight",
+                            "attn.add_v_proj.weight": "attn.w1v.weight",
+                            "attn.to_add_out.weight": "attn.w1o.weight",
+                            "ff.linear_1.weight": "mlpX.c_fc1.weight",
+                            "ff.linear_2.weight": "mlpX.c_fc2.weight",
+                            "ff.out_projection.weight": "mlpX.c_proj.weight",
+                            "ff_context.linear_1.weight": "mlpC.c_fc1.weight",
+                            "ff_context.linear_2.weight": "mlpC.c_fc2.weight",
+                            "ff_context.out_projection.weight": "mlpC.c_proj.weight",
+                            "norm1.linear.weight": "modX.1.weight",
+                            "norm1_context.linear.weight": "modC.1.weight",
+                        }
+        else:
+            index = i - n_double_layers
+            prefix_from = "single_transformer_blocks"
+            prefix_to = "{}single_layers".format(output_prefix)
+
+            block_map = {
+                            "attn.to_q.weight": "attn.w1q.weight",
+                            "attn.to_k.weight": "attn.w1k.weight",
+                            "attn.to_v.weight": "attn.w1v.weight",
+                            "attn.to_out.0.weight": "attn.w1o.weight",
+                            "norm1.linear.weight": "modCX.1.weight",
+                            "ff.linear_1.weight": "mlp.c_fc1.weight",
+                            "ff.linear_2.weight": "mlp.c_fc2.weight",
+                            "ff.out_projection.weight": "mlp.c_proj.weight"
+                        }
+
+        for k in block_map:
+            key_map["{}.{}.{}".format(prefix_from, index, k)] = "{}.{}.{}".format(prefix_to, index, block_map[k])
+
+    MAP_BASIC = {
+        ("positional_encoding", "pos_embed.pos_embed"),
+        ("register_tokens", "register_tokens"),
+        ("t_embedder.mlp.0.weight", "time_step_proj.linear_1.weight"),
+        ("t_embedder.mlp.0.bias", "time_step_proj.linear_1.bias"),
+        ("t_embedder.mlp.2.weight", "time_step_proj.linear_2.weight"),
+        ("t_embedder.mlp.2.bias", "time_step_proj.linear_2.bias"),
+        ("cond_seq_linear.weight", "context_embedder.weight"),
+        ("init_x_linear.weight", "pos_embed.proj.weight"),
+        ("init_x_linear.bias", "pos_embed.proj.bias"),
+        ("final_linear.weight", "proj_out.weight"),
+        ("modF.1.weight", "norm_out.linear.weight", swap_scale_shift),
+    }
+
+    for k in MAP_BASIC:
+        if len(k) > 2:
+            key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+        else:
+            key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
+
+def flux_to_diffusers(mmdit_config, output_prefix=""):
+    n_double_layers = mmdit_config.get("depth", 0)
+    n_single_layers = mmdit_config.get("depth_single_blocks", 0)
+    hidden_size = mmdit_config.get("hidden_size", 0)
+
+    key_map = {}
+    for index in range(n_double_layers):
+        prefix_from = "transformer_blocks.{}".format(index)
+        prefix_to = "{}double_blocks.{}".format(output_prefix, index)
+
+        for end in ("weight", "bias"):
+            k = "{}.attn.".format(prefix_from)
+            qkv = "{}.img_attn.qkv.{}".format(prefix_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+
+            k = "{}.attn.".format(prefix_from)
+            qkv = "{}.txt_attn.qkv.{}".format(prefix_to, end)
+            key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+
+        block_map = {
+                        "attn.to_out.0.weight": "img_attn.proj.weight",
+                        "attn.to_out.0.bias": "img_attn.proj.bias",
+                        "norm1.linear.weight": "img_mod.lin.weight",
+                        "norm1.linear.bias": "img_mod.lin.bias",
+                        "norm1_context.linear.weight": "txt_mod.lin.weight",
+                        "norm1_context.linear.bias": "txt_mod.lin.bias",
+                        "attn.to_add_out.weight": "txt_attn.proj.weight",
+                        "attn.to_add_out.bias": "txt_attn.proj.bias",
+                        "ff.net.0.proj.weight": "img_mlp.0.weight",
+                        "ff.net.0.proj.bias": "img_mlp.0.bias",
+                        "ff.net.2.weight": "img_mlp.2.weight",
+                        "ff.net.2.bias": "img_mlp.2.bias",
+                        "ff_context.net.0.proj.weight": "txt_mlp.0.weight",
+                        "ff_context.net.0.proj.bias": "txt_mlp.0.bias",
+                        "ff_context.net.2.weight": "txt_mlp.2.weight",
+                        "ff_context.net.2.bias": "txt_mlp.2.bias",
+                        "ff.linear_in.weight": "img_mlp.0.weight",  # LyCoris LoKr
+                        "ff.linear_in.bias": "img_mlp.0.bias",
+                        "ff.linear_out.weight": "img_mlp.2.weight",
+                        "ff.linear_out.bias": "img_mlp.2.bias",
+                        "ff_context.linear_in.weight": "txt_mlp.0.weight",
+                        "ff_context.linear_in.bias": "txt_mlp.0.bias",
+                        "ff_context.linear_out.weight": "txt_mlp.2.weight",
+                        "ff_context.linear_out.bias": "txt_mlp.2.bias",
+                        "attn.norm_q.weight": "img_attn.norm.query_norm.weight",
+                        "attn.norm_k.weight": "img_attn.norm.key_norm.weight",
+                        "attn.norm_added_q.weight": "txt_attn.norm.query_norm.weight",
+                        "attn.norm_added_k.weight": "txt_attn.norm.key_norm.weight",
+                    }
+
+        for k in block_map:
+            key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k])
+
+    for index in range(n_single_layers):
+        prefix_from = "single_transformer_blocks.{}".format(index)
+        prefix_to = "{}single_blocks.{}".format(output_prefix, index)
+
+        for end in ("weight", "bias"):
+            k = "{}.attn.".format(prefix_from)
+            qkv = "{}.linear1.{}".format(prefix_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+            key_map["{}.proj_mlp.{}".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4))
+
+        block_map = {
+                        "norm.linear.weight": "modulation.lin.weight",
+                        "norm.linear.bias": "modulation.lin.bias",
+                        "proj_out.weight": "linear2.weight",
+                        "proj_out.bias": "linear2.bias",
+                        "attn.norm_q.weight": "norm.query_norm.weight",
+                        "attn.norm_k.weight": "norm.key_norm.weight",
+                        "attn.to_qkv_mlp_proj.weight": "linear1.weight", # Flux 2
+                        "attn.to_out.weight": "linear2.weight", # Flux 2
+                    }
+
+        for k in block_map:
+            key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k])
+
+    MAP_BASIC = {
+        ("final_layer.linear.bias", "proj_out.bias"),
+        ("final_layer.linear.weight", "proj_out.weight"),
+        ("img_in.bias", "x_embedder.bias"),
+        ("img_in.weight", "x_embedder.weight"),
+        ("time_in.in_layer.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
+        ("time_in.in_layer.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
+        ("time_in.out_layer.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
+        ("time_in.out_layer.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
+        ("txt_in.bias", "context_embedder.bias"),
+        ("txt_in.weight", "context_embedder.weight"),
+        ("vector_in.in_layer.bias", "time_text_embed.text_embedder.linear_1.bias"),
+        ("vector_in.in_layer.weight", "time_text_embed.text_embedder.linear_1.weight"),
+        ("vector_in.out_layer.bias", "time_text_embed.text_embedder.linear_2.bias"),
+        ("vector_in.out_layer.weight", "time_text_embed.text_embedder.linear_2.weight"),
+        ("guidance_in.in_layer.bias", "time_text_embed.guidance_embedder.linear_1.bias"),
+        ("guidance_in.in_layer.weight", "time_text_embed.guidance_embedder.linear_1.weight"),
+        ("guidance_in.out_layer.bias", "time_text_embed.guidance_embedder.linear_2.bias"),
+        ("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"),
+        ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
+        ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
+        ("pos_embed_input.bias", "controlnet_x_embedder.bias"),
+        ("pos_embed_input.weight", "controlnet_x_embedder.weight"),
+    }
+
+    for k in MAP_BASIC:
+        if len(k) > 2:
+            key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+        else:
+            key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
+
+def z_image_to_diffusers(mmdit_config, output_prefix=""):
+    n_layers = mmdit_config.get("n_layers", 0)
+    hidden_size = mmdit_config.get("dim", 0)
+    n_context_refiner = mmdit_config.get("n_refiner_layers", 2)
+    n_noise_refiner = mmdit_config.get("n_refiner_layers", 2)
+    key_map = {}
+
+    def add_block_keys(prefix_from, prefix_to, has_adaln=True):
+        for end in ("weight", "bias"):
+            k = "{}.attention.".format(prefix_from)
+            qkv = "{}.attention.qkv.{}".format(prefix_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+
+        block_map = {
+            "attention.norm_q.weight": "attention.q_norm.weight",
+            "attention.norm_k.weight": "attention.k_norm.weight",
+            "attention.to_out.0.weight": "attention.out.weight",
+            "attention.to_out.0.bias": "attention.out.bias",
+            "attention_norm1.weight": "attention_norm1.weight",
+            "attention_norm2.weight": "attention_norm2.weight",
+            "feed_forward.w1.weight": "feed_forward.w1.weight",
+            "feed_forward.w2.weight": "feed_forward.w2.weight",
+            "feed_forward.w3.weight": "feed_forward.w3.weight",
+            "ffn_norm1.weight": "ffn_norm1.weight",
+            "ffn_norm2.weight": "ffn_norm2.weight",
+        }
+        if has_adaln:
+            block_map["adaLN_modulation.0.weight"] = "adaLN_modulation.0.weight"
+            block_map["adaLN_modulation.0.bias"] = "adaLN_modulation.0.bias"
+        for k, v in block_map.items():
+            key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, v)
+
+    for i in range(n_layers):
+        add_block_keys("layers.{}".format(i), "{}layers.{}".format(output_prefix, i))
+
+    for i in range(n_context_refiner):
+        add_block_keys("context_refiner.{}".format(i), "{}context_refiner.{}".format(output_prefix, i))
+
+    for i in range(n_noise_refiner):
+        add_block_keys("noise_refiner.{}".format(i), "{}noise_refiner.{}".format(output_prefix, i))
+
+    MAP_BASIC = [
+        ("final_layer.linear.weight", "all_final_layer.2-1.linear.weight"),
+        ("final_layer.linear.bias", "all_final_layer.2-1.linear.bias"),
+        ("final_layer.adaLN_modulation.1.weight", "all_final_layer.2-1.adaLN_modulation.1.weight"),
+        ("final_layer.adaLN_modulation.1.bias", "all_final_layer.2-1.adaLN_modulation.1.bias"),
+        ("x_embedder.weight", "all_x_embedder.2-1.weight"),
+        ("x_embedder.bias", "all_x_embedder.2-1.bias"),
+        ("x_pad_token", "x_pad_token"),
+        ("cap_embedder.0.weight", "cap_embedder.0.weight"),
+        ("cap_embedder.1.weight", "cap_embedder.1.weight"),
+        ("cap_embedder.1.bias", "cap_embedder.1.bias"),
+        ("cap_pad_token", "cap_pad_token"),
+        ("t_embedder.mlp.0.weight", "t_embedder.mlp.0.weight"),
+        ("t_embedder.mlp.0.bias", "t_embedder.mlp.0.bias"),
+        ("t_embedder.mlp.2.weight", "t_embedder.mlp.2.weight"),
+        ("t_embedder.mlp.2.bias", "t_embedder.mlp.2.bias"),
+    ]
+
+    for c, diffusers in MAP_BASIC:
+        key_map[diffusers] = "{}{}".format(output_prefix, c)
+
+    return key_map
+
+def krea2_to_diffusers(mmdit_config, output_prefix=""):
+    n_layers = mmdit_config.get("layers", 0)
+    n_txt_layerwise = 2  # TextFusionTransformer hardcodes 2 layerwise + 2 refiner blocks
+    n_txt_refiner = 2
+    key_map = {}
+
+    def add_block(prefix_to, prefix_from):
+        block_map = {
+            "attn.to_q": "attn.wq", "attn.to_k": "attn.wk", "attn.to_v": "attn.wv",
+            "attn.to_gate": "attn.gate", "attn.to_out.0": "attn.wo",
+            "attn.to_out": "attn.wo",  # some tools drop the ".0" on to_out
+            "ff.gate": "mlp.gate", "ff.up": "mlp.up", "ff.down": "mlp.down",
+        }
+        for d, c in block_map.items():
+            key_map["{}.{}.weight".format(prefix_to, d)] = "{}{}.{}.weight".format(output_prefix, prefix_from, c)
+
+    for i in range(n_layers):
+        add_block("transformer_blocks.{}".format(i), "blocks.{}".format(i))
+    for i in range(n_txt_layerwise):
+        add_block("text_fusion.layerwise_blocks.{}".format(i), "txtfusion.layerwise_blocks.{}".format(i))
+    for i in range(n_txt_refiner):
+        add_block("text_fusion.refiner_blocks.{}".format(i), "txtfusion.refiner_blocks.{}".format(i))
+
+    MAP_BASIC = [
+        ("img_in", "first"),
+        ("time_embed.linear_1", "tmlp.0"),
+        ("time_embed.linear_2", "tmlp.2"),
+        ("time_mod_proj", "tproj.1"),
+        ("txt_in.linear_1", "txtmlp.1"),
+        ("txt_in.linear_2", "txtmlp.3"),
+        ("text_fusion.projector", "txtfusion.projector"),
+        ("final_layer.linear", "last.linear"),
+    ]
+    for d, c in MAP_BASIC:
+        key_map["{}.weight".format(d)] = "{}{}.weight".format(output_prefix, c)
+
+    return key_map
+
+def repeat_to_batch_size(tensor, batch_size, dim=0):
+    if tensor.shape[dim] > batch_size:
+        return tensor.narrow(dim, 0, batch_size)
+    elif tensor.shape[dim] < batch_size:
+        return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size)
+    return tensor
+
+def resize_to_batch_size(tensor, batch_size):
+    in_batch_size = tensor.shape[0]
+    if in_batch_size == batch_size:
+        return tensor
+
+    if batch_size <= 1:
+        return tensor[:batch_size]
+
+    output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device)
+    if batch_size < in_batch_size:
+        scale = (in_batch_size - 1) / (batch_size - 1)
+        for i in range(batch_size):
+            output[i] = tensor[min(round(i * scale), in_batch_size - 1)]
+    else:
+        scale = in_batch_size / batch_size
+        for i in range(batch_size):
+            output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)]
+
+    return output
+
+def resize_list_to_batch_size(l, batch_size):
+    in_batch_size = len(l)
+    if in_batch_size == batch_size or in_batch_size == 0:
+        return l
+
+    if batch_size <= 1:
+        return l[:batch_size]
+
+    output = []
+    if batch_size < in_batch_size:
+        scale = (in_batch_size - 1) / (batch_size - 1)
+        for i in range(batch_size):
+            output.append(l[min(round(i * scale), in_batch_size - 1)])
+    else:
+        scale = in_batch_size / batch_size
+        for i in range(batch_size):
+           output.append(l[min(math.floor((i + 0.5) * scale), in_batch_size - 1)])
+
+    return output
+
+def convert_sd_to(state_dict, dtype):
+    keys = list(state_dict.keys())
+    for k in keys:
+        state_dict[k] = state_dict[k].to(dtype)
+    return state_dict
+
+def safetensors_header(safetensors_path, max_size=100*1024*1024):
+    with open(safetensors_path, "rb") as f:
+        header = f.read(8)
+        length_of_header = struct.unpack('<Q', header)[0]
+        if length_of_header > max_size:
+            return None
+        return f.read(length_of_header)
+
+ATTR_UNSET={}
+
+def resolve_attr(obj, attr):
+    attrs = attr.split(".")
+    for name in attrs[:-1]:
+        obj = getattr(obj, name)
+    return obj, attrs[-1]
+
+def set_attr(obj, attr, value):
+    obj, name = resolve_attr(obj, attr)
+    prev = getattr(obj, name, ATTR_UNSET)
+    if value is ATTR_UNSET:
+        delattr(obj, name)
+    else:
+        setattr(obj, name, value)
+    return prev
+
+def set_attr_param(obj, attr, value):
+    # Clone inference tensors (created under torch.inference_mode) since
+    # their version counter is frozen and nn.Parameter() cannot wrap them.
+    if (not torch.is_inference_mode_enabled()) and value.is_inference():
+        value = value.clone()
+    return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
+
+def set_attr_buffer(obj, attr, value):
+    obj, name = resolve_attr(obj, attr)
+    prev = getattr(obj, name, ATTR_UNSET)
+    persistent = name not in getattr(obj, "_non_persistent_buffers_set", set())
+    obj.register_buffer(name, value, persistent=persistent)
+    return prev
+
+def copy_to_param(obj, attr, value):
+    # inplace update tensor instead of replacing it
+    attrs = attr.split(".")
+    for name in attrs[:-1]:
+        obj = getattr(obj, name)
+    prev = getattr(obj, attrs[-1])
+    prev.data.copy_(value)
+
+def get_attr(obj, attr: str):
+    """Retrieves a nested attribute from an object using dot notation.
+
+    Args:
+        obj: The object to get the attribute from
+        attr (str): The attribute path using dot notation (e.g. "model.layer.weight")
+
+    Returns:
+        The value of the requested attribute
+
+    Example:
+        model = MyModel()
+        weight = get_attr(model, "layer1.conv.weight")
+        # Equivalent to: model.layer1.conv.weight
+
+    Important:
+        Always prefer `comfy.model_patcher.ModelPatcher.get_model_object` when
+        accessing nested model objects under `ModelPatcher.model`.
+    """
+    attrs = attr.split(".")
+    for name in attrs:
+        obj = getattr(obj, name)
+    return obj
+
+def bislerp(samples, width, height):
+    def slerp(b1, b2, r):
+        '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''
+
+        c = b1.shape[-1]
+
+        #norms
+        b1_norms = torch.norm(b1, dim=-1, keepdim=True)
+        b2_norms = torch.norm(b2, dim=-1, keepdim=True)
+
+        #normalize
+        b1_normalized = b1 / b1_norms
+        b2_normalized = b2 / b2_norms
+
+        #zero when norms are zero
+        b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0
+        b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0
+
+        #slerp
+        dot = (b1_normalized*b2_normalized).sum(1)
+        omega = torch.acos(dot)
+        so = torch.sin(omega)
+
+        #technically not mathematically correct, but more pleasing?
+        res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized
+        res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c)
+
+        #edge cases for same or polar opposites
+        res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5]
+        res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
+        return res
+
+    def generate_bilinear_data(length_old, length_new, device):
+        coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1))
+        coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
+        ratios = coords_1 - coords_1.floor()
+        coords_1 = coords_1.to(torch.int64)
+
+        coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1
+        coords_2[:,:,:,-1] -= 1
+        coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
+        coords_2 = coords_2.to(torch.int64)
+        return ratios, coords_1, coords_2
+
+    orig_dtype = samples.dtype
+    samples = samples.float()
+    n,c,h,w = samples.shape
+    h_new, w_new = (height, width)
+
+    #linear w
+    ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device)
+    coords_1 = coords_1.expand((n, c, h, -1))
+    coords_2 = coords_2.expand((n, c, h, -1))
+    ratios = ratios.expand((n, 1, h, -1))
+
+    pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c))
+    pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c))
+    ratios = ratios.movedim(1, -1).reshape((-1,1))
+
+    result = slerp(pass_1, pass_2, ratios)
+    result = result.reshape(n, h, w_new, c).movedim(-1, 1)
+
+    #linear h
+    ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device)
+    coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
+    coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
+    ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))
+
+    pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c))
+    pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c))
+    ratios = ratios.movedim(1, -1).reshape((-1,1))
+
+    result = slerp(pass_1, pass_2, ratios)
+    result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
+    return result.to(orig_dtype)
+
+def lanczos(samples, width, height):
+    #the below API is strict and expects grayscale to be squeezed
+    if samples.ndim == 4:
+        samples = samples.squeeze(1) if samples.shape[1] == 1 else samples.movedim(1, -1)
+    images = [Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]
+    images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images]
+    images = [torch.from_numpy(t).movedim(-1, 0) if (t := np.array(image).astype(np.float32) / 255.0).ndim == 3 else torch.from_numpy(t) for image in images]
+    result = torch.stack(images)
+    return result.to(samples.device, samples.dtype)
+
+def common_upscale(samples, width, height, upscale_method, crop):
+        orig_shape = tuple(samples.shape)
+        if len(orig_shape) > 4:
+            samples = samples.reshape(samples.shape[0], samples.shape[1], -1, samples.shape[-2], samples.shape[-1])
+            samples = samples.movedim(2, 1)
+            samples = samples.reshape(-1, orig_shape[1], orig_shape[-2], orig_shape[-1])
+        if crop == "center":
+            old_width = samples.shape[-1]
+            old_height = samples.shape[-2]
+            old_aspect = old_width / old_height
+            new_aspect = width / height
+            x = 0
+            y = 0
+            if old_aspect > new_aspect:
+                x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
+            elif old_aspect < new_aspect:
+                y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
+            s = samples.narrow(-2, y, old_height - y * 2).narrow(-1, x, old_width - x * 2)
+        else:
+            s = samples
+
+        if upscale_method == "bislerp":
+            out = bislerp(s, width, height)
+        elif upscale_method == "lanczos":
+            out = lanczos(s, width, height)
+        else:
+            out = torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
+
+        if len(orig_shape) == 4:
+            return out
+
+        out = out.reshape((orig_shape[0], -1, orig_shape[1]) + (height, width))
+        return out.movedim(2, 1).reshape(orig_shape[:-2] + (height, width))
+
+def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
+    rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap))
+    cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap))
+    return rows * cols
+
+@torch.inference_mode()
+def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3, output_device="cpu", downscale=False, index_formulas=None, pbar=None):
+    dims = len(tile)
+
+    if not (isinstance(upscale_amount, (tuple, list))):
+        upscale_amount = [upscale_amount] * dims
+
+    if not (isinstance(overlap, (tuple, list))):
+        overlap = [overlap] * dims
+
+    if index_formulas is None:
+        index_formulas = upscale_amount
+
+    if not (isinstance(index_formulas, (tuple, list))):
+        index_formulas = [index_formulas] * dims
+
+    def get_upscale(dim, val):
+        up = upscale_amount[dim]
+        if callable(up):
+            return up(val)
+        else:
+            return up * val
+
+    def get_downscale(dim, val):
+        up = upscale_amount[dim]
+        if callable(up):
+            return up(val)
+        else:
+            return val / up
+
+    def get_upscale_pos(dim, val):
+        up = index_formulas[dim]
+        if callable(up):
+            return up(val)
+        else:
+            return up * val
+
+    def get_downscale_pos(dim, val):
+        up = index_formulas[dim]
+        if callable(up):
+            return up(val)
+        else:
+            return val / up
+
+    if downscale:
+        get_scale = get_downscale
+        get_pos = get_downscale_pos
+    else:
+        get_scale = get_upscale
+        get_pos = get_upscale_pos
+
+    def mult_list_upscale(a):
+        out = []
+        for i in range(len(a)):
+            out.append(round(get_scale(i, a[i])))
+        return out
+
+    output = torch.empty([samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), device=output_device)
+
+    for b in range(samples.shape[0]):
+        s = samples[b:b+1]
+
+        # handle entire input fitting in a single tile
+        if all(s.shape[d+2] <= tile[d] for d in range(dims)):
+            output[b:b+1] = function(s).to(output_device)
+            if pbar is not None:
+                pbar.update(1)
+            continue
+
+        out = output[b:b+1].zero_()
+        out_div = torch.zeros([s.shape[0], 1] + mult_list_upscale(s.shape[2:]), device=output_device)
+
+        positions = [range(0, s.shape[d+2] - overlap[d], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)]
+
+        for it in itertools.product(*positions):
+            s_in = s
+            upscaled = []
+
+            for d in range(dims):
+                pos = max(0, min(s.shape[d + 2] - overlap[d], it[d]))
+                l = min(tile[d], s.shape[d + 2] - pos)
+                s_in = s_in.narrow(d + 2, pos, l)
+                upscaled.append(round(get_pos(d, pos)))
+
+            ps = function(s_in).to(output_device)
+            mask = torch.ones([1, 1] + list(ps.shape[2:]), device=output_device)
+
+            for d in range(2, dims + 2):
+                feather = round(get_scale(d - 2, overlap[d - 2]))
+                if feather >= mask.shape[d]:
+                    continue
+                for t in range(feather):
+                    a = (t + 1) / feather
+                    mask.narrow(d, t, 1).mul_(a)
+                    mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a)
+
+            o = out
+            o_d = out_div
+            ps_view = ps
+            mask_view = mask
+            for d in range(dims):
+                l = min(ps_view.shape[d + 2], o.shape[d + 2] - upscaled[d])
+                o = o.narrow(d + 2, upscaled[d], l)
+                o_d = o_d.narrow(d + 2, upscaled[d], l)
+                if l < ps_view.shape[d + 2]:
+                    ps_view = ps_view.narrow(d + 2, 0, l)
+                    mask_view = mask_view.narrow(d + 2, 0, l)
+
+            o.add_(ps_view * mask_view)
+            o_d.add_(mask_view)
+
+            if pbar is not None:
+                pbar.update(1)
+
+        out.div_(out_div)
+    return output
+
+def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
+    return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar)
+
+def model_trange(*args, **kwargs):
+    if not comfy.memory_management.aimdo_enabled:
+        return trange(*args, **kwargs)
+
+    pbar = trange(*args, **kwargs, smoothing=1.0)
+    pbar._i = 0
+    pbar.set_postfix_str("  Model Initializing ...  ")
+
+    _update = pbar.update
+
+    def warmup_update(n=1):
+        pbar._i += 1
+        if pbar._i == 1:
+            pbar.i1_time = time.time()
+            pbar.set_postfix_str(" Model Initialization complete!  ")
+        elif pbar._i == 2:
+            #bring forward the effective start time based the diff between first and second iteration
+            #to attempt to remove load overhead from the final step rate estimate.
+            pbar.start_t = pbar.i1_time - (time.time() - pbar.i1_time)
+            pbar.set_postfix_str("")
+
+        _update(n)
+
+    pbar.update = warmup_update
+    return pbar
+
+PROGRESS_BAR_ENABLED = True
+def set_progress_bar_enabled(enabled):
+    global PROGRESS_BAR_ENABLED
+    PROGRESS_BAR_ENABLED = enabled
+
+PROGRESS_BAR_HOOK = None
+def set_progress_bar_global_hook(function):
+    global PROGRESS_BAR_HOOK
+    PROGRESS_BAR_HOOK = function
+
+# Throttle settings for progress bar updates to reduce WebSocket flooding
+PROGRESS_THROTTLE_MIN_INTERVAL = 0.1  # 100ms minimum between updates
+PROGRESS_THROTTLE_MIN_PERCENT = 0.5   # 0.5% minimum progress change
+
+class ProgressBar:
+    def __init__(self, total, node_id=None):
+        global PROGRESS_BAR_HOOK
+        self.total = total
+        self.current = 0
+        self.hook = PROGRESS_BAR_HOOK
+        self.node_id = node_id
+        self._last_update_time = 0.0
+        self._last_sent_value = -1
+
+    def update_absolute(self, value, total=None, preview=None):
+        if total is not None:
+            self.total = total
+        if value > self.total:
+            value = self.total
+        self.current = value
+        if self.hook is not None:
+            current_time = time.perf_counter()
+            is_first = (self._last_sent_value < 0)
+            is_final = (value >= self.total)
+            has_preview = (preview is not None)
+
+            # Always send immediately for previews, first update, or final update
+            if has_preview or is_first or is_final:
+                self.hook(self.current, self.total, preview, node_id=self.node_id)
+                self._last_update_time = current_time
+                self._last_sent_value = value
+                return
+
+            # Apply throttling for regular progress updates
+            if self.total > 0:
+                percent_changed = ((value - max(0, self._last_sent_value)) / self.total) * 100
+            else:
+                percent_changed = 100
+            time_elapsed = current_time - self._last_update_time
+
+            if time_elapsed >= PROGRESS_THROTTLE_MIN_INTERVAL and percent_changed >= PROGRESS_THROTTLE_MIN_PERCENT:
+                self.hook(self.current, self.total, preview, node_id=self.node_id)
+                self._last_update_time = current_time
+                self._last_sent_value = value
+
+    def update(self, value):
+        self.update_absolute(self.current + value)
+
+def reshape_mask(input_mask, output_shape):
+    dims = len(output_shape) - 2
+
+    if dims == 1:
+        scale_mode = "linear"
+
+    if dims == 2:
+        input_mask = input_mask.reshape((-1, 1, input_mask.shape[-2], input_mask.shape[-1]))
+        scale_mode = "bilinear"
+
+    if dims == 3:
+        if len(input_mask.shape) < 5:
+            input_mask = input_mask.reshape((1, 1, -1, input_mask.shape[-2], input_mask.shape[-1]))
+        scale_mode = "trilinear"
+
+    mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode)
+    if mask.shape[1] < output_shape[1]:
+        mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]
+    mask = repeat_to_batch_size(mask, output_shape[0])
+    return mask
+
+def upscale_dit_mask(mask: torch.Tensor, img_size_in, img_size_out):
+        hi, wi = img_size_in
+        ho, wo = img_size_out
+        # if it's already the correct size, no need to do anything
+        if (hi, wi) == (ho, wo):
+            return mask
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        if mask.ndim != 3:
+            raise ValueError(f"Got a mask of shape {list(mask.shape)}, expected [b, q, k] or [q, k]")
+        txt_tokens = mask.shape[1] - (hi * wi)
+        # quadrants of the mask
+        txt_to_txt = mask[:, :txt_tokens, :txt_tokens]
+        txt_to_img = mask[:, :txt_tokens, txt_tokens:]
+        img_to_img = mask[:, txt_tokens:, txt_tokens:]
+        img_to_txt = mask[:, txt_tokens:, :txt_tokens]
+
+        # convert to 1d x 2d, interpolate, then back to 1d x 1d
+        txt_to_img = rearrange  (txt_to_img, "b t (h w) -> b t h w", h=hi, w=wi)
+        txt_to_img = interpolate(txt_to_img, size=img_size_out, mode="bilinear")
+        txt_to_img = rearrange  (txt_to_img, "b t h w -> b t (h w)")
+        # this one is hard because we have to do it twice
+        # convert to 1d x 2d, interpolate, then to 2d x 1d, interpolate, then 1d x 1d
+        img_to_img = rearrange  (img_to_img, "b hw (h w) -> b hw h w", h=hi, w=wi)
+        img_to_img = interpolate(img_to_img, size=img_size_out, mode="bilinear")
+        img_to_img = rearrange  (img_to_img, "b (hk wk) hq wq -> b (hq wq) hk wk", hk=hi, wk=wi)
+        img_to_img = interpolate(img_to_img, size=img_size_out, mode="bilinear")
+        img_to_img = rearrange  (img_to_img, "b (hq wq) hk wk -> b (hk wk) (hq wq)", hq=ho, wq=wo)
+        # convert to 2d x 1d, interpolate, then back to 1d x 1d
+        img_to_txt = rearrange  (img_to_txt, "b (h w) t -> b t h w", h=hi, w=wi)
+        img_to_txt = interpolate(img_to_txt, size=img_size_out, mode="bilinear")
+        img_to_txt = rearrange  (img_to_txt, "b t h w -> b (h w) t")
+
+        # reassemble the mask from blocks
+        out = torch.cat([
+            torch.cat([txt_to_txt, txt_to_img], dim=2),
+            torch.cat([img_to_txt, img_to_img], dim=2)],
+            dim=1
+        )
+        return out
+
+def pack_latents(latents):
+    latent_shapes = []
+    tensors = []
+    for tensor in latents:
+        latent_shapes.append(tensor.shape)
+        tensors.append(tensor.reshape(tensor.shape[0], 1, -1))
+
+    latent = torch.cat(tensors, dim=-1)
+    return latent, latent_shapes
+
+def unpack_latents(combined_latent, latent_shapes):
+    if len(latent_shapes) > 1:
+        output_tensors = []
+        for shape in latent_shapes:
+            cut = math.prod(shape[1:])
+            tens = combined_latent[:, :, :cut]
+            combined_latent = combined_latent[:, :, cut:]
+            output_tensors.append(tens.reshape([tens.shape[0]] + list(shape)[1:]))
+    else:
+        output_tensors = [combined_latent]
+    return output_tensors
+
+def detect_layer_quantization(state_dict, prefix):
+    for k in state_dict:
+        if k.startswith(prefix) and k.endswith(".comfy_quant"):
+            logging.info("Found quantization metadata version 1")
+            return {"mixed_ops": True}
+    return None
+
+def convert_old_quants(state_dict, model_prefix="", metadata={}):
+    if metadata is None:
+        metadata = {}
+
+    quant_metadata = None
+    if "_quantization_metadata" not in metadata:
+        scaled_fp8_key = "{}scaled_fp8".format(model_prefix)
+
+        if scaled_fp8_key in state_dict:
+            scaled_fp8_weight = state_dict[scaled_fp8_key]
+            scaled_fp8_dtype = scaled_fp8_weight.dtype
+            if scaled_fp8_dtype == torch.float32:
+                scaled_fp8_dtype = torch.float8_e4m3fn
+
+            if scaled_fp8_weight.nelement() == 2:
+                full_precision_matrix_mult = True
+            else:
+                full_precision_matrix_mult = False
+
+            out_sd = {}
+            layers = {}
+            for k in list(state_dict.keys()):
+                if k == scaled_fp8_key:
+                    continue
+                if not k.startswith(model_prefix):
+                    out_sd[k] = state_dict[k]
+                    continue
+                k_out = k
+                w = state_dict.pop(k)
+                layer = None
+                if k_out.endswith(".scale_weight"):
+                    layer = k_out[:-len(".scale_weight")]
+                    k_out = "{}.weight_scale".format(layer)
+
+                if layer is not None:
+                    layer_conf = {"format": "float8_e4m3fn"}
+                    if full_precision_matrix_mult:
+                        layer_conf["full_precision_matrix_mult"] = full_precision_matrix_mult
+                    layers[layer] = layer_conf
+
+                if k_out.endswith(".scale_input"):
+                    layer = k_out[:-len(".scale_input")]
+                    k_out = "{}.input_scale".format(layer)
+                    if w.item() == 1.0:
+                        continue
+
+                out_sd[k_out] = w
+
+            state_dict = out_sd
+            quant_metadata = {"layers": layers}
+    else:
+        quant_metadata = json.loads(metadata["_quantization_metadata"])
+
+    if quant_metadata is not None:
+        layers = quant_metadata["layers"]
+        for k, v in layers.items():
+            state_dict["{}.comfy_quant".format(k)] = torch.tensor(list(json.dumps(v).encode('utf-8')), dtype=torch.uint8)
+
+    return state_dict, metadata
+
+def string_to_seed(data):
+    crc = 0xFFFFFFFF
+    for byte in data:
+        if isinstance(byte, str):
+            byte = ord(byte)
+        crc ^= byte
+        for _ in range(8):
+            if crc & 1:
+                crc = (crc >> 1) ^ 0xEDB88320
+            else:
+                crc >>= 1
+    return crc ^ 0xFFFFFFFF
+
+def deepcopy_list_dict(obj, memo=None):
+    if memo is None:
+        memo = {}
+
+    obj_id = id(obj)
+    if obj_id in memo:
+        return memo[obj_id]
+
+    if isinstance(obj, dict):
+        res = {deepcopy_list_dict(k, memo): deepcopy_list_dict(v, memo) for k, v in obj.items()}
+    elif isinstance(obj, list):
+        res = [deepcopy_list_dict(i, memo) for i in obj]
+    else:
+        res = obj
+
+    memo[obj_id] = res
+    return res
+
+def bit_reverse_range(index, bits):
+    result = 0
+    for _ in range(bits):
+        result = (result << 1) | (index & 1)
+        index >>= 1
+    return result
diff --git a/comfy/weight_adapter/__init__.py b/comfy/weight_adapter/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2a11418216c3e4f322aca9cade507b3116a1253
--- /dev/null
+++ b/comfy/weight_adapter/__init__.py
@@ -0,0 +1,42 @@
+from .base import WeightAdapterBase, WeightAdapterTrainBase
+from .lora import LoRAAdapter
+from .loha import LoHaAdapter
+from .lokr import LoKrAdapter
+from .glora import GLoRAAdapter
+from .oft import OFTAdapter
+from .boft import BOFTAdapter
+from .bypass import (
+    BypassInjectionManager,
+    BypassForwardHook,
+    create_bypass_injections_from_patches,
+)
+
+
+adapters: list[type[WeightAdapterBase]] = [
+    LoRAAdapter,
+    LoHaAdapter,
+    LoKrAdapter,
+    GLoRAAdapter,
+    OFTAdapter,
+    BOFTAdapter,
+]
+adapter_maps: dict[str, type[WeightAdapterBase]] = {
+    "LoRA": LoRAAdapter,
+    "LoHa": LoHaAdapter,
+    "LoKr": LoKrAdapter,
+    "OFT": OFTAdapter,
+    ## We disable not implemented algo for now
+    # "GLoRA": GLoRAAdapter,
+    # "BOFT": BOFTAdapter,
+}
+
+
+__all__ = [
+    "WeightAdapterBase",
+    "WeightAdapterTrainBase",
+    "adapters",
+    "adapter_maps",
+    "BypassInjectionManager",
+    "BypassForwardHook",
+    "create_bypass_injections_from_patches",
+] + [a.__name__ for a in adapters]
diff --git a/comfy/weight_adapter/base.py b/comfy/weight_adapter/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a9333413949bf4e61b42a03aedc4767377ad2d4
--- /dev/null
+++ b/comfy/weight_adapter/base.py
@@ -0,0 +1,396 @@
+from typing import Callable, Optional
+
+import torch
+import torch.nn as nn
+
+import comfy.model_management
+
+
+class WeightAdapterBase:
+    """
+    Base class for weight adapters (LoRA, LoHa, LoKr, OFT, etc.)
+
+    Bypass Mode:
+        All adapters follow the pattern: bypass(f)(x) = g(f(x) + h(x))
+
+        - h(x): Additive component (LoRA path). Returns delta to add to base output.
+        - g(y): Output transformation. Applied after base + h(x).
+
+        For LoRA/LoHa/LoKr: g = identity, h = adapter(x)
+        For OFT/BOFT: g = transform, h = 0
+    """
+
+    name: str
+    loaded_keys: set[str]
+    weights: list[torch.Tensor]
+
+    # Attributes set by bypass system
+    multiplier: float = 1.0
+    shape: tuple = None  # (out_features, in_features) or (out_ch, in_ch, *kernel)
+
+    @classmethod
+    def load(
+        cls,
+        x: str,
+        lora: dict[str, torch.Tensor],
+        alpha: float,
+        dora_scale: torch.Tensor,
+    ) -> Optional["WeightAdapterBase"]:
+        raise NotImplementedError
+
+    def to_train(self) -> "WeightAdapterTrainBase":
+        raise NotImplementedError
+
+    @classmethod
+    def create_train(cls, weight, *args) -> "WeightAdapterTrainBase":
+        """
+        weight: The original weight tensor to be modified.
+        *args: Additional arguments for configuration, such as rank, alpha etc.
+        """
+        raise NotImplementedError
+
+    def calculate_shape(
+        self,
+        key
+    ):
+        return None
+
+    def calculate_weight(
+        self,
+        weight,
+        key,
+        strength,
+        strength_model,
+        offset,
+        function,
+        intermediate_dtype=torch.float32,
+        original_weight=None,
+    ):
+        raise NotImplementedError
+
+    # ===== Bypass Mode Methods =====
+    #
+    # IMPORTANT: Bypass mode is designed for quantized models where original weights
+    # may not be accessible in a usable format. Therefore, h() and bypass_forward()
+    # do NOT take org_weight as a parameter. All necessary information (out_channels,
+    # in_channels, conv params, etc.) is provided via attributes set by BypassForwardHook.
+
+    def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:
+        """
+        Additive bypass component: h(x, base_out)
+
+        Computes the adapter's contribution to be added to base forward output.
+        For adapters that only transform output (OFT/BOFT), returns zeros.
+
+        Note:
+            This method does NOT access original model weights. Bypass mode is
+            designed for quantized models where weights may not be in a usable format.
+            All shape info comes from module attributes set by BypassForwardHook.
+
+        Args:
+            x: Input tensor
+            base_out: Output from base forward f(x), can be used for shape reference
+
+        Returns:
+            Delta tensor to add to base output. Shape matches base output.
+
+        Reference: LyCORIS LoConModule.bypass_forward_diff
+        """
+        # Default: no additive component (for OFT/BOFT)
+        # Simply return zeros matching base_out shape
+        return torch.zeros_like(base_out)
+
+    def g(self, y: torch.Tensor) -> torch.Tensor:
+        """
+        Output transformation: g(y)
+
+        Applied after base forward + h(x). For most adapters this is identity.
+        OFT/BOFT override this to apply orthogonal transformation.
+
+        Args:
+            y: Combined output (base + h(x))
+
+        Returns:
+            Transformed output
+
+        Reference: LyCORIS OFTModule applies orthogonal transform here
+        """
+        # Default: identity (for LoRA/LoHa/LoKr)
+        return y
+
+    def bypass_forward(
+        self,
+        org_forward: Callable,
+        x: torch.Tensor,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        Full bypass forward: g(f(x) + h(x, f(x)))
+
+        Note:
+            This method does NOT take org_weight/org_bias parameters. Bypass mode
+            is designed for quantized models where weights may not be accessible.
+            The original forward function handles weight access internally.
+
+        Args:
+            org_forward: Original module forward function
+            x: Input tensor
+            *args, **kwargs: Additional arguments for org_forward
+
+        Returns:
+            Output with adapter applied in bypass mode
+
+        Reference: LyCORIS LoConModule.bypass_forward
+        """
+        # Base forward: f(x)
+        base_out = org_forward(x, *args, **kwargs)
+
+        # Additive component: h(x, base_out) - base_out provided for shape reference
+        h_out = self.h(x, base_out)
+
+        # Output transformation: g(base + h)
+        return self.g(base_out + h_out)
+
+
+class WeightAdapterTrainBase(nn.Module):
+    """
+    Base class for trainable weight adapters (LoRA, LoHa, LoKr, OFT, etc.)
+
+    Bypass Mode:
+        All adapters follow the pattern: bypass(f)(x) = g(f(x) + h(x))
+
+        - h(x): Additive component (LoRA path). Returns delta to add to base output.
+        - g(y): Output transformation. Applied after base + h(x).
+
+        For LoRA/LoHa/LoKr: g = identity, h = adapter(x)
+        For OFT: g = transform, h = 0
+
+    Note:
+        Unlike WeightAdapterBase, TrainBase classes have simplified weight formats
+        with fewer branches (e.g., LoKr only has w1/w2, not w1_a/w1_b decomposition).
+
+    We follow the scheme of PR #7032
+    """
+
+    # Attributes set by bypass system (BypassForwardHook)
+    # These are set before h()/g()/bypass_forward() are called
+    multiplier: float = 1.0
+    is_conv: bool = False
+    conv_dim: int = 0  # 0=linear, 1=conv1d, 2=conv2d, 3=conv3d
+    kw_dict: dict = {}  # Conv kwargs: stride, padding, dilation, groups
+    kernel_size: tuple = ()
+    in_channels: int = None
+    out_channels: int = None
+
+    def __init__(self):
+        super().__init__()
+
+    def __call__(self, w):
+        """
+        Weight modification mode: returns modified weight.
+
+        Args:
+            w: The original weight tensor to be modified.
+
+        Returns:
+            Modified weight tensor.
+        """
+        raise NotImplementedError
+
+    # ===== Bypass Mode Methods =====
+
+    def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:
+        """
+        Additive bypass component: h(x, base_out)
+
+        Computes the adapter's contribution to be added to base forward output.
+        For adapters that only transform output (OFT), returns zeros.
+
+        Args:
+            x: Input tensor
+            base_out: Output from base forward f(x), can be used for shape reference
+
+        Returns:
+            Delta tensor to add to base output. Shape matches base output.
+
+        Subclasses should override this method.
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__}.h() not implemented. "
+            "Subclasses must implement h() for bypass mode."
+        )
+
+    def g(self, y: torch.Tensor) -> torch.Tensor:
+        """
+        Output transformation: g(y)
+
+        Applied after base forward + h(x). For most adapters this is identity.
+        OFT overrides this to apply orthogonal transformation.
+
+        Args:
+            y: Combined output (base + h(x))
+
+        Returns:
+            Transformed output
+        """
+        # Default: identity (for LoRA/LoHa/LoKr)
+        return y
+
+    def bypass_forward(
+        self,
+        org_forward: Callable,
+        x: torch.Tensor,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        Full bypass forward: g(f(x) + h(x, f(x)))
+
+        Args:
+            org_forward: Original module forward function
+            x: Input tensor
+            *args, **kwargs: Additional arguments for org_forward
+
+        Returns:
+            Output with adapter applied in bypass mode
+        """
+        # Base forward: f(x)
+        base_out = org_forward(x, *args, **kwargs)
+
+        # Additive component: h(x, base_out) - base_out provided for shape reference
+        h_out = self.h(x, base_out)
+
+        # Output transformation: g(base + h)
+        return self.g(base_out + h_out)
+
+    def passive_memory_usage(self):
+        raise NotImplementedError("passive_memory_usage is not implemented")
+
+    def move_to(self, device):
+        self.to(device)
+        return self.passive_memory_usage()
+
+
+def weight_decompose(
+    dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function
+):
+    dora_scale = comfy.model_management.cast_to_device(
+        dora_scale, weight.device, intermediate_dtype
+    )
+    lora_diff *= alpha
+    weight_calc = weight + function(lora_diff).type(weight.dtype)
+
+    wd_on_output_axis = dora_scale.shape[0] == weight_calc.shape[0]
+    if wd_on_output_axis:
+        weight_norm = (
+            weight.reshape(weight.shape[0], -1)
+            .norm(dim=1, keepdim=True)
+            .reshape(weight.shape[0], *[1] * (weight.dim() - 1))
+        )
+    else:
+        weight_norm = (
+            weight_calc.transpose(0, 1)
+            .reshape(weight_calc.shape[1], -1)
+            .norm(dim=1, keepdim=True)
+            .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1))
+            .transpose(0, 1)
+        )
+    weight_norm = weight_norm + torch.finfo(weight.dtype).eps
+
+    weight_calc *= (dora_scale / weight_norm).type(weight.dtype)
+    if strength != 1.0:
+        weight_calc -= weight
+        weight += strength * (weight_calc)
+    else:
+        weight[:] = weight_calc
+    return weight
+
+
+def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor:
+    """
+    Pad a tensor to a new shape with zeros.
+
+    Args:
+        tensor (torch.Tensor): The original tensor to be padded.
+        new_shape (List[int]): The desired shape of the padded tensor.
+
+    Returns:
+        torch.Tensor: A new tensor padded with zeros to the specified shape.
+
+    Note:
+        If the new shape is smaller than the original tensor in any dimension,
+        the original tensor will be truncated in that dimension.
+    """
+    if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]):
+        raise ValueError(
+            "The new shape must be larger than the original tensor in all dimensions"
+        )
+
+    if len(new_shape) != len(tensor.shape):
+        raise ValueError(
+            "The new shape must have the same number of dimensions as the original tensor"
+        )
+
+    # Create a new tensor filled with zeros
+    padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device)
+
+    # Create slicing tuples for both tensors
+    orig_slices = tuple(slice(0, dim) for dim in tensor.shape)
+    new_slices = tuple(slice(0, dim) for dim in tensor.shape)
+
+    # Copy the original tensor into the new tensor
+    padded_tensor[new_slices] = tensor[orig_slices]
+
+    return padded_tensor
+
+
+def tucker_weight_from_conv(up, down, mid):
+    up = up.reshape(up.size(0), up.size(1))
+    down = down.reshape(down.size(0), down.size(1))
+    return torch.einsum("m n ..., i m, n j -> i j ...", mid, up, down)
+
+
+def tucker_weight(wa, wb, t):
+    temp = torch.einsum("i j ..., j r -> i r ...", t, wb)
+    return torch.einsum("i j ..., i r -> r j ...", temp, wa)
+
+
+def factorization(dimension: int, factor: int = -1) -> tuple[int, int]:
+    """
+    return a tuple of two value of input dimension decomposed by the number closest to factor
+    second value is higher or equal than first value.
+
+    examples)
+    factor
+        -1               2                4               8               16               ...
+    127 -> 1, 127   127 -> 1, 127    127 -> 1, 127   127 -> 1, 127   127 -> 1, 127
+    128 -> 8, 16    128 -> 2, 64     128 -> 4, 32    128 -> 8, 16    128 -> 8, 16
+    250 -> 10, 25   250 -> 2, 125    250 -> 2, 125   250 -> 5, 50    250 -> 10, 25
+    360 -> 8, 45    360 -> 2, 180    360 -> 4, 90    360 -> 8, 45    360 -> 12, 30
+    512 -> 16, 32   512 -> 2, 256    512 -> 4, 128   512 -> 8, 64    512 -> 16, 32
+    1024 -> 32, 32  1024 -> 2, 512   1024 -> 4, 256  1024 -> 8, 128  1024 -> 16, 64
+    """
+
+    if factor > 0 and (dimension % factor) == 0 and dimension >= factor**2:
+        m = factor
+        n = dimension // factor
+        if m > n:
+            n, m = m, n
+        return m, n
+    if factor < 0:
+        factor = dimension
+    m, n = 1, dimension
+    length = m + n
+    while m < n:
+        new_m = m + 1
+        while dimension % new_m != 0:
+            new_m += 1
+        new_n = dimension // new_m
+        if new_m + new_n > length or new_m > factor:
+            break
+        else:
+            m, n = new_m, new_n
+    if m > n:
+        n, m = m, n
+    return m, n
diff --git a/comfy/weight_adapter/boft.py b/comfy/weight_adapter/boft.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cbf69fd085bd0e8816191903d132fd9db908152
--- /dev/null
+++ b/comfy/weight_adapter/boft.py
@@ -0,0 +1,218 @@
+import logging
+from typing import Optional
+
+import torch
+import comfy.model_management
+from .base import WeightAdapterBase, weight_decompose
+
+
+class BOFTAdapter(WeightAdapterBase):
+    name = "boft"
+
+    def __init__(self, loaded_keys, weights):
+        self.loaded_keys = loaded_keys
+        self.weights = weights
+
+    @classmethod
+    def load(
+        cls,
+        x: str,
+        lora: dict[str, torch.Tensor],
+        alpha: float,
+        dora_scale: torch.Tensor,
+        loaded_keys: set[str] = None,
+    ) -> Optional["BOFTAdapter"]:
+        if loaded_keys is None:
+            loaded_keys = set()
+        blocks_name = "{}.oft_blocks".format(x)
+        rescale_name = "{}.rescale".format(x)
+
+        blocks = None
+        if blocks_name in lora.keys():
+            blocks = lora[blocks_name]
+            if blocks.ndim == 4:
+                loaded_keys.add(blocks_name)
+            else:
+                blocks = None
+        if blocks is None:
+            return None
+
+        rescale = None
+        if rescale_name in lora.keys():
+            rescale = lora[rescale_name]
+            loaded_keys.add(rescale_name)
+
+        weights = (blocks, rescale, alpha, dora_scale)
+        return cls(loaded_keys, weights)
+
+    def calculate_weight(
+        self,
+        weight,
+        key,
+        strength,
+        strength_model,
+        offset,
+        function,
+        intermediate_dtype=torch.float32,
+        original_weight=None,
+    ):
+        v = self.weights
+        blocks = v[0]
+        rescale = v[1]
+        alpha = v[2]
+        dora_scale = v[3]
+
+        blocks = comfy.model_management.cast_to_device(
+            blocks, weight.device, intermediate_dtype
+        )
+        if rescale is not None:
+            rescale = comfy.model_management.cast_to_device(
+                rescale, weight.device, intermediate_dtype
+            )
+
+        boft_m, block_num, boft_b, *_ = blocks.shape
+
+        try:
+            # Get r
+            I = torch.eye(boft_b, device=blocks.device, dtype=blocks.dtype)
+            # for Q = -Q^T
+            q = blocks - blocks.transpose(-1, -2)
+            normed_q = q
+            if alpha > 0:  # alpha in boft/bboft is for constraint
+                q_norm = torch.norm(q) + 1e-8
+                if q_norm > alpha:
+                    normed_q = q * alpha / q_norm
+            # use float() to prevent unsupported type in .inverse()
+            r = (I + normed_q) @ (I - normed_q).float().inverse()
+            r = r.to(weight)
+            inp = org = weight
+
+            r_b = boft_b // 2
+            for i in range(boft_m):
+                bi = r[i]
+                g = 2
+                k = 2**i * r_b
+                if strength != 1:
+                    bi = bi * strength + (1 - strength) * I
+                inp = (
+                    inp.unflatten(0, (-1, g, k))
+                    .transpose(1, 2)
+                    .flatten(0, 2)
+                    .unflatten(0, (-1, boft_b))
+                )
+                inp = torch.einsum("b i j, b j ...-> b i ...", bi, inp)
+                inp = (
+                    inp.flatten(0, 1)
+                    .unflatten(0, (-1, k, g))
+                    .transpose(1, 2)
+                    .flatten(0, 2)
+                )
+
+            if rescale is not None:
+                inp = inp * rescale
+
+            lora_diff = inp - org
+            lora_diff = comfy.model_management.cast_to_device(
+                lora_diff, weight.device, intermediate_dtype
+            )
+            if dora_scale is not None:
+                weight = weight_decompose(
+                    dora_scale,
+                    weight,
+                    lora_diff,
+                    alpha,
+                    strength,
+                    intermediate_dtype,
+                    function,
+                )
+            else:
+                weight += function((strength * lora_diff).type(weight.dtype))
+        except Exception as e:
+            logging.error("ERROR {} {} {}".format(self.name, key, e))
+        return weight
+
+    def _get_orthogonal_matrices(self, device, dtype):
+        """Compute the orthogonal rotation matrices R from BOFT blocks."""
+        v = self.weights
+        blocks = v[0].to(device=device, dtype=dtype)
+        alpha = v[2]
+        if alpha is None:
+            alpha = 0
+
+        boft_m, block_num, boft_b, _ = blocks.shape
+        I = torch.eye(boft_b, device=device, dtype=dtype)
+
+        # Q = blocks - blocks^T (skew-symmetric)
+        q = blocks - blocks.transpose(-1, -2)
+        normed_q = q
+
+        # Apply constraint if alpha > 0
+        if alpha > 0:
+            q_norm = torch.norm(q) + 1e-8
+            if q_norm > alpha:
+                normed_q = q * alpha / q_norm
+
+        # Cayley transform: R = (I + Q)(I - Q)^-1
+        r = (I + normed_q) @ (I - normed_q).float().inverse()
+        return r, boft_m, boft_b
+
+    def g(self, y: torch.Tensor) -> torch.Tensor:
+        """
+        Output transformation for BOFT: applies butterfly orthogonal transform.
+
+        BOFT uses multiple stages of butterfly-structured orthogonal transforms.
+
+        Reference: LyCORIS ButterflyOFTModule._bypass_forward
+        """
+        v = self.weights
+        rescale = v[1]
+
+        r, boft_m, boft_b = self._get_orthogonal_matrices(y.device, y.dtype)
+        r_b = boft_b // 2
+
+        # Apply multiplier
+        multiplier = getattr(self, "multiplier", 1.0)
+        I = torch.eye(boft_b, device=y.device, dtype=y.dtype)
+
+        # Use module info from bypass injection to determine conv vs linear
+        is_conv = getattr(self, "is_conv", y.dim() > 2)
+
+        if is_conv:
+            # Conv output: (N, C, H, W, ...) -> transpose to (N, H, W, ..., C)
+            y = y.transpose(1, -1)
+
+        # Apply butterfly transform stages
+        inp = y
+        for i in range(boft_m):
+            bi = r[i]  # (block_num, boft_b, boft_b)
+            g = 2
+            k = 2**i * r_b
+
+            # Interpolate with identity based on multiplier
+            if multiplier != 1:
+                bi = bi * multiplier + (1 - multiplier) * I
+
+            # Reshape for butterfly: unflatten last dim, transpose, flatten, unflatten
+            inp = (
+                inp.unflatten(-1, (-1, g, k))
+                .transpose(-2, -1)
+                .flatten(-3)
+                .unflatten(-1, (-1, boft_b))
+            )
+            # Apply block-diagonal orthogonal transform
+            inp = torch.einsum("b i j, ... b j -> ... b i", bi, inp)
+            # Reshape back
+            inp = (
+                inp.flatten(-2).unflatten(-1, (-1, k, g)).transpose(-2, -1).flatten(-3)
+            )
+
+        # Apply rescale if present
+        if rescale is not None:
+            rescale = rescale.to(device=y.device, dtype=y.dtype)
+            inp = inp * rescale.transpose(0, -1)
+
+        if is_conv:
+            # Transpose back: (N, H, W, ..., C) -> (N, C, H, W, ...)
+            inp = inp.transpose(1, -1)
+
+        return inp
diff --git a/comfy/weight_adapter/bypass.py b/comfy/weight_adapter/bypass.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca71b2c2aa7c2bf42751f4544b1bed988d431e4d
--- /dev/null
+++ b/comfy/weight_adapter/bypass.py
@@ -0,0 +1,441 @@
+"""
+Bypass mode implementation for weight adapters (LoRA, LoKr, LoHa, etc.)
+
+Bypass mode applies adapters during forward pass without modifying base weights:
+    bypass(f)(x) = g(f(x) + h(x))
+
+Where:
+    - f(x): Original layer forward
+    - h(x): Additive component from adapter (LoRA path)
+    - g(y): Output transformation (identity for most adapters)
+
+This is useful for:
+    - Training with gradient checkpointing
+    - Avoiding weight modifications when weights are offloaded
+    - Supporting multiple adapters with different strengths dynamically
+"""
+
+import logging
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+
+import comfy.model_management
+from .base import WeightAdapterBase, WeightAdapterTrainBase
+from comfy.patcher_extension import PatcherInjection
+
+# Type alias for adapters that support bypass mode
+BypassAdapter = Union[WeightAdapterBase, WeightAdapterTrainBase]
+
+
+def get_module_type_info(module: nn.Module) -> dict:
+    """
+    Determine module type and extract conv parameters from module class.
+
+    This is more reliable than checking weight.ndim, especially for quantized layers
+    where weight shape might be different.
+
+    Returns:
+        dict with keys: is_conv, conv_dim, stride, padding, dilation, groups
+    """
+    info = {
+        "is_conv": False,
+        "conv_dim": 0,
+        "stride": (1,),
+        "padding": (0,),
+        "dilation": (1,),
+        "groups": 1,
+        "kernel_size": (1,),
+        "in_channels": None,
+        "out_channels": None,
+    }
+
+    # Determine conv type
+    if isinstance(module, nn.Conv1d):
+        info["is_conv"] = True
+        info["conv_dim"] = 1
+    elif isinstance(module, nn.Conv2d):
+        info["is_conv"] = True
+        info["conv_dim"] = 2
+    elif isinstance(module, nn.Conv3d):
+        info["is_conv"] = True
+        info["conv_dim"] = 3
+    elif isinstance(module, nn.Linear):
+        info["is_conv"] = False
+        info["conv_dim"] = 0
+    else:
+        # Try to infer from class name for custom/quantized layers
+        class_name = type(module).__name__.lower()
+        if "conv3d" in class_name:
+            info["is_conv"] = True
+            info["conv_dim"] = 3
+        elif "conv2d" in class_name:
+            info["is_conv"] = True
+            info["conv_dim"] = 2
+        elif "conv1d" in class_name:
+            info["is_conv"] = True
+            info["conv_dim"] = 1
+        elif "conv" in class_name:
+            info["is_conv"] = True
+            info["conv_dim"] = 2
+
+    # Extract conv parameters if it's a conv layer
+    if info["is_conv"]:
+        # Try to get stride, padding, dilation, groups, kernel_size from module
+        info["stride"] = getattr(module, "stride", (1,) * info["conv_dim"])
+        info["padding"] = getattr(module, "padding", (0,) * info["conv_dim"])
+        info["dilation"] = getattr(module, "dilation", (1,) * info["conv_dim"])
+        info["groups"] = getattr(module, "groups", 1)
+        info["kernel_size"] = getattr(module, "kernel_size", (1,) * info["conv_dim"])
+        info["in_channels"] = getattr(module, "in_channels", None)
+        info["out_channels"] = getattr(module, "out_channels", None)
+
+        # Ensure they're tuples
+        if isinstance(info["stride"], int):
+            info["stride"] = (info["stride"],) * info["conv_dim"]
+        if isinstance(info["padding"], int):
+            info["padding"] = (info["padding"],) * info["conv_dim"]
+        if isinstance(info["dilation"], int):
+            info["dilation"] = (info["dilation"],) * info["conv_dim"]
+        if isinstance(info["kernel_size"], int):
+            info["kernel_size"] = (info["kernel_size"],) * info["conv_dim"]
+
+    return info
+
+
+class BypassForwardHook:
+    """
+    Hook that wraps a layer's forward to apply adapter in bypass mode.
+
+    Stores the original forward and replaces it with bypass version.
+
+    Supports both:
+        - WeightAdapterBase: Inference adapters (uses self.weights tuple)
+        - WeightAdapterTrainBase: Training adapters (nn.Module with parameters)
+    """
+
+    def __init__(
+        self,
+        module: nn.Module,
+        adapter: BypassAdapter,
+        multiplier: float = 1.0,
+    ):
+        self.module = module
+        self.adapter = adapter
+        self.multiplier = multiplier
+        self.original_forward = None
+
+        # Determine layer type and conv params from module class (works for quantized layers)
+        module_info = get_module_type_info(module)
+
+        # Set multiplier and layer type info on adapter for use in h()
+        adapter.multiplier = multiplier
+        adapter.is_conv = module_info["is_conv"]
+        adapter.conv_dim = module_info["conv_dim"]
+        adapter.kernel_size = module_info["kernel_size"]
+        adapter.in_channels = module_info["in_channels"]
+        adapter.out_channels = module_info["out_channels"]
+        # Store kw_dict for conv operations (like LyCORIS extra_args)
+        if module_info["is_conv"]:
+            adapter.kw_dict = {
+                "stride": module_info["stride"],
+                "padding": module_info["padding"],
+                "dilation": module_info["dilation"],
+                "groups": module_info["groups"],
+            }
+        else:
+            adapter.kw_dict = {}
+
+    def _bypass_forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+        """Bypass forward: uses adapter's bypass_forward or default g(f(x) + h(x))
+
+        Note:
+            Bypass mode does NOT access original model weights (org_weight).
+            This is intentional - bypass mode is designed for quantized models
+            where weights may not be in a usable format. All necessary shape
+            information is provided via adapter attributes set during inject().
+        """
+        # Check if adapter has custom bypass_forward (e.g., GLoRA)
+        adapter_bypass = getattr(self.adapter, "bypass_forward", None)
+        if adapter_bypass is not None:
+            # Check if it's overridden (not the base class default)
+            # Need to check both base classes since adapter could be either type
+            adapter_type = type(self.adapter)
+            is_default_bypass = (
+                adapter_type.bypass_forward is WeightAdapterBase.bypass_forward
+                or adapter_type.bypass_forward is WeightAdapterTrainBase.bypass_forward
+            )
+            if not is_default_bypass:
+                return adapter_bypass(self.original_forward, x, *args, **kwargs)
+
+        # Default bypass: g(f(x) + h(x, f(x)))
+        base_out = self.original_forward(x, *args, **kwargs)
+        h_out = self.adapter.h(x, base_out)
+        return self.adapter.g(base_out + h_out)
+
+    def inject(self):
+        """Replace module forward with bypass version."""
+        if self.original_forward is not None:
+            logging.debug(
+                f"[BypassHook] Already injected for {type(self.module).__name__}"
+            )
+            return  # Already injected
+
+        # Move adapter weights to compute device (GPU)
+        # Use get_torch_device() instead of module.weight.device because
+        # with offloading, module weights may be on CPU while compute happens on GPU
+        device = comfy.model_management.get_torch_device()
+
+        # Get dtype from module weight if available
+        dtype = None
+        if hasattr(self.module, "weight") and self.module.weight is not None:
+            dtype = self.module.weight.dtype
+
+        # Only use dtype if it's a standard float type, not quantized
+        if dtype is not None and dtype not in (torch.float32, torch.float16, torch.bfloat16):
+            dtype = None
+
+        self._move_adapter_weights_to_device(device, dtype)
+
+        self.original_forward = self.module.forward
+        self.module.forward = self._bypass_forward
+        logging.debug(
+            f"[BypassHook] Injected bypass forward for {type(self.module).__name__} (adapter={type(self.adapter).__name__})"
+        )
+
+    def _move_adapter_weights_to_device(self, device, dtype=None):
+        """Move adapter weights to specified device to avoid per-forward transfers.
+
+        Handles both:
+            - WeightAdapterBase: has self.weights tuple of tensors
+            - WeightAdapterTrainBase: nn.Module with parameters, uses .to() method
+        """
+        adapter = self.adapter
+
+        # Check if adapter is an nn.Module (WeightAdapterTrainBase)
+        if isinstance(adapter, nn.Module):
+            # In training mode we don't touch dtype as trainer will handle it
+            adapter.to(device=device)
+            logging.debug(
+                f"[BypassHook] Moved training adapter (nn.Module) to {device}"
+            )
+            return
+
+        # WeightAdapterBase: handle self.weights tuple
+        if not hasattr(adapter, "weights") or adapter.weights is None:
+            return
+
+        weights = adapter.weights
+        if isinstance(weights, (list, tuple)):
+            new_weights = []
+            for w in weights:
+                if isinstance(w, torch.Tensor):
+                    if dtype is not None:
+                        new_weights.append(w.to(device=device, dtype=dtype))
+                    else:
+                        new_weights.append(w.to(device=device))
+                else:
+                    new_weights.append(w)
+            adapter.weights = (
+                tuple(new_weights) if isinstance(weights, tuple) else new_weights
+            )
+        elif isinstance(weights, torch.Tensor):
+            if dtype is not None:
+                adapter.weights = weights.to(device=device, dtype=dtype)
+            else:
+                adapter.weights = weights.to(device=device)
+
+        logging.debug(f"[BypassHook] Moved adapter weights to {device}")
+
+    def eject(self):
+        """Restore original module forward."""
+        if self.original_forward is None:
+            logging.debug(f"[BypassHook] Not injected for {type(self.module).__name__}")
+            return  # Not injected
+
+        self.module.forward = self.original_forward
+        self.original_forward = None
+        logging.debug(
+            f"[BypassHook] Ejected bypass forward for {type(self.module).__name__}"
+        )
+
+
+class BypassInjectionManager:
+    """
+    Manages bypass mode injection for a collection of adapters.
+
+    Creates PatcherInjection objects that can be used with ModelPatcher.
+
+    Supports both inference adapters (WeightAdapterBase) and training adapters
+    (WeightAdapterTrainBase).
+
+    Usage:
+        manager = BypassInjectionManager()
+        manager.add_adapter("model.layers.0.self_attn.q_proj", lora_adapter, strength=0.8)
+        manager.add_adapter("model.layers.0.self_attn.k_proj", lora_adapter, strength=0.8)
+
+        injections = manager.create_injections(model)
+        model_patcher.set_injections("bypass_lora", injections)
+    """
+
+    def __init__(self):
+        self.adapters: dict[str, tuple[BypassAdapter, float]] = {}
+        self.hooks: list[BypassForwardHook] = []
+
+    def add_adapter(
+        self,
+        key: str,
+        adapter: BypassAdapter,
+        strength: float = 1.0,
+    ):
+        """
+        Add an adapter for a specific weight key.
+
+        Args:
+            key: Weight key (e.g., "model.layers.0.self_attn.q_proj.weight")
+            adapter: The weight adapter (LoRAAdapter, LoKrAdapter, etc.)
+            strength: Multiplier for adapter effect
+        """
+        # Remove .weight suffix if present for module lookup
+        module_key = key
+        if module_key.endswith(".weight"):
+            module_key = module_key[:-7]
+            logging.debug(
+                f"[BypassManager] Stripped .weight suffix: {key} -> {module_key}"
+            )
+
+        self.adapters[module_key] = (adapter, strength)
+        logging.debug(
+            f"[BypassManager] Added adapter: {module_key} (type={type(adapter).__name__}, strength={strength})"
+        )
+
+    def clear_adapters(self):
+        """Remove all adapters."""
+        self.adapters.clear()
+
+    def _get_module_by_key(self, model: nn.Module, key: str) -> Optional[nn.Module]:
+        """Get a submodule by dot-separated key."""
+        parts = key.split(".")
+        module = model
+        try:
+            for i, part in enumerate(parts):
+                if part.isdigit():
+                    module = module[int(part)]
+                else:
+                    module = getattr(module, part)
+            logging.debug(
+                f"[BypassManager] Found module for key {key}: {type(module).__name__}"
+            )
+            return module
+        except (AttributeError, IndexError, KeyError) as e:
+            logging.error(f"[BypassManager] Failed to find module for key {key}: {e}")
+            logging.error(
+                f"[BypassManager] Failed at part index {i}, part={part}, current module type={type(module).__name__}"
+            )
+            return None
+
+    def create_injections(self, model: nn.Module) -> list[PatcherInjection]:
+        """
+        Create PatcherInjection objects for all registered adapters.
+
+        Args:
+            model: The model to inject into (e.g., model_patcher.model)
+
+        Returns:
+            List of PatcherInjection objects to use with model_patcher.set_injections()
+        """
+        self.hooks.clear()
+
+        logging.debug(
+            f"[BypassManager] create_injections called with {len(self.adapters)} adapters"
+        )
+        logging.debug(f"[BypassManager] Model type: {type(model).__name__}")
+
+        for key, (adapter, strength) in self.adapters.items():
+            logging.debug(f"[BypassManager] Looking for module: {key}")
+            module = self._get_module_by_key(model, key)
+
+            if module is None:
+                logging.warning(f"[BypassManager] Module not found for key {key}")
+                continue
+
+            if not hasattr(module, "weight"):
+                logging.warning(
+                    f"[BypassManager] Module {key} has no weight attribute (type={type(module).__name__})"
+                )
+                continue
+
+            logging.debug(
+                f"[BypassManager] Creating hook for {key} (module type={type(module).__name__}, weight shape={module.weight.shape})"
+            )
+            hook = BypassForwardHook(module, adapter, multiplier=strength)
+            self.hooks.append(hook)
+
+        logging.debug(f"[BypassManager] Created {len(self.hooks)} hooks")
+
+        # Create single injection that manages all hooks
+        def inject_all(model_patcher):
+            logging.debug(
+                f"[BypassManager] inject_all called, injecting {len(self.hooks)} hooks"
+            )
+            for hook in self.hooks:
+                hook.inject()
+                logging.debug(
+                    f"[BypassManager] Injected hook for {type(hook.module).__name__}"
+                )
+
+        def eject_all(model_patcher):
+            logging.debug(
+                f"[BypassManager] eject_all called, ejecting {len(self.hooks)} hooks"
+            )
+            for hook in self.hooks:
+                hook.eject()
+
+        return [PatcherInjection(inject=inject_all, eject=eject_all)]
+
+    def get_hook_count(self) -> int:
+        """Return number of hooks that will be/are injected."""
+        return len(self.hooks)
+
+
+def create_bypass_injections_from_patches(
+    model: nn.Module,
+    patches: dict,
+    strength: float = 1.0,
+) -> list[PatcherInjection]:
+    """
+    Convenience function to create bypass injections from a patches dict.
+
+    This is useful when you have patches in the format used by model_patcher.add_patches()
+    and want to apply them in bypass mode instead.
+
+    Args:
+        model: The model to inject into
+        patches: Dict mapping weight keys to adapter data
+        strength: Global strength multiplier
+
+    Returns:
+        List of PatcherInjection objects
+    """
+    manager = BypassInjectionManager()
+
+    for key, patch_list in patches.items():
+        if not patch_list:
+            continue
+
+        # patches format: list of (strength_patch, patch_data, strength_model, offset, function)
+        for patch in patch_list:
+            patch_strength, patch_data, strength_model, offset, function = patch
+
+            # patch_data should be a WeightAdapterBase/WeightAdapterTrainBase or tuple
+            if isinstance(patch_data, (WeightAdapterBase, WeightAdapterTrainBase)):
+                adapter = patch_data
+            else:
+                # Skip non-adapter patches
+                continue
+
+            combined_strength = strength * patch_strength
+            manager.add_adapter(key, adapter, strength=combined_strength)
+
+    return manager.create_injections(model)
diff --git a/comfy/weight_adapter/glora.py b/comfy/weight_adapter/glora.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fdaca6589086f446e4b9db5d7cc795240468a63
--- /dev/null
+++ b/comfy/weight_adapter/glora.py
@@ -0,0 +1,290 @@
+import logging
+from typing import Callable, Optional
+
+import torch
+import torch.nn.functional as F
+import comfy.model_management
+from .base import WeightAdapterBase, weight_decompose
+
+
+class GLoRAAdapter(WeightAdapterBase):
+    name = "glora"
+
+    def __init__(self, loaded_keys, weights):
+        self.loaded_keys = loaded_keys
+        self.weights = weights
+
+    @classmethod
+    def load(
+        cls,
+        x: str,
+        lora: dict[str, torch.Tensor],
+        alpha: float,
+        dora_scale: torch.Tensor,
+        loaded_keys: set[str] = None,
+    ) -> Optional["GLoRAAdapter"]:
+        if loaded_keys is None:
+            loaded_keys = set()
+        a1_name = "{}.a1.weight".format(x)
+        a2_name = "{}.a2.weight".format(x)
+        b1_name = "{}.b1.weight".format(x)
+        b2_name = "{}.b2.weight".format(x)
+        if a1_name in lora:
+            weights = (
+                lora[a1_name],
+                lora[a2_name],
+                lora[b1_name],
+                lora[b2_name],
+                alpha,
+                dora_scale,
+            )
+            loaded_keys.add(a1_name)
+            loaded_keys.add(a2_name)
+            loaded_keys.add(b1_name)
+            loaded_keys.add(b2_name)
+            return cls(loaded_keys, weights)
+        else:
+            return None
+
+    def calculate_weight(
+        self,
+        weight,
+        key,
+        strength,
+        strength_model,
+        offset,
+        function,
+        intermediate_dtype=torch.float32,
+        original_weight=None,
+    ):
+        v = self.weights
+        dora_scale = v[5]
+
+        old_glora = False
+        if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]:
+            rank = v[0].shape[0]
+            old_glora = True
+
+        if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]:
+            if (
+                old_glora
+                and v[1].shape[0] == weight.shape[0]
+                and weight.shape[0] == weight.shape[1]
+            ):
+                pass
+            else:
+                old_glora = False
+                rank = v[1].shape[0]
+
+        a1 = comfy.model_management.cast_to_device(
+            v[0].flatten(start_dim=1), weight.device, intermediate_dtype
+        )
+        a2 = comfy.model_management.cast_to_device(
+            v[1].flatten(start_dim=1), weight.device, intermediate_dtype
+        )
+        b1 = comfy.model_management.cast_to_device(
+            v[2].flatten(start_dim=1), weight.device, intermediate_dtype
+        )
+        b2 = comfy.model_management.cast_to_device(
+            v[3].flatten(start_dim=1), weight.device, intermediate_dtype
+        )
+
+        if v[4] is not None:
+            alpha = v[4] / rank
+        else:
+            alpha = 1.0
+
+        try:
+            if old_glora:
+                lora_diff = (
+                    torch.mm(b2, b1)
+                    + torch.mm(
+                        torch.mm(
+                            weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2
+                        ),
+                        a1,
+                    )
+                ).reshape(
+                    weight.shape
+                )  # old lycoris glora
+            else:
+                if weight.dim() > 2:
+                    lora_diff = torch.einsum(
+                        "o i ..., i j -> o j ...",
+                        torch.einsum(
+                            "o i ..., i j -> o j ...",
+                            weight.to(dtype=intermediate_dtype),
+                            a1,
+                        ),
+                        a2,
+                    ).reshape(weight.shape)
+                else:
+                    lora_diff = torch.mm(
+                        torch.mm(weight.to(dtype=intermediate_dtype), a1), a2
+                    ).reshape(weight.shape)
+                lora_diff += torch.mm(b1, b2).reshape(weight.shape)
+
+            if dora_scale is not None:
+                weight = weight_decompose(
+                    dora_scale,
+                    weight,
+                    lora_diff,
+                    alpha,
+                    strength,
+                    intermediate_dtype,
+                    function,
+                )
+            else:
+                weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+        except Exception as e:
+            logging.error("ERROR {} {} {}".format(self.name, key, e))
+        return weight
+
+    def _compute_paths(self, x: torch.Tensor):
+        """
+        Compute A path and B path outputs for GLoRA bypass.
+
+        GLoRA: f(x) = Wx + WAx + Bx
+        - A path: a1(a2(x)) - modifies input to base forward
+        - B path: b1(b2(x)) - additive component
+
+        Note:
+            Does not access original model weights - bypass mode is designed
+            for quantized models where weights may not be accessible.
+
+        Returns: (a_out, b_out)
+        """
+        v = self.weights
+        # v = (a1, a2, b1, b2, alpha, dora_scale)
+        a1 = v[0]
+        a2 = v[1]
+        b1 = v[2]
+        b2 = v[3]
+        alpha = v[4]
+
+        dtype = x.dtype
+
+        # Cast dtype (weights should already be on correct device from inject())
+        a1 = a1.to(dtype=dtype)
+        a2 = a2.to(dtype=dtype)
+        b1 = b1.to(dtype=dtype)
+        b2 = b2.to(dtype=dtype)
+
+        # Determine rank and scale
+        # Check for old vs new glora format
+        old_glora = False
+        if b2.shape[1] == b1.shape[0] == a1.shape[0] == a2.shape[1]:
+            rank = a1.shape[0]
+            old_glora = True
+
+        if b2.shape[0] == b1.shape[1] == a1.shape[1] == a2.shape[0]:
+            if old_glora and a2.shape[0] == x.shape[-1] and x.shape[-1] == x.shape[-1]:
+                pass
+            else:
+                old_glora = False
+                rank = a2.shape[0]
+
+        if alpha is not None:
+            scale = alpha / rank
+        else:
+            scale = 1.0
+
+        # Apply multiplier
+        multiplier = getattr(self, "multiplier", 1.0)
+        scale = scale * multiplier
+
+        # Use module info from bypass injection, not input tensor shape
+        is_conv = getattr(self, "is_conv", False)
+        conv_dim = getattr(self, "conv_dim", 0)
+        kw_dict = getattr(self, "kw_dict", {})
+
+        if is_conv:
+            # Conv case - conv_dim is 1/2/3 for conv1d/2d/3d
+            conv_fn = (F.conv1d, F.conv2d, F.conv3d)[conv_dim - 1]
+
+            # Get module's stride/padding for spatial dimension handling
+            module_stride = kw_dict.get("stride", (1,) * conv_dim)
+            module_padding = kw_dict.get("padding", (0,) * conv_dim)
+            kernel_size = getattr(self, "kernel_size", (1,) * conv_dim)
+            in_channels = getattr(self, "in_channels", None)
+
+            # Ensure weights are in conv shape
+            # a1, a2, b1 are always 1x1 kernels
+            if a1.ndim == 2:
+                a1 = a1.view(*a1.shape, *([1] * conv_dim))
+            if a2.ndim == 2:
+                a2 = a2.view(*a2.shape, *([1] * conv_dim))
+            if b1.ndim == 2:
+                b1 = b1.view(*b1.shape, *([1] * conv_dim))
+            # b2 has actual kernel_size (like LoRA down)
+            if b2.ndim == 2:
+                if in_channels is not None:
+                    b2 = b2.view(b2.shape[0], in_channels, *kernel_size)
+                else:
+                    b2 = b2.view(*b2.shape, *([1] * conv_dim))
+
+            # A path: a2(x) -> a1(...) - 1x1 convs, no stride/padding needed, a_out is added to x
+            a2_out = conv_fn(x, a2)
+            a_out = conv_fn(a2_out, a1) * scale
+
+            # B path: b2(x) with kernel/stride/padding -> b1(...) 1x1
+            b2_out = conv_fn(x, b2, stride=module_stride, padding=module_padding)
+            b_out = conv_fn(b2_out, b1) * scale
+        else:
+            # Linear case
+            if old_glora:
+                # Old format: a1 @ a2 @ x, b2 @ b1
+                a_out = F.linear(F.linear(x, a2), a1) * scale
+                b_out = F.linear(F.linear(x, b1), b2) * scale
+            else:
+                # New format: x @ a1 @ a2, b1 @ b2
+                a_out = F.linear(F.linear(x, a1), a2) * scale
+                b_out = F.linear(F.linear(x, b2), b1) * scale
+
+        return a_out, b_out
+
+    def bypass_forward(
+        self,
+        org_forward: Callable,
+        x: torch.Tensor,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        GLoRA bypass forward: f(x + a(x)) + b(x)
+
+        Unlike standard adapters, GLoRA modifies the input to the base forward
+        AND adds the B path output.
+
+        Note:
+            Does not access original model weights - bypass mode is designed
+            for quantized models where weights may not be accessible.
+
+        Reference: LyCORIS GLoRAModule._bypass_forward
+        """
+        a_out, b_out = self._compute_paths(x)
+
+        # Call base forward with modified input
+        base_out = org_forward(x + a_out, *args, **kwargs)
+
+        # Add B path
+        return base_out + b_out
+
+    def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:
+        """
+        For GLoRA, h() returns the B path output.
+
+        Note:
+            GLoRA's full bypass requires overriding bypass_forward() since
+            it also modifies the input to org_forward. This h() is provided for
+            compatibility but bypass_forward() should be used for correct behavior.
+
+            Does not access original model weights - bypass mode is designed
+            for quantized models where weights may not be accessible.
+
+        Args:
+            x: Input tensor
+            base_out: Output from base forward (unused, for API consistency)
+        """
+        _, b_out = self._compute_paths(x)
+        return b_out
diff --git a/comfy/weight_adapter/loha.py b/comfy/weight_adapter/loha.py
new file mode 100644
index 0000000000000000000000000000000000000000..387a9258bf21e90bdb5622540a72cbd925c0a589
--- /dev/null
+++ b/comfy/weight_adapter/loha.py
@@ -0,0 +1,378 @@
+import logging
+from functools import cache
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+import comfy.model_management
+from .base import WeightAdapterBase, WeightAdapterTrainBase, weight_decompose
+
+
+@cache
+def _warn_loha_bypass_inefficient():
+    """One-time warning about LoHa bypass inefficiency."""
+    logging.warning(
+        "LoHa bypass mode is inefficient: full weight diff is computed each forward pass. "
+        "Consider using LoRA or LoKr for training with bypass mode."
+    )
+
+
+class HadaWeight(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, w1u, w1d, w2u, w2d, scale=torch.tensor(1)):
+        ctx.save_for_backward(w1d, w1u, w2d, w2u, scale)
+        diff_weight = ((w1u @ w1d) * (w2u @ w2d)) * scale
+        return diff_weight
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        (w1d, w1u, w2d, w2u, scale) = ctx.saved_tensors
+        grad_out = grad_out * scale
+        temp = grad_out * (w2u @ w2d)
+        grad_w1u = temp @ w1d.T
+        grad_w1d = w1u.T @ temp
+
+        temp = grad_out * (w1u @ w1d)
+        grad_w2u = temp @ w2d.T
+        grad_w2d = w2u.T @ temp
+
+        del temp
+        return grad_w1u, grad_w1d, grad_w2u, grad_w2d, None
+
+
+class HadaWeightTucker(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, t1, w1u, w1d, t2, w2u, w2d, scale=torch.tensor(1)):
+        ctx.save_for_backward(t1, w1d, w1u, t2, w2d, w2u, scale)
+
+        rebuild1 = torch.einsum("i j ..., j r, i p -> p r ...", t1, w1d, w1u)
+        rebuild2 = torch.einsum("i j ..., j r, i p -> p r ...", t2, w2d, w2u)
+
+        return rebuild1 * rebuild2 * scale
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        (t1, w1d, w1u, t2, w2d, w2u, scale) = ctx.saved_tensors
+        grad_out = grad_out * scale
+
+        temp = torch.einsum("i j ..., j r -> i r ...", t2, w2d)
+        rebuild = torch.einsum("i j ..., i r -> r j ...", temp, w2u)
+
+        grad_w = rebuild * grad_out
+        del rebuild
+
+        grad_w1u = torch.einsum("r j ..., i j ... -> r i", temp, grad_w)
+        grad_temp = torch.einsum("i j ..., i r -> r j ...", grad_w, w1u.T)
+        del grad_w, temp
+
+        grad_w1d = torch.einsum("i r ..., i j ... -> r j", t1, grad_temp)
+        grad_t1 = torch.einsum("i j ..., j r -> i r ...", grad_temp, w1d.T)
+        del grad_temp
+
+        temp = torch.einsum("i j ..., j r -> i r ...", t1, w1d)
+        rebuild = torch.einsum("i j ..., i r -> r j ...", temp, w1u)
+
+        grad_w = rebuild * grad_out
+        del rebuild
+
+        grad_w2u = torch.einsum("r j ..., i j ... -> r i", temp, grad_w)
+        grad_temp = torch.einsum("i j ..., i r -> r j ...", grad_w, w2u.T)
+        del grad_w, temp
+
+        grad_w2d = torch.einsum("i r ..., i j ... -> r j", t2, grad_temp)
+        grad_t2 = torch.einsum("i j ..., j r -> i r ...", grad_temp, w2d.T)
+        del grad_temp
+        return grad_t1, grad_w1u, grad_w1d, grad_t2, grad_w2u, grad_w2d, None
+
+
+class LohaDiff(WeightAdapterTrainBase):
+    def __init__(self, weights):
+        super().__init__()
+        # Unpack weights tuple from LoHaAdapter
+        w1a, w1b, alpha, w2a, w2b, t1, t2, _ = weights
+
+        # Create trainable parameters
+        self.hada_w1_a = torch.nn.Parameter(w1a)
+        self.hada_w1_b = torch.nn.Parameter(w1b)
+        self.hada_w2_a = torch.nn.Parameter(w2a)
+        self.hada_w2_b = torch.nn.Parameter(w2b)
+
+        self.use_tucker = False
+        if t1 is not None and t2 is not None:
+            self.use_tucker = True
+            self.hada_t1 = torch.nn.Parameter(t1)
+            self.hada_t2 = torch.nn.Parameter(t2)
+        else:
+            # Keep the attributes for consistent access
+            self.hada_t1 = None
+            self.hada_t2 = None
+
+        # Store rank and non-trainable alpha
+        self.rank = w1b.shape[0]
+        self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False)
+
+    def __call__(self, w):
+        org_dtype = w.dtype
+
+        scale = self.alpha / self.rank
+        if self.use_tucker:
+            diff_weight = HadaWeightTucker.apply(
+                self.hada_t1,
+                self.hada_w1_a,
+                self.hada_w1_b,
+                self.hada_t2,
+                self.hada_w2_a,
+                self.hada_w2_b,
+                scale,
+            )
+        else:
+            diff_weight = HadaWeight.apply(
+                self.hada_w1_a, self.hada_w1_b, self.hada_w2_a, self.hada_w2_b, scale
+            )
+
+        # Add the scaled difference to the original weight
+        weight = w.to(diff_weight) + diff_weight.reshape(w.shape)
+
+        return weight.to(org_dtype)
+
+    def passive_memory_usage(self):
+        """Calculates memory usage of the trainable parameters."""
+        return sum(param.numel() * param.element_size() for param in self.parameters())
+
+
+class LoHaAdapter(WeightAdapterBase):
+    name = "loha"
+
+    def __init__(self, loaded_keys, weights):
+        self.loaded_keys = loaded_keys
+        self.weights = weights
+
+    @classmethod
+    def create_train(cls, weight, rank=1, alpha=1.0):
+        out_dim = weight.shape[0]
+        in_dim = weight.shape[1:].numel()
+        mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=torch.float32)
+        mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=torch.float32)
+        torch.nn.init.normal_(mat1, 0.1)
+        torch.nn.init.constant_(mat2, 0.0)
+        mat3 = torch.empty(out_dim, rank, device=weight.device, dtype=torch.float32)
+        mat4 = torch.empty(rank, in_dim, device=weight.device, dtype=torch.float32)
+        torch.nn.init.normal_(mat3, 0.1)
+        torch.nn.init.normal_(mat4, 0.01)
+        return LohaDiff((mat1, mat2, alpha, mat3, mat4, None, None, None))
+
+    def to_train(self):
+        return LohaDiff(self.weights)
+
+    @classmethod
+    def load(
+        cls,
+        x: str,
+        lora: dict[str, torch.Tensor],
+        alpha: float,
+        dora_scale: torch.Tensor,
+        loaded_keys: set[str] = None,
+    ) -> Optional["LoHaAdapter"]:
+        if loaded_keys is None:
+            loaded_keys = set()
+
+        hada_w1_a_name = "{}.hada_w1_a".format(x)
+        hada_w1_b_name = "{}.hada_w1_b".format(x)
+        hada_w2_a_name = "{}.hada_w2_a".format(x)
+        hada_w2_b_name = "{}.hada_w2_b".format(x)
+        hada_t1_name = "{}.hada_t1".format(x)
+        hada_t2_name = "{}.hada_t2".format(x)
+        if hada_w1_a_name in lora.keys():
+            hada_t1 = None
+            hada_t2 = None
+            if hada_t1_name in lora.keys():
+                hada_t1 = lora[hada_t1_name]
+                hada_t2 = lora[hada_t2_name]
+                loaded_keys.add(hada_t1_name)
+                loaded_keys.add(hada_t2_name)
+
+            weights = (
+                lora[hada_w1_a_name],
+                lora[hada_w1_b_name],
+                alpha,
+                lora[hada_w2_a_name],
+                lora[hada_w2_b_name],
+                hada_t1,
+                hada_t2,
+                dora_scale,
+            )
+            loaded_keys.add(hada_w1_a_name)
+            loaded_keys.add(hada_w1_b_name)
+            loaded_keys.add(hada_w2_a_name)
+            loaded_keys.add(hada_w2_b_name)
+            return cls(loaded_keys, weights)
+        else:
+            return None
+
+    def calculate_weight(
+        self,
+        weight,
+        key,
+        strength,
+        strength_model,
+        offset,
+        function,
+        intermediate_dtype=torch.float32,
+        original_weight=None,
+    ):
+        v = self.weights
+        w1a = v[0]
+        w1b = v[1]
+        if v[2] is not None:
+            alpha = v[2] / w1b.shape[0]
+        else:
+            alpha = 1.0
+
+        w2a = v[3]
+        w2b = v[4]
+        dora_scale = v[7]
+        if v[5] is not None:  # cp decomposition
+            t1 = v[5]
+            t2 = v[6]
+            m1 = torch.einsum(
+                "i j k l, j r, i p -> p r k l",
+                comfy.model_management.cast_to_device(
+                    t1, weight.device, intermediate_dtype
+                ),
+                comfy.model_management.cast_to_device(
+                    w1b, weight.device, intermediate_dtype
+                ),
+                comfy.model_management.cast_to_device(
+                    w1a, weight.device, intermediate_dtype
+                ),
+            )
+
+            m2 = torch.einsum(
+                "i j k l, j r, i p -> p r k l",
+                comfy.model_management.cast_to_device(
+                    t2, weight.device, intermediate_dtype
+                ),
+                comfy.model_management.cast_to_device(
+                    w2b, weight.device, intermediate_dtype
+                ),
+                comfy.model_management.cast_to_device(
+                    w2a, weight.device, intermediate_dtype
+                ),
+            )
+        else:
+            m1 = torch.mm(
+                comfy.model_management.cast_to_device(
+                    w1a, weight.device, intermediate_dtype
+                ),
+                comfy.model_management.cast_to_device(
+                    w1b, weight.device, intermediate_dtype
+                ),
+            )
+            m2 = torch.mm(
+                comfy.model_management.cast_to_device(
+                    w2a, weight.device, intermediate_dtype
+                ),
+                comfy.model_management.cast_to_device(
+                    w2b, weight.device, intermediate_dtype
+                ),
+            )
+
+        try:
+            lora_diff = (m1 * m2).reshape(weight.shape)
+            if dora_scale is not None:
+                weight = weight_decompose(
+                    dora_scale,
+                    weight,
+                    lora_diff,
+                    alpha,
+                    strength,
+                    intermediate_dtype,
+                    function,
+                )
+            else:
+                weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+        except Exception as e:
+            logging.error("ERROR {} {} {}".format(self.name, key, e))
+        return weight
+
+    def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:
+        """
+        Additive bypass component for LoHa: h(x) = diff_weight @ x
+
+        WARNING: Inefficient - computes full Hadamard product each forward.
+
+        Note:
+            Does not access original model weights - bypass mode is designed
+            for quantized models where weights may not be accessible.
+
+        Args:
+            x: Input tensor
+            base_out: Output from base forward (unused, for API consistency)
+
+        Reference: LyCORIS functional/loha.py bypass_forward_diff
+        """
+        _warn_loha_bypass_inefficient()
+
+        # FUNC_LIST: [None, None, F.linear, F.conv1d, F.conv2d, F.conv3d]
+        FUNC_LIST = [None, None, F.linear, F.conv1d, F.conv2d, F.conv3d]
+
+        v = self.weights
+        # v[0]=w1a, v[1]=w1b, v[2]=alpha, v[3]=w2a, v[4]=w2b, v[5]=t1, v[6]=t2, v[7]=dora
+        w1a = v[0]
+        w1b = v[1]
+        alpha = v[2]
+        w2a = v[3]
+        w2b = v[4]
+        t1 = v[5]
+        t2 = v[6]
+
+        # Compute scale
+        rank = w1b.shape[0]
+        scale = (alpha / rank if alpha is not None else 1.0) * getattr(
+            self, "multiplier", 1.0
+        )
+
+        # Cast dtype
+        w1a = w1a.to(dtype=x.dtype)
+        w1b = w1b.to(dtype=x.dtype)
+        w2a = w2a.to(dtype=x.dtype)
+        w2b = w2b.to(dtype=x.dtype)
+
+        # Use module info from bypass injection, not weight dimension
+        is_conv = getattr(self, "is_conv", False)
+        conv_dim = getattr(self, "conv_dim", 0)
+        kw_dict = getattr(self, "kw_dict", {})
+
+        # Compute diff weight using Hadamard product
+        if t1 is not None and t2 is not None:
+            t1 = t1.to(dtype=x.dtype)
+            t2 = t2.to(dtype=x.dtype)
+            m1 = torch.einsum("i j k l, j r, i p -> p r k l", t1, w1b, w1a)
+            m2 = torch.einsum("i j k l, j r, i p -> p r k l", t2, w2b, w2a)
+            diff_weight = (m1 * m2) * scale
+        else:
+            m1 = w1a @ w1b
+            m2 = w2a @ w2b
+            diff_weight = (m1 * m2) * scale
+
+        if is_conv:
+            op = FUNC_LIST[conv_dim + 2]
+            kernel_size = getattr(self, "kernel_size", (1,) * conv_dim)
+            in_channels = getattr(self, "in_channels", None)
+
+            # Reshape 2D diff_weight to conv format using kernel_size
+            # diff_weight: [out_channels, in_channels * prod(kernel_size)] -> [out_channels, in_channels, *kernel_size]
+            if diff_weight.dim() == 2:
+                if in_channels is not None:
+                    diff_weight = diff_weight.view(
+                        diff_weight.shape[0], in_channels, *kernel_size
+                    )
+                else:
+                    diff_weight = diff_weight.view(
+                        *diff_weight.shape, *([1] * conv_dim)
+                    )
+        else:
+            op = F.linear
+            kw_dict = {}
+
+        return op(x, diff_weight, **kw_dict)
diff --git a/comfy/weight_adapter/lokr.py b/comfy/weight_adapter/lokr.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5a6014513390bac8d6f5d2d55588764d86c5dc4
--- /dev/null
+++ b/comfy/weight_adapter/lokr.py
@@ -0,0 +1,481 @@
+import logging
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+import comfy.model_management
+from .base import (
+    WeightAdapterBase,
+    WeightAdapterTrainBase,
+    weight_decompose,
+    factorization,
+)
+
+
+class LokrDiff(WeightAdapterTrainBase):
+    def __init__(self, weights):
+        super().__init__()
+        (
+            lokr_w1,
+            lokr_w2,
+            alpha,
+            lokr_w1_a,
+            lokr_w1_b,
+            lokr_w2_a,
+            lokr_w2_b,
+            lokr_t2,
+            dora_scale,
+        ) = weights
+        self.use_tucker = False
+        if lokr_w1_a is not None:
+            _, rank_a = lokr_w1_a.shape[0], lokr_w1_a.shape[1]
+            rank_a, _ = lokr_w1_b.shape[0], lokr_w1_b.shape[1]
+            self.lokr_w1_a = torch.nn.Parameter(lokr_w1_a)
+            self.lokr_w1_b = torch.nn.Parameter(lokr_w1_b)
+            self.w1_rebuild = True
+            self.ranka = rank_a
+
+        if lokr_w2_a is not None:
+            _, rank_b = lokr_w2_a.shape[0], lokr_w2_a.shape[1]
+            rank_b, _ = lokr_w2_b.shape[0], lokr_w2_b.shape[1]
+            self.lokr_w2_a = torch.nn.Parameter(lokr_w2_a)
+            self.lokr_w2_b = torch.nn.Parameter(lokr_w2_b)
+            if lokr_t2 is not None:
+                self.use_tucker = True
+                self.lokr_t2 = torch.nn.Parameter(lokr_t2)
+            self.w2_rebuild = True
+            self.rankb = rank_b
+
+        if lokr_w1 is not None:
+            self.lokr_w1 = torch.nn.Parameter(lokr_w1)
+            self.w1_rebuild = False
+
+        if lokr_w2 is not None:
+            self.lokr_w2 = torch.nn.Parameter(lokr_w2)
+            self.w2_rebuild = False
+
+        self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False)
+
+    @property
+    def w1(self):
+        if self.w1_rebuild:
+            return (self.lokr_w1_a @ self.lokr_w1_b) * (self.alpha / self.ranka)
+        else:
+            return self.lokr_w1
+
+    @property
+    def w2(self):
+        if self.w2_rebuild:
+            if self.use_tucker:
+                w2 = torch.einsum(
+                    "i j k l, j r, i p -> p r k l",
+                    self.lokr_t2,
+                    self.lokr_w2_b,
+                    self.lokr_w2_a,
+                )
+            else:
+                w2 = self.lokr_w2_a @ self.lokr_w2_b
+            return w2 * (self.alpha / self.rankb)
+        else:
+            return self.lokr_w2
+
+    def __call__(self, w):
+        w1 = self.w1
+        w2 = self.w2
+        # Unsqueeze w1 to match w2 dims for proper kron product (like LyCORIS make_kron)
+        for _ in range(w2.dim() - w1.dim()):
+            w1 = w1.unsqueeze(-1)
+        diff = torch.kron(w1, w2)
+        return w + diff.reshape(w.shape).to(w)
+
+    def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:
+        """
+        Additive bypass component for LoKr training: efficient Kronecker product.
+
+        Uses w1/w2 properties which handle both direct and decomposed cases.
+        For create_train (direct w1/w2), no alpha scaling in properties.
+        For to_train (decomposed), alpha/rank scaling is in properties.
+
+        Args:
+            x: Input tensor
+            base_out: Output from base forward (unused, for API consistency)
+        """
+        # Get w1, w2 from properties (handles rebuild vs direct)
+        w1 = self.w1
+        w2 = self.w2
+
+        # Multiplier from bypass injection
+        multiplier = getattr(self, "multiplier", 1.0)
+
+        # Get module info from bypass injection
+        is_conv = getattr(self, "is_conv", False)
+        conv_dim = getattr(self, "conv_dim", 0)
+        kw_dict = getattr(self, "kw_dict", {})
+
+        # Efficient Kronecker application without materializing full weight
+        # kron(w1, w2) @ x can be computed as nested operations
+        # w1: [out_l, in_m], w2: [out_k, in_n, *k_size]
+        # Full weight would be [out_l*out_k, in_m*in_n, *k_size]
+
+        uq = w1.size(1)  # in_m - inner grouping dimension
+
+        if is_conv:
+            conv_fn = (F.conv1d, F.conv2d, F.conv3d)[conv_dim - 1]
+
+            B, C_in, *spatial = x.shape
+            # Reshape input for grouped application: [B * uq, C_in // uq, *spatial]
+            h_in_group = x.reshape(B * uq, -1, *spatial)
+
+            # Ensure w2 has conv dims
+            if w2.dim() == 2:
+                w2 = w2.view(*w2.shape, *([1] * conv_dim))
+
+            # Apply w2 path with stride/padding
+            hb = conv_fn(h_in_group, w2, **kw_dict)
+
+            # Reshape for cross-group operation
+            hb = hb.view(B, -1, *hb.shape[1:])
+            h_cross = hb.transpose(1, -1)
+
+            # Apply w1 (always 2D, applied as linear on channel dim)
+            hc = F.linear(h_cross, w1)
+            hc = hc.transpose(1, -1)
+
+            # Reshape to output
+            out = hc.reshape(B, -1, *hc.shape[3:])
+        else:
+            # Linear case
+            # Reshape input: [..., in_m * in_n] -> [..., uq (in_m), in_n]
+            h_in_group = x.reshape(*x.shape[:-1], uq, -1)
+
+            # Apply w2: [..., uq, in_n] @ [out_k, in_n].T -> [..., uq, out_k]
+            hb = F.linear(h_in_group, w2)
+
+            # Transpose for w1: [..., uq, out_k] -> [..., out_k, uq]
+            h_cross = hb.transpose(-1, -2)
+
+            # Apply w1: [..., out_k, uq] @ [out_l, uq].T -> [..., out_k, out_l]
+            hc = F.linear(h_cross, w1)
+
+            # Transpose back and flatten: [..., out_k, out_l] -> [..., out_l * out_k]
+            hc = hc.transpose(-1, -2)
+            out = hc.reshape(*hc.shape[:-2], -1)
+
+        return out * multiplier
+
+    def passive_memory_usage(self):
+        return sum(param.numel() * param.element_size() for param in self.parameters())
+
+
+class LoKrAdapter(WeightAdapterBase):
+    name = "lokr"
+
+    def __init__(self, loaded_keys, weights):
+        self.loaded_keys = loaded_keys
+        self.weights = weights
+
+    @classmethod
+    def create_train(cls, weight, rank=1, alpha=1.0):
+        out_dim = weight.shape[0]
+        in_dim = weight.shape[1]  # Just in_channels, not flattened with kernel
+        k_size = weight.shape[2:] if weight.dim() > 2 else ()
+
+        out_l, out_k = factorization(out_dim, rank)
+        in_m, in_n = factorization(in_dim, rank)
+
+        # w1: [out_l, in_m]
+        mat1 = torch.empty(out_l, in_m, device=weight.device, dtype=torch.float32)
+        # w2: [out_k, in_n, *k_size] for conv, [out_k, in_n] for linear
+        mat2 = torch.empty(
+            out_k, in_n, *k_size, device=weight.device, dtype=torch.float32
+        )
+
+        torch.nn.init.kaiming_uniform_(mat2, a=5**0.5)
+        torch.nn.init.constant_(mat1, 0.0)
+        return LokrDiff((mat1, mat2, alpha, None, None, None, None, None, None))
+
+    def to_train(self):
+        return LokrDiff(self.weights)
+
+    @classmethod
+    def load(
+        cls,
+        x: str,
+        lora: dict[str, torch.Tensor],
+        alpha: float,
+        dora_scale: torch.Tensor,
+        loaded_keys: set[str] = None,
+    ) -> Optional["LoKrAdapter"]:
+        if loaded_keys is None:
+            loaded_keys = set()
+        lokr_w1_name = "{}.lokr_w1".format(x)
+        lokr_w2_name = "{}.lokr_w2".format(x)
+        lokr_w1_a_name = "{}.lokr_w1_a".format(x)
+        lokr_w1_b_name = "{}.lokr_w1_b".format(x)
+        lokr_t2_name = "{}.lokr_t2".format(x)
+        lokr_w2_a_name = "{}.lokr_w2_a".format(x)
+        lokr_w2_b_name = "{}.lokr_w2_b".format(x)
+
+        lokr_w1 = None
+        if lokr_w1_name in lora.keys():
+            lokr_w1 = lora[lokr_w1_name]
+            loaded_keys.add(lokr_w1_name)
+
+        lokr_w2 = None
+        if lokr_w2_name in lora.keys():
+            lokr_w2 = lora[lokr_w2_name]
+            loaded_keys.add(lokr_w2_name)
+
+        lokr_w1_a = None
+        if lokr_w1_a_name in lora.keys():
+            lokr_w1_a = lora[lokr_w1_a_name]
+            loaded_keys.add(lokr_w1_a_name)
+
+        lokr_w1_b = None
+        if lokr_w1_b_name in lora.keys():
+            lokr_w1_b = lora[lokr_w1_b_name]
+            loaded_keys.add(lokr_w1_b_name)
+
+        lokr_w2_a = None
+        if lokr_w2_a_name in lora.keys():
+            lokr_w2_a = lora[lokr_w2_a_name]
+            loaded_keys.add(lokr_w2_a_name)
+
+        lokr_w2_b = None
+        if lokr_w2_b_name in lora.keys():
+            lokr_w2_b = lora[lokr_w2_b_name]
+            loaded_keys.add(lokr_w2_b_name)
+
+        lokr_t2 = None
+        if lokr_t2_name in lora.keys():
+            lokr_t2 = lora[lokr_t2_name]
+            loaded_keys.add(lokr_t2_name)
+
+        if (
+            (lokr_w1 is not None)
+            or (lokr_w2 is not None)
+            or (lokr_w1_a is not None)
+            or (lokr_w2_a is not None)
+        ):
+            weights = (
+                lokr_w1,
+                lokr_w2,
+                alpha,
+                lokr_w1_a,
+                lokr_w1_b,
+                lokr_w2_a,
+                lokr_w2_b,
+                lokr_t2,
+                dora_scale,
+            )
+            return cls(loaded_keys, weights)
+        else:
+            return None
+
+    def calculate_weight(
+        self,
+        weight,
+        key,
+        strength,
+        strength_model,
+        offset,
+        function,
+        intermediate_dtype=torch.float32,
+        original_weight=None,
+    ):
+        v = self.weights
+        w1 = v[0]
+        w2 = v[1]
+        w1_a = v[3]
+        w1_b = v[4]
+        w2_a = v[5]
+        w2_b = v[6]
+        t2 = v[7]
+        dora_scale = v[8]
+        dim = None
+
+        if w1 is None:
+            dim = w1_b.shape[0]
+            w1 = torch.mm(
+                comfy.model_management.cast_to_device(
+                    w1_a, weight.device, intermediate_dtype
+                ),
+                comfy.model_management.cast_to_device(
+                    w1_b, weight.device, intermediate_dtype
+                ),
+            )
+        else:
+            w1 = comfy.model_management.cast_to_device(
+                w1, weight.device, intermediate_dtype
+            )
+
+        if w2 is None:
+            dim = w2_b.shape[0]
+            if t2 is None:
+                w2 = torch.mm(
+                    comfy.model_management.cast_to_device(
+                        w2_a, weight.device, intermediate_dtype
+                    ),
+                    comfy.model_management.cast_to_device(
+                        w2_b, weight.device, intermediate_dtype
+                    ),
+                )
+            else:
+                w2 = torch.einsum(
+                    "i j k l, j r, i p -> p r k l",
+                    comfy.model_management.cast_to_device(
+                        t2, weight.device, intermediate_dtype
+                    ),
+                    comfy.model_management.cast_to_device(
+                        w2_b, weight.device, intermediate_dtype
+                    ),
+                    comfy.model_management.cast_to_device(
+                        w2_a, weight.device, intermediate_dtype
+                    ),
+                )
+        else:
+            w2 = comfy.model_management.cast_to_device(
+                w2, weight.device, intermediate_dtype
+            )
+
+        if len(w2.shape) == 4:
+            w1 = w1.unsqueeze(2).unsqueeze(2)
+        if v[2] is not None and dim is not None:
+            alpha = v[2] / dim
+        else:
+            alpha = 1.0
+
+        try:
+            lora_diff = torch.kron(w1, w2).reshape(weight.shape)
+            if dora_scale is not None:
+                weight = weight_decompose(
+                    dora_scale,
+                    weight,
+                    lora_diff,
+                    alpha,
+                    strength,
+                    intermediate_dtype,
+                    function,
+                )
+            else:
+                weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+        except Exception as e:
+            logging.error("ERROR {} {} {}".format(self.name, key, e))
+        return weight
+
+    def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:
+        """
+        Additive bypass component for LoKr: efficient Kronecker product application.
+
+        Note:
+            Does not access original model weights - bypass mode is designed
+            for quantized models where weights may not be accessible.
+
+        Args:
+            x: Input tensor
+            base_out: Output from base forward (unused, for API consistency)
+
+        Reference: LyCORIS functional/lokr.py bypass_forward_diff
+        """
+        # FUNC_LIST: [None, None, F.linear, F.conv1d, F.conv2d, F.conv3d]
+        FUNC_LIST = [None, None, F.linear, F.conv1d, F.conv2d, F.conv3d]
+
+        v = self.weights
+        # v[0]=w1, v[1]=w2, v[2]=alpha, v[3]=w1_a, v[4]=w1_b, v[5]=w2_a, v[6]=w2_b, v[7]=t2, v[8]=dora
+        w1 = v[0]
+        w2 = v[1]
+        alpha = v[2]
+        w1_a = v[3]
+        w1_b = v[4]
+        w2_a = v[5]
+        w2_b = v[6]
+        t2 = v[7]
+
+        use_w1 = w1 is not None
+        use_w2 = w2 is not None
+        tucker = t2 is not None
+
+        # Use module info from bypass injection, not weight dimension
+        is_conv = getattr(self, "is_conv", False)
+        conv_dim = getattr(self, "conv_dim", 0)
+        kw_dict = getattr(self, "kw_dict", {}) if is_conv else {}
+
+        if is_conv:
+            op = FUNC_LIST[conv_dim + 2]
+        else:
+            op = F.linear
+
+        # Determine rank and scale
+        rank = w1_b.size(0) if not use_w1 else w2_b.size(0) if not use_w2 else alpha
+        scale = (alpha / rank if alpha is not None else 1.0) * getattr(
+            self, "multiplier", 1.0
+        )
+
+        # Build c (w1)
+        if use_w1:
+            c = w1.to(dtype=x.dtype)
+        else:
+            c = w1_a.to(dtype=x.dtype) @ w1_b.to(dtype=x.dtype)
+        uq = c.size(1)
+
+        # Build w2 components
+        if use_w2:
+            ba = w2.to(dtype=x.dtype)
+        else:
+            a = w2_b.to(dtype=x.dtype)
+            b = w2_a.to(dtype=x.dtype)
+            if is_conv:
+                if tucker:
+                    # Tucker: a, b get 1s appended (kernel is in t2)
+                    if a.dim() == 2:
+                        a = a.view(*a.shape, *([1] * conv_dim))
+                    if b.dim() == 2:
+                        b = b.view(*b.shape, *([1] * conv_dim))
+                else:
+                    # Non-tucker conv: b may need 1s appended
+                    if b.dim() == 2:
+                        b = b.view(*b.shape, *([1] * conv_dim))
+
+        # Reshape input by uq groups
+        if is_conv:
+            B, _, *rest = x.shape
+            h_in_group = x.reshape(B * uq, -1, *rest)
+        else:
+            h_in_group = x.reshape(*x.shape[:-1], uq, -1)
+
+        # Apply w2 path
+        if use_w2:
+            hb = op(h_in_group, ba, **kw_dict)
+        else:
+            if is_conv:
+                if tucker:
+                    t = t2.to(dtype=x.dtype)
+                    if t.dim() == 2:
+                        t = t.view(*t.shape, *([1] * conv_dim))
+                    ha = op(h_in_group, a)
+                    ht = op(ha, t, **kw_dict)
+                    hb = op(ht, b)
+                else:
+                    ha = op(h_in_group, a, **kw_dict)
+                    hb = op(ha, b)
+            else:
+                ha = op(h_in_group, a)
+                hb = op(ha, b)
+
+        # Reshape and apply c (w1)
+        if is_conv:
+            hb = hb.view(B, -1, *hb.shape[1:])
+            h_cross_group = hb.transpose(1, -1)
+        else:
+            h_cross_group = hb.transpose(-1, -2)
+
+        hc = F.linear(h_cross_group, c)
+
+        if is_conv:
+            hc = hc.transpose(1, -1)
+            out = hc.reshape(B, -1, *hc.shape[3:])
+        else:
+            hc = hc.transpose(-1, -2)
+            out = hc.reshape(*hc.shape[:-2], -1)
+
+        return out * scale
diff --git a/comfy/weight_adapter/lora.py b/comfy/weight_adapter/lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..30fce2b23aacfbaba09f29008212f2602e570c93
--- /dev/null
+++ b/comfy/weight_adapter/lora.py
@@ -0,0 +1,368 @@
+import logging
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+import comfy.model_management
+from .base import (
+    WeightAdapterBase,
+    WeightAdapterTrainBase,
+    weight_decompose,
+    pad_tensor_to_shape,
+    tucker_weight_from_conv,
+)
+
+
+class LoraDiff(WeightAdapterTrainBase):
+    def __init__(self, weights):
+        super().__init__()
+        mat1, mat2, alpha, mid, dora_scale, reshape = weights
+        out_dim, rank = mat1.shape[0], mat1.shape[1]
+        rank, in_dim = mat2.shape[0], mat2.shape[1]
+        if mid is not None:
+            convdim = mid.ndim - 2
+            layer = (torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d)[convdim]
+        else:
+            layer = torch.nn.Linear
+        self.lora_up = layer(rank, out_dim, bias=False)
+        self.lora_down = layer(in_dim, rank, bias=False)
+        self.lora_up.weight.data.copy_(mat1)
+        self.lora_down.weight.data.copy_(mat2)
+        if mid is not None:
+            self.lora_mid = layer(mid, rank, bias=False)
+            self.lora_mid.weight.data.copy_(mid)
+        else:
+            self.lora_mid = None
+        self.rank = rank
+        self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False)
+
+    def __call__(self, w):
+        org_dtype = w.dtype
+        if self.lora_mid is None:
+            diff = self.lora_up.weight @ self.lora_down.weight
+        else:
+            diff = tucker_weight_from_conv(
+                self.lora_up.weight, self.lora_down.weight, self.lora_mid.weight
+            )
+        scale = self.alpha / self.rank
+        weight = w + scale * diff.reshape(w.shape)
+        return weight.to(org_dtype)
+
+    def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:
+        """
+        Additive bypass component for LoRA training: h(x) = up(down(x)) * scale
+
+        Simple implementation using the nn.Module weights directly.
+        No mid/dora/reshape branches (create_train doesn't create them).
+
+        Args:
+            x: Input tensor
+            base_out: Output from base forward (unused, for API consistency)
+        """
+        # Compute scale = alpha / rank * multiplier
+        scale = (self.alpha / self.rank) * getattr(self, "multiplier", 1.0)
+
+        # Get module info from bypass injection
+        is_conv = getattr(self, "is_conv", False)
+        conv_dim = getattr(self, "conv_dim", 0)
+        kw_dict = getattr(self, "kw_dict", {})
+
+        # Get weights (keep in original dtype for numerical stability)
+        down_weight = self.lora_down.weight
+        up_weight = self.lora_up.weight
+
+        if is_conv:
+            # Conv path: use functional conv
+            # conv_dim: 1=conv1d, 2=conv2d, 3=conv3d
+            conv_fn = (F.conv1d, F.conv2d, F.conv3d)[conv_dim - 1]
+
+            # Reshape 2D weights to conv format if needed
+            # down: [rank, in_features] -> [rank, in_channels, *kernel_size]
+            # up: [out_features, rank] -> [out_features, rank, 1, 1, ...]
+            if down_weight.dim() == 2:
+                kernel_size = getattr(self, "kernel_size", (1,) * conv_dim)
+                in_channels = getattr(self, "in_channels", None)
+                if in_channels is not None:
+                    down_weight = down_weight.view(
+                        down_weight.shape[0], in_channels, *kernel_size
+                    )
+                else:
+                    # Fallback: assume 1x1 kernel
+                    down_weight = down_weight.view(
+                        *down_weight.shape, *([1] * conv_dim)
+                    )
+            if up_weight.dim() == 2:
+                # up always uses 1x1 kernel
+                up_weight = up_weight.view(*up_weight.shape, *([1] * conv_dim))
+
+            # down conv uses stride/padding from module, up is 1x1
+            hidden = conv_fn(x, down_weight, **kw_dict)
+
+            # mid layer if exists (tucker decomposition)
+            if self.lora_mid is not None:
+                mid_weight = self.lora_mid.weight
+                if mid_weight.dim() == 2:
+                    mid_weight = mid_weight.view(*mid_weight.shape, *([1] * conv_dim))
+                hidden = conv_fn(hidden, mid_weight)
+
+            # up conv is always 1x1 (no stride/padding)
+            out = conv_fn(hidden, up_weight)
+        else:
+            # Linear path: simple matmul chain
+            hidden = F.linear(x, down_weight)
+
+            # mid layer if exists
+            if self.lora_mid is not None:
+                mid_weight = self.lora_mid.weight
+                hidden = F.linear(hidden, mid_weight)
+
+            out = F.linear(hidden, up_weight)
+
+        return out * scale
+
+    def passive_memory_usage(self):
+        return sum(param.numel() * param.element_size() for param in self.parameters())
+
+
+class LoRAAdapter(WeightAdapterBase):
+    name = "lora"
+
+    def __init__(self, loaded_keys, weights):
+        self.loaded_keys = loaded_keys
+        self.weights = weights
+
+    @classmethod
+    def create_train(cls, weight, rank=1, alpha=1.0):
+        out_dim = weight.shape[0]
+        in_dim = weight.shape[1:].numel()
+        mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=torch.float32)
+        mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=torch.float32)
+        torch.nn.init.kaiming_uniform_(mat1, a=5**0.5)
+        torch.nn.init.constant_(mat2, 0.0)
+        return LoraDiff((mat1, mat2, alpha, None, None, None))
+
+    def to_train(self):
+        return LoraDiff(self.weights)
+
+    @classmethod
+    def load(
+        cls,
+        x: str,
+        lora: dict[str, torch.Tensor],
+        alpha: float,
+        dora_scale: torch.Tensor,
+        loaded_keys: set[str] = None,
+    ) -> Optional["LoRAAdapter"]:
+        if loaded_keys is None:
+            loaded_keys = set()
+
+        reshape_name = "{}.reshape_weight".format(x)
+        regular_lora = "{}.lora_up.weight".format(x)
+        diffusers_lora = "{}_lora.up.weight".format(x)
+        diffusers2_lora = "{}.lora_B.weight".format(x)
+        diffusers3_lora = "{}.lora.up.weight".format(x)
+        mochi_lora = "{}.lora_B".format(x)
+        transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
+        qwen_default_lora = "{}.lora_B.default.weight".format(x)
+        A_name = None
+
+        if regular_lora in lora.keys():
+            A_name = regular_lora
+            B_name = "{}.lora_down.weight".format(x)
+            mid_name = "{}.lora_mid.weight".format(x)
+        elif diffusers_lora in lora.keys():
+            A_name = diffusers_lora
+            B_name = "{}_lora.down.weight".format(x)
+            mid_name = None
+        elif diffusers2_lora in lora.keys():
+            A_name = diffusers2_lora
+            B_name = "{}.lora_A.weight".format(x)
+            mid_name = None
+        elif diffusers3_lora in lora.keys():
+            A_name = diffusers3_lora
+            B_name = "{}.lora.down.weight".format(x)
+            mid_name = None
+        elif mochi_lora in lora.keys():
+            A_name = mochi_lora
+            B_name = "{}.lora_A".format(x)
+            mid_name = None
+        elif transformers_lora in lora.keys():
+            A_name = transformers_lora
+            B_name = "{}.lora_linear_layer.down.weight".format(x)
+            mid_name = None
+        elif qwen_default_lora in lora.keys():
+            A_name = qwen_default_lora
+            B_name = "{}.lora_A.default.weight".format(x)
+            mid_name = None
+
+        if A_name is not None:
+            mid = None
+            if mid_name is not None and mid_name in lora.keys():
+                mid = lora[mid_name]
+                loaded_keys.add(mid_name)
+            reshape = None
+            if reshape_name in lora.keys():
+                try:
+                    reshape = lora[reshape_name].tolist()
+                    loaded_keys.add(reshape_name)
+                except:
+                    pass
+            weights = (lora[A_name], lora[B_name], alpha, mid, dora_scale, reshape)
+            loaded_keys.add(A_name)
+            loaded_keys.add(B_name)
+            return cls(loaded_keys, weights)
+        else:
+            return None
+
+    def calculate_shape(
+        self,
+        key
+    ):
+        reshape = self.weights[5]
+        return tuple(reshape) if reshape is not None else None
+
+    def calculate_weight(
+        self,
+        weight,
+        key,
+        strength,
+        strength_model,
+        offset,
+        function,
+        intermediate_dtype=torch.float32,
+        original_weight=None,
+    ):
+        v = self.weights
+        mat1 = comfy.model_management.cast_to_device(
+            v[0], weight.device, intermediate_dtype
+        )
+        mat2 = comfy.model_management.cast_to_device(
+            v[1], weight.device, intermediate_dtype
+        )
+        dora_scale = v[4]
+        reshape = v[5]
+
+        if reshape is not None:
+            weight = pad_tensor_to_shape(weight, reshape)
+
+        if v[2] is not None:
+            alpha = v[2] / mat2.shape[0]
+        else:
+            alpha = 1.0
+
+        if v[3] is not None:
+            # locon mid weights, hopefully the math is fine because I didn't properly test it
+            mat3 = comfy.model_management.cast_to_device(
+                v[3], weight.device, intermediate_dtype
+            )
+            final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
+            mat2 = (
+                torch.mm(
+                    mat2.transpose(0, 1).flatten(start_dim=1),
+                    mat3.transpose(0, 1).flatten(start_dim=1),
+                )
+                .reshape(final_shape)
+                .transpose(0, 1)
+            )
+        try:
+            lora_diff = torch.mm(
+                mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)
+            ).reshape(weight.shape)
+            del mat1, mat2
+            if dora_scale is not None:
+                weight = weight_decompose(
+                    dora_scale,
+                    weight,
+                    lora_diff,
+                    alpha,
+                    strength,
+                    intermediate_dtype,
+                    function,
+                )
+            else:
+                weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+        except Exception as e:
+            logging.error("ERROR {} {} {}".format(self.name, key, e))
+        return weight
+
+    def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:
+        """
+        Additive bypass component for LoRA: h(x) = up(down(x)) * scale
+
+        Note:
+            Does not access original model weights - bypass mode is designed
+            for quantized models where weights may not be accessible.
+
+        Args:
+            x: Input tensor
+            base_out: Output from base forward (unused, for API consistency)
+
+        Reference: LyCORIS functional/locon.py bypass_forward_diff
+        """
+        # FUNC_LIST: [None, None, F.linear, F.conv1d, F.conv2d, F.conv3d]
+        FUNC_LIST = [None, None, F.linear, F.conv1d, F.conv2d, F.conv3d]
+
+        v = self.weights
+        # v[0]=up, v[1]=down, v[2]=alpha, v[3]=mid, v[4]=dora_scale, v[5]=reshape
+        up = v[0]
+        down = v[1]
+        alpha = v[2]
+        mid = v[3]
+
+        # Compute scale = alpha / rank
+        rank = down.shape[0]
+        if alpha is not None:
+            scale = alpha / rank
+        else:
+            scale = 1.0
+        scale = scale * getattr(self, "multiplier", 1.0)
+
+        # Cast dtype
+        up = up.to(dtype=x.dtype)
+        down = down.to(dtype=x.dtype)
+
+        # Use module info from bypass injection, not weight dimension
+        is_conv = getattr(self, "is_conv", False)
+        conv_dim = getattr(self, "conv_dim", 0)
+        kw_dict = getattr(self, "kw_dict", {})
+
+        if is_conv:
+            op = FUNC_LIST[
+                conv_dim + 2
+            ]  # conv_dim 1->conv1d(3), 2->conv2d(4), 3->conv3d(5)
+            kernel_size = getattr(self, "kernel_size", (1,) * conv_dim)
+            in_channels = getattr(self, "in_channels", None)
+
+            # Reshape 2D weights to conv format using kernel_size
+            # down: [rank, in_channels * prod(kernel_size)] -> [rank, in_channels, *kernel_size]
+            # up: [out_channels, rank] -> [out_channels, rank, 1, 1, ...] (1x1 kernel)
+            if down.dim() == 2:
+                # down.shape[1] = in_channels * prod(kernel_size)
+                if in_channels is not None:
+                    down = down.view(down.shape[0], in_channels, *kernel_size)
+                else:
+                    # Fallback: assume 1x1 kernel if in_channels unknown
+                    down = down.view(*down.shape, *([1] * conv_dim))
+            if up.dim() == 2:
+                # up always uses 1x1 kernel
+                up = up.view(*up.shape, *([1] * conv_dim))
+            if mid is not None:
+                mid = mid.to(dtype=x.dtype)
+                if mid.dim() == 2:
+                    mid = mid.view(*mid.shape, *([1] * conv_dim))
+        else:
+            op = F.linear
+            kw_dict = {}  # linear doesn't take stride/padding
+
+        # Simple chain: down -> mid (if tucker) -> up
+        if mid is not None:
+            if not is_conv:
+                mid = mid.to(dtype=x.dtype)
+            hidden = op(x, down)
+            hidden = op(hidden, mid, **kw_dict)
+            out = op(hidden, up)
+        else:
+            hidden = op(x, down, **kw_dict)
+            out = op(hidden, up)
+
+        return out * scale
diff --git a/comfy/weight_adapter/oft.py b/comfy/weight_adapter/oft.py
new file mode 100644
index 0000000000000000000000000000000000000000..657801fd3ebcadcddfdc83aca002308fdee386d4
--- /dev/null
+++ b/comfy/weight_adapter/oft.py
@@ -0,0 +1,327 @@
+import logging
+from typing import Optional
+
+import torch
+import comfy.model_management
+from .base import (
+    WeightAdapterBase,
+    WeightAdapterTrainBase,
+    weight_decompose,
+    factorization,
+)
+
+
+class OFTDiff(WeightAdapterTrainBase):
+    def __init__(self, weights):
+        super().__init__()
+        # Unpack weights tuple from OFTAdapter
+        blocks, rescale, alpha, _ = weights
+
+        # Create trainable parameters
+        self.oft_blocks = torch.nn.Parameter(blocks)
+        if rescale is not None:
+            self.rescale = torch.nn.Parameter(rescale)
+            self.rescaled = True
+        else:
+            self.rescaled = False
+        self.block_num, self.block_size, _ = blocks.shape
+        self.constraint = float(alpha)
+        self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False)
+
+    def __call__(self, w):
+        org_dtype = w.dtype
+        I = torch.eye(self.block_size, device=self.oft_blocks.device)
+
+        ## generate r
+        # for Q = -Q^T
+        q = self.oft_blocks - self.oft_blocks.transpose(1, 2)
+        normed_q = q
+        if self.constraint:
+            q_norm = torch.norm(q) + 1e-8
+            if q_norm > self.constraint:
+                normed_q = q * self.constraint / q_norm
+        # use float() to prevent unsupported type
+        r = (I + normed_q) @ (I - normed_q).float().inverse()
+
+        ## Apply chunked matmul on weight
+        _, *shape = w.shape
+        org_weight = w.to(dtype=r.dtype)
+        org_weight = org_weight.unflatten(0, (self.block_num, self.block_size))
+        # Init R=0, so add I on it to ensure the output of step0 is original model output
+        weight = torch.einsum(
+            "k n m, k n ... -> k m ...",
+            r,
+            org_weight,
+        ).flatten(0, 1)
+        if self.rescaled:
+            weight = self.rescale * weight
+        return weight.to(org_dtype)
+
+    def _get_orthogonal_matrix(self, device, dtype):
+        """Compute the orthogonal rotation matrix R from OFT blocks."""
+        blocks = self.oft_blocks.to(device=device, dtype=dtype)
+        I = torch.eye(self.block_size, device=device, dtype=dtype)
+
+        # Q = blocks - blocks^T (skew-symmetric)
+        q = blocks - blocks.transpose(1, 2)
+        normed_q = q
+
+        # Apply constraint if set
+        if self.constraint:
+            q_norm = torch.norm(q) + 1e-8
+            if q_norm > self.constraint:
+                normed_q = q * self.constraint / q_norm
+
+        # Cayley transform: R = (I + Q)(I - Q)^-1
+        r = (I + normed_q) @ (I - normed_q).float().inverse()
+        return r.to(dtype)
+
+    def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:
+        """
+        OFT has no additive component - returns zeros matching base_out shape.
+
+        OFT only transforms the output via g(), it doesn't add to it.
+        """
+        return torch.zeros_like(base_out)
+
+    def g(self, y: torch.Tensor) -> torch.Tensor:
+        """
+        Output transformation for OFT: applies orthogonal rotation.
+
+        OFT transforms output channels using block-diagonal orthogonal matrices.
+        """
+        r = self._get_orthogonal_matrix(y.device, y.dtype)
+
+        # Apply multiplier to interpolate between identity and full transform
+        multiplier = getattr(self, "multiplier", 1.0)
+        I = torch.eye(self.block_size, device=y.device, dtype=y.dtype)
+        r = r * multiplier + (1 - multiplier) * I
+
+        # Use module info from bypass injection
+        is_conv = getattr(self, "is_conv", y.dim() > 2)
+
+        if is_conv:
+            # Conv output: (N, C, H, W, ...) -> transpose to (N, H, W, ..., C)
+            y = y.transpose(1, -1)
+
+        # y now has channels in last dim
+        *batch_shape, out_features = y.shape
+
+        # Reshape to apply block-diagonal transform
+        # (*, out_features) -> (*, block_num, block_size)
+        y_blocked = y.reshape(*batch_shape, self.block_num, self.block_size)
+
+        # Apply orthogonal transform: R @ y for each block
+        # r: (block_num, block_size, block_size), y_blocked: (*, block_num, block_size)
+        out_blocked = torch.einsum("k n m, ... k n -> ... k m", r, y_blocked)
+
+        # Reshape back: (*, block_num, block_size) -> (*, out_features)
+        out = out_blocked.reshape(*batch_shape, out_features)
+
+        # Apply rescale if present
+        if self.rescaled:
+            rescale = self.rescale.to(device=y.device, dtype=y.dtype)
+            out = out * rescale.view(-1)
+
+        if is_conv:
+            # Transpose back: (N, H, W, ..., C) -> (N, C, H, W, ...)
+            out = out.transpose(1, -1)
+
+        return out
+
+    def passive_memory_usage(self):
+        """Calculates memory usage of the trainable parameters."""
+        return sum(param.numel() * param.element_size() for param in self.parameters())
+
+
+class OFTAdapter(WeightAdapterBase):
+    name = "oft"
+
+    def __init__(self, loaded_keys, weights):
+        self.loaded_keys = loaded_keys
+        self.weights = weights
+
+    @classmethod
+    def create_train(cls, weight, rank=1, alpha=1.0):
+        out_dim = weight.shape[0]
+        block_size, block_num = factorization(out_dim, rank)
+        block = torch.zeros(
+            block_num, block_size, block_size, device=weight.device, dtype=torch.float32
+        )
+        return OFTDiff((block, None, alpha, None))
+
+    def to_train(self):
+        return OFTDiff(self.weights)
+
+    @classmethod
+    def load(
+        cls,
+        x: str,
+        lora: dict[str, torch.Tensor],
+        alpha: float,
+        dora_scale: torch.Tensor,
+        loaded_keys: set[str] = None,
+    ) -> Optional["OFTAdapter"]:
+        if loaded_keys is None:
+            loaded_keys = set()
+        blocks_name = "{}.oft_blocks".format(x)
+        rescale_name = "{}.rescale".format(x)
+
+        blocks = None
+        if blocks_name in lora.keys():
+            blocks = lora[blocks_name]
+            if blocks.ndim == 3:
+                loaded_keys.add(blocks_name)
+            else:
+                blocks = None
+        if blocks is None:
+            return None
+
+        rescale = None
+        if rescale_name in lora.keys():
+            rescale = lora[rescale_name]
+            loaded_keys.add(rescale_name)
+
+        weights = (blocks, rescale, alpha, dora_scale)
+        return cls(loaded_keys, weights)
+
+    def calculate_weight(
+        self,
+        weight,
+        key,
+        strength,
+        strength_model,
+        offset,
+        function,
+        intermediate_dtype=torch.float32,
+        original_weight=None,
+    ):
+        v = self.weights
+        blocks = v[0]
+        rescale = v[1]
+        alpha = v[2]
+        if alpha is None:
+            alpha = 0
+        dora_scale = v[3]
+
+        blocks = comfy.model_management.cast_to_device(
+            blocks, weight.device, intermediate_dtype
+        )
+        if rescale is not None:
+            rescale = comfy.model_management.cast_to_device(
+                rescale, weight.device, intermediate_dtype
+            )
+
+        block_num, block_size, *_ = blocks.shape
+
+        try:
+            # Get r
+            I = torch.eye(block_size, device=blocks.device, dtype=blocks.dtype)
+            # for Q = -Q^T
+            q = blocks - blocks.transpose(1, 2)
+            normed_q = q
+            if alpha > 0:  # alpha in oft/boft is for constraint
+                q_norm = torch.norm(q) + 1e-8
+                if q_norm > alpha:
+                    normed_q = q * alpha / q_norm
+            # use float() to prevent unsupported type in .inverse()
+            r = (I + normed_q) @ (I - normed_q).float().inverse()
+            r = r.to(weight)
+            # Create I in weight's dtype for the einsum
+            I_w = torch.eye(block_size, device=weight.device, dtype=weight.dtype)
+            _, *shape = weight.shape
+            lora_diff = torch.einsum(
+                "k n m, k n ... -> k m ...",
+                (r * strength) - strength * I_w,
+                weight.view(block_num, block_size, *shape),
+            ).view(-1, *shape)
+            if dora_scale is not None:
+                weight = weight_decompose(
+                    dora_scale,
+                    weight,
+                    lora_diff,
+                    alpha,
+                    strength,
+                    intermediate_dtype,
+                    function,
+                )
+            else:
+                weight += function((strength * lora_diff).type(weight.dtype))
+        except Exception as e:
+            logging.error("ERROR {} {} {}".format(self.name, key, e))
+        return weight
+
+    def _get_orthogonal_matrix(self, device, dtype):
+        """Compute the orthogonal rotation matrix R from OFT blocks."""
+        v = self.weights
+        blocks = v[0].to(device=device, dtype=dtype)
+        alpha = v[2]
+        if alpha is None:
+            alpha = 0
+
+        block_num, block_size, _ = blocks.shape
+        I = torch.eye(block_size, device=device, dtype=dtype)
+
+        # Q = blocks - blocks^T (skew-symmetric)
+        q = blocks - blocks.transpose(1, 2)
+        normed_q = q
+
+        # Apply constraint if alpha > 0
+        if alpha > 0:
+            q_norm = torch.norm(q) + 1e-8
+            if q_norm > alpha:
+                normed_q = q * alpha / q_norm
+
+        # Cayley transform: R = (I + Q)(I - Q)^-1
+        r = (I + normed_q) @ (I - normed_q).float().inverse()
+        return r, block_num, block_size
+
+    def g(self, y: torch.Tensor) -> torch.Tensor:
+        """
+        Output transformation for OFT: applies orthogonal rotation to output.
+
+        OFT transforms the output channels using block-diagonal orthogonal matrices.
+
+        Reference: LyCORIS DiagOFTModule._bypass_forward
+        """
+        v = self.weights
+        rescale = v[1]
+
+        r, block_num, block_size = self._get_orthogonal_matrix(y.device, y.dtype)
+
+        # Apply multiplier to interpolate between identity and full transform
+        multiplier = getattr(self, "multiplier", 1.0)
+        I = torch.eye(block_size, device=y.device, dtype=y.dtype)
+        r = r * multiplier + (1 - multiplier) * I
+
+        # Use module info from bypass injection to determine conv vs linear
+        is_conv = getattr(self, "is_conv", y.dim() > 2)
+
+        if is_conv:
+            # Conv output: (N, C, H, W, ...) -> transpose to (N, H, W, ..., C)
+            y = y.transpose(1, -1)
+
+        # y now has channels in last dim
+        *batch_shape, out_features = y.shape
+
+        # Reshape to apply block-diagonal transform
+        # (*, out_features) -> (*, block_num, block_size)
+        y_blocked = y.view(*batch_shape, block_num, block_size)
+
+        # Apply orthogonal transform: R @ y for each block
+        # r: (block_num, block_size, block_size), y_blocked: (*, block_num, block_size)
+        out_blocked = torch.einsum("k n m, ... k n -> ... k m", r, y_blocked)
+
+        # Reshape back: (*, block_num, block_size) -> (*, out_features)
+        out = out_blocked.view(*batch_shape, out_features)
+
+        # Apply rescale if present
+        if rescale is not None:
+            rescale = rescale.to(device=y.device, dtype=y.dtype)
+            out = out * rescale.view(-1)
+
+        if is_conv:
+            # Transpose back: (N, H, W, ..., C) -> (N, C, H, W, ...)
+            out = out.transpose(1, -1)
+
+        return out
diff --git a/comfy_api/feature_flags.py b/comfy_api/feature_flags.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f182d0834b4b7b0faa6d76bd15373c3ae52f0ea
--- /dev/null
+++ b/comfy_api/feature_flags.py
@@ -0,0 +1,166 @@
+"""
+Feature flags module for ComfyUI WebSocket protocol negotiation.
+
+This module handles capability negotiation between frontend and backend,
+allowing graceful protocol evolution while maintaining backward compatibility.
+"""
+
+import logging
+from typing import Any, TypedDict
+
+from comfy.cli_args import args
+
+
+class FeatureFlagInfo(TypedDict):
+    type: str
+    default: Any
+    description: str
+
+
+# Registry of known CLI-settable feature flags.
+# Launchers can query this via --list-feature-flags to discover valid flags.
+CLI_FEATURE_FLAG_REGISTRY: dict[str, FeatureFlagInfo] = {
+    "show_signin_button": {
+        "type": "bool",
+        "default": False,
+        "description": "Show the sign-in button in the frontend even when not signed in",
+    },
+    "enable_telemetry": {
+        "type": "bool",
+        "default": False,
+        "description": "Signal the frontend that telemetry collection is enabled",
+    },
+}
+
+
+def _coerce_bool(v: str) -> bool:
+    """Strict bool coercion: only 'true'/'false' (case-insensitive).
+
+    Anything else raises ValueError so the caller can warn and drop the flag,
+    rather than silently treating typos like 'ture' or 'yes' as False.
+    """
+    lower = v.lower()
+    if lower == "true":
+        return True
+    if lower == "false":
+        return False
+    raise ValueError(f"expected 'true' or 'false', got {v!r}")
+
+
+_COERCE_FNS: dict[str, Any] = {
+    "bool": _coerce_bool,
+    "int": lambda v: int(v),
+    "float": lambda v: float(v),
+}
+
+
+def _coerce_flag_value(key: str, raw_value: str) -> Any:
+    """Coerce a raw string value using the registry type, or keep as string.
+
+    Returns the raw string if the key is unregistered or the type is unknown.
+    Raises ValueError/TypeError if the key is registered with a known type but
+    the value cannot be coerced; callers are expected to warn and drop the flag.
+    """
+    info = CLI_FEATURE_FLAG_REGISTRY.get(key)
+    if info is None:
+        return raw_value
+    coerce = _COERCE_FNS.get(info["type"])
+    if coerce is None:
+        return raw_value
+    return coerce(raw_value)
+
+
+def _parse_cli_feature_flags() -> dict[str, Any]:
+    """Parse --feature-flag key=value pairs from CLI args into a dict.
+
+    Items without '=' default to the value 'true' (bare flag form).
+    Flags whose value cannot be coerced to the registered type are dropped
+    with a warning, so a typo like '--feature-flag some_bool=ture' does not
+    silently take effect as the wrong value.
+    """
+    result: dict[str, Any] = {}
+    for item in getattr(args, "feature_flag", []):
+        key, sep, raw_value = item.partition("=")
+        key = key.strip()
+        if not key:
+            continue
+        if not sep:
+            raw_value = "true"
+        try:
+            result[key] = _coerce_flag_value(key, raw_value.strip())
+        except (ValueError, TypeError) as e:
+            info = CLI_FEATURE_FLAG_REGISTRY.get(key, {})
+            logging.warning(
+                "Could not coerce --feature-flag %s=%r to %s (%s); dropping flag.",
+                key, raw_value.strip(), info.get("type", "?"), e,
+            )
+    return result
+
+
+# Default server capabilities
+_CORE_FEATURE_FLAGS: dict[str, Any] = {
+    "supports_preview_metadata": True,
+    "supports_model_type_tags": True,
+    "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
+    "extension": {"manager": {"supports_v4": True}},
+    "node_replacements": True,
+    "assets": args.enable_assets,
+}
+
+# CLI-provided flags cannot overwrite core flags
+_cli_flags = {k: v for k, v in _parse_cli_feature_flags().items() if k not in _CORE_FEATURE_FLAGS}
+
+SERVER_FEATURE_FLAGS: dict[str, Any] = {**_CORE_FEATURE_FLAGS, **_cli_flags}
+
+
+def get_connection_feature(
+    sockets_metadata: dict[str, dict[str, Any]],
+    sid: str,
+    feature_name: str,
+    default: Any = False
+) -> Any:
+    """
+    Get a feature flag value for a specific connection.
+
+    Args:
+        sockets_metadata: Dictionary of socket metadata
+        sid: Session ID of the connection
+        feature_name: Name of the feature to check
+        default: Default value if feature not found
+
+    Returns:
+        Feature value or default if not found
+    """
+    if sid not in sockets_metadata:
+        return default
+
+    return sockets_metadata[sid].get("feature_flags", {}).get(feature_name, default)
+
+
+def supports_feature(
+    sockets_metadata: dict[str, dict[str, Any]],
+    sid: str,
+    feature_name: str
+) -> bool:
+    """
+    Check if a connection supports a specific feature.
+
+    Args:
+        sockets_metadata: Dictionary of socket metadata
+        sid: Session ID of the connection
+        feature_name: Name of the feature to check
+
+    Returns:
+        Boolean indicating if feature is supported
+    """
+    return get_connection_feature(sockets_metadata, sid, feature_name, False) is True
+
+
+def get_server_features() -> dict[str, Any]:
+    """
+    Get the server's feature flags.
+
+    Returns:
+        Dictionary of server feature flags
+    """
+    return SERVER_FEATURE_FLAGS.copy()
diff --git a/comfy_api/generate_api_stubs.py b/comfy_api/generate_api_stubs.py
new file mode 100644
index 0000000000000000000000000000000000000000..679f6cb82c9d832cda9973e87f35b175f8e027eb
--- /dev/null
+++ b/comfy_api/generate_api_stubs.py
@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+"""
+Script to generate .pyi stub files for the synchronous API wrappers.
+This allows generating stubs without running the full ComfyUI application.
+"""
+
+import os
+import sys
+import logging
+import importlib
+
+# Add ComfyUI to path so we can import modules
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from comfy_api.internal.async_to_sync import AsyncToSyncConverter
+from comfy_api.version_list import supported_versions
+
+
+def generate_stubs_for_module(module_name: str) -> None:
+    """Generate stub files for a specific module that exports ComfyAPI and ComfyAPISync."""
+    try:
+        # Import the module
+        module = importlib.import_module(module_name)
+
+        # Check if module has ComfyAPISync (the sync wrapper)
+        if hasattr(module, "ComfyAPISync"):
+            # Module already has a sync class
+            api_class = getattr(module, "ComfyAPI", None)
+            sync_class = getattr(module, "ComfyAPISync")
+
+            if api_class:
+                # Generate the stub file
+                AsyncToSyncConverter.generate_stub_file(api_class, sync_class)
+                logging.info(f"Generated stub file for {module_name}")
+            else:
+                logging.warning(
+                    f"Module {module_name} has ComfyAPISync but no ComfyAPI"
+                )
+
+        elif hasattr(module, "ComfyAPI"):
+            # Module only has async API, need to create sync wrapper first
+            from comfy_api.internal.async_to_sync import create_sync_class
+
+            api_class = getattr(module, "ComfyAPI")
+            sync_class = create_sync_class(api_class)
+
+            # Generate the stub file
+            AsyncToSyncConverter.generate_stub_file(api_class, sync_class)
+            logging.info(f"Generated stub file for {module_name}")
+        else:
+            logging.warning(
+                f"Module {module_name} does not export ComfyAPI or ComfyAPISync"
+            )
+
+    except Exception as e:
+        logging.error(f"Failed to generate stub for {module_name}: {e}")
+        import traceback
+
+        traceback.print_exc()
+
+
+def main():
+    """Main function to generate all API stub files."""
+    logging.basicConfig(level=logging.INFO)
+
+    logging.info("Starting stub generation...")
+
+    # Dynamically get module names from supported_versions
+    api_modules = []
+    for api_class in supported_versions:
+        # Extract module name from the class
+        module_name = api_class.__module__
+        if module_name not in api_modules:
+            api_modules.append(module_name)
+
+    logging.info(f"Found {len(api_modules)} API modules: {api_modules}")
+
+    # Generate stubs for each module
+    for module_name in api_modules:
+        generate_stubs_for_module(module_name)
+
+    logging.info("Stub generation complete!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/comfy_api/input/__init__.py b/comfy_api/input/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c539f0e622f17b390a4302cd901dae2f3cffab28
--- /dev/null
+++ b/comfy_api/input/__init__.py
@@ -0,0 +1,26 @@
+# This file only exists for backwards compatibility.
+from comfy_api.latest._input import (
+    ImageInput,
+    AudioInput,
+    MaskInput,
+    LatentInput,
+    VideoInput,
+    CurvePoint,
+    CurveInput,
+    MonotoneCubicCurve,
+    LinearCurve,
+    RangeInput,
+)
+
+__all__ = [
+    "ImageInput",
+    "AudioInput",
+    "MaskInput",
+    "LatentInput",
+    "VideoInput",
+    "CurvePoint",
+    "CurveInput",
+    "MonotoneCubicCurve",
+    "LinearCurve",
+    "RangeInput",
+]
diff --git a/comfy_api/input/basic_types.py b/comfy_api/input/basic_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb2f005d67526a923a57cc3a05350872746c6c68
--- /dev/null
+++ b/comfy_api/input/basic_types.py
@@ -0,0 +1,14 @@
+# This file only exists for backwards compatibility.
+from comfy_api.latest._input.basic_types import (
+    ImageInput,
+    AudioInput,
+    MaskInput,
+    LatentInput,
+)
+
+__all__ = [
+    "ImageInput",
+    "AudioInput",
+    "MaskInput",
+    "LatentInput",
+]
diff --git a/comfy_api/input/video_types.py b/comfy_api/input/video_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..b13035d6582daf71b280d69eb1752c4fd586ed62
--- /dev/null
+++ b/comfy_api/input/video_types.py
@@ -0,0 +1,6 @@
+# This file only exists for backwards compatibility.
+from comfy_api.latest._input.video_types import VideoInput
+
+__all__ = [
+    "VideoInput",
+]
diff --git a/comfy_api/input_impl/__init__.py b/comfy_api/input_impl/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbaa0938b0d7ef518ab772d99cd7fd56d1d30131
--- /dev/null
+++ b/comfy_api/input_impl/__init__.py
@@ -0,0 +1,7 @@
+# This file only exists for backwards compatibility.
+from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents
+
+__all__ = [
+    "VideoFromFile",
+    "VideoFromComponents",
+]
diff --git a/comfy_api/input_impl/video_types.py b/comfy_api/input_impl/video_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1ae8f343d7d728bd1e0997f7468d87d0ca0a03c
--- /dev/null
+++ b/comfy_api/input_impl/video_types.py
@@ -0,0 +1,2 @@
+# This file only exists for backwards compatibility.
+from comfy_api.latest._input_impl.video_types import *  # noqa: F403
diff --git a/comfy_api/internal/__init__.py b/comfy_api/internal/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4acd6381e13f4e5498b9527d65a6709d4aab6e19
--- /dev/null
+++ b/comfy_api/internal/__init__.py
@@ -0,0 +1,150 @@
+# Internal infrastructure for ComfyAPI
+from .api_registry import (
+    ComfyAPIBase as ComfyAPIBase,
+    ComfyAPIWithVersion as ComfyAPIWithVersion,
+    register_versions as register_versions,
+    get_all_versions as get_all_versions,
+)
+
+import asyncio
+from dataclasses import asdict
+from typing import Callable, Optional
+
+
+def first_real_override(cls: type, name: str, *, base: type=None) -> Optional[Callable]:
+    """Return the *callable* override of `name` visible on `cls`, or None if every
+    implementation up to (and including) `base` is the placeholder defined on `base`.
+
+    If base is not provided, it will assume cls has a GET_BASE_CLASS
+    """
+    if base is None:
+        if not hasattr(cls, "GET_BASE_CLASS"):
+            raise ValueError("base is required if cls does not have a GET_BASE_CLASS; is this a valid ComfyNode subclass?")
+        base = cls.GET_BASE_CLASS()
+    base_attr = getattr(base, name, None)
+    if base_attr is None:
+        return None
+    base_func = base_attr.__func__
+    for c in cls.mro():                       # NodeB, NodeA, ComfyNode, object …
+        if c is base:                         # reached the placeholder – we're done
+            break
+        if name in c.__dict__:                # first class that *defines* the attr
+            func = getattr(c, name).__func__
+            if func is not base_func:         # real override
+                return getattr(cls, name)     # bound to *cls*
+    return None
+
+
+class _ComfyNodeInternal:
+    """Class that all V3-based APIs inherit from for ComfyNode.
+
+    This is intended to only be referenced within execution.py, as it has to handle all V3 APIs going forward."""
+    @classmethod
+    def GET_NODE_INFO_V1(cls):
+        ...
+
+
+class _NodeOutputInternal:
+    """Class that all V3-based APIs inherit from for NodeOutput.
+
+    This is intended to only be referenced within execution.py, as it has to handle all V3 APIs going forward."""
+    ...
+
+
+def as_pruned_dict(dataclass_obj):
+    '''Return dict of dataclass object with pruned None values.'''
+    return prune_dict(asdict(dataclass_obj))
+
+def prune_dict(d: dict):
+    return {k: v for k,v in d.items() if v is not None}
+
+
+def is_class(obj):
+    '''
+    Returns True if is a class type.
+    Returns False if is a class instance.
+    '''
+    return isinstance(obj, type)
+
+
+def copy_class(cls: type) -> type:
+    '''
+    Copy a class and its attributes.
+    '''
+    if cls is None:
+        return None
+    cls_dict = {
+            k: v for k, v in cls.__dict__.items()
+            if k not in ('__dict__', '__weakref__', '__module__', '__doc__')
+        }
+    # new class
+    new_cls = type(
+        cls.__name__,
+        (cls,),
+        cls_dict
+    )
+    # metadata preservation
+    new_cls.__module__ = cls.__module__
+    new_cls.__doc__ = cls.__doc__
+    return new_cls
+
+
+class classproperty(object):
+    def __init__(self, f):
+        self.f = f
+    def __get__(self, obj, owner):
+        return self.f(owner)
+
+
+# NOTE: this was ai generated and validated by hand
+def shallow_clone_class(cls, new_name=None):
+    '''
+    Shallow clone a class while preserving super() functionality.
+    '''
+    new_name = new_name or f"{cls.__name__}Clone"
+    # Include the original class in the bases to maintain proper inheritance
+    new_bases = (cls,) + cls.__bases__
+    return type(new_name, new_bases, dict(cls.__dict__))
+
+# NOTE: this was ai generated and validated by hand
+def lock_class(cls):
+    '''
+    Lock a class so that its top-levelattributes cannot be modified.
+    '''
+    # Locked instance __setattr__
+    def locked_instance_setattr(self, name, value):
+        raise AttributeError(
+            f"Cannot set attribute '{name}' on immutable instance of {type(self).__name__}"
+        )
+    # Locked metaclass
+    class LockedMeta(type(cls)):
+        def __setattr__(cls_, name, value):
+            raise AttributeError(
+                f"Cannot modify class attribute '{name}' on locked class '{cls_.__name__}'"
+            )
+    # Rebuild class with locked behavior
+    locked_dict = dict(cls.__dict__)
+    locked_dict['__setattr__'] = locked_instance_setattr
+
+    return LockedMeta(cls.__name__, cls.__bases__, locked_dict)
+
+
+def make_locked_method_func(type_obj, func, class_clone):
+    """
+    Returns a function that, when called with **inputs, will execute:
+    getattr(type_obj, func).__func__(lock_class(class_clone), **inputs)
+
+    Supports both synchronous and asynchronous methods.
+    """
+    locked_class = lock_class(class_clone)
+    method = getattr(type_obj, func).__func__
+
+    # Check if the original method is async
+    if asyncio.iscoroutinefunction(method):
+        async def wrapped_async_func(**inputs):
+            return await method(locked_class, **inputs)
+        return wrapped_async_func
+    else:
+        def wrapped_func(**inputs):
+            return method(locked_class, **inputs)
+        return wrapped_func
diff --git a/comfy_api/internal/api_registry.py b/comfy_api/internal/api_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f3fa6074500977e743e3c4f9727329a1f1018f6
--- /dev/null
+++ b/comfy_api/internal/api_registry.py
@@ -0,0 +1,39 @@
+from typing import NamedTuple
+from comfy_api.internal.singleton import ProxiedSingleton
+from packaging import version as packaging_version
+
+
+class ComfyAPIBase(ProxiedSingleton):
+    def __init__(self):
+        pass
+
+
+class ComfyAPIWithVersion(NamedTuple):
+    version: str
+    api_class: type[ComfyAPIBase]
+
+
+def parse_version(version_str: str) -> packaging_version.Version:
+    """
+    Parses a version string into a packaging_version.Version object.
+    Raises ValueError if the version string is invalid.
+    """
+    if version_str == "latest":
+        return packaging_version.parse("9999999.9999999.9999999")
+    return packaging_version.parse(version_str)
+
+
+registered_versions: list[ComfyAPIWithVersion] = []
+
+
+def register_versions(versions: list[ComfyAPIWithVersion]):
+    versions.sort(key=lambda x: parse_version(x.version))
+    global registered_versions
+    registered_versions = versions
+
+
+def get_all_versions() -> list[ComfyAPIWithVersion]:
+    """
+    Returns a list of all registered ComfyAPI versions.
+    """
+    return registered_versions
diff --git a/comfy_api/internal/async_to_sync.py b/comfy_api/internal/async_to_sync.py
new file mode 100644
index 0000000000000000000000000000000000000000..46f1a9078908506c72e665fdaaafbc52042c0e70
--- /dev/null
+++ b/comfy_api/internal/async_to_sync.py
@@ -0,0 +1,1002 @@
+import asyncio
+import concurrent.futures
+import contextvars
+import functools
+import inspect
+import logging
+import os
+import textwrap
+import threading
+from enum import Enum
+from typing import Optional, get_origin, get_args, get_type_hints
+
+
+class TypeTracker:
+    """Tracks types discovered during stub generation for automatic import generation."""
+
+    def __init__(self):
+        self.discovered_types = {}  # type_name -> (module, qualname)
+        self.builtin_types = {
+            "Any",
+            "Dict",
+            "List",
+            "Optional",
+            "Tuple",
+            "Union",
+            "Set",
+            "Sequence",
+            "cast",
+            "NamedTuple",
+            "str",
+            "int",
+            "float",
+            "bool",
+            "None",
+            "bytes",
+            "object",
+            "type",
+            "dict",
+            "list",
+            "tuple",
+            "set",
+        }
+        self.already_imported = (
+            set()
+        )  # Track types already imported to avoid duplicates
+
+    def track_type(self, annotation):
+        """Track a type annotation and record its module/import info."""
+        if annotation is None or annotation is type(None):
+            return
+
+        # Skip builtins and typing module types we already import
+        type_name = getattr(annotation, "__name__", None)
+        if type_name and (
+            type_name in self.builtin_types or type_name in self.already_imported
+        ):
+            return
+
+        # Get module and qualname
+        module = getattr(annotation, "__module__", None)
+        qualname = getattr(annotation, "__qualname__", type_name or "")
+
+        # Skip types from typing module (they're already imported)
+        if module == "typing":
+            return
+
+        # Skip UnionType and GenericAlias from types module as they're handled specially
+        if module == "types" and type_name in ("UnionType", "GenericAlias"):
+            return
+
+        if module and module not in ["builtins", "__main__"]:
+            # Store the type info
+            if type_name:
+                self.discovered_types[type_name] = (module, qualname)
+
+    def get_imports(self, main_module_name: str) -> list[str]:
+        """Generate import statements for all discovered types."""
+        imports = []
+        imports_by_module = {}
+
+        for type_name, (module, qualname) in sorted(self.discovered_types.items()):
+            # Skip types from the main module (they're already imported)
+            if main_module_name and module == main_module_name:
+                continue
+
+            if module not in imports_by_module:
+                imports_by_module[module] = []
+            if type_name not in imports_by_module[module]:  # Avoid duplicates
+                imports_by_module[module].append(type_name)
+
+        # Generate import statements
+        for module, types in sorted(imports_by_module.items()):
+            if len(types) == 1:
+                imports.append(f"from {module} import {types[0]}")
+            else:
+                imports.append(f"from {module} import {', '.join(sorted(set(types)))}")
+
+        return imports
+
+
+class AsyncToSyncConverter:
+    """
+    Provides utilities to convert async classes to sync classes with proper type hints.
+    """
+
+    _thread_pool: Optional[concurrent.futures.ThreadPoolExecutor] = None
+    _thread_pool_lock = threading.Lock()
+    _thread_pool_initialized = False
+
+    @classmethod
+    def get_thread_pool(cls, max_workers=None) -> concurrent.futures.ThreadPoolExecutor:
+        """Get or create the shared thread pool with proper thread-safe initialization."""
+        # Fast path - check if already initialized without acquiring lock
+        if cls._thread_pool_initialized:
+            assert cls._thread_pool is not None, "Thread pool should be initialized"
+            return cls._thread_pool
+
+        # Slow path - acquire lock and create pool if needed
+        with cls._thread_pool_lock:
+            if not cls._thread_pool_initialized:
+                cls._thread_pool = concurrent.futures.ThreadPoolExecutor(
+                    max_workers=max_workers, thread_name_prefix="async_to_sync_"
+                )
+                cls._thread_pool_initialized = True
+
+        # This should never be None at this point, but add assertion for type checker
+        assert cls._thread_pool is not None
+        return cls._thread_pool
+
+    @classmethod
+    def run_async_in_thread(cls, coro_func, *args, **kwargs):
+        """
+        Run an async function in a separate thread from the thread pool.
+        Blocks until the async function completes.
+        Properly propagates contextvars between threads and manages event loops.
+        """
+        # Capture current context - this includes all context variables
+        context = contextvars.copy_context()
+
+        # Store the result and any exception that occurs
+        result_container: dict = {"result": None, "exception": None}
+
+        # Function that runs in the thread pool
+        def run_in_thread():
+            # Create new event loop for this thread
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+
+            try:
+                # Create the coroutine within the context
+                async def run_with_context():
+                    # The coroutine function might access context variables
+                    return await coro_func(*args, **kwargs)
+
+                # Run the coroutine with the captured context
+                # This ensures all context variables are available in the async function
+                result = context.run(loop.run_until_complete, run_with_context())
+                result_container["result"] = result
+            except Exception as e:
+                # Store the exception to re-raise in the calling thread
+                result_container["exception"] = e
+            finally:
+                # Ensure event loop is properly closed to prevent warnings
+                try:
+                    # Cancel any remaining tasks
+                    pending = asyncio.all_tasks(loop)
+                    for task in pending:
+                        task.cancel()
+
+                    # Run the loop briefly to handle cancellations
+                    if pending:
+                        loop.run_until_complete(
+                            asyncio.gather(*pending, return_exceptions=True)
+                        )
+                except Exception:
+                    pass  # Ignore errors during cleanup
+
+                # Close the event loop
+                loop.close()
+
+                # Clear the event loop from the thread
+                asyncio.set_event_loop(None)
+
+        # Submit to thread pool and wait for result
+        thread_pool = cls.get_thread_pool()
+        future = thread_pool.submit(run_in_thread)
+        future.result()  # Wait for completion
+
+        # Re-raise any exception that occurred in the thread
+        if result_container["exception"] is not None:
+            raise result_container["exception"]
+
+        return result_container["result"]
+
+    @classmethod
+    def create_sync_class(cls, async_class: type, thread_pool_size=10) -> type:
+        """
+        Creates a new class with synchronous versions of all async methods.
+
+        Args:
+            async_class: The async class to convert
+            thread_pool_size: Size of thread pool to use
+
+        Returns:
+            A new class with sync versions of all async methods
+        """
+        sync_class_name = "ComfyAPISyncStub"
+        cls.get_thread_pool(thread_pool_size)
+
+        # Create a proper class with docstrings and proper base classes
+        sync_class_dict = {
+            "__doc__": async_class.__doc__,
+            "__module__": async_class.__module__,
+            "__qualname__": sync_class_name,
+            "__orig_class__": async_class,  # Store original class for typing references
+        }
+
+        # Create __init__ method
+        def __init__(self, *args, **kwargs):
+            self._async_instance = async_class(*args, **kwargs)
+
+            # Handle annotated class attributes (like execution: Execution)
+            # Get all annotations from the class hierarchy and resolve string annotations
+            try:
+                # get_type_hints resolves string annotations to actual type objects
+                # This handles classes using 'from __future__ import annotations'
+                all_annotations = get_type_hints(async_class)
+            except Exception:
+                # Fallback to raw annotations if get_type_hints fails
+                # (e.g., for undefined forward references)
+                all_annotations = {}
+                for base_class in reversed(inspect.getmro(async_class)):
+                    if hasattr(base_class, "__annotations__"):
+                        all_annotations.update(base_class.__annotations__)
+
+            # For each annotated attribute, check if it needs to be created or wrapped
+            for attr_name, attr_type in all_annotations.items():
+                if hasattr(self._async_instance, attr_name):
+                    # Attribute exists on the instance
+                    attr = getattr(self._async_instance, attr_name)
+                    # Check if this attribute needs a sync wrapper
+                    if hasattr(attr, "__class__"):
+                        from comfy_api.internal.singleton import ProxiedSingleton
+
+                        if isinstance(attr, ProxiedSingleton):
+                            # Create a sync version of this attribute
+                            try:
+                                sync_attr_class = cls.create_sync_class(attr.__class__)
+                                # Create instance of the sync wrapper with the async instance
+                                sync_attr = object.__new__(sync_attr_class)  # type: ignore
+                                sync_attr._async_instance = attr
+                                setattr(self, attr_name, sync_attr)
+                            except Exception:
+                                # If we can't create a sync version, keep the original
+                                setattr(self, attr_name, attr)
+                        else:
+                            # Not async, just copy the reference
+                            setattr(self, attr_name, attr)
+                else:
+                    # Attribute doesn't exist, but is annotated - create it
+                    # This handles cases like execution: Execution
+                    if isinstance(attr_type, type):
+                        # Check if the type is defined as an inner class
+                        if hasattr(async_class, attr_type.__name__):
+                            inner_class = getattr(async_class, attr_type.__name__)
+                            from comfy_api.internal.singleton import ProxiedSingleton
+
+                            # Create an instance of the inner class
+                            try:
+                                # For ProxiedSingleton classes, get or create the singleton instance
+                                if issubclass(inner_class, ProxiedSingleton):
+                                    async_instance = inner_class.get_instance()
+                                else:
+                                    async_instance = inner_class()
+
+                                # Create sync wrapper
+                                sync_attr_class = cls.create_sync_class(inner_class)
+                                sync_attr = object.__new__(sync_attr_class)  # type: ignore
+                                sync_attr._async_instance = async_instance
+                                setattr(self, attr_name, sync_attr)
+                                # Also set on the async instance for consistency
+                                setattr(self._async_instance, attr_name, async_instance)
+                            except Exception as e:
+                                logging.warning(
+                                    f"Failed to create instance for {attr_name}: {e}"
+                                )
+
+            # Handle other instance attributes that might not be annotated
+            for name, attr in inspect.getmembers(self._async_instance):
+                if name.startswith("_") or hasattr(self, name):
+                    continue
+
+                # If attribute is an instance of a class, and that class is defined in the original class
+                # we need to check if it needs a sync wrapper
+                if isinstance(attr, object) and not isinstance(
+                    attr, (str, int, float, bool, list, dict, tuple)
+                ):
+                    from comfy_api.internal.singleton import ProxiedSingleton
+
+                    if isinstance(attr, ProxiedSingleton):
+                        # Create a sync version of this nested class
+                        try:
+                            sync_attr_class = cls.create_sync_class(attr.__class__)
+                            # Create instance of the sync wrapper with the async instance
+                            sync_attr = object.__new__(sync_attr_class)  # type: ignore
+                            sync_attr._async_instance = attr
+                            setattr(self, name, sync_attr)
+                        except Exception:
+                            # If we can't create a sync version, keep the original
+                            setattr(self, name, attr)
+
+        sync_class_dict["__init__"] = __init__
+
+        # Process methods from the async class
+        for name, method in inspect.getmembers(
+            async_class, predicate=inspect.isfunction
+        ):
+            if name.startswith("_"):
+                continue
+
+            # Extract the actual return type from a coroutine
+            if inspect.iscoroutinefunction(method):
+                # Create sync version of async method with proper signature
+                @functools.wraps(method)
+                def sync_method(self, *args, _method_name=name, **kwargs):
+                    async_method = getattr(self._async_instance, _method_name)
+                    return AsyncToSyncConverter.run_async_in_thread(
+                        async_method, *args, **kwargs
+                    )
+
+                # Add to the class dict
+                sync_class_dict[name] = sync_method
+            else:
+                # For regular methods, create a proxy method
+                @functools.wraps(method)
+                def proxy_method(self, *args, _method_name=name, **kwargs):
+                    method = getattr(self._async_instance, _method_name)
+                    return method(*args, **kwargs)
+
+                # Add to the class dict
+                sync_class_dict[name] = proxy_method
+
+        # Handle property access
+        for name, prop in inspect.getmembers(
+            async_class, lambda x: isinstance(x, property)
+        ):
+
+            def make_property(name, prop_obj):
+                def getter(self):
+                    value = getattr(self._async_instance, name)
+                    if inspect.iscoroutinefunction(value):
+
+                        def sync_fn(*args, **kwargs):
+                            return AsyncToSyncConverter.run_async_in_thread(
+                                value, *args, **kwargs
+                            )
+
+                        return sync_fn
+                    return value
+
+                def setter(self, value):
+                    setattr(self._async_instance, name, value)
+
+                return property(getter, setter if prop_obj.fset else None)
+
+            sync_class_dict[name] = make_property(name, prop)
+
+        # Create the class
+        sync_class = type(sync_class_name, (object,), sync_class_dict)
+
+        return sync_class
+
+    @classmethod
+    def _format_type_annotation(
+        cls, annotation, type_tracker: Optional[TypeTracker] = None
+    ) -> str:
+        """Convert a type annotation to its string representation for stub files."""
+        if (
+            annotation is inspect.Parameter.empty
+            or annotation is inspect.Signature.empty
+        ):
+            return "Any"
+
+        # Handle None type
+        if annotation is type(None):
+            return "None"
+
+        # Track the type if we have a tracker
+        if type_tracker:
+            type_tracker.track_type(annotation)
+
+        # Try using typing.get_origin/get_args for Python 3.8+
+        try:
+            origin = get_origin(annotation)
+            args = get_args(annotation)
+
+            if origin is not None:
+                # Track the origin type
+                if type_tracker:
+                    type_tracker.track_type(origin)
+
+                # Get the origin name
+                origin_name = getattr(origin, "__name__", str(origin))
+                if "." in origin_name:
+                    origin_name = origin_name.split(".")[-1]
+
+                # Special handling for types.UnionType (Python 3.10+ pipe operator)
+                # Convert to old-style Union for compatibility
+                if str(origin) == "<class 'types.UnionType'>" or origin_name == "UnionType":
+                    origin_name = "Union"
+
+                # Format arguments recursively
+                if args:
+                    formatted_args = []
+                    for arg in args:
+                        # Track each type in the union
+                        if type_tracker:
+                            type_tracker.track_type(arg)
+                        formatted_args.append(cls._format_type_annotation(arg, type_tracker))
+                    return f"{origin_name}[{', '.join(formatted_args)}]"
+                else:
+                    return origin_name
+        except (AttributeError, TypeError):
+            # Fallback for older Python versions or non-generic types
+            pass
+
+        # Handle generic types the old way for compatibility
+        if hasattr(annotation, "__origin__") and hasattr(annotation, "__args__"):
+            origin = annotation.__origin__
+            origin_name = (
+                origin.__name__
+                if hasattr(origin, "__name__")
+                else str(origin).split("'")[1]
+            )
+
+            # Format each type argument
+            args = []
+            for arg in annotation.__args__:
+                args.append(cls._format_type_annotation(arg, type_tracker))
+
+            return f"{origin_name}[{', '.join(args)}]"
+
+        # Handle regular types with __name__
+        if hasattr(annotation, "__name__"):
+            return annotation.__name__
+
+        # Handle special module types (like types from typing module)
+        if hasattr(annotation, "__module__") and hasattr(annotation, "__qualname__"):
+            # For types like typing.Literal, typing.TypedDict, etc.
+            return annotation.__qualname__
+
+        # Last resort: string conversion with cleanup
+        type_str = str(annotation)
+
+        # Clean up common patterns more robustly
+        if type_str.startswith("<class '") and type_str.endswith("'>"):
+            type_str = type_str[8:-2]  # Remove "<class '" and "'>"
+
+        # Remove module prefixes for common modules
+        for prefix in ["typing.", "builtins.", "types."]:
+            if type_str.startswith(prefix):
+                type_str = type_str[len(prefix) :]
+
+        # Handle special cases
+        if type_str in ("_empty", "inspect._empty"):
+            return "None"
+
+        # Fix NoneType (this should rarely be needed now)
+        if type_str == "NoneType":
+            return "None"
+
+        return type_str
+
+    @classmethod
+    def _extract_coroutine_return_type(cls, annotation):
+        """Extract the actual return type from a Coroutine annotation."""
+        if hasattr(annotation, "__args__") and len(annotation.__args__) > 2:
+            # Coroutine[Any, Any, ReturnType] -> extract ReturnType
+            return annotation.__args__[2]
+        return annotation
+
+    @classmethod
+    def _format_parameter_default(cls, default_value) -> str:
+        """Format a parameter's default value for stub files."""
+        if default_value is inspect.Parameter.empty:
+            return ""
+        elif default_value is None:
+            return " = None"
+        elif isinstance(default_value, bool):
+            return f" = {default_value}"
+        elif default_value == {}:
+            return " = {}"
+        elif default_value == []:
+            return " = []"
+        else:
+            return f" = {default_value}"
+
+    @classmethod
+    def _format_method_parameters(
+        cls,
+        sig: inspect.Signature,
+        skip_self: bool = True,
+        type_hints: Optional[dict] = None,
+        type_tracker: Optional[TypeTracker] = None,
+    ) -> str:
+        """Format method parameters for stub files."""
+        params = []
+        if type_hints is None:
+            type_hints = {}
+
+        for i, (param_name, param) in enumerate(sig.parameters.items()):
+            if i == 0 and param_name == "self" and skip_self:
+                params.append("self")
+            else:
+                # Get type annotation from type hints if available, otherwise from signature
+                annotation = type_hints.get(param_name, param.annotation)
+                type_str = cls._format_type_annotation(annotation, type_tracker)
+
+                # Get default value
+                default_str = cls._format_parameter_default(param.default)
+
+                # Combine parameter parts
+                if annotation is inspect.Parameter.empty:
+                    params.append(f"{param_name}: Any{default_str}")
+                else:
+                    params.append(f"{param_name}: {type_str}{default_str}")
+
+        return ", ".join(params)
+
+    @classmethod
+    def _generate_method_signature(
+        cls,
+        method_name: str,
+        method,
+        is_async: bool = False,
+        type_tracker: Optional[TypeTracker] = None,
+    ) -> str:
+        """Generate a complete method signature for stub files."""
+        sig = inspect.signature(method)
+
+        # Try to get evaluated type hints to resolve string annotations
+        try:
+            from typing import get_type_hints
+            type_hints = get_type_hints(method)
+        except Exception:
+            # Fallback to empty dict if we can't get type hints
+            type_hints = {}
+
+        # For async methods, extract the actual return type
+        return_annotation = type_hints.get('return', sig.return_annotation)
+        if is_async and inspect.iscoroutinefunction(method):
+            return_annotation = cls._extract_coroutine_return_type(return_annotation)
+
+        # Format parameters with type hints
+        params_str = cls._format_method_parameters(sig, type_hints=type_hints, type_tracker=type_tracker)
+
+        # Format return type
+        return_type = cls._format_type_annotation(return_annotation, type_tracker)
+        if return_annotation is inspect.Signature.empty:
+            return_type = "None"
+
+        return f"def {method_name}({params_str}) -> {return_type}: ..."
+
+    @classmethod
+    def _generate_imports(
+        cls, async_class: type, type_tracker: TypeTracker
+    ) -> list[str]:
+        """Generate import statements for the stub file."""
+        imports = []
+
+        # Add standard typing imports
+        imports.append(
+            "from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple"
+        )
+
+        # Add imports from the original module
+        if async_class.__module__ != "builtins":
+            module = inspect.getmodule(async_class)
+            additional_types = []
+
+            if module:
+                # Check if module has __all__ defined
+                module_all = getattr(module, "__all__", None)
+
+                for name, obj in sorted(inspect.getmembers(module)):
+                    if isinstance(obj, type):
+                        # Skip if __all__ is defined and this name isn't in it
+                        # unless it's already been tracked as used in type annotations
+                        if module_all is not None and name not in module_all:
+                            # Check if this type was actually used in annotations
+                            if name not in type_tracker.discovered_types:
+                                continue
+
+                        # Check for NamedTuple
+                        if issubclass(obj, tuple) and hasattr(obj, "_fields"):
+                            additional_types.append(name)
+                            # Mark as already imported
+                            type_tracker.already_imported.add(name)
+                        # Check for Enum
+                        elif issubclass(obj, Enum) and name != "Enum":
+                            additional_types.append(name)
+                            # Mark as already imported
+                            type_tracker.already_imported.add(name)
+
+            if additional_types:
+                type_imports = ", ".join([async_class.__name__] + additional_types)
+                imports.append(f"from {async_class.__module__} import {type_imports}")
+            else:
+                imports.append(
+                    f"from {async_class.__module__} import {async_class.__name__}"
+                )
+
+        # Add imports for all discovered types
+        # Pass the main module name to avoid duplicate imports
+        imports.extend(
+            type_tracker.get_imports(main_module_name=async_class.__module__)
+        )
+
+        # Add base module import if needed
+        if hasattr(inspect.getmodule(async_class), "__name__"):
+            module_name = inspect.getmodule(async_class).__name__
+            if "." in module_name:
+                base_module = module_name.split(".")[0]
+                # Only add if not already importing from it
+                if not any(imp.startswith(f"from {base_module}") for imp in imports):
+                    imports.append(f"import {base_module}")
+
+        return imports
+
+    @classmethod
+    def _get_class_attributes(cls, async_class: type) -> list[tuple[str, type]]:
+        """Extract class attributes that are classes themselves."""
+        class_attributes = []
+
+        # Get resolved type hints to handle string annotations
+        try:
+            type_hints = get_type_hints(async_class)
+        except Exception:
+            type_hints = {}
+
+        # Look for class attributes that are classes
+        for name, attr in sorted(inspect.getmembers(async_class)):
+            if isinstance(attr, type) and not name.startswith("_"):
+                class_attributes.append((name, attr))
+            elif name in type_hints:
+                # Use resolved type hint instead of raw annotation
+                annotation = type_hints[name]
+                if isinstance(annotation, type):
+                    class_attributes.append((name, annotation))
+
+        return class_attributes
+
+    @classmethod
+    def _generate_inner_class_stub(
+        cls,
+        name: str,
+        attr: type,
+        indent: str = "    ",
+        type_tracker: Optional[TypeTracker] = None,
+    ) -> list[str]:
+        """Generate stub for an inner class."""
+        stub_lines = []
+        stub_lines.append(f"{indent}class {name}Sync:")
+
+        # Add docstring if available
+        if hasattr(attr, "__doc__") and attr.__doc__:
+            stub_lines.extend(
+                cls._format_docstring_for_stub(attr.__doc__, f"{indent}    ")
+            )
+
+        # Add __init__ if it exists
+        if hasattr(attr, "__init__"):
+            try:
+                init_method = getattr(attr, "__init__")
+                init_sig = inspect.signature(init_method)
+
+                # Try to get type hints
+                try:
+                    from typing import get_type_hints
+                    init_hints = get_type_hints(init_method)
+                except Exception:
+                    init_hints = {}
+
+                # Format parameters
+                params_str = cls._format_method_parameters(
+                    init_sig, type_hints=init_hints, type_tracker=type_tracker
+                )
+                # Add __init__ docstring if available (before the method)
+                if hasattr(init_method, "__doc__") and init_method.__doc__:
+                    stub_lines.extend(
+                        cls._format_docstring_for_stub(
+                            init_method.__doc__, f"{indent}    "
+                        )
+                    )
+                stub_lines.append(
+                    f"{indent}    def __init__({params_str}) -> None: ..."
+                )
+            except (ValueError, TypeError):
+                stub_lines.append(
+                    f"{indent}    def __init__(self, *args, **kwargs) -> None: ..."
+                )
+
+        # Add methods to the inner class
+        has_methods = False
+        for method_name, method in sorted(
+            inspect.getmembers(attr, predicate=inspect.isfunction)
+        ):
+            if method_name.startswith("_"):
+                continue
+
+            has_methods = True
+            try:
+                # Add method docstring if available (before the method signature)
+                if method.__doc__:
+                    stub_lines.extend(
+                        cls._format_docstring_for_stub(method.__doc__, f"{indent}    ")
+                    )
+
+                method_sig = cls._generate_method_signature(
+                    method_name, method, is_async=True, type_tracker=type_tracker
+                )
+                stub_lines.append(f"{indent}    {method_sig}")
+            except (ValueError, TypeError):
+                stub_lines.append(
+                    f"{indent}    def {method_name}(self, *args, **kwargs): ..."
+                )
+
+        if not has_methods:
+            stub_lines.append(f"{indent}    pass")
+
+        return stub_lines
+
+    @classmethod
+    def _format_docstring_for_stub(
+        cls, docstring: str, indent: str = "    "
+    ) -> list[str]:
+        """Format a docstring for inclusion in a stub file with proper indentation."""
+        if not docstring:
+            return []
+
+        # First, dedent the docstring to remove any existing indentation
+        dedented = textwrap.dedent(docstring).strip()
+
+        # Split into lines
+        lines = dedented.split("\n")
+
+        # Build the properly indented docstring
+        result = []
+        result.append(f'{indent}"""')
+
+        for line in lines:
+            if line.strip():  # Non-empty line
+                result.append(f"{indent}{line}")
+            else:  # Empty line
+                result.append("")
+
+        result.append(f'{indent}"""')
+        return result
+
+    @classmethod
+    def _post_process_stub_content(cls, stub_content: list[str]) -> list[str]:
+        """Post-process stub content to fix any remaining issues."""
+        processed = []
+
+        for line in stub_content:
+            # Skip processing imports
+            if line.startswith(("from ", "import ")):
+                processed.append(line)
+                continue
+
+            # Fix method signatures missing return types
+            if (
+                line.strip().startswith("def ")
+                and line.strip().endswith(": ...")
+                and ") -> " not in line
+            ):
+                # Add -> None for methods without return annotation
+                line = line.replace(": ...", " -> None: ...")
+
+            processed.append(line)
+
+        return processed
+
+    @classmethod
+    def generate_stub_file(cls, async_class: type, sync_class: type) -> None:
+        """
+        Generate a .pyi stub file for the sync class to help IDEs with type checking.
+        """
+        try:
+            # Only generate stub if we can determine module path
+            if async_class.__module__ == "__main__":
+                return
+
+            module = inspect.getmodule(async_class)
+            if not module:
+                return
+
+            module_path = module.__file__
+            if not module_path:
+                return
+
+            # Create stub file path in a 'generated' subdirectory
+            module_dir = os.path.dirname(module_path)
+            stub_dir = os.path.join(module_dir, "generated")
+
+            # Ensure the generated directory exists
+            os.makedirs(stub_dir, exist_ok=True)
+
+            module_name = os.path.basename(module_path)
+            if module_name.endswith(".py"):
+                module_name = module_name[:-3]
+
+            sync_stub_path = os.path.join(stub_dir, f"{sync_class.__name__}.pyi")
+
+            # Create a type tracker for this stub generation
+            type_tracker = TypeTracker()
+
+            stub_content = []
+
+            # We'll generate imports after processing all methods to capture all types
+            # Leave a placeholder for imports
+            imports_placeholder_index = len(stub_content)
+            stub_content.append("")  # Will be replaced with imports later
+
+            # Class definition
+            stub_content.append(f"class {sync_class.__name__}:")
+
+            # Docstring
+            if async_class.__doc__:
+                stub_content.extend(
+                    cls._format_docstring_for_stub(async_class.__doc__, "    ")
+                )
+
+            # Generate __init__
+            try:
+                init_method = async_class.__init__
+                init_signature = inspect.signature(init_method)
+
+                # Try to get type hints for __init__
+                try:
+                    from typing import get_type_hints
+                    init_hints = get_type_hints(init_method)
+                except Exception:
+                    init_hints = {}
+
+                # Format parameters
+                params_str = cls._format_method_parameters(
+                    init_signature, type_hints=init_hints, type_tracker=type_tracker
+                )
+                # Add __init__ docstring if available (before the method)
+                if hasattr(init_method, "__doc__") and init_method.__doc__:
+                    stub_content.extend(
+                        cls._format_docstring_for_stub(init_method.__doc__, "    ")
+                    )
+                stub_content.append(f"    def __init__({params_str}) -> None: ...")
+            except (ValueError, TypeError):
+                stub_content.append(
+                    "    def __init__(self, *args, **kwargs) -> None: ..."
+                )
+
+            stub_content.append("")  # Add newline after __init__
+
+            # Get class attributes
+            class_attributes = cls._get_class_attributes(async_class)
+
+            # Generate inner classes
+            for name, attr in class_attributes:
+                inner_class_stub = cls._generate_inner_class_stub(
+                    name, attr, type_tracker=type_tracker
+                )
+                stub_content.extend(inner_class_stub)
+                stub_content.append("")  # Add newline after the inner class
+
+            # Add methods to the main class
+            processed_methods = set()  # Keep track of methods we've processed
+            for name, method in sorted(
+                inspect.getmembers(async_class, predicate=inspect.isfunction)
+            ):
+                if name.startswith("_") or name in processed_methods:
+                    continue
+
+                processed_methods.add(name)
+
+                try:
+                    method_sig = cls._generate_method_signature(
+                        name, method, is_async=True, type_tracker=type_tracker
+                    )
+
+                    # Add docstring if available (before the method signature for proper formatting)
+                    if method.__doc__:
+                        stub_content.extend(
+                            cls._format_docstring_for_stub(method.__doc__, "    ")
+                        )
+
+                    stub_content.append(f"    {method_sig}")
+
+                    stub_content.append("")  # Add newline after each method
+
+                except (ValueError, TypeError):
+                    # If we can't get the signature, just add a simple stub
+                    stub_content.append(f"    def {name}(self, *args, **kwargs): ...")
+                    stub_content.append("")  # Add newline
+
+            # Add properties
+            for name, prop in sorted(
+                inspect.getmembers(async_class, lambda x: isinstance(x, property))
+            ):
+                stub_content.append("    @property")
+                stub_content.append(f"    def {name}(self) -> Any: ...")
+                if prop.fset:
+                    stub_content.append(f"    @{name}.setter")
+                    stub_content.append(
+                        f"    def {name}(self, value: Any) -> None: ..."
+                    )
+                stub_content.append("")  # Add newline after each property
+
+            # Add placeholders for the nested class instances
+            # Check the actual attribute names from class annotations and attributes
+            attribute_mappings = {}
+
+            # First check annotations for typed attributes (including from parent classes)
+            # Resolve string annotations to actual types
+            try:
+                all_annotations = get_type_hints(async_class)
+            except Exception:
+                # Fallback to raw annotations
+                all_annotations = {}
+                for base_class in reversed(inspect.getmro(async_class)):
+                    if hasattr(base_class, "__annotations__"):
+                        all_annotations.update(base_class.__annotations__)
+
+            for attr_name, attr_type in sorted(all_annotations.items()):
+                for class_name, class_type in class_attributes:
+                    # If the class type matches the annotated type
+                    if (
+                        attr_type == class_type
+                        or (hasattr(attr_type, "__name__") and attr_type.__name__ == class_name)
+                        or (isinstance(attr_type, str) and attr_type == class_name)
+                    ):
+                        attribute_mappings[class_name] = attr_name
+
+            # Remove the extra checking - annotations should be sufficient
+
+            # Add the attribute declarations with proper names
+            for class_name, class_type in class_attributes:
+                # Check if there's a mapping from annotation
+                attr_name = attribute_mappings.get(class_name, class_name)
+                # Use the annotation name if it exists, even if the attribute doesn't exist yet
+                # This is because the attribute might be created at runtime
+                stub_content.append(f"    {attr_name}: {class_name}Sync")
+
+            stub_content.append("")  # Add a final newline
+
+            # Now generate imports with all discovered types
+            imports = cls._generate_imports(async_class, type_tracker)
+
+            # Deduplicate imports while preserving order
+            seen = set()
+            unique_imports = []
+            for imp in imports:
+                if imp not in seen:
+                    seen.add(imp)
+                    unique_imports.append(imp)
+                else:
+                    logging.warning(f"Duplicate import detected: {imp}")
+
+            # Replace the placeholder with actual imports
+            stub_content[imports_placeholder_index : imports_placeholder_index + 1] = (
+                unique_imports
+            )
+
+            # Post-process stub content
+            stub_content = cls._post_process_stub_content(stub_content)
+
+            # Write stub file
+            with open(sync_stub_path, "w") as f:
+                f.write("\n".join(stub_content))
+
+            logging.info(f"Generated stub file: {sync_stub_path}")
+
+        except Exception as e:
+            # If stub generation fails, log the error but don't break the main functionality
+            logging.error(
+                f"Error generating stub file for {sync_class.__name__}: {str(e)}"
+            )
+            import traceback
+
+            logging.error(traceback.format_exc())
+
+
+def create_sync_class(async_class: type, thread_pool_size=10) -> type:
+    """
+    Creates a sync version of an async class
+
+    Args:
+        async_class: The async class to convert
+        thread_pool_size: Size of thread pool to use
+
+    Returns:
+        A new class with sync versions of all async methods
+    """
+    return AsyncToSyncConverter.create_sync_class(async_class, thread_pool_size)
diff --git a/comfy_api/internal/singleton.py b/comfy_api/internal/singleton.py
new file mode 100644
index 0000000000000000000000000000000000000000..897204b60511835cec66b190d46bf81205aa1e82
--- /dev/null
+++ b/comfy_api/internal/singleton.py
@@ -0,0 +1,33 @@
+from typing import TypeVar
+
+class SingletonMetaclass(type):
+    T = TypeVar("T", bound="SingletonMetaclass")
+    _instances = {}
+
+    def __call__(cls, *args, **kwargs):
+        if cls not in cls._instances:
+            cls._instances[cls] = super(SingletonMetaclass, cls).__call__(
+                *args, **kwargs
+            )
+        return cls._instances[cls]
+
+    def inject_instance(cls: type[T], instance: T) -> None:
+        assert cls not in SingletonMetaclass._instances, (
+            "Cannot inject instance after first instantiation"
+        )
+        SingletonMetaclass._instances[cls] = instance
+
+    def get_instance(cls: type[T], *args, **kwargs) -> T:
+        """
+        Gets the singleton instance of the class, creating it if it doesn't exist.
+        """
+        if cls not in SingletonMetaclass._instances:
+            SingletonMetaclass._instances[cls] = super(
+                SingletonMetaclass, cls
+            ).__call__(*args, **kwargs)
+        return cls._instances[cls]
+
+
+class ProxiedSingleton(object, metaclass=SingletonMetaclass):
+    def __init__(self):
+        super().__init__()
diff --git a/comfy_api/latest/__init__.py b/comfy_api/latest/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a0b296b67e5b054525e812918d77fae7d743e09
--- /dev/null
+++ b/comfy_api/latest/__init__.py
@@ -0,0 +1,177 @@
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING
+from comfy_api.internal import ComfyAPIBase
+from comfy_api.internal.singleton import ProxiedSingleton
+from comfy_api.internal.async_to_sync import create_sync_class
+from ._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
+from ._input_impl import VideoFromFile, VideoFromComponents
+from ._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL, SPLAT, File3D
+from . import _io_public as io
+from . import _ui_public as ui
+from comfy_execution.utils import get_executing_context
+from comfy_execution.progress import get_progress_state, PreviewImageTuple
+from PIL import Image
+from comfy.cli_args import args
+import numpy as np
+
+
+class ComfyAPI_latest(ComfyAPIBase):
+    VERSION = "latest"
+    STABLE = False
+
+    def __init__(self):
+        super().__init__()
+        self.node_replacement = self.NodeReplacement()
+        self.execution = self.Execution()
+        self.caching = self.Caching()
+
+    class NodeReplacement(ProxiedSingleton):
+        async def register(self, node_replace: io.NodeReplace) -> None:
+            """Register a node replacement mapping."""
+            from server import PromptServer
+            PromptServer.instance.node_replace_manager.register(node_replace)
+
+    class Execution(ProxiedSingleton):
+        async def set_progress(
+            self,
+            value: float,
+            max_value: float,
+            node_id: str | None = None,
+            preview_image: Image.Image | ImageInput | None = None,
+            ignore_size_limit: bool = False,
+        ) -> None:
+            """
+            Update the progress bar displayed in the ComfyUI interface.
+
+            This function allows custom nodes and API calls to report their progress
+            back to the user interface, providing visual feedback during long operations.
+
+            Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK
+            """
+            executing_context = get_executing_context()
+            if node_id is None and executing_context is not None:
+                node_id = executing_context.node_id
+            if node_id is None:
+                raise ValueError("node_id must be provided if not in executing context")
+
+            # Convert preview_image to PreviewImageTuple if needed
+            to_display: PreviewImageTuple | Image.Image | ImageInput | None = preview_image
+            if to_display is not None:
+                # First convert to PIL Image if needed
+                if isinstance(to_display, ImageInput):
+                    # Convert ImageInput (torch.Tensor) to PIL Image
+                    # Handle tensor shape [B, H, W, C] -> get first image if batch
+                    tensor = to_display
+                    if len(tensor.shape) == 4:
+                        tensor = tensor[0]
+
+                    # Convert to numpy array and scale to 0-255
+                    image_np = (tensor.cpu().numpy() * 255).astype(np.uint8)
+                    to_display = Image.fromarray(image_np)
+
+                if isinstance(to_display, Image.Image):
+                    # Detect image format from PIL Image
+                    image_format = to_display.format if to_display.format else "JPEG"
+                    # Use None for preview_size if ignore_size_limit is True
+                    preview_size = None if ignore_size_limit else args.preview_size
+                    to_display = (image_format, to_display, preview_size)
+
+            get_progress_state().update_progress(
+                node_id=node_id,
+                value=value,
+                max_value=max_value,
+                image=to_display,
+            )
+
+    class Caching(ProxiedSingleton):
+        """
+        External cache provider API for sharing cached node outputs
+        across ComfyUI instances.
+
+        Example::
+
+            from comfy_api.latest import Caching
+
+            class MyCacheProvider(Caching.CacheProvider):
+                async def on_lookup(self, context):
+                    ...  # check external storage
+
+                async def on_store(self, context, value):
+                    ...  # store to external storage
+
+            Caching.register_provider(MyCacheProvider())
+        """
+        from ._caching import CacheProvider, CacheContext, CacheValue
+
+        async def register_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
+            """Register an external cache provider. Providers are called in registration order."""
+            from comfy_execution.cache_provider import register_cache_provider
+            register_cache_provider(provider)
+
+        async def unregister_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
+            """Unregister a previously registered cache provider."""
+            from comfy_execution.cache_provider import unregister_cache_provider
+            unregister_cache_provider(provider)
+
+class ComfyExtension(ABC):
+    async def on_load(self) -> None:
+        """
+        Called when an extension is loaded.
+        This should be used to initialize any global resources needed by the extension.
+        """
+
+    @abstractmethod
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        """
+        Returns a list of nodes that this extension provides.
+        """
+
+class Input:
+    Image = ImageInput
+    Audio = AudioInput
+    Mask = MaskInput
+    Latent = LatentInput
+    Video = VideoInput
+
+class InputImpl:
+    VideoFromFile = VideoFromFile
+    VideoFromComponents = VideoFromComponents
+
+class Types:
+    VideoCodec = VideoCodec
+    VideoContainer = VideoContainer
+    VideoComponents = VideoComponents
+    MESH = MESH
+    VOXEL = VOXEL
+    SPLAT = SPLAT
+    File3D = File3D
+
+
+Caching = ComfyAPI_latest.Caching
+
+ComfyAPI = ComfyAPI_latest
+
+# Create a synchronous version of the API
+if TYPE_CHECKING:
+    import comfy_api.latest.generated.ComfyAPISyncStub  # type: ignore
+
+    ComfyAPISync: type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub]
+ComfyAPISync = create_sync_class(ComfyAPI_latest)
+
+# create new aliases for io and ui
+IO = io
+UI = ui
+
+__all__ = [
+    "ComfyAPI",
+    "ComfyAPISync",
+    "Input",
+    "InputImpl",
+    "Types",
+    "Caching",
+    "ComfyExtension",
+    "io",
+    "IO",
+    "ui",
+    "UI",
+]
diff --git a/comfy_api/latest/_caching.py b/comfy_api/latest/_caching.py
new file mode 100644
index 0000000000000000000000000000000000000000..88af833729f92e3097c9bcf8a0111002bd6cbfd9
--- /dev/null
+++ b/comfy_api/latest/_caching.py
@@ -0,0 +1,42 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+from dataclasses import dataclass
+
+
+@dataclass
+class CacheContext:
+    node_id: str
+    class_type: str
+    cache_key_hash: str  # SHA256 hex digest
+
+
+@dataclass
+class CacheValue:
+    outputs: list
+    ui: dict = None
+
+
+class CacheProvider(ABC):
+    """Abstract base class for external cache providers.
+    Exceptions from provider methods are caught by the caller and never break execution.
+    """
+
+    @abstractmethod
+    async def on_lookup(self, context: CacheContext) -> Optional[CacheValue]:
+        """Called on local cache miss. Return CacheValue if found, None otherwise."""
+        pass
+
+    @abstractmethod
+    async def on_store(self, context: CacheContext, value: CacheValue) -> None:
+        """Called after local store. Dispatched via asyncio.create_task."""
+        pass
+
+    def should_cache(self, context: CacheContext, value: Optional[CacheValue] = None) -> bool:
+        """Return False to skip external caching for this node. Default: True."""
+        return True
+
+    def on_prompt_start(self, prompt_id: str) -> None:
+        pass
+
+    def on_prompt_end(self, prompt_id: str) -> None:
+        pass
diff --git a/comfy_api/latest/_input/__init__.py b/comfy_api/latest/_input/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7dcdf42118db8b7062e71debe5675fa07e2ead3
--- /dev/null
+++ b/comfy_api/latest/_input/__init__.py
@@ -0,0 +1,17 @@
+from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput
+from .curve_types import CurvePoint, CurveInput, MonotoneCubicCurve, LinearCurve
+from .range_types import RangeInput
+from .video_types import VideoInput
+
+__all__ = [
+    "ImageInput",
+    "AudioInput",
+    "VideoInput",
+    "MaskInput",
+    "LatentInput",
+    "CurvePoint",
+    "CurveInput",
+    "MonotoneCubicCurve",
+    "LinearCurve",
+    "RangeInput",
+]
diff --git a/comfy_api/latest/_input/basic_types.py b/comfy_api/latest/_input/basic_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..b36d2ae5d50ad5bd854ab22e29643eda680a0f3c
--- /dev/null
+++ b/comfy_api/latest/_input/basic_types.py
@@ -0,0 +1,42 @@
+import torch
+from typing import TypedDict, Optional
+
+ImageInput = torch.Tensor
+"""
+An image in format [B, H, W, C] where B is the batch size, C is the number of channels,
+"""
+
+MaskInput = torch.Tensor
+"""
+A mask in format [B, H, W] where B is the batch size
+"""
+
+class AudioInput(TypedDict):
+    """
+    TypedDict representing audio input.
+    """
+
+    waveform: torch.Tensor
+    """
+    Tensor in the format [B, C, T] where B is the batch size, C is the number of channels,
+    """
+
+    sample_rate: int
+
+class LatentInput(TypedDict):
+    """
+    TypedDict representing latent input.
+    """
+
+    samples: torch.Tensor
+    """
+    Tensor in the format [B, C, H, W] where B is the batch size, C is the number of channels,
+    H is the height, and W is the width.
+    """
+
+    noise_mask: Optional[MaskInput]
+    """
+    Optional noise mask tensor in the same format as samples.
+    """
+
+    batch_index: Optional[list[int]]
diff --git a/comfy_api/latest/_input/curve_types.py b/comfy_api/latest/_input/curve_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..08030a3f5f9c5c98a33dc9789ebe012bb56a59ee
--- /dev/null
+++ b/comfy_api/latest/_input/curve_types.py
@@ -0,0 +1,219 @@
+from __future__ import annotations
+
+import logging
+import math
+from abc import ABC, abstractmethod
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+CurvePoint = tuple[float, float]
+
+
+class CurveInput(ABC):
+    """Abstract base class for curve inputs.
+
+    Subclasses represent different curve representations (control-point
+    interpolation, analytical functions, LUT-based, etc.) while exposing a
+    uniform evaluation interface to downstream nodes.
+    """
+
+    @property
+    @abstractmethod
+    def points(self) -> list[CurvePoint]:
+        """The control points that define this curve."""
+
+    @abstractmethod
+    def interp(self, x: float) -> float:
+        """Evaluate the curve at a single *x* value in [0, 1]."""
+
+    def interp_array(self, xs: np.ndarray) -> np.ndarray:
+        """Vectorised evaluation over a numpy array of x values.
+
+        Subclasses should override this for better performance. The default
+        falls back to scalar ``interp`` calls.
+        """
+        return np.fromiter((self.interp(float(x)) for x in xs), dtype=np.float64, count=len(xs))
+
+    def to_lut(self, size: int = 256) -> np.ndarray:
+        """Generate a float64 lookup table of *size* evenly-spaced samples in [0, 1]."""
+        return self.interp_array(np.linspace(0.0, 1.0, size))
+
+    @staticmethod
+    def from_raw(data) -> CurveInput:
+        """Convert raw curve data (dict or point list) to a CurveInput instance.
+
+        Accepts:
+        - A ``CurveInput`` instance (returned as-is).
+        - A dict with ``"points"`` and optional ``"interpolation"`` keys.
+        - A bare list/sequence of ``(x, y)`` pairs (defaults to monotone cubic).
+        """
+        if isinstance(data, CurveInput):
+            return data
+        if isinstance(data, dict):
+            raw_points = data["points"]
+            interpolation = data.get("interpolation", "monotone_cubic")
+        else:
+            raw_points = data
+            interpolation = "monotone_cubic"
+        points = [(float(x), float(y)) for x, y in raw_points]
+        if interpolation == "linear":
+            return LinearCurve(points)
+        if interpolation != "monotone_cubic":
+            logger.warning("Unknown curve interpolation %r, falling back to monotone_cubic", interpolation)
+        return MonotoneCubicCurve(points)
+
+
+class MonotoneCubicCurve(CurveInput):
+    """Monotone cubic Hermite interpolation over control points.
+
+    Mirrors the frontend ``createMonotoneInterpolator`` in
+    ``ComfyUI_frontend/src/components/curve/curveUtils.ts`` so that
+    backend evaluation matches the editor preview exactly.
+
+    All heavy work (sorting, slope computation) happens once at construction.
+    ``interp_array`` is fully vectorised with numpy.
+    """
+
+    def __init__(self, control_points: list[CurvePoint]):
+        sorted_pts = sorted(control_points, key=lambda p: p[0])
+        self._points = [(float(x), float(y)) for x, y in sorted_pts]
+        self._xs = np.array([p[0] for p in self._points], dtype=np.float64)
+        self._ys = np.array([p[1] for p in self._points], dtype=np.float64)
+        self._slopes = self._compute_slopes()
+
+    @property
+    def points(self) -> list[CurvePoint]:
+        return list(self._points)
+
+    def _compute_slopes(self) -> np.ndarray:
+        xs, ys = self._xs, self._ys
+        n = len(xs)
+        if n < 2:
+            return np.zeros(n, dtype=np.float64)
+
+        dx = np.diff(xs)
+        dy = np.diff(ys)
+        dx_safe = np.where(dx == 0, 1.0, dx)
+        deltas = np.where(dx == 0, 0.0, dy / dx_safe)
+
+        slopes = np.empty(n, dtype=np.float64)
+        slopes[0] = deltas[0]
+        slopes[-1] = deltas[-1]
+        for i in range(1, n - 1):
+            if deltas[i - 1] * deltas[i] <= 0:
+                slopes[i] = 0.0
+            else:
+                slopes[i] = (deltas[i - 1] + deltas[i]) / 2
+
+        for i in range(n - 1):
+            if deltas[i] == 0:
+                slopes[i] = 0.0
+                slopes[i + 1] = 0.0
+            else:
+                alpha = slopes[i] / deltas[i]
+                beta = slopes[i + 1] / deltas[i]
+                s = alpha * alpha + beta * beta
+                if s > 9:
+                    t = 3 / math.sqrt(s)
+                    slopes[i] = t * alpha * deltas[i]
+                    slopes[i + 1] = t * beta * deltas[i]
+        return slopes
+
+    def interp(self, x: float) -> float:
+        xs, ys, slopes = self._xs, self._ys, self._slopes
+        n = len(xs)
+        if n == 0:
+            return 0.0
+        if n == 1:
+            return float(ys[0])
+        if x <= xs[0]:
+            return float(ys[0])
+        if x >= xs[-1]:
+            return float(ys[-1])
+
+        hi = int(np.searchsorted(xs, x, side='right'))
+        hi = min(hi, n - 1)
+        lo = hi - 1
+
+        dx = xs[hi] - xs[lo]
+        if dx == 0:
+            return float(ys[lo])
+
+        t = (x - xs[lo]) / dx
+        t2 = t * t
+        t3 = t2 * t
+        h00 = 2 * t3 - 3 * t2 + 1
+        h10 = t3 - 2 * t2 + t
+        h01 = -2 * t3 + 3 * t2
+        h11 = t3 - t2
+        return float(h00 * ys[lo] + h10 * dx * slopes[lo] + h01 * ys[hi] + h11 * dx * slopes[hi])
+
+    def interp_array(self, xs_in: np.ndarray) -> np.ndarray:
+        """Fully vectorised evaluation using numpy."""
+        xs, ys, slopes = self._xs, self._ys, self._slopes
+        n = len(xs)
+        if n == 0:
+            return np.zeros_like(xs_in, dtype=np.float64)
+        if n == 1:
+            return np.full_like(xs_in, ys[0], dtype=np.float64)
+
+        hi = np.searchsorted(xs, xs_in, side='right').clip(1, n - 1)
+        lo = hi - 1
+
+        dx = xs[hi] - xs[lo]
+        dx_safe = np.where(dx == 0, 1.0, dx)
+        t = np.where(dx == 0, 0.0, (xs_in - xs[lo]) / dx_safe)
+        t2 = t * t
+        t3 = t2 * t
+
+        h00 = 2 * t3 - 3 * t2 + 1
+        h10 = t3 - 2 * t2 + t
+        h01 = -2 * t3 + 3 * t2
+        h11 = t3 - t2
+
+        result = h00 * ys[lo] + h10 * dx * slopes[lo] + h01 * ys[hi] + h11 * dx * slopes[hi]
+        result = np.where(xs_in <= xs[0], ys[0], result)
+        result = np.where(xs_in >= xs[-1], ys[-1], result)
+        return result
+
+    def __repr__(self) -> str:
+        return f"MonotoneCubicCurve(points={self._points})"
+
+
+class LinearCurve(CurveInput):
+    """Piecewise linear interpolation over control points.
+
+    Mirrors the frontend ``createLinearInterpolator`` in
+    ``ComfyUI_frontend/src/components/curve/curveUtils.ts``.
+    """
+
+    def __init__(self, control_points: list[CurvePoint]):
+        sorted_pts = sorted(control_points, key=lambda p: p[0])
+        self._points = [(float(x), float(y)) for x, y in sorted_pts]
+        self._xs = np.array([p[0] for p in self._points], dtype=np.float64)
+        self._ys = np.array([p[1] for p in self._points], dtype=np.float64)
+
+    @property
+    def points(self) -> list[CurvePoint]:
+        return list(self._points)
+
+    def interp(self, x: float) -> float:
+        xs, ys = self._xs, self._ys
+        n = len(xs)
+        if n == 0:
+            return 0.0
+        if n == 1:
+            return float(ys[0])
+        return float(np.interp(x, xs, ys))
+
+    def interp_array(self, xs_in: np.ndarray) -> np.ndarray:
+        if len(self._xs) == 0:
+            return np.zeros_like(xs_in, dtype=np.float64)
+        if len(self._xs) == 1:
+            return np.full_like(xs_in, self._ys[0], dtype=np.float64)
+        return np.interp(xs_in, self._xs, self._ys)
+
+    def __repr__(self) -> str:
+        return f"LinearCurve(points={self._points})"
diff --git a/comfy_api/latest/_input/range_types.py b/comfy_api/latest/_input/range_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..7557b31410a145994da0c439d1aabb9ffac4befc
--- /dev/null
+++ b/comfy_api/latest/_input/range_types.py
@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+import logging
+import math
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+class RangeInput:
+    """Represents a levels/range adjustment: input range [min, max] with
+    optional midpoint (gamma control).
+
+    Generates a 1D LUT identical to GIMP's levels mapping:
+        1. Normalize input to [0, 1] using [min, max]
+        2. Apply gamma correction: pow(value, 1/gamma)
+        3. Clamp to [0, 1]
+
+    The midpoint field is a position in [0, 1] representing where the
+    midtone falls within [min, max]. It maps to gamma via:
+        gamma = -log2(midpoint)
+    So midpoint=0.5 → gamma=1.0 (linear).
+    """
+
+    def __init__(self, min_val: float, max_val: float, midpoint: float | None = None):
+        self.min_val = min_val
+        self.max_val = max_val
+        self.midpoint = midpoint
+
+    @staticmethod
+    def from_raw(data) -> RangeInput:
+        if isinstance(data, RangeInput):
+            return data
+        if isinstance(data, dict):
+            return RangeInput(
+                min_val=float(data.get("min", 0.0)),
+                max_val=float(data.get("max", 1.0)),
+                midpoint=float(data["midpoint"]) if data.get("midpoint") is not None else None,
+            )
+        raise TypeError(f"Cannot convert {type(data)} to RangeInput")
+
+    def to_lut(self, size: int = 256) -> np.ndarray:
+        """Generate a float64 lookup table mapping [0, 1] input through this
+        levels adjustment.
+
+        The LUT maps normalized input values (0..1) to output values (0..1),
+        matching the GIMP levels formula.
+        """
+        xs = np.linspace(0.0, 1.0, size, dtype=np.float64)
+
+        in_range = self.max_val - self.min_val
+        if abs(in_range) < 1e-10:
+            return np.where(xs >= self.min_val, 1.0, 0.0).astype(np.float64)
+
+        # Normalize: map [min, max] → [0, 1]
+        result = (xs - self.min_val) / in_range
+        result = np.clip(result, 0.0, 1.0)
+
+        # Gamma correction from midpoint
+        if self.midpoint is not None and self.midpoint > 0 and self.midpoint != 0.5:
+            gamma = max(-math.log2(self.midpoint), 0.001)
+            inv_gamma = 1.0 / gamma
+            mask = result > 0
+            result[mask] = np.power(result[mask], inv_gamma)
+
+        return result
+
+    def __repr__(self) -> str:
+        mid = f", midpoint={self.midpoint}" if self.midpoint is not None else ""
+        return f"RangeInput(min={self.min_val}, max={self.max_val}{mid})"
diff --git a/comfy_api/latest/_input/video_types.py b/comfy_api/latest/_input/video_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..d59b18280ead04bfab2258570a577ab7f4f31a4e
--- /dev/null
+++ b/comfy_api/latest/_input/video_types.py
@@ -0,0 +1,198 @@
+from __future__ import annotations
+from abc import ABC, abstractmethod
+from fractions import Fraction
+from typing import Optional, Union, IO
+import io
+import av
+from .._util import VideoContainer, VideoCodec, VideoComponents, normalize_crop_rect
+
+class VideoInput(ABC):
+    """
+    Abstract base class for video input types.
+    """
+
+    @abstractmethod
+    def get_components(self) -> VideoComponents:
+        """
+        Abstract method to get the video components (images, audio, and frame rate).
+
+        Returns:
+            VideoComponents containing images, audio, and frame rate
+        """
+        pass
+
+    @abstractmethod
+    def save_to(
+        self,
+        path: Union[str, IO[bytes]],
+        format: VideoContainer = VideoContainer.AUTO,
+        codec: VideoCodec = VideoCodec.AUTO,
+        metadata: Optional[dict] = None,
+        bit_depth: int | None = None,
+        crf: float | None = None,
+        color_space: str | None = None,
+        preset: str | None = None,
+    ):
+        """
+        Abstract method to save the video input to a file.
+
+        bit_depth selects the encoded bit depth; None keeps the video's native depth.
+        crf selects the H.264 or AV1 constant rate factor; None uses the encoder default.
+        preset selects the H.264 encoder speed/compression trade-off (e.g. "ultrafast");
+        None uses the encoder default. Ignored for other codecs.
+        color_space="sRGB" selects SDR BT.709/sRGB, "HDR" selects BT.2020/HLG, and "HDR PQ"
+        selects BT.2020/PQ. Bit depth is selected independently.
+        Tensor-created videos default to sRGB when color_space is None. Loaded videos keep matching recognized native color
+        properties; other input pixels must already use the selected color space.
+        """
+        pass
+
+    def get_color_space(self) -> str:
+        """Return the video's color space as sRGB, HDR, HDR PQ, or auto when unspecified."""
+        return "auto"
+
+    @abstractmethod
+    def as_trimmed(
+        self,
+        start_time: float | None = None,
+        duration: float | None = None,
+        strict_duration: bool = False,
+    ) -> VideoInput | None:
+        """
+        Create a new VideoInput which is trimmed to have the corresponding start_time and duration
+
+        Returns:
+            A new VideoInput, or None if the result would have negative duration
+        """
+        pass
+
+    def as_cropped(
+        self,
+        x: int = 0,
+        y: int = 0,
+        width: int = 0,
+        height: int = 0,
+    ) -> VideoInput:
+        """
+        Create a new VideoInput spatially cropped to the given pixel rectangle.
+
+        The rectangle is clamped to the frame and even-aligned for encoder
+        compatibility. An empty or full-frame rectangle returns the input
+        unchanged.
+
+        Default implementation materializes the video via get_components();
+        subclasses should override with lazier strategies when possible.
+        """
+        components = self.get_components()
+        rect = normalize_crop_rect(
+            x, y, width, height, components.images.shape[2], components.images.shape[1]
+        )
+        if rect is None:
+            return self
+        from .._input_impl.video_types import VideoFromComponents
+
+        cx, cy, cw, ch = rect
+        return VideoFromComponents(
+            VideoComponents(
+                images=components.images[:, cy:cy + ch, cx:cx + cw, :].clone(),
+                audio=components.audio,
+                frame_rate=components.frame_rate,
+                metadata=components.metadata,
+                alpha=components.alpha[:, cy:cy + ch, cx:cx + cw].clone()
+                if components.alpha is not None
+                else None,
+            ),
+            bit_depth=self.get_bit_depth(),
+        )
+
+    def get_stream_source(self) -> Union[str, io.BytesIO]:
+        """
+        Get a streamable source for the video. This allows processing without
+        loading the entire video into memory.
+
+        Returns:
+            Either a file path (str) or a BytesIO object that can be opened with av.
+
+        Default implementation creates a BytesIO buffer, but subclasses should
+        override this for better performance when possible.
+        """
+        buffer = io.BytesIO()
+        self.save_to(buffer)
+        buffer.seek(0)
+        return buffer
+
+    def get_active_trim_window(self) -> tuple[float, float]:
+        """Return the active trim as ``(start_time, duration)`` in seconds (start_time normalized
+        to ``>= 0``; ``duration == 0`` means "until the end"). Default: no trim; trimmable subclasses override.
+        """
+        return 0.0, 0.0
+
+    # Provide a default implementation, but subclasses can provide optimized versions
+    # if possible.
+    def get_dimensions(self) -> tuple[int, int]:
+        """
+        Returns the dimensions of the video input.
+
+        Returns:
+            Tuple of (width, height)
+        """
+        components = self.get_components()
+        return components.images.shape[2], components.images.shape[1]
+
+    def get_bit_depth(self) -> int:
+        """
+        Returns the bit depth of the video (e.g. 8 or 10).
+
+        Default implementation returns 8; subclasses report their real depth.
+        """
+        return 8
+
+    def get_duration(self) -> float:
+        """
+        Returns the duration of the video in seconds.
+
+        Returns:
+            Duration in seconds
+        """
+        components = self.get_components()
+        frame_count = components.images.shape[0]
+        return float(frame_count / components.frame_rate)
+
+    def get_frame_count(self) -> int:
+        """
+        Returns the number of frames in the video.
+
+        Default implementation uses :meth:`get_components`, which may require
+        loading all frames into memory. File-based implementations should
+        override this method and use container/stream metadata instead.
+
+        Returns:
+            Total number of frames as an integer.
+        """
+        return int(self.get_components().images.shape[0])
+
+    def get_frame_rate(self) -> Fraction:
+        """
+        Returns the frame rate of the video.
+
+        Default implementation materializes the video into memory via
+        `get_components()`. Subclasses that can inspect the underlying
+        container (e.g. `VideoFromFile`) should override this with a more
+        efficient implementation.
+
+        Returns:
+            Frame rate as a Fraction.
+        """
+        return self.get_components().frame_rate
+
+    def get_container_format(self) -> str:
+        """
+        Returns the container format of the video (e.g., 'mp4', 'mov', 'avi').
+
+        Returns:
+            Container format as string
+        """
+        # Default implementation - subclasses should override for better performance
+        source = self.get_stream_source()
+        with av.open(source, mode="r") as container:
+            return container.format.name
diff --git a/comfy_api/latest/_input_impl/__init__.py b/comfy_api/latest/_input_impl/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1397702153ac4c069af1e00ce9731ee8952a60c
--- /dev/null
+++ b/comfy_api/latest/_input_impl/__init__.py
@@ -0,0 +1,7 @@
+from .video_types import VideoFromFile, VideoFromComponents
+
+__all__ = [
+    # Implementations
+    "VideoFromFile",
+    "VideoFromComponents",
+]
diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ea9248d0712d5e3ae2d2c234f8d3ecbe81ef27b
--- /dev/null
+++ b/comfy_api/latest/_input_impl/video_types.py
@@ -0,0 +1,1212 @@
+from av.bitstream import BitStreamFilterContext
+from av.container import InputContainer
+from av.subtitles.stream import SubtitleStream
+from av.video.reformatter import ColorPrimaries, ColorRange, ColorTrc
+from fractions import Fraction
+from typing import Optional
+from .._input import AudioInput, VideoInput
+import av
+import io
+import itertools
+import json
+import numpy as np
+import math
+import os
+import torch
+from .._util import VideoContainer, VideoCodec, VideoComponents, normalize_crop_rect
+import comfy.utils
+import logging
+
+
+VIDEO_ENCODERS = {
+    VideoCodec.H264: "h264",
+    VideoCodec.AV1: "libsvtav1",
+}
+VIDEO_CONTAINER_FORMATS = {
+    VideoContainer.MP4: "mp4",
+    VideoContainer.MKV: "matroska",
+    VideoContainer.WEBM: "webm",
+}
+WEBM_STREAM_CODECS = {
+    "video": {"av1", "vp8", "vp9"},
+    "audio": {"opus", "vorbis"},
+    "subtitle": {"webvtt"},
+}
+BT2020_NCL = 9
+BT709_NCL = 1
+HDR_COLOR_TRANSFERS = {
+    "HDR": ColorTrc.ARIB_STD_B67,
+    "HDR PQ": ColorTrc.SMPTE2084,
+}
+VIDEO_COLOR_TRANSFERS = {
+    "sRGB": ColorTrc.IEC61966_2_1,
+    **HDR_COLOR_TRANSFERS,
+}
+VIDEO_TRANSFER_COLOR_SPACES = {
+    ColorTrc.BT709: "sRGB",
+    ColorTrc.IEC61966_2_1: "sRGB",
+    ColorTrc.ARIB_STD_B67: "HDR",
+    ColorTrc.SMPTE2084: "HDR PQ",
+}
+
+
+def container_to_output_format(container_format: str | None) -> str | None:
+    """
+    A container's `format` may be a comma-separated list of formats.
+    E.g., iso container's `format` may be `mov,mp4,m4a,3gp,3g2,mj2`.
+    However, writing to a file/stream with `av.open` requires a single format,
+    or `None` to auto-detect.
+    """
+    if not container_format:
+        return None  # Auto-detect
+
+    if "," not in container_format:
+        return container_format
+
+    formats = container_format.split(",")
+    return formats[0]
+
+def get_open_write_kwargs(
+    dest: str | io.BytesIO, container_format: str, to_format: str | None
+) -> dict:
+    """Get kwargs for writing a `VideoFromFile` to a file/stream with `av.open`"""
+    is_write_to_buffer = isinstance(dest, io.BytesIO)
+    open_kwargs = {"mode": "w"}
+
+    if is_write_to_buffer:
+        # Set output format explicitly, since it cannot be inferred from file extension
+        if to_format == VideoContainer.AUTO:
+            to_format = container_format.lower()
+        elif isinstance(to_format, VideoContainer):
+            to_format = VIDEO_CONTAINER_FORMATS[to_format]
+        elif isinstance(to_format, str):
+            to_format = to_format.lower()
+        open_kwargs["format"] = container_to_output_format(to_format)
+
+    output_format = open_kwargs["format"] if is_write_to_buffer else os.path.splitext(dest)[1].lower().lstrip(".")
+    if output_format in ("mov", "mp4"):
+        # Preserve custom metadata tags (workflow, prompt, extra_pnginfo) in isobmff.
+        movflags = "use_metadata_tags" if is_write_to_buffer else "use_metadata_tags+faststart"
+        open_kwargs["options"] = {"movflags": movflags}
+
+    return open_kwargs
+
+
+def video_stream_bit_depth(stream) -> int:
+    if stream is None or stream.format is None or not stream.format.components:
+        return 8
+    return max(component.bits for component in stream.format.components)
+
+
+def isobmff_hevc_filter(output_container, stream, out_stream):
+    """Apple players need the 'hvc1' sample entry, not FFmpeg's default 'hev1'. Annex B input without
+    extradata makes the muxer build hvcC from the first packet and strip in-band parameter sets;
+    'hvc1' sources already have a complete hvcC and only need the tag PyAV reset."""
+    if output_container.format.name not in ("mp4", "mov") or stream.codec.canonical_name != "hevc":
+        return None
+    try:
+        codec_tag = stream.codec_context.codec_tag
+    except UnicodeDecodeError:
+        codec_tag = ""
+    if codec_tag == "hvc1":
+        out_stream.codec_context.codec_tag = "hvc1"
+        return None
+    hevc_filter = BitStreamFilterContext("hevc_mp4toannexb", stream, out_stream)
+    out_stream.codec_context.codec_tag = "hvc1"
+    out_stream.codec_context.extradata = None
+    return hevc_filter
+
+
+def filter_hevc_packet(hevc_filter, packet):
+    if packet.has_sidedata("new_extradata"):
+        raise ValueError("HEVC with multiple sample descriptions cannot be remuxed as hvc1; re-encode it instead")
+    return hevc_filter.filter(packet)
+
+
+def last_decodable_audio_stream(container: InputContainer):
+    """Streams FFmpeg has no decoder for have no codec context, and decoding their
+    packets crashes the process (e.g. APAC spatial-audio track in iPhone)."""
+    stream = next(
+        (s for s in reversed(container.streams.audio) if s.codec_context is not None),
+        None,
+    )
+    if stream is None and len(container.streams.audio):
+        logging.warning("No decodable audio stream found in video; ignoring audio.")
+    return stream
+
+
+def probe_audio_params(container: InputContainer, audio_stream, max_packets: int = 200):
+    """Containers probed only up to a window (mpegts) leave audio codec parameters unset when
+    audio starts beyond it; learn them by decoding ahead. The caller must seek back afterwards.
+    Returns (sample_rate, channels), zeros when the stream never yields a decodable frame."""
+    for i, packet in enumerate(container.demux(audio_stream)):
+        try:
+            frames = packet.decode()
+        except av.error.FFmpegError:
+            frames = ()
+        if frames:
+            return frames[0].sample_rate, frames[0].layout.nb_channels
+        if i >= max_packets:
+            break
+    return 0, 0
+
+
+def write_output_metadata(container: InputContainer, output, metadata: dict | None):
+    """Copy the source container's metadata, then overlay the caller's tags."""
+    for key, value in container.metadata.items():
+        if metadata is None or key not in metadata:
+            output.metadata[key] = value
+    if metadata is not None:
+        for key, value in metadata.items():
+            output.metadata[key] = value if isinstance(value, str) else json.dumps(value)
+
+
+def video_output_config(path: str | io.BytesIO, format: VideoContainer, codec: VideoCodec) -> tuple[dict, VideoContainer, VideoCodec]:
+    if isinstance(format, str):
+        format = VideoContainer(format)
+    if isinstance(codec, str):
+        codec = VideoCodec(codec)
+
+    if format == VideoContainer.AUTO:
+        extension = os.path.splitext(os.fspath(path))[1].lower() if isinstance(path, (str, os.PathLike)) else ""
+        format = {
+            ".mkv": VideoContainer.MKV,
+            ".webm": VideoContainer.WEBM,
+        }.get(extension, VideoContainer.MP4)
+    if codec == VideoCodec.AUTO:
+        codec = VideoCodec.AV1 if format == VideoContainer.WEBM else VideoCodec.H264
+    if format == VideoContainer.WEBM and codec != VideoCodec.AV1:
+        raise ValueError("WebM output requires the AV1 codec")
+
+    # FFmpeg's faststart pass reopens the output by filename, so it cannot be used with file-like objects.
+    open_kwargs = {"mode": "w", "format": VIDEO_CONTAINER_FORMATS[format]}
+    if format == VideoContainer.MP4:
+        movflags = "use_metadata_tags+faststart" if isinstance(path, (str, os.PathLike)) else "use_metadata_tags"
+        open_kwargs["options"] = {"movflags": movflags}
+    return open_kwargs, format, codec
+
+
+def set_video_color_properties(target, color_space):
+    is_hdr = color_space in HDR_COLOR_TRANSFERS
+    target.color_primaries = ColorPrimaries.BT2020 if is_hdr else ColorPrimaries.BT709
+    target.color_trc = VIDEO_COLOR_TRANSFERS[color_space]
+    target.colorspace = BT2020_NCL if is_hdr else BT709_NCL
+    target.color_range = ColorRange.MPEG
+
+
+def copy_color_properties(source, target):
+    target.color_primaries = source.color_primaries
+    target.color_trc = source.color_trc
+    target.colorspace = source.colorspace
+    target.color_range = source.color_range
+
+
+def video_stream_color_space(stream) -> str | None:
+    if stream is None:
+        return None
+    return VIDEO_TRANSFER_COLOR_SPACES.get(stream.color_trc)
+
+
+def video_encoder_options(
+    codec: VideoCodec, crf: float | None, preset: str | None = None
+) -> dict[str, str]:
+    options = {}
+    if preset is not None and codec == VideoCodec.H264:
+        options["preset"] = preset
+    if crf is not None:
+        if codec == VideoCodec.AV1 and crf == 0:
+            options["svtav1-params"] = "lossless=1"
+        else:
+            options["crf"] = str(crf)
+    return options
+
+
+def webm_streams_compatible(streams) -> bool:
+    for stream in streams:
+        allowed_codecs = WEBM_STREAM_CODECS.get(stream.type)
+        if allowed_codecs is not None and stream.codec_context is not None and stream.codec.canonical_name not in allowed_codecs:
+            return False
+    return True
+
+
+def _rotation_quadrant(frame: av.VideoFrame) -> int:
+    return int(round(frame.rotation // 90)) % 4 if frame.rotation else 0
+
+
+class VideoFromFile(VideoInput):
+    """
+    Class representing video input from a file.
+    """
+
+    def __init__(self, file: str | io.BytesIO, *, start_time: float=0, duration: float=0,
+                 crop: tuple[int, int, int, int] | None = None):
+        """
+        Initialize the VideoFromFile object based off of either a path on disk or a BytesIO object
+        containing the file contents.
+        """
+        self.__file = file
+        self.__start_time = start_time
+        self.__duration = duration
+        self.__crop = crop
+
+    def get_stream_source(self) -> str | io.BytesIO:
+        """
+        Return the underlying file source for efficient streaming.
+        This avoids unnecessary memory copies when the source is already a file path.
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+        return self.__file
+
+    def get_active_trim_window(self) -> tuple[float, float]:
+        start_time = self.__start_time
+        if start_time < 0:
+            start_time = max(self._get_raw_duration() + start_time, 0.0)
+        return float(start_time), float(self.__duration)
+
+    def get_dimensions(self) -> tuple[int, int]:
+        """
+        Returns the dimensions of the video input.
+
+        Returns:
+            Tuple of (width, height)
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)  # Reset the BytesIO object to the beginning
+        with av.open(self.__file, mode='r') as container:
+            for stream in container.streams:
+                if stream.type == 'video':
+                    assert isinstance(stream, av.VideoStream)
+                    if self.__crop is None:
+                        return stream.width, stream.height
+
+                    display_width, display_height = self._get_display_dimensions()
+                    rect = normalize_crop_rect(*self.__crop, display_width, display_height)
+                    if rect is not None:
+                        return rect[2], rect[3]
+                    return display_width, display_height
+        raise ValueError(f"No video stream found in file '{self.__file}'")
+
+    def _get_display_dimensions(self) -> tuple[int, int]:
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+        with av.open(self.__file, mode='r') as container:
+            for stream in container.streams:
+                if stream.type == 'video':
+                    assert isinstance(stream, av.VideoStream)
+                    width, height = stream.width, stream.height
+                    try:
+                        frame = next(container.decode(stream), None)
+                    except av.error.FFmpegError:
+                        frame = None
+                    if frame is not None and _rotation_quadrant(frame) % 2:
+                        width, height = height, width
+                    return width, height
+        raise ValueError(f"No video stream found in file '{self.__file}'")
+
+    def get_bit_depth(self) -> int:
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)  # Reset the BytesIO object to the beginning
+        with av.open(self.__file, mode="r") as container:
+            video_stream = container.streams.video[0] if len(container.streams.video) > 0 else None
+            return video_stream_bit_depth(video_stream)
+
+    def get_color_space(self) -> str:
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+        with av.open(self.__file, mode="r") as container:
+            video_stream = container.streams.video[0] if len(container.streams.video) > 0 else None
+            return video_stream_color_space(video_stream) or "sRGB"
+
+    def get_duration(self) -> float:
+        """
+        Returns the duration of the video in seconds.
+
+        Returns:
+            Duration in seconds
+        """
+        raw_duration = self._get_raw_duration()
+        if self.__start_time < 0:
+            duration_from_start = min(raw_duration, -self.__start_time)
+        else:
+            duration_from_start = raw_duration - self.__start_time
+        if self.__duration:
+            return min(self.__duration, duration_from_start)
+        return duration_from_start
+
+    def _get_raw_duration(self) -> float:
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+        with av.open(self.__file, mode="r") as container:
+            if container.duration is not None:
+                return float(container.duration / av.time_base)
+
+            # Fallback: calculate from frame count and frame rate
+            video_stream = next(
+                (s for s in container.streams if s.type == "video"), None
+            )
+            if video_stream and video_stream.frames and video_stream.average_rate:
+                return float(video_stream.frames / video_stream.average_rate)
+
+            # Last resort: decode frames to count them
+            if video_stream and video_stream.average_rate:
+                frame_count = 0
+                container.seek(0)
+                frame_iterator = (
+                    container.decode(video_stream)
+                    if video_stream.codec.capabilities & 0x100
+                    else container.demux(video_stream)
+                )
+                for packet in frame_iterator:
+                    frame_count += 1
+                if frame_count > 0:
+                    return float(frame_count / video_stream.average_rate)
+
+        raise ValueError(f"Could not determine duration for file '{self.__file}'")
+
+    def get_frame_count(self) -> int:
+        """
+        Returns the number of frames in the video without materializing them as
+        torch tensors.
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+
+        with av.open(self.__file, mode="r") as container:
+            video_stream = self._get_first_video_stream(container)
+            # 1. Prefer the frames field if available and usable
+            if (
+                video_stream.frames
+                and video_stream.frames > 0
+                and not self.__start_time
+                and not self.__duration
+            ):
+                return int(video_stream.frames)
+
+            # 2. Try to estimate from duration and average_rate using only metadata
+            if (
+                getattr(video_stream, "duration", None) is not None
+                and getattr(video_stream, "time_base", None) is not None
+                and video_stream.average_rate
+            ):
+                raw_duration = float(video_stream.duration * video_stream.time_base)
+                if self.__start_time < 0:
+                    duration_from_start = min(raw_duration, -self.__start_time)
+                else:
+                    duration_from_start = raw_duration - self.__start_time
+                duration_seconds = min(self.__duration, duration_from_start)
+                estimated_frames = int(round(duration_seconds * float(video_stream.average_rate)))
+                if estimated_frames > 0:
+                    return estimated_frames
+
+            # 3. Last resort: decode frames and count them (streaming)
+            start_time, duration = self.get_active_trim_window()
+            frame_count = 1
+            start_pts = int(start_time / video_stream.time_base)
+            end_pts = int((start_time + duration) / video_stream.time_base)
+            container.seek(start_pts, stream=video_stream)
+            frame_iterator = (
+                container.decode(video_stream)
+                if video_stream.codec.capabilities & 0x100
+                else container.demux(video_stream)
+            )
+            for frame in frame_iterator:
+                if frame.pts >= start_pts:
+                    break
+            else:
+                raise ValueError(f"Could not determine frame count for file '{self.__file}'\nNo frames exist for start_time {self.__start_time}")
+            for frame in frame_iterator:
+                if frame.pts >= end_pts:
+                    break
+                frame_count += 1
+            return frame_count
+
+    def get_frame_rate(self) -> Fraction:
+        """
+        Returns the average frame rate of the video using container metadata
+        without decoding all frames.
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+
+        with av.open(self.__file, mode="r") as container:
+            video_stream = self._get_first_video_stream(container)
+            # Preferred: use PyAV's average_rate (usually already a Fraction-like)
+            if video_stream.average_rate:
+                return Fraction(video_stream.average_rate)
+
+            # Fallback: estimate from frames + duration if available
+            if video_stream.frames and container.duration:
+                duration_seconds = float(container.duration / av.time_base)
+                if duration_seconds > 0:
+                    return Fraction(video_stream.frames / duration_seconds).limit_denominator()
+
+            # Last resort: match get_components_internal default
+            return Fraction(1)
+
+    def get_container_format(self) -> str:
+        """
+        Returns the container format of the video (e.g., 'mp4', 'mov', 'avi').
+
+        Returns:
+            Container format as string
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+        with av.open(self.__file, mode='r') as container:
+            return container.format.name
+
+    def get_components_internal(self, container: InputContainer) -> VideoComponents:
+        video_stream = self._get_first_video_stream(container)
+        video_stream.thread_type = "AUTO"
+        start_time, duration = self.get_active_trim_window()
+
+        # Get video frames
+        frames = []
+        audio_frames = []
+        alphas = None
+        start_pts = int(start_time / video_stream.time_base)
+        end_pts = int((start_time + duration) / video_stream.time_base)
+
+        if start_pts != 0:
+            container.seek(start_pts, stream=video_stream)
+
+        image_format = 'gbrpf32le'
+        process_image_format = lambda a: a
+        align_graph = None
+        audio = None
+
+        streams = [video_stream]
+        has_first_audio_frame = False
+        checked_alpha = False
+        crop_rect = None
+        crop_resolved = False
+
+        # Default to False so we decode until EOF if duration is 0
+        video_done = False
+        audio_done = True
+
+        audio_stream = last_decodable_audio_stream(container)
+        if audio_stream is not None:
+            streams += [audio_stream]
+            resampler = av.audio.resampler.AudioResampler(format='fltp')
+            audio_done = False
+
+        for packet in container.demux(*streams):
+            if video_done and audio_done:
+                break
+
+            if packet.stream.type == "video":
+                if video_done:
+                    continue
+                try:
+                    for frame in packet.decode():
+                        if frame.pts < start_pts:
+                            continue
+                        if duration and frame.pts >= end_pts:
+                            video_done = True
+                            break
+
+                        if not checked_alpha:
+                            alpha_channel = False
+                            for comp in frame.format.components:
+                                if comp.is_alpha or frame.format.name == "pal8":
+                                    alphas = []
+                                    alpha_channel = True
+                                    break
+                            if frame.format.name in ("yuvj420p", "yuvj422p", "yuvj444p", "rgb24", "rgba", "pal8"):
+                                process_image_format = lambda a: a.float() / 255.0
+                                if alpha_channel:
+                                    image_format = 'rgba'
+                                else:
+                                    image_format = 'rgb24'
+                            else:
+                                process_image_format = lambda a: a
+                                if alpha_channel:
+                                    image_format = 'gbrapf32le'
+                                else:
+                                    image_format = 'gbrpf32le'
+
+                            checked_alpha = True
+
+                        # Fix non-deterministic video decode when the video width is not a multiple of 32
+                        # For non-yuvj pixel formats: most H.264/H.265 video and static images (e.g. lossy WebP via LoadImage)
+                        # Pad both axes to a multiple of 32 and smear the border so the alignment padding never bleeds into the cropped edges
+                        if image_format in ('gbrpf32le', 'gbrapf32le') and frame.width % 32 != 0:
+                            if align_graph is None:
+                                pad_w = ((frame.width + 31) // 32) * 32
+                                pad_h = ((frame.height + 31) // 32) * 32
+                                g = av.filter.Graph()
+                                g_src = g.add_buffer(width=frame.width, height=frame.height,
+                                                     format=frame.format.name, time_base=video_stream.time_base)
+                                g_pad = g.add('pad', f'{pad_w}:{pad_h}:0:0')
+                                g_fill = g.add('fillborders', f'left=0:right={pad_w - frame.width}:top=0:bottom={pad_h - frame.height}:mode=smear')
+                                g_sink = g.add('buffersink')
+                                g_src.link_to(g_pad)
+                                g_pad.link_to(g_fill)
+                                g_fill.link_to(g_sink)
+                                g.configure()
+                                align_graph = (g, g_src, g_sink)
+                            align_graph[1].push(frame)
+                            img = np.ascontiguousarray(align_graph[2].pull().to_ndarray(format=image_format)[:frame.height, :frame.width])
+                        else:
+                            img = frame.to_ndarray(format=image_format)
+                        rotation_quadrant = _rotation_quadrant(frame)
+                        if rotation_quadrant:
+                            img = np.rot90(img, k=rotation_quadrant, axes=(0, 1)).copy()
+                        if self.__crop is not None:
+                            if not crop_resolved:
+                                crop_rect = normalize_crop_rect(*self.__crop, img.shape[1], img.shape[0])
+                                crop_resolved = True
+                            if crop_rect is not None:
+                                cx, cy, cw, ch = crop_rect
+                                img = np.ascontiguousarray(img[cy:cy + ch, cx:cx + cw])
+                        if alphas is None:
+                            frames.append(torch.from_numpy(img))
+                        else:
+                            frames.append(torch.from_numpy(img[..., :-1]))
+                            alphas.append(torch.from_numpy(img[..., -1:]))
+                except av.error.InvalidDataError:
+                    logging.info("pyav decode error")
+
+            elif packet.stream.type == "audio":
+                if audio_done:
+                    continue
+
+                aframes = itertools.chain.from_iterable(
+                    map(resampler.resample, packet.decode())
+                )
+                for frame in aframes:
+                    if duration and frame.time > start_time + duration:
+                        audio_done = True
+                        break
+
+                    if not has_first_audio_frame:
+                        offset_seconds = start_time - frame.pts * audio_stream.time_base
+                        to_skip = max(0, int(offset_seconds * audio_stream.sample_rate))
+                        if to_skip < frame.samples:
+                            has_first_audio_frame = True
+                            audio_frames.append(frame.to_ndarray()[..., to_skip:])
+                    else:
+                        audio_frames.append(frame.to_ndarray())
+
+        images = process_image_format(torch.stack(frames)) if len(frames) > 0 else torch.zeros(0, 0, 0, 3)
+        if alphas is not None:
+            alphas = process_image_format(torch.stack(alphas)) if len(alphas) > 0 else torch.zeros(0, 0, 0, 1)
+
+        # Get frame rate
+        frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1)
+
+        if len(audio_frames) > 0:
+            audio_data = np.concatenate(audio_frames, axis=1)  # shape: (channels, total_samples)
+            if duration:
+                audio_data = audio_data[..., :int(duration * audio_stream.sample_rate)]
+
+            audio_tensor = torch.from_numpy(audio_data).unsqueeze(0)  # shape: (1, channels, total_samples)
+            audio = AudioInput({
+                "waveform": audio_tensor,
+                "sample_rate": int(audio_stream.sample_rate) if audio_stream.sample_rate else 1,
+            })
+
+        metadata = container.metadata
+        return VideoComponents(images=images, alpha=alphas, audio=audio, frame_rate=frame_rate, metadata=metadata)
+
+    def get_components(self) -> VideoComponents:
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)  # Reset the BytesIO object to the beginning
+        with av.open(self.__file, mode='r') as container:
+            return self.get_components_internal(container)
+        raise ValueError(f"No video stream found in file '{self.__file}'")
+
+    def save_to(
+        self,
+        path: str | io.BytesIO,
+        format: VideoContainer = VideoContainer.AUTO,
+        codec: VideoCodec = VideoCodec.AUTO,
+        metadata: Optional[dict] = None,
+        bit_depth: int | None = None,
+        crf: float | None = None,
+        color_space: str | None = None,
+        preset: str | None = None,
+    ):
+        if color_space is not None and color_space not in VIDEO_COLOR_TRANSFERS:
+            raise ValueError(f"Unsupported video color space: {color_space}")
+        _, output_format, _ = video_output_config(path, format, codec)
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)  # Reset the BytesIO object to the beginning
+        with av.open(self.__file, mode='r') as container:
+            container_format = container.format.name
+            video_stream = container.streams.video[0] if len(container.streams.video) > 0 else None
+            video_encoding = video_stream.codec.canonical_name if video_stream is not None else None
+            source_bit_depth = video_stream_bit_depth(video_stream)
+            source_color_space = video_stream_color_space(video_stream)
+            if source_color_space is not None and color_space is not None and source_color_space != color_space:
+                raise ValueError(
+                    f"Cannot save {source_color_space} video as {color_space} without color conversion; "
+                    f"use auto or {source_color_space}"
+                )
+            reuse_streams = True
+            if format != VideoContainer.AUTO and VIDEO_CONTAINER_FORMATS[VideoContainer(format)] not in container_format.split(","):
+                reuse_streams = False
+            if output_format == VideoContainer.WEBM and not webm_streams_compatible(container.streams):
+                reuse_streams = False
+            if codec != VideoCodec.AUTO and codec != video_encoding and video_encoding is not None:
+                reuse_streams = False
+            if bit_depth is not None and video_encoding is not None and bit_depth != source_bit_depth:
+                reuse_streams = False
+            if crf is not None:
+                reuse_streams = False
+            if color_space is not None:
+                reuse_streams = False
+            if self.__start_time or self.__duration:
+                reuse_streams = False
+            if self.__crop is not None:
+                reuse_streams = False
+
+            if not reuse_streams:
+                if bit_depth is None:
+                    bit_depth = source_bit_depth
+                return self._save_transcoded(container, path, format=format, codec=codec, metadata=metadata, bit_depth=bit_depth, crf=crf, color_space=color_space, preset=preset)
+
+            streams = container.streams
+
+            open_kwargs = get_open_write_kwargs(path, container_format, format)
+            with av.open(path, **open_kwargs) as output_container:
+                # Add metadata before writing any streams
+                write_output_metadata(container, output_container, metadata)
+
+                # Add streams to the new container. Streams with no codec context cannot be used as an output template.
+                stream_map = {}
+                hevc_filters = {}
+                for stream in streams:
+                    if isinstance(stream, (av.VideoStream, av.AudioStream, SubtitleStream)):
+                        if stream.codec_context is None:
+                            logging.warning("Skipping %s stream %d with unsupported codec", stream.type, stream.index)
+                            continue
+                        out_stream = output_container.add_stream_from_template(template=stream, opaque=True)
+                        hevc_filter = isobmff_hevc_filter(output_container, stream, out_stream)
+                        if hevc_filter is not None:
+                            hevc_filters[stream] = hevc_filter
+                        stream_map[stream] = out_stream
+
+                # Write packets to the new container
+                for packet in container.demux():
+                    if packet.stream in stream_map and packet.dts is not None:
+                        out_stream = stream_map[packet.stream]
+                        hevc_filter = hevc_filters.get(packet.stream)
+                        for out_packet in filter_hevc_packet(hevc_filter, packet) if hevc_filter else (packet,):
+                            out_packet.stream = out_stream
+                            output_container.mux(out_packet)
+
+    def _save_transcoded(
+        self,
+        container: InputContainer,
+        path: str | io.BytesIO,
+        format: VideoContainer,
+        codec: VideoCodec,
+        metadata: dict | None,
+        bit_depth: int,
+        crf: float | None = None,
+        color_space: str | None = None,
+        preset: str | None = None,
+    ):
+        """Re-encode one frame at a time; peak memory does not scale with video length."""
+        open_kwargs, output_format, output_codec = video_output_config(path, format, codec)
+        video_stream = self._get_first_video_stream(container)
+        video_stream.thread_type = "AUTO"
+        start_time, duration = self.get_active_trim_window()
+        start_pts = int(start_time / video_stream.time_base)
+        end_pts = int((start_time + duration) / video_stream.time_base) if duration else None
+        stream_end_pts = None
+        if video_stream.duration is not None:
+            stream_end_pts = (video_stream.start_time or 0) + video_stream.duration
+        output_end_pts = end_pts
+        if stream_end_pts is not None and (output_end_pts is None or stream_end_pts < output_end_pts):
+            output_end_pts = stream_end_pts
+        if start_pts != 0:
+            container.seek(start_pts, stream=video_stream)
+
+        audio_stream = last_decodable_audio_stream(container)
+        source_color_space = video_stream_color_space(video_stream)
+        preserve_source_color = source_color_space is not None
+        pix_fmt = "yuv420p10le" if bit_depth >= 10 else "yuv420p"
+        rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1)
+
+        resampler = None
+        sample_rate = 0
+        audio_time_base = None
+        duration_cap = None
+        if audio_stream is not None:
+            sample_rate = audio_stream.codec_context.sample_rate
+            channels = audio_stream.codec_context.channels
+            if not sample_rate:
+                sample_rate, channels = probe_audio_params(container, audio_stream)
+                container.seek(start_pts, stream=video_stream)
+                if sample_rate:
+                    audio_stream.codec_context.flush_buffers()
+                else:
+                    logging.warning("Audio stream parameters could not be determined; ignoring audio.")
+                    audio_stream = None
+        if audio_stream is not None:
+            if output_format == VideoContainer.WEBM:
+                sample_rate = 48000
+            audio_time_base = Fraction(1, sample_rate)
+            layout = {1: "mono", 2: "stereo", 6: "5.1"}.get(channels, "stereo")
+            resampler = av.audio.resampler.AudioResampler(format="fltp", layout=layout, rate=sample_rate)
+            if duration:
+                duration_cap = math.ceil(duration * sample_rate)
+
+        if duration:
+            window_seconds = duration
+        else:
+            try:
+                window_seconds = max(self._get_raw_duration() - start_time, 0.0)
+            except ValueError:
+                window_seconds = 0.0
+        progress_total = max(1, int(round(window_seconds * float(rate))))
+        pbar = comfy.utils.ProgressBar(progress_total)
+
+        streams = [video_stream] if audio_stream is None else [video_stream, audio_stream]
+        pts_step = max(1, int(round((1 / rate) / video_stream.time_base)))
+        video_done = False
+        audio_done = audio_stream is None
+        video_pts_offset = None
+        last_video_pts = None
+        last_video_end = None
+        # rebased pts -> true display duration: the mp4 muxer pads the last sample with 1/rate otherwise
+        video_frame_durations = {}
+        source_size = None
+        rotation_k = 0
+        rotation_filter = None
+        crop_rect = None
+        crop_filter = None
+        audio_started = False
+        samples_written = 0
+        pending_audio = []
+        # The output opens lazily on the first kept frame: it decides the geometry (90/270 rotation swaps dims),
+        # and never seeking back keeps webm/mkv leading audio intact.
+        output = None
+        out_video = None
+        out_audio = None
+
+        def audio_frame_from_ndarray(nd_planar):
+            frame = av.AudioFrame.from_ndarray(np.ascontiguousarray(nd_planar), format="fltp", layout=layout)
+            frame.sample_rate = sample_rate
+            return frame
+
+        def drain_audio(final=False):
+            # Audio may cover the pts span of the video written so far, capped by the requested duration
+            nonlocal samples_written, audio_done
+            if last_video_end is None:
+                cap = 0
+            else:
+                cap = math.ceil(last_video_end * video_stream.time_base * sample_rate)
+            if duration_cap is not None:
+                cap = min(cap, duration_cap)
+            while pending_audio and not audio_done:
+                frame = pending_audio[0]
+                if samples_written + frame.samples <= cap:
+                    frame.pts = samples_written
+                    frame.time_base = audio_time_base
+                    output.mux(out_audio.encode(frame))
+                    samples_written += frame.samples
+                    pending_audio.pop(0)
+                    continue
+                if final:
+                    keep = frame.to_ndarray()[..., :cap - samples_written]
+                    if keep.shape[-1] > 0:
+                        tail = audio_frame_from_ndarray(keep)
+                        tail.pts = samples_written
+                        tail.time_base = audio_time_base
+                        output.mux(out_audio.encode(tail))
+                        samples_written += keep.shape[-1]
+                    pending_audio.clear()
+                break
+            if duration_cap is not None and samples_written >= duration_cap:
+                audio_done = True
+            return cap
+
+        try:
+            for packet in container.demux(*streams):
+                if video_done and audio_done:
+                    break
+
+                if packet.stream == video_stream and not video_done:
+                    try:
+                        frames = packet.decode()
+                    except av.error.InvalidDataError:
+                        logging.info("pyav decode error")
+                        continue
+                    for frame in frames:
+                        if frame.pts is not None and frame.pts < start_pts:
+                            continue
+                        if end_pts is not None and frame.pts is not None and frame.pts >= end_pts:
+                            video_done = True
+                            if last_video_pts is not None:
+                                # the source continues past the window: hold the last kept frame to the window end
+                                end_offset = video_pts_offset if video_pts_offset is not None else start_pts
+                                last_video_end = max(last_video_end, end_pts - end_offset)
+                            break
+                        # the source's true display duration of this frame; average_rate is not a
+                        # frame duration (sparse/VFR sources), so it is only the fallback
+                        frame_duration = frame.duration if frame.duration else pts_step
+                        if end_pts is not None and frame.pts is not None:
+                            frame_duration = min(frame_duration, end_pts - frame.pts)
+                        if output is None:
+                            rotation_k = _rotation_quadrant(frame)
+                            if rotation_k % 2:
+                                out_width, out_height = frame.height, frame.width
+                            else:
+                                out_width, out_height = frame.width, frame.height
+                            if self.__crop is not None:
+                                crop_rect = normalize_crop_rect(*self.__crop, out_width, out_height)
+                                if crop_rect is not None:
+                                    out_width, out_height = crop_rect[2], crop_rect[3]
+                            if (out_width % 2 or out_height % 2) and crop_rect is None:
+                                even_width = out_width - out_width % 2
+                                even_height = out_height - out_height % 2
+                                if even_width > 0 and even_height > 0:
+                                    crop_rect = (0, 0, even_width, even_height)
+                                    out_width, out_height = even_width, even_height
+                            if out_width % 2 or out_height % 2:
+                                raise ValueError(f"{output_codec.value.upper()} output requires even dimensions, got {out_width}x{out_height}")
+                            if any(component.is_alpha for component in frame.format.components):
+                                logging.warning(
+                                    "Transcoded video output does not support alpha; the alpha channel will be discarded."
+                                )
+                            source_size = (frame.width, frame.height)
+                            output = av.open(path, **open_kwargs)
+                            # Add metadata before writing any streams
+                            write_output_metadata(container, output, metadata)
+                            out_video = output.add_stream(VIDEO_ENCODERS[output_codec], rate=rate)
+                            # no B-frames: reordering makes mp4 sample durations follow decode order,
+                            # so irregular-VFR spans and trim windows land wrong
+                            out_video.codec_context.max_b_frames = 0
+                            out_video.width = out_width
+                            out_video.height = out_height
+                            out_video.pix_fmt = pix_fmt
+                            out_video.options = video_encoder_options(output_codec, crf, preset)
+                            if preserve_source_color:
+                                copy_color_properties(video_stream, out_video.codec_context)
+                            elif color_space is not None:
+                                set_video_color_properties(out_video.codec_context, color_space)
+                            # source pts pass through (rebased to 0), so variable frame rate survives
+                            out_video.codec_context.time_base = video_stream.time_base
+                            if audio_stream is not None:
+                                audio_codec = "libopus" if output_format == VideoContainer.WEBM else "aac"
+                                out_audio = output.add_stream(audio_codec, rate=sample_rate, layout=layout)
+                        if (frame.width, frame.height) != source_size:
+                            # encoding would silently rescale the new geometry into the old one
+                            raise ValueError(
+                                f"Video resolution changes mid-stream "
+                                f"({source_size[0]}x{source_size[1]} -> {frame.width}x{frame.height}); cannot transcode"
+                            )
+                        if rotation_k:
+                            if rotation_filter is None:
+                                g = av.filter.Graph()
+                                g_src = g.add_buffer(width=frame.width, height=frame.height,
+                                                     format=frame.format.name, time_base=video_stream.time_base)
+                                tail = g_src
+                                for filter_name, filter_args in {1: [("transpose", "cclock")],
+                                                                 2: [("hflip", None), ("vflip", None)],
+                                                                 3: [("transpose", "clock")]}[rotation_k]:
+                                    step = g.add(filter_name, filter_args)
+                                    tail.link_to(step)
+                                    tail = step
+                                g_sink = g.add("buffersink")
+                                tail.link_to(g_sink)
+                                g.configure()
+                                rotation_filter = (g_src, g_sink)
+                            rotation_filter[0].push(frame)
+                            frame = rotation_filter[1].pull()
+                        if crop_rect is not None:
+                            if crop_filter is None:
+                                g = av.filter.Graph()
+                                g_src = g.add_buffer(width=frame.width, height=frame.height,
+                                                     format=frame.format.name, time_base=video_stream.time_base)
+                                g_crop = g.add("crop", f"{crop_rect[2]}:{crop_rect[3]}:{crop_rect[0]}:{crop_rect[1]}")
+                                g_sink = g.add("buffersink")
+                                g_src.link_to(g_crop)
+                                g_crop.link_to(g_sink)
+                                g.configure()
+                                crop_filter = (g_src, g_sink)
+                            crop_filter[0].push(frame)
+                            frame = crop_filter[1].pull()
+                        if frame.color_range == ColorRange.JPEG and not preserve_source_color:
+                            # compress full-range sources (yuvj/MJPEG) to limited range
+                            frame = frame.reformat(format=pix_fmt, src_color_range="JPEG", dst_color_range="MPEG")
+                        else:
+                            frame = frame.reformat(format=pix_fmt)
+                        if preserve_source_color:
+                            copy_color_properties(video_stream, frame)
+                        elif color_space is not None:
+                            set_video_color_properties(frame, color_space)
+                        frame_output_end = None
+                        if frame.pts is not None:
+                            if video_pts_offset is None:
+                                video_pts_offset = frame.pts
+                            frame.pts -= video_pts_offset
+                            if output_end_pts is not None:
+                                frame_output_end = output_end_pts - video_pts_offset
+                                if frame.pts + frame_duration > frame_output_end:
+                                    clamped_pts = frame_output_end - frame_duration
+                                    if clamped_pts >= 0 and (last_video_pts is None or clamped_pts > last_video_pts):
+                                        frame.pts = min(frame.pts, clamped_pts)
+                                    elif frame.pts < frame_output_end:
+                                        frame_duration = frame_output_end - frame.pts
+                                    else:
+                                        continue
+                        if frame.pts is None or (last_video_pts is not None and frame.pts <= last_video_pts):
+                            # broken sources emit missing/backward timestamps mid-stream, which the
+                            # muxer rejects; nudge them forward by one nominal frame interval
+                            frame.pts = 0 if last_video_pts is None else last_video_pts + pts_step
+                            if frame_output_end is not None and frame.pts + frame_duration > frame_output_end:
+                                if frame.pts >= frame_output_end:
+                                    continue
+                                frame_duration = frame_output_end - frame.pts
+                        last_video_pts = frame.pts
+                        last_video_end = frame.pts + frame_duration
+                        video_frame_durations[frame.pts] = frame_duration
+                        # the decoded pict_type would force x264's frame types (intra-only
+                        # sources like MJPEG/ProRes would come out all-keyframe)
+                        frame.pict_type = 0
+                        for out_packet in out_video.encode(frame):
+                            out_packet.duration = video_frame_durations.pop(out_packet.pts, 0)
+                            output.mux(out_packet)
+                        drain_audio()
+                        pbar.update(1)
+
+                elif packet.stream == audio_stream and not audio_done:
+                    for resampled in itertools.chain.from_iterable(map(resampler.resample, packet.decode())):
+                        frame_start = None
+                        if resampled.pts is not None:
+                            # passthrough frames keep the source stream's time base
+                            tb = resampled.time_base if resampled.time_base else audio_time_base
+                            frame_start = float(resampled.pts * tb)
+                            if duration and not audio_started and frame_start >= start_time + duration:
+                                audio_done = True
+                                break
+                        if not audio_started:
+                            if frame_start is None:
+                                frame_start = 0.0
+                            to_skip = max(0, int((start_time - frame_start) * sample_rate))
+                            if to_skip >= resampled.samples:
+                                continue
+                            audio_started = True
+                            if duration and frame_start > start_time:
+                                duration_cap = min(duration_cap, math.ceil((start_time + duration - frame_start) * sample_rate))
+                            if to_skip:
+                                pending_audio.append(audio_frame_from_ndarray(resampled.to_ndarray()[..., to_skip:]))
+                                continue
+                        pending_audio.append(resampled)
+                        if video_done:
+                            # the video window is complete so the cap is final, but containers
+                            # that interleave audio behind video (fragmented mp4) still owe most
+                            # of it: stop only once the demuxed audio covers the cap
+                            cap = drain_audio()
+                            if pending_audio or samples_written >= cap:
+                                drain_audio(final=True)
+                                audio_done = True
+                                break
+
+            if output is None:
+                raise ValueError(f"No decodable video frames found in file '{self.__file}'")
+            if out_audio is not None and not audio_done:
+                drain_audio(final=True)
+            window_fill = last_video_end - last_video_pts if video_done and last_video_pts is not None else 0
+            for out_packet in out_video.encode(None):
+                duration = video_frame_durations.pop(out_packet.pts, 0)
+                if out_packet.pts == last_video_pts:
+                    duration = max(duration, window_fill)
+                out_packet.duration = duration
+                output.mux(out_packet)
+            if out_audio is not None:
+                output.mux(out_audio.encode(None))
+        except BaseException:
+            if output is not None:
+                output.close()
+                if isinstance(path, (str, os.PathLike)) and os.path.exists(path):
+                    os.remove(path)
+            raise
+        else:
+            if output is not None:
+                output.close()
+
+    def _get_first_video_stream(self, container: InputContainer):
+        if len(container.streams.video):
+            return container.streams.video[0]
+        raise ValueError(f"No video stream found in file '{self.__file}'")
+
+    def as_trimmed(
+        self, start_time: float = 0, duration: float = 0, strict_duration: bool = True
+    ) -> VideoInput | None:
+        trimmed = VideoFromFile(
+            self.get_stream_source(),
+            start_time=start_time + self.__start_time,
+            duration=duration,
+            crop=self.__crop,
+        )
+        if strict_duration and duration and trimmed.get_duration() < duration:
+            return None
+        return trimmed
+
+    def as_cropped(
+        self, x: int = 0, y: int = 0, width: int = 0, height: int = 0
+    ) -> VideoInput:
+        if int(width) <= 0 or int(height) <= 0:
+            return self
+
+        display_width, display_height = self._get_display_dimensions()
+        outer = (
+            normalize_crop_rect(*self.__crop, display_width, display_height)
+            if self.__crop is not None
+            else None
+        )
+        if outer is None:
+            rect = normalize_crop_rect(x, y, width, height, display_width, display_height)
+        else:
+            inner = normalize_crop_rect(x, y, width, height, outer[2], outer[3])
+            rect = (
+                (outer[0] + inner[0], outer[1] + inner[1], inner[2], inner[3])
+                if inner is not None
+                else None
+            )
+        if rect is None:
+            return self
+        return VideoFromFile(
+            self.get_stream_source(),
+            start_time=self.__start_time,
+            duration=self.__duration,
+            crop=rect,
+        )
+
+
+class VideoFromComponents(VideoInput):
+    """
+    Class representing video input from tensors.
+    """
+
+    def __init__(self, components: VideoComponents, bit_depth: int = 8, color_space: str = "sRGB"):
+        if color_space not in VIDEO_COLOR_TRANSFERS:
+            raise ValueError(f"Unsupported video color space: {color_space}")
+        self.__components = components
+        # Tensor components have no inherent bit depth; this is the depth used when encoding.
+        self.__bit_depth = bit_depth
+        self.__color_space = color_space
+
+    def get_components(self) -> VideoComponents:
+        return VideoComponents(
+            images=self.__components.images,
+            audio=self.__components.audio,
+            frame_rate=self.__components.frame_rate,
+            metadata=self.__components.metadata,
+            alpha=self.__components.alpha,
+        )
+
+    def get_bit_depth(self) -> int:
+        return self.__bit_depth
+
+    def get_color_space(self) -> str:
+        return self.__color_space
+
+    def save_to(
+        self,
+        path: str,
+        format: VideoContainer = VideoContainer.AUTO,
+        codec: VideoCodec = VideoCodec.AUTO,
+        metadata: Optional[dict] = None,
+        bit_depth: int | None = None,
+        crf: float | None = None,
+        color_space: str | None = None,
+        preset: str | None = None,
+    ):
+        """Save the video to a file path or BytesIO buffer."""
+        if color_space is None:
+            color_space = self.__color_space
+        if color_space is not None and color_space not in VIDEO_COLOR_TRANSFERS:
+            raise ValueError(f"Unsupported video color space: {color_space}")
+        open_kwargs, output_format, output_codec = video_output_config(path, format, codec)
+        # None means "use the depth this video was created with" (CreateVideo's choice).
+        if bit_depth is None:
+            bit_depth = self.__bit_depth
+        is_10bit = bit_depth >= 10
+        with av.open(path, **open_kwargs) as output:
+            # Add metadata before writing any streams
+            if metadata is not None:
+                for key, value in metadata.items():
+                    output.metadata[key] = json.dumps(value)
+
+            frame_rate = Fraction(round(self.__components.frame_rate * 1000), 1000)
+            # Create a video stream
+            pix_fmt = "yuv420p10le" if is_10bit else "yuv420p"
+            video_stream = output.add_stream(VIDEO_ENCODERS[output_codec], rate=frame_rate)
+            video_stream.width = self.__components.images.shape[2]
+            video_stream.height = self.__components.images.shape[1]
+            video_stream.pix_fmt = pix_fmt
+            video_stream.options = video_encoder_options(output_codec, crf, preset)
+            if color_space is not None:
+                set_video_color_properties(video_stream.codec_context, color_space)
+
+            # Create an audio stream
+            audio_sample_rate = 1
+            audio_resampler = None
+            audio_stream: Optional[av.AudioStream] = None
+            if self.__components.audio:
+                source_audio_sample_rate = int(self.__components.audio['sample_rate'])
+                audio_sample_rate = 48000 if output_format == VideoContainer.WEBM else source_audio_sample_rate
+                waveform = self.__components.audio['waveform']
+                waveform = waveform[0, :, :math.ceil((source_audio_sample_rate / frame_rate) * self.__components.images.shape[0])]
+                layout = {1: 'mono', 2: 'stereo', 6: '5.1'}.get(waveform.shape[0], 'stereo')
+                audio_codec = "libopus" if output_format == VideoContainer.WEBM else "aac"
+                audio_stream = output.add_stream(audio_codec, rate=audio_sample_rate, layout=layout)
+                if audio_sample_rate != source_audio_sample_rate:
+                    audio_resampler = av.audio.resampler.AudioResampler(format="fltp", layout=layout, rate=audio_sample_rate)
+
+            # Encode video
+            for i, frame in enumerate(self.__components.images):
+                if is_10bit:
+                    # 16-bit RGB keeps float precision through the conversion to 10-bit YUV.
+                    img = (frame.float() * 65535).clamp(0, 65535).cpu().numpy().astype(np.uint16)  # shape: (H, W, 3)
+                    frame = av.VideoFrame.from_ndarray(img, format="rgb48le")
+                else:
+                    img = (frame * 255).clamp(0, 255).byte().cpu().numpy() # shape: (H, W, 3)
+                    frame = av.VideoFrame.from_ndarray(img, format='rgb24')
+                dst_colorspace = None
+                if color_space == "sRGB":
+                    dst_colorspace = BT709_NCL
+                elif color_space in HDR_COLOR_TRANSFERS:
+                    dst_colorspace = BT2020_NCL
+                frame = frame.reformat(format=pix_fmt, dst_colorspace=dst_colorspace)
+                if color_space is not None:
+                    set_video_color_properties(frame, color_space)
+                packet = video_stream.encode(frame)
+                output.mux(packet)
+
+            # Flush video
+            packet = video_stream.encode(None)
+            output.mux(packet)
+
+            if audio_stream and self.__components.audio:
+                frame = av.AudioFrame.from_ndarray(waveform.float().cpu().contiguous().numpy(), format='fltp', layout=layout)
+                frame.sample_rate = source_audio_sample_rate
+                frame.pts = 0
+                frames = [frame] if audio_resampler is None else audio_resampler.resample(frame)
+                for frame in frames:
+                    output.mux(audio_stream.encode(frame))
+                if audio_resampler is not None:
+                    for frame in audio_resampler.resample(None):
+                        output.mux(audio_stream.encode(frame))
+
+                # Flush encoder
+                output.mux(audio_stream.encode(None))
+
+    def as_trimmed(
+        self,
+        start_time: float | None = None,
+        duration: float | None = None,
+        strict_duration: bool = True,
+    ) -> VideoInput | None:
+        if self.get_duration() < start_time + duration:
+            return None
+        #TODO Consider tracking duration and trimming at time of save?
+        return VideoFromFile(self.get_stream_source(), start_time=start_time, duration=duration)
diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e635903fcb6a72f81f566bbb0dc73096b765d57
--- /dev/null
+++ b/comfy_api/latest/_io.py
@@ -0,0 +1,2533 @@
+from __future__ import annotations
+
+import copy
+import inspect
+from abc import ABC, abstractmethod
+from collections import Counter
+from collections.abc import Iterable
+from dataclasses import asdict, dataclass, field
+from enum import Enum
+from typing import Any, Callable, Literal, TypedDict, TypeVar, TYPE_CHECKING
+from typing_extensions import NotRequired, final
+
+# used for type hinting
+import torch
+
+if TYPE_CHECKING:
+    from spandrel import ImageModelDescriptor
+    from comfy.clip_vision import ClipVisionModel
+    from comfy.clip_vision import Output as ClipVisionOutput_
+    from comfy.bg_removal_model import BackgroundRemovalModel
+    from comfy.controlnet import ControlNet
+    from comfy.hooks import HookGroup, HookKeyframeGroup
+    from comfy.model_patcher import ModelPatcher
+    from comfy.samplers import CFGGuider, Sampler
+    from comfy.sd import CLIP, VAE
+    from comfy.sd import StyleModel as StyleModel_
+    from comfy_api.input import VideoInput, CurveInput as CurveInput_
+from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class,
+    prune_dict, shallow_clone_class)
+from comfy_execution.graph_utils import ExecutionBlocker
+from ._util import MESH, VOXEL, SPLAT, SVG as _SVG, File3D
+
+
+class FolderType(str, Enum):
+    input = "input"
+    output = "output"
+    temp = "temp"
+
+
+class UploadType(str, Enum):
+    image = "image_upload"
+    audio = "audio_upload"
+    video = "video_upload"
+    model = "file_upload"
+
+
+class RemoteOptions:
+    def __init__(self, route: str, refresh_button: bool, control_after_refresh: Literal["first", "last"]="first",
+                 timeout: int=None, max_retries: int=None, refresh: int=None):
+        self.route = route
+        """The route to the remote source."""
+        self.refresh_button = refresh_button
+        """Specifies whether to show a refresh button in the UI below the widget."""
+        self.control_after_refresh = control_after_refresh
+        """Specifies the control after the refresh button is clicked. If "first", the first item will be automatically selected, and so on."""
+        self.timeout = timeout
+        """The maximum amount of time to wait for a response from the remote source in milliseconds."""
+        self.max_retries = max_retries
+        """The maximum number of retries before aborting the request."""
+        self.refresh = refresh
+        """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed."""
+
+    def as_dict(self):
+        return prune_dict({
+            "route": self.route,
+            "refresh_button": self.refresh_button,
+            "control_after_refresh": self.control_after_refresh,
+            "timeout": self.timeout,
+            "max_retries": self.max_retries,
+            "refresh": self.refresh,
+        })
+
+
+class NumberDisplay(str, Enum):
+    number = "number"
+    slider = "slider"
+    gradient_slider = "gradientslider"
+
+
+class ControlAfterGenerate(str, Enum):
+    fixed = "fixed"
+    increment = "increment"
+    decrement = "decrement"
+    randomize = "randomize"
+
+class _ComfyType(ABC):
+    Type = Any
+    io_type: str = None
+
+# NOTE: this is a workaround to make the decorator return the correct type
+T = TypeVar("T", bound=type)
+def comfytype(io_type: str, **kwargs):
+    '''
+    Decorator to mark nested classes as ComfyType; io_type will be bound to the class.
+
+    A ComfyType may have the following attributes:
+    - Type = <type hint here>
+    - class Input(Input): ...
+    - class Output(Output): ...
+    '''
+    def decorator(cls: T) -> T:
+        if isinstance(cls, _ComfyType) or issubclass(cls, _ComfyType):
+            # clone Input and Output classes to avoid modifying the original class
+            new_cls = cls
+            if hasattr(new_cls, "Input"):
+                new_cls.Input = copy_class(new_cls.Input)
+            if hasattr(new_cls, "Output"):
+                new_cls.Output = copy_class(new_cls.Output)
+        else:
+            # copy class attributes except for special ones that shouldn't be in type()
+            cls_dict = {
+                k: v for k, v in cls.__dict__.items()
+                if k not in ('__dict__', '__weakref__', '__module__', '__doc__')
+            }
+            # new class
+            new_cls: ComfyTypeIO = type(
+                cls.__name__,
+                (cls, ComfyTypeIO),
+                cls_dict
+            )
+            # metadata preservation
+            new_cls.__module__ = cls.__module__
+            new_cls.__doc__ = cls.__doc__
+            # assign ComfyType attributes, if needed
+        new_cls.io_type = io_type
+        if hasattr(new_cls, "Input") and new_cls.Input is not None:
+            new_cls.Input.Parent = new_cls
+        if hasattr(new_cls, "Output") and new_cls.Output is not None:
+            new_cls.Output.Parent = new_cls
+        return new_cls
+    return decorator
+
+def Custom(io_type: str) -> type[ComfyTypeIO]:
+    '''Create a ComfyType for a custom io_type.'''
+    @comfytype(io_type=io_type)
+    class CustomComfyType(ComfyTypeIO):
+        ...
+    return CustomComfyType
+
+class _IO_V3:
+    '''
+    Base class for V3 Inputs and Outputs.
+    '''
+    Parent: _ComfyType = None
+
+    def __init__(self):
+        pass
+
+    def validate(self):
+        pass
+
+    @property
+    def io_type(self):
+        return self.Parent.io_type
+
+    @property
+    def Type(self):
+        return self.Parent.Type
+
+class Input(_IO_V3):
+    '''
+    Base class for a V3 Input.
+    '''
+    def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+        super().__init__()
+        self.id = id
+        self.display_name = display_name
+        self.optional = optional
+        self.tooltip = tooltip
+        self.lazy = lazy
+        self.extra_dict = extra_dict if extra_dict is not None else {}
+        self.rawLink = raw_link
+        self.advanced = advanced
+
+    def as_dict(self):
+        return prune_dict({
+            "display_name": self.display_name,
+            "optional": self.optional,
+            "tooltip": self.tooltip,
+            "lazy": self.lazy,
+            "rawLink": self.rawLink,
+            "advanced": self.advanced,
+        }) | prune_dict(self.extra_dict)
+
+    def get_io_type(self):
+        return self.io_type
+
+    def get_all(self) -> list[Input]:
+        return [self]
+
+class WidgetInput(Input):
+    '''
+    Base class for a V3 Input with widget.
+    '''
+    def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
+                 default: Any=None,
+                 socketless: bool=None, widget_type: str=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+        super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link, advanced)
+        self.default = default
+        self.socketless = socketless
+        self.widget_type = widget_type
+        self.force_input = force_input
+
+    def as_dict(self):
+        return super().as_dict() | prune_dict({
+            "default": self.default,
+            "socketless": self.socketless,
+            "widgetType": self.widget_type,
+            "forceInput": self.force_input,
+        })
+
+    def get_io_type(self):
+        return self.widget_type if self.widget_type is not None else super().get_io_type()
+
+
+class Output(_IO_V3):
+    def __init__(self, id: str=None, display_name: str=None, tooltip: str=None,
+                 is_output_list=False):
+        self.id = id
+        self.display_name = display_name if display_name else id
+        self.tooltip = tooltip
+        self.is_output_list = is_output_list
+
+    def as_dict(self):
+        display_name = self.display_name if self.display_name else self.id
+        return prune_dict({
+            "display_name": display_name,
+            "tooltip": self.tooltip,
+            "is_output_list": self.is_output_list,
+        })
+
+    def get_io_type(self):
+        return self.io_type
+
+
+class ComfyTypeI(_ComfyType):
+    '''ComfyType subclass that only has a default Input class - intended for types that only have Inputs.'''
+    class Input(Input):
+        ...
+
+class ComfyTypeIO(ComfyTypeI):
+    '''ComfyType subclass that has default Input and Output classes; useful for types with both Inputs and Outputs.'''
+    class Output(Output):
+        ...
+
+
+@comfytype(io_type="BOOLEAN")
+class Boolean(ComfyTypeIO):
+    Type = bool
+
+    class Input(WidgetInput):
+        '''Boolean input.'''
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
+                    default: bool=None, label_on: str=None, label_off: str=None,
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
+            self.label_on = label_on
+            self.label_off = label_off
+            self.default: bool
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "label_on": self.label_on,
+                "label_off": self.label_off,
+            })
+
+@comfytype(io_type="INT")
+class Int(ComfyTypeIO):
+    Type = int
+
+    class Input(WidgetInput):
+        '''Integer input.'''
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
+                    default: int=None, min: int=None, max: int=None, step: int=None, control_after_generate: bool | ControlAfterGenerate=None,
+                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
+            self.min = min
+            self.max = max
+            self.step = step
+            self.control_after_generate = control_after_generate
+            self.display_mode = display_mode
+            self.default: int
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "min": self.min,
+                "max": self.max,
+                "step": self.step,
+                "control_after_generate": self.control_after_generate,
+                "display": self.display_mode.value if self.display_mode else None,
+            })
+
+@comfytype(io_type="FLOAT")
+class Float(ComfyTypeIO):
+    Type = float
+
+    class Input(WidgetInput):
+        '''Float input.'''
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
+                    default: float=None, min: float=None, max: float=None, step: float=None, round: float=None,
+                    display_mode: NumberDisplay=None, gradient_stops: list[dict]=None,
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
+            self.min = min
+            self.max = max
+            self.step = step
+            self.round = round
+            self.display_mode = display_mode
+            self.gradient_stops = gradient_stops
+            self.default: float
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "min": self.min,
+                "max": self.max,
+                "step": self.step,
+                "round": self.round,
+                "display": self.display_mode,
+                "gradient_stops": self.gradient_stops,
+            })
+
+@comfytype(io_type="STRING")
+class String(ComfyTypeIO):
+    Type = str
+
+    class Input(WidgetInput):
+        '''String input.'''
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
+                    multiline=False, placeholder: str=None, default: str=None, dynamic_prompts: bool=None,
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
+            self.multiline = multiline
+            self.placeholder = placeholder
+            self.dynamic_prompts = dynamic_prompts
+            self.default: str
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "multiline": self.multiline,
+                "placeholder": self.placeholder,
+                "dynamicPrompts": self.dynamic_prompts,
+            })
+
+@comfytype(io_type="COMBO")
+class Combo(ComfyTypeIO):
+    Type = str
+    class Input(WidgetInput):
+        """Combo input (dropdown)."""
+        Type = str
+        def __init__(
+            self,
+            id: str,
+            options: list[str] | list[int] | type[Enum] = None,
+            display_name: str=None,
+            optional=False,
+            tooltip: str=None,
+            lazy: bool=None,
+            default: str | int | Enum = None,
+            control_after_generate: bool | ControlAfterGenerate=None,
+            upload: UploadType=None,
+            image_folder: FolderType=None,
+            remote: RemoteOptions=None,
+            socketless: bool=None,
+            extra_dict=None,
+            raw_link: bool=None,
+            advanced: bool=None,
+        ):
+            if isinstance(options, type) and issubclass(options, Enum):
+                options = [v.value for v in options]
+            if isinstance(default, Enum):
+                default = default.value
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, None, extra_dict, raw_link, advanced)
+            self.multiselect = False
+            self.options = options
+            self.control_after_generate = control_after_generate
+            self.upload = upload
+            self.image_folder = image_folder
+            self.remote = remote
+            self.default: str
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "multiselect": self.multiselect,
+                "options": self.options,
+                "control_after_generate": self.control_after_generate,
+                **({self.upload.value: True} if self.upload is not None else {}),
+                "image_folder": self.image_folder.value if self.image_folder else None,
+                "remote": self.remote.as_dict() if self.remote else None,
+            })
+
+    class Output(Output):
+        def __init__(self, id: str=None, display_name: str=None, options: list[str]=None, tooltip: str=None, is_output_list=False):
+            super().__init__(id, display_name, tooltip, is_output_list)
+            self.options = options if options is not None else []
+
+@comfytype(io_type="COMBO")
+class MultiCombo(ComfyTypeI):
+    '''Multiselect Combo input (dropdown for selecting potentially more than one value).'''
+    Type = list[str]
+    class Input(Combo.Input):
+        def __init__(self, id: str, options: list[str], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
+                    default: list[str]=None, placeholder: str=None, chip: bool=None, control_after_generate: bool | ControlAfterGenerate=None,
+                    socketless: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless=socketless, extra_dict=extra_dict, raw_link=raw_link, advanced=advanced)
+            self.multiselect = True
+            self.placeholder = placeholder
+            self.chip = chip
+            self.default: list[str]
+
+        def as_dict(self):
+            # Frontend expects `multi_select` to be an object config (not a boolean).
+            # Keep top-level `multiselect` from Combo.Input for backwards compatibility.
+            return super().as_dict() | prune_dict({
+                "multi_select": prune_dict({
+                    "placeholder": self.placeholder,
+                    "chip": self.chip,
+                }),
+            })
+
+@comfytype(io_type="IMAGE")
+class Image(ComfyTypeIO):
+    Type = torch.Tensor
+
+
+@comfytype(io_type="WAN_CAMERA_EMBEDDING")
+class WanCameraEmbedding(ComfyTypeIO):
+    Type = torch.Tensor
+
+
+@comfytype(io_type="WEBCAM")
+class Webcam(ComfyTypeIO):
+    Type = str
+
+    class Input(WidgetInput):
+        """Webcam input."""
+        Type = str
+        def __init__(
+                self, id: str, display_name: str=None, optional=False,
+                tooltip: str=None, lazy: bool=None, default: str=None, socketless: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None
+        ):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, None, extra_dict, raw_link, advanced)
+
+
+@comfytype(io_type="MASK")
+class Mask(ComfyTypeIO):
+    Type = torch.Tensor
+
+@comfytype(io_type="LATENT")
+class Latent(ComfyTypeIO):
+    '''Latents are stored as a dictionary.'''
+    class LatentDict(TypedDict):
+        samples: torch.Tensor
+        '''Latent tensors.'''
+        noise_mask: NotRequired[torch.Tensor]
+        batch_index: NotRequired[list[int]]
+        type: NotRequired[str]
+        '''Only needed if dealing with these types: audio, hunyuan3dv2'''
+    Type = LatentDict
+
+@comfytype(io_type="CONDITIONING")
+class Conditioning(ComfyTypeIO):
+    class PooledDict(TypedDict):
+        pooled_output: torch.Tensor
+        '''Pooled output from CLIP.'''
+        control: NotRequired[ControlNet]
+        '''ControlNet to apply to conditioning.'''
+        control_apply_to_uncond: NotRequired[bool]
+        '''Whether to apply ControlNet to matching negative conditioning at sample time, if applicable.'''
+        cross_attn_controlnet: NotRequired[torch.Tensor]
+        '''CrossAttn from CLIP to use for controlnet only.'''
+        pooled_output_controlnet: NotRequired[torch.Tensor]
+        '''Pooled output from CLIP to use for controlnet only.'''
+        gligen: NotRequired[tuple[str, Gligen, list[tuple[torch.Tensor, int, ...]]]]
+        '''GLIGEN to apply to conditioning.'''
+        area: NotRequired[tuple[int, ...] | tuple[str, float, ...]]
+        '''Set area of conditioning. First half of values apply to dimensions, the second half apply to coordinates.
+        By default, the dimensions are based on total pixel amount, but the first value can be set to "percentage" to use a percentage of the image size instead.
+
+        (1024, 1024, 0, 0) would apply conditioning to the top-left 1024x1024 pixels.
+
+        ("percentage", 0.5, 0.5, 0, 0) would apply conditioning to the top-left 50% of the image.''' # TODO: verify its actually top-left
+        strength: NotRequired[float]
+        '''Strength of conditioning. Default strength is 1.0.'''
+        mask: NotRequired[torch.Tensor]
+        '''Mask to apply conditioning to.'''
+        mask_strength: NotRequired[float]
+        '''Strength of conditioning mask. Default strength is 1.0.'''
+        set_area_to_bounds: NotRequired[bool]
+        '''Whether conditioning mask should determine bounds of area - if set to false, latents are sampled at full resolution and result is applied in mask.'''
+        concat_latent_image: NotRequired[torch.Tensor]
+        '''Used for inpainting and specific models.'''
+        concat_mask: NotRequired[torch.Tensor]
+        '''Used for inpainting and specific models.'''
+        concat_image: NotRequired[torch.Tensor]
+        '''Used by SD_4XUpscale_Conditioning.'''
+        noise_augmentation: NotRequired[float]
+        '''Used by SD_4XUpscale_Conditioning.'''
+        hooks: NotRequired[HookGroup]
+        '''Applies hooks to conditioning.'''
+        default: NotRequired[bool]
+        '''Whether to this conditioning is 'default'; default conditioning gets applied to any areas of the image that have no masks/areas applied, assuming at least one area/mask is present during sampling.'''
+        start_percent: NotRequired[float]
+        '''Determines relative step to begin applying conditioning, expressed as a float between 0.0 and 1.0.'''
+        end_percent: NotRequired[float]
+        '''Determines relative step to end applying conditioning, expressed as a float between 0.0 and 1.0.'''
+        clip_start_percent: NotRequired[float]
+        '''Internal variable for conditioning scheduling - start of application, expressed as a float between 0.0 and 1.0.'''
+        clip_end_percent: NotRequired[float]
+        '''Internal variable for conditioning scheduling - end of application, expressed as a float between 0.0 and 1.0.'''
+        attention_mask: NotRequired[torch.Tensor]
+        '''Masks text conditioning; used by StyleModel among others.'''
+        attention_mask_img_shape: NotRequired[tuple[int, ...]]
+        '''Masks text conditioning; used by StyleModel among others.'''
+        unclip_conditioning: NotRequired[list[dict]]
+        '''Used by unCLIP.'''
+        conditioning_lyrics: NotRequired[torch.Tensor]
+        '''Used by AceT5Model.'''
+        seconds_start: NotRequired[float]
+        '''Used by StableAudio.'''
+        seconds_total: NotRequired[float]
+        '''Used by StableAudio.'''
+        lyrics_strength: NotRequired[float]
+        '''Used by AceStepAudio.'''
+        width: NotRequired[int]
+        '''Used by certain models (e.g. CLIPTextEncodeSDXL/Refiner, PixArtAlpha).'''
+        height: NotRequired[int]
+        '''Used by certain models (e.g. CLIPTextEncodeSDXL/Refiner, PixArtAlpha).'''
+        aesthetic_score: NotRequired[float]
+        '''Used by CLIPTextEncodeSDXL/Refiner.'''
+        crop_w: NotRequired[int]
+        '''Used by CLIPTextEncodeSDXL.'''
+        crop_h: NotRequired[int]
+        '''Used by CLIPTextEncodeSDXL.'''
+        target_width: NotRequired[int]
+        '''Used by CLIPTextEncodeSDXL.'''
+        target_height: NotRequired[int]
+        '''Used by CLIPTextEncodeSDXL.'''
+        reference_latents: NotRequired[list[torch.Tensor]]
+        '''Used by ReferenceLatent.'''
+        guidance: NotRequired[float]
+        '''Used by Flux-like models with guidance embed.'''
+        guiding_frame_index: NotRequired[int]
+        '''Used by Hunyuan ImageToVideo.'''
+        ref_latent: NotRequired[torch.Tensor]
+        '''Used by Hunyuan ImageToVideo.'''
+        keyframe_idxs: NotRequired[list[int]]
+        '''Used by LTXV.'''
+        frame_rate: NotRequired[float]
+        '''Used by LTXV.'''
+        stable_cascade_prior: NotRequired[torch.Tensor]
+        '''Used by StableCascade.'''
+        elevation: NotRequired[list[float]]
+        '''Used by SV3D.'''
+        azimuth: NotRequired[list[float]]
+        '''Used by SV3D.'''
+        motion_bucket_id: NotRequired[int]
+        '''Used by SVD-like models.'''
+        fps: NotRequired[int]
+        '''Used by SVD-like models.'''
+        augmentation_level: NotRequired[float]
+        '''Used by SVD-like models.'''
+        clip_vision_output: NotRequired[ClipVisionOutput_]
+        '''Used by WAN-like models.'''
+        vace_frames: NotRequired[torch.Tensor]
+        '''Used by WAN VACE.'''
+        vace_mask: NotRequired[torch.Tensor]
+        '''Used by WAN VACE.'''
+        vace_strength: NotRequired[float]
+        '''Used by WAN VACE.'''
+        camera_conditions: NotRequired[Any] # TODO: assign proper type once defined
+        '''Used by WAN Camera.'''
+        time_dim_concat: NotRequired[torch.Tensor]
+        '''Used by WAN Phantom Subject.'''
+        time_dim_replace: NotRequired[torch.Tensor]
+        '''Used by Kandinsky5 I2V.'''
+
+    CondList = list[tuple[torch.Tensor, PooledDict]]
+    Type = CondList
+
+@comfytype(io_type="SAMPLER")
+class Sampler(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = Sampler
+
+@comfytype(io_type="SIGMAS")
+class Sigmas(ComfyTypeIO):
+    Type = torch.Tensor
+
+@comfytype(io_type="NOISE")
+class Noise(ComfyTypeIO):
+    Type = torch.Tensor
+
+@comfytype(io_type="GUIDER")
+class Guider(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = CFGGuider
+
+@comfytype(io_type="CLIP")
+class Clip(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = CLIP
+
+@comfytype(io_type="CONTROL_NET")
+class ControlNet(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = ControlNet
+
+@comfytype(io_type="VAE")
+class Vae(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = VAE
+
+@comfytype(io_type="MODEL")
+class Model(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = ModelPatcher
+
+@comfytype(io_type="BACKGROUND_REMOVAL")
+class BackgroundRemoval(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = BackgroundRemovalModel
+
+@comfytype(io_type="CLIP_VISION")
+class ClipVision(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = ClipVisionModel
+
+@comfytype(io_type="CLIP_VISION_OUTPUT")
+class ClipVisionOutput(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = ClipVisionOutput_
+
+@comfytype(io_type="STYLE_MODEL")
+class StyleModel(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = StyleModel_
+
+@comfytype(io_type="GLIGEN")
+class Gligen(ComfyTypeIO):
+    '''ModelPatcher that wraps around a 'Gligen' model.'''
+    if TYPE_CHECKING:
+        Type = ModelPatcher
+
+@comfytype(io_type="UPSCALE_MODEL")
+class UpscaleModel(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = ImageModelDescriptor
+
+@comfytype(io_type="LATENT_UPSCALE_MODEL")
+class LatentUpscaleModel(ComfyTypeIO):
+    Type = Any
+
+@comfytype(io_type="AUDIO")
+class Audio(ComfyTypeIO):
+    class AudioDict(TypedDict):
+        waveform: torch.Tensor
+        sampler_rate: int
+    Type = AudioDict
+
+@comfytype(io_type="VIDEO")
+class Video(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = VideoInput
+
+@comfytype(io_type="SVG")
+class SVG(ComfyTypeIO):
+    Type = _SVG
+
+@comfytype(io_type="LORA_MODEL")
+class LoraModel(ComfyTypeIO):
+    Type = dict[str, torch.Tensor]
+
+@comfytype(io_type="LOSS_MAP")
+class LossMap(ComfyTypeIO):
+    class LossMapDict(TypedDict):
+        loss: list[torch.Tensor]
+    Type = LossMapDict
+
+@comfytype(io_type="VOXEL")
+class Voxel(ComfyTypeIO):
+    Type = VOXEL
+
+@comfytype(io_type="MESH")
+class Mesh(ComfyTypeIO):
+    Type = MESH
+
+@comfytype(io_type="SPLAT")
+class Splat(ComfyTypeIO):
+    Type = SPLAT
+
+
+@comfytype(io_type="FILE_3D")
+class File3DAny(ComfyTypeIO):
+    """General 3D file type - accepts any supported 3D format."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_GLB")
+class File3DGLB(ComfyTypeIO):
+    """GLB format 3D file - binary glTF, best for web and cross-platform."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_GLTF")
+class File3DGLTF(ComfyTypeIO):
+    """GLTF format 3D file - JSON-based glTF with external resources."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_FBX")
+class File3DFBX(ComfyTypeIO):
+    """FBX format 3D file - best for game engines and animation."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_OBJ")
+class File3DOBJ(ComfyTypeIO):
+    """OBJ format 3D file - simple geometry format."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_STL")
+class File3DSTL(ComfyTypeIO):
+    """STL format 3D file - best for 3D printing."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_USDZ")
+class File3DUSDZ(ComfyTypeIO):
+    """USDZ format 3D file - Apple AR format."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_PLY")
+class File3DPLY(ComfyTypeIO):
+    """PLY format 3D file - point cloud or Gaussian splat."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_SPLAT")
+class File3DSPLAT(ComfyTypeIO):
+    """SPLAT format 3D file - 3D Gaussian splat."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_SPZ")
+class File3DSPZ(ComfyTypeIO):
+    """SPZ format 3D file - compressed 3D Gaussian splat."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_KSPLAT")
+class File3DKSPLAT(ComfyTypeIO):
+    """KSPLAT format 3D file - 3D Gaussian splat."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_SPLAT_ANY")
+class File3DSplatAny(ComfyTypeIO):
+    """General 3D Gaussian splat file type - accepts any supported splat container (.ply / .spz / .splat / .ksplat)."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_POINT_CLOUD_ANY")
+class File3DPointCloudAny(ComfyTypeIO):
+    """General point cloud file type - accepts any supported point cloud container (currently .ply)."""
+    Type = File3D
+
+
+@comfytype(io_type="HOOKS")
+class Hooks(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = HookGroup
+
+@comfytype(io_type="HOOK_KEYFRAMES")
+class HookKeyframes(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = HookKeyframeGroup
+
+@comfytype(io_type="TIMESTEPS_RANGE")
+class TimestepsRange(ComfyTypeIO):
+    '''Range defined by start and endpoint, between 0.0 and 1.0.'''
+    Type = tuple[int, int]
+
+@comfytype(io_type="LATENT_OPERATION")
+class LatentOperation(ComfyTypeIO):
+    Type = Callable[[torch.Tensor], torch.Tensor]
+
+@comfytype(io_type="FLOW_CONTROL")
+class FlowControl(ComfyTypeIO):
+    # NOTE: only used in testing_nodes right now
+    Type = tuple[str, Any]
+
+@comfytype(io_type="ACCUMULATION")
+class Accumulation(ComfyTypeIO):
+    # NOTE: only used in testing_nodes right now
+    class AccumulationDict(TypedDict):
+        accum: list[Any]
+    Type = AccumulationDict
+
+
+@comfytype(io_type="LOAD3D_CAMERA")
+class Load3DCamera(ComfyTypeIO):
+    class CameraInfo(TypedDict):
+        # Coordinate system: right-handed, Y-up, camera looks down -Z
+        position: dict[str, float | int]  # scene units
+        target: dict[str, float | int]  # scene units; OrbitControls focus point
+        zoom: float | int  # dimensionless, 1 = 100%
+        cameraType: str  # 'perspective' | 'orthographic'
+        quaternion: NotRequired[dict[str, float | int]]  # normalized, dimensionless; camera world rotation
+        fov: NotRequired[float | int]  # degrees, vertical FOV (perspective only)
+        aspect: NotRequired[float | int]  # width / height (perspective only)
+        near: NotRequired[float | int]  # scene units
+        far: NotRequired[float | int]  # scene units
+        frustum: NotRequired[dict[str, float | int]]  # orthographic only: {left, right, top, bottom} in scene units
+
+    Type = CameraInfo
+
+
+@comfytype(io_type="LOAD3D_MODEL_INFO")
+class Load3DModelInfo(ComfyTypeIO):
+    class Model3DTransform(TypedDict):
+        # Coordinate system: right-handed, Y-up, world space
+        position: dict[str, float | int]  # scene units
+        quaternion: dict[str, float | int]  # normalized, dimensionless; world rotation
+        scale: dict[str, float | int]  # dimensionless multiplier
+
+    Type = list[Model3DTransform]
+
+
+@comfytype(io_type="LOAD_3D")
+class Load3D(ComfyTypeIO):
+    """3D models are stored as a dictionary."""
+    class Model3DDict(TypedDict):
+        image: str
+        mask: str
+        normal: str
+        camera_info: Load3DCamera.CameraInfo
+        recording: NotRequired[str]
+        model_3d_info: NotRequired[list[Load3DModelInfo.Model3DTransform]]
+
+    Type = Model3DDict
+
+
+@comfytype(io_type="LOAD_3D_ANIMATION")
+class Load3DAnimation(Load3D):
+    ...
+
+
+@comfytype(io_type="LAYERS")
+class Layers(ComfyTypeIO):
+    BlendMode = Literal[
+        "normal", "multiply", "screen", "overlay", "darken", "lighten",
+        "color-dodge", "color-burn", "hard-light", "soft-light", "difference",
+        "exclusion", "linear-dodge", "linear-burn", "vivid-light", "pin-light",
+        "linear-light", "hard-mix", "subtract", "divide", "grain-extract",
+        "grain-merge", "hue", "saturation", "color", "luminosity",
+    ]
+
+    class LayerItem(TypedDict):
+        image: torch.Tensor
+        type: Literal["raster"]
+        x: NotRequired[int]
+        y: NotRequired[int]
+        mask: NotRequired[torch.Tensor]
+        z_index: int
+        name: NotRequired[str]
+        opacity: NotRequired[float]
+        blend_mode: NotRequired["Layers.BlendMode"]
+        visible: NotRequired[bool]
+        flip_h: NotRequired[bool]
+        flip_v: NotRequired[bool]
+        rotation: NotRequired[float]
+        w: NotRequired[int]
+        h: NotRequired[int]
+
+    class Document(TypedDict):
+        version: int
+        canvas: NotRequired[tuple[int, int]]
+        layers: list["Layers.LayerItem"]
+
+    Type = Document
+
+
+@comfytype(io_type="COMPOSITOR")
+class Compositor(ComfyTypeIO):
+    class LayerState(TypedDict):
+        version: NotRequired[int]
+        canvas: dict
+        background: NotRequired[dict]
+        inputs: NotRequired[list[str]]
+        order: NotRequired[list[int]]
+        layers: list[dict]
+
+    Type = LayerState
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: dict=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = {}
+
+
+@comfytype(io_type="PHOTOMAKER")
+class Photomaker(ComfyTypeIO):
+    Type = Any
+
+
+@comfytype(io_type="POINT")
+class Point(ComfyTypeIO):
+    Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist?
+
+@comfytype(io_type="FACE_ANALYSIS")
+class FaceAnalysis(ComfyTypeIO):
+    Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist?
+
+@comfytype(io_type="BBOX")
+class BBOX(ComfyTypeIO):
+    Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist?
+
+@comfytype(io_type="SEGS")
+class SEGS(ComfyTypeIO):
+    Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist?
+
+@comfytype(io_type="*")
+class AnyType(ComfyTypeIO):
+    Type = Any
+
+@comfytype(io_type="MODEL_PATCH")
+class ModelPatch(ComfyTypeIO):
+    Type = Any
+
+@comfytype(io_type="AUDIO_ENCODER")
+class AudioEncoder(ComfyTypeIO):
+    Type = Any
+
+@comfytype(io_type="AUDIO_ENCODER_OUTPUT")
+class AudioEncoderOutput(ComfyTypeIO):
+    Type = Any
+
+@comfytype(io_type="TRACKS")
+class Tracks(ComfyTypeIO):
+    class TrackDict(TypedDict):
+        track_path: torch.Tensor
+        track_visibility: torch.Tensor
+    Type = TrackDict
+
+@comfytype(io_type="DICT")
+class Dict(ComfyTypeIO):
+    Type = dict
+
+@comfytype(io_type="ARRAY")
+class Array(ComfyTypeIO):
+    Type = list
+
+@comfytype(io_type="COMFY_MULTITYPED_V3")
+class MultiType:
+    Type = Any
+    class Input(Input):
+        '''
+        Input that permits more than one input type; if `id` is an instance of `ComfyType.Input`, then that input will be used to create a widget (if applicable) with overridden values.
+        '''
+        def __init__(self, id: str | Input, types: list[type[_ComfyType] | _ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            # if id is an Input, then use that Input with overridden values
+            self.input_override = None
+            if isinstance(id, Input):
+                self.input_override = copy.copy(id)
+                optional = id.optional if id.optional is True else optional
+                tooltip = id.tooltip if id.tooltip is not None else tooltip
+                display_name = id.display_name if id.display_name is not None else display_name
+                lazy = id.lazy if id.lazy is not None else lazy
+                id = id.id
+                # if is a widget input, make sure widget_type is set appropriately
+                if isinstance(self.input_override, WidgetInput):
+                    self.input_override.widget_type = self.input_override.get_io_type()
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link, advanced)
+            self._io_types = types
+
+        @property
+        def io_types(self) -> list[type[Input]]:
+            '''
+            Returns list of Input class types permitted.
+            '''
+            io_types = []
+            for x in self._io_types:
+                if not is_class(x):
+                    io_types.append(type(x))
+                else:
+                    io_types.append(x)
+            return io_types
+
+        def get_io_type(self):
+            # ensure types are unique and order is preserved
+            str_types = [x.io_type for x in self.io_types]
+            if self.input_override is not None:
+                str_types.insert(0, self.input_override.get_io_type())
+            return ",".join(list(dict.fromkeys(str_types)))
+
+        def as_dict(self):
+            if self.input_override is not None:
+                return self.input_override.as_dict() | super().as_dict()
+            else:
+                return super().as_dict()
+
+@comfytype(io_type="COMFY_MATCHTYPE_V3")
+class MatchType(ComfyTypeIO):
+    class Template:
+        def __init__(self, template_id: str, allowed_types: _ComfyType | list[_ComfyType] = AnyType):
+            self.template_id = template_id
+            # account for syntactic sugar
+            if not isinstance(allowed_types, Iterable):
+                allowed_types = [allowed_types]
+            for t in allowed_types:
+                if not isinstance(t, type):
+                    if not isinstance(t, _ComfyType):
+                        raise ValueError(f"Allowed types must be a ComfyType or a list of ComfyTypes, got {t.__class__.__name__}")
+                else:
+                    if not issubclass(t, _ComfyType):
+                        raise ValueError(f"Allowed types must be a ComfyType or a list of ComfyTypes, got {t.__name__}")
+            self.allowed_types = allowed_types
+
+        def as_dict(self):
+            return {
+                "template_id": self.template_id,
+                "allowed_types": ",".join([t.io_type for t in self.allowed_types]),
+            }
+
+    class Input(Input):
+        def __init__(self, id: str, template: MatchType.Template,
+                    display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link, advanced)
+            self.template = template
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "template": self.template.as_dict(),
+            })
+
+    class Output(Output):
+        def __init__(self, template: MatchType.Template, id: str=None, display_name: str=None, tooltip: str=None,
+                     is_output_list=False):
+            if not id and not display_name:
+                display_name = "MATCHTYPE"
+            super().__init__(id, display_name, tooltip, is_output_list)
+            self.template = template
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "template": self.template.as_dict(),
+            })
+
+class DynamicInput(Input, ABC):
+    '''
+    Abstract class for dynamic input registration.
+    '''
+    pass
+
+
+class DynamicOutput(Output, ABC):
+    '''
+    Abstract class for dynamic output registration.
+    '''
+    pass
+
+
+def handle_prefix(prefix_list: list[str] | None, id: str | None = None) -> list[str]:
+    if prefix_list is None:
+        prefix_list = []
+    if id is not None:
+        prefix_list = prefix_list + [id]
+    return prefix_list
+
+def finalize_prefix(prefix_list: list[str] | None, id: str | None = None) -> str:
+    assert not (prefix_list is None and id is None)
+    if prefix_list is None:
+        return id
+    elif id is not None:
+        prefix_list = prefix_list + [id]
+    return ".".join(prefix_list)
+
+@comfytype(io_type="COMFY_AUTOGROW_V3")
+class Autogrow(ComfyTypeI):
+    Type = dict[str, Any]
+    _MaxNames = 100  # NOTE: max 100 names for sanity
+
+    class _AutogrowTemplate:
+        def __init__(self, input: Input):
+            # dynamic inputs are not allowed as the template input
+            assert(not isinstance(input, DynamicInput))
+            self.input = copy.copy(input)
+            if isinstance(self.input, WidgetInput):
+                self.input.force_input = True
+            self.names: list[str] = []
+            self.cached_inputs = {}
+
+        def _create_input(self, input: Input, name: str):
+            new_input = copy.copy(self.input)
+            new_input.id = name
+            return new_input
+
+        def _create_cached_inputs(self):
+            for name in self.names:
+                self.cached_inputs[name] = self._create_input(self.input, name)
+
+        def get_all(self) -> list[Input]:
+            return list(self.cached_inputs.values())
+
+        def as_dict(self):
+            return prune_dict({
+                "input": create_input_dict_v1([self.input]),
+            })
+
+        def validate(self):
+            self.input.validate()
+
+    class TemplatePrefix(_AutogrowTemplate):
+        def __init__(self, input: Input, prefix: str, min: int=1, max: int=10):
+            super().__init__(input)
+            self.prefix = prefix
+            assert(min >= 0)
+            assert(max >= 1)
+            assert(max <= Autogrow._MaxNames)
+            self.min = min
+            self.max = max
+            self.names = [f"{self.prefix}{i}" for i in range(self.max)]
+            self._create_cached_inputs()
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "prefix": self.prefix,
+                "min": self.min,
+                "max": self.max,
+            })
+
+    class TemplateNames(_AutogrowTemplate):
+        def __init__(self, input: Input, names: list[str], min: int=1):
+            super().__init__(input)
+            self.names = names[:Autogrow._MaxNames]
+            assert(min >= 0)
+            self.min = min
+            self._create_cached_inputs()
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "names": self.names,
+                "min": self.min,
+            })
+
+    class Input(DynamicInput):
+        def __init__(self, id: str, template: Autogrow.TemplatePrefix | Autogrow.TemplateNames,
+                     display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            self.template = template
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "template": self.template.as_dict(),
+            })
+
+        def get_all(self) -> list[Input]:
+            return [self] + self.template.get_all()
+
+        def validate(self):
+            self.template.validate()
+
+    @staticmethod
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
+        # NOTE: purposely do not include self in out_dict; instead use only the template inputs
+        # need to figure out names based on template type
+        is_names = ("names" in value[1]["template"])
+        is_prefix = ("prefix" in value[1]["template"])
+        input = value[1]["template"]["input"]
+        if is_names:
+            min = value[1]["template"]["min"]
+            names = value[1]["template"]["names"]
+            max = len(names)
+        elif is_prefix:
+            prefix = value[1]["template"]["prefix"]
+            min = value[1]["template"]["min"]
+            max = value[1]["template"]["max"]
+            names = [f"{prefix}{i}" for i in range(max)]
+        # need to create a new input based on the contents of input
+        template_input = None
+        template_required = True
+        for _input_type, dict_input in input.items():
+            # for now, get just the first value from dict_input; if not required, min can be ignored
+            if len(dict_input) == 0:
+                continue
+            template_input = list(dict_input.values())[0]
+            template_required = _input_type == "required"
+            break
+        if template_input is None:
+            raise Exception("template_input could not be determined from required or optional; this should never happen.")
+        new_dict = {}
+        new_dict_added_to = False
+        # first, add possible inputs into out_dict
+        for i, name in enumerate(names):
+            expected_id = finalize_prefix(curr_prefix, name)
+            # required
+            if i < min and template_required:
+                out_dict["required"][expected_id] = template_input
+                type_dict = new_dict.setdefault("required", {})
+            # optional
+            else:
+                out_dict["optional"][expected_id] = template_input
+                type_dict = new_dict.setdefault("optional", {})
+            if expected_id in live_inputs:
+                # NOTE: prefix gets added in parse_class_inputs
+                type_dict[name] = template_input
+                new_dict_added_to = True
+        # account for the edge case that all inputs are optional and no values are received
+        if not new_dict_added_to:
+            finalized_prefix = finalize_prefix(curr_prefix)
+            out_dict["dynamic_paths"][finalized_prefix] = finalized_prefix
+            out_dict["dynamic_paths_default_value"][finalized_prefix] = DynamicPathsDefaultValue.EMPTY_DICT
+        parse_class_inputs(out_dict, live_inputs, new_dict, curr_prefix)
+
+@comfytype(io_type="COMFY_DYNAMICCOMBO_V3")
+class DynamicCombo(ComfyTypeI):
+    Type = dict[str, Any]
+
+    class Option:
+        def __init__(self, key: str, inputs: list[Input]):
+            self.key = key
+            self.inputs = inputs
+
+        def as_dict(self):
+            return {
+                "key": self.key,
+                "inputs": create_input_dict_v1(self.inputs),
+            }
+
+    class Input(DynamicInput):
+        def __init__(self, id: str, options: list[DynamicCombo.Option],
+                    display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            self.options = options
+
+        def get_all(self) -> list[Input]:
+            return [self] + [input for option in self.options for input in option.inputs]
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "options": [o.as_dict() for o in self.options],
+            })
+
+        def validate(self):
+            # make sure all nested inputs are validated
+            for option in self.options:
+                for input in option.inputs:
+                    input.validate()
+
+    @staticmethod
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
+        finalized_id = finalize_prefix(curr_prefix)
+        if finalized_id in live_inputs:
+            key = live_inputs[finalized_id]
+            selected_option = None
+            # get options from dict
+            options: list[dict[str, str | dict[str, Any]]] = value[1]["options"]
+            for option in options:
+                if option["key"] == key:
+                    selected_option = option
+                    break
+            if selected_option is not None:
+                parse_class_inputs(out_dict, live_inputs, selected_option["inputs"], curr_prefix)
+                # add self to inputs
+                out_dict[input_type][finalized_id] = value
+                out_dict["dynamic_paths"][finalized_id] = finalize_prefix(curr_prefix, curr_prefix[-1])
+
+@comfytype(io_type="COMFY_DYNAMICSLOT_V3")
+class DynamicSlot(ComfyTypeI):
+    Type = dict[str, Any]
+
+    class Input(DynamicInput):
+        def __init__(self, slot: Input, inputs: list[Input],
+                    display_name: str=None, tooltip: str=None, lazy: bool=None, extra_dict=None):
+            assert(not isinstance(slot, DynamicInput))
+            self.slot = copy.copy(slot)
+            self.slot.display_name = slot.display_name if slot.display_name is not None else display_name
+            optional = True
+            self.slot.tooltip = slot.tooltip if slot.tooltip is not None else tooltip
+            self.slot.lazy = slot.lazy if slot.lazy is not None else lazy
+            self.slot.extra_dict = slot.extra_dict if slot.extra_dict is not None else extra_dict
+            super().__init__(slot.id, self.slot.display_name, optional, self.slot.tooltip, self.slot.lazy, self.slot.extra_dict)
+            self.inputs = inputs
+            self.force_input = None
+            # force widget inputs to have no widgets, otherwise this would be awkward
+            if isinstance(self.slot, WidgetInput):
+                self.force_input = True
+                self.slot.force_input = True
+
+        def get_all(self) -> list[Input]:
+            return [self.slot] + self.inputs
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "slotType": str(self.slot.get_io_type()),
+                "inputs": create_input_dict_v1(self.inputs),
+                "forceInput": self.force_input,
+            })
+
+        def validate(self):
+            self.slot.validate()
+            for input in self.inputs:
+                input.validate()
+
+    @staticmethod
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
+        finalized_id = finalize_prefix(curr_prefix)
+        if finalized_id in live_inputs:
+            inputs = value[1]["inputs"]
+            parse_class_inputs(out_dict, live_inputs, inputs, curr_prefix)
+            # add self to inputs
+            out_dict[input_type][finalized_id] = value
+            out_dict["dynamic_paths"][finalized_id] = finalize_prefix(curr_prefix, curr_prefix[-1])
+
+@comfytype(io_type="IMAGECOMPARE")
+class ImageCompare(ComfyTypeI):
+  Type = dict
+
+  class Input(WidgetInput):
+      def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                   socketless: bool=True, advanced: bool=None):
+          super().__init__(id, display_name, optional, tooltip, None, None, socketless, None, None, None, None, advanced)
+
+      def as_dict(self):
+          return super().as_dict()
+
+
+@comfytype(io_type="COLOR")
+class Color(ComfyTypeIO):
+  Type = str
+
+  class Input(WidgetInput):
+      def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                   socketless: bool=True, advanced: bool=None, default: str="#ffffff"):
+          super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+          self.default: str
+
+      def as_dict(self):
+          return super().as_dict()
+
+
+@comfytype(io_type="COLORS")
+class Colors(ComfyTypeIO):
+    Type = list[Color.Type]
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: list[str]=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = []
+
+
+@comfytype(io_type="BOUNDING_BOX")
+class BoundingBox(ComfyTypeIO):
+    class BoundingBoxDict(TypedDict):
+        x: int
+        y: int
+        width: int
+        height: int
+    Type = BoundingBoxDict
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: dict=None, component: str=None, force_input: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless)
+            self.component = component
+            self.force_input = force_input
+            if default is None:
+                self.default = {"x": 0, "y": 0, "width": 512, "height": 512}
+
+        def as_dict(self):
+            d = super().as_dict()
+            if self.component:
+                d["component"] = self.component
+            if self.force_input is not None:
+                d["forceInput"] = self.force_input
+            return d
+
+
+@comfytype(io_type="CURVE")
+class Curve(ComfyTypeIO):
+    from comfy_api.input import CurvePoint
+    if TYPE_CHECKING:
+        Type = CurveInput_
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: list[tuple[float, float]]=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = [(0.0, 0.0), (1.0, 1.0)]
+
+        def as_dict(self):
+            d = super().as_dict()
+            if self.default is not None:
+                d["default"] = {"points": [list(p) for p in self.default], "interpolation": "monotone_cubic"}
+            return d
+
+
+@comfytype(io_type="BOUNDING_BOXES")
+class BoundingBoxes(ComfyTypeIO):
+    class BoundingBoxWithMetadata(BoundingBox.BoundingBoxDict):
+        metadata: dict
+    Type = list[BoundingBoxWithMetadata]
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: list[dict]=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = []
+
+
+@comfytype(io_type="VIDEO_EDIT")
+class VideoEdit(ComfyTypeIO):
+    class VideoTrimSection(TypedDict):
+        start_time: float
+        duration: float
+
+    class VideoCropSection(TypedDict):
+        x: int
+        y: int
+        width: int
+        height: int
+
+    class VideoEditDict(TypedDict, total=False):
+        trim: 'VideoEdit.VideoTrimSection'
+        crop: 'VideoEdit.VideoCropSection'
+    Type = VideoEditDict
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: dict=None, features: list[str]=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            self.features = features if features is not None else ["trim", "crop"]
+            if default is None:
+                self.default = {}
+                if "trim" in self.features:
+                    self.default["trim"] = {"start_time": 0.0, "duration": 0.0}
+                if "crop" in self.features:
+                    self.default["crop"] = {"x": 0, "y": 0, "width": 0, "height": 0}
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "features": self.features,
+            })
+
+
+@comfytype(io_type="HISTOGRAM")
+class Histogram(ComfyTypeIO):
+    """A histogram represented as a list of bin counts."""
+    Type = list[int]
+
+
+@comfytype(io_type="RANGE")
+class Range(ComfyTypeIO):
+    from comfy_api.input import RangeInput
+    if TYPE_CHECKING:
+        Type = RangeInput
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: dict=None,
+                     display: str=None,
+                     gradient_stops: list=None,
+                     show_midpoint: bool=None,
+                     midpoint_scale: str=None,
+                     value_min: float=None,
+                     value_max: float=None,
+                     advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = {"min": 0.0, "max": 1.0}
+            self.display = display
+            self.gradient_stops = gradient_stops
+            self.show_midpoint = show_midpoint
+            self.midpoint_scale = midpoint_scale
+            self.value_min = value_min
+            self.value_max = value_max
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "display": self.display,
+                "gradient_stops": self.gradient_stops,
+                "show_midpoint": self.show_midpoint,
+                "midpoint_scale": self.midpoint_scale,
+                "value_min": self.value_min,
+                "value_max": self.value_max,
+            })
+
+
+DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
+def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
+    DYNAMIC_INPUT_LOOKUP[io_type] = func
+
+def get_dynamic_input_func(io_type: str) -> Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]:
+    return DYNAMIC_INPUT_LOOKUP[io_type]
+
+def setup_dynamic_input_funcs():
+    # Autogrow.Input
+    register_dynamic_input_func(Autogrow.io_type, Autogrow._expand_schema_for_dynamic)
+    # DynamicCombo.Input
+    register_dynamic_input_func(DynamicCombo.io_type, DynamicCombo._expand_schema_for_dynamic)
+    # DynamicSlot.Input
+    register_dynamic_input_func(DynamicSlot.io_type, DynamicSlot._expand_schema_for_dynamic)
+
+if len(DYNAMIC_INPUT_LOOKUP) == 0:
+    setup_dynamic_input_funcs()
+
+class V3Data(TypedDict):
+    hidden_inputs: dict[str, Any]
+    'Dictionary where the keys are the hidden input ids and the values are the values of the hidden inputs.'
+    dynamic_paths: dict[str, Any]
+    'Dictionary where the keys are the input ids and the values dictate how to turn the inputs into a nested dictionary.'
+    dynamic_paths_default_value: dict[str, Any]
+    'Dictionary where the keys are the input ids and the values are a string from DynamicPathsDefaultValue for the inputs if value is None.'
+    create_dynamic_tuple: bool
+    'When True, the value of the dynamic input will be in the format (value, path_key).'
+
+class HiddenHolder:
+    def __init__(self, unique_id: str, prompt: Any,
+                 extra_pnginfo: Any, dynprompt: Any,
+                 auth_token_comfy_org: str, api_key_comfy_org: str,
+                 comfy_usage_source: str = None, **kwargs):
+        self.unique_id = unique_id
+        """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages)."""
+        self.prompt = prompt
+        """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description."""
+        self.extra_pnginfo = extra_pnginfo
+        """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node)."""
+        self.dynprompt = dynprompt
+        """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion."""
+        self.auth_token_comfy_org = auth_token_comfy_org
+        """AUTH_TOKEN_COMFY_ORG is a token acquired from signing into a ComfyOrg account on frontend."""
+        self.api_key_comfy_org = api_key_comfy_org
+        """API_KEY_COMFY_ORG is an API Key generated by ComfyOrg that allows skipping signing into a ComfyOrg account on frontend."""
+        self.comfy_usage_source = comfy_usage_source
+        """COMFY_USAGE_SOURCE identifies the client that submitted the prompt (e.g. comfyui-frontend, comfy-cli, comfyui-mcp); forwarded to API nodes' upstream requests via the Comfy-Usage-Source header."""
+
+    def __getattr__(self, key: str):
+        '''If hidden variable not found, return None.'''
+        return None
+
+    @classmethod
+    def from_dict(cls, d: dict | None):
+        if d is None:
+            d = {}
+        return cls(
+            unique_id=d.get(Hidden.unique_id, None),
+            prompt=d.get(Hidden.prompt, None),
+            extra_pnginfo=d.get(Hidden.extra_pnginfo, None),
+            dynprompt=d.get(Hidden.dynprompt, None),
+            auth_token_comfy_org=d.get(Hidden.auth_token_comfy_org, None),
+            api_key_comfy_org=d.get(Hidden.api_key_comfy_org, None),
+            comfy_usage_source=d.get(Hidden.comfy_usage_source, None),
+        )
+
+    @classmethod
+    def from_v3_data(cls, v3_data: V3Data | None) -> HiddenHolder:
+        return cls.from_dict(v3_data["hidden_inputs"] if v3_data else None)
+
+class Hidden(str, Enum):
+    '''
+    Enumerator for requesting hidden variables in nodes.
+    '''
+    unique_id = "UNIQUE_ID"
+    """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages)."""
+    prompt = "PROMPT"
+    """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description."""
+    extra_pnginfo = "EXTRA_PNGINFO"
+    """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node)."""
+    dynprompt = "DYNPROMPT"
+    """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion."""
+    auth_token_comfy_org = "AUTH_TOKEN_COMFY_ORG"
+    """AUTH_TOKEN_COMFY_ORG is a token acquired from signing into a ComfyOrg account on frontend."""
+    api_key_comfy_org = "API_KEY_COMFY_ORG"
+    """API_KEY_COMFY_ORG is an API Key generated by ComfyOrg that allows skipping signing into a ComfyOrg account on frontend."""
+    comfy_usage_source = "COMFY_USAGE_SOURCE"
+    """COMFY_USAGE_SOURCE identifies the client that submitted the prompt (e.g. comfyui-frontend, comfy-cli, comfyui-mcp); forwarded to API nodes' upstream requests via the Comfy-Usage-Source header."""
+
+
+@dataclass
+class NodeInfoV1:
+    input: dict=None
+    input_order: dict[str, list[str]]=None
+    is_input_list: bool=None
+    output: list[str]=None
+    output_is_list: list[bool]=None
+    output_name: list[str]=None
+    output_tooltips: list[str]=None
+    output_matchtypes: list[str]=None
+    name: str=None
+    display_name: str=None
+    description: str=None
+    python_module: Any=None
+    category: str=None
+    output_node: bool=None
+    deprecated: bool=None
+    experimental: bool=None
+    dev_only: bool=None
+    api_node: bool=None
+    price_badge: dict | None = None
+    search_aliases: list[str]=None
+    essentials_category: str=None
+    has_intermediate_output: bool=None
+
+
+@dataclass
+class PriceBadgeDepends:
+    widgets: list[str] = field(default_factory=list)
+    inputs: list[str] = field(default_factory=list)
+    input_groups: list[str] = field(default_factory=list)
+
+    def validate(self) -> None:
+        if not isinstance(self.widgets, list) or any(not isinstance(x, str) for x in self.widgets):
+            raise ValueError("PriceBadgeDepends.widgets must be a list[str].")
+        if not isinstance(self.inputs, list) or any(not isinstance(x, str) for x in self.inputs):
+            raise ValueError("PriceBadgeDepends.inputs must be a list[str].")
+        if not isinstance(self.input_groups, list) or any(not isinstance(x, str) for x in self.input_groups):
+            raise ValueError("PriceBadgeDepends.input_groups must be a list[str].")
+
+    def as_dict(self, schema_inputs: list["Input"]) -> dict[str, Any]:
+        # Build lookup: widget_id -> io_type
+        input_types: dict[str, str] = {}
+        for inp in schema_inputs:
+            all_inputs = inp.get_all()
+            input_types[inp.id] = inp.get_io_type()  # First input is always the parent itself
+            for nested_inp in all_inputs[1:]:
+                # For DynamicCombo/DynamicSlot, nested inputs are prefixed with parent ID
+                # to match frontend naming convention (e.g., "should_texture.enable_pbr")
+                prefixed_id = f"{inp.id}.{nested_inp.id}"
+                input_types[prefixed_id] = nested_inp.get_io_type()
+
+        # Enrich widgets with type information, raising error for unknown widgets
+        widgets_data: list[dict[str, str]] = []
+        for w in self.widgets:
+            if w not in input_types:
+                raise ValueError(
+                    f"PriceBadge depends_on.widgets references unknown widget '{w}'. "
+                    f"Available widgets: {list(input_types.keys())}"
+                )
+            widgets_data.append({"name": w, "type": input_types[w]})
+
+        return {
+            "widgets": widgets_data,
+            "inputs": self.inputs,
+            "input_groups": self.input_groups,
+        }
+
+
+@dataclass
+class PriceBadge:
+    expr: str
+    depends_on: PriceBadgeDepends = field(default_factory=PriceBadgeDepends)
+    engine: str = field(default="jsonata")
+
+    def validate(self) -> None:
+        if self.engine != "jsonata":
+            raise ValueError(f"Unsupported PriceBadge.engine '{self.engine}'. Only 'jsonata' is supported.")
+        if not isinstance(self.expr, str) or not self.expr.strip():
+            raise ValueError("PriceBadge.expr must be a non-empty string.")
+        self.depends_on.validate()
+
+    def as_dict(self, schema_inputs: list["Input"]) -> dict[str, Any]:
+        return {
+            "engine": self.engine,
+            "depends_on": self.depends_on.as_dict(schema_inputs),
+            "expr": self.expr,
+        }
+
+
+@dataclass
+class Schema:
+    """Definition of V3 node properties."""
+
+    node_id: str
+    """ID of node - should be globally unique. If this is a custom node, add a prefix or postfix to avoid name clashes."""
+    display_name: str = None
+    """Display name of node."""
+    category: str = "sd"
+    """The category of the node, as per the "Add Node" menu."""
+    inputs: list[Input] = field(default_factory=list)
+    outputs: list[Output] = field(default_factory=list)
+    hidden: list[Hidden] = field(default_factory=list)
+    description: str=""
+    """Node description, shown as a tooltip when hovering over the node."""
+    search_aliases: list[str] = field(default_factory=list)
+    """Alternative names for search. Useful for synonyms, abbreviations, or old names after renaming."""
+    is_input_list: bool = False
+    """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes.
+
+    All inputs of ``type`` will become ``list[type]``, regardless of how many items are passed in.  This also affects ``check_lazy_status``.
+
+    From the docs:
+
+    A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``.
+
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing
+    """
+    is_output_node: bool=False
+    """Flags this node as an output node, causing any inputs it requires to be executed.
+
+    If a node is not connected to any output nodes, that node will not be executed.  Usage::
+
+    From the docs:
+
+    By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is.
+
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node
+    """
+    is_deprecated: bool=False
+    """Flags a node as deprecated, indicating to users that they should find alternatives to this node."""
+    is_experimental: bool=False
+    """Flags a node as experimental, informing users that it may change or not work as expected."""
+    is_dev_only: bool=False
+    """Flags a node as dev-only, hiding it from search/menus unless dev mode is enabled."""
+    is_api_node: bool=False
+    """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview."""
+    price_badge: PriceBadge | None = None
+    """Optional client-evaluated pricing badge declaration for this node."""
+    not_idempotent: bool=False
+    """Flags a node as not idempotent; when True, the node will run and not reuse the cached outputs when identical inputs are provided on a different node in the graph."""
+    enable_expand: bool=False
+    """Flags a node as expandable, allowing NodeOutput to include 'expand' property."""
+    accept_all_inputs: bool=False
+    """When True, all inputs from the prompt will be passed to the node as kwargs, even if not defined in the schema."""
+    essentials_category: str | None = None
+    """Optional category for the Essentials tab. Path-based like category field (e.g., 'Basic', 'Image Tools/Editing')."""
+    has_intermediate_output: bool=False
+    """Flags this node as having intermediate output that should persist across page refreshes.
+
+    Nodes with this flag behave like output nodes (their UI results are cached and resent
+    to the frontend) but do NOT automatically get added to the execution list. This means
+    they will only execute if they are on the dependency path of a real output node.
+
+    Use this for nodes with interactive/operable UI regions that produce intermediate outputs
+    (e.g., Image Crop, Painter) rather than final outputs (e.g., Save Image).
+    """
+
+    def validate(self):
+        '''Validate the schema:
+        - verify ids on inputs and outputs are unique - both internally and in relation to each other
+        '''
+        nested_inputs: list[Input] = []
+        for input in self.inputs:
+            if not isinstance(input, DynamicInput):
+                nested_inputs.extend(input.get_all())
+        input_ids = [i.id for i in nested_inputs]
+        output_ids = [o.id for o in self.outputs]
+        input_set = set(input_ids)
+        output_set = set(output_ids)
+        issues: list[str] = []
+        # verify ids are unique per list
+        if len(input_set) != len(input_ids):
+            issues.append(f"Input ids must be unique, but {[item for item, count in Counter(input_ids).items() if count > 1]} are not.")
+        if len(output_set) != len(output_ids):
+            issues.append(f"Output ids must be unique, but {[item for item, count in Counter(output_ids).items() if count > 1]} are not.")
+        if len(issues) > 0:
+            raise ValueError("\n".join(issues))
+        # validate inputs and outputs
+        for input in self.inputs:
+            input.validate()
+        for output in self.outputs:
+            output.validate()
+        if self.price_badge is not None:
+            self.price_badge.validate()
+
+    def finalize(self):
+        """Add hidden based on selected schema options, and give outputs without ids default ids."""
+        # ensure inputs, outputs, and hidden are lists
+        if self.inputs is None:
+            self.inputs = []
+        if self.outputs is None:
+            self.outputs = []
+        if self.hidden is None:
+            self.hidden = []
+        # if is an api_node, will need key-related hidden
+        if self.is_api_node:
+            if Hidden.auth_token_comfy_org not in self.hidden:
+                self.hidden.append(Hidden.auth_token_comfy_org)
+            if Hidden.api_key_comfy_org not in self.hidden:
+                self.hidden.append(Hidden.api_key_comfy_org)
+            if Hidden.comfy_usage_source not in self.hidden:
+                self.hidden.append(Hidden.comfy_usage_source)
+        # if is an output_node, will need prompt and extra_pnginfo
+        if self.is_output_node:
+            if Hidden.prompt not in self.hidden:
+                self.hidden.append(Hidden.prompt)
+            if Hidden.extra_pnginfo not in self.hidden:
+                self.hidden.append(Hidden.extra_pnginfo)
+        # give outputs without ids default ids
+        for i, output in enumerate(self.outputs):
+            if output.id is None:
+                output.id = f"_{i}_{output.io_type}_"
+
+    def get_v1_info(self, cls) -> NodeInfoV1:
+        # get V1 inputs
+        input = create_input_dict_v1(self.inputs)
+        if self.hidden:
+            for hidden in self.hidden:
+                input.setdefault("hidden", {})[hidden.name] = (hidden.value,)
+        # create separate lists from output fields
+        output = []
+        output_is_list = []
+        output_name = []
+        output_tooltips = []
+        output_matchtypes = []
+        any_matchtypes = False
+        if self.outputs:
+            for o in self.outputs:
+                output.append(o.io_type)
+                output_is_list.append(o.is_output_list)
+                output_name.append(o.display_name if o.display_name else o.io_type)
+                output_tooltips.append(o.tooltip if o.tooltip else None)
+                # special handling for MatchType
+                if isinstance(o, MatchType.Output):
+                    output_matchtypes.append(o.template.template_id)
+                    any_matchtypes = True
+                else:
+                    output_matchtypes.append(None)
+
+        # clear out lists that are all None
+        if not any_matchtypes:
+            output_matchtypes = None
+
+        info = NodeInfoV1(
+            input=input,
+            input_order={key: list(value.keys()) for (key, value) in input.items()},
+            is_input_list=self.is_input_list,
+            output=output,
+            output_is_list=output_is_list,
+            output_name=output_name,
+            output_tooltips=output_tooltips,
+            output_matchtypes=output_matchtypes,
+            name=self.node_id,
+            display_name=self.display_name,
+            category=self.category,
+            description=self.description,
+            output_node=self.is_output_node,
+            has_intermediate_output=self.has_intermediate_output,
+            deprecated=self.is_deprecated,
+            experimental=self.is_experimental,
+            dev_only=self.is_dev_only,
+            api_node=self.is_api_node,
+            python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes"),
+            price_badge=self.price_badge.as_dict(self.inputs) if self.price_badge is not None else None,
+            search_aliases=self.search_aliases if self.search_aliases else None,
+            essentials_category=self.essentials_category,
+        )
+        return info
+
+def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], include_hidden=False) -> tuple[dict[str, Any], V3Data]:
+    out_dict = {
+        "required": {},
+        "optional": {},
+        "dynamic_paths": {},
+        "dynamic_paths_default_value": {},
+    }
+    d = d.copy()
+    # ignore hidden for parsing
+    hidden = d.pop("hidden", None)
+    parse_class_inputs(out_dict, live_inputs, d)
+    if hidden is not None and include_hidden:
+        out_dict["hidden"] = hidden
+    v3_data = {}
+    dynamic_paths = out_dict.pop("dynamic_paths", None)
+    if dynamic_paths is not None and len(dynamic_paths) > 0:
+        v3_data["dynamic_paths"] = dynamic_paths
+    # this list is used for autogrow, in the case all inputs are optional and no values are passed
+    dynamic_paths_default_value = out_dict.pop("dynamic_paths_default_value", None)
+    if dynamic_paths_default_value is not None and len(dynamic_paths_default_value) > 0:
+        v3_data["dynamic_paths_default_value"] = dynamic_paths_default_value
+    return out_dict, hidden, v3_data
+
+def parse_class_inputs(out_dict: dict[str, Any], live_inputs: dict[str, Any], curr_dict: dict[str, Any], curr_prefix: list[str] | None=None) -> None:
+    for input_type, inner_d in curr_dict.items():
+        for id, value in inner_d.items():
+            io_type = value[0]
+            if io_type in DYNAMIC_INPUT_LOOKUP:
+                # dynamic inputs need to be handled with lookup functions
+                dynamic_input_func = get_dynamic_input_func(io_type)
+                new_prefix = handle_prefix(curr_prefix, id)
+                dynamic_input_func(out_dict, live_inputs, value, input_type, new_prefix)
+            else:
+                # non-dynamic inputs get directly transferred
+                finalized_id = finalize_prefix(curr_prefix, id)
+                out_dict[input_type][finalized_id] = value
+                if curr_prefix:
+                    out_dict["dynamic_paths"][finalized_id] = finalized_id
+
+def create_input_dict_v1(inputs: list[Input]) -> dict:
+    input = {
+        "required": {}
+    }
+    for i in inputs:
+        add_to_dict_v1(i, input)
+    return input
+
+def add_to_dict_v1(i: Input, d: dict):
+    key = "optional" if i.optional else "required"
+    as_dict = i.as_dict()
+    # for v1, we don't want to include the optional key
+    as_dict.pop("optional", None)
+    d.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict)
+
+class DynamicPathsDefaultValue:
+    EMPTY_DICT = "empty_dict"
+
+def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
+    paths = v3_data.get("dynamic_paths", None)
+    default_value_dict = v3_data.get("dynamic_paths_default_value", {})
+    if paths is None:
+        return values
+    values = values.copy()
+
+    result = {}
+
+    create_tuple = v3_data.get("create_dynamic_tuple", False)
+
+    for key, path in paths.items():
+        parts = path.split(".")
+        current = result
+
+        for i, p in enumerate(parts):
+            is_last = (i == len(parts) - 1)
+
+            if is_last:
+                value = values.pop(key, None)
+                if value is None:
+                    # see if a default value was provided for this key
+                    default_option = default_value_dict.get(key, None)
+                    if default_option == DynamicPathsDefaultValue.EMPTY_DICT:
+                        value = {}
+                if create_tuple:
+                    value = (value, key)
+                current[p] = value
+            else:
+                current = current.setdefault(p, {})
+
+    values.update(result)
+    return values
+
+
+class _ComfyNodeBaseInternal(_ComfyNodeInternal):
+    """Common base class for storing internal methods and properties; DO NOT USE for defining nodes."""
+
+    RELATIVE_PYTHON_MODULE = None
+    SCHEMA = None
+
+    # filled in during execution
+    hidden: HiddenHolder = None
+
+    @classmethod
+    @abstractmethod
+    def define_schema(cls) -> Schema:
+        """Override this function with one that returns a Schema instance."""
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def execute(cls, **kwargs) -> NodeOutput:
+        """Override this function with one that performs node's actions."""
+        raise NotImplementedError
+
+    @classmethod
+    def validate_inputs(cls, **kwargs) -> bool | str:
+        """Optionally, define this function to validate inputs; equivalent to V1's VALIDATE_INPUTS.
+
+        If the function returns a string, it will be used as the validation error message for the node.
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def fingerprint_inputs(cls, **kwargs) -> Any:
+        """Optionally, define this function to fingerprint inputs; equivalent to V1's IS_CHANGED.
+
+        If this function returns the same value as last run, the node will not be executed."""
+        raise NotImplementedError
+
+    @classmethod
+    def check_lazy_status(cls, **kwargs) -> list[str]:
+        """Optionally, define this function to return a list of input names that should be evaluated.
+
+        This basic mixin impl. requires all inputs.
+
+        :kwargs: All node inputs will be included here.  If the input is ``None``, it should be assumed that it has not yet been evaluated.  \
+            When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``.
+
+        Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name).
+        Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params).
+
+        Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status
+        """
+        return [name for name in kwargs if kwargs[name] is None]
+
+    def __init__(self):
+        self.__class__.VALIDATE_CLASS()
+
+    @classmethod
+    def GET_BASE_CLASS(cls):
+        return _ComfyNodeBaseInternal
+
+    @final
+    @classmethod
+    def VALIDATE_CLASS(cls):
+        if first_real_override(cls, "define_schema") is None:
+            raise Exception(f"No define_schema function was defined for node class {cls.__name__}.")
+        if first_real_override(cls, "execute") is None:
+            raise Exception(f"No execute function was defined for node class {cls.__name__}.")
+
+    @classproperty
+    def FUNCTION(cls):  # noqa
+        if inspect.iscoroutinefunction(cls.execute):
+            return "EXECUTE_NORMALIZED_ASYNC"
+        return "EXECUTE_NORMALIZED"
+
+    @final
+    @classmethod
+    def EXECUTE_NORMALIZED(cls, *args, **kwargs) -> NodeOutput:
+        to_return = cls.execute(*args, **kwargs)
+        if to_return is None:
+            to_return = NodeOutput()
+        elif isinstance(to_return, NodeOutput):
+            pass
+        elif isinstance(to_return, tuple):
+            to_return = NodeOutput(*to_return)
+        elif isinstance(to_return, dict):
+            to_return = NodeOutput.from_dict(to_return)
+        elif isinstance(to_return, ExecutionBlocker):
+            to_return = NodeOutput(block_execution=to_return.message)
+        else:
+            raise Exception(f"Invalid return type from node: {type(to_return)}")
+        if to_return.expand is not None and not cls.SCHEMA.enable_expand:
+            raise Exception(f"Node {cls.__name__} is not expandable, but expand included in NodeOutput; developer should set enable_expand=True on node's Schema to allow this.")
+        return to_return
+
+    @final
+    @classmethod
+    async def EXECUTE_NORMALIZED_ASYNC(cls, *args, **kwargs) -> NodeOutput:
+        to_return = await cls.execute(*args, **kwargs)
+        if to_return is None:
+            to_return = NodeOutput()
+        elif isinstance(to_return, NodeOutput):
+            pass
+        elif isinstance(to_return, tuple):
+            to_return = NodeOutput(*to_return)
+        elif isinstance(to_return, dict):
+            to_return = NodeOutput.from_dict(to_return)
+        elif isinstance(to_return, ExecutionBlocker):
+            to_return = NodeOutput(block_execution=to_return.message)
+        else:
+            raise Exception(f"Invalid return type from node: {type(to_return)}")
+        if to_return.expand is not None and not cls.SCHEMA.enable_expand:
+            raise Exception(f"Node {cls.__name__} is not expandable, but expand included in NodeOutput; developer should set enable_expand=True on node's Schema to allow this.")
+        return to_return
+
+    @final
+    @classmethod
+    def PREPARE_CLASS_CLONE(cls, v3_data: V3Data | None) -> type[ComfyNode]:
+        """Creates clone of real node class to prevent monkey-patching."""
+        c_type: type[ComfyNode] = cls if is_class(cls) else type(cls)
+        type_clone: type[ComfyNode] = shallow_clone_class(c_type)
+        # set hidden
+        type_clone.hidden = HiddenHolder.from_v3_data(v3_data)
+        return type_clone
+    #############################################
+    # V1 Backwards Compatibility code
+    #--------------------------------------------
+    @final
+    @classmethod
+    def GET_NODE_INFO_V1(cls) -> dict[str, Any]:
+        schema = cls.GET_SCHEMA()
+        info = schema.get_v1_info(cls)
+        return asdict(info)
+
+    _DESCRIPTION = None
+    @final
+    @classproperty
+    def DESCRIPTION(cls):  # noqa
+        if cls._DESCRIPTION is None:
+            cls.GET_SCHEMA()
+        return cls._DESCRIPTION
+
+    _CATEGORY = None
+    @final
+    @classproperty
+    def CATEGORY(cls):  # noqa
+        if cls._CATEGORY is None:
+            cls.GET_SCHEMA()
+        return cls._CATEGORY
+
+    _EXPERIMENTAL = None
+    @final
+    @classproperty
+    def EXPERIMENTAL(cls):  # noqa
+        if cls._EXPERIMENTAL is None:
+            cls.GET_SCHEMA()
+        return cls._EXPERIMENTAL
+
+    _DEPRECATED = None
+    @final
+    @classproperty
+    def DEPRECATED(cls):  # noqa
+        if cls._DEPRECATED is None:
+            cls.GET_SCHEMA()
+        return cls._DEPRECATED
+
+    _DEV_ONLY = None
+    @final
+    @classproperty
+    def DEV_ONLY(cls):  # noqa
+        if cls._DEV_ONLY is None:
+            cls.GET_SCHEMA()
+        return cls._DEV_ONLY
+
+    _API_NODE = None
+    @final
+    @classproperty
+    def API_NODE(cls):  # noqa
+        if cls._API_NODE is None:
+            cls.GET_SCHEMA()
+        return cls._API_NODE
+
+    _OUTPUT_NODE = None
+    @final
+    @classproperty
+    def OUTPUT_NODE(cls):  # noqa
+        if cls._OUTPUT_NODE is None:
+            cls.GET_SCHEMA()
+        return cls._OUTPUT_NODE
+
+    _HAS_INTERMEDIATE_OUTPUT = None
+    @final
+    @classproperty
+    def HAS_INTERMEDIATE_OUTPUT(cls):  # noqa
+        if cls._HAS_INTERMEDIATE_OUTPUT is None:
+            cls.GET_SCHEMA()
+        return cls._HAS_INTERMEDIATE_OUTPUT
+
+    _INPUT_IS_LIST = None
+    @final
+    @classproperty
+    def INPUT_IS_LIST(cls):  # noqa
+        if cls._INPUT_IS_LIST is None:
+            cls.GET_SCHEMA()
+        return cls._INPUT_IS_LIST
+    _OUTPUT_IS_LIST = None
+
+    @final
+    @classproperty
+    def OUTPUT_IS_LIST(cls):  # noqa
+        if cls._OUTPUT_IS_LIST is None:
+            cls.GET_SCHEMA()
+        return cls._OUTPUT_IS_LIST
+
+    _RETURN_TYPES = None
+    @final
+    @classproperty
+    def RETURN_TYPES(cls):  # noqa
+        if cls._RETURN_TYPES is None:
+            cls.GET_SCHEMA()
+        return cls._RETURN_TYPES
+
+    _RETURN_NAMES = None
+    @final
+    @classproperty
+    def RETURN_NAMES(cls):  # noqa
+        if cls._RETURN_NAMES is None:
+            cls.GET_SCHEMA()
+        return cls._RETURN_NAMES
+
+    _OUTPUT_TOOLTIPS = None
+    @final
+    @classproperty
+    def OUTPUT_TOOLTIPS(cls):  # noqa
+        if cls._OUTPUT_TOOLTIPS is None:
+            cls.GET_SCHEMA()
+        return cls._OUTPUT_TOOLTIPS
+
+    _NOT_IDEMPOTENT = None
+    @final
+    @classproperty
+    def NOT_IDEMPOTENT(cls):  # noqa
+        if cls._NOT_IDEMPOTENT is None:
+            cls.GET_SCHEMA()
+        return cls._NOT_IDEMPOTENT
+
+    _ACCEPT_ALL_INPUTS = None
+    @final
+    @classproperty
+    def ACCEPT_ALL_INPUTS(cls):  # noqa
+        if cls._ACCEPT_ALL_INPUTS is None:
+            cls.GET_SCHEMA()
+        return cls._ACCEPT_ALL_INPUTS
+
+    @final
+    @classmethod
+    def INPUT_TYPES(cls) -> dict[str, dict]:
+        schema = cls.FINALIZE_SCHEMA()
+        info = schema.get_v1_info(cls)
+        return info.input
+
+    @final
+    @classmethod
+    def FINALIZE_SCHEMA(cls):
+        """Call define_schema and finalize it."""
+        schema = cls.define_schema()
+        schema.finalize()
+        return schema
+
+    @final
+    @classmethod
+    def GET_SCHEMA(cls) -> Schema:
+        """Validate node class, finalize schema, validate schema, and set expected class properties."""
+        cls.VALIDATE_CLASS()
+        schema = cls.FINALIZE_SCHEMA()
+        schema.validate()
+        if cls._DESCRIPTION is None:
+            cls._DESCRIPTION = schema.description
+        if cls._CATEGORY is None:
+            cls._CATEGORY = schema.category
+        if cls._EXPERIMENTAL is None:
+            cls._EXPERIMENTAL = schema.is_experimental
+        if cls._DEPRECATED is None:
+            cls._DEPRECATED = schema.is_deprecated
+        if cls._DEV_ONLY is None:
+            cls._DEV_ONLY = schema.is_dev_only
+        if cls._API_NODE is None:
+            cls._API_NODE = schema.is_api_node
+        if cls._OUTPUT_NODE is None:
+            cls._OUTPUT_NODE = schema.is_output_node
+        if cls._HAS_INTERMEDIATE_OUTPUT is None:
+            cls._HAS_INTERMEDIATE_OUTPUT = schema.has_intermediate_output
+        if cls._INPUT_IS_LIST is None:
+            cls._INPUT_IS_LIST = schema.is_input_list
+        if cls._NOT_IDEMPOTENT is None:
+            cls._NOT_IDEMPOTENT = schema.not_idempotent
+        if cls._ACCEPT_ALL_INPUTS is None:
+            cls._ACCEPT_ALL_INPUTS = schema.accept_all_inputs
+
+        if cls._RETURN_TYPES is None:
+            output = []
+            output_name = []
+            output_is_list = []
+            output_tooltips = []
+            if schema.outputs:
+                for o in schema.outputs:
+                    output.append(o.io_type)
+                    output_name.append(o.display_name if o.display_name else o.io_type)
+                    output_is_list.append(o.is_output_list)
+                    output_tooltips.append(o.tooltip if o.tooltip else None)
+
+            cls._RETURN_TYPES = output
+            cls._RETURN_NAMES = output_name
+            cls._OUTPUT_IS_LIST = output_is_list
+            cls._OUTPUT_TOOLTIPS = output_tooltips
+        cls.SCHEMA = schema
+        return schema
+    #--------------------------------------------
+    #############################################
+
+
+class ComfyNode(_ComfyNodeBaseInternal):
+    """Common base class for all V3 nodes."""
+
+    @classmethod
+    @abstractmethod
+    def define_schema(cls) -> Schema:
+        """Override this function with one that returns a Schema instance."""
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def execute(cls, **kwargs) -> NodeOutput:
+        """Override this function with one that performs node's actions."""
+        raise NotImplementedError
+
+    @classmethod
+    def validate_inputs(cls, **kwargs) -> bool | str:
+        """Optionally, define this function to validate inputs; equivalent to V1's VALIDATE_INPUTS."""
+        raise NotImplementedError
+
+    @classmethod
+    def fingerprint_inputs(cls, **kwargs) -> Any:
+        """Optionally, define this function to fingerprint inputs; equivalent to V1's IS_CHANGED."""
+        raise NotImplementedError
+
+    @classmethod
+    def check_lazy_status(cls, **kwargs) -> list[str]:
+        """Optionally, define this function to return a list of input names that should be evaluated.
+
+        This basic mixin impl. requires all inputs.
+
+        :kwargs: All node inputs will be included here.  If the input is ``None``, it should be assumed that it has not yet been evaluated.  \
+            When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``.
+
+        Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name).
+        Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params).
+
+        Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status
+        """
+        return [name for name in kwargs if kwargs[name] is None]
+
+    @final
+    @classmethod
+    def GET_BASE_CLASS(cls):
+        """DO NOT override this class. Will break things in execution.py."""
+        return ComfyNode
+
+
+class NodeOutput(_NodeOutputInternal):
+    '''
+    Standardized output of a node; can pass in any number of args and/or a UIOutput into 'ui' kwarg.
+    '''
+    def __init__(self, *args: Any, ui: _UIOutput | dict=None, expand: dict=None, block_execution: str=None):
+        self.args = args
+        self.ui = ui
+        self.expand = expand
+        self.block_execution = block_execution
+
+    @property
+    def result(self):
+        return self.args if len(self.args) > 0 else None
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> NodeOutput:
+        args = ()
+        ui = None
+        expand = None
+        if "result" in data:
+            result = data["result"]
+            if isinstance(result, ExecutionBlocker):
+                return cls(block_execution=result.message)
+            args = result
+        if "ui" in data:
+            ui = data["ui"]
+        if "expand" in data:
+            expand = data["expand"]
+        return cls(*args, ui=ui, expand=expand)
+
+    def __getitem__(self, index) -> Any:
+        return self.args[index]
+
+class _UIOutput(ABC):
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def as_dict(self) -> dict:
+        ...
+
+
+class InputMapOldId(TypedDict):
+    """Map an old node input to a new node input by ID."""
+    new_id: str
+    old_id: str
+
+class InputMapSetValue(TypedDict):
+    """Set a specific value for a new node input."""
+    new_id: str
+    set_value: Any
+
+InputMap = InputMapOldId | InputMapSetValue
+"""
+Input mapping for node replacement. Type is inferred by dictionary keys:
+- {"new_id": str, "old_id": str} - maps old input to new input
+- {"new_id": str, "set_value": Any} - sets a specific value for new input
+"""
+
+class OutputMap(TypedDict):
+    """Map outputs of node replacement via indexes."""
+    new_idx: int
+    old_idx: int
+
+class NodeReplace:
+    """
+    Defines a possible node replacement, mapping inputs and outputs of the old node to the new node.
+
+    Also supports assigning specific values to the input widgets of the new node.
+
+    Args:
+        new_node_id: The class name of the new replacement node.
+        old_node_id: The class name of the deprecated node.
+        old_widget_ids: Ordered list of input IDs for widgets that may not have an input slot
+            connected. The workflow JSON stores widget values by their relative position index,
+            not by ID. This list maps those positional indexes to input IDs, enabling the
+            replacement system to correctly identify widget values during node migration.
+        input_mapping: List of input mappings from old node to new node.
+        output_mapping: List of output mappings from old node to new node.
+    """
+    def __init__(self,
+        new_node_id: str,
+        old_node_id: str,
+        old_widget_ids: list[str] | None=None,
+        input_mapping: list[InputMap] | None=None,
+        output_mapping: list[OutputMap] | None=None,
+    ):
+        self.new_node_id = new_node_id
+        self.old_node_id = old_node_id
+        self.old_widget_ids = old_widget_ids
+        self.input_mapping = input_mapping
+        self.output_mapping = output_mapping
+
+    def as_dict(self):
+        """Create serializable representation of the node replacement."""
+        return {
+            "new_node_id": self.new_node_id,
+            "old_node_id": self.old_node_id,
+            "old_widget_ids": self.old_widget_ids,
+            "input_mapping": list(self.input_mapping) if self.input_mapping else None,
+            "output_mapping": list(self.output_mapping) if self.output_mapping else None,
+        }
+
+
+__all__ = [
+    "FolderType",
+    "UploadType",
+    "RemoteOptions",
+    "NumberDisplay",
+    "ControlAfterGenerate",
+
+    "comfytype",
+    "Custom",
+    "Input",
+    "WidgetInput",
+    "Output",
+    "ComfyTypeI",
+    "ComfyTypeIO",
+    # Supported Types
+    "Boolean",
+    "Int",
+    "Float",
+    "String",
+    "Combo",
+    "MultiCombo",
+    "Image",
+    "WanCameraEmbedding",
+    "Webcam",
+    "Mask",
+    "Latent",
+    "Conditioning",
+    "Sampler",
+    "Sigmas",
+    "Noise",
+    "Guider",
+    "Clip",
+    "ControlNet",
+    "Vae",
+    "Model",
+    "ModelPatch",
+    "ClipVision",
+    "ClipVisionOutput",
+    "BackgroundRemoval",
+    "AudioEncoder",
+    "AudioEncoderOutput",
+    "StyleModel",
+    "Gligen",
+    "UpscaleModel",
+    "LatentUpscaleModel",
+    "Audio",
+    "Video",
+    "SVG",
+    "LoraModel",
+    "LossMap",
+    "Voxel",
+    "Mesh",
+    "Splat",
+    "File3DAny",
+    "File3DGLB",
+    "File3DGLTF",
+    "File3DFBX",
+    "File3DOBJ",
+    "File3DSTL",
+    "File3DUSDZ",
+    "File3DPLY",
+    "File3DSPLAT",
+    "File3DSPZ",
+    "File3DKSPLAT",
+    "File3DSplatAny",
+    "File3DPointCloudAny",
+    "Hooks",
+    "HookKeyframes",
+    "TimestepsRange",
+    "LatentOperation",
+    "FlowControl",
+    "Accumulation",
+    "Load3DCamera",
+    "Load3DModelInfo",
+    "Load3D",
+    "Load3DAnimation",
+    "Compositor",
+    "Layers",
+    "Photomaker",
+    "Point",
+    "FaceAnalysis",
+    "BBOX",
+    "SEGS",
+    "AnyType",
+    "MultiType",
+    "Tracks",
+    "Dict",
+    "Array",
+    "Color",
+    # Dynamic Types
+    "MatchType",
+    "DynamicCombo",
+    "Autogrow",
+    # Other classes
+    "HiddenHolder",
+    "Hidden",
+    "NodeInfoV1",
+    "Schema",
+    "ComfyNode",
+    "NodeOutput",
+    "add_to_dict_v1",
+    "V3Data",
+    "ImageCompare",
+    "PriceBadgeDepends",
+    "PriceBadge",
+    "BoundingBox",
+    "BoundingBoxes",
+    "Colors",
+    "Curve",
+    "Histogram",
+    "Range",
+    "VideoEdit",
+    "NodeReplace",
+]
diff --git a/comfy_api/latest/_io_public.py b/comfy_api/latest/_io_public.py
new file mode 100644
index 0000000000000000000000000000000000000000..d58cea54c699184855c7a7b29d9c3fac3fa13bad
--- /dev/null
+++ b/comfy_api/latest/_io_public.py
@@ -0,0 +1 @@
+from ._io import *  # noqa: F403
diff --git a/comfy_api/latest/_ui.py b/comfy_api/latest/_ui.py
new file mode 100644
index 0000000000000000000000000000000000000000..9aafcde205452e8fe67f394df1973a730424bdde
--- /dev/null
+++ b/comfy_api/latest/_ui.py
@@ -0,0 +1,490 @@
+from __future__ import annotations
+
+import json
+import os
+import random
+import uuid
+from io import BytesIO
+
+import av
+import numpy as np
+import torch
+try:
+    import torchaudio
+    TORCH_AUDIO_AVAILABLE = True
+except:
+    TORCH_AUDIO_AVAILABLE = False
+from PIL import Image as PILImage
+from PIL.PngImagePlugin import PngInfo
+
+import folder_paths
+
+# used for image preview
+from comfy.cli_args import args
+from ._io import ComfyNode, FolderType, Image, _UIOutput
+
+
+class SavedResult(dict):
+    def __init__(self, filename: str, subfolder: str, type: FolderType):
+        super().__init__(filename=filename, subfolder=subfolder,type=type.value)
+
+    @property
+    def filename(self) -> str:
+        return self["filename"]
+
+    @property
+    def subfolder(self) -> str:
+        return self["subfolder"]
+
+    @property
+    def type(self) -> FolderType:
+        return FolderType(self["type"])
+
+
+class SavedImages(_UIOutput):
+    """A UI output class to represent one or more saved images, potentially animated."""
+    def __init__(self, results: list[SavedResult], is_animated: bool = False):
+        super().__init__()
+        self.results = results
+        self.is_animated = is_animated
+
+    def as_dict(self) -> dict:
+        data = {"images": self.results}
+        if self.is_animated:
+            data["animated"] = (True,)
+        return data
+
+
+class SavedAudios(_UIOutput):
+    """UI wrapper around one or more audio files on disk (FLAC / MP3 / Opus)."""
+    def __init__(self, results: list[SavedResult]):
+        super().__init__()
+        self.results = results
+
+    def as_dict(self) -> dict:
+        return {"audio": self.results}
+
+
+def _get_directory_by_folder_type(folder_type: FolderType) -> str:
+    if folder_type == FolderType.input:
+        return folder_paths.get_input_directory()
+    if folder_type == FolderType.output:
+        return folder_paths.get_output_directory()
+    return folder_paths.get_temp_directory()
+
+
+class ImageSaveHelper:
+    """A helper class with static methods to handle image saving and metadata."""
+
+    @staticmethod
+    def _convert_tensor_to_pil(image_tensor: torch.Tensor) -> PILImage.Image:
+        """Converts a single torch tensor to a PIL Image."""
+        return PILImage.fromarray(np.clip(255.0 * image_tensor.cpu().numpy(), 0, 255).astype(np.uint8))
+
+    @staticmethod
+    def _create_png_metadata(cls: type[ComfyNode] | None) -> PngInfo | None:
+        """Creates a PngInfo object with prompt and extra_pnginfo."""
+        if args.disable_metadata or cls is None or not cls.hidden:
+            return None
+        metadata = PngInfo()
+        if cls.hidden.prompt:
+            metadata.add_text("prompt", json.dumps(cls.hidden.prompt))
+        if cls.hidden.extra_pnginfo:
+            for x in cls.hidden.extra_pnginfo:
+                metadata.add_text(x, json.dumps(cls.hidden.extra_pnginfo[x]))
+        return metadata
+
+    @staticmethod
+    def _create_animated_png_metadata(cls: type[ComfyNode] | None) -> PngInfo | None:
+        """Creates a PngInfo object with prompt and extra_pnginfo for animated PNGs (APNG)."""
+        if args.disable_metadata or cls is None or not cls.hidden:
+            return None
+        metadata = PngInfo()
+        if cls.hidden.prompt:
+            metadata.add(
+                b"comf",
+                "prompt".encode("latin-1", "strict")
+                + b"\0"
+                + json.dumps(cls.hidden.prompt).encode("latin-1", "strict"),
+                after_idat=True,
+            )
+        if cls.hidden.extra_pnginfo:
+            for x in cls.hidden.extra_pnginfo:
+                metadata.add(
+                    b"comf",
+                    x.encode("latin-1", "strict")
+                    + b"\0"
+                    + json.dumps(cls.hidden.extra_pnginfo[x]).encode("latin-1", "strict"),
+                    after_idat=True,
+                )
+        return metadata
+
+    @staticmethod
+    def _create_webp_metadata(pil_image: PILImage.Image, cls: type[ComfyNode] | None) -> PILImage.Exif:
+        """Creates EXIF metadata bytes for WebP images."""
+        exif_data = pil_image.getexif()
+        if args.disable_metadata or cls is None or cls.hidden is None:
+            return exif_data
+        if cls.hidden.prompt is not None:
+            exif_data[0x0110] = "prompt:{}".format(json.dumps(cls.hidden.prompt))  # EXIF 0x0110 = Model
+        if cls.hidden.extra_pnginfo is not None:
+            inital_exif_tag = 0x010F  # EXIF 0x010f = Make
+            for key, value in cls.hidden.extra_pnginfo.items():
+                exif_data[inital_exif_tag] = "{}:{}".format(key, json.dumps(value))
+                inital_exif_tag -= 1
+        return exif_data
+
+    @staticmethod
+    def save_images(
+        images, filename_prefix: str, folder_type: FolderType, cls: type[ComfyNode] | None, compress_level = 4,
+    ) -> list[SavedResult]:
+        """Saves a batch of images as individual PNG files."""
+        full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(
+            filename_prefix, _get_directory_by_folder_type(folder_type), images[0].shape[1], images[0].shape[0]
+        )
+        results = []
+        metadata = ImageSaveHelper._create_png_metadata(cls)
+        for batch_number, image_tensor in enumerate(images):
+            img = ImageSaveHelper._convert_tensor_to_pil(image_tensor)
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.png"
+            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level)
+            results.append(SavedResult(file, subfolder, folder_type))
+            counter += 1
+        return results
+
+    @staticmethod
+    def get_save_images_ui(images, filename_prefix: str, cls: type[ComfyNode] | None, compress_level=4) -> SavedImages:
+        """Saves a batch of images and returns a UI object for the node output."""
+        return SavedImages(
+                ImageSaveHelper.save_images(
+                images,
+                filename_prefix=filename_prefix,
+                folder_type=FolderType.output,
+                cls=cls,
+                compress_level=compress_level,
+            )
+        )
+
+    @staticmethod
+    def save_animated_png(
+        images, filename_prefix: str, folder_type: FolderType, cls: type[ComfyNode] | None, fps: float, compress_level: int
+    ) -> SavedResult:
+        """Saves a batch of images as a single animated PNG."""
+        full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(
+            filename_prefix, _get_directory_by_folder_type(folder_type), images[0].shape[1], images[0].shape[0]
+        )
+        pil_images = [ImageSaveHelper._convert_tensor_to_pil(img) for img in images]
+        metadata = ImageSaveHelper._create_animated_png_metadata(cls)
+        file = f"{filename}_{counter:05}_.png"
+        save_path = os.path.join(full_output_folder, file)
+        pil_images[0].save(
+            save_path,
+            pnginfo=metadata,
+            compress_level=compress_level,
+            save_all=True,
+            duration=int(1000.0 / fps),
+            append_images=pil_images[1:],
+        )
+        return SavedResult(file, subfolder, folder_type)
+
+    @staticmethod
+    def get_save_animated_png_ui(
+        images, filename_prefix: str, cls: type[ComfyNode] | None, fps: float, compress_level: int
+    ) -> SavedImages:
+        """Saves an animated PNG and returns a UI object for the node output."""
+        result = ImageSaveHelper.save_animated_png(
+            images,
+            filename_prefix=filename_prefix,
+            folder_type=FolderType.output,
+            cls=cls,
+            fps=fps,
+            compress_level=compress_level,
+        )
+        return SavedImages([result], is_animated=len(images) > 1)
+
+    @staticmethod
+    def save_animated_webp(
+        images,
+        filename_prefix: str,
+        folder_type: FolderType,
+        cls: type[ComfyNode] | None,
+        fps: float,
+        lossless: bool,
+        quality: int,
+        method: int,
+    ) -> SavedResult:
+        """Saves a batch of images as a single animated WebP."""
+        full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(
+            filename_prefix, _get_directory_by_folder_type(folder_type), images[0].shape[1], images[0].shape[0]
+        )
+        pil_images = [ImageSaveHelper._convert_tensor_to_pil(img) for img in images]
+        pil_exif = ImageSaveHelper._create_webp_metadata(pil_images[0], cls)
+        file = f"{filename}_{counter:05}_.webp"
+        pil_images[0].save(
+            os.path.join(full_output_folder, file),
+            save_all=True,
+            duration=int(1000.0 / fps),
+            append_images=pil_images[1:],
+            exif=pil_exif,
+            lossless=lossless,
+            quality=quality,
+            method=method,
+        )
+        return SavedResult(file, subfolder, folder_type)
+
+    @staticmethod
+    def get_save_animated_webp_ui(
+        images,
+        filename_prefix: str,
+        cls: type[ComfyNode] | None,
+        fps: float,
+        lossless: bool,
+        quality: int,
+        method: int,
+    ) -> SavedImages:
+        """Saves an animated WebP and returns a UI object for the node output."""
+        result = ImageSaveHelper.save_animated_webp(
+            images,
+            filename_prefix=filename_prefix,
+            folder_type=FolderType.output,
+            cls=cls,
+            fps=fps,
+            lossless=lossless,
+            quality=quality,
+            method=method,
+        )
+        return SavedImages([result], is_animated=len(images) > 1)
+
+
+class AudioSaveHelper:
+    """A helper class with static methods to handle audio saving and metadata."""
+    _OPUS_RATES = [8000, 12000, 16000, 24000, 48000]
+    _FORMATS = {"flac", "mp3", "opus"}
+
+    @staticmethod
+    def save_audio(
+        audio: dict,
+        filename_prefix: str,
+        folder_type: FolderType,
+        cls: type[ComfyNode] | None,
+        format: str = "flac",
+        quality: str = "128k",
+    ) -> list[SavedResult]:
+        if format not in AudioSaveHelper._FORMATS:
+            raise ValueError(f"Unsupported audio format: {format!r}")
+
+        full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(
+            filename_prefix, _get_directory_by_folder_type(folder_type)
+        )
+
+        metadata = {}
+        if not args.disable_metadata and cls is not None:
+            if cls.hidden.prompt is not None:
+                metadata["prompt"] = json.dumps(cls.hidden.prompt)
+            if cls.hidden.extra_pnginfo is not None:
+                for x in cls.hidden.extra_pnginfo:
+                    metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
+
+        results = []
+        for batch_number, waveform in enumerate(audio["waveform"].cpu()):
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}.{format}"
+            output_path = os.path.join(full_output_folder, file)
+
+            # Use original sample rate initially
+            sample_rate = audio["sample_rate"]
+
+            # Handle Opus sample rate requirements
+            if format == "opus":
+                if sample_rate > 48000:
+                    sample_rate = 48000
+                elif sample_rate not in AudioSaveHelper._OPUS_RATES:
+                    # Find the next highest supported rate
+                    for rate in sorted(AudioSaveHelper._OPUS_RATES):
+                        if rate > sample_rate:
+                            sample_rate = rate
+                            break
+                    if sample_rate not in AudioSaveHelper._OPUS_RATES:  # Fallback if still not supported
+                        sample_rate = 48000
+
+                # Resample if necessary
+                if sample_rate != audio["sample_rate"]:
+                    if not TORCH_AUDIO_AVAILABLE:
+                        raise Exception("torchaudio is not available; cannot resample audio.")
+                    waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate)
+
+            # Create output with specified format
+            output_buffer = BytesIO()
+            output_container = av.open(output_buffer, mode="w", format=format)
+
+            # Set metadata on the container
+            for key, value in metadata.items():
+                output_container.metadata[key] = value
+
+            layout = "mono" if waveform.shape[0] == 1 else "stereo"
+            # Set up the output stream with appropriate properties
+            if format == "opus":
+                out_stream = output_container.add_stream("libopus", rate=sample_rate, layout=layout)
+                if quality == "64k":
+                    out_stream.bit_rate = 64000
+                elif quality == "96k":
+                    out_stream.bit_rate = 96000
+                elif quality == "128k":
+                    out_stream.bit_rate = 128000
+                elif quality == "192k":
+                    out_stream.bit_rate = 192000
+                elif quality == "320k":
+                    out_stream.bit_rate = 320000
+            elif format == "mp3":
+                out_stream = output_container.add_stream("libmp3lame", rate=sample_rate, layout=layout)
+                if quality == "V0":
+                    # TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
+                    out_stream.codec_context.qscale = 1
+                elif quality == "128k":
+                    out_stream.bit_rate = 128000
+                elif quality == "320k":
+                    out_stream.bit_rate = 320000
+            else:  # format == "flac":
+                out_stream = output_container.add_stream("flac", rate=sample_rate, layout=layout)
+
+            frame = av.AudioFrame.from_ndarray(
+                waveform.movedim(0, 1).reshape(1, -1).float().numpy(),
+                format="flt",
+                layout=layout,
+            )
+            frame.sample_rate = sample_rate
+            frame.pts = 0
+            output_container.mux(out_stream.encode(frame))
+
+            # Flush encoder
+            output_container.mux(out_stream.encode(None))
+
+            # Close containers
+            output_container.close()
+
+            # Write the output to file
+            output_buffer.seek(0)
+            with open(output_path, "wb") as f:
+                f.write(output_buffer.getbuffer())
+
+            results.append(SavedResult(file, subfolder, folder_type))
+            counter += 1
+
+        return results
+
+    @staticmethod
+    def get_save_audio_ui(
+        audio, filename_prefix: str, cls: type[ComfyNode] | None, format: str = "flac", quality: str = "128k",
+    ) -> SavedAudios:
+        """Save and instantly wrap for UI."""
+        return SavedAudios(
+            AudioSaveHelper.save_audio(
+                audio,
+                filename_prefix=filename_prefix,
+                folder_type=FolderType.output,
+                cls=cls,
+                format=format,
+                quality=quality,
+            )
+        )
+
+
+class PreviewImage(_UIOutput):
+    def __init__(self, image: Image.Type, animated: bool = False, cls: type[ComfyNode] = None, **kwargs):
+        self.values = ImageSaveHelper.save_images(
+            image,
+            filename_prefix="ComfyUI_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for _ in range(5)),
+            folder_type=FolderType.temp,
+            cls=cls,
+            compress_level=1,
+        )
+        self.animated = animated
+
+    def as_dict(self):
+        return {
+            "images": self.values,
+            "animated": (self.animated,)
+        }
+
+
+class PreviewMask(PreviewImage):
+    def __init__(self, mask: PreviewMask.Type, animated: bool=False, cls: ComfyNode=None, **kwargs):
+        preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
+        super().__init__(preview, animated, cls, **kwargs)
+
+
+class PreviewAudio(_UIOutput):
+    def __init__(self, audio: dict, cls: type[ComfyNode] = None, **kwargs):
+        self.values = AudioSaveHelper.save_audio(
+            audio,
+            filename_prefix="ComfyUI_temp_" + "".join(random.choice("abcdefghijklmnopqrstuvwxyz") for _ in range(5)),
+            folder_type=FolderType.temp,
+            cls=cls,
+            format="flac",
+            quality="128k",
+        )
+
+    def as_dict(self) -> dict:
+        return {"audio": self.values}
+
+
+class PreviewVideo(_UIOutput):
+    def __init__(self, values: list[SavedResult | dict], **kwargs):
+        self.values = values
+
+    def as_dict(self):
+        return {"images": self.values, "animated": (True,)}
+
+
+class PreviewUI3D(_UIOutput):
+    def __init__(self, model_file, camera_info, **kwargs):
+        self.model_file = model_file
+        self.camera_info = camera_info
+        self.bg_image_path = None
+        bg_image = kwargs.get("bg_image", None)
+        if bg_image is not None:
+            img_array = (bg_image[0].cpu().numpy() * 255).astype(np.uint8)
+            img = PILImage.fromarray(img_array)
+            temp_dir = folder_paths.get_temp_directory()
+            filename = f"bg_{uuid.uuid4().hex}.png"
+            bg_image_path = os.path.join(temp_dir, filename)
+            img.save(bg_image_path, compress_level=1)
+            self.bg_image_path = f"temp/{filename}"
+
+    def as_dict(self):
+        return {"result": [self.model_file, self.camera_info, self.bg_image_path]}
+
+
+class PreviewUI3DAdvanced(_UIOutput):
+    def __init__(self, model_file, camera_info, model_3d_info):
+        self.model_file = model_file
+        self.camera_info = camera_info
+        self.model_3d_info = model_3d_info
+
+    def as_dict(self):
+        return {"result": [self.model_file, self.camera_info, self.model_3d_info]}
+
+
+class PreviewText(_UIOutput):
+    def __init__(self, value: str, **kwargs):
+        self.value = value
+
+    def as_dict(self):
+        return {"text": (self.value,)}
+
+
+__all__ = [
+    "SavedResult",
+    "SavedImages",
+    "SavedAudios",
+    "ImageSaveHelper",
+    "AudioSaveHelper",
+    "PreviewImage",
+    "PreviewMask",
+    "PreviewAudio",
+    "PreviewVideo",
+    "PreviewUI3D",
+    "PreviewUI3DAdvanced",
+    "PreviewText",
+]
diff --git a/comfy_api/latest/_ui_public.py b/comfy_api/latest/_ui_public.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf531b8214e2c18d690b2f5f816460ac0ef052f2
--- /dev/null
+++ b/comfy_api/latest/_ui_public.py
@@ -0,0 +1 @@
+from ._ui import *  # noqa: F403
diff --git a/comfy_api/latest/_util/__init__.py b/comfy_api/latest/_util/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbbc0fc47015ea9d1a5126d499e6c119eb03942d
--- /dev/null
+++ b/comfy_api/latest/_util/__init__.py
@@ -0,0 +1,16 @@
+from .video_types import VideoContainer, VideoCodec, VideoComponents, normalize_crop_rect
+from .geometry_types import VOXEL, MESH, SPLAT, File3D
+from .image_types import SVG
+
+__all__ = [
+    # Utility Types
+    "VideoContainer",
+    "VideoCodec",
+    "VideoComponents",
+    "normalize_crop_rect",
+    "VOXEL",
+    "MESH",
+    "SPLAT",
+    "File3D",
+    "SVG",
+]
diff --git a/comfy_api/latest/_util/geometry_types.py b/comfy_api/latest/_util/geometry_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..2edfaffb8f206027707b2ba27245b3cc28809216
--- /dev/null
+++ b/comfy_api/latest/_util/geometry_types.py
@@ -0,0 +1,145 @@
+import shutil
+from io import BytesIO
+from pathlib import Path
+from typing import IO
+
+import torch
+
+
+class VOXEL:
+    def __init__(self, data: torch.Tensor, voxel_colors=None, resolution=None):
+        self.data = data
+        self.voxel_colors = voxel_colors
+        self.resolution = resolution # each 3d model has its own resolution
+
+class SPLAT:
+    """A batch of 3D Gaussian splats in render-ready (activated, world-space) form.
+
+    Tensors are (B, N, ...) and zero-padded to a common N across the batch; `counts` (B,) holds the
+    real per-item lengths (None when rows are uniform and no slicing is needed). SH coefficients are
+    stored as (B, N, K, 3) with K = (sh_degree + 1)**2; the DC (diffuse) term is sh[..., 0, :].
+    """
+
+    def __init__(self, positions: torch.Tensor, scales: torch.Tensor, rotations: torch.Tensor,
+                 opacities: torch.Tensor, sh: torch.Tensor, counts: torch.Tensor | None = None):
+        self.positions = positions    # (B, N, 3) world-space centers
+        self.scales = scales          # (B, N, 3) linear (positive) per-axis std
+        self.rotations = rotations    # (B, N, 4) quaternion wxyz (normalized)
+        self.opacities = opacities    # (B, N, 1) in [0, 1]
+        self.sh = sh                  # (B, N, K, 3) spherical-harmonic color coefficients
+        self.counts = counts          # (B,) real lengths, or None
+
+
+class MESH:
+    def __init__(self, vertices: torch.Tensor, faces: torch.Tensor,
+                 uvs: torch.Tensor | None = None,
+                 vertex_colors: torch.Tensor | None = None,
+                 texture: torch.Tensor | None = None,
+                 metallic_roughness: torch.Tensor | None = None,
+                 vertex_counts: torch.Tensor | None = None,
+                 face_counts: torch.Tensor | None = None,
+                 unlit: bool = False,
+                 normals: torch.Tensor | None = None,
+                 tangents: torch.Tensor | None = None,
+                 normal_map: torch.Tensor | None = None,
+                 occlusion_in_mr: bool = False,
+                 material: dict | None = None,
+                 emissive: torch.Tensor | None = None):
+
+        assert (vertex_counts is None) == (face_counts is None), \
+            "vertex_counts and face_counts must be provided together (both or neither)"
+        self.vertices = vertices            # vertices: (B, N, 3)
+        self.faces = faces                  # faces: (B, M, 3)
+        self.uvs = uvs                      # uvs: (B, N, 2)
+        self.vertex_colors = vertex_colors  # vertex_colors: (B, N, 3 or 4)
+        # Optional per-vertex normals: (B, N, 3). When None, SaveGLB computes smooth
+        # area-weighted normals so viewers don't fall back to flat (per-face) shading.
+        self.normals = normals
+        self.texture = texture              # texture (baseColor): (B, H, W, 3)
+        # glTF metallicRoughness texture: (B, H, W, 3), R unused, G=roughness, B=metallic
+        self.metallic_roughness = metallic_roughness
+        # When vertices/faces are zero-padded to a common N/M across the batch (variable-size mesh batch),
+        # these hold the real per-item lengths (B,). None means rows are uniform and no slicing is needed.
+        self.vertex_counts = vertex_counts
+        self.face_counts = face_counts
+        # Render flat / emissive (no scene lighting) when saved, e.g. for gaussian-splat-derived meshes.
+        self.unlit = unlit
+        # Extra maps / material overrides attached by bake, normal/AO, and SetMeshMaterial nodes;
+        # consumed by SaveGLB. Declared here (with defaults) so consumers read them directly.
+        self.tangents = tangents            # (B, N, 4) per-vertex tangents for normal mapping
+        self.normal_map = normal_map        # tangent-space normal map: (B, H, W, 3)
+        self.occlusion_in_mr = occlusion_in_mr  # True = R channel of metallic_roughness holds AO (ORM)
+        self.material = material             # SetMeshMaterial scalar/factor overrides
+        self.emissive = emissive             # emissive map: (B, H, W, 3)
+
+
+class File3D:
+    """Class representing a 3D file from a file path or binary stream.
+
+    Supports both disk-backed (file path) and memory-backed (BytesIO) storage.
+    """
+
+    def __init__(self, source: str | IO[bytes], file_format: str = ""):
+        self._source = source
+        self._format = file_format or self._infer_format()
+
+    def _infer_format(self) -> str:
+        if isinstance(self._source, str):
+            return Path(self._source).suffix.lstrip(".").lower()
+        return ""
+
+    @property
+    def format(self) -> str:
+        return self._format
+
+    @format.setter
+    def format(self, value: str) -> None:
+        self._format = value.lstrip(".").lower() if value else ""
+
+    @property
+    def is_disk_backed(self) -> bool:
+        return isinstance(self._source, str)
+
+    def get_source(self) -> str | IO[bytes]:
+        if isinstance(self._source, str):
+            return self._source
+        if hasattr(self._source, "seek"):
+            self._source.seek(0)
+        return self._source
+
+    def get_data(self) -> BytesIO:
+        if isinstance(self._source, str):
+            with open(self._source, "rb") as f:
+                result = BytesIO(f.read())
+            return result
+        if hasattr(self._source, "seek"):
+            self._source.seek(0)
+        if isinstance(self._source, BytesIO):
+            return self._source
+        return BytesIO(self._source.read())
+
+    def save_to(self, path: str) -> str:
+        dest = Path(path)
+        dest.parent.mkdir(parents=True, exist_ok=True)
+
+        if isinstance(self._source, str):
+            if Path(self._source).resolve() != dest.resolve():
+                shutil.copy2(self._source, dest)
+        else:
+            if hasattr(self._source, "seek"):
+                self._source.seek(0)
+            with open(dest, "wb") as f:
+                f.write(self._source.read())
+        return str(dest)
+
+    def get_bytes(self) -> bytes:
+        if isinstance(self._source, str):
+            return Path(self._source).read_bytes()
+        if hasattr(self._source, "seek"):
+            self._source.seek(0)
+        return self._source.read()
+
+    def __repr__(self) -> str:
+        if isinstance(self._source, str):
+            return f"File3D(source={self._source!r}, format={self._format!r})"
+        return f"File3D(<stream>, format={self._format!r})"
diff --git a/comfy_api/latest/_util/image_types.py b/comfy_api/latest/_util/image_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..36b66b26ab70a4d06f3c847986e77185e099cb99
--- /dev/null
+++ b/comfy_api/latest/_util/image_types.py
@@ -0,0 +1,18 @@
+from io import BytesIO
+
+
+class SVG:
+    """Stores SVG representations via a list of BytesIO objects."""
+
+    def __init__(self, data: list[BytesIO]):
+        self.data = data
+
+    def combine(self, other: 'SVG') -> 'SVG':
+        return SVG(self.data + other.data)
+
+    @staticmethod
+    def combine_all(svgs: list['SVG']) -> 'SVG':
+        all_svgs_list: list[BytesIO] = []
+        for svg_item in svgs:
+            all_svgs_list.extend(svg_item.data)
+        return SVG(all_svgs_list)
diff --git a/comfy_api/latest/_util/video_types.py b/comfy_api/latest/_util/video_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc9bb2744acf97b2267eca78b6b001cc5ffcb5f9
--- /dev/null
+++ b/comfy_api/latest/_util/video_types.py
@@ -0,0 +1,79 @@
+from dataclasses import dataclass
+from enum import Enum
+from fractions import Fraction
+from typing import Optional
+from .._input import ImageInput, AudioInput, MaskInput
+
+class VideoCodec(str, Enum):
+    AUTO = "auto"
+    H264 = "h264"
+    AV1 = "av1"
+
+    @classmethod
+    def as_input(cls) -> list[str]:
+        """
+        Returns a list of codec names that can be used as node input.
+        """
+        return [member.value for member in cls]
+
+class VideoContainer(str, Enum):
+    AUTO = "auto"
+    MP4 = "mp4"
+    MKV = "mkv"
+    WEBM = "webm"
+
+    @classmethod
+    def as_input(cls) -> list[str]:
+        """
+        Returns a list of container names that can be used as node input.
+        """
+        return [member.value for member in cls]
+
+    @classmethod
+    def get_extension(cls, value) -> str:
+        """
+        Returns the file extension for the container.
+        """
+        if isinstance(value, str):
+            value = cls(value)
+        if value == VideoContainer.MP4 or value == VideoContainer.AUTO:
+            return "mp4"
+        if value == VideoContainer.MKV:
+            return "mkv"
+        if value == VideoContainer.WEBM:
+            return "webm"
+        return ""
+
+@dataclass
+class VideoComponents:
+    """
+    Dataclass representing the components of a video.
+    """
+
+    images: ImageInput
+    frame_rate: Fraction
+    audio: Optional[AudioInput] = None
+    metadata: Optional[dict] = None
+    alpha: Optional[MaskInput] = None
+
+
+def normalize_crop_rect(
+    x: int, y: int, width: int, height: int, source_width: int, source_height: int
+) -> Optional[tuple[int, int, int, int]]:
+    width = int(width)
+    height = int(height)
+    if width <= 0 or height <= 0:
+        return None
+    x = max(0, min(int(x), source_width - 1))
+    y = max(0, min(int(y), source_height - 1))
+    x -= x % 2
+    y -= y % 2
+    width = min(width, source_width - x)
+    height = min(height, source_height - y)
+    if x == 0 and y == 0 and width == source_width and height == source_height:
+        return None
+    width -= width % 2
+    height -= height % 2
+    if width <= 0 or height <= 0:
+        return None
+    return x, y, width, height
diff --git a/comfy_api/latest/generated/ComfyAPISyncStub.pyi b/comfy_api/latest/generated/ComfyAPISyncStub.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b6903c5df67c5408f3be92470f800e135fb51321
--- /dev/null
+++ b/comfy_api/latest/generated/ComfyAPISyncStub.pyi
@@ -0,0 +1,20 @@
+from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple
+from comfy_api.latest import ComfyAPI_latest
+from PIL.Image import Image
+from torch import Tensor
+class ComfyAPISyncStub:
+    def __init__(self) -> None: ...
+
+    class ExecutionSync:
+        def __init__(self) -> None: ...
+        """
+        Update the progress bar displayed in the ComfyUI interface.
+
+        This function allows custom nodes and API calls to report their progress
+        back to the user interface, providing visual feedback during long operations.
+
+        Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK
+        """
+        def set_progress(self, value: float, max_value: float, node_id: Union[str, None] = None, preview_image: Union[Image, Tensor, None] = None, ignore_size_limit: bool = False) -> None: ...
+
+    execution: ExecutionSync
diff --git a/comfy_api/torch_helpers/__init__.py b/comfy_api/torch_helpers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..555754ba31f672e1f54a1e61ea3929dc4878ca07
--- /dev/null
+++ b/comfy_api/torch_helpers/__init__.py
@@ -0,0 +1,5 @@
+from .torch_compile import set_torch_compile_wrapper
+
+__all__ = [
+    "set_torch_compile_wrapper",
+]
diff --git a/comfy_api/torch_helpers/torch_compile.py b/comfy_api/torch_helpers/torch_compile.py
new file mode 100644
index 0000000000000000000000000000000000000000..0bff31881bc1562fa2b0ad440f2b3d28b2b026c1
--- /dev/null
+++ b/comfy_api/torch_helpers/torch_compile.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+import torch
+
+import comfy.utils
+from comfy.patcher_extension import WrappersMP
+from typing import TYPE_CHECKING, Callable, Optional
+if TYPE_CHECKING:
+    from comfy.model_patcher import ModelPatcher
+    from comfy.patcher_extension import WrapperExecutor
+
+
+COMPILE_KEY = "torch.compile"
+TORCH_COMPILE_KWARGS = "torch_compile_kwargs"
+
+
+def apply_torch_compile_factory(compiled_module_dict: dict[str, Callable]) -> Callable:
+    '''
+    Create a wrapper that will refer to the compiled_diffusion_model.
+    '''
+    def apply_torch_compile_wrapper(executor: WrapperExecutor, *args, **kwargs):
+        try:
+            orig_modules = {}
+            for key, value in compiled_module_dict.items():
+                orig_modules[key] = comfy.utils.get_attr(executor.class_obj, key)
+                comfy.utils.set_attr(executor.class_obj, key, value)
+            return executor(*args, **kwargs)
+        finally:
+            for key, value in orig_modules.items():
+                comfy.utils.set_attr(executor.class_obj, key, value)
+    return apply_torch_compile_wrapper
+
+
+def set_torch_compile_wrapper(model: ModelPatcher, backend: str, options: Optional[dict[str,str]]=None,
+                              mode: Optional[str]=None, fullgraph=False, dynamic: Optional[bool]=None,
+                              keys: list[str]=["diffusion_model"], *args, **kwargs):
+    '''
+    Perform torch.compile that will be applied at sample time for either the whole model or specific params of the BaseModel instance.
+
+    When keys is None, it will default to using ["diffusion_model"], compiling the whole diffusion_model.
+    When a list of keys is provided, it will perform torch.compile on only the selected modules.
+    '''
+    # clear out any other torch.compile wrappers
+    model.remove_wrappers_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY)
+    # if no keys, default to 'diffusion_model'
+    if not keys:
+        keys = ["diffusion_model"]
+    # create kwargs dict that can be referenced later
+    compile_kwargs = {
+        "backend": backend,
+        "options": options,
+        "mode": mode,
+        "fullgraph": fullgraph,
+        "dynamic": dynamic,
+    }
+    # get a dict of compiled keys
+    compiled_modules = {}
+    for key in keys:
+        compiled_modules[key] = torch.compile(
+                model=model.get_model_object(key),
+                **compile_kwargs,
+            )
+    # add torch.compile wrapper
+    wrapper_func = apply_torch_compile_factory(
+        compiled_module_dict=compiled_modules,
+    )
+    # store wrapper to run on BaseModel's apply_model function
+    model.add_wrapper_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY, wrapper_func)
+    # keep compile kwargs for reference
+    model.model_options[TORCH_COMPILE_KWARGS] = compile_kwargs
diff --git a/comfy_api/util.py b/comfy_api/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd1b2712cf579a6fd9ae48899bf3589e13c35a6b
--- /dev/null
+++ b/comfy_api/util.py
@@ -0,0 +1,8 @@
+# This file only exists for backwards compatibility.
+from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents
+
+__all__ = [
+    "VideoCodec",
+    "VideoContainer",
+    "VideoComponents",
+]
diff --git a/comfy_api/util/__init__.py b/comfy_api/util/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e6593426f8d8be3f641c15445dd0fc6913d8bcb
--- /dev/null
+++ b/comfy_api/util/__init__.py
@@ -0,0 +1,8 @@
+# This file only exists for backwards compatibility.
+from comfy_api.latest._util import VideoContainer, VideoCodec, VideoComponents
+
+__all__ = [
+    "VideoContainer",
+    "VideoCodec",
+    "VideoComponents",
+]
diff --git a/comfy_api/util/video_types.py b/comfy_api/util/video_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f474264e74e2ce11467b3d22fb150202fd9d82a
--- /dev/null
+++ b/comfy_api/util/video_types.py
@@ -0,0 +1,12 @@
+# This file only exists for backwards compatibility.
+from comfy_api.latest._util.video_types import (
+    VideoContainer,
+    VideoCodec,
+    VideoComponents,
+)
+
+__all__ = [
+    "VideoContainer",
+    "VideoCodec",
+    "VideoComponents",
+]
diff --git a/comfy_api/v0_0_1/__init__.py b/comfy_api/v0_0_1/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6da986dd38afd6592784e0bad5f82cd0b66c2869
--- /dev/null
+++ b/comfy_api/v0_0_1/__init__.py
@@ -0,0 +1,42 @@
+from comfy_api.v0_0_2 import (
+    ComfyAPIAdapter_v0_0_2,
+    Input as Input_v0_0_2,
+    InputImpl as InputImpl_v0_0_2,
+    Types as Types_v0_0_2,
+)
+from typing import Type, TYPE_CHECKING
+from comfy_api.internal.async_to_sync import create_sync_class
+
+
+# This version only exists to serve as a template for future version adapters.
+# There is no reason anyone should ever use it.
+class ComfyAPIAdapter_v0_0_1(ComfyAPIAdapter_v0_0_2):
+    VERSION = "0.0.1"
+    STABLE = True
+
+class Input(Input_v0_0_2):
+    pass
+
+class InputImpl(InputImpl_v0_0_2):
+    pass
+
+class Types(Types_v0_0_2):
+    pass
+
+ComfyAPI = ComfyAPIAdapter_v0_0_1
+
+# Create a synchronous version of the API
+if TYPE_CHECKING:
+    from comfy_api.v0_0_1.generated.ComfyAPISyncStub import ComfyAPISyncStub  # type: ignore
+
+    ComfyAPISync: Type[ComfyAPISyncStub]
+
+ComfyAPISync = create_sync_class(ComfyAPIAdapter_v0_0_1)
+
+__all__ = [
+    "ComfyAPI",
+    "ComfyAPISync",
+    "Input",
+    "InputImpl",
+    "Types",
+]
diff --git a/comfy_api/v0_0_1/generated/ComfyAPISyncStub.pyi b/comfy_api/v0_0_1/generated/ComfyAPISyncStub.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d3047ef0e0b89e9e709e7f67b90e70049f13e650
--- /dev/null
+++ b/comfy_api/v0_0_1/generated/ComfyAPISyncStub.pyi
@@ -0,0 +1,20 @@
+from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple
+from comfy_api.v0_0_1 import ComfyAPIAdapter_v0_0_1
+from PIL.Image import Image
+from torch import Tensor
+class ComfyAPISyncStub:
+    def __init__(self) -> None: ...
+
+    class ExecutionSync:
+        def __init__(self) -> None: ...
+        """
+        Update the progress bar displayed in the ComfyUI interface.
+
+        This function allows custom nodes and API calls to report their progress
+        back to the user interface, providing visual feedback during long operations.
+
+        Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK
+        """
+        def set_progress(self, value: float, max_value: float, node_id: Union[str, None] = None, preview_image: Union[Image, Tensor, None] = None, ignore_size_limit: bool = False) -> None: ...
+
+    execution: ExecutionSync
diff --git a/comfy_api/v0_0_2/__init__.py b/comfy_api/v0_0_2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..94b34a2d75c7726a87393624068b4a39af1ce669
--- /dev/null
+++ b/comfy_api/v0_0_2/__init__.py
@@ -0,0 +1,49 @@
+from comfy_api.latest import (
+    ComfyAPI_latest,
+    Input as Input_latest,
+    InputImpl as InputImpl_latest,
+    Types as Types_latest,
+)
+from typing import Type, TYPE_CHECKING
+from comfy_api.internal.async_to_sync import create_sync_class
+from comfy_api.latest import io, ui, IO, UI, ComfyExtension  #noqa: F401
+
+
+class ComfyAPIAdapter_v0_0_2(ComfyAPI_latest):
+    VERSION = "0.0.2"
+    STABLE = False
+
+
+class Input(Input_latest):
+    pass
+
+
+class InputImpl(InputImpl_latest):
+    pass
+
+
+class Types(Types_latest):
+    pass
+
+
+ComfyAPI = ComfyAPIAdapter_v0_0_2
+
+# Create a synchronous version of the API
+if TYPE_CHECKING:
+    from comfy_api.v0_0_2.generated.ComfyAPISyncStub import ComfyAPISyncStub  # type: ignore
+
+    ComfyAPISync: Type[ComfyAPISyncStub]
+ComfyAPISync = create_sync_class(ComfyAPIAdapter_v0_0_2)
+
+__all__ = [
+    "ComfyAPI",
+    "ComfyAPISync",
+    "Input",
+    "InputImpl",
+    "Types",
+    "ComfyExtension",
+    "io",
+    "IO",
+    "ui",
+    "UI",
+]
diff --git a/comfy_api/v0_0_2/generated/ComfyAPISyncStub.pyi b/comfy_api/v0_0_2/generated/ComfyAPISyncStub.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9d9fb06421918f2097cb5969118a0fbd8a14e38a
--- /dev/null
+++ b/comfy_api/v0_0_2/generated/ComfyAPISyncStub.pyi
@@ -0,0 +1,20 @@
+from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple
+from comfy_api.v0_0_2 import ComfyAPIAdapter_v0_0_2
+from PIL.Image import Image
+from torch import Tensor
+class ComfyAPISyncStub:
+    def __init__(self) -> None: ...
+
+    class ExecutionSync:
+        def __init__(self) -> None: ...
+        """
+        Update the progress bar displayed in the ComfyUI interface.
+
+        This function allows custom nodes and API calls to report their progress
+        back to the user interface, providing visual feedback during long operations.
+
+        Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK
+        """
+        def set_progress(self, value: float, max_value: float, node_id: Union[str, None] = None, preview_image: Union[Image, Tensor, None] = None, ignore_size_limit: bool = False) -> None: ...
+
+    execution: ExecutionSync
diff --git a/comfy_api/version_list.py b/comfy_api/version_list.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d1629eeafa14416c5f70a17f6bbc3a64e82f9dc
--- /dev/null
+++ b/comfy_api/version_list.py
@@ -0,0 +1,11 @@
+from comfy_api.latest import ComfyAPI_latest
+from comfy_api.v0_0_2 import ComfyAPIAdapter_v0_0_2
+from comfy_api.v0_0_1 import ComfyAPIAdapter_v0_0_1
+from comfy_api.internal import ComfyAPIBase
+
+supported_versions: list[type[ComfyAPIBase]] = [
+    ComfyAPI_latest,
+    ComfyAPIAdapter_v0_0_2,
+    ComfyAPIAdapter_v0_0_1,
+]
+
diff --git a/comfy_api_nodes/__init__.py b/comfy_api_nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/comfy_api_nodes/apis/__init__.py b/comfy_api_nodes/apis/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..567e06f97388e3f05400223ff84e12a9809ff90a
--- /dev/null
+++ b/comfy_api_nodes/apis/__init__.py
@@ -0,0 +1,6147 @@
+# generated by datamodel-codegen:
+#   filename:  filtered-openapi.yaml
+#   timestamp: 2025-07-30T08:54:00+00:00
+
+# pylint: disable
+
+from datetime import date, datetime
+from enum import Enum
+from typing import Any, Dict, List, Literal, Optional, Union
+from uuid import UUID
+
+from pydantic import AnyUrl, BaseModel, ConfigDict, Field, RootModel, StrictBytes
+
+
+class APIKey(BaseModel):
+    created_at: Optional[datetime] = None
+    description: Optional[str] = None
+    id: Optional[str] = None
+    key_prefix: Optional[str] = None
+    name: Optional[str] = None
+
+
+class APIKeyWithPlaintext(APIKey):
+    plaintext_key: Optional[str] = Field(
+        None, description='The full API key (only returned at creation)'
+    )
+
+
+class AuditLog(BaseModel):
+    createdAt: Optional[datetime] = Field(
+        None, description='The date and time the event was created'
+    )
+    event_id: Optional[str] = Field(None, description='the id of the event')
+    event_type: Optional[str] = Field(None, description='the type of the event')
+    params: Optional[Dict[str, Any]] = Field(
+        None, description='data related to the event'
+    )
+
+
+class BFLAsyncResponse(BaseModel):
+    id: str = Field(..., title='Id')
+    polling_url: str = Field(..., title='Polling Url')
+
+
+class BFLAsyncWebhookResponse(BaseModel):
+    id: str = Field(..., title='Id')
+    status: str = Field(..., title='Status')
+    webhook_url: str = Field(..., title='Webhook Url')
+
+
+class CannyHighThreshold(RootModel[int]):
+    root: int = Field(
+        ...,
+        description='High threshold for Canny edge detection',
+        ge=0,
+        le=500,
+        title='Canny High Threshold',
+    )
+
+
+class CannyLowThreshold(RootModel[int]):
+    root: int = Field(
+        ...,
+        description='Low threshold for Canny edge detection',
+        ge=0,
+        le=500,
+        title='Canny Low Threshold',
+    )
+
+
+class Guidance(RootModel[float]):
+    root: float = Field(
+        ...,
+        description='Guidance strength for the image generation process',
+        ge=1.0,
+        le=100.0,
+        title='Guidance',
+    )
+
+
+class Steps(RootModel[int]):
+    root: int = Field(
+        ...,
+        description='Number of steps for the image generation process',
+        ge=15,
+        le=50,
+        title='Steps',
+    )
+
+
+class WebhookUrl(RootModel[AnyUrl]):
+    root: AnyUrl = Field(
+        ..., description='URL to receive webhook notifications', title='Webhook Url'
+    )
+
+
+class BFLFluxKontextMaxGenerateRequest(BaseModel):
+    guidance: Optional[float] = Field(
+        3, description='The guidance scale for generation', ge=1.0, le=20.0
+    )
+    input_image: str = Field(..., description='Base64 encoded image to be edited')
+    prompt: str = Field(
+        ..., description='The text prompt describing what to edit on the image'
+    )
+    steps: Optional[int] = Field(
+        50, description='Number of inference steps', ge=1, le=50
+    )
+
+
+class BFLFluxKontextMaxGenerateResponse(BaseModel):
+    id: str = Field(..., description='Job ID for tracking')
+    polling_url: str = Field(..., description='URL to poll for results')
+
+
+class BFLFluxKontextProGenerateRequest(BaseModel):
+    guidance: Optional[float] = Field(
+        3, description='The guidance scale for generation', ge=1.0, le=20.0
+    )
+    input_image: str = Field(..., description='Base64 encoded image to be edited')
+    prompt: str = Field(
+        ..., description='The text prompt describing what to edit on the image'
+    )
+    steps: Optional[int] = Field(
+        50, description='Number of inference steps', ge=1, le=50
+    )
+
+
+class BFLFluxKontextProGenerateResponse(BaseModel):
+    id: str = Field(..., description='Job ID for tracking')
+    polling_url: str = Field(..., description='URL to poll for results')
+
+
+class OutputFormat(str, Enum):
+    jpeg = 'jpeg'
+    png = 'png'
+
+
+class BFLFluxPro11GenerateRequest(BaseModel):
+    height: int = Field(..., description='Height of the generated image')
+    image_prompt: Optional[str] = Field(None, description='Optional image prompt')
+    output_format: Optional[OutputFormat] = Field(
+        None, description='Output image format'
+    )
+    prompt: str = Field(..., description='The main text prompt for image generation')
+    prompt_upsampling: Optional[bool] = Field(
+        None, description='Whether to use prompt upsampling'
+    )
+    safety_tolerance: Optional[int] = Field(None, description='Safety tolerance level')
+    seed: Optional[int] = Field(None, description='Random seed for reproducibility')
+    webhook_secret: Optional[str] = Field(
+        None, description='Optional webhook secret for async processing'
+    )
+    webhook_url: Optional[str] = Field(
+        None, description='Optional webhook URL for async processing'
+    )
+    width: int = Field(..., description='Width of the generated image')
+
+
+class BFLFluxPro11GenerateResponse(BaseModel):
+    id: str = Field(..., description='Job ID for tracking')
+    polling_url: str = Field(..., description='URL to poll for results')
+
+
+class Bottom(RootModel[int]):
+    root: int = Field(
+        ...,
+        description='Number of pixels to expand at the bottom of the image',
+        ge=0,
+        le=2048,
+        title='Bottom',
+    )
+
+
+class Guidance2(RootModel[float]):
+    root: float = Field(
+        ...,
+        description='Guidance strength for the image generation process',
+        ge=1.5,
+        le=100.0,
+        title='Guidance',
+    )
+
+
+class Left(RootModel[int]):
+    root: int = Field(
+        ...,
+        description='Number of pixels to expand on the left side of the image',
+        ge=0,
+        le=2048,
+        title='Left',
+    )
+
+
+class Right(RootModel[int]):
+    root: int = Field(
+        ...,
+        description='Number of pixels to expand on the right side of the image',
+        ge=0,
+        le=2048,
+        title='Right',
+    )
+
+
+class Steps2(RootModel[int]):
+    root: int = Field(
+        ...,
+        description='Number of steps for the image generation process',
+        examples=[50],
+        ge=15,
+        le=50,
+        title='Steps',
+    )
+
+
+class Top(RootModel[int]):
+    root: int = Field(
+        ...,
+        description='Number of pixels to expand at the top of the image',
+        ge=0,
+        le=2048,
+        title='Top',
+    )
+
+
+class BFLFluxProGenerateRequest(BaseModel):
+    guidance_scale: Optional[float] = Field(
+        None, description='The guidance scale for generation.', ge=1.0, le=20.0
+    )
+    height: int = Field(
+        ..., description='The height of the image to generate.', ge=64, le=2048
+    )
+    negative_prompt: Optional[str] = Field(
+        None, description='The negative prompt for image generation.'
+    )
+    num_images: Optional[int] = Field(
+        None, description='The number of images to generate.', ge=1, le=4
+    )
+    num_inference_steps: Optional[int] = Field(
+        None, description='The number of inference steps.', ge=1, le=100
+    )
+    prompt: str = Field(..., description='The text prompt for image generation.')
+    seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
+    width: int = Field(
+        ..., description='The width of the image to generate.', ge=64, le=2048
+    )
+
+
+class BFLFluxProGenerateResponse(BaseModel):
+    id: str = Field(..., description='The unique identifier for the generation task.')
+    polling_url: str = Field(..., description='URL to poll for the generation result.')
+
+
+class BFLOutputFormat(str, Enum):
+    jpeg = 'jpeg'
+    png = 'png'
+
+
+class BFLValidationError(BaseModel):
+    loc: List[Union[str, int]] = Field(..., title='Location')
+    msg: str = Field(..., title='Message')
+    type: str = Field(..., title='Error Type')
+
+
+class Status(str, Enum):
+    success = 'success'
+    not_found = 'not_found'
+    error = 'error'
+
+
+class ClaimMyNodeRequest(BaseModel):
+    GH_TOKEN: str = Field(
+        ..., description='GitHub token to verify if the user owns the repo of the node'
+    )
+
+
+class ComfyNode(BaseModel):
+    category: Optional[str] = Field(
+        None,
+        description='UI category where the node is listed, used for grouping nodes.',
+    )
+    comfy_node_name: Optional[str] = Field(
+        None, description='Unique identifier for the node'
+    )
+    deprecated: Optional[bool] = Field(
+        None,
+        description='Indicates if the node is deprecated. Deprecated nodes are hidden in the UI.',
+    )
+    description: Optional[str] = Field(
+        None, description="Brief description of the node's functionality or purpose."
+    )
+    experimental: Optional[bool] = Field(
+        None,
+        description='Indicates if the node is experimental, subject to changes or removal.',
+    )
+    function: Optional[str] = Field(
+        None, description='Name of the entry-point function to execute the node.'
+    )
+    input_types: Optional[str] = Field(None, description='Defines input parameters')
+    output_is_list: Optional[List[bool]] = Field(
+        None, description='Boolean values indicating if each output is a list.'
+    )
+    return_names: Optional[str] = Field(
+        None, description='Names of the outputs for clarity in workflows.'
+    )
+    return_types: Optional[str] = Field(
+        None, description='Specifies the types of outputs produced by the node.'
+    )
+
+
+class ComfyNodeCloudBuildInfo(BaseModel):
+    build_id: Optional[str] = None
+    location: Optional[str] = None
+    project_id: Optional[str] = None
+    project_number: Optional[str] = None
+
+
+class Status1(str, Enum):
+    in_progress = 'in_progress'
+    completed = 'completed'
+    incomplete = 'incomplete'
+
+
+class Type(str, Enum):
+    computer_call = 'computer_call'
+
+
+class ComputerToolCall(BaseModel):
+    action: Dict[str, Any]
+    call_id: str = Field(
+        ...,
+        description='An identifier used when responding to the tool call with output.\n',
+    )
+    id: str = Field(..., description='The unique ID of the computer call.')
+    status: Status1 = Field(
+        ...,
+        description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n',
+    )
+    type: Type = Field(
+        ..., description='The type of the computer call. Always `computer_call`.'
+    )
+
+
+class Environment(str, Enum):
+    windows = 'windows'
+    mac = 'mac'
+    linux = 'linux'
+    ubuntu = 'ubuntu'
+    browser = 'browser'
+
+
+class Type1(str, Enum):
+    computer_use_preview = 'computer_use_preview'
+
+
+class ComputerUsePreviewTool(BaseModel):
+    display_height: int = Field(..., description='The height of the computer display.')
+    display_width: int = Field(..., description='The width of the computer display.')
+    environment: Environment = Field(
+        ..., description='The type of computer environment to control.'
+    )
+    type: Literal['ComputerUsePreviewTool'] = Field(
+        ...,
+        description='The type of the computer use tool. Always `computer_use_preview`.',
+    )
+
+
+class CreateAPIKeyRequest(BaseModel):
+    description: Optional[str] = None
+    name: str
+
+
+class Customer(BaseModel):
+    createdAt: Optional[datetime] = Field(
+        None, description='The date and time the user was created'
+    )
+    email: Optional[str] = Field(None, description='The email address for this user')
+    has_fund: Optional[bool] = Field(None, description='Whether the user has funds')
+    id: str = Field(..., description='The firebase UID of the user')
+    is_admin: Optional[bool] = Field(None, description='Whether the user is an admin')
+    metronome_id: Optional[str] = Field(None, description='The Metronome customer ID')
+    name: Optional[str] = Field(None, description='The name for this user')
+    stripe_id: Optional[str] = Field(None, description='The Stripe customer ID')
+    updatedAt: Optional[datetime] = Field(
+        None, description='The date and time the user was last updated'
+    )
+
+
+class CustomerStorageResourceResponse(BaseModel):
+    download_url: Optional[str] = Field(
+        None,
+        description='The signed URL to use for downloading the file from the specified path',
+    )
+    existing_file: Optional[bool] = Field(
+        None, description='Whether an existing file with the same hash was found'
+    )
+    expires_at: Optional[datetime] = Field(
+        None, description='When the signed URL will expire'
+    )
+    upload_url: Optional[str] = Field(
+        None,
+        description='The signed URL to use for uploading the file to the specified path',
+    )
+
+
+class Role(str, Enum):
+    user = 'user'
+    assistant = 'assistant'
+    system = 'system'
+    developer = 'developer'
+
+
+class Type2(str, Enum):
+    message = 'message'
+
+
+class Error(BaseModel):
+    details: Optional[List[str]] = Field(
+        None,
+        description='Optional detailed information about the error or hints for resolving it.',
+    )
+    message: Optional[str] = Field(
+        None, description='A clear and concise description of the error.'
+    )
+
+
+class ErrorResponse(BaseModel):
+    error: str
+    message: str
+
+
+class Type3(str, Enum):
+    file_search = 'file_search'
+
+
+class FileSearchTool(BaseModel):
+    type: Literal['FileSearchTool'] = Field(..., description='The type of tool')
+    vector_store_ids: List[str] = Field(
+        ..., description='IDs of vector stores to search in'
+    )
+
+
+class Result(BaseModel):
+    file_id: Optional[str] = Field(None, description='The unique ID of the file.\n')
+    filename: Optional[str] = Field(None, description='The name of the file.\n')
+    score: Optional[float] = Field(
+        None, description='The relevance score of the file - a value between 0 and 1.\n'
+    )
+    text: Optional[str] = Field(
+        None, description='The text that was retrieved from the file.\n'
+    )
+
+
+class Status2(str, Enum):
+    in_progress = 'in_progress'
+    searching = 'searching'
+    completed = 'completed'
+    incomplete = 'incomplete'
+    failed = 'failed'
+
+
+class Type4(str, Enum):
+    file_search_call = 'file_search_call'
+
+
+class FileSearchToolCall(BaseModel):
+    id: str = Field(..., description='The unique ID of the file search tool call.\n')
+    queries: List[str] = Field(
+        ..., description='The queries used to search for files.\n'
+    )
+    results: Optional[List[Result]] = Field(
+        None, description='The results of the file search tool call.\n'
+    )
+    status: Status2 = Field(
+        ...,
+        description='The status of the file search tool call. One of `in_progress`, \n`searching`, `incomplete` or `failed`,\n',
+    )
+    type: Type4 = Field(
+        ...,
+        description='The type of the file search tool call. Always `file_search_call`.\n',
+    )
+
+
+class Type5(str, Enum):
+    function = 'function'
+
+
+class FunctionTool(BaseModel):
+    description: Optional[str] = Field(
+        None, description='Description of what the function does'
+    )
+    name: str = Field(..., description='Name of the function')
+    parameters: Dict[str, Any] = Field(
+        ..., description='JSON Schema object describing the function parameters'
+    )
+    type: Literal['FunctionTool'] = Field(..., description='The type of tool')
+
+
+class Status3(str, Enum):
+    in_progress = 'in_progress'
+    completed = 'completed'
+    incomplete = 'incomplete'
+
+
+class Type6(str, Enum):
+    function_call = 'function_call'
+
+
+class FunctionToolCall(BaseModel):
+    arguments: str = Field(
+        ..., description='A JSON string of the arguments to pass to the function.\n'
+    )
+    call_id: str = Field(
+        ...,
+        description='The unique ID of the function tool call generated by the model.\n',
+    )
+    id: Optional[str] = Field(
+        None, description='The unique ID of the function tool call.\n'
+    )
+    name: str = Field(..., description='The name of the function to run.\n')
+    status: Optional[Status3] = Field(
+        None,
+        description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n',
+    )
+    type: Type6 = Field(
+        ..., description='The type of the function tool call. Always `function_call`.\n'
+    )
+
+
+class GeminiCitation(BaseModel):
+    authors: Optional[List[str]] = None
+    endIndex: Optional[int] = None
+    license: Optional[str] = None
+    publicationDate: Optional[date] = None
+    startIndex: Optional[int] = None
+    title: Optional[str] = None
+    uri: Optional[str] = None
+
+
+class GeminiCitationMetadata(BaseModel):
+    citations: Optional[List[GeminiCitation]] = None
+
+
+class Role1(str, Enum):
+    user = 'user'
+    model = 'model'
+
+
+class GeminiFunctionDeclaration(BaseModel):
+    description: Optional[str] = None
+    name: str
+    parameters: Dict[str, Any] = Field(
+        ..., description='JSON schema for the function parameters'
+    )
+
+
+class GeminiGenerationConfig(BaseModel):
+    maxOutputTokens: Optional[int] = Field(
+        None,
+        description='Maximum number of tokens that can be generated in the response. A token is approximately 4 characters. 100 tokens correspond to roughly 60-80 words.\n',
+        examples=[2048],
+        ge=16,
+        le=8192,
+    )
+    seed: Optional[int] = Field(
+        None,
+        description="When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used. Available for the following models:, gemini-2.5-flash-preview-04-1, gemini-2.5-pro-preview-05-0, gemini-2.0-flash-lite-00, gemini-2.0-flash-001\n",
+        examples=[343940597],
+    )
+    stopSequences: Optional[List[str]] = None
+    temperature: Optional[float] = Field(
+        1,
+        description="The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible. If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature\n",
+        ge=0.0,
+        le=2.0,
+    )
+    topK: Optional[int] = Field(
+        40,
+        description="Top-K changes how the model selects tokens for output. A top-K of 1 means the next selected token is the most probable among all tokens in the model's vocabulary. A top-K of 3 means that the next token is selected from among the 3 most probable tokens by using temperature.\n",
+        examples=[40],
+        ge=1,
+    )
+    topP: Optional[float] = Field(
+        0.95,
+        description='If specified, nucleus sampling is used.\nTop-P changes how the model selects tokens for output. Tokens are selected from the most (see top-K) to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-P value is 0.5, then the model will select either A or B as the next token by using temperature and excludes C as a candidate.\nSpecify a lower value for less random responses and a higher value for more random responses.\n',
+        ge=0.0,
+        le=1.0,
+    )
+
+
+class GeminiMimeType(str, Enum):
+    application_pdf = 'application/pdf'
+    audio_mpeg = 'audio/mpeg'
+    audio_mp3 = 'audio/mp3'
+    audio_wav = 'audio/wav'
+    image_png = 'image/png'
+    image_jpeg = 'image/jpeg'
+    image_webp = 'image/webp'
+    text_plain = 'text/plain'
+    video_mov = 'video/mov'
+    video_mpeg = 'video/mpeg'
+    video_mp4 = 'video/mp4'
+    video_mpg = 'video/mpg'
+    video_avi = 'video/avi'
+    video_wmv = 'video/wmv'
+    video_mpegps = 'video/mpegps'
+    video_flv = 'video/flv'
+
+
+class GeminiOffset(BaseModel):
+    nanos: Optional[int] = Field(
+        None,
+        description='Signed fractions of a second at nanosecond resolution. Negative second values with fractions must still have non-negative nanos values.\n',
+        examples=[0],
+        ge=0,
+        le=999999999,
+    )
+    seconds: Optional[int] = Field(
+        None,
+        description='Signed seconds of the span of time. Must be from -315,576,000,000 to +315,576,000,000 inclusive.\n',
+        examples=[60],
+        ge=-315576000000,
+        le=315576000000,
+    )
+
+
+class GeminiSafetyCategory(str, Enum):
+    HARM_CATEGORY_SEXUALLY_EXPLICIT = 'HARM_CATEGORY_SEXUALLY_EXPLICIT'
+    HARM_CATEGORY_HATE_SPEECH = 'HARM_CATEGORY_HATE_SPEECH'
+    HARM_CATEGORY_HARASSMENT = 'HARM_CATEGORY_HARASSMENT'
+    HARM_CATEGORY_DANGEROUS_CONTENT = 'HARM_CATEGORY_DANGEROUS_CONTENT'
+
+
+class Probability(str, Enum):
+    NEGLIGIBLE = 'NEGLIGIBLE'
+    LOW = 'LOW'
+    MEDIUM = 'MEDIUM'
+    HIGH = 'HIGH'
+    UNKNOWN = 'UNKNOWN'
+
+
+class GeminiSafetyRating(BaseModel):
+    category: Optional[GeminiSafetyCategory] = None
+    probability: Optional[Probability] = Field(
+        None,
+        description='The probability that the content violates the specified safety category',
+    )
+
+
+class GeminiSafetyThreshold(str, Enum):
+    OFF = 'OFF'
+    BLOCK_NONE = 'BLOCK_NONE'
+    BLOCK_LOW_AND_ABOVE = 'BLOCK_LOW_AND_ABOVE'
+    BLOCK_MEDIUM_AND_ABOVE = 'BLOCK_MEDIUM_AND_ABOVE'
+    BLOCK_ONLY_HIGH = 'BLOCK_ONLY_HIGH'
+
+
+class GeminiTextPart(BaseModel):
+    text: Optional[str] = Field(
+        None,
+        description='A text prompt or code snippet.',
+        examples=['Answer as concisely as possible'],
+    )
+
+
+class GeminiTool(BaseModel):
+    functionDeclarations: Optional[List[GeminiFunctionDeclaration]] = None
+
+
+class GeminiVideoMetadata(BaseModel):
+    endOffset: Optional[GeminiOffset] = None
+    startOffset: Optional[GeminiOffset] = None
+
+
+class GitCommitSummary(BaseModel):
+    author: Optional[str] = Field(None, description='The author of the commit')
+    branch_name: Optional[str] = Field(
+        None, description='The branch where the commit was made'
+    )
+    commit_hash: Optional[str] = Field(None, description='The hash of the commit')
+    commit_name: Optional[str] = Field(None, description='The name of the commit')
+    status_summary: Optional[Dict[str, str]] = Field(
+        None, description='A map of operating system to status pairs'
+    )
+    timestamp: Optional[datetime] = Field(
+        None, description='The timestamp when the commit was made'
+    )
+
+
+class GithubEnterprise(BaseModel):
+    avatar_url: str = Field(..., description='URL to the enterprise avatar')
+    created_at: datetime = Field(..., description='When the enterprise was created')
+    description: Optional[str] = Field(None, description='The enterprise description')
+    html_url: str = Field(..., description='The HTML URL of the enterprise')
+    id: int = Field(..., description='The enterprise ID')
+    name: str = Field(..., description='The enterprise name')
+    node_id: str = Field(..., description='The enterprise node ID')
+    slug: str = Field(..., description='The enterprise slug')
+    updated_at: datetime = Field(
+        ..., description='When the enterprise was last updated'
+    )
+    website_url: Optional[str] = Field(None, description='The enterprise website URL')
+
+
+class RepositorySelection(str, Enum):
+    selected = 'selected'
+    all = 'all'
+
+
+class GithubOrganization(BaseModel):
+    avatar_url: str = Field(..., description="URL to the organization's avatar")
+    description: Optional[str] = Field(None, description='The organization description')
+    events_url: str = Field(..., description="The API URL of the organization's events")
+    hooks_url: str = Field(..., description="The API URL of the organization's hooks")
+    id: int = Field(..., description='The organization ID')
+    issues_url: str = Field(..., description="The API URL of the organization's issues")
+    login: str = Field(..., description="The organization's login name")
+    members_url: str = Field(
+        ..., description="The API URL of the organization's members"
+    )
+    node_id: str = Field(..., description='The organization node ID')
+    public_members_url: str = Field(
+        ..., description="The API URL of the organization's public members"
+    )
+    repos_url: str = Field(
+        ..., description="The API URL of the organization's repositories"
+    )
+    url: str = Field(..., description='The API URL of the organization')
+
+
+class State(str, Enum):
+    uploaded = 'uploaded'
+    open = 'open'
+
+
+class Action(str, Enum):
+    published = 'published'
+    unpublished = 'unpublished'
+    created = 'created'
+    edited = 'edited'
+    deleted = 'deleted'
+    prereleased = 'prereleased'
+    released = 'released'
+
+
+class Type7(str, Enum):
+    Bot = 'Bot'
+    User = 'User'
+    Organization = 'Organization'
+
+
+class GithubUser(BaseModel):
+    avatar_url: str = Field(..., description="URL to the user's avatar")
+    gravatar_id: Optional[str] = Field(None, description="The user's gravatar ID")
+    html_url: str = Field(..., description='The HTML URL of the user')
+    id: int = Field(..., description="The user's ID")
+    login: str = Field(..., description="The user's login name")
+    node_id: str = Field(..., description="The user's node ID")
+    site_admin: bool = Field(..., description='Whether the user is a site admin')
+    type: Type7 = Field(..., description='The type of user')
+    url: str = Field(..., description='The API URL of the user')
+
+
+class IdeogramColorPalette1(BaseModel):
+    name: str = Field(..., description='Name of the preset color palette')
+
+
+class Member(BaseModel):
+    color: Optional[str] = Field(
+        None, description='Hexadecimal color code', pattern='^#[0-9A-Fa-f]{6}$'
+    )
+    weight: Optional[float] = Field(
+        None, description='Optional weight for the color (0-1)', ge=0.0, le=1.0
+    )
+
+
+class IdeogramColorPalette2(BaseModel):
+    members: List[Member] = Field(
+        ..., description='Array of color definitions with optional weights'
+    )
+
+
+class IdeogramColorPalette(
+    RootModel[Union[IdeogramColorPalette1, IdeogramColorPalette2]]
+):
+    root: Union[IdeogramColorPalette1, IdeogramColorPalette2] = Field(
+        ...,
+        description='A color palette specification that can either use a preset name or explicit color definitions with weights',
+    )
+
+
+class ImageRequest(BaseModel):
+    aspect_ratio: Optional[str] = Field(
+        None,
+        description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.",
+    )
+    color_palette: Optional[Dict[str, Any]] = Field(
+        None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.'
+    )
+    magic_prompt_option: Optional[str] = Field(
+        None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')."
+    )
+    model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')")
+    negative_prompt: Optional[str] = Field(
+        None,
+        description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.',
+    )
+    num_images: Optional[int] = Field(
+        1,
+        description='Optional. Number of images to generate (1-8). Defaults to 1.',
+        ge=1,
+        le=8,
+    )
+    prompt: str = Field(
+        ..., description='Required. The prompt to use to generate the image.'
+    )
+    resolution: Optional[str] = Field(
+        None,
+        description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.",
+    )
+    seed: Optional[int] = Field(
+        None,
+        description='Optional. A number between 0 and 2147483647.',
+        ge=0,
+        le=2147483647,
+    )
+    style_type: Optional[str] = Field(
+        None,
+        description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.",
+    )
+
+
+class IdeogramGenerateRequest(BaseModel):
+    image_request: ImageRequest = Field(
+        ..., description='The image generation request parameters.'
+    )
+
+
+class Datum(BaseModel):
+    is_image_safe: Optional[bool] = Field(
+        None, description='Indicates whether the image is considered safe.'
+    )
+    prompt: Optional[str] = Field(
+        None, description='The prompt used to generate this image.'
+    )
+    resolution: Optional[str] = Field(
+        None, description="The resolution of the generated image (e.g., '1024x1024')."
+    )
+    seed: Optional[int] = Field(
+        None, description='The seed value used for this generation.'
+    )
+    style_type: Optional[str] = Field(
+        None,
+        description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').",
+    )
+    url: Optional[str] = Field(None, description='URL to the generated image.')
+
+
+class IdeogramGenerateResponse(BaseModel):
+    created: Optional[datetime] = Field(
+        None, description='Timestamp when the generation was created.'
+    )
+    data: Optional[List[Datum]] = Field(
+        None, description='Array of generated image information.'
+    )
+
+
+class StyleCode(RootModel[str]):
+    root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$')
+
+
+class Datum1(BaseModel):
+    is_image_safe: Optional[bool] = None
+    prompt: Optional[str] = None
+    resolution: Optional[str] = None
+    seed: Optional[int] = None
+    style_type: Optional[str] = None
+    url: Optional[str] = None
+
+
+class IdeogramV3IdeogramResponse(BaseModel):
+    created: Optional[datetime] = None
+    data: Optional[List[Datum1]] = None
+
+
+class RenderingSpeed1(str, Enum):
+    TURBO = 'TURBO'
+    DEFAULT = 'DEFAULT'
+    QUALITY = 'QUALITY'
+
+
+class IdeogramV3ReframeRequest(BaseModel):
+    color_palette: Optional[Dict[str, Any]] = None
+    image: Optional[StrictBytes] = None
+    num_images: Optional[int] = Field(None, ge=1, le=8)
+    rendering_speed: Optional[RenderingSpeed1] = None
+    resolution: str
+    seed: Optional[int] = Field(None, ge=0, le=2147483647)
+    style_codes: Optional[List[str]] = None
+    style_reference_images: Optional[List[StrictBytes]] = None
+
+
+class MagicPrompt(str, Enum):
+    AUTO = 'AUTO'
+    ON = 'ON'
+    OFF = 'OFF'
+
+
+class StyleType(str, Enum):
+    AUTO = 'AUTO'
+    GENERAL = 'GENERAL'
+    REALISTIC = 'REALISTIC'
+    DESIGN = 'DESIGN'
+
+
+class IdeogramV3RemixRequest(BaseModel):
+    aspect_ratio: Optional[str] = None
+    color_palette: Optional[Dict[str, Any]] = None
+    image: Optional[StrictBytes] = None
+    image_weight: Optional[int] = Field(50, ge=1, le=100)
+    magic_prompt: Optional[MagicPrompt] = None
+    negative_prompt: Optional[str] = None
+    num_images: Optional[int] = Field(None, ge=1, le=8)
+    prompt: str
+    rendering_speed: Optional[RenderingSpeed1] = None
+    resolution: Optional[str] = None
+    seed: Optional[int] = Field(None, ge=0, le=2147483647)
+    style_codes: Optional[List[str]] = None
+    style_reference_images: Optional[List[StrictBytes]] = None
+    style_type: Optional[StyleType] = None
+
+
+class IdeogramV3ReplaceBackgroundRequest(BaseModel):
+    color_palette: Optional[Dict[str, Any]] = None
+    image: Optional[StrictBytes] = None
+    magic_prompt: Optional[MagicPrompt] = None
+    num_images: Optional[int] = Field(None, ge=1, le=8)
+    prompt: str
+    rendering_speed: Optional[RenderingSpeed1] = None
+    seed: Optional[int] = Field(None, ge=0, le=2147483647)
+    style_codes: Optional[List[str]] = None
+    style_reference_images: Optional[List[StrictBytes]] = None
+
+
+class ColorPalette(BaseModel):
+    name: str = Field(..., description='Name of the color palette', examples=['PASTEL'])
+
+
+class MagicPrompt2(str, Enum):
+    ON = 'ON'
+    OFF = 'OFF'
+
+
+class StyleType1(str, Enum):
+    AUTO = 'AUTO'
+    GENERAL = 'GENERAL'
+    REALISTIC = 'REALISTIC'
+    DESIGN = 'DESIGN'
+    FICTION = 'FICTION'
+
+
+class ImagenImageGenerationInstance(BaseModel):
+    prompt: str = Field(..., description='Text prompt for image generation')
+
+
+class AspectRatio(str, Enum):
+    field_1_1 = '1:1'
+    field_9_16 = '9:16'
+    field_16_9 = '16:9'
+    field_3_4 = '3:4'
+    field_4_3 = '4:3'
+
+
+class PersonGeneration(str, Enum):
+    dont_allow = 'dont_allow'
+    allow_adult = 'allow_adult'
+    allow_all = 'allow_all'
+
+
+class SafetySetting(str, Enum):
+    block_most = 'block_most'
+    block_some = 'block_some'
+    block_few = 'block_few'
+    block_fewest = 'block_fewest'
+
+
+class ImagenImagePrediction(BaseModel):
+    bytesBase64Encoded: Optional[str] = Field(
+        None, description='Base64-encoded image content'
+    )
+    mimeType: Optional[str] = Field(
+        None, description='MIME type of the generated image'
+    )
+    prompt: Optional[str] = Field(
+        None, description='Enhanced or rewritten prompt used to generate this image'
+    )
+
+
+class MimeType(str, Enum):
+    image_png = 'image/png'
+    image_jpeg = 'image/jpeg'
+
+
+class ImagenOutputOptions(BaseModel):
+    compressionQuality: Optional[int] = Field(None, ge=0, le=100)
+    mimeType: Optional[MimeType] = None
+
+
+class Includable(str, Enum):
+    file_search_call_results = 'file_search_call.results'
+    message_input_image_image_url = 'message.input_image.image_url'
+    computer_call_output_output_image_url = 'computer_call_output.output.image_url'
+
+
+class Type8(str, Enum):
+    input_file = 'input_file'
+
+
+class InputFileContent(BaseModel):
+    file_data: Optional[str] = Field(
+        None, description='The content of the file to be sent to the model.\n'
+    )
+    file_id: Optional[str] = Field(
+        None, description='The ID of the file to be sent to the model.'
+    )
+    filename: Optional[str] = Field(
+        None, description='The name of the file to be sent to the model.'
+    )
+    type: Type8 = Field(
+        ..., description='The type of the input item. Always `input_file`.'
+    )
+
+
+class Detail(str, Enum):
+    low = 'low'
+    high = 'high'
+    auto = 'auto'
+
+
+class Type9(str, Enum):
+    input_image = 'input_image'
+
+
+class InputImageContent(BaseModel):
+    detail: Detail = Field(
+        ...,
+        description='The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`.',
+    )
+    file_id: Optional[str] = Field(
+        None, description='The ID of the file to be sent to the model.'
+    )
+    image_url: Optional[str] = Field(
+        None,
+        description='The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.',
+    )
+    type: Type9 = Field(
+        ..., description='The type of the input item. Always `input_image`.'
+    )
+
+
+class Role3(str, Enum):
+    user = 'user'
+    system = 'system'
+    developer = 'developer'
+
+
+class Type10(str, Enum):
+    message = 'message'
+
+
+class Type11(str, Enum):
+    input_text = 'input_text'
+
+
+class InputTextContent(BaseModel):
+    text: str = Field(..., description='The text input to the model.')
+    type: Type11 = Field(
+        ..., description='The type of the input item. Always `input_text`.'
+    )
+
+
+class KlingAudioUploadType(str, Enum):
+    file = 'file'
+    url = 'url'
+
+
+class KlingCameraConfig(BaseModel):
+    horizontal: Optional[float] = Field(
+        None,
+        description="Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right.",
+        ge=-10.0,
+        le=10.0,
+    )
+    pan: Optional[float] = Field(
+        None,
+        description="Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.",
+        ge=-10.0,
+        le=10.0,
+    )
+    roll: Optional[float] = Field(
+        None,
+        description="Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.",
+        ge=-10.0,
+        le=10.0,
+    )
+    tilt: Optional[float] = Field(
+        None,
+        description="Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.",
+        ge=-10.0,
+        le=10.0,
+    )
+    vertical: Optional[float] = Field(
+        None,
+        description="Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward.",
+        ge=-10.0,
+        le=10.0,
+    )
+    zoom: Optional[float] = Field(
+        None,
+        description="Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.",
+        ge=-10.0,
+        le=10.0,
+    )
+
+
+class KlingCameraControlType(str, Enum):
+    simple = 'simple'
+    down_back = 'down_back'
+    forward_up = 'forward_up'
+    right_turn_forward = 'right_turn_forward'
+    left_turn_forward = 'left_turn_forward'
+
+
+class KlingCharacterEffectModelName(str, Enum):
+    kling_v1 = 'kling-v1'
+    kling_v1_5 = 'kling-v1-5'
+    kling_v1_6 = 'kling-v1-6'
+
+
+class KlingDualCharacterEffectsScene(str, Enum):
+    hug = 'hug'
+    kiss = 'kiss'
+    heart_gesture = 'heart_gesture'
+
+
+class KlingDualCharacterImages(RootModel[List[str]]):
+    root: List[str] = Field(..., max_length=2, min_length=2)
+
+
+class KlingErrorResponse(BaseModel):
+    code: int = Field(
+        ...,
+        description='- 1000: Authentication failed\n- 1001: Authorization is empty\n- 1002: Authorization is invalid\n- 1003: Authorization is not yet valid\n- 1004: Authorization has expired\n- 1100: Account exception\n- 1101: Account in arrears (postpaid scenario)\n- 1102: Resource pack depleted or expired (prepaid scenario)\n- 1103: Unauthorized access to requested resource\n- 1200: Invalid request parameters\n- 1201: Invalid parameters\n- 1202: Invalid request method\n- 1203: Requested resource does not exist\n- 1300: Trigger platform strategy\n- 1301: Trigger content security policy\n- 1302: API request too frequent\n- 1303: Concurrency/QPS exceeds limit\n- 1304: Trigger IP whitelist policy\n- 5000: Internal server error\n- 5001: Service temporarily unavailable\n- 5002: Server internal timeout\n',
+    )
+    message: str = Field(..., description='Human-readable error message')
+    request_id: str = Field(
+        ..., description='Request ID for tracking and troubleshooting'
+    )
+
+
+class Trajectory(BaseModel):
+    x: Optional[int] = Field(
+        None,
+        description='The horizontal coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).',
+    )
+    y: Optional[int] = Field(
+        None,
+        description='The vertical coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).',
+    )
+
+
+class DynamicMask(BaseModel):
+    mask: Optional[AnyUrl] = Field(
+        None,
+        description='Dynamic Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.',
+    )
+    trajectories: Optional[List[Trajectory]] = None
+
+
+class TaskInfo(BaseModel):
+    external_task_id: Optional[str] = None
+
+
+class KlingImageGenAspectRatio(str, Enum):
+    field_16_9 = '16:9'
+    field_9_16 = '9:16'
+    field_1_1 = '1:1'
+    field_4_3 = '4:3'
+    field_3_4 = '3:4'
+    field_3_2 = '3:2'
+    field_2_3 = '2:3'
+    field_21_9 = '21:9'
+
+
+class KlingImageGenImageReferenceType(str, Enum):
+    subject = 'subject'
+    face = 'face'
+
+
+class KlingImageGenerationsRequest(BaseModel):
+    aspect_ratio: Optional[KlingImageGenAspectRatio] = '16:9'
+    callback_url: Optional[AnyUrl] = Field(
+        None, description='The callback notification address'
+    )
+    human_fidelity: Optional[float] = Field(
+        0.45, description='Subject reference similarity', ge=0.0, le=1.0
+    )
+    image: Optional[str] = Field(
+        None, description='Reference Image - Base64 encoded string or image URL'
+    )
+    image_fidelity: Optional[float] = Field(
+        0.5, description='Reference intensity for user-uploaded images', ge=0.0, le=1.0
+    )
+    image_reference: Optional[KlingImageGenImageReferenceType] = None
+    model_name: str = Field(...)
+    n: Optional[int] = Field(1, description='Number of generated images', ge=1, le=9)
+    negative_prompt: Optional[str] = Field(
+        None, description='Negative text prompt', max_length=200
+    )
+    prompt: str = Field(..., description='Positive text prompt', max_length=500)
+
+
+class KlingImageResult(BaseModel):
+    index: Optional[int] = Field(None, description='Image Number (0-9)')
+    url: Optional[AnyUrl] = Field(None, description='URL for generated image')
+
+
+class KlingLipSyncMode(str, Enum):
+    text2video = 'text2video'
+    audio2video = 'audio2video'
+
+
+class KlingLipSyncVoiceLanguage(str, Enum):
+    zh = 'zh'
+    en = 'en'
+
+
+class ResourcePackType(str, Enum):
+    decreasing_total = 'decreasing_total'
+    constant_period = 'constant_period'
+
+
+class Status5(str, Enum):
+    toBeOnline = 'toBeOnline'
+    online = 'online'
+    expired = 'expired'
+    runOut = 'runOut'
+
+
+class ResourcePackSubscribeInfo(BaseModel):
+    effective_time: Optional[int] = Field(
+        None, description='Effective time, Unix timestamp in ms'
+    )
+    invalid_time: Optional[int] = Field(
+        None, description='Expiration time, Unix timestamp in ms'
+    )
+    purchase_time: Optional[int] = Field(
+        None, description='Purchase time, Unix timestamp in ms'
+    )
+    remaining_quantity: Optional[float] = Field(
+        None, description='Remaining quantity (updated with a 12-hour delay)'
+    )
+    resource_pack_id: Optional[str] = Field(None, description='Resource package ID')
+    resource_pack_name: Optional[str] = Field(None, description='Resource package name')
+    resource_pack_type: Optional[ResourcePackType] = Field(
+        None,
+        description='Resource package type (decreasing_total=decreasing total, constant_period=constant periodicity)',
+    )
+    status: Optional[Status5] = Field(None, description='Resource Package Status')
+    total_quantity: Optional[float] = Field(None, description='Total quantity')
+
+
+class Data3(BaseModel):
+    code: Optional[int] = Field(None, description='Error code; 0 indicates success')
+    msg: Optional[str] = Field(None, description='Error information')
+    resource_pack_subscribe_infos: Optional[List[ResourcePackSubscribeInfo]] = Field(
+        None, description='Resource package list'
+    )
+
+
+class KlingResourcePackageResponse(BaseModel):
+    code: Optional[int] = Field(None, description='Error code; 0 indicates success')
+    data: Optional[Data3] = None
+    message: Optional[str] = Field(None, description='Error information')
+    request_id: Optional[str] = Field(
+        None,
+        description='Request ID, generated by the system, used to track requests and troubleshoot problems',
+    )
+
+
+class KlingSingleImageEffectDuration(str, Enum):
+    field_5 = '5'
+
+
+class KlingSingleImageEffectModelName(str, Enum):
+    kling_v1_6 = 'kling-v1-6'
+
+
+class KlingSingleImageEffectsScene(str, Enum):
+    bloombloom = 'bloombloom'
+    dizzydizzy = 'dizzydizzy'
+    fuzzyfuzzy = 'fuzzyfuzzy'
+    squish = 'squish'
+    expansion = 'expansion'
+
+
+class KlingTaskStatus(str, Enum):
+    submitted = 'submitted'
+    processing = 'processing'
+    succeed = 'succeed'
+    failed = 'failed'
+
+
+class KlingVideoGenAspectRatio(str, Enum):
+    field_16_9 = '16:9'
+    field_9_16 = '9:16'
+    field_1_1 = '1:1'
+
+
+class KlingVideoGenCfgScale(RootModel[float]):
+    root: float = Field(
+        ...,
+        description="Flexibility in video generation. The higher the value, the lower the model's degree of flexibility, and the stronger the relevance to the user's prompt.",
+        ge=0.0,
+        le=1.0,
+    )
+
+
+class KlingVideoGenDuration(str, Enum):
+    field_5 = '5'
+    field_10 = '10'
+
+
+class KlingVideoGenMode(str, Enum):
+    std = 'std'
+    pro = 'pro'
+
+
+class KlingVideoGenModelName(str, Enum):
+    kling_v1 = 'kling-v1'
+    kling_v1_5 = 'kling-v1-5'
+    kling_v1_6 = 'kling-v1-6'
+    kling_v2_master = 'kling-v2-master'
+    kling_v2_1 = 'kling-v2-1'
+    kling_v2_1_master = 'kling-v2-1-master'
+    kling_v2_5_turbo = 'kling-v2-5-turbo'
+
+
+class KlingVideoResult(BaseModel):
+    duration: Optional[str] = Field(None, description='Total video duration')
+    id: Optional[str] = Field(None, description='Generated video ID')
+    url: Optional[AnyUrl] = Field(None, description='URL for generated video')
+
+
+class KlingVirtualTryOnModelName(str, Enum):
+    kolors_virtual_try_on_v1 = 'kolors-virtual-try-on-v1'
+    kolors_virtual_try_on_v1_5 = 'kolors-virtual-try-on-v1-5'
+
+
+class KlingVirtualTryOnRequest(BaseModel):
+    callback_url: Optional[AnyUrl] = Field(
+        None, description='The callback notification address'
+    )
+    cloth_image: Optional[str] = Field(
+        None,
+        description='Reference clothing image - Base64 encoded string or image URL',
+    )
+    human_image: str = Field(
+        ..., description='Reference human image - Base64 encoded string or image URL'
+    )
+    model_name: Optional[KlingVirtualTryOnModelName] = 'kolors-virtual-try-on-v1'
+
+
+class TaskResult6(BaseModel):
+    images: Optional[List[KlingImageResult]] = None
+
+
+class Data7(BaseModel):
+    created_at: Optional[int] = Field(None, description='Task creation time')
+    task_id: Optional[str] = Field(None, description='Task ID')
+    task_result: Optional[TaskResult6] = None
+    task_status: Optional[KlingTaskStatus] = None
+    task_status_msg: Optional[str] = Field(None, description='Task status information')
+    updated_at: Optional[int] = Field(None, description='Task update time')
+
+
+class KlingVirtualTryOnResponse(BaseModel):
+    code: Optional[int] = Field(None, description='Error code')
+    data: Optional[Data7] = None
+    message: Optional[str] = Field(None, description='Error message')
+    request_id: Optional[str] = Field(None, description='Request ID')
+
+
+class LumaAspectRatio(str, Enum):
+    field_1_1 = '1:1'
+    field_16_9 = '16:9'
+    field_9_16 = '9:16'
+    field_4_3 = '4:3'
+    field_3_4 = '3:4'
+    field_21_9 = '21:9'
+    field_9_21 = '9:21'
+
+
+class LumaAssets(BaseModel):
+    image: Optional[AnyUrl] = Field(None, description='The URL of the image')
+    progress_video: Optional[AnyUrl] = Field(
+        None, description='The URL of the progress video'
+    )
+    video: Optional[AnyUrl] = Field(None, description='The URL of the video')
+
+
+class GenerationType(str, Enum):
+    add_audio = 'add_audio'
+
+
+class LumaAudioGenerationRequest(BaseModel):
+    callback_url: Optional[AnyUrl] = Field(
+        None, description='The callback URL for the audio'
+    )
+    generation_type: Optional[GenerationType] = 'add_audio'
+    negative_prompt: Optional[str] = Field(
+        None, description='The negative prompt of the audio'
+    )
+    prompt: Optional[str] = Field(None, description='The prompt of the audio')
+
+
+class LumaError(BaseModel):
+    detail: Optional[str] = Field(None, description='The error message')
+
+
+class Type12(str, Enum):
+    generation = 'generation'
+
+
+class LumaGenerationReference(BaseModel):
+    id: UUID = Field(..., description='The ID of the generation')
+    type: Literal['generation']
+
+
+class GenerationType1(str, Enum):
+    video = 'video'
+
+
+class LumaGenerationType(str, Enum):
+    video = 'video'
+    image = 'image'
+
+
+class GenerationType2(str, Enum):
+    image = 'image'
+
+
+class LumaImageIdentity(BaseModel):
+    images: Optional[List[AnyUrl]] = Field(
+        None, description='The URLs of the image identity'
+    )
+
+
+class LumaImageModel(str, Enum):
+    photon_1 = 'photon-1'
+    photon_flash_1 = 'photon-flash-1'
+
+
+class LumaImageRef(BaseModel):
+    url: Optional[AnyUrl] = Field(None, description='The URL of the image reference')
+    weight: Optional[float] = Field(
+        None, description='The weight of the image reference'
+    )
+
+
+class Type13(str, Enum):
+    image = 'image'
+
+
+class LumaImageReference(BaseModel):
+    type: Literal['image']
+    url: AnyUrl = Field(..., description='The URL of the image')
+
+
+class LumaKeyframe(RootModel[Union[LumaGenerationReference, LumaImageReference]]):
+    root: Union[LumaGenerationReference, LumaImageReference] = Field(
+        ...,
+        description='A keyframe can be either a Generation reference, an Image, or a Video',
+        discriminator='type',
+    )
+
+
+class LumaKeyframes(BaseModel):
+    frame0: Optional[LumaKeyframe] = None
+    frame1: Optional[LumaKeyframe] = None
+
+
+class LumaModifyImageRef(BaseModel):
+    url: Optional[AnyUrl] = Field(None, description='The URL of the image reference')
+    weight: Optional[float] = Field(
+        None, description='The weight of the modify image reference'
+    )
+
+
+class LumaState(str, Enum):
+    queued = 'queued'
+    dreaming = 'dreaming'
+    completed = 'completed'
+    failed = 'failed'
+
+
+class GenerationType3(str, Enum):
+    upscale_video = 'upscale_video'
+
+
+class LumaVideoModel(str, Enum):
+    ray_2 = 'ray-2'
+    ray_flash_2 = 'ray-flash-2'
+    ray_1_6 = 'ray-1-6'
+
+
+class LumaVideoModelOutputDuration1(str, Enum):
+    field_5s = '5s'
+    field_9s = '9s'
+
+
+class LumaVideoModelOutputDuration(
+    RootModel[Union[LumaVideoModelOutputDuration1, str]]
+):
+    root: Union[LumaVideoModelOutputDuration1, str]
+
+
+class LumaVideoModelOutputResolution1(str, Enum):
+    field_540p = '540p'
+    field_720p = '720p'
+    field_1080p = '1080p'
+    field_4k = '4k'
+
+
+class LumaVideoModelOutputResolution(
+    RootModel[Union[LumaVideoModelOutputResolution1, str]]
+):
+    root: Union[LumaVideoModelOutputResolution1, str]
+
+
+class MachineStats(BaseModel):
+    cpu_capacity: Optional[str] = Field(None, description='Total CPU on the machine.')
+    disk_capacity: Optional[str] = Field(
+        None, description='Total disk capacity on the machine.'
+    )
+    gpu_type: Optional[str] = Field(
+        None, description='The GPU type. eg. NVIDIA Tesla K80'
+    )
+    initial_cpu: Optional[str] = Field(
+        None, description='Initial CPU available before the job starts.'
+    )
+    initial_disk: Optional[str] = Field(
+        None, description='Initial disk available before the job starts.'
+    )
+    initial_ram: Optional[str] = Field(
+        None, description='Initial RAM available before the job starts.'
+    )
+    machine_name: Optional[str] = Field(None, description='Name of the machine.')
+    memory_capacity: Optional[str] = Field(
+        None, description='Total memory on the machine.'
+    )
+    os_version: Optional[str] = Field(
+        None, description='The operating system version. eg. Ubuntu Linux 20.04'
+    )
+    pip_freeze: Optional[str] = Field(None, description='The pip freeze output')
+    vram_time_series: Optional[Dict[str, Any]] = Field(
+        None, description='Time series of VRAM usage.'
+    )
+
+
+class MinimaxBaseResponse(BaseModel):
+    status_code: int = Field(
+        ...,
+        description='Status code. 0 indicates success, other values indicate errors.',
+    )
+    status_msg: str = Field(
+        ..., description='Specific error details or success message.'
+    )
+
+
+class File(BaseModel):
+    bytes: Optional[int] = Field(None, description='File size in bytes')
+    created_at: Optional[int] = Field(
+        None, description='Unix timestamp when the file was created, in seconds'
+    )
+    download_url: Optional[str] = Field(
+        None, description='The URL to download the video'
+    )
+    file_id: Optional[int] = Field(None, description='Unique identifier for the file')
+    filename: Optional[str] = Field(None, description='The name of the file')
+    purpose: Optional[str] = Field(None, description='The purpose of using the file')
+
+
+class MinimaxFileRetrieveResponse(BaseModel):
+    base_resp: MinimaxBaseResponse
+    file: File
+
+
+class Status6(str, Enum):
+    Queueing = 'Queueing'
+    Preparing = 'Preparing'
+    Processing = 'Processing'
+    Success = 'Success'
+    Fail = 'Fail'
+
+
+class MinimaxTaskResultResponse(BaseModel):
+    base_resp: MinimaxBaseResponse
+    file_id: Optional[str] = Field(
+        None,
+        description='After the task status changes to Success, this field returns the file ID corresponding to the generated video.',
+    )
+    status: Status6 = Field(
+        ...,
+        description="Task status: 'Queueing' (in queue), 'Preparing' (task is preparing), 'Processing' (generating), 'Success' (task completed successfully), or 'Fail' (task failed).",
+    )
+    task_id: str = Field(..., description='The task ID being queried.')
+
+
+class MiniMaxModel(str, Enum):
+    T2V_01_Director = 'T2V-01-Director'
+    I2V_01_Director = 'I2V-01-Director'
+    S2V_01 = 'S2V-01'
+    I2V_01 = 'I2V-01'
+    I2V_01_live = 'I2V-01-live'
+    T2V_01 = 'T2V-01'
+    Hailuo_02 = 'MiniMax-Hailuo-02'
+
+
+class SubjectReferenceItem(BaseModel):
+    image: Optional[str] = Field(
+        None, description='URL or base64 encoding of the subject reference image.'
+    )
+    mask: Optional[str] = Field(
+        None,
+        description='URL or base64 encoding of the mask for the subject reference image.',
+    )
+
+
+class MinimaxVideoGenerationRequest(BaseModel):
+    callback_url: Optional[str] = Field(
+        None,
+        description='Optional. URL to receive real-time status updates about the video generation task.',
+    )
+    first_frame_image: Optional[str] = Field(
+        None,
+        description='URL or base64 encoding of the first frame image. Required when model is I2V-01, I2V-01-Director, or I2V-01-live.',
+    )
+    model: MiniMaxModel = Field(
+        ...,
+        description='Required. ID of model. Options: T2V-01-Director, I2V-01-Director, S2V-01, I2V-01, I2V-01-live, T2V-01',
+    )
+    prompt: Optional[str] = Field(
+        None,
+        description='Description of the video. Should be less than 2000 characters. Supports camera movement instructions in [brackets].',
+        max_length=2000,
+    )
+    prompt_optimizer: Optional[bool] = Field(
+        True,
+        description='If true (default), the model will automatically optimize the prompt. Set to false for more precise control.',
+    )
+    subject_reference: Optional[List[SubjectReferenceItem]] = Field(
+        None,
+        description='Only available when model is S2V-01. The model will generate a video based on the subject uploaded through this parameter.',
+    )
+    duration: Optional[int] = Field(
+        None,
+        description="The length of the output video in seconds."
+    )
+    resolution: Optional[str] = Field(
+        None,
+        description="The dimensions of the video display. 1080p corresponds to 1920 x 1080 pixels, 768p corresponds to 1366 x 768 pixels."
+    )
+
+
+class MinimaxVideoGenerationResponse(BaseModel):
+    base_resp: MinimaxBaseResponse
+    task_id: str = Field(
+        ..., description='The task ID for the asynchronous video generation task.'
+    )
+
+
+class Modality(str, Enum):
+    MODALITY_UNSPECIFIED = 'MODALITY_UNSPECIFIED'
+    TEXT = 'TEXT'
+    IMAGE = 'IMAGE'
+    VIDEO = 'VIDEO'
+    AUDIO = 'AUDIO'
+    DOCUMENT = 'DOCUMENT'
+
+
+class ModalityTokenCount(BaseModel):
+    modality: Optional[Modality] = None
+    tokenCount: Optional[int] = Field(
+        None, description='Number of tokens for the given modality.'
+    )
+
+
+class Truncation(str, Enum):
+    disabled = 'disabled'
+    auto = 'auto'
+
+
+class ModelResponseProperties(BaseModel):
+    instructions: Optional[str] = Field(
+        None, description='Instructions for the model on how to generate the response'
+    )
+    max_output_tokens: Optional[int] = Field(
+        None, description='Maximum number of tokens to generate'
+    )
+    model: Optional[str] = Field(
+        None, description='The model used to generate the response'
+    )
+    temperature: Optional[float] = Field(
+        1, description='Controls randomness in the response', ge=0.0, le=2.0
+    )
+    top_p: Optional[float] = Field(
+        1,
+        description='Controls diversity of the response via nucleus sampling',
+        ge=0.0,
+        le=1.0,
+    )
+    truncation: Optional[Truncation] = Field(
+        'disabled', description='How to handle truncation of the response'
+    )
+
+
+class Keyframes(BaseModel):
+    image_url: Optional[str] = None
+
+
+class MoonvalleyPromptResponse(BaseModel):
+    error: Optional[Dict[str, Any]] = None
+    frame_conditioning: Optional[Dict[str, Any]] = None
+    id: Optional[str] = None
+    inference_params: Optional[Dict[str, Any]] = None
+    meta: Optional[Dict[str, Any]] = None
+    model_params: Optional[Dict[str, Any]] = None
+    output_url: Optional[str] = None
+    prompt_text: Optional[str] = None
+    status: Optional[str] = None
+
+
+class MoonvalleyTextToVideoInferenceParams(BaseModel):
+    add_quality_guidance: Optional[bool] = Field(
+        True, description='Whether to add quality guidance'
+    )
+    caching_coefficient: Optional[float] = Field(
+        0.3, description='Caching coefficient for optimization'
+    )
+    caching_cooldown: Optional[int] = Field(
+        3, description='Number of caching cooldown steps'
+    )
+    caching_warmup: Optional[int] = Field(
+        3, description='Number of caching warmup steps'
+    )
+    clip_value: Optional[float] = Field(
+        3, description='CLIP value for generation control'
+    )
+    conditioning_frame_index: Optional[int] = Field(
+        0, description='Index of the conditioning frame'
+    )
+    cooldown_steps: Optional[int] = Field(
+        75, description='Number of cooldown steps (calculated based on num_frames)'
+    )
+    fps: Optional[int] = Field(
+        24, description='Frames per second of the generated video'
+    )
+    guidance_scale: Optional[float] = Field(
+        10, description='Guidance scale for generation control'
+    )
+    height: Optional[int] = Field(
+        1080, description='Height of the generated video in pixels'
+    )
+    negative_prompt: Optional[str] = Field(None, description='Negative prompt text')
+    num_frames: Optional[int] = Field(64, description='Number of frames to generate')
+    seed: Optional[int] = Field(
+        None, description='Random seed for generation (default: random)'
+    )
+    shift_value: Optional[float] = Field(
+        3, description='Shift value for generation control'
+    )
+    steps: Optional[int] = Field(80, description='Number of denoising steps')
+    use_guidance_schedule: Optional[bool] = Field(
+        True, description='Whether to use guidance scheduling'
+    )
+    use_negative_prompts: Optional[bool] = Field(
+        False, description='Whether to use negative prompts'
+    )
+    use_timestep_transform: Optional[bool] = Field(
+        True, description='Whether to use timestep transformation'
+    )
+    warmup_steps: Optional[int] = Field(
+        0, description='Number of warmup steps (calculated based on num_frames)'
+    )
+    width: Optional[int] = Field(
+        1920, description='Width of the generated video in pixels'
+    )
+
+
+class MoonvalleyTextToVideoRequest(BaseModel):
+    image_url: Optional[str] = None
+    inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None
+    prompt_text: Optional[str] = None
+    webhook_url: Optional[str] = None
+
+
+class MoonvalleyUploadFileRequest(BaseModel):
+    file: Optional[StrictBytes] = None
+
+
+class MoonvalleyUploadFileResponse(BaseModel):
+    access_url: Optional[str] = None
+
+
+class MoonvalleyVideoToVideoInferenceParams(BaseModel):
+    add_quality_guidance: Optional[bool] = Field(
+        True, description='Whether to add quality guidance'
+    )
+    caching_coefficient: Optional[float] = Field(
+        0.3, description='Caching coefficient for optimization'
+    )
+    caching_cooldown: Optional[int] = Field(
+        3, description='Number of caching cooldown steps'
+    )
+    caching_warmup: Optional[int] = Field(
+        3, description='Number of caching warmup steps'
+    )
+    clip_value: Optional[float] = Field(
+        3, description='CLIP value for generation control'
+    )
+    conditioning_frame_index: Optional[int] = Field(
+        0, description='Index of the conditioning frame'
+    )
+    cooldown_steps: Optional[int] = Field(
+        36, description='Number of cooldown steps (calculated based on num_frames)'
+    )
+    guidance_scale: Optional[float] = Field(
+        15, description='Guidance scale for generation control'
+    )
+    negative_prompt: Optional[str] = Field(None, description='Negative prompt text')
+    seed: Optional[int] = Field(
+        None, description='Random seed for generation (default: random)'
+    )
+    shift_value: Optional[float] = Field(
+        3, description='Shift value for generation control'
+    )
+    steps: Optional[int] = Field(80, description='Number of denoising steps')
+    use_guidance_schedule: Optional[bool] = Field(
+        True, description='Whether to use guidance scheduling'
+    )
+    use_negative_prompts: Optional[bool] = Field(
+        False, description='Whether to use negative prompts'
+    )
+    use_timestep_transform: Optional[bool] = Field(
+        True, description='Whether to use timestep transformation'
+    )
+    warmup_steps: Optional[int] = Field(
+        24, description='Number of warmup steps (calculated based on num_frames)'
+    )
+
+
+class ControlType(str, Enum):
+    motion_control = 'motion_control'
+    pose_control = 'pose_control'
+
+
+class MoonvalleyVideoToVideoRequest(BaseModel):
+    control_type: ControlType = Field(
+        ..., description='Supported types for video control'
+    )
+    inference_params: Optional[MoonvalleyVideoToVideoInferenceParams] = None
+    prompt_text: str = Field(..., description='Describes the video to generate')
+    video_url: str = Field(..., description='Url to control video')
+    webhook_url: Optional[str] = Field(
+        None, description='Optional webhook URL for notifications'
+    )
+
+
+class NodeStatus(str, Enum):
+    NodeStatusActive = 'NodeStatusActive'
+    NodeStatusDeleted = 'NodeStatusDeleted'
+    NodeStatusBanned = 'NodeStatusBanned'
+
+
+class NodeVersionIdentifier(BaseModel):
+    node_id: str = Field(..., description='The unique identifier of the node')
+    version: str = Field(..., description='The version of the node')
+
+
+class NodeVersionStatus(str, Enum):
+    NodeVersionStatusActive = 'NodeVersionStatusActive'
+    NodeVersionStatusDeleted = 'NodeVersionStatusDeleted'
+    NodeVersionStatusBanned = 'NodeVersionStatusBanned'
+    NodeVersionStatusPending = 'NodeVersionStatusPending'
+    NodeVersionStatusFlagged = 'NodeVersionStatusFlagged'
+
+
+class NodeVersionUpdateRequest(BaseModel):
+    changelog: Optional[str] = Field(
+        None, description='The changelog describing the version changes.'
+    )
+    deprecated: Optional[bool] = Field(
+        None, description='Whether the version is deprecated.'
+    )
+
+
+class Moderation(str, Enum):
+    low = 'low'
+    auto = 'auto'
+
+
+class OutputFormat1(str, Enum):
+    png = 'png'
+    webp = 'webp'
+    jpeg = 'jpeg'
+
+
+class OpenAIImageEditRequest(BaseModel):
+    background: Optional[str] = Field(
+        None, description='Background transparency', examples=['opaque']
+    )
+    model: str = Field(
+        ..., description='The model to use for image editing', examples=['gpt-image-1']
+    )
+    moderation: Optional[Moderation] = Field(
+        None, description='Content moderation setting', examples=['auto']
+    )
+    n: Optional[int] = Field(
+        None, description='The number of images to generate', examples=[1]
+    )
+    output_compression: Optional[int] = Field(
+        None, description='Compression level for JPEG or WebP (0-100)', examples=[100]
+    )
+    output_format: Optional[OutputFormat1] = Field(
+        None, description='Format of the output image', examples=['png']
+    )
+    prompt: str = Field(
+        ...,
+        description='A text description of the desired edit',
+        examples=['Give the rocketship rainbow coloring'],
+    )
+    quality: Optional[str] = Field(
+        None, description='The quality of the edited image', examples=['low']
+    )
+    size: Optional[str] = Field(
+        None, description='Size of the output image', examples=['1024x1024']
+    )
+    user: Optional[str] = Field(
+        None,
+        description='A unique identifier for end-user monitoring',
+        examples=['user-1234'],
+    )
+
+
+class Background(str, Enum):
+    transparent = 'transparent'
+    opaque = 'opaque'
+
+
+class Quality(str, Enum):
+    low = 'low'
+    medium = 'medium'
+    high = 'high'
+    standard = 'standard'
+    hd = 'hd'
+
+
+class ResponseFormat(str, Enum):
+    url = 'url'
+    b64_json = 'b64_json'
+
+
+class Style(str, Enum):
+    vivid = 'vivid'
+    natural = 'natural'
+
+
+class OpenAIImageGenerationRequest(BaseModel):
+    background: Optional[Background] = Field(
+        None, description='Background transparency', examples=['opaque']
+    )
+    model: Optional[str] = Field(
+        None, description='The model to use for image generation', examples=['dall-e-3']
+    )
+    moderation: Optional[Moderation] = Field(
+        None, description='Content moderation setting', examples=['auto']
+    )
+    n: Optional[int] = Field(
+        None,
+        description='The number of images to generate (1-10). Only 1 supported for dall-e-3.',
+        examples=[1],
+    )
+    output_compression: Optional[int] = Field(
+        None, description='Compression level for JPEG or WebP (0-100)', examples=[100]
+    )
+    output_format: Optional[OutputFormat1] = Field(
+        None, description='Format of the output image', examples=['png']
+    )
+    prompt: str = Field(
+        ...,
+        description='A text description of the desired image',
+        examples=['Draw a rocket in front of a blackhole in deep space'],
+    )
+    quality: Optional[Quality] = Field(
+        None, description='The quality of the generated image', examples=['high']
+    )
+    response_format: Optional[ResponseFormat] = Field(
+        None, description='Response format of image data', examples=['b64_json']
+    )
+    size: Optional[str] = Field(
+        None,
+        description='Size of the image (e.g., 1024x1024, 1536x1024, auto)',
+        examples=['1024x1536'],
+    )
+    style: Optional[Style] = Field(
+        None, description='Style of the image (only for dall-e-3)', examples=['vivid']
+    )
+    user: Optional[str] = Field(
+        None,
+        description='A unique identifier for end-user monitoring',
+        examples=['user-1234'],
+    )
+
+
+class Datum2(BaseModel):
+    b64_json: Optional[str] = Field(None, description='Base64 encoded image data')
+    revised_prompt: Optional[str] = Field(None, description='Revised prompt')
+    url: Optional[str] = Field(None, description='URL of the image')
+
+
+class InputTokensDetails(BaseModel):
+    image_tokens: Optional[int] = None
+    text_tokens: Optional[int] = None
+
+
+class Usage(BaseModel):
+    input_tokens: Optional[int] = None
+    input_tokens_details: Optional[InputTokensDetails] = None
+    output_tokens: Optional[int] = None
+    total_tokens: Optional[int] = None
+
+
+class OpenAIImageGenerationResponse(BaseModel):
+    data: Optional[List[Datum2]] = None
+    usage: Optional[Usage] = None
+
+
+class OpenAIModels(str, Enum):
+    gpt_4 = 'gpt-4'
+    gpt_4_0314 = 'gpt-4-0314'
+    gpt_4_0613 = 'gpt-4-0613'
+    gpt_4_32k = 'gpt-4-32k'
+    gpt_4_32k_0314 = 'gpt-4-32k-0314'
+    gpt_4_32k_0613 = 'gpt-4-32k-0613'
+    gpt_4_0125_preview = 'gpt-4-0125-preview'
+    gpt_4_turbo = 'gpt-4-turbo'
+    gpt_4_turbo_2024_04_09 = 'gpt-4-turbo-2024-04-09'
+    gpt_4_turbo_preview = 'gpt-4-turbo-preview'
+    gpt_4_1106_preview = 'gpt-4-1106-preview'
+    gpt_4_vision_preview = 'gpt-4-vision-preview'
+    gpt_3_5_turbo = 'gpt-3.5-turbo'
+    gpt_3_5_turbo_16k = 'gpt-3.5-turbo-16k'
+    gpt_3_5_turbo_0301 = 'gpt-3.5-turbo-0301'
+    gpt_3_5_turbo_0613 = 'gpt-3.5-turbo-0613'
+    gpt_3_5_turbo_1106 = 'gpt-3.5-turbo-1106'
+    gpt_3_5_turbo_0125 = 'gpt-3.5-turbo-0125'
+    gpt_3_5_turbo_16k_0613 = 'gpt-3.5-turbo-16k-0613'
+    gpt_4_1 = 'gpt-4.1'
+    gpt_4_1_mini = 'gpt-4.1-mini'
+    gpt_4_1_nano = 'gpt-4.1-nano'
+    gpt_4_1_2025_04_14 = 'gpt-4.1-2025-04-14'
+    gpt_4_1_mini_2025_04_14 = 'gpt-4.1-mini-2025-04-14'
+    gpt_4_1_nano_2025_04_14 = 'gpt-4.1-nano-2025-04-14'
+    o1 = 'o1'
+    o1_mini = 'o1-mini'
+    o1_preview = 'o1-preview'
+    o1_pro = 'o1-pro'
+    o1_2024_12_17 = 'o1-2024-12-17'
+    o1_preview_2024_09_12 = 'o1-preview-2024-09-12'
+    o1_mini_2024_09_12 = 'o1-mini-2024-09-12'
+    o1_pro_2025_03_19 = 'o1-pro-2025-03-19'
+    o3 = 'o3'
+    o3_mini = 'o3-mini'
+    o3_2025_04_16 = 'o3-2025-04-16'
+    o3_mini_2025_01_31 = 'o3-mini-2025-01-31'
+    o4_mini = 'o4-mini'
+    o4_mini_2025_04_16 = 'o4-mini-2025-04-16'
+    gpt_4o = 'gpt-4o'
+    gpt_4o_mini = 'gpt-4o-mini'
+    gpt_4o_2024_11_20 = 'gpt-4o-2024-11-20'
+    gpt_4o_2024_08_06 = 'gpt-4o-2024-08-06'
+    gpt_4o_2024_05_13 = 'gpt-4o-2024-05-13'
+    gpt_4o_mini_2024_07_18 = 'gpt-4o-mini-2024-07-18'
+    gpt_4o_audio_preview = 'gpt-4o-audio-preview'
+    gpt_4o_audio_preview_2024_10_01 = 'gpt-4o-audio-preview-2024-10-01'
+    gpt_4o_audio_preview_2024_12_17 = 'gpt-4o-audio-preview-2024-12-17'
+    gpt_4o_mini_audio_preview = 'gpt-4o-mini-audio-preview'
+    gpt_4o_mini_audio_preview_2024_12_17 = 'gpt-4o-mini-audio-preview-2024-12-17'
+    gpt_4o_search_preview = 'gpt-4o-search-preview'
+    gpt_4o_mini_search_preview = 'gpt-4o-mini-search-preview'
+    gpt_4o_search_preview_2025_03_11 = 'gpt-4o-search-preview-2025-03-11'
+    gpt_4o_mini_search_preview_2025_03_11 = 'gpt-4o-mini-search-preview-2025-03-11'
+    computer_use_preview = 'computer-use-preview'
+    computer_use_preview_2025_03_11 = 'computer-use-preview-2025-03-11'
+    chatgpt_4o_latest = 'chatgpt-4o-latest'
+
+
+class Reason(str, Enum):
+    max_output_tokens = 'max_output_tokens'
+    content_filter = 'content_filter'
+
+
+class IncompleteDetails(BaseModel):
+    reason: Optional[Reason] = Field(
+        None, description='The reason why the response is incomplete.'
+    )
+
+
+class Object(str, Enum):
+    response = 'response'
+
+
+class Status7(str, Enum):
+    completed = 'completed'
+    failed = 'failed'
+    in_progress = 'in_progress'
+    incomplete = 'incomplete'
+
+
+class Type14(str, Enum):
+    output_audio = 'output_audio'
+
+
+class OutputAudioContent(BaseModel):
+    data: str = Field(..., description='Base64-encoded audio data')
+    transcript: str = Field(..., description='Transcript of the audio')
+    type: Type14 = Field(..., description='The type of output content')
+
+
+class Role4(str, Enum):
+    assistant = 'assistant'
+
+
+class Type15(str, Enum):
+    message = 'message'
+
+
+class Type16(str, Enum):
+    output_text = 'output_text'
+
+
+class OutputTextContent(BaseModel):
+    text: str = Field(..., description='The text content')
+    type: Type16 = Field(..., description='The type of output content')
+
+
+class PersonalAccessToken(BaseModel):
+    createdAt: Optional[datetime] = Field(
+        None, description='[Output Only]The date and time the token was created.'
+    )
+    description: Optional[str] = Field(
+        None,
+        description="Optional. A more detailed description of the token's intended use.",
+    )
+    id: Optional[UUID] = Field(None, description='Unique identifier for the GitCommit')
+    name: Optional[str] = Field(
+        None,
+        description='Required. The name of the token. Can be a simple description.',
+    )
+    token: Optional[str] = Field(
+        None,
+        description='[Output Only]. The personal access token. Only returned during creation.',
+    )
+
+
+class AspectRatio1(RootModel[float]):
+    root: float = Field(
+        ...,
+        description='Aspect ratio (width / height)',
+        ge=0.4,
+        le=2.5,
+        title='Aspectratio',
+    )
+
+
+class IngredientsMode(str, Enum):
+    creative = 'creative'
+    precise = 'precise'
+
+
+class PikaBodyGenerate22C2vGenerate22PikascenesPost(BaseModel):
+    aspectRatio: Optional[AspectRatio1] = Field(
+        None, description='Aspect ratio (width / height)', title='Aspectratio'
+    )
+    duration: Optional[int] = Field(5, title='Duration')
+    images: Optional[List[StrictBytes]] = Field(None, title='Images')
+    ingredientsMode: IngredientsMode = Field(..., title='Ingredientsmode')
+    negativePrompt: Optional[str] = Field(None, title='Negativeprompt')
+    promptText: Optional[str] = Field(None, title='Prompttext')
+    resolution: Optional[str] = Field('1080p', title='Resolution')
+    seed: Optional[int] = Field(None, title='Seed')
+
+
+class PikaBodyGeneratePikadditionsGeneratePikadditionsPost(BaseModel):
+    image: Optional[StrictBytes] = Field(None, title='Image')
+    negativePrompt: Optional[str] = Field(None, title='Negativeprompt')
+    promptText: Optional[str] = Field(None, title='Prompttext')
+    seed: Optional[int] = Field(None, title='Seed')
+    video: Optional[StrictBytes] = Field(None, title='Video')
+
+
+class PikaBodyGeneratePikaswapsGeneratePikaswapsPost(BaseModel):
+    image: Optional[StrictBytes] = Field(None, title='Image')
+    modifyRegionMask: Optional[StrictBytes] = Field(
+        None,
+        description='A mask image that specifies the region to modify, where the mask is white and the background is black',
+        title='Modifyregionmask',
+    )
+    modifyRegionRoi: Optional[str] = Field(
+        None,
+        description='Plaintext description of the object / region to modify',
+        title='Modifyregionroi',
+    )
+    negativePrompt: Optional[str] = Field(None, title='Negativeprompt')
+    promptText: Optional[str] = Field(None, title='Prompttext')
+    seed: Optional[int] = Field(None, title='Seed')
+    video: Optional[StrictBytes] = Field(None, title='Video')
+
+
+class PikaDurationEnum(int, Enum):
+    integer_5 = 5
+    integer_10 = 10
+
+
+class PikaGenerateResponse(BaseModel):
+    video_id: str = Field(..., title='Video Id')
+
+
+class PikaResolutionEnum(str, Enum):
+    field_1080p = '1080p'
+    field_720p = '720p'
+
+
+class PikaStatusEnum(str, Enum):
+    queued = 'queued'
+    started = 'started'
+    finished = 'finished'
+
+
+class PikaValidationError(BaseModel):
+    loc: List[Union[str, int]] = Field(..., title='Location')
+    msg: str = Field(..., title='Message')
+    type: str = Field(..., title='Error Type')
+
+
+class PikaVideoResponse(BaseModel):
+    id: str = Field(..., title='Id')
+    progress: Optional[int] = Field(None, title='Progress')
+    status: PikaStatusEnum
+    url: Optional[str] = Field(None, title='Url')
+
+
+class Pikaffect(str, Enum):
+    Cake_ify = 'Cake-ify'
+    Crumble = 'Crumble'
+    Crush = 'Crush'
+    Decapitate = 'Decapitate'
+    Deflate = 'Deflate'
+    Dissolve = 'Dissolve'
+    Explode = 'Explode'
+    Eye_pop = 'Eye-pop'
+    Inflate = 'Inflate'
+    Levitate = 'Levitate'
+    Melt = 'Melt'
+    Peel = 'Peel'
+    Poke = 'Poke'
+    Squish = 'Squish'
+    Ta_da = 'Ta-da'
+    Tear = 'Tear'
+
+
+class Resp(BaseModel):
+    img_id: Optional[int] = None
+
+
+class PixverseImageUploadResponse(BaseModel):
+    ErrCode: Optional[int] = None
+    ErrMsg: Optional[str] = None
+    Resp_1: Optional[Resp] = Field(None, alias='Resp')
+
+
+class Duration(int, Enum):
+    integer_5 = 5
+    integer_8 = 8
+
+
+class Model1(str, Enum):
+    v3_5 = 'v3.5'
+
+
+class MotionMode(str, Enum):
+    normal = 'normal'
+    fast = 'fast'
+
+
+class Quality1(str, Enum):
+    field_360p = '360p'
+    field_540p = '540p'
+    field_720p = '720p'
+    field_1080p = '1080p'
+
+
+class Style1(str, Enum):
+    anime = 'anime'
+    field_3d_animation = '3d_animation'
+    clay = 'clay'
+    comic = 'comic'
+    cyberpunk = 'cyberpunk'
+
+
+class PixverseImageVideoRequest(BaseModel):
+    duration: Duration
+    img_id: int
+    model: Model1
+    motion_mode: Optional[MotionMode] = None
+    prompt: str
+    quality: Quality1
+    seed: Optional[int] = None
+    style: Optional[Style1] = None
+    template_id: Optional[int] = None
+    water_mark: Optional[bool] = None
+
+
+class AspectRatio2(str, Enum):
+    field_16_9 = '16:9'
+    field_4_3 = '4:3'
+    field_1_1 = '1:1'
+    field_3_4 = '3:4'
+    field_9_16 = '9:16'
+
+
+class PixverseTextVideoRequest(BaseModel):
+    aspect_ratio: AspectRatio2
+    duration: Duration
+    model: Model1
+    motion_mode: Optional[MotionMode] = None
+    negative_prompt: Optional[str] = None
+    prompt: str
+    quality: Quality1
+    seed: Optional[int] = None
+    style: Optional[Style1] = None
+    template_id: Optional[int] = None
+    water_mark: Optional[bool] = None
+
+
+class PixverseTransitionVideoRequest(BaseModel):
+    duration: Duration
+    first_frame_img: int
+    last_frame_img: int
+    model: Model1
+    motion_mode: MotionMode
+    prompt: str
+    quality: Quality1
+    seed: int
+    style: Optional[Style1] = None
+    template_id: Optional[int] = None
+    water_mark: Optional[bool] = None
+
+
+class Resp1(BaseModel):
+    video_id: Optional[int] = None
+
+
+class PixverseVideoResponse(BaseModel):
+    ErrCode: Optional[int] = None
+    ErrMsg: Optional[str] = None
+    Resp: Optional[Resp1] = None
+
+
+class Status8(int, Enum):
+    integer_1 = 1
+    integer_5 = 5
+    integer_6 = 6
+    integer_7 = 7
+    integer_8 = 8
+
+
+class Resp2(BaseModel):
+    create_time: Optional[str] = None
+    id: Optional[int] = None
+    modify_time: Optional[str] = None
+    negative_prompt: Optional[str] = None
+    outputHeight: Optional[int] = None
+    outputWidth: Optional[int] = None
+    prompt: Optional[str] = None
+    resolution_ratio: Optional[int] = None
+    seed: Optional[int] = None
+    size: Optional[int] = None
+    status: Optional[Status8] = Field(
+        None,
+        description='Video generation status codes:\n* 1 - Generation successful\n* 5 - Generating\n* 6 - Deleted\n* 7 - Contents moderation failed\n* 8 - Generation failed\n',
+    )
+    style: Optional[str] = None
+    url: Optional[str] = None
+
+
+class PixverseVideoResultResponse(BaseModel):
+    ErrCode: Optional[int] = None
+    ErrMsg: Optional[str] = None
+    Resp: Optional[Resp2] = None
+
+
+class PublisherStatus(str, Enum):
+    PublisherStatusActive = 'PublisherStatusActive'
+    PublisherStatusBanned = 'PublisherStatusBanned'
+
+
+class PublisherUser(BaseModel):
+    email: Optional[str] = Field(None, description='The email address for this user.')
+    id: Optional[str] = Field(None, description='The unique id for this user.')
+    name: Optional[str] = Field(None, description='The name for this user.')
+
+
+class RgbItem(RootModel[int]):
+    root: int = Field(..., ge=0, le=255)
+
+
+class RGBColor(BaseModel):
+    rgb: List[RgbItem] = Field(..., max_length=3, min_length=3)
+
+
+class GenerateSummary(str, Enum):
+    auto = 'auto'
+    concise = 'concise'
+    detailed = 'detailed'
+
+
+class Summary(str, Enum):
+    auto = 'auto'
+    concise = 'concise'
+    detailed = 'detailed'
+
+
+class ReasoningEffort(str, Enum):
+    low = 'low'
+    medium = 'medium'
+    high = 'high'
+
+
+class Status9(str, Enum):
+    in_progress = 'in_progress'
+    completed = 'completed'
+    incomplete = 'incomplete'
+
+
+class Type17(str, Enum):
+    summary_text = 'summary_text'
+
+
+class SummaryItem(BaseModel):
+    text: str = Field(
+        ...,
+        description='A short summary of the reasoning used by the model when generating\nthe response.\n',
+    )
+    type: Type17 = Field(
+        ..., description='The type of the object. Always `summary_text`.\n'
+    )
+
+
+class Type18(str, Enum):
+    reasoning = 'reasoning'
+
+
+class ReasoningItem(BaseModel):
+    id: str = Field(
+        ..., description='The unique identifier of the reasoning content.\n'
+    )
+    status: Optional[Status9] = Field(
+        None,
+        description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n',
+    )
+    summary: List[SummaryItem] = Field(..., description='Reasoning text contents.\n')
+    type: Type18 = Field(
+        ..., description='The type of the object. Always `reasoning`.\n'
+    )
+
+
+class RecraftImageColor(BaseModel):
+    rgb: Optional[List[int]] = None
+    std: Optional[List[float]] = None
+    weight: Optional[float] = None
+
+
+class RecraftImageFeatures(BaseModel):
+    nsfw_score: Optional[float] = None
+
+
+class RecraftImageFormat(str, Enum):
+    webp = 'webp'
+    png = 'png'
+
+
+class Controls(BaseModel):
+    artistic_level: Optional[int] = Field(
+        None,
+        description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity.',
+        ge=0,
+        le=5,
+    )
+    background_color: Optional[RGBColor] = None
+    colors: Optional[List[RGBColor]] = Field(
+        None, description='An array of preferable colors'
+    )
+    no_text: Optional[bool] = Field(None, description='Do not embed text layouts')
+
+
+class RecraftImageGenerationRequest(BaseModel):
+    controls: Optional[Controls] = Field(
+        None, description='The controls for the generated image'
+    )
+    model: str = Field(
+        ..., description='The model to use for generation (e.g., "recraftv3")'
+    )
+    n: int = Field(..., description='The number of images to generate', ge=1, le=4)
+    prompt: str = Field(
+        ..., description='The text prompt describing the image to generate'
+    )
+    size: str = Field(
+        ..., description='The size of the generated image (e.g., "1024x1024")'
+    )
+    style: Optional[str] = Field(
+        None,
+        description='The style to apply to the generated image (e.g., "digital_illustration")',
+    )
+    style_id: Optional[str] = Field(
+        None,
+        description='The style ID to apply to the generated image (e.g., "123e4567-e89b-12d3-a456-426614174000"). If style_id is provided, style should not be provided.',
+    )
+
+
+class Datum3(BaseModel):
+    image_id: Optional[str] = Field(
+        None, description='Unique identifier for the generated image'
+    )
+    url: Optional[str] = Field(None, description='URL to access the generated image')
+
+
+class RecraftImageGenerationResponse(BaseModel):
+    created: int = Field(
+        ..., description='Unix timestamp when the generation was created'
+    )
+    credits: int = Field(..., description='Number of credits used for the generation')
+    data: List[Datum3] = Field(..., description='Array of generated image information')
+
+
+class RecraftImageStyle(str, Enum):
+    digital_illustration = 'digital_illustration'
+    icon = 'icon'
+    realistic_image = 'realistic_image'
+    vector_illustration = 'vector_illustration'
+
+
+class RecraftImageSubStyle(str, Enum):
+    field_2d_art_poster = '2d_art_poster'
+    field_3d = '3d'
+    field_80s = '80s'
+    glow = 'glow'
+    grain = 'grain'
+    hand_drawn = 'hand_drawn'
+    infantile_sketch = 'infantile_sketch'
+    kawaii = 'kawaii'
+    pixel_art = 'pixel_art'
+    psychedelic = 'psychedelic'
+    seamless = 'seamless'
+    voxel = 'voxel'
+    watercolor = 'watercolor'
+    broken_line = 'broken_line'
+    colored_outline = 'colored_outline'
+    colored_shapes = 'colored_shapes'
+    colored_shapes_gradient = 'colored_shapes_gradient'
+    doodle_fill = 'doodle_fill'
+    doodle_offset_fill = 'doodle_offset_fill'
+    offset_fill = 'offset_fill'
+    outline = 'outline'
+    outline_gradient = 'outline_gradient'
+    uneven_fill = 'uneven_fill'
+    field_70s = '70s'
+    cartoon = 'cartoon'
+    doodle_line_art = 'doodle_line_art'
+    engraving = 'engraving'
+    flat_2 = 'flat_2'
+    kawaii_1 = 'kawaii'
+    line_art = 'line_art'
+    linocut = 'linocut'
+    seamless_1 = 'seamless'
+    b_and_w = 'b_and_w'
+    enterprise = 'enterprise'
+    hard_flash = 'hard_flash'
+    hdr = 'hdr'
+    motion_blur = 'motion_blur'
+    natural_light = 'natural_light'
+    studio_portrait = 'studio_portrait'
+    line_circuit = 'line_circuit'
+    field_2d_art_poster_2 = '2d_art_poster_2'
+    engraving_color = 'engraving_color'
+    flat_air_art = 'flat_air_art'
+    hand_drawn_outline = 'hand_drawn_outline'
+    handmade_3d = 'handmade_3d'
+    stickers_drawings = 'stickers_drawings'
+    plastic = 'plastic'
+    pictogram = 'pictogram'
+
+
+class RecraftResponseFormat(str, Enum):
+    url = 'url'
+    b64_json = 'b64_json'
+
+
+class RecraftTextLayoutItem(BaseModel):
+    bbox: List[List[float]]
+    text: str
+
+
+class RecraftTransformModel(str, Enum):
+    refm1 = 'refm1'
+    recraft20b = 'recraft20b'
+    recraftv2 = 'recraftv2'
+    recraftv3 = 'recraftv3'
+    flux1_1pro = 'flux1_1pro'
+    flux1dev = 'flux1dev'
+    imagen3 = 'imagen3'
+    hidream_i1_dev = 'hidream_i1_dev'
+
+
+class RecraftUserControls(BaseModel):
+    artistic_level: Optional[int] = None
+    background_color: Optional[RecraftImageColor] = None
+    colors: Optional[List[RecraftImageColor]] = None
+    no_text: Optional[bool] = None
+
+
+class Attention(str, Enum):
+    low = 'low'
+    medium = 'medium'
+    high = 'high'
+
+
+class Project(str, Enum):
+    comfyui = 'comfyui'
+    comfyui_frontend = 'comfyui_frontend'
+    desktop = 'desktop'
+
+
+class ReleaseNote(BaseModel):
+    attention: Attention = Field(
+        ..., description='The attention level for this release'
+    )
+    content: str = Field(
+        ..., description='The content of the release note in markdown format'
+    )
+    id: int = Field(..., description='Unique identifier for the release note')
+    project: Project = Field(
+        ..., description='The project this release note belongs to'
+    )
+    published_at: datetime = Field(
+        ..., description='When the release note was published'
+    )
+    version: str = Field(..., description='The version of the release')
+
+
+class RenderingSpeed(str, Enum):
+    DEFAULT = 'DEFAULT'
+    TURBO = 'TURBO'
+    QUALITY = 'QUALITY'
+
+
+class Type19(str, Enum):
+    response_completed = 'response.completed'
+
+
+class Type20(str, Enum):
+    response_content_part_added = 'response.content_part.added'
+
+
+class Type21(str, Enum):
+    response_content_part_done = 'response.content_part.done'
+
+
+class Type22(str, Enum):
+    response_created = 'response.created'
+
+
+class ResponseErrorCode(str, Enum):
+    server_error = 'server_error'
+    rate_limit_exceeded = 'rate_limit_exceeded'
+    invalid_prompt = 'invalid_prompt'
+    vector_store_timeout = 'vector_store_timeout'
+    invalid_image = 'invalid_image'
+    invalid_image_format = 'invalid_image_format'
+    invalid_base64_image = 'invalid_base64_image'
+    invalid_image_url = 'invalid_image_url'
+    image_too_large = 'image_too_large'
+    image_too_small = 'image_too_small'
+    image_parse_error = 'image_parse_error'
+    image_content_policy_violation = 'image_content_policy_violation'
+    invalid_image_mode = 'invalid_image_mode'
+    image_file_too_large = 'image_file_too_large'
+    unsupported_image_media_type = 'unsupported_image_media_type'
+    empty_image_file = 'empty_image_file'
+    failed_to_download_image = 'failed_to_download_image'
+    image_file_not_found = 'image_file_not_found'
+
+
+class Type23(str, Enum):
+    error = 'error'
+
+
+class ResponseErrorEvent(BaseModel):
+    code: str = Field(..., description='The error code.\n')
+    message: str = Field(..., description='The error message.\n')
+    param: str = Field(..., description='The error parameter.\n')
+    type: Type23 = Field(..., description='The type of the event. Always `error`.\n')
+
+
+class Type24(str, Enum):
+    response_failed = 'response.failed'
+
+
+class Type25(str, Enum):
+    json_object = 'json_object'
+
+
+class ResponseFormatJsonObject(BaseModel):
+    type: Type25 = Field(
+        ...,
+        description='The type of response format being defined. Always `json_object`.',
+    )
+
+
+class ResponseFormatJsonSchemaSchema(BaseModel):
+    pass
+    model_config = ConfigDict(
+        extra='allow',
+    )
+
+
+class Type26(str, Enum):
+    text = 'text'
+
+
+class ResponseFormatText(BaseModel):
+    type: Type26 = Field(
+        ..., description='The type of response format being defined. Always `text`.'
+    )
+
+
+class Type27(str, Enum):
+    response_in_progress = 'response.in_progress'
+
+
+class Type28(str, Enum):
+    response_incomplete = 'response.incomplete'
+
+
+class Type29(str, Enum):
+    response_output_item_added = 'response.output_item.added'
+
+
+class Type30(str, Enum):
+    response_output_item_done = 'response.output_item.done'
+
+
+class Truncation1(str, Enum):
+    auto = 'auto'
+    disabled = 'disabled'
+
+
+class InputTokensDetails1(BaseModel):
+    cached_tokens: int = Field(
+        ...,
+        description='The number of tokens that were retrieved from the cache. \n[More on prompt caching](/docs/guides/prompt-caching).\n',
+    )
+
+
+class OutputTokensDetails(BaseModel):
+    reasoning_tokens: int = Field(..., description='The number of reasoning tokens.')
+
+
+class ResponseUsage(BaseModel):
+    input_tokens: int = Field(..., description='The number of input tokens.')
+    input_tokens_details: InputTokensDetails1 = Field(
+        ..., description='A detailed breakdown of the input tokens.'
+    )
+    output_tokens: int = Field(..., description='The number of output tokens.')
+    output_tokens_details: OutputTokensDetails = Field(
+        ..., description='A detailed breakdown of the output tokens.'
+    )
+    total_tokens: int = Field(..., description='The total number of tokens used.')
+
+
+class Rodin3DCheckStatusRequest(BaseModel):
+    subscription_key: str = Field(
+        ..., description='subscription from generate endpoint'
+    )
+
+
+class Rodin3DDownloadRequest(BaseModel):
+    task_uuid: str = Field(..., description='Task UUID')
+
+
+class RodinGenerateJobsData(BaseModel):
+    subscription_key: Optional[str] = Field(None, description='Subscription Key.')
+    uuids: Optional[List[str]] = Field(None, description='subjobs uuid.')
+
+
+class RodinMaterialType(str, Enum):
+    PBR = 'PBR'
+    Shaded = 'Shaded'
+
+
+class RodinMeshModeType(str, Enum):
+    Quad = 'Quad'
+    Raw = 'Raw'
+
+
+class RodinQualityType(str, Enum):
+    extra_low = 'extra-low'
+    low = 'low'
+    medium = 'medium'
+    high = 'high'
+
+
+class RodinResourceItem(BaseModel):
+    name: Optional[str] = Field(None, description='File name')
+    url: Optional[str] = Field(None, description='Download url')
+
+
+class RodinStatusOptions(str, Enum):
+    Done = 'Done'
+    Failed = 'Failed'
+    Generating = 'Generating'
+    Waiting = 'Waiting'
+
+
+class RodinTierType(str, Enum):
+    Regular = 'Regular'
+    Sketch = 'Sketch'
+    Detail = 'Detail'
+    Smooth = 'Smooth'
+
+
+class RunwayAspectRatioEnum(str, Enum):
+    field_1280_720 = '1280:720'
+    field_720_1280 = '720:1280'
+    field_1104_832 = '1104:832'
+    field_832_1104 = '832:1104'
+    field_960_960 = '960:960'
+    field_1584_672 = '1584:672'
+    field_1280_768 = '1280:768'
+    field_768_1280 = '768:1280'
+
+
+class RunwayDurationEnum(int, Enum):
+    integer_5 = 5
+    integer_10 = 10
+
+
+class RunwayImageToVideoResponse(BaseModel):
+    id: Optional[str] = Field(None, description='Task ID')
+
+
+class RunwayModelEnum(str, Enum):
+    gen4_turbo = 'gen4_turbo'
+    gen3a_turbo = 'gen3a_turbo'
+
+
+class Position(str, Enum):
+    first = 'first'
+    last = 'last'
+
+
+class RunwayPromptImageDetailedObject(BaseModel):
+    position: Position = Field(
+        ...,
+        description="The position of the image in the output video. 'last' is currently supported for gen3a_turbo only.",
+    )
+    uri: str = Field(
+        ..., description='A HTTPS URL or data URI containing an encoded image.'
+    )
+
+
+class RunwayPromptImageObject(
+    RootModel[Union[str, List[RunwayPromptImageDetailedObject]]]
+):
+    root: Union[str, List[RunwayPromptImageDetailedObject]] = Field(
+        ...,
+        description='Image(s) to use for the video generation. Can be a single URI or an array of image objects with positions.',
+    )
+
+
+class RunwayTaskStatusEnum(str, Enum):
+    SUCCEEDED = 'SUCCEEDED'
+    RUNNING = 'RUNNING'
+    FAILED = 'FAILED'
+    PENDING = 'PENDING'
+    CANCELLED = 'CANCELLED'
+    THROTTLED = 'THROTTLED'
+
+
+class RunwayTaskStatusResponse(BaseModel):
+    createdAt: datetime = Field(..., description='Task creation timestamp')
+    id: str = Field(..., description='Task ID')
+    output: Optional[List[str]] = Field(None, description='Array of output video URLs')
+    progress: Optional[float] = Field(
+        None,
+        description='Float value between 0 and 1 representing the progress of the task. Only available if status is RUNNING.',
+        ge=0.0,
+        le=1.0,
+    )
+    status: RunwayTaskStatusEnum
+
+
+class RunwayTextToImageAspectRatioEnum(str, Enum):
+    field_1920_1080 = '1920:1080'
+    field_1080_1920 = '1080:1920'
+    field_1024_1024 = '1024:1024'
+    field_1360_768 = '1360:768'
+    field_1080_1080 = '1080:1080'
+    field_1168_880 = '1168:880'
+    field_1440_1080 = '1440:1080'
+    field_1080_1440 = '1080:1440'
+    field_1808_768 = '1808:768'
+    field_2112_912 = '2112:912'
+
+
+class Model4(str, Enum):
+    gen4_image = 'gen4_image'
+
+
+class ReferenceImage(BaseModel):
+    uri: Optional[str] = Field(
+        None, description='A HTTPS URL or data URI containing an encoded image'
+    )
+
+
+class RunwayTextToImageRequest(BaseModel):
+    model: Model4 = Field(..., description='Model to use for generation')
+    promptText: str = Field(
+        ..., description='Text prompt for the image generation', max_length=1000
+    )
+    ratio: RunwayTextToImageAspectRatioEnum
+    referenceImages: Optional[List[ReferenceImage]] = Field(
+        None, description='Array of reference images to guide the generation'
+    )
+
+
+class RunwayTextToImageResponse(BaseModel):
+    id: Optional[str] = Field(None, description='Task ID')
+
+
+class Name(str, Enum):
+    content_moderation = 'content_moderation'
+
+
+class StabilityContentModerationResponse(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new) you file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: Name = Field(
+        ...,
+        description='Our content moderation system has flagged some part of your request and subsequently denied it.  You were not charged for this request.  While this may at times be frustrating, it is necessary to maintain the integrity of our platform and ensure a safe experience for all users. If you would like to provide feedback, please use the [Support Form](https://kb.stability.ai/knowledge-base/kb-tickets/new).',
+    )
+
+
+class StabilityCreativity(RootModel[float]):
+    root: float = Field(
+        ...,
+        description='Controls the likelihood of creating additional details not heavily conditioned by the init image.',
+        ge=0.2,
+        le=0.5,
+    )
+
+
+class StabilityError(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[[{'some-field': 'is required'}]],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new) you file, as it will greatly assist us in diagnosing the root cause of the problem.\n',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityGenerationID(RootModel[str]):
+    root: str = Field(
+        ...,
+        description='The `id` of a generation, typically used for async generations, that can be used to check the status of the generation or retrieve the result.',
+        examples=['a6dc6c6e20acda010fe14d71f180658f2896ed9b4ec25aa99a6ff06c796987c4'],
+        max_length=64,
+        min_length=64,
+    )
+
+
+class Status10(str, Enum):
+    in_progress = 'in-progress'
+
+
+class StabilityGetResultResponse202(BaseModel):
+    id: Optional[str] = Field(
+        None, description='The ID of the generation result.', examples=[1234567890]
+    )
+    status: Optional[Status10] = None
+
+
+class AspectRatio3(str, Enum):
+    field_21_9 = '21:9'
+    field_16_9 = '16:9'
+    field_3_2 = '3:2'
+    field_5_4 = '5:4'
+    field_1_1 = '1:1'
+    field_4_5 = '4:5'
+    field_2_3 = '2:3'
+    field_9_16 = '9:16'
+    field_9_21 = '9:21'
+
+
+class Mode(str, Enum):
+    text_to_image = 'text-to-image'
+    image_to_image = 'image-to-image'
+
+
+class Model5(str, Enum):
+    sd3_5_large = 'sd3.5-large'
+    sd3_5_large_turbo = 'sd3.5-large-turbo'
+    sd3_5_medium = 'sd3.5-medium'
+
+
+class OutputFormat3(str, Enum):
+    png = 'png'
+    jpeg = 'jpeg'
+
+
+class StylePreset(str, Enum):
+    enhance = 'enhance'
+    anime = 'anime'
+    photographic = 'photographic'
+    digital_art = 'digital-art'
+    comic_book = 'comic-book'
+    fantasy_art = 'fantasy-art'
+    line_art = 'line-art'
+    analog_film = 'analog-film'
+    neon_punk = 'neon-punk'
+    isometric = 'isometric'
+    low_poly = 'low-poly'
+    origami = 'origami'
+    modeling_compound = 'modeling-compound'
+    cinematic = 'cinematic'
+    field_3d_model = '3d-model'
+    pixel_art = 'pixel-art'
+    tile_texture = 'tile-texture'
+
+
+class StabilityImageGenerationSD3Request(BaseModel):
+    aspect_ratio: Optional[AspectRatio3] = Field(
+        '1:1',
+        description='Controls the aspect ratio of the generated image. Defaults to 1:1.\n\n> **Important:** This parameter is only valid for **text-to-image** requests.',
+    )
+    cfg_scale: Optional[float] = Field(
+        None,
+        description='How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt). The _Large_ and _Medium_ models use a default of `4`. The _Turbo_ model uses a default of `1`.',
+        ge=1.0,
+        le=10.0,
+    )
+    image: Optional[StrictBytes] = Field(
+        None,
+        description='The image to use as the starting point for the generation.\n\nSupported formats:\n\n\n\n  - jpeg\n  - png\n  - webp\n\nSupported dimensions:\n\n\n\n  - Every side must be at least 64 pixels\n\n> **Important:** This parameter is only valid for **image-to-image** requests.',
+    )
+    mode: Optional[Mode] = Field(
+        'text-to-image',
+        description='Controls whether this is a text-to-image or image-to-image generation, which affects which parameters are required:\n- **text-to-image** requires only the `prompt` parameter\n- **image-to-image** requires the `prompt`, `image`, and `strength` parameters',
+        title='GenerationMode',
+    )
+    model: Optional[Model5] = Field(
+        'sd3.5-large',
+        description='The model to use for generation.\n\n- `sd3.5-large` requires 6.5 credits per generation\n- `sd3.5-large-turbo` requires 4 credits per generation\n- `sd3.5-medium` requires 3.5 credits per generation\n- As of the April 17, 2025, `sd3-large`, `sd3-large-turbo` and `sd3-medium`\n\n\n\n  are re-routed to their `sd3.5-[model version]` equivalent, at the same price.',
+    )
+    negative_prompt: Optional[str] = Field(
+        None,
+        description='Keywords of what you **do not** wish to see in the output image.\nThis is an advanced feature.',
+        max_length=10000,
+    )
+    output_format: Optional[OutputFormat3] = Field(
+        'png', description='Dictates the `content-type` of the generated image.'
+    )
+    prompt: str = Field(
+        ...,
+        description='What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.',
+        max_length=10000,
+        min_length=1,
+    )
+    seed: Optional[float] = Field(
+        0,
+        description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)",
+        ge=0.0,
+        le=4294967294.0,
+    )
+    strength: Optional[float] = Field(
+        None,
+        description='Sometimes referred to as _denoising_, this parameter controls how much influence the\n`image` parameter has on the generated image.  A value of 0 would yield an image that\nis identical to the input.  A value of 1 would be as if you passed in no image at all.\n\n> **Important:** This parameter is only valid for **image-to-image** requests.',
+        ge=0.0,
+        le=1.0,
+    )
+    style_preset: Optional[StylePreset] = Field(
+        None, description='Guides the image model towards a particular style.'
+    )
+
+
+class FinishReason(str, Enum):
+    SUCCESS = 'SUCCESS'
+    CONTENT_FILTERED = 'CONTENT_FILTERED'
+
+
+class StabilityImageGenrationSD3Response200(BaseModel):
+    finish_reason: FinishReason = Field(
+        ...,
+        description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.',
+        examples=['SUCCESS'],
+    )
+    image: str = Field(
+        ...,
+        description='The generated image, encoded to base64.',
+        examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'],
+    )
+    seed: Optional[float] = Field(
+        0,
+        description='The seed used as random noise for this generation.',
+        examples=[343940597],
+        ge=0.0,
+        le=4294967294.0,
+    )
+
+
+class StabilityImageGenrationSD3Response400(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationSD3Response413(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationSD3Response422(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationSD3Response429(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationSD3Response500(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class OutputFormat4(str, Enum):
+    jpeg = 'jpeg'
+    png = 'png'
+    webp = 'webp'
+
+
+class StabilityImageGenrationUpscaleConservativeRequest(BaseModel):
+    creativity: Optional[StabilityCreativity] = Field(
+        default_factory=lambda: StabilityCreativity.model_validate(0.35)
+    )
+    image: StrictBytes = Field(
+        ...,
+        description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Every side must be at least 64 pixels\n- Total pixel count must be between 4,096 and 9,437,184 pixels\n- The aspect ratio must be between 1:2.5 and 2.5:1',
+        examples=['./some/image.png'],
+    )
+    negative_prompt: Optional[str] = Field(
+        None,
+        description='A blurb of text describing what you **do not** wish to see in the output image.\nThis is an advanced feature.',
+        max_length=10000,
+    )
+    output_format: Optional[OutputFormat4] = Field(
+        'png', description='Dictates the `content-type` of the generated image.'
+    )
+    prompt: str = Field(
+        ...,
+        description="What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.\n\nTo control the weight of a given word use the format `(word:weight)`,\nwhere `word` is the word you'd like to control the weight of and `weight`\nis a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`\nwould convey a sky that was blue and green, but more green than blue.",
+        max_length=10000,
+        min_length=1,
+    )
+    seed: Optional[float] = Field(
+        0,
+        description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)",
+        ge=0.0,
+        le=4294967294.0,
+    )
+
+
+class StabilityImageGenrationUpscaleConservativeResponse200(BaseModel):
+    finish_reason: FinishReason = Field(
+        ...,
+        description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.',
+        examples=['SUCCESS'],
+    )
+    image: str = Field(
+        ...,
+        description='The generated image, encoded to base64.',
+        examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'],
+    )
+    seed: Optional[float] = Field(
+        0,
+        description='The seed used as random noise for this generation.',
+        examples=[343940597],
+        ge=0.0,
+        le=4294967294.0,
+    )
+
+
+class StabilityImageGenrationUpscaleConservativeResponse400(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleConservativeResponse413(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleConservativeResponse422(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleConservativeResponse429(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleConservativeResponse500(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleCreativeRequest(BaseModel):
+    creativity: Optional[float] = Field(
+        0.3,
+        description='Indicates how creative the model should be when upscaling an image.\nHigher values will result in more details being added to the image during upscaling.',
+        ge=0.1,
+        le=0.5,
+    )
+    image: StrictBytes = Field(
+        ...,
+        description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Every side must be at least 64 pixels\n- Total pixel count must be between 4,096 and 1,048,576 pixels',
+        examples=['./some/image.png'],
+    )
+    negative_prompt: Optional[str] = Field(
+        None,
+        description='A blurb of text describing what you **do not** wish to see in the output image.\nThis is an advanced feature.',
+        max_length=10000,
+    )
+    output_format: Optional[OutputFormat4] = Field(
+        'png', description='Dictates the `content-type` of the generated image.'
+    )
+    prompt: str = Field(
+        ...,
+        description="What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.\n\nTo control the weight of a given word use the format `(word:weight)`,\nwhere `word` is the word you'd like to control the weight of and `weight`\nis a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`\nwould convey a sky that was blue and green, but more green than blue.",
+        max_length=10000,
+        min_length=1,
+    )
+    seed: Optional[float] = Field(
+        0,
+        description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)",
+        ge=0.0,
+        le=4294967294.0,
+    )
+    style_preset: Optional[StylePreset] = Field(
+        None, description='Guides the image model towards a particular style.'
+    )
+
+
+class StabilityImageGenrationUpscaleCreativeResponse200(BaseModel):
+    id: StabilityGenerationID
+
+
+class StabilityImageGenrationUpscaleCreativeResponse400(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleCreativeResponse413(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleCreativeResponse422(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleCreativeResponse429(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleCreativeResponse500(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleFastRequest(BaseModel):
+    image: StrictBytes = Field(
+        ...,
+        description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Width must be between 32 and 1,536 pixels\n- Height must be between 32 and 1,536 pixels\n- Total pixel count must be between 1,024 and 1,048,576 pixels',
+        examples=['./some/image.png'],
+    )
+    output_format: Optional[OutputFormat4] = Field(
+        'png', description='Dictates the `content-type` of the generated image.'
+    )
+
+
+class StabilityImageGenrationUpscaleFastResponse200(BaseModel):
+    finish_reason: FinishReason = Field(
+        ...,
+        description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.',
+        examples=['SUCCESS'],
+    )
+    image: str = Field(
+        ...,
+        description='The generated image, encoded to base64.',
+        examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'],
+    )
+    seed: Optional[float] = Field(
+        0,
+        description='The seed used as random noise for this generation.',
+        examples=[343940597],
+        ge=0.0,
+        le=4294967294.0,
+    )
+
+
+class StabilityImageGenrationUpscaleFastResponse400(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleFastResponse413(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleFastResponse422(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleFastResponse429(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityImageGenrationUpscaleFastResponse500(BaseModel):
+    errors: List[str] = Field(
+        ...,
+        description='One or more error messages indicating what went wrong.',
+        examples=[['some-field: is required']],
+        min_length=1,
+    )
+    id: str = Field(
+        ...,
+        description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.',
+        examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'],
+        min_length=1,
+    )
+    name: str = Field(
+        ...,
+        description='Short-hand name for an error, useful for discriminating between errors with the same status code.',
+        examples=['bad_request'],
+        min_length=1,
+    )
+
+
+class StabilityStabilityClientID(RootModel[str]):
+    root: str = Field(
+        ...,
+        description='The name of your application, used to help us communicate app-specific debugging or moderation issues to you.',
+        examples=['my-awesome-app'],
+        max_length=256,
+    )
+
+
+class StabilityStabilityClientUserID(RootModel[str]):
+    root: str = Field(
+        ...,
+        description='A unique identifier for your end user. Used to help us communicate user-specific debugging or moderation issues to you. Feel free to obfuscate this value to protect user privacy.',
+        examples=['DiscordUser#9999'],
+        max_length=256,
+    )
+
+
+class StabilityStabilityClientVersion(RootModel[str]):
+    root: str = Field(
+        ...,
+        description='The version of your application, used to help us communicate version-specific debugging or moderation issues to you.',
+        examples=['1.2.1'],
+        max_length=256,
+    )
+
+
+class StorageFile(BaseModel):
+    file_path: Optional[str] = Field(None, description='Path to the file in storage')
+    id: Optional[UUID] = Field(
+        None, description='Unique identifier for the storage file'
+    )
+    public_url: Optional[str] = Field(None, description='Public URL')
+
+
+class StripeAddress(BaseModel):
+    city: Optional[str] = None
+    country: Optional[str] = None
+    line1: Optional[str] = None
+    line2: Optional[str] = None
+    postal_code: Optional[str] = None
+    state: Optional[str] = None
+
+
+class StripeAmountDetails(BaseModel):
+    tip: Optional[Dict[str, Any]] = None
+
+
+class StripeBillingDetails(BaseModel):
+    address: Optional[StripeAddress] = None
+    email: Optional[str] = None
+    name: Optional[str] = None
+    phone: Optional[str] = None
+    tax_id: Optional[Any] = None
+
+
+class Checks(BaseModel):
+    address_line1_check: Optional[Any] = None
+    address_postal_code_check: Optional[Any] = None
+    cvc_check: Optional[str] = None
+
+
+class ExtendedAuthorization(BaseModel):
+    status: Optional[str] = None
+
+
+class IncrementalAuthorization(BaseModel):
+    status: Optional[str] = None
+
+
+class Multicapture(BaseModel):
+    status: Optional[str] = None
+
+
+class NetworkToken(BaseModel):
+    used: Optional[bool] = None
+
+
+class Overcapture(BaseModel):
+    maximum_amount_capturable: Optional[int] = None
+    status: Optional[str] = None
+
+
+class StripeCardDetails(BaseModel):
+    amount_authorized: Optional[int] = None
+    authorization_code: Optional[Any] = None
+    brand: Optional[str] = None
+    checks: Optional[Checks] = None
+    country: Optional[str] = None
+    exp_month: Optional[int] = None
+    exp_year: Optional[int] = None
+    extended_authorization: Optional[ExtendedAuthorization] = None
+    fingerprint: Optional[str] = None
+    funding: Optional[str] = None
+    incremental_authorization: Optional[IncrementalAuthorization] = None
+    installments: Optional[Any] = None
+    last4: Optional[str] = None
+    mandate: Optional[Any] = None
+    multicapture: Optional[Multicapture] = None
+    network: Optional[str] = None
+    network_token: Optional[NetworkToken] = None
+    network_transaction_id: Optional[str] = None
+    overcapture: Optional[Overcapture] = None
+    regulated_status: Optional[str] = None
+    three_d_secure: Optional[Any] = None
+    wallet: Optional[Any] = None
+
+
+class Object1(str, Enum):
+    charge = 'charge'
+
+
+class Object2(str, Enum):
+    event = 'event'
+
+
+class Type31(str, Enum):
+    payment_intent_succeeded = 'payment_intent.succeeded'
+
+
+class StripeOutcome(BaseModel):
+    advice_code: Optional[Any] = None
+    network_advice_code: Optional[Any] = None
+    network_decline_code: Optional[Any] = None
+    network_status: Optional[str] = None
+    reason: Optional[Any] = None
+    risk_level: Optional[str] = None
+    risk_score: Optional[int] = None
+    seller_message: Optional[str] = None
+    type: Optional[str] = None
+
+
+class Object3(str, Enum):
+    payment_intent = 'payment_intent'
+
+
+class StripePaymentMethodDetails(BaseModel):
+    card: Optional[StripeCardDetails] = None
+    type: Optional[str] = None
+
+
+class Card(BaseModel):
+    installments: Optional[Any] = None
+    mandate_options: Optional[Any] = None
+    network: Optional[Any] = None
+    request_three_d_secure: Optional[str] = None
+
+
+class StripePaymentMethodOptions(BaseModel):
+    card: Optional[Card] = None
+
+
+class StripeRefundList(BaseModel):
+    data: Optional[List[Dict[str, Any]]] = None
+    has_more: Optional[bool] = None
+    object: Optional[str] = None
+    total_count: Optional[int] = None
+    url: Optional[str] = None
+
+
+class StripeRequestInfo(BaseModel):
+    id: Optional[str] = None
+    idempotency_key: Optional[str] = None
+
+
+class StripeShipping(BaseModel):
+    address: Optional[StripeAddress] = None
+    carrier: Optional[str] = None
+    name: Optional[str] = None
+    phone: Optional[str] = None
+    tracking_number: Optional[str] = None
+
+
+class Type32(str, Enum):
+    json_schema = 'json_schema'
+
+
+class TextResponseFormatJsonSchema(BaseModel):
+    description: Optional[str] = Field(
+        None,
+        description='A description of what the response format is for, used by the model to\ndetermine how to respond in the format.\n',
+    )
+    name: str = Field(
+        ...,
+        description='The name of the response format. Must be a-z, A-Z, 0-9, or contain\nunderscores and dashes, with a maximum length of 64.\n',
+    )
+    schema_: ResponseFormatJsonSchemaSchema = Field(..., alias='schema')
+    strict: Optional[bool] = Field(
+        False,
+        description='Whether to enable strict schema adherence when generating the output.\nIf set to true, the model will always follow the exact schema defined\nin the `schema` field. Only a subset of JSON Schema is supported when\n`strict` is `true`. To learn more, read the [Structured Outputs\nguide](/docs/guides/structured-outputs).\n',
+    )
+    type: Type32 = Field(
+        ...,
+        description='The type of response format being defined. Always `json_schema`.',
+    )
+
+
+class Type33(str, Enum):
+    function = 'function'
+
+
+class ToolChoiceFunction(BaseModel):
+    name: str = Field(..., description='The name of the function to call.')
+    type: Type33 = Field(
+        ..., description='For function calling, the type is always `function`.'
+    )
+
+
+class ToolChoiceOptions(str, Enum):
+    none = 'none'
+    auto = 'auto'
+    required = 'required'
+
+
+class Type34(str, Enum):
+    file_search = 'file_search'
+    web_search_preview = 'web_search_preview'
+    computer_use_preview = 'computer_use_preview'
+    web_search_preview_2025_03_11 = 'web_search_preview_2025_03_11'
+
+
+class ToolChoiceTypes(BaseModel):
+    type: Type34 = Field(
+        ...,
+        description='The type of hosted tool the model should to use. Learn more about\n[built-in tools](/docs/guides/tools).\n\nAllowed values are:\n- `file_search`\n- `web_search_preview`\n- `computer_use_preview`\n',
+    )
+
+
+class TripoAnimation(str, Enum):
+    preset_idle = 'preset:idle'
+    preset_walk = 'preset:walk'
+    preset_climb = 'preset:climb'
+    preset_jump = 'preset:jump'
+    preset_run = 'preset:run'
+    preset_slash = 'preset:slash'
+    preset_shoot = 'preset:shoot'
+    preset_hurt = 'preset:hurt'
+    preset_fall = 'preset:fall'
+    preset_turn = 'preset:turn'
+
+
+class TripoBalance(BaseModel):
+    balance: float
+    frozen: float
+
+
+class TripoConvertFormat(str, Enum):
+    GLTF = 'GLTF'
+    USDZ = 'USDZ'
+    FBX = 'FBX'
+    OBJ = 'OBJ'
+    STL = 'STL'
+    field_3MF = '3MF'
+
+
+class Code(int, Enum):
+    integer_1001 = 1001
+    integer_2000 = 2000
+    integer_2001 = 2001
+    integer_2002 = 2002
+    integer_2003 = 2003
+    integer_2004 = 2004
+    integer_2006 = 2006
+    integer_2007 = 2007
+    integer_2008 = 2008
+    integer_2010 = 2010
+
+
+class TripoErrorResponse(BaseModel):
+    code: Code
+    message: str
+    suggestion: str
+
+
+class TripoImageToModel(str, Enum):
+    image_to_model = 'image_to_model'
+
+
+class TripoModelStyle(str, Enum):
+    person_person2cartoon = 'person:person2cartoon'
+    animal_venom = 'animal:venom'
+    object_clay = 'object:clay'
+    object_steampunk = 'object:steampunk'
+    object_christmas = 'object:christmas'
+    object_barbie = 'object:barbie'
+    gold = 'gold'
+    ancient_bronze = 'ancient_bronze'
+
+
+class TripoModelVersion(str, Enum):
+    v2_5_20250123 = 'v2.5-20250123'
+    v2_0_20240919 = 'v2.0-20240919'
+    v1_4_20240625 = 'v1.4-20240625'
+
+
+class TripoMultiviewMode(str, Enum):
+    LEFT = 'LEFT'
+    RIGHT = 'RIGHT'
+
+
+class TripoMultiviewToModel(str, Enum):
+    multiview_to_model = 'multiview_to_model'
+
+
+class TripoOrientation(str, Enum):
+    align_image = 'align_image'
+    default = 'default'
+
+
+class TripoResponseSuccessCode(RootModel[int]):
+    root: int = Field(
+        ...,
+        description='Standard success code for Tripo API responses. Typically 0 for success.',
+        examples=[0],
+    )
+
+
+class TripoSpec(str, Enum):
+    mixamo = 'mixamo'
+    tripo = 'tripo'
+
+
+class TripoStandardFormat(str, Enum):
+    glb = 'glb'
+    fbx = 'fbx'
+
+
+class TripoStylizeOptions(str, Enum):
+    lego = 'lego'
+    voxel = 'voxel'
+    voronoi = 'voronoi'
+    minecraft = 'minecraft'
+
+
+class Code1(int, Enum):
+    integer_0 = 0
+
+
+class Data9(BaseModel):
+    task_id: str = Field(..., description='used for getTask')
+
+
+class TripoSuccessTask(BaseModel):
+    code: Code1
+    data: Data9
+
+
+class Topology(str, Enum):
+    bip = 'bip'
+    quad = 'quad'
+
+
+class Output(BaseModel):
+    base_model: Optional[str] = None
+    model: Optional[str] = None
+    pbr_model: Optional[str] = None
+    rendered_image: Optional[str] = None
+    riggable: Optional[bool] = None
+    topology: Optional[Topology] = None
+
+
+class Status11(str, Enum):
+    queued = 'queued'
+    running = 'running'
+    success = 'success'
+    failed = 'failed'
+    cancelled = 'cancelled'
+    unknown = 'unknown'
+    banned = 'banned'
+    expired = 'expired'
+
+
+class TripoTask(BaseModel):
+    create_time: int
+    input: Dict[str, Any]
+    output: Output
+    progress: int = Field(..., ge=0, le=100)
+    status: Status11
+    task_id: str
+    type: str
+
+
+class TripoTextToModel(str, Enum):
+    text_to_model = 'text_to_model'
+
+
+class TripoTextureAlignment(str, Enum):
+    original_image = 'original_image'
+    geometry = 'geometry'
+
+
+class TripoTextureFormat(str, Enum):
+    BMP = 'BMP'
+    DPX = 'DPX'
+    HDR = 'HDR'
+    JPEG = 'JPEG'
+    OPEN_EXR = 'OPEN_EXR'
+    PNG = 'PNG'
+    TARGA = 'TARGA'
+    TIFF = 'TIFF'
+    WEBP = 'WEBP'
+
+
+class TripoTextureQuality(str, Enum):
+    standard = 'standard'
+    detailed = 'detailed'
+
+
+class TripoTopology(str, Enum):
+    bip = 'bip'
+    quad = 'quad'
+
+
+class TripoTypeAnimatePrerigcheck(str, Enum):
+    animate_prerigcheck = 'animate_prerigcheck'
+
+
+class TripoTypeAnimateRetarget(str, Enum):
+    animate_retarget = 'animate_retarget'
+
+
+class TripoTypeAnimateRig(str, Enum):
+    animate_rig = 'animate_rig'
+
+
+class TripoTypeConvertModel(str, Enum):
+    convert_model = 'convert_model'
+
+
+class TripoTypeRefineModel(str, Enum):
+    refine_model = 'refine_model'
+
+
+class TripoTypeStylizeModel(str, Enum):
+    stylize_model = 'stylize_model'
+
+
+class TripoTypeTextureModel(str, Enum):
+    texture_model = 'texture_model'
+
+
+class User(BaseModel):
+    email: Optional[str] = Field(None, description='The email address for this user.')
+    id: Optional[str] = Field(None, description='The unique id for this user.')
+    isAdmin: Optional[bool] = Field(
+        None, description='Indicates if the user has admin privileges.'
+    )
+    isApproved: Optional[bool] = Field(
+        None, description='Indicates if the user is approved.'
+    )
+    name: Optional[str] = Field(None, description='The name for this user.')
+
+
+class Veo2GenVidPollRequest(BaseModel):
+    operationName: str = Field(
+        ...,
+        description='Full operation name (from predict response)',
+        examples=[
+            'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID'
+        ],
+    )
+
+
+class Error1(BaseModel):
+    code: Optional[int] = Field(None, description='Error code')
+    message: Optional[str] = Field(None, description='Error message')
+
+
+class Video(BaseModel):
+    bytesBase64Encoded: Optional[str] = Field(
+        None, description='Base64-encoded video content'
+    )
+    gcsUri: Optional[str] = Field(None, description='Cloud Storage URI of the video')
+    mimeType: Optional[str] = Field(None, description='Video MIME type')
+
+
+class Response(BaseModel):
+    field_type: Optional[str] = Field(
+        None,
+        alias='@type',
+        examples=[
+            'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse'
+        ],
+    )
+    raiMediaFilteredCount: Optional[int] = Field(
+        None, description='Count of media filtered by responsible AI policies'
+    )
+    raiMediaFilteredReasons: Optional[List[str]] = Field(
+        None, description='Reasons why media was filtered by responsible AI policies'
+    )
+    videos: Optional[List[Video]] = None
+
+
+class Veo2GenVidPollResponse(BaseModel):
+    done: Optional[bool] = None
+    error: Optional[Error1] = Field(
+        None, description='Error details if operation failed'
+    )
+    name: Optional[str] = None
+    response: Optional[Response] = Field(
+        None, description='The actual prediction response if done is true'
+    )
+
+
+class Image(BaseModel):
+    bytesBase64Encoded: str
+    gcsUri: Optional[str] = None
+    mimeType: Optional[str] = None
+
+
+class Image1(BaseModel):
+    bytesBase64Encoded: Optional[str] = None
+    gcsUri: str
+    mimeType: Optional[str] = None
+
+
+class Instance(BaseModel):
+    image: Optional[Union[Image, Image1]] = Field(
+        None, description='Optional image to guide video generation'
+    )
+    prompt: str = Field(..., description='Text description of the video')
+
+
+class PersonGeneration1(str, Enum):
+    ALLOW = 'ALLOW'
+    BLOCK = 'BLOCK'
+
+
+class Parameters(BaseModel):
+    aspectRatio: Optional[str] = Field(None, examples=['16:9'])
+    durationSeconds: Optional[int] = None
+    enhancePrompt: Optional[bool] = None
+    negativePrompt: Optional[str] = None
+    personGeneration: Optional[PersonGeneration1] = None
+    sampleCount: Optional[int] = None
+    seed: Optional[int] = None
+    storageUri: Optional[str] = Field(
+        None, description='Optional Cloud Storage URI to upload the video'
+    )
+
+
+class Veo2GenVidRequest(BaseModel):
+    instances: Optional[List[Instance]] = None
+    parameters: Optional[Parameters] = None
+
+
+class Veo2GenVidResponse(BaseModel):
+    name: str = Field(
+        ...,
+        description='Operation resource name',
+        examples=[
+            'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8'
+        ],
+    )
+
+
+class VeoGenVidPollRequest(BaseModel):
+    operationName: str = Field(
+        ...,
+        description='Full operation name (from predict response)',
+        examples=[
+            'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID'
+        ],
+    )
+
+
+class Response1(BaseModel):
+    field_type: Optional[str] = Field(
+        None,
+        alias='@type',
+        examples=[
+            'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse'
+        ],
+    )
+    raiMediaFilteredCount: Optional[int] = Field(
+        None, description='Count of media filtered by responsible AI policies'
+    )
+    raiMediaFilteredReasons: Optional[List[str]] = Field(
+        None, description='Reasons why media was filtered by responsible AI policies'
+    )
+    videos: Optional[List[Video]] = None
+
+
+class VeoGenVidPollResponse(BaseModel):
+    done: Optional[bool] = None
+    error: Optional[Error1] = Field(
+        None, description='Error details if operation failed'
+    )
+    name: Optional[str] = None
+    response: Optional[Response1] = Field(
+        None, description='The actual prediction response if done is true'
+    )
+
+
+class Image2(BaseModel):
+    bytesBase64Encoded: str
+    gcsUri: Optional[str] = None
+    mimeType: Optional[str] = None
+
+
+class Image3(BaseModel):
+    bytesBase64Encoded: Optional[str] = None
+    gcsUri: str
+    mimeType: Optional[str] = None
+
+
+class Instance1(BaseModel):
+    image: Optional[Union[Image2, Image3]] = Field(
+        None, description='Optional image to guide video generation'
+    )
+    prompt: str = Field(..., description='Text description of the video')
+
+
+class Parameters1(BaseModel):
+    aspectRatio: Optional[str] = Field(None, examples=['16:9'])
+    durationSeconds: Optional[int] = None
+    enhancePrompt: Optional[bool] = None
+    generateAudio: Optional[bool] = Field(
+        None,
+        description='Generate audio for the video. Only supported by veo 3 models.',
+    )
+    negativePrompt: Optional[str] = None
+    personGeneration: Optional[PersonGeneration1] = None
+    sampleCount: Optional[int] = None
+    seed: Optional[int] = None
+    storageUri: Optional[str] = Field(
+        None, description='Optional Cloud Storage URI to upload the video'
+    )
+
+
+class VeoGenVidRequest(BaseModel):
+    instances: Optional[List[Instance1]] = None
+    parameters: Optional[Parameters1] = None
+
+
+class VeoGenVidResponse(BaseModel):
+    name: str = Field(
+        ...,
+        description='Operation resource name',
+        examples=[
+            'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8'
+        ],
+    )
+
+
+class SearchContextSize(str, Enum):
+    low = 'low'
+    medium = 'medium'
+    high = 'high'
+
+
+class Type35(str, Enum):
+    web_search_preview = 'web_search_preview'
+    web_search_preview_2025_03_11 = 'web_search_preview_2025_03_11'
+
+
+class WebSearchPreviewTool(BaseModel):
+    search_context_size: Optional[SearchContextSize] = Field(
+        None,
+        description='High level guidance for the amount of context window space to use for the search. One of `low`, `medium`, or `high`. `medium` is the default.',
+    )
+    type: Literal['WebSearchPreviewTool'] = Field(
+        ...,
+        description='The type of the web search tool. One of `web_search_preview` or `web_search_preview_2025_03_11`.',
+    )
+
+
+class Status12(str, Enum):
+    in_progress = 'in_progress'
+    searching = 'searching'
+    completed = 'completed'
+    failed = 'failed'
+
+
+class Type36(str, Enum):
+    web_search_call = 'web_search_call'
+
+
+class WebSearchToolCall(BaseModel):
+    id: str = Field(..., description='The unique ID of the web search tool call.\n')
+    status: Status12 = Field(
+        ..., description='The status of the web search tool call.\n'
+    )
+    type: Type36 = Field(
+        ...,
+        description='The type of the web search tool call. Always `web_search_call`.\n',
+    )
+
+
+class WorkflowRunStatus(str, Enum):
+    WorkflowRunStatusStarted = 'WorkflowRunStatusStarted'
+    WorkflowRunStatusFailed = 'WorkflowRunStatusFailed'
+    WorkflowRunStatusCompleted = 'WorkflowRunStatusCompleted'
+
+
+class ActionJobResult(BaseModel):
+    action_job_id: Optional[str] = Field(
+        None, description='Identifier of the job this result belongs to'
+    )
+    action_run_id: Optional[str] = Field(
+        None, description='Identifier of the run this result belongs to'
+    )
+    author: Optional[str] = Field(None, description='The author of the commit')
+    avg_vram: Optional[int] = Field(
+        None, description='The average VRAM used by the job'
+    )
+    branch_name: Optional[str] = Field(
+        None, description='Name of the relevant git branch'
+    )
+    comfy_run_flags: Optional[str] = Field(
+        None, description='The comfy run flags. E.g. `--low-vram`'
+    )
+    commit_hash: Optional[str] = Field(None, description='The hash of the commit')
+    commit_id: Optional[str] = Field(None, description='The ID of the commit')
+    commit_message: Optional[str] = Field(None, description='The message of the commit')
+    commit_time: Optional[int] = Field(
+        None, description='The Unix timestamp when the commit was made'
+    )
+    cuda_version: Optional[str] = Field(None, description='CUDA version used')
+    end_time: Optional[int] = Field(
+        None, description='The end time of the job as a Unix timestamp.'
+    )
+    git_repo: Optional[str] = Field(None, description='The repository name')
+    id: Optional[UUID] = Field(None, description='Unique identifier for the job result')
+    job_trigger_user: Optional[str] = Field(
+        None, description='The user who triggered the job.'
+    )
+    machine_stats: Optional[MachineStats] = None
+    operating_system: Optional[str] = Field(None, description='Operating system used')
+    peak_vram: Optional[int] = Field(None, description='The peak VRAM used by the job')
+    pr_number: Optional[str] = Field(None, description='The pull request number')
+    python_version: Optional[str] = Field(None, description='PyTorch version used')
+    pytorch_version: Optional[str] = Field(None, description='PyTorch version used')
+    start_time: Optional[int] = Field(
+        None, description='The start time of the job as a Unix timestamp.'
+    )
+    status: Optional[WorkflowRunStatus] = None
+    storage_file: Optional[StorageFile] = None
+    workflow_name: Optional[str] = Field(None, description='Name of the workflow')
+
+
+class BFLCannyInputs(BaseModel):
+    canny_high_threshold: Optional[CannyHighThreshold] = Field(
+        default_factory=lambda: CannyHighThreshold.model_validate(200),
+        description='High threshold for Canny edge detection',
+        title='Canny High Threshold',
+    )
+    canny_low_threshold: Optional[CannyLowThreshold] = Field(
+        default_factory=lambda: CannyLowThreshold.model_validate(50),
+        description='Low threshold for Canny edge detection',
+        title='Canny Low Threshold',
+    )
+    control_image: Optional[str] = Field(
+        None,
+        description='Base64 encoded image to use as control input if no preprocessed image is provided',
+        title='Control Image',
+    )
+    guidance: Optional[Guidance] = Field(
+        default_factory=lambda: Guidance.model_validate(30),
+        description='Guidance strength for the image generation process',
+        title='Guidance',
+    )
+    output_format: Optional[BFLOutputFormat] = Field(
+        'jpeg',
+        description="Output format for the generated image. Can be 'jpeg' or 'png'.",
+    )
+    preprocessed_image: Optional[str] = Field(
+        None,
+        description='Optional pre-processed image that will bypass the control preprocessing step',
+        title='Preprocessed Image',
+    )
+    prompt: str = Field(
+        ...,
+        description='Text prompt for image generation',
+        examples=['ein fantastisches bild'],
+        title='Prompt',
+    )
+    prompt_upsampling: Optional[bool] = Field(
+        False,
+        description='Whether to perform upsampling on the prompt',
+        title='Prompt Upsampling',
+    )
+    safety_tolerance: Optional[int] = Field(
+        2,
+        description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.',
+        ge=0,
+        le=6,
+        title='Safety Tolerance',
+    )
+    seed: Optional[int] = Field(
+        None,
+        description='Optional seed for reproducibility',
+        examples=[42],
+        title='Seed',
+    )
+    steps: Optional[Steps] = Field(
+        default_factory=lambda: Steps.model_validate(50),
+        description='Number of steps for the image generation process',
+        title='Steps',
+    )
+    webhook_secret: Optional[str] = Field(
+        None,
+        description='Optional secret for webhook signature verification',
+        title='Webhook Secret',
+    )
+    webhook_url: Optional[WebhookUrl] = Field(
+        None, description='URL to receive webhook notifications', title='Webhook Url'
+    )
+
+
+class BFLDepthInputs(BaseModel):
+    control_image: Optional[str] = Field(
+        None,
+        description='Base64 encoded image to use as control input',
+        title='Control Image',
+    )
+    guidance: Optional[Guidance] = Field(
+        default_factory=lambda: Guidance.model_validate(15),
+        description='Guidance strength for the image generation process',
+        title='Guidance',
+    )
+    output_format: Optional[BFLOutputFormat] = Field(
+        'jpeg',
+        description="Output format for the generated image. Can be 'jpeg' or 'png'.",
+    )
+    preprocessed_image: Optional[str] = Field(
+        None,
+        description='Optional pre-processed image that will bypass the control preprocessing step',
+        title='Preprocessed Image',
+    )
+    prompt: str = Field(
+        ...,
+        description='Text prompt for image generation',
+        examples=['ein fantastisches bild'],
+        title='Prompt',
+    )
+    prompt_upsampling: Optional[bool] = Field(
+        False,
+        description='Whether to perform upsampling on the prompt',
+        title='Prompt Upsampling',
+    )
+    safety_tolerance: Optional[int] = Field(
+        2,
+        description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.',
+        ge=0,
+        le=6,
+        title='Safety Tolerance',
+    )
+    seed: Optional[int] = Field(
+        None,
+        description='Optional seed for reproducibility',
+        examples=[42],
+        title='Seed',
+    )
+    steps: Optional[Steps] = Field(
+        default_factory=lambda: Steps.model_validate(50),
+        description='Number of steps for the image generation process',
+        title='Steps',
+    )
+    webhook_secret: Optional[str] = Field(
+        None,
+        description='Optional secret for webhook signature verification',
+        title='Webhook Secret',
+    )
+    webhook_url: Optional[WebhookUrl] = Field(
+        None, description='URL to receive webhook notifications', title='Webhook Url'
+    )
+
+
+class BFLFluxProExpandInputs(BaseModel):
+    bottom: Optional[Bottom] = Field(
+        0,
+        description='Number of pixels to expand at the bottom of the image',
+        title='Bottom',
+    )
+    guidance: Optional[Guidance2] = Field(
+        default_factory=lambda: Guidance2.model_validate(60),
+        description='Guidance strength for the image generation process',
+        title='Guidance',
+    )
+    image: str = Field(
+        ...,
+        description='A Base64-encoded string representing the image you wish to expand.',
+        title='Image',
+    )
+    left: Optional[Left] = Field(
+        0,
+        description='Number of pixels to expand on the left side of the image',
+        title='Left',
+    )
+    output_format: Optional[BFLOutputFormat] = Field(
+        'jpeg',
+        description="Output format for the generated image. Can be 'jpeg' or 'png'.",
+    )
+    prompt: Optional[str] = Field(
+        '',
+        description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.',
+        examples=['ein fantastisches bild'],
+        title='Prompt',
+    )
+    prompt_upsampling: Optional[bool] = Field(
+        False,
+        description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation',
+        title='Prompt Upsampling',
+    )
+    right: Optional[Right] = Field(
+        0,
+        description='Number of pixels to expand on the right side of the image',
+        title='Right',
+    )
+    safety_tolerance: Optional[int] = Field(
+        2,
+        description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.',
+        examples=[2],
+        ge=0,
+        le=6,
+        title='Safety Tolerance',
+    )
+    seed: Optional[int] = Field(
+        None, description='Optional seed for reproducibility', title='Seed'
+    )
+    steps: Optional[Steps2] = Field(
+        default_factory=lambda: Steps2.model_validate(50),
+        description='Number of steps for the image generation process',
+        examples=[50],
+        title='Steps',
+    )
+    top: Optional[Top] = Field(
+        0, description='Number of pixels to expand at the top of the image', title='Top'
+    )
+    webhook_secret: Optional[str] = Field(
+        None,
+        description='Optional secret for webhook signature verification',
+        title='Webhook Secret',
+    )
+    webhook_url: Optional[WebhookUrl] = Field(
+        None, description='URL to receive webhook notifications', title='Webhook Url'
+    )
+
+
+class BFLFluxProFillInputs(BaseModel):
+    guidance: Optional[Guidance2] = Field(
+        default_factory=lambda: Guidance2.model_validate(60),
+        description='Guidance strength for the image generation process',
+        title='Guidance',
+    )
+    image: str = Field(
+        ...,
+        description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.',
+        title='Image',
+    )
+    mask: Optional[str] = Field(
+        None,
+        description='A Base64-encoded string representing a mask for the areas you want to modify in the image. The mask should be the same dimensions as the image and in black and white. Black areas (0%) indicate no modification, while white areas (100%) specify areas for inpainting. Optional if you provide an alpha mask in the original image. Validation: The endpoint verifies that the dimensions of the mask match the original image.',
+        title='Mask',
+    )
+    output_format: Optional[BFLOutputFormat] = Field(
+        'jpeg',
+        description="Output format for the generated image. Can be 'jpeg' or 'png'.",
+    )
+    prompt: Optional[str] = Field(
+        '',
+        description='The description of the changes you want to make. This text guides the inpainting process, allowing you to specify features, styles, or modifications for the masked area.',
+        examples=['ein fantastisches bild'],
+        title='Prompt',
+    )
+    prompt_upsampling: Optional[bool] = Field(
+        False,
+        description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation',
+        title='Prompt Upsampling',
+    )
+    safety_tolerance: Optional[int] = Field(
+        2,
+        description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.',
+        examples=[2],
+        ge=0,
+        le=6,
+        title='Safety Tolerance',
+    )
+    seed: Optional[int] = Field(
+        None, description='Optional seed for reproducibility', title='Seed'
+    )
+    steps: Optional[Steps2] = Field(
+        default_factory=lambda: Steps2.model_validate(50),
+        description='Number of steps for the image generation process',
+        examples=[50],
+        title='Steps',
+    )
+    webhook_secret: Optional[str] = Field(
+        None,
+        description='Optional secret for webhook signature verification',
+        title='Webhook Secret',
+    )
+    webhook_url: Optional[WebhookUrl] = Field(
+        None, description='URL to receive webhook notifications', title='Webhook Url'
+    )
+
+
+class BFLHTTPValidationError(BaseModel):
+    detail: Optional[List[BFLValidationError]] = Field(None, title='Detail')
+
+
+class BulkNodeVersionsRequest(BaseModel):
+    node_versions: List[NodeVersionIdentifier] = Field(
+        ..., description='List of node ID and version pairs to retrieve'
+    )
+
+
+CreateModelResponseProperties = ModelResponseProperties
+
+
+class GeminiInlineData(BaseModel):
+    data: Optional[str] = Field(
+        None,
+        description='The base64 encoding of the image, PDF, or video to include inline in the prompt. When including media inline, you must also specify the media type (mimeType) of the data. Size limit: 20MB\n',
+    )
+    mimeType: Optional[GeminiMimeType] = None
+
+
+class GeminiPart(BaseModel):
+    inlineData: Optional[GeminiInlineData] = None
+    text: Optional[str] = Field(
+        None,
+        description='A text prompt or code snippet.',
+        examples=['Write a story about a robot learning to paint'],
+    )
+
+
+class GeminiPromptFeedback(BaseModel):
+    blockReason: Optional[str] = None
+    blockReasonMessage: Optional[str] = None
+    safetyRatings: Optional[List[GeminiSafetyRating]] = None
+
+
+class GeminiSafetySetting(BaseModel):
+    category: GeminiSafetyCategory
+    threshold: GeminiSafetyThreshold
+
+
+class GeminiSystemInstructionContent(BaseModel):
+    parts: List[GeminiTextPart] = Field(
+        ...,
+        description='A list of ordered parts that make up a single message. Different parts may have different IANA MIME types. For limits on the inputs, such as the maximum number of tokens or the number of images, see the model specifications on the Google models page.\n',
+    )
+    role: Role1 = Field(
+        ...,
+        description='The identity of the entity that creates the message. The following values are supported: user: This indicates that the message is sent by a real person, typically a user-generated message. model: This indicates that the message is generated by the model. The model value is used to insert messages from the model into the conversation during multi-turn conversations. For non-multi-turn conversations, this field can be left blank or unset.\n',
+        examples=['user'],
+    )
+
+
+class GeminiUsageMetadata(BaseModel):
+    cachedContentTokenCount: Optional[int] = Field(
+        None,
+        description='Output only. Number of tokens in the cached part in the input (the cached content).',
+    )
+    candidatesTokenCount: Optional[int] = Field(
+        None, description='Number of tokens in the response(s).'
+    )
+    candidatesTokensDetails: Optional[List[ModalityTokenCount]] = Field(
+        None, description='Breakdown of candidate tokens by modality.'
+    )
+    promptTokenCount: Optional[int] = Field(
+        None,
+        description='Number of tokens in the request. When cachedContent is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content.',
+    )
+    promptTokensDetails: Optional[List[ModalityTokenCount]] = Field(
+        None, description='Breakdown of prompt tokens by modality.'
+    )
+    thoughtsTokenCount: Optional[int] = Field(
+        None, description='Number of tokens present in thoughts output.'
+    )
+    toolUsePromptTokenCount: Optional[int] = Field(
+        None, description='Number of tokens present in tool-use prompt(s).'
+    )
+
+
+class GithubInstallation(BaseModel):
+    access_tokens_url: str = Field(..., description='The API URL for access tokens')
+    account: GithubUser
+    app_id: int = Field(..., description='The GitHub App ID')
+    created_at: datetime = Field(..., description='When the installation was created')
+    events: List[str] = Field(
+        ..., description='The events the installation subscribes to'
+    )
+    html_url: str = Field(..., description='The HTML URL of the installation')
+    id: int = Field(..., description='The installation ID')
+    permissions: Dict[str, Any] = Field(..., description='The installation permissions')
+    repositories_url: str = Field(..., description='The API URL for repositories')
+    repository_selection: RepositorySelection = Field(
+        ..., description='Repository selection for the installation'
+    )
+    single_file_name: Optional[str] = Field(
+        None, description='The single file name if applicable'
+    )
+    target_id: int = Field(..., description='The target ID')
+    target_type: str = Field(..., description='The target type')
+    updated_at: datetime = Field(
+        ..., description='When the installation was last updated'
+    )
+
+
+class GithubReleaseAsset(BaseModel):
+    browser_download_url: str = Field(..., description='The browser download URL')
+    content_type: str = Field(..., description='The content type of the asset')
+    created_at: datetime = Field(..., description='When the asset was created')
+    download_count: int = Field(..., description='The number of downloads')
+    id: int = Field(..., description='The asset ID')
+    label: Optional[str] = Field(None, description='The label of the asset')
+    name: str = Field(..., description='The name of the asset')
+    node_id: str = Field(..., description='The asset node ID')
+    size: int = Field(..., description='The size of the asset in bytes')
+    state: State = Field(..., description='The state of the asset')
+    updated_at: datetime = Field(..., description='When the asset was last updated')
+    uploader: GithubUser
+
+
+class Release(BaseModel):
+    assets: List[GithubReleaseAsset] = Field(..., description='Array of release assets')
+    assets_url: Optional[str] = Field(None, description='The URL to the release assets')
+    author: GithubUser
+    body: Optional[str] = Field(None, description='The release notes/body')
+    created_at: datetime = Field(..., description='When the release was created')
+    draft: bool = Field(..., description='Whether the release is a draft')
+    html_url: str = Field(..., description='The HTML URL of the release')
+    id: int = Field(..., description='The ID of the release')
+    name: Optional[str] = Field(None, description='The name of the release')
+    node_id: str = Field(..., description='The node ID of the release')
+    prerelease: bool = Field(..., description='Whether the release is a prerelease')
+    published_at: Optional[datetime] = Field(
+        None, description='When the release was published'
+    )
+    tag_name: str = Field(..., description='The tag name of the release')
+    tarball_url: str = Field(..., description='URL to the tarball')
+    target_commitish: str = Field(
+        ..., description='The branch or commit the release was created from'
+    )
+    upload_url: Optional[str] = Field(
+        None, description='The URL to upload release assets'
+    )
+    url: str = Field(..., description='The API URL of the release')
+    zipball_url: str = Field(..., description='URL to the zipball')
+
+
+class GithubRepository(BaseModel):
+    clone_url: str = Field(..., description='The clone URL of the repository')
+    created_at: datetime = Field(..., description='When the repository was created')
+    default_branch: str = Field(..., description='The default branch of the repository')
+    description: Optional[str] = Field(None, description='The repository description')
+    fork: bool = Field(..., description='Whether the repository is a fork')
+    full_name: str = Field(
+        ..., description='The full name of the repository (owner/repo)'
+    )
+    git_url: str = Field(..., description='The git URL of the repository')
+    html_url: str = Field(..., description='The HTML URL of the repository')
+    id: int = Field(..., description='The repository ID')
+    name: str = Field(..., description='The name of the repository')
+    node_id: str = Field(..., description='The repository node ID')
+    owner: GithubUser
+    private: bool = Field(..., description='Whether the repository is private')
+    pushed_at: datetime = Field(
+        ..., description='When the repository was last pushed to'
+    )
+    ssh_url: str = Field(..., description='The SSH URL of the repository')
+    updated_at: datetime = Field(
+        ..., description='When the repository was last updated'
+    )
+    url: str = Field(..., description='The API URL of the repository')
+
+
+class IdeogramV3EditRequest(BaseModel):
+    color_palette: Optional[IdeogramColorPalette] = None
+    image: Optional[StrictBytes] = Field(
+        None,
+        description='The image being edited (max size 10MB); only JPEG, WebP and PNG formats are supported at this time.',
+    )
+    magic_prompt: Optional[str] = Field(
+        None,
+        description='Determine if MagicPrompt should be used in generating the request or not.',
+    )
+    mask: Optional[StrictBytes] = Field(
+        None,
+        description='A black and white image of the same size as the image being edited (max size 10MB). Black regions in the mask should match up with the regions of the image that you would like to edit; only JPEG, WebP and PNG formats are supported at this time.',
+    )
+    num_images: Optional[int] = Field(
+        None, description='The number of images to generate.'
+    )
+    prompt: str = Field(
+        ..., description='The prompt used to describe the edited result.'
+    )
+    rendering_speed: RenderingSpeed
+    seed: Optional[int] = Field(
+        None, description='Random seed. Set for reproducible generation.'
+    )
+    style_codes: Optional[List[StyleCode]] = Field(
+        None,
+        description='A list of 8 character hexadecimal codes representing the style of the image. Cannot be used in conjunction with style_reference_images or style_type.',
+    )
+    style_reference_images: Optional[List[StrictBytes]] = Field(
+        None,
+        description='A set of images to use as style references (maximum total size 10MB across all style references). The images should be in JPEG, PNG or WebP format.',
+    )
+    character_reference_images: Optional[List[str]] = Field(
+        None,
+        description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+    character_reference_images_mask: Optional[List[str]] = Field(
+        None,
+        description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+
+
+class IdeogramV3Request(BaseModel):
+    aspect_ratio: Optional[str] = Field(
+        None, description='Aspect ratio in format WxH', examples=['1x3']
+    )
+    color_palette: Optional[ColorPalette] = None
+    magic_prompt: Optional[MagicPrompt2] = Field(
+        None, description='Whether to enable magic prompt enhancement'
+    )
+    negative_prompt: Optional[str] = Field(
+        None, description='Text prompt specifying what to avoid in the generation'
+    )
+    num_images: Optional[int] = Field(
+        None, description='Number of images to generate', ge=1
+    )
+    prompt: str = Field(..., description='The text prompt for image generation')
+    rendering_speed: RenderingSpeed
+    resolution: Optional[str] = Field(
+        None, description='Image resolution in format WxH', examples=['1280x800']
+    )
+    seed: Optional[int] = Field(
+        None, description='Seed value for reproducible generation'
+    )
+    style_codes: Optional[List[StyleCode]] = Field(
+        None, description='Array of style codes in hexadecimal format'
+    )
+    style_reference_images: Optional[List[str]] = Field(
+        None, description='Array of reference image URLs or identifiers'
+    )
+    style_type: Optional[StyleType1] = Field(
+        None, description='The type of style to apply'
+    )
+    character_reference_images: Optional[List[str]] = Field(
+        None,
+        description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+    character_reference_images_mask: Optional[List[str]] = Field(
+        None,
+        description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+
+
+class ImagenGenerateImageResponse(BaseModel):
+    predictions: Optional[List[ImagenImagePrediction]] = None
+
+
+class ImagenImageGenerationParameters(BaseModel):
+    addWatermark: Optional[bool] = None
+    aspectRatio: Optional[AspectRatio] = None
+    enhancePrompt: Optional[bool] = None
+    includeRaiReason: Optional[bool] = None
+    includeSafetyAttributes: Optional[bool] = None
+    outputOptions: Optional[ImagenOutputOptions] = None
+    personGeneration: Optional[PersonGeneration] = None
+    safetySetting: Optional[SafetySetting] = None
+    sampleCount: Optional[int] = Field(None, ge=1, le=4)
+    seed: Optional[int] = None
+    storageUri: Optional[AnyUrl] = None
+
+
+class InputContent(
+    RootModel[Union[InputTextContent, InputImageContent, InputFileContent]]
+):
+    root: Union[InputTextContent, InputImageContent, InputFileContent]
+
+
+class InputMessageContentList(RootModel[List[InputContent]]):
+    root: List[InputContent] = Field(
+        ...,
+        description='A list of one or many input items to the model, containing different content \ntypes.\n',
+        title='Input item content list',
+    )
+
+
+class KlingCameraControl(BaseModel):
+    config: Optional[KlingCameraConfig] = None
+    type: Optional[KlingCameraControlType] = None
+
+
+class KlingDualCharacterEffectInput(BaseModel):
+    duration: KlingVideoGenDuration
+    images: KlingDualCharacterImages
+    mode: Optional[KlingVideoGenMode] = 'std'
+    model_name: Optional[KlingCharacterEffectModelName] = 'kling-v1'
+
+
+class KlingImage2VideoRequest(BaseModel):
+    aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9'
+    callback_url: Optional[AnyUrl] = Field(
+        None,
+        description='The callback notification address. Server will notify when the task status changes.',
+    )
+    camera_control: Optional[KlingCameraControl] = None
+    cfg_scale: Optional[KlingVideoGenCfgScale] = Field(
+        default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5)
+    )
+    duration: Optional[KlingVideoGenDuration] = '5'
+    dynamic_masks: Optional[List[DynamicMask]] = Field(
+        None,
+        description='Dynamic Brush Configuration List (up to 6 groups). For 5-second videos, trajectory length must not exceed 77 coordinates.',
+    )
+    external_task_id: Optional[str] = Field(
+        None,
+        description='Customized Task ID. Must be unique within a single user account.',
+    )
+    image: Optional[str] = Field(
+        None,
+        description='Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.',
+    )
+    image_tail: Optional[str] = Field(
+        None,
+        description='Reference Image - End frame control. URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px. Base64 should not include data:image prefix.',
+    )
+    mode: Optional[KlingVideoGenMode] = 'std'
+    model_name: Optional[KlingVideoGenModelName] = 'kling-v2-master'
+    negative_prompt: Optional[str] = Field(
+        None, description='Negative text prompt', max_length=2500
+    )
+    prompt: Optional[str] = Field(
+        None, description='Positive text prompt', max_length=2500
+    )
+    static_mask: Optional[str] = Field(
+        None,
+        description='Static Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.',
+    )
+
+
+class TaskResult(BaseModel):
+    videos: Optional[List[KlingVideoResult]] = None
+
+
+class Data(BaseModel):
+    created_at: Optional[int] = Field(None, description='Task creation time')
+    task_id: Optional[str] = Field(None, description='Task ID')
+    task_info: Optional[TaskInfo] = None
+    task_result: Optional[TaskResult] = None
+    task_status: Optional[KlingTaskStatus] = None
+    updated_at: Optional[int] = Field(None, description='Task update time')
+
+
+class KlingImage2VideoResponse(BaseModel):
+    code: Optional[int] = Field(None, description='Error code')
+    data: Optional[Data] = None
+    message: Optional[str] = Field(None, description='Error message')
+    request_id: Optional[str] = Field(None, description='Request ID')
+
+
+class TaskResult1(BaseModel):
+    images: Optional[List[KlingImageResult]] = None
+
+
+class Data1(BaseModel):
+    created_at: Optional[int] = Field(None, description='Task creation time')
+    task_id: Optional[str] = Field(None, description='Task ID')
+    task_result: Optional[TaskResult1] = None
+    task_status: Optional[KlingTaskStatus] = None
+    task_status_msg: Optional[str] = Field(None, description='Task status information')
+    updated_at: Optional[int] = Field(None, description='Task update time')
+
+
+class KlingImageGenerationsResponse(BaseModel):
+    code: Optional[int] = Field(None, description='Error code')
+    data: Optional[Data1] = None
+    message: Optional[str] = Field(None, description='Error message')
+    request_id: Optional[str] = Field(None, description='Request ID')
+
+
+class KlingLipSyncInputObject(BaseModel):
+    audio_file: Optional[str] = Field(
+        None,
+        description='Local Path of Audio File. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB. Base64 code.',
+    )
+    audio_type: Optional[KlingAudioUploadType] = None
+    audio_url: Optional[str] = Field(
+        None,
+        description='Audio File Download URL. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB.',
+    )
+    mode: KlingLipSyncMode
+    text: Optional[str] = Field(
+        None,
+        description='Text Content for Lip-Sync Video Generation. Required when mode is text2video. Maximum length is 120 characters.',
+    )
+    video_id: Optional[str] = Field(
+        None,
+        description='The ID of the video generated by Kling AI. Only supports 5-second and 10-second videos generated within the last 30 days.',
+    )
+    video_url: Optional[str] = Field(
+        None,
+        description='Get link for uploaded video. Video files support .mp4/.mov, file size does not exceed 100MB, video length between 2-10s.',
+    )
+    voice_id: Optional[str] = Field(
+        None,
+        description='Voice ID. Required when mode is text2video. The system offers a variety of voice options to choose from.',
+    )
+    voice_language: Optional[KlingLipSyncVoiceLanguage] = 'en'
+    voice_speed: Optional[float] = Field(
+        1,
+        description='Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.',
+        ge=0.8,
+        le=2.0,
+    )
+
+
+class KlingLipSyncRequest(BaseModel):
+    callback_url: Optional[AnyUrl] = Field(
+        None,
+        description='The callback notification address. Server will notify when the task status changes.',
+    )
+    input: KlingLipSyncInputObject
+
+
+class TaskResult2(BaseModel):
+    videos: Optional[List[KlingVideoResult]] = None
+
+
+class Data2(BaseModel):
+    created_at: Optional[int] = Field(None, description='Task creation time')
+    task_id: Optional[str] = Field(None, description='Task ID')
+    task_info: Optional[TaskInfo] = None
+    task_result: Optional[TaskResult2] = None
+    task_status: Optional[KlingTaskStatus] = None
+    updated_at: Optional[int] = Field(None, description='Task update time')
+
+
+class KlingLipSyncResponse(BaseModel):
+    code: Optional[int] = Field(None, description='Error code')
+    data: Optional[Data2] = None
+    message: Optional[str] = Field(None, description='Error message')
+    request_id: Optional[str] = Field(None, description='Request ID')
+
+
+class KlingSingleImageEffectInput(BaseModel):
+    duration: KlingSingleImageEffectDuration
+    image: str = Field(
+        ...,
+        description='Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1.',
+    )
+    model_name: KlingSingleImageEffectModelName
+
+
+class KlingText2VideoRequest(BaseModel):
+    aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9'
+    callback_url: Optional[AnyUrl] = Field(
+        None, description='The callback notification address'
+    )
+    camera_control: Optional[KlingCameraControl] = None
+    cfg_scale: Optional[KlingVideoGenCfgScale] = Field(
+        default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5)
+    )
+    duration: Optional[KlingVideoGenDuration] = '5'
+    external_task_id: Optional[str] = Field(None, description='Customized Task ID')
+    mode: Optional[KlingVideoGenMode] = 'std'
+    model_name: Optional[str] = 'kling-v1'
+    negative_prompt: Optional[str] = Field(
+        None, description='Negative text prompt', max_length=2500
+    )
+    prompt: Optional[str] = Field(
+        None, description='Positive text prompt', max_length=2500
+    )
+
+
+class Data4(BaseModel):
+    created_at: Optional[int] = Field(None, description='Task creation time')
+    task_id: Optional[str] = Field(None, description='Task ID')
+    task_info: Optional[TaskInfo] = None
+    task_result: Optional[TaskResult2] = None
+    task_status: Optional[KlingTaskStatus] = None
+    updated_at: Optional[int] = Field(None, description='Task update time')
+
+
+class KlingText2VideoResponse(BaseModel):
+    code: Optional[int] = Field(None, description='Error code')
+    data: Optional[Data4] = None
+    message: Optional[str] = Field(None, description='Error message')
+    request_id: Optional[str] = Field(None, description='Request ID')
+
+
+class KlingVideoEffectsInput(
+    RootModel[Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput]]
+):
+    root: Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput]
+
+
+class KlingVideoEffectsRequest(BaseModel):
+    callback_url: Optional[AnyUrl] = Field(
+        None,
+        description='The callback notification address for the result of this task.',
+    )
+    effect_scene: Union[KlingDualCharacterEffectsScene, KlingSingleImageEffectsScene]
+    external_task_id: Optional[str] = Field(
+        None,
+        description='Customized Task ID. Must be unique within a single user account.',
+    )
+    input: KlingVideoEffectsInput
+
+
+class Data5(BaseModel):
+    created_at: Optional[int] = Field(None, description='Task creation time')
+    task_id: Optional[str] = Field(None, description='Task ID')
+    task_info: Optional[TaskInfo] = None
+    task_result: Optional[TaskResult2] = None
+    task_status: Optional[KlingTaskStatus] = None
+    updated_at: Optional[int] = Field(None, description='Task update time')
+
+
+class KlingVideoEffectsResponse(BaseModel):
+    code: Optional[int] = Field(None, description='Error code')
+    data: Optional[Data5] = None
+    message: Optional[str] = Field(None, description='Error message')
+    request_id: Optional[str] = Field(None, description='Request ID')
+
+
+class KlingVideoExtendRequest(BaseModel):
+    callback_url: Optional[AnyUrl] = Field(
+        None,
+        description='The callback notification address. Server will notify when the task status changes.',
+    )
+    cfg_scale: Optional[KlingVideoGenCfgScale] = Field(
+        default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5)
+    )
+    negative_prompt: Optional[str] = Field(
+        None,
+        description='Negative text prompt for elements to avoid in the extended video',
+        max_length=2500,
+    )
+    prompt: Optional[str] = Field(
+        None,
+        description='Positive text prompt for guiding the video extension',
+        max_length=2500,
+    )
+    video_id: Optional[str] = Field(
+        None,
+        description='The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.',
+    )
+
+
+class Data6(BaseModel):
+    created_at: Optional[int] = Field(None, description='Task creation time')
+    task_id: Optional[str] = Field(None, description='Task ID')
+    task_info: Optional[TaskInfo] = None
+    task_result: Optional[TaskResult2] = None
+    task_status: Optional[KlingTaskStatus] = None
+    updated_at: Optional[int] = Field(None, description='Task update time')
+
+
+class KlingVideoExtendResponse(BaseModel):
+    code: Optional[int] = Field(None, description='Error code')
+    data: Optional[Data6] = None
+    message: Optional[str] = Field(None, description='Error message')
+    request_id: Optional[str] = Field(None, description='Request ID')
+
+
+class LumaGenerationRequest(BaseModel):
+    aspect_ratio: LumaAspectRatio
+    callback_url: Optional[AnyUrl] = Field(
+        None,
+        description='The callback URL of the generation, a POST request with Generation object will be sent to the callback URL when the generation is dreaming, completed, or failed',
+    )
+    duration: LumaVideoModelOutputDuration
+    generation_type: Optional[GenerationType1] = 'video'
+    keyframes: Optional[LumaKeyframes] = None
+    loop: Optional[bool] = Field(None, description='Whether to loop the video')
+    model: LumaVideoModel
+    prompt: str = Field(..., description='The prompt of the generation')
+    resolution: LumaVideoModelOutputResolution
+
+
+class CharacterRef(BaseModel):
+    identity0: Optional[LumaImageIdentity] = None
+
+
+class LumaImageGenerationRequest(BaseModel):
+    aspect_ratio: Optional[LumaAspectRatio] = '16:9'
+    callback_url: Optional[AnyUrl] = Field(
+        None, description='The callback URL for the generation'
+    )
+    character_ref: Optional[CharacterRef] = None
+    generation_type: Optional[GenerationType2] = 'image'
+    image_ref: Optional[List[LumaImageRef]] = None
+    model: Optional[LumaImageModel] = 'photon-1'
+    modify_image_ref: Optional[LumaModifyImageRef] = None
+    prompt: Optional[str] = Field(None, description='The prompt of the generation')
+    style_ref: Optional[List[LumaImageRef]] = None
+
+
+class LumaUpscaleVideoGenerationRequest(BaseModel):
+    callback_url: Optional[AnyUrl] = Field(
+        None, description='The callback URL for the upscale'
+    )
+    generation_type: Optional[GenerationType3] = 'upscale_video'
+    resolution: Optional[LumaVideoModelOutputResolution] = None
+
+
+class MoonvalleyImageToVideoRequest(MoonvalleyTextToVideoRequest):
+    keyframes: Optional[Dict[str, Keyframes]] = None
+
+
+class MoonvalleyResizeVideoRequest(MoonvalleyVideoToVideoRequest):
+    frame_position: Optional[List[int]] = Field(None, max_length=2, min_length=2)
+    frame_resolution: Optional[List[int]] = Field(None, max_length=2, min_length=2)
+    scale: Optional[List[int]] = Field(None, max_length=2, min_length=2)
+
+
+class MoonvalleyTextToImageRequest(BaseModel):
+    image_url: Optional[str] = None
+    inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None
+    prompt_text: Optional[str] = None
+    webhook_url: Optional[str] = None
+
+
+class NodeVersion(BaseModel):
+    changelog: Optional[str] = Field(
+        None, description='Summary of changes made in this version'
+    )
+    comfy_node_extract_status: Optional[str] = Field(
+        None, description='The status of comfy node extraction process.'
+    )
+    createdAt: Optional[datetime] = Field(
+        None, description='The date and time the version was created.'
+    )
+    dependencies: Optional[List[str]] = Field(
+        None, description='A list of pip dependencies required by the node.'
+    )
+    deprecated: Optional[bool] = Field(
+        None, description='Indicates if this version is deprecated.'
+    )
+    downloadUrl: Optional[str] = Field(
+        None, description='[Output Only] URL to download this version of the node'
+    )
+    id: Optional[str] = None
+    node_id: Optional[str] = Field(
+        None, description='The unique identifier of the node.'
+    )
+    status: Optional[NodeVersionStatus] = None
+    status_reason: Optional[str] = Field(
+        None, description='The reason for the status change.'
+    )
+    supported_accelerators: Optional[List[str]] = Field(
+        None,
+        description='List of accelerators (e.g. CUDA, DirectML, ROCm) that this node supports',
+    )
+    supported_comfyui_frontend_version: Optional[str] = Field(
+        None, description='Supported versions of ComfyUI frontend'
+    )
+    supported_comfyui_version: Optional[str] = Field(
+        None, description='Supported versions of ComfyUI'
+    )
+    supported_os: Optional[List[str]] = Field(
+        None, description='List of operating systems that this node supports'
+    )
+    version: Optional[str] = Field(
+        None,
+        description='The version identifier, following semantic versioning. Must be unique for the node.',
+    )
+
+
+class OutputContent(RootModel[Union[OutputTextContent, OutputAudioContent]]):
+    root: Union[OutputTextContent, OutputAudioContent]
+
+
+class OutputMessage(BaseModel):
+    content: List[OutputContent] = Field(..., description='The content of the message')
+    role: Role4 = Field(..., description='The role of the message')
+    type: Type15 = Field(..., description='The type of output item')
+
+
+class PikaBodyGenerate22I2vGenerate22I2vPost(BaseModel):
+    duration: Optional[PikaDurationEnum] = 5
+    image: Optional[StrictBytes] = Field(None, title='Image')
+    negativePrompt: Optional[str] = Field(None, title='Negativeprompt')
+    promptText: Optional[str] = Field(None, title='Prompttext')
+    resolution: Optional[PikaResolutionEnum] = '1080p'
+    seed: Optional[int] = Field(None, title='Seed')
+
+
+class PikaBodyGenerate22KeyframeGenerate22PikaframesPost(BaseModel):
+    duration: Optional[int] = Field(None, ge=5, le=10, title='Duration')
+    keyFrames: Optional[List[StrictBytes]] = Field(
+        None, description='Array of keyframe images', title='Keyframes'
+    )
+    negativePrompt: Optional[str] = Field(None, title='Negativeprompt')
+    promptText: str = Field(..., title='Prompttext')
+    resolution: Optional[PikaResolutionEnum] = '1080p'
+    seed: Optional[int] = Field(None, title='Seed')
+
+
+class PikaBodyGenerate22T2vGenerate22T2vPost(BaseModel):
+    aspectRatio: Optional[float] = Field(
+        1.7777777777777777,
+        description='Aspect ratio (width / height)',
+        ge=0.4,
+        le=2.5,
+        title='Aspectratio',
+    )
+    duration: Optional[PikaDurationEnum] = 5
+    negativePrompt: Optional[str] = Field(None, title='Negativeprompt')
+    promptText: str = Field(..., title='Prompttext')
+    resolution: Optional[PikaResolutionEnum] = '1080p'
+    seed: Optional[int] = Field(None, title='Seed')
+
+
+class PikaBodyGeneratePikaffectsGeneratePikaffectsPost(BaseModel):
+    image: Optional[StrictBytes] = Field(None, title='Image')
+    negativePrompt: Optional[str] = Field(None, title='Negativeprompt')
+    pikaffect: Optional[Pikaffect] = None
+    promptText: Optional[str] = Field(None, title='Prompttext')
+    seed: Optional[int] = Field(None, title='Seed')
+
+
+class PikaHTTPValidationError(BaseModel):
+    detail: Optional[List[PikaValidationError]] = Field(None, title='Detail')
+
+
+class PublisherMember(BaseModel):
+    id: Optional[str] = Field(
+        None, description='The unique identifier for the publisher member.'
+    )
+    role: Optional[str] = Field(
+        None, description='The role of the user in the publisher.'
+    )
+    user: Optional[PublisherUser] = None
+
+
+class Reasoning(BaseModel):
+    effort: Optional[ReasoningEffort] = 'medium'
+    generate_summary: Optional[GenerateSummary] = Field(
+        None,
+        description="**Deprecated:** use `summary` instead.\n\nA summary of the reasoning performed by the model. This can be\nuseful for debugging and understanding the model's reasoning process.\nOne of `auto`, `concise`, or `detailed`.\n",
+    )
+    summary: Optional[Summary] = Field(
+        None,
+        description="A summary of the reasoning performed by the model. This can be\nuseful for debugging and understanding the model's reasoning process.\nOne of `auto`, `concise`, or `detailed`.\n",
+    )
+
+
+class RecraftImage(BaseModel):
+    b64_json: Optional[str] = None
+    features: Optional[RecraftImageFeatures] = None
+    image_id: UUID
+    revised_prompt: Optional[str] = None
+    url: Optional[str] = None
+
+
+class RecraftProcessImageRequest(BaseModel):
+    image: StrictBytes
+    image_format: Optional[RecraftImageFormat] = None
+    response_format: Optional[RecraftResponseFormat] = None
+
+
+class RecraftProcessImageResponse(BaseModel):
+    created: int
+    credits: int
+    image: RecraftImage
+
+
+class RecraftTextLayout(RootModel[List[RecraftTextLayoutItem]]):
+    root: List[RecraftTextLayoutItem]
+
+
+class RecraftTransformImageWithMaskRequest(BaseModel):
+    block_nsfw: Optional[bool] = None
+    calculate_features: Optional[bool] = None
+    image: StrictBytes
+    image_format: Optional[RecraftImageFormat] = None
+    mask: StrictBytes
+    model: Optional[RecraftTransformModel] = None
+    n: Optional[int] = None
+    negative_prompt: Optional[str] = None
+    prompt: str
+    response_format: Optional[RecraftResponseFormat] = None
+    style: Optional[RecraftImageStyle] = None
+    style_id: Optional[UUID] = None
+    substyle: Optional[RecraftImageSubStyle] = None
+    text_layout: Optional[RecraftTextLayout] = None
+
+
+class ResponseContentPartAddedEvent(BaseModel):
+    content_index: int = Field(
+        ..., description='The index of the content part that was added.'
+    )
+    item_id: str = Field(
+        ..., description='The ID of the output item that the content part was added to.'
+    )
+    output_index: int = Field(
+        ...,
+        description='The index of the output item that the content part was added to.',
+    )
+    part: OutputContent
+    type: Type20 = Field(
+        ..., description='The type of the event. Always `response.content_part.added`.'
+    )
+
+
+class ResponseContentPartDoneEvent(BaseModel):
+    content_index: int = Field(
+        ..., description='The index of the content part that is done.'
+    )
+    item_id: str = Field(
+        ..., description='The ID of the output item that the content part was added to.'
+    )
+    output_index: int = Field(
+        ...,
+        description='The index of the output item that the content part was added to.',
+    )
+    part: OutputContent
+    type: Type21 = Field(
+        ..., description='The type of the event. Always `response.content_part.done`.'
+    )
+
+
+class ResponseError(BaseModel):
+    code: ResponseErrorCode
+    message: str = Field(..., description='A human-readable description of the error.')
+
+
+class Rodin3DDownloadResponse(BaseModel):
+    list: Optional[List[RodinResourceItem]] = None
+
+
+class Rodin3DGenerateRequest(BaseModel):
+    images: str = Field(..., description='The reference images to generate 3D Assets.')
+    material: Optional[RodinMaterialType] = None
+    mesh_mode: Optional[RodinMeshModeType] = None
+    quality: Optional[RodinQualityType] = None
+    seed: Optional[int] = Field(None, description='Seed.')
+    tier: Optional[RodinTierType] = None
+
+
+class Rodin3DGenerateResponse(BaseModel):
+    jobs: Optional[RodinGenerateJobsData] = None
+    message: Optional[str] = Field(None, description='message')
+    prompt: Optional[str] = Field(None, description='prompt')
+    submit_time: Optional[str] = Field(None, description='Time')
+    uuid: Optional[str] = Field(None, description='Task UUID')
+
+
+class RodinCheckStatusJobItem(BaseModel):
+    status: Optional[RodinStatusOptions] = None
+    uuid: Optional[str] = Field(None, description='sub uuid')
+
+
+class RunwayImageToVideoRequest(BaseModel):
+    duration: RunwayDurationEnum
+    model: RunwayModelEnum
+    promptImage: RunwayPromptImageObject
+    promptText: Optional[str] = Field(
+        None, description='Text prompt for the generation', max_length=1000
+    )
+    ratio: RunwayAspectRatioEnum
+    seed: int = Field(
+        ..., description='Random seed for generation', ge=0, le=4294967295
+    )
+
+
+class StripeCharge(BaseModel):
+    amount: Optional[int] = None
+    amount_captured: Optional[int] = None
+    amount_refunded: Optional[int] = None
+    application: Optional[str] = None
+    application_fee: Optional[str] = None
+    application_fee_amount: Optional[int] = None
+    balance_transaction: Optional[str] = None
+    billing_details: Optional[StripeBillingDetails] = None
+    calculated_statement_descriptor: Optional[str] = None
+    captured: Optional[bool] = None
+    created: Optional[int] = None
+    currency: Optional[str] = None
+    customer: Optional[str] = None
+    description: Optional[str] = None
+    destination: Optional[Any] = None
+    dispute: Optional[Any] = None
+    disputed: Optional[bool] = None
+    failure_balance_transaction: Optional[Any] = None
+    failure_code: Optional[Any] = None
+    failure_message: Optional[Any] = None
+    fraud_details: Optional[Dict[str, Any]] = None
+    id: Optional[str] = None
+    invoice: Optional[Any] = None
+    livemode: Optional[bool] = None
+    metadata: Optional[Dict[str, Any]] = None
+    object: Optional[Object1] = None
+    on_behalf_of: Optional[Any] = None
+    order: Optional[Any] = None
+    outcome: Optional[StripeOutcome] = None
+    paid: Optional[bool] = None
+    payment_intent: Optional[str] = None
+    payment_method: Optional[str] = None
+    payment_method_details: Optional[StripePaymentMethodDetails] = None
+    radar_options: Optional[Dict[str, Any]] = None
+    receipt_email: Optional[str] = None
+    receipt_number: Optional[str] = None
+    receipt_url: Optional[str] = None
+    refunded: Optional[bool] = None
+    refunds: Optional[StripeRefundList] = None
+    review: Optional[Any] = None
+    shipping: Optional[StripeShipping] = None
+    source: Optional[Any] = None
+    source_transfer: Optional[Any] = None
+    statement_descriptor: Optional[Any] = None
+    statement_descriptor_suffix: Optional[Any] = None
+    status: Optional[str] = None
+    transfer_data: Optional[Any] = None
+    transfer_group: Optional[Any] = None
+
+
+class StripeChargeList(BaseModel):
+    data: Optional[List[StripeCharge]] = None
+    has_more: Optional[bool] = None
+    object: Optional[str] = None
+    total_count: Optional[int] = None
+    url: Optional[str] = None
+
+
+class StripePaymentIntent(BaseModel):
+    amount: Optional[int] = None
+    amount_capturable: Optional[int] = None
+    amount_details: Optional[StripeAmountDetails] = None
+    amount_received: Optional[int] = None
+    application: Optional[str] = None
+    application_fee_amount: Optional[int] = None
+    automatic_payment_methods: Optional[Any] = None
+    canceled_at: Optional[int] = None
+    cancellation_reason: Optional[str] = None
+    capture_method: Optional[str] = None
+    charges: Optional[StripeChargeList] = None
+    client_secret: Optional[str] = None
+    confirmation_method: Optional[str] = None
+    created: Optional[int] = None
+    currency: Optional[str] = None
+    customer: Optional[str] = None
+    description: Optional[str] = None
+    id: Optional[str] = None
+    invoice: Optional[str] = None
+    last_payment_error: Optional[Any] = None
+    latest_charge: Optional[str] = None
+    livemode: Optional[bool] = None
+    metadata: Optional[Dict[str, Any]] = None
+    next_action: Optional[Any] = None
+    object: Optional[Object3] = None
+    on_behalf_of: Optional[Any] = None
+    payment_method: Optional[str] = None
+    payment_method_configuration_details: Optional[Any] = None
+    payment_method_options: Optional[StripePaymentMethodOptions] = None
+    payment_method_types: Optional[List[str]] = None
+    processing: Optional[Any] = None
+    receipt_email: Optional[str] = None
+    review: Optional[Any] = None
+    setup_future_usage: Optional[Any] = None
+    shipping: Optional[StripeShipping] = None
+    source: Optional[Any] = None
+    statement_descriptor: Optional[Any] = None
+    statement_descriptor_suffix: Optional[Any] = None
+    status: Optional[str] = None
+    transfer_data: Optional[Any] = None
+    transfer_group: Optional[Any] = None
+
+
+class TextResponseFormatConfiguration(
+    RootModel[
+        Union[
+            ResponseFormatText, TextResponseFormatJsonSchema, ResponseFormatJsonObject
+        ]
+    ]
+):
+    root: Union[
+        ResponseFormatText, TextResponseFormatJsonSchema, ResponseFormatJsonObject
+    ] = Field(
+        ...,
+        description='An object specifying the format that the model must output.\n\nConfiguring `{ "type": "json_schema" }` enables Structured Outputs, \nwhich ensures the model will match your supplied JSON schema. Learn more in the \n[Structured Outputs guide](/docs/guides/structured-outputs).\n\nThe default format is `{ "type": "text" }` with no additional options.\n\n**Not recommended for gpt-4o and newer models:**\n\nSetting to `{ "type": "json_object" }` enables the older JSON mode, which\nensures the message the model generates is valid JSON. Using `json_schema`\nis preferred for models that support it.\n',
+    )
+
+
+class Tool(
+    RootModel[
+        Union[
+            FileSearchTool, FunctionTool, WebSearchPreviewTool, ComputerUsePreviewTool
+        ]
+    ]
+):
+    root: Union[
+        FileSearchTool, FunctionTool, WebSearchPreviewTool, ComputerUsePreviewTool
+    ] = Field(..., discriminator='type')
+
+
+class BulkNodeVersionResult(BaseModel):
+    error_message: Optional[str] = Field(
+        None,
+        description='Error message if retrieval failed (only present if status is error)',
+    )
+    identifier: NodeVersionIdentifier
+    node_version: Optional[NodeVersion] = None
+    status: Status = Field(..., description='Status of the retrieval operation')
+
+
+class BulkNodeVersionsResponse(BaseModel):
+    node_versions: List[BulkNodeVersionResult] = Field(
+        ..., description='List of retrieved node versions with their status'
+    )
+
+
+class EasyInputMessage(BaseModel):
+    content: Union[str, InputMessageContentList] = Field(
+        ...,
+        description='Text, image, or audio input to the model, used to generate a response.\nCan also contain previous assistant responses.\n',
+    )
+    role: Role = Field(
+        ...,
+        description='The role of the message input. One of `user`, `assistant`, `system`, or\n`developer`.\n',
+    )
+    type: Optional[Type2] = Field(
+        None, description='The type of the message input. Always `message`.\n'
+    )
+
+
+class GeminiContent(BaseModel):
+    parts: List[GeminiPart]
+    role: Role1 = Field(..., examples=['user'])
+
+
+class GeminiGenerateContentRequest(BaseModel):
+    contents: List[GeminiContent]
+    generationConfig: Optional[GeminiGenerationConfig] = None
+    safetySettings: Optional[List[GeminiSafetySetting]] = None
+    systemInstruction: Optional[GeminiSystemInstructionContent] = None
+    tools: Optional[List[GeminiTool]] = None
+    videoMetadata: Optional[GeminiVideoMetadata] = None
+
+
+class GithubReleaseWebhook(BaseModel):
+    action: Action = Field(..., description='The action performed on the release')
+    enterprise: Optional[GithubEnterprise] = None
+    installation: Optional[GithubInstallation] = None
+    organization: Optional[GithubOrganization] = None
+    release: Release = Field(..., description='The release object')
+    repository: GithubRepository
+    sender: GithubUser
+
+
+class ImagenGenerateImageRequest(BaseModel):
+    instances: List[ImagenImageGenerationInstance]
+    parameters: ImagenImageGenerationParameters
+
+
+class InputMessage(BaseModel):
+    content: Optional[InputMessageContentList] = None
+    role: Optional[Role3] = None
+    status: Optional[Status3] = None
+    type: Optional[Type10] = None
+
+
+class Item(
+    RootModel[
+        Union[
+            InputMessage,
+            OutputMessage,
+            FileSearchToolCall,
+            ComputerToolCall,
+            WebSearchToolCall,
+            FunctionToolCall,
+            ReasoningItem,
+        ]
+    ]
+):
+    root: Union[
+        InputMessage,
+        OutputMessage,
+        FileSearchToolCall,
+        ComputerToolCall,
+        WebSearchToolCall,
+        FunctionToolCall,
+        ReasoningItem,
+    ] = Field(..., description='Content item used to generate a response.\n')
+
+
+class LumaGeneration(BaseModel):
+    assets: Optional[LumaAssets] = None
+    created_at: Optional[datetime] = Field(
+        None, description='The date and time when the generation was created'
+    )
+    failure_reason: Optional[str] = Field(
+        None, description='The reason for the state of the generation'
+    )
+    generation_type: Optional[LumaGenerationType] = None
+    id: Optional[UUID] = Field(None, description='The ID of the generation')
+    model: Optional[str] = Field(None, description='The model used for the generation')
+    request: Optional[
+        Union[
+            LumaGenerationRequest,
+            LumaImageGenerationRequest,
+            LumaUpscaleVideoGenerationRequest,
+            LumaAudioGenerationRequest,
+        ]
+    ] = Field(None, description='The request of the generation')
+    state: Optional[LumaState] = None
+
+
+class OutputItem(
+    RootModel[
+        Union[
+            OutputMessage,
+            FileSearchToolCall,
+            FunctionToolCall,
+            WebSearchToolCall,
+            ComputerToolCall,
+            ReasoningItem,
+        ]
+    ]
+):
+    root: Union[
+        OutputMessage,
+        FileSearchToolCall,
+        FunctionToolCall,
+        WebSearchToolCall,
+        ComputerToolCall,
+        ReasoningItem,
+    ]
+
+
+class Publisher(BaseModel):
+    createdAt: Optional[datetime] = Field(
+        None, description='The date and time the publisher was created.'
+    )
+    description: Optional[str] = None
+    id: Optional[str] = Field(
+        None,
+        description="The unique identifier for the publisher. It's akin to a username. Should be lowercase.",
+    )
+    logo: Optional[str] = Field(None, description="URL to the publisher's logo.")
+    members: Optional[List[PublisherMember]] = Field(
+        None, description='A list of members in the publisher.'
+    )
+    name: Optional[str] = None
+    source_code_repo: Optional[str] = None
+    status: Optional[PublisherStatus] = None
+    support: Optional[str] = None
+    website: Optional[str] = None
+
+
+class RecraftGenerateImageResponse(BaseModel):
+    created: int
+    credits: int
+    data: List[RecraftImage]
+
+
+class RecraftImageToImageRequest(BaseModel):
+    block_nsfw: Optional[bool] = None
+    calculate_features: Optional[bool] = None
+    controls: Optional[RecraftUserControls] = None
+    image: StrictBytes
+    image_format: Optional[RecraftImageFormat] = None
+    model: Optional[RecraftTransformModel] = None
+    n: Optional[int] = None
+    negative_prompt: Optional[str] = None
+    prompt: str
+    response_format: Optional[RecraftResponseFormat] = None
+    strength: float
+    style: Optional[RecraftImageStyle] = None
+    style_id: Optional[UUID] = None
+    substyle: Optional[RecraftImageSubStyle] = None
+    text_layout: Optional[RecraftTextLayout] = None
+
+
+class ResponseOutputItemAddedEvent(BaseModel):
+    item: OutputItem
+    output_index: int = Field(
+        ..., description='The index of the output item that was added.\n'
+    )
+    type: Type29 = Field(
+        ..., description='The type of the event. Always `response.output_item.added`.\n'
+    )
+
+
+class ResponseOutputItemDoneEvent(BaseModel):
+    item: OutputItem
+    output_index: int = Field(
+        ..., description='The index of the output item that was marked done.\n'
+    )
+    type: Type30 = Field(
+        ..., description='The type of the event. Always `response.output_item.done`.\n'
+    )
+
+
+class Text(BaseModel):
+    format: Optional[TextResponseFormatConfiguration] = None
+
+
+class ResponseProperties(BaseModel):
+    instructions: Optional[str] = Field(
+        None,
+        description="Inserts a system (or developer) message as the first item in the model's context.\n\nWhen using along with `previous_response_id`, the instructions from a previous\nresponse will not be carried over to the next response. This makes it simple\nto swap out system (or developer) messages in new responses.\n",
+    )
+    max_output_tokens: Optional[int] = Field(
+        None,
+        description='An upper bound for the number of tokens that can be generated for a response, including visible output tokens and [reasoning tokens](/docs/guides/reasoning).\n',
+    )
+    model: Optional[OpenAIModels] = None
+    previous_response_id: Optional[str] = Field(
+        None,
+        description='The unique ID of the previous response to the model. Use this to\ncreate multi-turn conversations. Learn more about \n[conversation state](/docs/guides/conversation-state).\n',
+    )
+    reasoning: Optional[Reasoning] = None
+    text: Optional[Text] = None
+    tool_choice: Optional[
+        Union[ToolChoiceOptions, ToolChoiceTypes, ToolChoiceFunction]
+    ] = Field(
+        None,
+        description='How the model should select which tool (or tools) to use when generating\na response. See the `tools` parameter to see how to specify which tools\nthe model can call.\n',
+    )
+    tools: Optional[List[Tool]] = None
+    truncation: Optional[Truncation1] = Field(
+        'disabled',
+        description="The truncation strategy to use for the model response.\n- `auto`: If the context of this response and previous ones exceeds\n  the model's context window size, the model will truncate the \n  response to fit the context window by dropping input items in the\n  middle of the conversation. \n- `disabled` (default): If a model response will exceed the context window \n  size for a model, the request will fail with a 400 error.\n",
+    )
+
+
+class Rodin3DCheckStatusResponse(BaseModel):
+    jobs: Optional[List[RodinCheckStatusJobItem]] = Field(
+        None, description='Details for the generation status.'
+    )
+
+
+class Data8(BaseModel):
+    object: Optional[StripePaymentIntent] = None
+
+
+class StripeEvent(BaseModel):
+    api_version: Optional[str] = None
+    created: Optional[int] = None
+    data: Data8
+    id: str
+    livemode: Optional[bool] = None
+    object: Object2
+    pending_webhooks: Optional[int] = None
+    request: Optional[StripeRequestInfo] = None
+    type: Type31
+
+
+class GeminiCandidate(BaseModel):
+    citationMetadata: Optional[GeminiCitationMetadata] = None
+    content: Optional[GeminiContent] = None
+    finishReason: Optional[str] = None
+    safetyRatings: Optional[List[GeminiSafetyRating]] = None
+
+
+class GeminiGenerateContentResponse(BaseModel):
+    candidates: Optional[List[GeminiCandidate]] = None
+    promptFeedback: Optional[GeminiPromptFeedback] = None
+    usageMetadata: Optional[GeminiUsageMetadata] = None
+
+
+class InputItem(RootModel[Union[EasyInputMessage, Item]]):
+    root: Union[EasyInputMessage, Item]
+
+
+class Node(BaseModel):
+    author: Optional[str] = None
+    banner_url: Optional[str] = Field(None, description="URL to the node's banner.")
+    category: Optional[str] = Field(None, description='The category of the node.')
+    created_at: Optional[datetime] = Field(
+        None, description='The date and time when the node was created'
+    )
+    description: Optional[str] = None
+    downloads: Optional[int] = Field(
+        None, description='The number of downloads of the node.'
+    )
+    github_stars: Optional[int] = Field(
+        None, description='Number of stars on the GitHub repository.'
+    )
+    icon: Optional[str] = Field(None, description="URL to the node's icon.")
+    id: Optional[str] = Field(None, description='The unique identifier of the node.')
+    latest_version: Optional[NodeVersion] = None
+    license: Optional[str] = Field(
+        None, description="The path to the LICENSE file in the node's repository."
+    )
+    name: Optional[str] = Field(None, description='The display name of the node.')
+    preempted_comfy_node_names: Optional[List[str]] = Field(
+        None, description='A list of Comfy node names that are preempted by this node.'
+    )
+    publisher: Optional[Publisher] = None
+    rating: Optional[float] = Field(None, description='The average rating of the node.')
+    repository: Optional[str] = Field(None, description="URL to the node's repository.")
+    search_ranking: Optional[int] = Field(
+        None,
+        description="A numerical value representing the node's search ranking, used for sorting search results.",
+    )
+    status: Optional[NodeStatus] = None
+    status_detail: Optional[str] = Field(
+        None, description='The status detail of the node.'
+    )
+    supported_accelerators: Optional[List[str]] = Field(
+        None,
+        description='List of accelerators (e.g. CUDA, DirectML, ROCm) that this node supports',
+    )
+    supported_comfyui_frontend_version: Optional[str] = Field(
+        None, description='Supported versions of ComfyUI frontend'
+    )
+    supported_comfyui_version: Optional[str] = Field(
+        None, description='Supported versions of ComfyUI'
+    )
+    supported_os: Optional[List[str]] = Field(
+        None, description='List of operating systems that this node supports'
+    )
+    tags: Optional[List[str]] = None
+    translations: Optional[Dict[str, Dict[str, Any]]] = Field(
+        None, description='Translations of node metadata in different languages.'
+    )
+
+
+class OpenAICreateResponse(CreateModelResponseProperties, ResponseProperties):
+    include: Optional[List[Includable]] = Field(
+        None,
+        description='Specify additional output data to include in the model response. Currently\nsupported values are:\n- `file_search_call.results`: Include the search results of\n  the file search tool call.\n- `message.input_image.image_url`: Include image urls from the input message.\n- `computer_call_output.output.image_url`: Include image urls from the computer call output.\n',
+    )
+    input: Union[str, List[InputItem]] = Field(
+        ...,
+        description='Text, image, or file inputs to the model, used to generate a response.\n\nLearn more:\n- [Text inputs and outputs](/docs/guides/text)\n- [Image inputs](/docs/guides/images)\n- [File inputs](/docs/guides/pdf-files)\n- [Conversation state](/docs/guides/conversation-state)\n- [Function calling](/docs/guides/function-calling)\n',
+    )
+    parallel_tool_calls: Optional[bool] = Field(
+        True, description='Whether to allow the model to run tool calls in parallel.\n'
+    )
+    store: Optional[bool] = Field(
+        True,
+        description='Whether to store the generated model response for later retrieval via\nAPI.\n',
+    )
+    stream: Optional[bool] = Field(
+        False,
+        description='If set to true, the model response data will be streamed to the client\nas it is generated using [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).\nSee the [Streaming section below](/docs/api-reference/responses-streaming)\nfor more information.\n',
+    )
+    usage: Optional[ResponseUsage] = None
+
+
+class OpenAIResponse(ModelResponseProperties, ResponseProperties):
+    created_at: Optional[float] = Field(
+        None,
+        description='Unix timestamp (in seconds) of when this Response was created.',
+    )
+    error: Optional[ResponseError] = None
+    id: Optional[str] = Field(None, description='Unique identifier for this Response.')
+    incomplete_details: Optional[IncompleteDetails] = Field(
+        None, description='Details about why the response is incomplete.\n'
+    )
+    object: Optional[Object] = Field(
+        None, description='The object type of this resource - always set to `response`.'
+    )
+    output: Optional[List[OutputItem]] = Field(
+        None,
+        description="An array of content items generated by the model.\n\n- The length and order of items in the `output` array is dependent\n  on the model's response.\n- Rather than accessing the first item in the `output` array and \n  assuming it's an `assistant` message with the content generated by\n  the model, you might consider using the `output_text` property where\n  supported in SDKs.\n",
+    )
+    output_text: Optional[str] = Field(
+        None,
+        description='SDK-only convenience property that contains the aggregated text output \nfrom all `output_text` items in the `output` array, if any are present. \nSupported in the Python and JavaScript SDKs.\n',
+    )
+    parallel_tool_calls: Optional[bool] = Field(
+        True, description='Whether to allow the model to run tool calls in parallel.\n'
+    )
+    status: Optional[Status7] = Field(
+        None,
+        description='The status of the response generation. One of `completed`, `failed`, `in_progress`, or `incomplete`.',
+    )
+    usage: Optional[ResponseUsage] = None
+
+
+class ResponseCompletedEvent(BaseModel):
+    response: OpenAIResponse
+    type: Type19 = Field(
+        ..., description='The type of the event. Always `response.completed`.'
+    )
+
+
+class ResponseCreatedEvent(BaseModel):
+    response: OpenAIResponse
+    type: Type22 = Field(
+        ..., description='The type of the event. Always `response.created`.'
+    )
+
+
+class ResponseFailedEvent(BaseModel):
+    response: OpenAIResponse
+    type: Type24 = Field(
+        ..., description='The type of the event. Always `response.failed`.\n'
+    )
+
+
+class ResponseInProgressEvent(BaseModel):
+    response: OpenAIResponse
+    type: Type27 = Field(
+        ..., description='The type of the event. Always `response.in_progress`.\n'
+    )
+
+
+class ResponseIncompleteEvent(BaseModel):
+    response: OpenAIResponse
+    type: Type28 = Field(
+        ..., description='The type of the event. Always `response.incomplete`.\n'
+    )
+
+
+class OpenAIResponseStreamEvent(
+    RootModel[
+        Union[
+            ResponseCreatedEvent,
+            ResponseInProgressEvent,
+            ResponseCompletedEvent,
+            ResponseFailedEvent,
+            ResponseIncompleteEvent,
+            ResponseOutputItemAddedEvent,
+            ResponseOutputItemDoneEvent,
+            ResponseContentPartAddedEvent,
+            ResponseContentPartDoneEvent,
+            ResponseErrorEvent,
+        ]
+    ]
+):
+    root: Union[
+        ResponseCreatedEvent,
+        ResponseInProgressEvent,
+        ResponseCompletedEvent,
+        ResponseFailedEvent,
+        ResponseIncompleteEvent,
+        ResponseOutputItemAddedEvent,
+        ResponseOutputItemDoneEvent,
+        ResponseContentPartAddedEvent,
+        ResponseContentPartDoneEvent,
+        ResponseErrorEvent,
+    ] = Field(..., description='Events that can be emitted during response streaming')
diff --git a/comfy_api_nodes/apis/anthropic.py b/comfy_api_nodes/apis/anthropic.py
new file mode 100644
index 0000000000000000000000000000000000000000..115d0c19ab84eb0edae495ef9b9c7d645f89d229
--- /dev/null
+++ b/comfy_api_nodes/apis/anthropic.py
@@ -0,0 +1,98 @@
+from enum import Enum
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class AnthropicRole(str, Enum):
+    user = "user"
+    assistant = "assistant"
+
+
+class AnthropicTextContent(BaseModel):
+    type: Literal["text"] = "text"
+    text: str = Field(...)
+
+
+class AnthropicImageSourceBase64(BaseModel):
+    type: Literal["base64"] = "base64"
+    media_type: str = Field(..., description="MIME type of the image, e.g. image/png, image/jpeg")
+    data: str = Field(..., description="Base64-encoded image data")
+
+
+class AnthropicImageSourceUrl(BaseModel):
+    type: Literal["url"] = "url"
+    url: str = Field(...)
+
+
+class AnthropicImageContent(BaseModel):
+    type: Literal["image"] = "image"
+    source: AnthropicImageSourceBase64 | AnthropicImageSourceUrl = Field(...)
+
+
+class AnthropicMessage(BaseModel):
+    role: AnthropicRole = Field(...)
+    content: list[AnthropicTextContent | AnthropicImageContent] = Field(...)
+
+
+class AnthropicThinkingConfig(BaseModel):
+    type: Literal["enabled", "disabled", "adaptive"] = Field(...)
+    budget_tokens: int | None = Field(
+        None, ge=1024,
+        description="Reasoning budget in tokens. Used when type is 'enabled'. Must be less than max_tokens.",
+    )
+
+
+class AnthropicOutputConfig(BaseModel):
+    """Used with `thinking.type='adaptive'` on models like Opus 4.7."""
+    effort: Literal["low", "medium", "high"] | None = Field(None)
+
+
+class AnthropicMessagesRequest(BaseModel):
+    model: str = Field(...)
+    messages: list[AnthropicMessage] = Field(...)
+    max_tokens: int = Field(..., ge=1)
+    system: str | None = Field(None, description="Top-level system prompt")
+    temperature: float | None = Field(None, ge=0.0, le=1.0)
+    top_p: float | None = Field(None, ge=0.0, le=1.0)
+    top_k: int | None = Field(None, ge=0)
+    stop_sequences: list[str] | None = Field(None)
+    thinking: AnthropicThinkingConfig | None = Field(None)
+    output_config: AnthropicOutputConfig | None = Field(None)
+
+
+class AnthropicResponseTextBlock(BaseModel):
+    type: Literal["text"] = "text"
+    text: str = Field(...)
+
+
+class AnthropicResponseThinkingBlock(BaseModel):
+    type: Literal["thinking"] = "thinking"
+    thinking: str = Field(...)
+
+
+AnthropicResponseBlock = AnthropicResponseTextBlock | AnthropicResponseThinkingBlock
+
+
+class AnthropicCacheCreationUsage(BaseModel):
+    ephemeral_5m_input_tokens: int | None = Field(None)
+    ephemeral_1h_input_tokens: int | None = Field(None)
+
+
+class AnthropicMessagesUsage(BaseModel):
+    input_tokens: int | None = Field(None)
+    output_tokens: int | None = Field(None)
+    cache_creation_input_tokens: int | None = Field(None)
+    cache_read_input_tokens: int | None = Field(None)
+    cache_creation: AnthropicCacheCreationUsage | None = Field(None)
+
+
+class AnthropicMessagesResponse(BaseModel):
+    id: str | None = Field(None)
+    type: str | None = Field(None)
+    role: str | None = Field(None)
+    model: str | None = Field(None)
+    content: list[AnthropicResponseBlock] | None = Field(None)
+    stop_reason: str | None = Field(None)
+    stop_sequence: str | None = Field(None)
+    usage: AnthropicMessagesUsage | None = Field(None)
diff --git a/comfy_api_nodes/apis/beeble.py b/comfy_api_nodes/apis/beeble.py
new file mode 100644
index 0000000000000000000000000000000000000000..52203d59727ce87e37ad87bb8eb37b8885310d59
--- /dev/null
+++ b/comfy_api_nodes/apis/beeble.py
@@ -0,0 +1,32 @@
+from pydantic import BaseModel, Field
+
+
+class CreateSwitchXRequest(BaseModel):
+    generation_type: str = Field(...)
+    source_uri: str = Field(...)
+    alpha_mode: str = Field(...)
+    prompt: str | None = Field(None, max_length=2000)
+    reference_image_uri: str | None = Field(None)
+    alpha_uri: str | None = Field(None)
+    max_resolution: int = Field(1080)
+    callback_url: str | None = Field(None)
+    idempotency_key: str | None = Field(None, max_length=256, min_length=1)
+
+
+class SwitchXOutputUrls(BaseModel):
+    render: str | None = Field(None)
+    source: str | None = Field(None)
+    alpha: str | None = Field(None)
+
+
+class SwitchXStatusResponse(BaseModel):
+    id: str = Field(...)
+    status: str = Field(...)
+    progress: int | None = Field(None)
+    generation_type: str | None = Field(None)
+    alpha_mode: str | None = Field(None)
+    output: SwitchXOutputUrls | None = Field(None)
+    error: str | None = Field(None)
+    created_at: str | None = Field(None)
+    modified_at: str | None = Field(None)
+    completed_at: str | None = Field(None)
diff --git a/comfy_api_nodes/apis/bfl.py b/comfy_api_nodes/apis/bfl.py
new file mode 100644
index 0000000000000000000000000000000000000000..58e915fa50bac8c3c85a1c46404c6d58de1cdc3f
--- /dev/null
+++ b/comfy_api_nodes/apis/bfl.py
@@ -0,0 +1,178 @@
+from enum import Enum
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class BFLFluxExpandImageRequest(BaseModel):
+    prompt: str = Field(...)
+    prompt_upsampling: bool | None = Field(None)
+    seed: int | None = Field(None)
+    top: int = Field(...)
+    bottom: int = Field(...)
+    left: int = Field(...)
+    right: int = Field(...)
+    steps: int = Field(...)
+    guidance: float = Field(...)
+    safety_tolerance: int = Field(6)
+    output_format: str = Field("png")
+    image: str = Field(None, description="A Base64-encoded string representing the image you wish to expand")
+
+
+class BFLFluxFillImageRequest(BaseModel):
+    prompt: str = Field(...)
+    prompt_upsampling: bool | None = Field(None)
+    seed: int | None = Field(None)
+    steps: int = Field(...)
+    guidance: float = Field(...)
+    safety_tolerance: int = Field(6)
+    output_format: str = Field("png")
+    image: str = Field(
+        None, description="Base64-encoded string representing the image to modify. Can contain alpha mask if desired.",
+    )
+    mask: str = Field(
+        None, description="Base64-encoded string representing the mask of the areas you wish to modify."
+    )
+
+
+class BFLFluxEraseRequest(BaseModel):
+    image: str = Field(..., description="A Base64-encoded string representing the image to erase from.")
+    mask: str = Field(
+        ...,
+        description="A Base64-encoded black/white mask matching the input dimensions; "
+        "white (255) marks areas to remove, black (0) marks areas to preserve.",
+    )
+    dilate_pixels: int = Field(10)
+    seed: int | None = Field(None)
+    output_format: str = Field("png")
+
+
+class BFLFluxVTORequest(BaseModel):
+    prompt: str = Field(
+        ..., description="Natural-language styling instruction. Required field, but may be an empty string."
+    )
+    person: str = Field(..., description="A Base64-encoded string representing the person image.")
+    garment: str = Field(..., description="A Base64-encoded string representing the garment reference image.")
+    seed: int | None = Field(None)
+    safety_tolerance: int = Field(5)
+    output_format: str = Field("png")
+
+
+class BFLFluxProGenerateRequest(BaseModel):
+    prompt: str = Field(...)
+    prompt_upsampling: bool | None = Field(None)
+    seed: int | None = Field(None)
+    width: int = Field(1024, description="Must be a multiple of 32.")
+    height: int = Field(768, description="Must be a multiple of 32.")
+    safety_tolerance: int = Field(6)
+    output_format: str = Field("png")
+    image_prompt: str | None = Field(None, description="Optional image to remix in base64 format")
+
+
+class Flux2ProGenerateRequest(BaseModel):
+    prompt: str = Field(...)
+    width: int = Field(1024, description="Must be a multiple of 32.")
+    height: int = Field(768, description="Must be a multiple of 32.")
+    seed: int | None = Field(None)
+    prompt_upsampling: bool | None = Field(None)
+    input_image: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_2: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_3: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_4: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_5: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_6: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_7: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_8: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_9: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    safety_tolerance: int = Field(5)
+    output_format: str = Field("png")
+
+
+class BFLFluxKontextProGenerateRequest(BaseModel):
+    prompt: str = Field(...)
+    input_image: str | None = Field(None, description="Image to edit in base64 format")
+    seed: int | None = Field(None)
+    guidance: float = Field(...)
+    steps: int = Field(...)
+    safety_tolerance: int = Field(2)
+    output_format: str = Field("png")
+    aspect_ratio: str | None = Field(None)
+    prompt_upsampling: bool | None = Field(None)
+
+
+class BFLFluxProUltraGenerateRequest(BaseModel):
+    prompt: str = Field(...)
+    prompt_upsampling: bool | None = Field(None)
+    seed: int | None = Field(None)
+    aspect_ratio: str | None = Field(None)
+    safety_tolerance: int = Field(6)
+    output_format: str = Field("png")
+    raw: bool | None = Field(None)
+    image_prompt: str | None = Field(None, description="Optional image to remix in base64 format")
+    image_prompt_strength: float | None = Field(None)
+
+
+class BFLFluxProGenerateResponse(BaseModel):
+    id: str = Field(...)
+    polling_url: str = Field(...)
+    cost: float | None = Field(None, description="Price in cents")
+
+
+class BFLStatus(str, Enum):
+    task_not_found = "Task not found"
+    pending = "Pending"
+    reasoning = "Reasoning"
+    generating = "Generating"
+    request_moderated = "Request Moderated"
+    content_moderated = "Content Moderated"
+    ready = "Ready"
+    error = "Error"
+
+
+class BFLFluxStatusResponse(BaseModel):
+    id: str = Field(...)
+    status: BFLStatus = Field(...)
+    result: dict[str, Any] | None = Field(None)
+    progress: float | None = Field(None, ge=0.0, le=1.0)
+
+
+class Flux3VideoRequest(BaseModel):
+    """Fields shared by every generation mode of /v1/flux-3-video."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    prompt: str = Field(...)
+    aspect_ratio: str = Field("auto")
+    duration: int | str = Field("auto", description="Whole seconds, or 'auto'.")
+    resolution: str = Field("hd", description="'hd' is the 720p class, 'fhd' the 1080p class.")
+    generate_audio: bool = Field(True)
+    safety_tolerance: int = Field(2, description="0 is the strictest; conditioned modes cap at 2.")
+
+
+class Flux3TextToVideoRequest(Flux3VideoRequest):
+    mode: str = Field("t2v")
+
+
+class Flux3ImageToVideoRequest(Flux3VideoRequest):
+    mode: str = Field("i2v")
+    keyframes: list[str] | list[tuple[float, str]] = Field(
+        ...,
+        description="Images (URL or base64), or [seconds, image] pairs pinning each to a time.",
+    )
+
+
+class Flux3VideoContinuationRequest(Flux3VideoRequest):
+    mode: str = Field("v2v")
+    start_video: str = Field(
+        ..., description="MP4 (URL or base64); the new clip carries on from its final frames."
+    )
+
+
+class BFLFluxVideoUpscaleRequest(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    input_video: str = Field(..., description="MP4 (URL or base64), 1 to 20 seconds.")
+    upscale_factor: float = Field(2.0, ge=1.5, le=3.0)
+    creativity: int = Field(1, description="0 preserves the source precisely, 1 enhances detail.")
+    prompt: str | None = Field(None)
+    safety_tolerance: int = Field(2, ge=0, le=4)
diff --git a/comfy_api_nodes/apis/bria.py b/comfy_api_nodes/apis/bria.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c8848828fb915be71c9f20731527eaf406ad66c
--- /dev/null
+++ b/comfy_api_nodes/apis/bria.py
@@ -0,0 +1,219 @@
+from typing import TypedDict
+
+from pydantic import BaseModel, Field
+
+
+class InputModerationSettings(TypedDict):
+    prompt_content_moderation: bool
+    visual_input_moderation: bool
+    visual_output_moderation: bool
+
+
+class BriaEditImageRequest(BaseModel):
+    instruction: str | None = Field(...)
+    structured_instruction: str | None = Field(
+        ...,
+        description="Use this instead of instruction for precise, programmatic control.",
+    )
+    images: list[str] = Field(
+        ...,
+        description="Required. Publicly available URL or Base64-encoded. Must contain exactly one item.",
+    )
+    mask: str | None = Field(
+        None,
+        description="Mask image (black and white). Black areas will be preserved, white areas will be edited. "
+        "If omitted, the edit applies to the entire image. "
+        "The input image and the input mask must be of the same size.",
+    )
+    negative_prompt: str | None = Field(None)
+    guidance_scale: float = Field(...)
+    model_version: str = Field(...)
+    steps_num: int = Field(...)
+    seed: int = Field(...)
+    ip_signal: bool = Field(
+        False,
+        description="If true, returns a warning for potential IP content in the instruction.",
+    )
+    prompt_content_moderation: bool = Field(
+        False, description="If true, returns 422 on instruction moderation failure."
+    )
+    visual_input_content_moderation: bool = Field(
+        False, description="If true, returns 422 on images or mask moderation failure."
+    )
+    visual_output_content_moderation: bool = Field(
+        False, description="If true, returns 422 on visual output moderation failure."
+    )
+
+
+class BriaRemoveBackgroundRequest(BaseModel):
+    image: str = Field(...)
+    sync: bool = Field(False)
+    visual_input_content_moderation: bool = Field(
+        False, description="If true, returns 422 on input image moderation failure."
+    )
+    visual_output_content_moderation: bool = Field(
+        False, description="If true, returns 422 on visual output moderation failure."
+    )
+    seed: int = Field(...)
+
+
+class BriaGenFillRequest(BaseModel):
+    image: str = Field(...)
+    mask: str = Field(
+        ...,
+        description="Binary mask defining the region to fill: white (255) pixels are generated, "
+        "black (0) pixels are preserved. Must have the same aspect ratio as the image.",
+    )
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    refine_prompt: bool = Field(True)
+    seed: int = Field(...)
+    prompt_content_moderation: bool = Field(False, description="If true, returns 422 on prompt moderation failure.")
+    visual_input_content_moderation: bool = Field(
+        False, description="If true, returns 422 on image or mask moderation failure."
+    )
+    visual_output_content_moderation: bool = Field(
+        False, description="If true, returns 422 on visual output moderation failure."
+    )
+
+
+class BriaEraseRequest(BaseModel):
+    image: str = Field(...)
+    mask: str = Field(
+        ...,
+        description="Binary mask defining the region to erase: white (255) pixels are removed, "
+        "black (0) pixels are preserved. Must have the same aspect ratio as the image.",
+    )
+    mask_type: str = Field("manual", description="'manual' for hand-drawn masks, 'automatic' for segmentation masks.")
+    visual_input_content_moderation: bool = Field(
+        False, description="If true, returns 422 on image or mask moderation failure."
+    )
+    visual_output_content_moderation: bool = Field(
+        False, description="If true, returns 422 on visual output moderation failure."
+    )
+
+
+class BriaExpandRequest(BaseModel):
+    image: str = Field(...)
+    aspect_ratio: str | float | None = Field(
+        None,
+        description="Target ratio: a preset string (1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9) "
+        "or a float between 0.5 and 3.0. When set, the canvas/placement fields are ignored.",
+    )
+    canvas_size: list[int] | None = Field(None, description="Output canvas [width, height]; area up to 5000x5000.")
+    original_image_size: list[int] | None = Field(
+        None, description="Size [width, height] of the original image inside the canvas."
+    )
+    original_image_location: list[int] | None = Field(
+        None,
+        description="Top-left corner [x, y] of the original image inside the canvas; "
+        "values may fall outside the canvas, cropping the image.",
+    )
+    prompt: str | None = Field(None, description="If omitted, Bria auto-generates a prompt from the image.")
+    negative_prompt: str | None = Field(None)
+    seed: int = Field(...)
+    prompt_content_moderation: bool = Field(False, description="If true, returns 422 on prompt moderation failure.")
+    visual_input_content_moderation: bool = Field(
+        False, description="If true, returns 422 on image moderation failure."
+    )
+    visual_output_content_moderation: bool = Field(
+        False, description="If true, returns 422 on visual output moderation failure."
+    )
+
+
+class BriaIncreaseResolutionRequest(BaseModel):
+    image: str = Field(...)
+    desired_increase: int = Field(..., description="Resolution multiplier, 2 or 4.")
+    visual_input_content_moderation: bool = Field(
+        False, description="If true, returns 422 on image moderation failure."
+    )
+    visual_output_content_moderation: bool = Field(
+        False, description="If true, returns 422 on visual output moderation failure."
+    )
+
+
+class BriaStatusResponse(BaseModel):
+    request_id: str = Field(...)
+    status_url: str = Field(...)
+    warning: str | None = Field(None)
+
+
+class BriaRemoveBackgroundResult(BaseModel):
+    image_url: str = Field(...)
+
+
+class BriaRemoveBackgroundResponse(BaseModel):
+    status: str = Field(...)
+    result: BriaRemoveBackgroundResult | None = Field(None)
+
+
+class BriaImageResult(BaseModel):
+    image_url: str = Field(...)
+
+
+class BriaImageResultResponse(BaseModel):
+    status: str = Field(...)
+    result: BriaImageResult | None = Field(None)
+
+
+class BriaExpandResult(BaseModel):
+    image_url: str = Field(...)
+    prompt: str | None = Field(None)
+    seed: int | None = Field(None)
+
+
+class BriaExpandResponse(BaseModel):
+    status: str = Field(...)
+    result: BriaExpandResult | None = Field(None)
+
+
+class BriaImageEditResult(BaseModel):
+    structured_prompt: str = Field(...)
+    image_url: str = Field(...)
+
+
+class BriaImageEditResponse(BaseModel):
+    status: str = Field(...)
+    result: BriaImageEditResult | None = Field(None)
+
+
+class BriaRemoveVideoBackgroundRequest(BaseModel):
+    video: str = Field(...)
+    background_color: str = Field(default="transparent", description="Background color for the output video.")
+    output_container_and_codec: str = Field(...)
+    preserve_audio: bool = Field(True)
+    seed: int = Field(...)
+
+
+class BriaRemoveVideoBackgroundResult(BaseModel):
+    video_url: str = Field(...)
+
+
+class BriaRemoveVideoBackgroundResponse(BaseModel):
+    status: str = Field(...)
+    result: BriaRemoveVideoBackgroundResult | None = Field(None)
+
+
+class BriaVideoGreenScreenRequest(BaseModel):
+    video: str = Field(..., description="Publicly accessible URL of the input video.")
+    green_shade: str = Field(
+        default="broadcast_green",
+        description="Solid chroma-key shade applied behind the foreground "
+        "(broadcast_green, chroma_green, or blue_screen).",
+    )
+    output_container_and_codec: str = Field(...)
+    preserve_audio: bool = Field(True)
+    seed: int = Field(...)
+
+
+class BriaVideoReplaceBackgroundRequest(BaseModel):
+    video: str = Field(..., description="Publicly accessible URL of the input (foreground) video.")
+    background_url: str = Field(
+        ...,
+        description="Publicly accessible URL of the background image or video to composite behind "
+        "the foreground. Stretched to the foreground frame; match its aspect ratio for "
+        "undistorted results.",
+    )
+    output_container_and_codec: str = Field(...)
+    preserve_audio: bool = Field(True)
+    seed: int = Field(...)
diff --git a/comfy_api_nodes/apis/bytedance.py b/comfy_api_nodes/apis/bytedance.py
new file mode 100644
index 0000000000000000000000000000000000000000..95d948550f5f069cc52768e5720401e6255513b8
--- /dev/null
+++ b/comfy_api_nodes/apis/bytedance.py
@@ -0,0 +1,430 @@
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+
+class Text2ImageTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(...)
+    response_format: str | None = Field("url")
+    size: str | None = Field(None)
+    seed: int | None = Field(0, ge=0, le=2147483647)
+    guidance_scale: float | None = Field(..., ge=1.0, le=10.0)
+    watermark: bool | None = Field(False)
+
+
+class Seedream4Options(BaseModel):
+    max_images: int = Field(15)
+
+
+class Seedream5OptimizePromptOptions(BaseModel):
+    thinking: Literal["auto", "enabled", "disabled"] | None = Field(None)
+    mode: Literal["standard", "fast"] | None = Field(None)
+
+
+class Seedream4TaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(...)
+    response_format: str = Field("url")
+    image: list[str] | None = Field(None, description="Image URLs")
+    size: str = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    sequential_image_generation: str | None = Field("disabled")
+    sequential_image_generation_options: Seedream4Options | None = Field(Seedream4Options(max_images=15))
+    watermark: bool = Field(False)
+    output_format: str | None = None
+    optimize_prompt_options: Seedream5OptimizePromptOptions | None = None
+
+
+class Seedream5LayerOptimizePromptOptions(BaseModel):
+    mode: Literal["standard", "fast"] = Field(...)
+
+
+class Seedream5LayerSeparationRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str | None = Field(None)
+    image: str = Field(..., description="Single image URL")
+    size: str = Field("auto")
+    seed: int = Field(..., ge=0, le=2147483647)
+    response_format: str = Field("url")
+    output_format: str = Field("png")
+    layer_decomposition: bool = Field(True)
+    watermark: bool = Field(False)
+    optimize_prompt_options: Seedream5LayerOptimizePromptOptions | None = Field(None)
+
+
+class ImageTaskCreationResponse(BaseModel):
+    model: str = Field(...)
+    created: int = Field(..., description="Unix timestamp (in seconds) indicating time when the request was created.")
+    data: list = Field([], description="Contains information about the generated image(s).")
+    error: dict = Field({}, description="Contains `code` and `message` fields in case of error.")
+
+
+class TaskTextContent(BaseModel):
+    type: str = Field("text")
+    text: str = Field(...)
+
+
+class TaskImageContentUrl(BaseModel):
+    url: str = Field(...)
+
+
+class TaskImageContent(BaseModel):
+    type: str = Field("image_url")
+    image_url: TaskImageContentUrl = Field(...)
+    role: Literal["first_frame", "last_frame", "reference_image"] | None = Field(None)
+
+
+class TaskVideoContentUrl(BaseModel):
+    url: str = Field(...)
+
+
+class TaskVideoContent(BaseModel):
+    type: str = Field("video_url")
+    video_url: TaskVideoContentUrl = Field(...)
+    role: str = Field("reference_video")
+
+
+class TaskAudioContentUrl(BaseModel):
+    url: str = Field(...)
+
+
+class TaskAudioContent(BaseModel):
+    type: str = Field("audio_url")
+    audio_url: TaskAudioContentUrl = Field(...)
+    role: str = Field("reference_audio")
+
+
+class Text2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    content: list[TaskTextContent] = Field(..., min_length=1)
+    generate_audio: bool | None = Field(...)
+
+
+class Image2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    content: list[TaskTextContent | TaskImageContent] = Field(..., min_length=2)
+    generate_audio: bool | None = Field(...)
+
+
+class Seedance2TaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    content: list[TaskTextContent | TaskImageContent | TaskVideoContent | TaskAudioContent] = Field(..., min_length=1)
+    generate_audio: bool | None = Field(None)
+    resolution: str | None = Field(None)
+    ratio: str | None = Field(None)
+    duration: int | None = Field(None)
+    seed: int | None = Field(None, ge=0, le=2147483647)
+    watermark: bool | None = Field(None)
+    output_format: str | None = Field(None)
+    omni_reference_task_type: str | None = Field(None, description="One of: auto, reference, edit, extend.")
+
+
+class TaskCreationResponse(BaseModel):
+    id: str = Field(...)
+
+
+class TaskStatusError(BaseModel):
+    code: str = Field(...)
+    message: str = Field(...)
+
+
+class TaskStatusResult(BaseModel):
+    video_url: str = Field(...)
+
+
+class TaskStatusUsage(BaseModel):
+    completion_tokens: int = Field(0)
+    total_tokens: int = Field(0)
+
+
+class TaskStatusResponse(BaseModel):
+    id: str = Field(...)
+    model: str = Field(...)
+    status: Literal["queued", "running", "cancelled", "succeeded", "failed"] = Field(...)
+    error: TaskStatusError | None = Field(None)
+    content: TaskStatusResult | None = Field(None)
+    usage: TaskStatusUsage | None = Field(None)
+
+
+class GetAssetResponse(BaseModel):
+    id: str = Field(...)
+    name: str | None = Field(None)
+    url: str | None = Field(None)
+    asset_type: str = Field(...)
+    group_id: str = Field(...)
+    status: str = Field(...)
+    error: TaskStatusError | None = Field(None)
+
+
+class SeedanceCreateVisualValidateSessionResponse(BaseModel):
+    session_id: str = Field(...)
+    h5_link: str = Field(...)
+
+
+class SeedanceGetVisualValidateSessionResponse(BaseModel):
+    session_id: str = Field(...)
+    status: str = Field(...)
+    group_id: str | None = Field(None)
+    error_code: str | None = Field(None)
+    error_message: str | None = Field(None)
+
+
+class SeedanceCreateAssetRequest(BaseModel):
+    group_id: str = Field(...)
+    url: str = Field(...)
+    asset_type: str = Field(...)
+    name: str | None = Field(None, max_length=64)
+    project_name: str | None = Field(None)
+
+
+class SeedanceCreateAssetResponse(BaseModel):
+    asset_id: str = Field(...)
+
+
+class SeedanceVirtualLibraryCreateAssetRequest(BaseModel):
+    url: str = Field(..., description="Publicly accessible URL of the asset to upload.")
+    hash: str = Field(..., description="Dedup key. Re-submitting the same hash returns the existing asset id.")
+    asset_type: str | None = Field(None, description="BytePlus asset type. Defaults to Image server-side when omitted.")
+
+
+RECOMMENDED_PRESETS = [
+    ("1024x1024 (1:1)", 1024, 1024),
+    ("864x1152 (3:4)", 864, 1152),
+    ("1152x864 (4:3)", 1152, 864),
+    ("1280x720 (16:9)", 1280, 720),
+    ("720x1280 (9:16)", 720, 1280),
+    ("832x1248 (2:3)", 832, 1248),
+    ("1248x832 (3:2)", 1248, 832),
+    ("1512x648 (21:9)", 1512, 648),
+    ("2048x2048 (1:1)", 2048, 2048),
+    ("Custom", None, None),
+]
+
+RECOMMENDED_PRESETS_SEEDREAM_4 = [
+    ("2048x2048 (1:1)", 2048, 2048),
+    ("2304x1728 (4:3)", 2304, 1728),
+    ("1728x2304 (3:4)", 1728, 2304),
+    ("2560x1440 (16:9)", 2560, 1440),
+    ("1440x2560 (9:16)", 1440, 2560),
+    ("2496x1664 (3:2)", 2496, 1664),
+    ("1664x2496 (2:3)", 1664, 2496),
+    ("3024x1296 (21:9)", 3024, 1296),
+    ("3072x3072 (1:1)", 3072, 3072),
+    ("4096x4096 (1:1)", 4096, 4096),
+    ("Custom", None, None),
+]
+
+_PRESETS_SEEDREAM_1K = [
+    ("(1K) 1024x1024 (1:1)", 1024, 1024),
+    ("(1K) 864x1152 (3:4)", 864, 1152),
+    ("(1K) 1152x864 (4:3)", 1152, 864),
+    ("(1K) 1312x736 (16:9)", 1312, 736),
+    ("(1K) 736x1312 (9:16)", 736, 1312),
+    ("(1K) 832x1248 (2:3)", 832, 1248),
+    ("(1K) 1248x832 (3:2)", 1248, 832),
+    ("(1K) 1568x672 (21:9)", 1568, 672),
+]
+
+_PRESETS_SEEDREAM_2K = [
+    ("(2K) 2048x2048 (1:1)", 2048, 2048),
+    ("(2K) 1728x2304 (3:4)", 1728, 2304),
+    ("(2K) 2304x1728 (4:3)", 2304, 1728),
+    ("(2K) 2848x1600 (16:9)", 2848, 1600),
+    ("(2K) 1600x2848 (9:16)", 1600, 2848),
+    ("(2K) 1664x2496 (2:3)", 1664, 2496),
+    ("(2K) 2496x1664 (3:2)", 2496, 1664),
+    ("(2K) 3136x1344 (21:9)", 3136, 1344),
+]
+
+_PRESETS_SEEDREAM_3K = [
+    ("(3K) 3072x3072 (1:1)", 3072, 3072),
+    ("(3K) 2592x3456 (3:4)", 2592, 3456),
+    ("(3K) 3456x2592 (4:3)", 3456, 2592),
+    ("(3K) 4096x2304 (16:9)", 4096, 2304),
+    ("(3K) 2304x4096 (9:16)", 2304, 4096),
+    ("(3K) 2496x3744 (2:3)", 2496, 3744),
+    ("(3K) 3744x2496 (3:2)", 3744, 2496),
+    ("(3K) 4704x2016 (21:9)", 4704, 2016),
+]
+
+_PRESETS_SEEDREAM_4K = [
+    ("(4K) 4096x4096 (1:1)", 4096, 4096),
+    ("(4K) 3520x4704 (3:4)", 3520, 4704),
+    ("(4K) 4704x3520 (4:3)", 4704, 3520),
+    ("(4K) 5504x3040 (16:9)", 5504, 3040),
+    ("(4K) 3040x5504 (9:16)", 3040, 5504),
+    ("(4K) 3328x4992 (2:3)", 3328, 4992),
+    ("(4K) 4992x3328 (3:2)", 4992, 3328),
+    ("(4K) 6240x2656 (21:9)", 6240, 2656),
+]
+
+_CUSTOM_PRESET = [("Custom", None, None)]
+
+_PRESETS_SEEDREAM_2K_PRO = [
+    ("(2K) 2048x2048 (1:1)", 2048, 2048),
+    ("(2K) 1728x2304 (3:4)", 1728, 2304),
+    ("(2K) 2304x1728 (4:3)", 2304, 1728),
+    # ("(2K) 2848x1600 (16:9)", 2848, 1600),  # 4,556,800 px - temporarily unavailable
+    # ("(2K) 1600x2848 (9:16)", 1600, 2848),  # 4,556,800 px - temporarily unavailable
+    ("(2K) 1664x2496 (2:3)", 1664, 2496),
+    ("(2K) 2496x1664 (3:2)", 2496, 1664),
+    # ("(2K) 3136x1344 (21:9)", 3136, 1344),  # 4,214,784 px - temporarily unavailable
+]
+RECOMMENDED_PRESETS_SEEDREAM_5_PRO = (
+    _PRESETS_SEEDREAM_1K + _PRESETS_SEEDREAM_2K_PRO + _CUSTOM_PRESET
+)
+RECOMMENDED_PRESETS_SEEDREAM_5_LITE = (
+    _PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_3K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
+)
+RECOMMENDED_PRESETS_SEEDREAM_4_5 = (
+    _PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
+)
+RECOMMENDED_PRESETS_SEEDREAM_4_0 = (
+    _PRESETS_SEEDREAM_1K + _PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
+)
+
+# Seedance 2.0 reference video pixel count limits per model and output resolution.
+SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = {
+    "dreamina-seedance-2-0-260128": {
+        "480p": {"min": 409_600, "max": 927_408},
+        "720p": {"min": 409_600, "max": 927_408},
+        "1080p": {"min": 409_600, "max": 2_073_600},
+    },
+    "dreamina-seedance-2-0-fast-260128": {
+        "480p": {"min": 409_600, "max": 927_408},
+        "720p": {"min": 409_600, "max": 927_408},
+    },
+    "dreamina-seedance-2-0-mini": {
+        "480p": {"min": 409_600, "max": 927_408},
+        "720p": {"min": 409_600, "max": 927_408},
+    },
+    "dreamina-seedance-2-5-260628": {
+        "480p": {"min": 409_600, "max": 8_295_044},
+        "720p": {"min": 409_600, "max": 8_295_044},
+        "1080p": {"min": 409_600, "max": 8_295_044},
+    },
+}
+
+SEEDANCE2_REFERENCE_LIMITS_DEFAULT = {
+    "max_images": 9,
+    "max_videos": 3,
+    "max_audios": 3,
+    "max_total_seconds": 15.1,
+}
+SEEDANCE2_REFERENCE_LIMITS = {
+    "dreamina-seedance-2-5-260628": {
+        "max_images": 30,
+        "max_videos": 10,
+        "max_audios": 10,
+        "max_total_seconds": 30.1,
+    },
+}
+
+
+def seedance2_reference_limits(model_id: str) -> dict:
+    return SEEDANCE2_REFERENCE_LIMITS.get(model_id, SEEDANCE2_REFERENCE_LIMITS_DEFAULT)
+
+# The time in this dictionary are given for 10 seconds duration.
+VIDEO_TASKS_EXECUTION_TIME = {
+    "seedance-1-0-lite-t2v-250428": {
+        "480p": 40,
+        "720p": 60,
+        "1080p": 90,
+    },
+    "seedance-1-0-lite-i2v-250428": {
+        "480p": 40,
+        "720p": 60,
+        "1080p": 90,
+    },
+    "seedance-1-0-pro-250528": {
+        "480p": 70,
+        "720p": 85,
+        "1080p": 115,
+    },
+    "seedance-1-0-pro-fast-251015": {
+        "480p": 50,
+        "720p": 65,
+        "1080p": 100,
+    },
+    "seedance-1-5-pro-251215": {
+        "480p": 80,
+        "720p": 100,
+        "1080p": 150,
+    },
+}
+
+
+class SeedAudioConfig(BaseModel):
+    format: str = Field(default="mp3")
+    sample_rate: int = Field(default=24000)
+    speech_rate: int = Field(default=0)
+    loudness_rate: int = Field(default=0)
+    pitch_rate: int = Field(default=0)
+
+
+class SeedAudioReference(BaseModel):
+    speaker: str | None = Field(default=None)
+    audio_data: str | None = Field(default=None)
+    audio_url: str | None = Field(default=None)
+    image_data: str | None = Field(default=None)
+    image_url: str | None = Field(default=None)
+
+
+class SeedAudioRequest(BaseModel):
+    model: str = Field(default="seed-audio-1.0")
+    text_prompt: str = Field(...)
+    references: list[SeedAudioReference] | None = Field(default=None)
+    audio_config: SeedAudioConfig = Field(default_factory=SeedAudioConfig)
+    watermark: dict[str, Any] = Field(default_factory=dict)
+
+
+class SeedAudioResponse(BaseModel):
+    audio: str | None = Field(default=None)
+    url: str | None = Field(default=None)
+    duration: float | None = Field(default=None)
+    original_duration: float | None = Field(default=None)
+    code: int | None = Field(default=None)
+    message: str | None = Field(default=None)
+
+
+class MediaKitVideoEnhanceRequest(BaseModel):
+    video_url: str = Field(...)
+    tool_version: str = Field(...)
+    scene: str | None = Field(None)
+    enhance_style: str | None = Field(None)
+    resolution: str | None = Field(None)
+    resolution_limit: int | None = Field(None)
+    fps: float | None = Field(None)
+    bitrate_level: str = Field(...)
+
+
+class MediaKitError(BaseModel):
+    code: str | None = Field(None)
+    type: str | None = Field(None)
+    message: str | None = Field(None)
+    param: str | None = Field(None)
+
+
+class MediaKitTaskCreateResponse(BaseModel):
+    success: bool = Field(...)
+    task_id: str | None = Field(None)
+    request_id: str | None = Field(None)
+    error: MediaKitError | None = Field(None)
+
+
+class MediaKitTaskResult(BaseModel):
+    video_url: str = Field(...)
+    duration: float | None = Field(None)
+    fps: float | None = Field(None)
+    resolution: str | None = Field(None)
+    tool_version: str | None = Field(None)
+
+
+class MediaKitTaskResponse(BaseModel):
+    success: bool = Field(...)
+    task_id: str | None = Field(None)
+    task_type: str | None = Field(None)
+    status: str | None = Field(None)
+    result: MediaKitTaskResult | None = Field(None)
+    error: MediaKitError | None = Field(None)
diff --git a/comfy_api_nodes/apis/bytedance_llm.py b/comfy_api_nodes/apis/bytedance_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3b8edc496112a81d988f4b23afd4dc4ceb2719c
--- /dev/null
+++ b/comfy_api_nodes/apis/bytedance_llm.py
@@ -0,0 +1,101 @@
+"""Pydantic models for BytePlus ModelArk Responses API.
+
+See: https://docs.byteplus.com/en/docs/ModelArk/1585128 (request)
+     https://docs.byteplus.com/en/docs/ModelArk/1783703 (response)
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class BytePlusInputText(BaseModel):
+    type: Literal["input_text"] = "input_text"
+    text: str = Field(...)
+
+
+class BytePlusInputImage(BaseModel):
+    type: Literal["input_image"] = "input_image"
+    image_url: str = Field(..., description="Image URL or `data:image/...;base64,...` payload")
+    detail: str = Field("auto", description="One of high, low, auto")
+
+
+class BytePlusInputVideo(BaseModel):
+    type: Literal["input_video"] = "input_video"
+    video_url: str = Field(..., description="Video URL or `data:video/...;base64,...` payload")
+    fps: float | None = Field(None, ge=0.2, le=5.0)
+
+
+BytePlusMessageContent = BytePlusInputText | BytePlusInputImage | BytePlusInputVideo
+
+
+class BytePlusInputMessage(BaseModel):
+    type: Literal["message"] = "message"
+    role: str = Field(..., description="One of user, system, assistant, developer")
+    content: list[BytePlusMessageContent] = Field(...)
+
+
+class BytePlusResponseCreateRequest(BaseModel):
+    model: str = Field(...)
+    input: list[BytePlusInputMessage] = Field(...)
+    instructions: str | None = Field(None)
+    max_output_tokens: int | None = Field(None, ge=1)
+    temperature: float | None = Field(None, ge=0.0, le=2.0)
+    store: bool | None = Field(False)
+    stream: bool | None = Field(False)
+
+
+class BytePlusOutputText(BaseModel):
+    type: Literal["output_text"] = "output_text"
+    text: str = Field(...)
+
+
+class BytePlusOutputRefusal(BaseModel):
+    type: Literal["refusal"] = "refusal"
+    refusal: str = Field(...)
+
+
+class BytePlusOutputContent(BaseModel):
+    type: str = Field(...)
+    text: str | None = Field(None)
+    refusal: str | None = Field(None)
+
+
+class BytePlusOutputMessage(BaseModel):
+    type: str = Field(...)
+    id: str | None = Field(None)
+    role: str | None = Field(None)
+    status: str | None = Field(None)
+    content: list[BytePlusOutputContent] | None = Field(None)
+
+
+class BytePlusInputTokensDetails(BaseModel):
+    cached_tokens: int | None = Field(None)
+
+
+class BytePlusOutputTokensDetails(BaseModel):
+    reasoning_tokens: int | None = Field(None)
+
+
+class BytePlusResponseUsage(BaseModel):
+    input_tokens: int | None = Field(None)
+    output_tokens: int | None = Field(None)
+    total_tokens: int | None = Field(None)
+    input_tokens_details: BytePlusInputTokensDetails | None = Field(None)
+    output_tokens_details: BytePlusOutputTokensDetails | None = Field(None)
+
+
+class BytePlusResponseError(BaseModel):
+    code: str = Field(...)
+    message: str = Field(...)
+
+
+class BytePlusResponseObject(BaseModel):
+    id: str | None = Field(None)
+    object: str | None = Field(None)
+    created_at: int | None = Field(None)
+    model: str | None = Field(None)
+    status: str | None = Field(None)
+    error: BytePlusResponseError | None = Field(None)
+    output: list[BytePlusOutputMessage] | None = Field(None)
+    usage: BytePlusResponseUsage | None = Field(None)
diff --git a/comfy_api_nodes/apis/elevenlabs.py b/comfy_api_nodes/apis/elevenlabs.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec996e39cd81a9226c16c40e968049fd5b1a007b
--- /dev/null
+++ b/comfy_api_nodes/apis/elevenlabs.py
@@ -0,0 +1,88 @@
+from pydantic import BaseModel, Field
+
+
+class SpeechToTextRequest(BaseModel):
+    model_id: str = Field(...)
+    cloud_storage_url: str = Field(...)
+    language_code: str | None = Field(None, description="ISO-639-1 or ISO-639-3 language code")
+    tag_audio_events: bool | None = Field(None, description="Annotate sounds like (laughter) in transcript")
+    num_speakers: int | None = Field(None, description="Max speakers predicted")
+    timestamps_granularity: str = Field(default="word", description="Timing precision: none, word, or character")
+    diarize: bool | None = Field(None, description="Annotate which speaker is talking")
+    diarization_threshold: float | None = Field(None, description="Speaker separation sensitivity")
+    temperature: float | None = Field(None, description="Randomness control")
+    seed: int = Field(..., description="Seed for deterministic sampling")
+
+
+class SpeechToTextWord(BaseModel):
+    text: str = Field(..., description="The word text")
+    type: str = Field(default="word", description="Type of text element (word, spacing, etc.)")
+    start: float | None = Field(None, description="Start time in seconds (when timestamps enabled)")
+    end: float | None = Field(None, description="End time in seconds (when timestamps enabled)")
+    speaker_id: str | None = Field(None, description="Speaker identifier when diarization is enabled")
+    logprob: float | None = Field(None, description="Log probability of the word")
+
+
+class SpeechToTextResponse(BaseModel):
+    language_code: str = Field(..., description="Detected or specified language code")
+    language_probability: float | None = Field(None, description="Confidence of language detection")
+    text: str = Field(..., description="Full transcript text")
+    words: list[SpeechToTextWord] | None = Field(None, description="Word-level timing information")
+
+
+class TextToSpeechVoiceSettings(BaseModel):
+    stability: float | None = Field(None, description="Voice stability")
+    similarity_boost: float | None = Field(None, description="Similarity boost")
+    style: float | None = Field(None, description="Style exaggeration")
+    use_speaker_boost: bool | None = Field(None, description="Boost similarity to original speaker")
+    speed: float | None = Field(None, description="Speech speed")
+
+
+class TextToSpeechRequest(BaseModel):
+    text: str = Field(..., description="Text to convert to speech")
+    model_id: str = Field(..., description="Model ID for TTS")
+    language_code: str | None = Field(None, description="ISO-639-1 or ISO-639-3 language code")
+    voice_settings: TextToSpeechVoiceSettings | None = Field(None, description="Voice settings")
+    seed: int = Field(..., description="Seed for deterministic sampling")
+    apply_text_normalization: str | None = Field(None, description="Text normalization mode: auto, on, off")
+
+
+class TextToSoundEffectsRequest(BaseModel):
+    text: str = Field(..., description="Text prompt to convert into a sound effect")
+    duration_seconds: float = Field(..., description="Duration of generated sound in seconds")
+    prompt_influence: float = Field(..., description="How closely generation follows the prompt")
+    loop: bool | None = Field(None, description="Whether to create a smoothly looping sound effect")
+
+
+class AddVoiceRequest(BaseModel):
+    name: str = Field(..., description="Name that identifies the voice")
+    remove_background_noise: bool = Field(..., description="Remove background noise from voice samples")
+
+
+class AddVoiceResponse(BaseModel):
+    voice_id: str = Field(..., description="The newly created voice's unique identifier")
+
+
+class SpeechToSpeechRequest(BaseModel):
+    model_id: str = Field(..., description="Model ID for speech-to-speech")
+    voice_settings: str = Field(..., description="JSON string of voice settings")
+    seed: int = Field(..., description="Seed for deterministic sampling")
+    remove_background_noise: bool = Field(..., description="Remove background noise from input audio")
+
+
+class DialogueInput(BaseModel):
+    text: str = Field(..., description="Text content to convert to speech")
+    voice_id: str = Field(..., description="Voice identifier for this dialogue segment")
+
+
+class DialogueSettings(BaseModel):
+    stability: float | None = Field(None, description="Voice stability (0-1)")
+
+
+class TextToDialogueRequest(BaseModel):
+    inputs: list[DialogueInput] = Field(..., description="List of dialogue segments")
+    model_id: str = Field(..., description="Model ID for dialogue generation")
+    language_code: str | None = Field(None, description="ISO-639-1 language code")
+    settings: DialogueSettings | None = Field(None, description="Voice settings")
+    seed: int | None = Field(None, description="Seed for deterministic sampling")
+    apply_text_normalization: str | None = Field(None, description="Text normalization mode: auto, on, off")
diff --git a/comfy_api_nodes/apis/fishaudio.py b/comfy_api_nodes/apis/fishaudio.py
new file mode 100644
index 0000000000000000000000000000000000000000..75a8cd3b41ce0492aff6831a14bafeb0c6997439
--- /dev/null
+++ b/comfy_api_nodes/apis/fishaudio.py
@@ -0,0 +1,49 @@
+from pydantic import BaseModel, Field
+
+
+class FishAudioProsody(BaseModel):
+    speed: float = Field(1.0, description="Speaking rate multiplier, 0.5-2.0")
+    volume: float = Field(0.0, description="Volume adjustment in decibels")
+
+
+class FishAudioTTSRequest(BaseModel):
+    text: str = Field(..., description="Text to synthesize")
+    reference_id: str | list[str] | None = Field(None, description="Voice model ID or list of IDs")
+    temperature: float = Field(0.7, description="Expressiveness, 0-1")
+    top_p: float = Field(0.7, description="Nucleus sampling diversity, (0, 1]")
+    prosody: FishAudioProsody = Field(..., description="Speed and volume adjustments")
+    normalize: bool = Field(True, description="Normalize numbers and text for English and Chinese")
+    format: str = Field("wav", description="Output audio format")
+
+
+class FishAudioASRRequest(BaseModel):
+    language: str | None = Field(None, description="Optional ISO 639-1 language hint")
+    ignore_timestamps: bool = Field(True, description="Skip precise timestamp computation")
+
+
+class FishAudioASRSegment(BaseModel):
+    text: str | None = Field(None, description="Segment text")
+    start: float | None = Field(None, description="Segment start time in seconds")
+    end: float | None = Field(None, description="Segment end time in seconds")
+
+
+class FishAudioASRResponse(BaseModel):
+    text: str | None = Field(None, description="Transcribed text")
+    duration: float | None = Field(None, description="Audio duration in seconds")
+    segments: list[FishAudioASRSegment] | None = Field(None, description="Timestamped transcript segments")
+    language_code: str | None = Field(None, description="Detected language as ISO 639-1 code")
+    language: str | None = Field(None, description="Detected language display name")
+
+
+class FishAudioCreateModelRequest(BaseModel):
+    type: str = Field("tts", description="Model type")
+    title: str = Field(..., description="Voice model name")
+    train_mode: str = Field("fast", description="Training mode; fast is instantly available")
+    visibility: str = Field("private", description="Model visibility")
+    enhance_audio_quality: bool = Field(..., description="Enhance reference audio quality")
+
+
+class FishAudioCreateModelResponse(BaseModel):
+    id: str = Field(..., alias="_id", description="Voice model ID for use as reference_id")
+    state: str | None = Field(None, description="Training state")
+    visibility: str | None = Field(None, description="Model visibility")
diff --git a/comfy_api_nodes/apis/gemini.py b/comfy_api_nodes/apis/gemini.py
new file mode 100644
index 0000000000000000000000000000000000000000..37bcd5557bad57a9ce8f7cd61086b431b633136c
--- /dev/null
+++ b/comfy_api_nodes/apis/gemini.py
@@ -0,0 +1,324 @@
+from datetime import date
+from enum import Enum
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+
+class GeminiSafetyCategory(str, Enum):
+    HARM_CATEGORY_SEXUALLY_EXPLICIT = "HARM_CATEGORY_SEXUALLY_EXPLICIT"
+    HARM_CATEGORY_HATE_SPEECH = "HARM_CATEGORY_HATE_SPEECH"
+    HARM_CATEGORY_HARASSMENT = "HARM_CATEGORY_HARASSMENT"
+    HARM_CATEGORY_DANGEROUS_CONTENT = "HARM_CATEGORY_DANGEROUS_CONTENT"
+
+
+class GeminiSafetyThreshold(str, Enum):
+    OFF = "OFF"
+    BLOCK_NONE = "BLOCK_NONE"
+    BLOCK_LOW_AND_ABOVE = "BLOCK_LOW_AND_ABOVE"
+    BLOCK_MEDIUM_AND_ABOVE = "BLOCK_MEDIUM_AND_ABOVE"
+    BLOCK_ONLY_HIGH = "BLOCK_ONLY_HIGH"
+
+
+class GeminiSafetySetting(BaseModel):
+    category: GeminiSafetyCategory
+    threshold: GeminiSafetyThreshold
+
+
+class GeminiRole(str, Enum):
+    user = "user"
+    model = "model"
+
+
+class GeminiMimeType(str, Enum):
+    application_pdf = "application/pdf"
+    audio_mpeg = "audio/mpeg"
+    audio_mp3 = "audio/mp3"
+    audio_wav = "audio/wav"
+    image_png = "image/png"
+    image_jpeg = "image/jpeg"
+    image_webp = "image/webp"
+    text_plain = "text/plain"
+    video_mov = "video/mov"
+    video_mpeg = "video/mpeg"
+    video_mp4 = "video/mp4"
+    video_mpg = "video/mpg"
+    video_avi = "video/avi"
+    video_wmv = "video/wmv"
+    video_mpegps = "video/mpegps"
+    video_flv = "video/flv"
+
+
+class GeminiInlineData(BaseModel):
+    data: str | None = Field(
+        None,
+        description="The base64 encoding of the image, PDF, or video to include inline in the prompt. "
+        "When including media inline, you must also specify the media type (mimeType) of the data. Size limit: 20MB",
+    )
+    mimeType: GeminiMimeType | None = Field(None)
+
+
+class GeminiFileData(BaseModel):
+    fileUri: str | None = Field(None)
+    mimeType: GeminiMimeType | None = Field(None)
+
+
+class GeminiPart(BaseModel):
+    inlineData: GeminiInlineData | None = Field(None)
+    fileData: GeminiFileData | None = Field(None)
+    text: str | None = Field(None)
+    thought: bool | None = Field(None)
+
+
+class GeminiTextPart(BaseModel):
+    text: str | None = Field(None)
+
+
+class GeminiContent(BaseModel):
+    parts: list[GeminiPart] = Field([])
+    role: GeminiRole = Field(..., examples=["user"])
+
+
+class GeminiSystemInstructionContent(BaseModel):
+    parts: list[GeminiTextPart] = Field(
+        ...,
+        description="A list of ordered parts that make up a single message. "
+        "Different parts may have different IANA MIME types.",
+    )
+    role: GeminiRole | None = Field(..., description="The role field of systemInstruction may be ignored.")
+
+
+class GeminiFunctionDeclaration(BaseModel):
+    description: str | None = Field(None)
+    name: str = Field(...)
+    parameters: dict[str, Any] = Field(..., description="JSON schema for the function parameters")
+
+
+class GeminiTool(BaseModel):
+    functionDeclarations: list[GeminiFunctionDeclaration] | None = Field(None)
+
+
+class GeminiOffset(BaseModel):
+    nanos: int | None = Field(None, ge=0, le=999999999)
+    seconds: int | None = Field(None, ge=-315576000000, le=315576000000)
+
+
+class GeminiVideoMetadata(BaseModel):
+    endOffset: GeminiOffset | None = Field(None)
+    startOffset: GeminiOffset | None = Field(None)
+
+
+class GeminiThinkingConfig(BaseModel):
+    includeThoughts: bool | None = Field(None)
+    thinkingLevel: str = Field(...)
+
+
+class GeminiGenerationConfig(BaseModel):
+    maxOutputTokens: int | None = Field(None, ge=16, le=65536)
+    seed: int | None = Field(None)
+    stopSequences: list[str] | None = Field(None)
+    temperature: float | None = Field(None, ge=0.0, le=2.0)
+    topK: int | None = Field(None, ge=1)
+    topP: float | None = Field(None, ge=0.0, le=1.0)
+    thinkingConfig: GeminiThinkingConfig | None = Field(None)
+    responseModalities: list[str] | None = Field(None)
+
+
+class GeminiImageOutputOptions(BaseModel):
+    mimeType: str = Field("image/png")
+    compressionQuality: int | None = Field(None)
+
+
+class GeminiImageConfig(BaseModel):
+    aspectRatio: str | None = Field(None)
+    imageSize: str | None = Field(None)
+    imageOutputOptions: GeminiImageOutputOptions = Field(default_factory=GeminiImageOutputOptions)
+
+
+class GeminiImageGenerationConfig(GeminiGenerationConfig):
+    responseModalities: list[str] | None = Field(None)
+    imageConfig: GeminiImageConfig | None = Field(None)
+    thinkingConfig: GeminiThinkingConfig | None = Field(None)
+
+
+class GeminiImageGenerateContentRequest(BaseModel):
+    contents: list[GeminiContent] = Field(...)
+    generationConfig: GeminiImageGenerationConfig | None = Field(None)
+    safetySettings: list[GeminiSafetySetting] | None = Field(None)
+    systemInstruction: GeminiSystemInstructionContent | None = Field(None)
+    tools: list[GeminiTool] | None = Field(None)
+    videoMetadata: GeminiVideoMetadata | None = Field(None)
+    uploadImagesToStorage: bool = Field(True)
+
+
+class GeminiGenerateContentRequest(BaseModel):
+    contents: list[GeminiContent] = Field(...)
+    generationConfig: GeminiGenerationConfig | None = Field(None)
+    safetySettings: list[GeminiSafetySetting] | None = Field(None)
+    systemInstruction: GeminiSystemInstructionContent | None = Field(None)
+    tools: list[GeminiTool] | None = Field(None)
+    videoMetadata: GeminiVideoMetadata | None = Field(None)
+
+
+class Modality(str, Enum):
+    MODALITY_UNSPECIFIED = "MODALITY_UNSPECIFIED"
+    TEXT = "TEXT"
+    IMAGE = "IMAGE"
+    VIDEO = "VIDEO"
+    AUDIO = "AUDIO"
+    DOCUMENT = "DOCUMENT"
+
+
+class ModalityTokenCount(BaseModel):
+    modality: Modality | None = None
+    tokenCount: int | None = Field(None, description="Number of tokens for the given modality.")
+
+
+class Probability(str, Enum):
+    NEGLIGIBLE = "NEGLIGIBLE"
+    LOW = "LOW"
+    MEDIUM = "MEDIUM"
+    HIGH = "HIGH"
+    UNKNOWN = "UNKNOWN"
+
+
+class GeminiSafetyRating(BaseModel):
+    category: GeminiSafetyCategory | None = None
+    probability: Probability | None = Field(
+        None,
+        description="The probability that the content violates the specified safety category",
+    )
+
+
+class GeminiCitation(BaseModel):
+    authors: list[str] | None = None
+    endIndex: int | None = None
+    license: str | None = None
+    publicationDate: date | None = None
+    startIndex: int | None = None
+    title: str | None = None
+    uri: str | None = None
+
+
+class GeminiCitationMetadata(BaseModel):
+    citations: list[GeminiCitation] | None = None
+
+
+class GeminiCandidate(BaseModel):
+    citationMetadata: GeminiCitationMetadata | None = None
+    content: GeminiContent | None = None
+    finishReason: str | None = None
+    safetyRatings: list[GeminiSafetyRating] | None = None
+
+
+class GeminiPromptFeedback(BaseModel):
+    blockReason: str | None = None
+    blockReasonMessage: str | None = None
+    safetyRatings: list[GeminiSafetyRating] | None = None
+
+
+class GeminiUsageMetadata(BaseModel):
+    cachedContentTokenCount: int | None = Field(
+        None,
+        description="Output only. Number of tokens in the cached part in the input (the cached content).",
+    )
+    candidatesTokenCount: int | None = Field(None, description="Number of tokens in the response(s).")
+    candidatesTokensDetails: list[ModalityTokenCount] | None = Field(
+        None, description="Breakdown of candidate tokens by modality."
+    )
+    promptTokenCount: int | None = Field(
+        None,
+        description="Number of tokens in the request. When cachedContent is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content.",
+    )
+    promptTokensDetails: list[ModalityTokenCount] | None = Field(
+        None, description="Breakdown of prompt tokens by modality."
+    )
+    thoughtsTokenCount: int | None = Field(None, description="Number of tokens present in thoughts output.")
+    toolUsePromptTokenCount: int | None = Field(None, description="Number of tokens present in tool-use prompt(s).")
+
+
+class GeminiGenerateContentResponse(BaseModel):
+    candidates: list[GeminiCandidate] | None = Field(None)
+    promptFeedback: GeminiPromptFeedback | None = Field(None)
+    usageMetadata: GeminiUsageMetadata | None = Field(None)
+    modelVersion: str | None = Field(None)
+
+
+class GeminiInteractionTextPart(BaseModel):
+    type: Literal["text"] = "text"
+    text: str = Field(...)
+
+
+class GeminiInteractionMediaPart(BaseModel):
+    type: str = Field(..., description="One of: image, video, audio, document.")
+    data: str | None = Field(None, description="Base64-encoded media bytes.")
+    uri: str | None = Field(None, description="URI of the media, as an alternative to inline data.")
+    mime_type: str | None = Field(None)
+
+
+class GeminiInteractionVideoConfig(BaseModel):
+    task: str | None = Field(
+        None, description="One of: text_to_video, image_to_video, reference_to_video, edit, extend."
+    )
+
+
+class GeminiInteractionGenerationConfig(BaseModel):
+    temperature: float | None = Field(None, ge=0.0, le=2.0)
+    top_p: float | None = Field(None, ge=0.0, le=1.0)
+    video_config: GeminiInteractionVideoConfig | None = Field(None)
+
+
+class GeminiInteractionResponseFormat(BaseModel):
+    type: Literal["video"] = "video"
+    resolution: str | None = Field(None, description="One of: 360p, 720p, 1080p, 4k.")
+    aspect_ratio: str | None = Field(None, description="One of: 16:9, 9:16.")
+    delivery: str | None = Field(
+        None, description="Set to 'uri' to receive a Files API URI instead of inline base64 data."
+    )
+
+
+class GeminiInteractionRequest(BaseModel):
+    model: str = Field(...)
+    input: list[GeminiInteractionTextPart | GeminiInteractionMediaPart] = Field(...)
+    generation_config: GeminiInteractionGenerationConfig | None = Field(None)
+    response_format: GeminiInteractionResponseFormat | None = Field(None)
+
+
+class GeminiInteractionModalityTokens(BaseModel):
+    modality: str | None = Field(None, description="One of: text, image, audio, video, document.")
+    tokens: int | None = Field(None)
+
+
+class GeminiInteractionUsage(BaseModel):
+    input_tokens_by_modality: list[GeminiInteractionModalityTokens] | None = Field(None)
+    output_tokens_by_modality: list[GeminiInteractionModalityTokens] | None = Field(None)
+    total_thought_tokens: int | None = Field(None)
+
+
+class GeminiInteractionContent(BaseModel):
+    type: str | None = Field(None)
+    text: str | None = Field(None)
+    data: str | None = Field(None)
+    uri: str | None = Field(None)
+    mime_type: str | None = Field(None)
+
+
+class GeminiInteractionStep(BaseModel):
+    type: str | None = Field(None)
+    content: list[GeminiInteractionContent] | None = Field(None)
+
+
+class GeminiInteraction(BaseModel):
+    id: str | None = Field(None)
+    status: str | None = Field(
+        None,
+        description="One of: in_progress, requires_action, completed, failed, cancelled, incomplete.",
+    )
+    steps: list[GeminiInteractionStep] | None = Field(None)
+    usage: GeminiInteractionUsage | None = Field(None)
+
+
+class GeminiFile(BaseModel):
+    name: str | None = Field(None, description="Resource name of the file, in the form 'files/<id>'.")
+    uri: str | None = Field(None)
+    state: str | None = Field(None, description="One of: PROCESSING, ACTIVE, FAILED.")
diff --git a/comfy_api_nodes/apis/grok.py b/comfy_api_nodes/apis/grok.py
new file mode 100644
index 0000000000000000000000000000000000000000..d933a698d7a3e9d9650d61bbb3b106f5949d1164
--- /dev/null
+++ b/comfy_api_nodes/apis/grok.py
@@ -0,0 +1,90 @@
+from pydantic import BaseModel, Field
+
+
+class ImageGenerationRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(...)
+    aspect_ratio: str = Field(...)
+    n: int = Field(...)
+    seed: int = Field(...)
+    response_format: str = Field("url")
+    resolution: str = Field(...)
+    quality: str | None = Field(None)
+
+
+class InputUrlObject(BaseModel):
+    url: str = Field(...)
+
+
+class VoiceReferenceObject(BaseModel):
+    voice_id: str = Field(...)
+
+
+class ImageEditRequest(BaseModel):
+    model: str = Field(...)
+    images: list[InputUrlObject] = Field(...)
+    prompt: str = Field(...)
+    resolution: str = Field(...)
+    n: int = Field(...)
+    seed: int = Field(...)
+    response_format: str = Field("url")
+    aspect_ratio: str | None = Field(...)
+    quality: str | None = Field(None)
+
+
+class VideoGenerationRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(...)
+    image: InputUrlObject | None = Field(None)
+    reference_images: list[InputUrlObject] | None = Field(None)
+    reference_audios: list[VoiceReferenceObject] | None = Field(None)
+    duration: int = Field(...)
+    aspect_ratio: str | None = Field(...)
+    resolution: str = Field(...)
+    seed: int = Field(...)
+
+
+class VideoExtensionRequest(BaseModel):
+    prompt: str = Field(...)
+    video: InputUrlObject = Field(...)
+    duration: int = Field(default=6)
+    model: str | None = Field(default=None)
+
+
+class VideoEditRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(...)
+    video: InputUrlObject = Field(...)
+    seed: int = Field(...)
+
+
+class ImageResponseObject(BaseModel):
+    url: str | None = Field(None)
+    b64_json: str | None = Field(None)
+    revised_prompt: str | None = Field(None)
+
+
+class UsageObject(BaseModel):
+    cost_in_usd_ticks: int | None = Field(None)
+
+
+class ImageGenerationResponse(BaseModel):
+    data: list[ImageResponseObject] = Field(...)
+    usage: UsageObject | None = Field(None)
+
+
+class VideoGenerationResponse(BaseModel):
+    request_id: str = Field(...)
+
+
+class VideoResponseObject(BaseModel):
+    url: str = Field(...)
+    upsampled_prompt: str | None = Field(None)
+    duration: int = Field(...)
+
+
+class VideoStatusResponse(BaseModel):
+    status: str | None = Field(None)
+    video: VideoResponseObject | None = Field(None)
+    model: str | None = Field(None)
+    usage: UsageObject | None = Field(None)
diff --git a/comfy_api_nodes/apis/heygen.py b/comfy_api_nodes/apis/heygen.py
new file mode 100644
index 0000000000000000000000000000000000000000..c40a319a38b80dc9674d9c17d1efcc4b5c55d85e
--- /dev/null
+++ b/comfy_api_nodes/apis/heygen.py
@@ -0,0 +1,452 @@
+# (label, avatar_id, avatar_type, supported engines)
+HEYGEN_AVATAR_LOOKS: list[tuple[str, str, str, tuple[str, ...]]] = [
+    (
+        "Annie Lounge Standing Side",
+        "Annie_Lounge_Standing_Side_public",
+        "studio_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Yara Modern Lecture Hall",
+        "fd6814ecc5e143cd899e615a80eaa2dc",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Brandon Business Sitting Front",
+        "Brandon_Business_Sitting_Front_public",
+        "studio_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Caroline Business Sitting Side",
+        "Caroline_Business_Sitting_Side_public",
+        "studio_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Ursula Lawyer Angle 4",
+        "f7173d2bb8584c00bfec6905c5e9a492",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Sofia Corporate Presenter 01 Angle 3",
+        "fe563971fd2d438e957372dac9e2be8c",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Seoyeon Health Nutrition Coach Angle 3",
+        "fe3c5d5028d941398d064b8fc64a2dea",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Sanne Fitness Coach Angle 4",
+        "d967f935a8bf4a0c8f0bccfd66c501d2",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    ("Sander", "f5cd7b94056f495ca0610602d64a9aa3", "photo_avatar", ("avatar_v", "avatar_iv", "avatar_iii")),
+    (
+        "Rupert Personal Development Coach Angle 4",
+        "f57b3e626adb4bc997b38f64884adce4",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Olivier Professor Angle 2",
+        "f6659bbb094b459c87c967edbb9ee481",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Obi Health Nutrition Coach Angle 5",
+        "f3dc2c38201d414382f506d2d8e8d029",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Matilda Modern Office Setting",
+        "fda889ac354a440da8dbecc410981273",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Mateo Traditional Law Office",
+        "ff172d6c499c4e47ba6fcc5de631e9fc",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Marlon Inviting Armchair Setting",
+        "f5a57db099ab462daa3e7c604a05dacc",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Margaret Professor Angle 1",
+        "fb472bc29ab04bcca576e3703978fecb",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Marek Therapy Coach Angle 3",
+        "e197768703f1463a93dc25ada1f421fb",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Maeve Warm, Professional Setting",
+        "faf66681d8cc48dc82c4283200b3e782",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Lorenzo Professor Angle 5",
+        "fc268dc244bb40d7a554663ce723dcf0",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    ("Luca", "Luca_public", "studio_avatar", ("avatar_iii",)),
+    ("Bruce", "Bruce_public", "studio_avatar", ("avatar_iii",)),
+    ("Nico", "Nico_public", "studio_avatar", ("avatar_iii",)),
+    ("Lisa", "Lisa_public", "studio_avatar", ("avatar_iii",)),
+    ("Sophie", "Sophie_public", "studio_avatar", ("avatar_iii",)),
+    ("Aiko", "Aiko_public", "studio_avatar", ("avatar_iii",)),
+    ("Rebecca (portrait)", "Rebecca_public", "studio_avatar", ("avatar_iii",)),
+    ("Daphne in Grey blazer (portrait)", "Daphne_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Bryce in Black t-shirt", "Bryce_public_5", "studio_avatar", ("avatar_iii",)),
+    ("Diora in White shirt", "Diora_public_3", "studio_avatar", ("avatar_iii",)),
+    ("Freja in White blazer", "Freja_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Albert in Blue blazer", "Albert_public_2", "studio_avatar", ("avatar_iii",)),
+    ("Emery in Red blazer", "Emery_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Minho in Blue shirt", "Minho_public_6", "studio_avatar", ("avatar_iii",)),
+    ("Aditya in Brown blazer", "Aditya_public_4", "studio_avatar", ("avatar_iii",)),
+    ("Nadim in Blue blazer", "Nadim_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Iker in Black blazer", "Iker_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Nour in Black blazer", "Nour_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Saskia in Blue blazer", "Saskia_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Lucien in Blue blazer", "Lucien_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Esmond in Blue suit", "Esmond_public_3", "studio_avatar", ("avatar_iii",)),
+    ("Jinwoo in Blue suit", "Jinwoo_public_5", "studio_avatar", ("avatar_iii",)),
+    ("Annelore in Red sweater (portrait)", "Annelore_public_3", "studio_avatar", ("avatar_iii",)),
+    ("Bastien in Blue shirt", "Bastien_public_4", "studio_avatar", ("avatar_iii",)),
+    ("Zosia in Khaki blazer", "Zosia_public_3", "studio_avatar", ("avatar_iii",)),
+    ("Tahlia in Dark blue suit", "Tahlia_public_4", "studio_avatar", ("avatar_iii",)),
+]
+HEYGEN_AVATAR_OPTIONS = [x[0] for x in HEYGEN_AVATAR_LOOKS]
+HEYGEN_AVATAR_MAP = {x[0]: (x[1], x[2], x[3]) for x in HEYGEN_AVATAR_LOOKS}
+
+# (label, voice_id) — Starfish-compatible voices for the TTS endpoint
+HEYGEN_VOICE_TTS: list[tuple[str, str]] = [
+    ("Chill Brian (English, male)", "d2f4f24783d04e22ab49ee8fdc3715e0"),
+    ("Zain (English, female)", "0047732240584155b1588455313e78ec"),
+    ("Narrator Mateo - Excited 🤩 (Spanish, male)", "0077225a877e457db4572ccaf245910b"),
+    ("Aria (English, female)", "007e1378fc454a9f976db570ba6164a7"),
+    ("Caryns (English, female)", "0082e70326864107823605db0d77f5e0"),
+    ("Klara (English, female)", "01209fdcd1c24a109c86dc24ee0f71c0"),
+    ("Bold Kasia - Excited 🤩 (Polish, female)", "015482a78b9a46ebae74bd0beb17765b"),
+    ("Shaun (English, male)", "01c42cddcfdc4665a57b8d89cba8ffc1"),
+    ("Senthil (English, male)", "01d674cfd32b4728a3fddd21b7e7d543"),
+    ("Cody (English, male)", "01f98ed43e6140349f47dbd37a416827"),
+    ("Saffron (English, female)", "0258bbc2cd8648cfa357adfb833f6d7b"),
+    ("Blanka - Lifelike (English, female)", "02880d1c6fd94b7799d91135581ed810"),
+    ("Rami (English, male)", "02d5366a90af4c7a87157808ff352e33"),
+    ("Rhodes (English, female)", "02dce0a169b3460084b6c914d18fb2c8"),
+    ("Michelle - Voice 1 (English, female)", "02X8sHnuxFpsq1caYWN0"),
+    ("Autumn - UGC 3 (English, female)", "03dca9ebfca441dba55fb14afa0791b7"),
+    ("Reassuring Rupert (English, male)", "03fcf8ecb0a94b6b94e9007edb7c35f8"),
+    ("Rose - UGC -2  (English, female)", "0495e14c2bd74eb3aeeef03583e0bce5"),
+    ("Derya - Lifelike - Broadcaster 🎙️ (English, female)", "04d0ae1d0af2489ca7d3bb402a39a890"),
+    ("Dynamic Derek (English, male)", "0516c2d857eb425c94e90b068241914e"),
+    ("Lotte (English, female)", "052fcfb83d1a4c2f8d0368c226fea4b9"),
+    ("Thanos - Broadcaster 🎙️ (English, male)", "054af44a167344d0af2722fdfef08d17"),
+    ("Marcia (English, female)", "05f19352e8f74b0392a8f411eba40de1"),
+    ("Camden (English, male)", "06468055edd4458aa131a1dfd813c1e9"),
+    ("Rumi (English, female)", "06672207805f41a9ad0af6797f8aa14b"),
+    ("Pippa (English, female)", "06b68c4dbb544935b9af984e80efa4fb"),
+    ("William Prescott - Broadcaster 🎙️ (English, male)", "06c816b952f14fa9b3a6c42aa151f731"),
+    ("Sammy (English, female)", "06e6facd99654b9dbb9308f67bf3a31c"),
+    ("Breezy Bagus (Indonesian, male)", "06e81a5d7c8b41818d3f0b38f7cf15a1"),
+    ("Ben (English, male)", "07ca39b243184dbcb82e7e0f0e524b21"),
+    ("Smooth Dev (English, male)", "07d2ba65847541feb97abc9b60181555"),
+    ("Daran inside booth (English, male)", "080d9383c0314056aef392892e009806"),
+    ("Peppy Stella (English, female)", "084760b4922a44599575c770070ec2d7"),
+    ("Silas (English, male)", "08f561403ec846dbbd8c691cc448f45a"),
+    ("Aditya (English, male)", "09c3d65e44e247dd8b78a97a903feb58"),
+    ("Christy (English, female)", "09d88c036bf449fa905900c08b235a37"),
+    ("Elio (English, male)", "0a0b38624ac64ec6afcd5842a977ca10"),
+    ("Luminous Laksh (Hindi, male)", "0adc547b76a5401c856274c379904eb7"),
+    ("Jeff (English, male)", "0add542e349f4ccaba6ecb3b7ced6034"),
+    ("Tahlia Brooks - Excited 🤩 (English, female)", "0b440d1ac2454d69a73302fc806522b1"),
+    ("Riya Mehta (Hindi, female)", "0b464b2f4e2249a4b5a05e60eaf41e7e"),
+    ("Ben Hart (English, male)", "0b47b5a637e944f9bfd49913999b344b"),
+    ("Skylar (English, female)", "0bbfbda5aa924a68a9d1da7b8496052a"),
+    ("Relaxed Reece (English, male)", "0c2151d538844c70a8b096de533f2828"),
+    ("Daniel (English, male)", "0c23804af39a4946ac6fda42bfff2738"),
+    ("Melani (English, female)", "0c54c6399ad64551a304e1a346677723"),
+    ("Clover (English, female)", "0ccb0bea067d4449ad367baeed7ea2e9"),
+    ("Pedro Lima - Serious 😐 (Portuguese, male)", "0d0e23e8170446e38b18a7380b2d30a8"),
+    ("Ana Carvalho (Portuguese, female)", "0d23c5b2f6004e909802a2e8bfcd52c2"),
+    ("Confident Connor - Excited 🤩 (English, male)", "0dd34c3eb79247238219eea35aeb58cd"),
+    ("Vibrant Victor (Spanish, male)", "1062976ea8bf42f4adc27c7e868b8fde"),
+    ("Young Olivier (French, male)", "1c5dc9a8f8cf4de0932f91d75f43a15d"),
+    ("Émile Noir (French, male)", "25a6a67280574d3da78e97b1935ebfc7"),
+    ("Steadfast Stefan (German, male)", "0eb85e6e8710473b82f7e88609ba3053"),
+    ("Deep Dieter (German, male)", "118949676b0a46629d1ad52981c3ef84"),
+    ("Serene Marco (Italian, male)", "72e922488a614041b5ab5f6ee07e3deb"),
+    ("Murmuring Matteo (Italian, male)", "755902b751654f30a6ef49e8bbcacfec"),
+    ("Gail in car (Multilingual, female)", "0214ac51f93e420f8711d568dcfbc50e"),
+    ("Daran outside walking (Multilingual, male)", "0ac81e725f4948dfa9638ceca216bcfa"),
+    ("BOB - Voice 1 (Chinese, unknown)", "dMkR1XwIkarpNqWUJLnX"),
+    ("Hakeem Hassan (Arabic, male)", "61a4359785664d01a59664ceb87ce6d4"),
+    ("Rami Idris (Arabic, male)", "a0bd2e5d41a74643be47ac75ca9171a2"),
+    ("Bold Kasia - Friendly 😊 (Polish, female)", "331624aec8b24a6c9287b8e16bdf54e8"),
+    ("Tranquil Tulin (Turkish, female)", "61646c861eb64e2d9036d8db51385356"),
+    ("Dynamic Derya (Turkish, female)", "664b73058b784aa89ddb2924c141d441"),
+    ("Quiet Dewa (Indonesian, male)", "1fa1193cf1d74f27ba58531c07ef9862"),
+    ("Cuong (Vietnamese, male)", "8af68d7ea38f4e7ca05cf46c3f7a590b"),
+]
+HEYGEN_VOICE_TTS_OPTIONS = [x[0] for x in HEYGEN_VOICE_TTS]
+HEYGEN_VOICE_TTS_MAP = dict(HEYGEN_VOICE_TTS)
+
+# (label, voice_id) — top-ranked voices for video narration (any engine)
+HEYGEN_VOICE_GENERAL: list[tuple[str, str]] = [
+    ("Cassidy (English, female)", "16a09e4706f74997ba4ed05ea11470f6"),
+    ("Hope (English, female)", "42d00d4aac5441279d8536cd6b52c53c"),
+    ("Archer (English, male)", "453c20e1525a429080e2ad9e4b26f2cd"),
+    ("Brittney (English, female)", "4754e1ec667544b0bd18cdf4bec7d6a7"),
+    ("Mark (English, male)", "5d8c378ba8c3434586081a52ac368738"),
+    ("Andrew (English, male)", "6be73833ef9a4eb0aeee399b8fe9d62b"),
+    ("Spuds Oxley (English, male)", "76940a9adcd0490a9ce2cfe9a64a2664"),
+    ("Patrick (English, male)", "7e157ec62c9c45f1adca12faae72c86f"),
+    ("David Castlemore (English, male)", "828b59f834fd4c7188da322b6d9b6c75"),
+    ("Michael C (English, male)", "8661cd40d6c44c709e2d0031c0186ada"),
+    ("Adam Stone (English, male)", "88bb9ee1c81b466eb2a08fdde86d3619"),
+    ("Alex (English, male)", "897d6a9b2c844f56aa077238768fe10a"),
+    ("Monika Sogam (English, female)", "97dd67ab8ce242b6a9e7689cb00c6414"),
+    ("Jessica Anne Bogart (English, female)", "b966c31caf124c2a99f19ff1479c964f"),
+    ("John Doe (English, male)", "c4a8ceb7a2954500bc047fb092bcff3f"),
+    ("Ivy (English, female)", "cef3bc4e0a84424cafcde6f2cf466c97"),
+    ("Chill Brian (English, male)", "d2f4f24783d04e22ab49ee8fdc3715e0"),
+    ("Allison (English, female)", "f8c69e517f424cafaecde32dde57096b"),
+    ("Mia Starset (Norwegian, female)", "000466f8ac6d47a49f5743d50b3778de"),
+    ("William Shanks (Spanish, male)", "001248bb63f847888d37b766ee8b3a47"),
+    ("Zain (English, female)", "0047732240584155b1588455313e78ec"),
+    ("Jora Slobod (Romanian, male)", "00631519159a402ab5d8f719e51532bb"),
+    ("Narrator Mateo - Excited 🤩 (Spanish, male)", "0077225a877e457db4572ccaf245910b"),
+    ("Aria (English, female)", "007e1378fc454a9f976db570ba6164a7"),
+    ("Caryns (English, female)", "0082e70326864107823605db0d77f5e0"),
+    ("Klara (English, female)", "01209fdcd1c24a109c86dc24ee0f71c0"),
+    ("Son Tran (Vietnamese, male)", "0132f85950a94d11ba180f885101bf84"),
+    ("Bold Kasia - Excited 🤩 (Polish, female)", "015482a78b9a46ebae74bd0beb17765b"),
+    ("Marc Aurèle (French, male)", "018a94cf15574491a0bab7f6799ac15b"),
+    ("Shaun (English, male)", "01c42cddcfdc4665a57b8d89cba8ffc1"),
+    ("Senthil (English, male)", "01d674cfd32b4728a3fddd21b7e7d543"),
+    ("Cody (English, male)", "01f98ed43e6140349f47dbd37a416827"),
+    ("Saffron (English, female)", "0258bbc2cd8648cfa357adfb833f6d7b"),
+    ("Blanka - Lifelike (English, female)", "02880d1c6fd94b7799d91135581ed810"),
+    ("Rami (English, male)", "02d5366a90af4c7a87157808ff352e33"),
+    ("Rhodes (English, female)", "02dce0a169b3460084b6c914d18fb2c8"),
+    ("Michelle - Voice 1 (English, female)", "02X8sHnuxFpsq1caYWN0"),
+    ("Tuba (, female)", "034ca0c32b6542028748d6d365d90d6a"),
+    ("Autumn - UGC 3 (English, female)", "03dca9ebfca441dba55fb14afa0791b7"),
+    ("Reassuring Rupert (English, male)", "03fcf8ecb0a94b6b94e9007edb7c35f8"),
+]
+HEYGEN_VOICE_GENERAL_OPTIONS = [x[0] for x in HEYGEN_VOICE_GENERAL]
+HEYGEN_VOICE_GENERAL_MAP = dict(HEYGEN_VOICE_GENERAL)
+
+HEYGEN_TRANSLATE_LANGUAGES = [
+    "English",
+    "Spanish",
+    "Spanish (Spain)",
+    "Spanish (Mexico)",
+    "French",
+    "French (France)",
+    "German",
+    "German (Germany)",
+    "Portuguese",
+    "Portuguese (Brazil)",
+    "Italian",
+    "Italian (Italy)",
+    "Japanese",
+    "Japanese (Japan)",
+    "Korean",
+    "Chinese (Mandarin, Simplified)",
+    "Arabic",
+    "Hindi",
+    "Hindi (India)",
+    "Russian",
+    "Russian (Russia)",
+    "Dutch",
+    "Polish",
+    "Turkish",
+    "Indonesian",
+    "Vietnamese",
+    "Ukrainian",
+    "Afrikaans (South Africa)",
+    "Albanian (Albania)",
+    "Amharic (Ethiopia)",
+    "Arabic (Algeria)",
+    "Arabic (Bahrain)",
+    "Arabic (Egypt)",
+    "Arabic (Iraq)",
+    "Arabic (Jordan)",
+    "Arabic (Kuwait)",
+    "Arabic (Lebanon)",
+    "Arabic (Libya)",
+    "Arabic (Morocco)",
+    "Arabic (Oman)",
+    "Arabic (Qatar)",
+    "Arabic (Saudi Arabia)",
+    "Arabic (Syria)",
+    "Arabic (Tunisia)",
+    "Arabic (United Arab Emirates)",
+    "Arabic (World)",
+    "Arabic (Yemen)",
+    "Armenian (Armenia)",
+    "Azerbaijani (Latin, Azerbaijan)",
+    "Bangla (Bangladesh)",
+    "Basque",
+    "Belarusian (Belarus)",
+    "Bengali (India)",
+    "Bosnian (Bosnia and Herzegovina)",
+    "Bulgarian",
+    "Bulgarian (Bulgaria)",
+    "Burmese (Myanmar)",
+    "Catalan",
+    "Chinese (Cantonese, Traditional)",
+    "Chinese (Jilu Mandarin, Simplified)",
+    "Chinese (Northeastern Mandarin, Simplified)",
+    "Chinese (Southwestern Mandarin, Simplified)",
+    "Chinese (Taiwanese Mandarin, Traditional)",
+    "Chinese (Wu, Simplified)",
+    "Chinese (Zhongyuan Mandarin Henan, Simplified)",
+    "Chinese (Zhongyuan Mandarin Shaanxi, Simplified)",
+    "Croatian",
+    "Croatian (Croatia)",
+    "Czech",
+    "Czech (Czechia)",
+    "Danish",
+    "Danish (Denmark)",
+    "Dutch (Belgium)",
+    "Dutch (Netherlands)",
+    "English (Australia)",
+    "English (Canada)",
+    "English (Hong Kong SAR)",
+    "English (India)",
+    "English (Ireland)",
+    "English (Kenya)",
+    "English (New Zealand)",
+    "English (Nigeria)",
+    "English (Philippines)",
+    "English (Singapore)",
+    "English (South Africa)",
+    "English (Tanzania)",
+    "English (UK)",
+    "English (United States)",
+    "Estonian (Estonia)",
+    "Filipino",
+    "Filipino (Cebuano)",
+    "Filipino (Philippines)",
+    "Finnish",
+    "Finnish (Finland)",
+    "French (Belgium)",
+    "French (Canada)",
+    "French (Switzerland)",
+    "Galician",
+    "Georgian (Georgia)",
+    "German (Austria)",
+    "German (Switzerland)",
+    "Greek",
+    "Greek (Greece)",
+    "Gujarati (India)",
+    "Haitian Creole (Haiti)",
+    "Hebrew (Israel)",
+    "Hungarian (Hungary)",
+    "Icelandic (Iceland)",
+    "Indonesian (Indonesia)",
+    "Irish (Ireland)",
+    "Javanese (Latin, Indonesia)",
+    "Kannada (India)",
+    "Kazakh (Kazakhstan)",
+    "Khmer (Cambodia)",
+    "Konkani (India)",
+    "Korean (Korea)",
+    "Lao (Laos)",
+    "Latin (Vatican City)",
+    "Latvian (Latvia)",
+    "Lithuanian (Lithuania)",
+    "Luxembourgish (Luxembourg)",
+    "Macedonian (North Macedonia)",
+    "Maithili (India)",
+    "Malagasy (Madagascar)",
+    "Malay",
+    "Malay (Malaysia)",
+    "Malayalam (India)",
+    "Maltese (Malta)",
+    "Mandarin",
+    "Marathi (India)",
+    "Mongolian (Mongolia)",
+    "Nepali (Nepal)",
+    "Norwegian Bokmål (Norway)",
+    "Norwegian Nynorsk (Norway)",
+    "Odia (India)",
+    "Pashto (Afghanistan)",
+    "Persian (Iran)",
+    "Polish (Poland)",
+    "Portuguese (Portugal)",
+    "Punjabi (India)",
+    "Romanian",
+    "Romanian (Romania)",
+    "Serbian (Latin, Serbia)",
+    "Sindhi (India)",
+    "Sinhala (Sri Lanka)",
+    "Slovak",
+    "Slovak (Slovakia)",
+    "Slovenian (Slovenia)",
+    "Somali (Somalia)",
+    "Spanish (Argentina)",
+    "Spanish (Bolivia)",
+    "Spanish (Chile)",
+    "Spanish (Colombia)",
+    "Spanish (Costa Rica)",
+    "Spanish (Cuba)",
+    "Spanish (Dominican Republic)",
+    "Spanish (Ecuador)",
+    "Spanish (El Salvador)",
+    "Spanish (Equatorial Guinea)",
+    "Spanish (Guatemala)",
+    "Spanish (Honduras)",
+    "Spanish (Latin America)",
+    "Spanish (Nicaragua)",
+    "Spanish (Panama)",
+    "Spanish (Paraguay)",
+    "Spanish (Peru)",
+    "Spanish (Puerto Rico)",
+    "Spanish (United States)",
+    "Spanish (Uruguay)",
+    "Spanish (Venezuela)",
+    "Sundanese (Indonesia)",
+    "Swahili (Kenya)",
+    "Swahili (Tanzania)",
+    "Swedish",
+    "Swedish (Sweden)",
+    "Tamil",
+    "Tamil (India)",
+    "Tamil (Malaysia)",
+    "Tamil (Singapore)",
+    "Tamil (Sri Lanka)",
+    "Telugu (India)",
+    "Thai (Thailand)",
+    "Turkish (Türkiye)",
+    "Ukrainian (Ukraine)",
+    "Urdu (India)",
+    "Urdu (Pakistan)",
+    "Uzbek (Latin, Uzbekistan)",
+    "Vietnamese (Vietnam)",
+    "Welsh (United Kingdom)",
+    "Zulu (South Africa)",
+]
diff --git a/comfy_api_nodes/apis/hitpaw.py b/comfy_api_nodes/apis/hitpaw.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d32d03fc6daf0794e955c0f75493e1164c909f6
--- /dev/null
+++ b/comfy_api_nodes/apis/hitpaw.py
@@ -0,0 +1,51 @@
+from typing import TypedDict
+
+from pydantic import BaseModel, Field
+
+
+class InputVideoModel(TypedDict):
+    model: str
+    resolution: str
+
+
+class ImageEnhanceTaskCreateRequest(BaseModel):
+    model_name: str = Field(...)
+    img_url: str = Field(...)
+    extension: str = Field(".png")
+    exif: bool = Field(False)
+    DPI: int | None = Field(None)
+
+
+class VideoEnhanceTaskCreateRequest(BaseModel):
+    video_url: str = Field(...)
+    extension: str = Field(".mp4")
+    model_name: str | None = Field(...)
+    resolution: list[int] = Field(..., description="Target resolution [width, height]")
+    original_resolution: list[int] = Field(..., description="Original video resolution [width, height]")
+
+
+class TaskCreateDataResponse(BaseModel):
+    job_id: str = Field(...)
+    consume_coins: int | None = Field(None)
+
+
+class TaskStatusPollRequest(BaseModel):
+    job_id: str = Field(...)
+
+
+class TaskCreateResponse(BaseModel):
+    code: int = Field(...)
+    message: str = Field(...)
+    data: TaskCreateDataResponse | None = Field(None)
+
+
+class TaskStatusDataResponse(BaseModel):
+    job_id: str = Field(...)
+    status: str = Field(...)
+    res_url: str = Field("")
+
+
+class TaskStatusResponse(BaseModel):
+    code: int = Field(...)
+    message: str = Field(...)
+    data: TaskStatusDataResponse = Field(...)
diff --git a/comfy_api_nodes/apis/hunyuan3d.py b/comfy_api_nodes/apis/hunyuan3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..43a06064dded744dcd21b936c3e272d113eabb16
--- /dev/null
+++ b/comfy_api_nodes/apis/hunyuan3d.py
@@ -0,0 +1,97 @@
+from typing import TypedDict
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class InputGenerateType(TypedDict):
+    generate_type: str
+    polygon_type: str
+    pbr: bool
+
+
+class Hunyuan3DViewImage(BaseModel):
+    ViewType: str = Field(..., description="Valid values: back, left, right.")
+    ViewImageUrl: str = Field(...)
+
+
+class To3DProTaskRequest(BaseModel):
+    Model: str = Field(...)
+    Prompt: str | None = Field(None)
+    ImageUrl: str | None = Field(None)
+    MultiViewImages: list[Hunyuan3DViewImage] | None = Field(None)
+    EnablePBR: bool | None = Field(...)
+    FaceCount: int | None = Field(...)
+    GenerateType: str | None = Field(...)
+    PolygonType: str | None = Field(...)
+
+
+class RequestError(BaseModel):
+    Code: str = Field("")
+    Message: str = Field("")
+
+
+class To3DProTaskCreateResponse(BaseModel):
+    JobId: str | None = Field(None)
+    Error: RequestError | None = Field(None)
+
+    @model_validator(mode="before")
+    @classmethod
+    def unwrap_data(cls, values: dict) -> dict:
+        if "Response" in values and isinstance(values["Response"], dict):
+            return values["Response"]
+        return values
+
+
+class ResultFile3D(BaseModel):
+    Type: str = Field(...)
+    Url: str = Field(...)
+    PreviewImageUrl: str = Field("")
+
+
+class To3DProTaskResultResponse(BaseModel):
+    ErrorCode: str = Field("")
+    ErrorMessage: str = Field("")
+    ResultFile3Ds: list[ResultFile3D] = Field([])
+    Status: str = Field(...)
+
+    @model_validator(mode="before")
+    @classmethod
+    def unwrap_data(cls, values: dict) -> dict:
+        if "Response" in values and isinstance(values["Response"], dict):
+            return values["Response"]
+        return values
+
+
+class To3DProTaskQueryRequest(BaseModel):
+    JobId: str = Field(...)
+
+
+class TaskFile3DInput(BaseModel):
+    Type: str = Field(..., description="File type: GLB, OBJ, or FBX")
+    Url: str = Field(...)
+
+
+class To3DUVTaskRequest(BaseModel):
+    File: TaskFile3DInput = Field(...)
+
+
+class To3DPartTaskRequest(BaseModel):
+    File: TaskFile3DInput = Field(...)
+    EnableStagedGeneration: bool | None = Field(None)
+
+
+class TextureEditImageInfo(BaseModel):
+    Url: str = Field(...)
+
+
+class TextureEditTaskRequest(BaseModel):
+    File3D: TaskFile3DInput = Field(...)
+    Image: TextureEditImageInfo | None = Field(None)
+    Prompt: str | None = Field(None)
+    EnablePBR: bool | None = Field(None)
+
+
+class SmartTopologyRequest(BaseModel):
+    File3D: TaskFile3DInput = Field(...)
+    PolygonType: str | None = Field(...)
+    FaceLevel: str | None = Field(...)
diff --git a/comfy_api_nodes/apis/ideogram.py b/comfy_api_nodes/apis/ideogram.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e8d02d8cbcd691e9267d932bd0d80e484bd72ea
--- /dev/null
+++ b/comfy_api_nodes/apis/ideogram.py
@@ -0,0 +1,266 @@
+from enum import Enum
+from typing import Optional, List, Dict, Any, Union
+from datetime import datetime
+
+from pydantic import BaseModel, Field, RootModel, StrictBytes
+
+
+class IdeogramColorPalette1(BaseModel):
+    name: str = Field(..., description='Name of the preset color palette')
+
+
+class Member(BaseModel):
+    color: Optional[str] = Field(
+        None, description='Hexadecimal color code', pattern='^#[0-9A-Fa-f]{6}$'
+    )
+    weight: Optional[float] = Field(
+        None, description='Optional weight for the color (0-1)', ge=0.0, le=1.0
+    )
+
+
+class IdeogramColorPalette2(BaseModel):
+    members: List[Member] = Field(
+        ..., description='Array of color definitions with optional weights'
+    )
+
+
+class IdeogramColorPalette(
+    RootModel[Union[IdeogramColorPalette1, IdeogramColorPalette2]]
+):
+    root: Union[IdeogramColorPalette1, IdeogramColorPalette2] = Field(
+        ...,
+        description='A color palette specification that can either use a preset name or explicit color definitions with weights',
+    )
+
+
+class Datum(BaseModel):
+    is_image_safe: Optional[bool] = Field(
+        None, description='Indicates whether the image is considered safe.'
+    )
+    prompt: Optional[str] = Field(
+        None, description='The prompt used to generate this image.'
+    )
+    resolution: Optional[str] = Field(
+        None, description="The resolution of the generated image (e.g., '1024x1024')."
+    )
+    seed: Optional[int] = Field(
+        None, description='The seed value used for this generation.'
+    )
+    style_type: Optional[str] = Field(
+        None,
+        description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').",
+    )
+    url: Optional[str] = Field(None, description='URL to the generated image.')
+
+
+class IdeogramGenerateResponse(BaseModel):
+    created: Optional[datetime] = Field(
+        None, description='Timestamp when the generation was created.'
+    )
+    data: Optional[List[Datum]] = Field(
+        None, description='Array of generated image information.'
+    )
+
+
+class StyleCode(RootModel[str]):
+    root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$')
+
+
+class RenderingSpeed1(str, Enum):
+    TURBO = 'TURBO'
+    DEFAULT = 'DEFAULT'
+    QUALITY = 'QUALITY'
+
+
+class IdeogramV3ReframeRequest(BaseModel):
+    color_palette: Optional[Dict[str, Any]] = None
+    image: Optional[StrictBytes] = None
+    num_images: Optional[int] = Field(None, ge=1, le=8)
+    rendering_speed: Optional[RenderingSpeed1] = None
+    resolution: str
+    seed: Optional[int] = Field(None, ge=0, le=2147483647)
+    style_codes: Optional[List[str]] = None
+    style_reference_images: Optional[List[StrictBytes]] = None
+
+
+class MagicPrompt(str, Enum):
+    AUTO = 'AUTO'
+    ON = 'ON'
+    OFF = 'OFF'
+
+
+class StyleType(str, Enum):
+    AUTO = 'AUTO'
+    GENERAL = 'GENERAL'
+    REALISTIC = 'REALISTIC'
+    DESIGN = 'DESIGN'
+
+
+class IdeogramV3RemixRequest(BaseModel):
+    aspect_ratio: Optional[str] = None
+    color_palette: Optional[Dict[str, Any]] = None
+    image: Optional[StrictBytes] = None
+    image_weight: Optional[int] = Field(50, ge=1, le=100)
+    magic_prompt: Optional[MagicPrompt] = None
+    negative_prompt: Optional[str] = None
+    num_images: Optional[int] = Field(None, ge=1, le=8)
+    prompt: str
+    rendering_speed: Optional[RenderingSpeed1] = None
+    resolution: Optional[str] = None
+    seed: Optional[int] = Field(None, ge=0, le=2147483647)
+    style_codes: Optional[List[str]] = None
+    style_reference_images: Optional[List[StrictBytes]] = None
+    style_type: Optional[StyleType] = None
+
+
+class IdeogramV3ReplaceBackgroundRequest(BaseModel):
+    color_palette: Optional[Dict[str, Any]] = None
+    image: Optional[StrictBytes] = None
+    magic_prompt: Optional[MagicPrompt] = None
+    num_images: Optional[int] = Field(None, ge=1, le=8)
+    prompt: str
+    rendering_speed: Optional[RenderingSpeed1] = None
+    seed: Optional[int] = Field(None, ge=0, le=2147483647)
+    style_codes: Optional[List[str]] = None
+    style_reference_images: Optional[List[StrictBytes]] = None
+
+
+class ColorPalette(BaseModel):
+    name: str = Field(..., description='Name of the color palette', examples=['PASTEL'])
+
+
+class MagicPrompt2(str, Enum):
+    ON = 'ON'
+    OFF = 'OFF'
+
+
+class StyleType1(str, Enum):
+    AUTO = 'AUTO'
+    GENERAL = 'GENERAL'
+    REALISTIC = 'REALISTIC'
+    DESIGN = 'DESIGN'
+    FICTION = 'FICTION'
+
+
+class RenderingSpeed(str, Enum):
+    DEFAULT = 'DEFAULT'
+    TURBO = 'TURBO'
+    QUALITY = 'QUALITY'
+
+
+class IdeogramV3EditRequest(BaseModel):
+    color_palette: Optional[IdeogramColorPalette] = None
+    image: Optional[StrictBytes] = Field(
+        None,
+        description='The image being edited (max size 10MB); only JPEG, WebP and PNG formats are supported at this time.',
+    )
+    magic_prompt: Optional[str] = Field(
+        None,
+        description='Determine if MagicPrompt should be used in generating the request or not.',
+    )
+    mask: Optional[StrictBytes] = Field(
+        None,
+        description='A black and white image of the same size as the image being edited (max size 10MB). Black regions in the mask should match up with the regions of the image that you would like to edit; only JPEG, WebP and PNG formats are supported at this time.',
+    )
+    num_images: Optional[int] = Field(
+        None, description='The number of images to generate.'
+    )
+    prompt: str = Field(
+        ..., description='The prompt used to describe the edited result.'
+    )
+    rendering_speed: RenderingSpeed
+    seed: Optional[int] = Field(
+        None, description='Random seed. Set for reproducible generation.'
+    )
+    style_codes: Optional[List[StyleCode]] = Field(
+        None,
+        description='A list of 8 character hexadecimal codes representing the style of the image. Cannot be used in conjunction with style_reference_images or style_type.',
+    )
+    style_reference_images: Optional[List[StrictBytes]] = Field(
+        None,
+        description='A set of images to use as style references (maximum total size 10MB across all style references). The images should be in JPEG, PNG or WebP format.',
+    )
+    character_reference_images: Optional[List[str]] = Field(
+        None,
+        description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+    character_reference_images_mask: Optional[List[str]] = Field(
+        None,
+        description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+
+
+class IdeogramV3Request(BaseModel):
+    aspect_ratio: Optional[str] = Field(
+        None, description='Aspect ratio in format WxH', examples=['1x3']
+    )
+    color_palette: Optional[ColorPalette] = None
+    magic_prompt: Optional[MagicPrompt2] = Field(
+        None, description='Whether to enable magic prompt enhancement'
+    )
+    negative_prompt: Optional[str] = Field(
+        None, description='Text prompt specifying what to avoid in the generation'
+    )
+    num_images: Optional[int] = Field(
+        None, description='Number of images to generate', ge=1
+    )
+    prompt: str = Field(..., description='The text prompt for image generation')
+    rendering_speed: RenderingSpeed
+    resolution: Optional[str] = Field(
+        None, description='Image resolution in format WxH', examples=['1280x800']
+    )
+    seed: Optional[int] = Field(
+        None, description='Seed value for reproducible generation'
+    )
+    style_codes: Optional[List[StyleCode]] = Field(
+        None, description='Array of style codes in hexadecimal format'
+    )
+    style_reference_images: Optional[List[str]] = Field(
+        None, description='Array of reference image URLs or identifiers'
+    )
+    style_type: Optional[StyleType1] = Field(
+        None, description='The type of style to apply'
+    )
+    character_reference_images: Optional[List[str]] = Field(
+        None,
+        description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+    character_reference_images_mask: Optional[List[str]] = Field(
+        None,
+        description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+
+
+class IdeogramPImageRequest(BaseModel):
+    prompt: str = Field(
+        ...,
+        description="The text prompt, or an Ideogram 4.0 structured JSON caption "
+        "(used verbatim when prompt_upsampling is 'OFF').",
+    )
+    quality: str | None = Field(
+        None, description="Generation tier: 'VERY_LOW', 'LOW', 'MEDIUM' or 'HIGH'."
+    )
+    resolution: str | None = Field(None, description="Output size class: '1K' or '2K'.")
+    aspect_ratio: str | None = Field(
+        None, description="Aspect ratio in WxH format", examples=['16x9']
+    )
+    prompt_upsampling: str | None = Field(
+        None, description="Prompt expansion: 'AUTO', 'ON' or 'OFF'."
+    )
+    seed: int | None = Field(None, ge=0, le=2147483647)
+
+
+class IdeogramV4Request(BaseModel):
+    text_prompt: str | None = Field(
+        None,
+        description="Natural-language prompt; Magic Prompt is applied automatically. "
+        "Supply exactly one of text_prompt or json_prompt.",
+    )
+    json_prompt: dict[str, Any] | None = Field(
+        None,
+        description="Structured V4 prompt object consumed directly (disables Magic Prompt). "
+        "Supply exactly one of text_prompt or json_prompt.",
+    )
+    resolution: str | None = Field(None, description="Output resolution in WIDTHxHEIGHT (e.g. '2048x2048').")
+    rendering_speed: str | None = Field(None, description="Rendering speed: 'TURBO', 'DEFAULT', or 'QUALITY'.")
+    enable_copyright_detection: bool | None = Field(None, description="Opt into post-generation copyright detection.")
diff --git a/comfy_api_nodes/apis/kling.py b/comfy_api_nodes/apis/kling.py
new file mode 100644
index 0000000000000000000000000000000000000000..79d058b05e3f167ced434107a8fcb4807699c43d
--- /dev/null
+++ b/comfy_api_nodes/apis/kling.py
@@ -0,0 +1,207 @@
+from pydantic import BaseModel, Field
+
+
+class MultiPromptEntry(BaseModel):
+    index: int = Field(...)
+    prompt: str = Field(...)
+    duration: str = Field(...)
+
+
+class OmniProText2VideoRequest(BaseModel):
+    model_name: str = Field(..., description="kling-video-o1")
+    aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
+    duration: str = Field(..., description="'5' or '10'")
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+    multi_shot: bool | None = Field(None)
+    multi_prompt: list[MultiPromptEntry] | None = Field(None)
+    shot_type: str | None = Field(None)
+    sound: str = Field(..., description="'on' or 'off'")
+
+
+class OmniParamImage(BaseModel):
+    image_url: str = Field(...)
+    type: str | None = Field(None, description="Can be 'first_frame' or 'end_frame'")
+
+
+class OmniParamVideo(BaseModel):
+    video_url: str = Field(...)
+    refer_type: str | None = Field(..., description="Can be 'base' or 'feature'")
+    keep_original_sound: str = Field(..., description="'yes' or 'no'")
+
+
+class OmniProFirstLastFrameRequest(BaseModel):
+    model_name: str = Field(..., description="kling-video-o1")
+    image_list: list[OmniParamImage] = Field(..., min_length=1, max_length=7)
+    duration: str = Field(..., description="'5' or '10'")
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+    sound: str | None = Field(None, description="'on' or 'off'")
+    multi_shot: bool | None = Field(None)
+    multi_prompt: list[MultiPromptEntry] | None = Field(None)
+    shot_type: str | None = Field(None)
+
+
+class OmniProReferences2VideoRequest(BaseModel):
+    model_name: str = Field(..., description="kling-video-o1")
+    aspect_ratio: str | None = Field(..., description="'16:9', '9:16' or '1:1'")
+    image_list: list[OmniParamImage] | None = Field(
+        None, max_length=7, description="Max length 4 when video is present."
+    )
+    video_list: list[OmniParamVideo] | None = Field(None, max_length=1)
+    duration: str | None = Field(..., description="From 3 to 10.")
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+    sound: str | None = Field(None, description="'on' or 'off'")
+    multi_shot: bool | None = Field(None)
+    multi_prompt: list[MultiPromptEntry] | None = Field(None)
+    shot_type: str | None = Field(None)
+
+
+class TaskStatusVideoResult(BaseModel):
+    duration: str | None = Field(None, description="Total video duration")
+    id: str | None = Field(None, description="Generated video ID")
+    url: str | None = Field(None, description="URL for generated video")
+
+
+class TaskStatusImageResult(BaseModel):
+    index: int = Field(..., description="Image Number，0-9")
+    url: str = Field(..., description="URL for generated image")
+
+
+class TaskStatusResults(BaseModel):
+    videos: list[TaskStatusVideoResult] | None = Field(None)
+    images: list[TaskStatusImageResult] | None = Field(None)
+    series_images: list[TaskStatusImageResult] | None = Field(None)
+
+
+class TaskStatusResponseData(BaseModel):
+    created_at: int | None = Field(None, description="Task creation time")
+    updated_at: int | None = Field(None, description="Task update time")
+    task_status: str | None = None
+    task_status_msg: str | None = Field(None, description="Additional failure reason. Only for polling endpoint.")
+    task_id: str | None = Field(None, description="Task ID")
+    task_result: TaskStatusResults | None = Field(None)
+
+
+class TaskStatusResponse(BaseModel):
+    code: int | None = Field(None, description="Error code")
+    message: str | None = Field(None, description="Error message")
+    request_id: str | None = Field(None, description="Request ID")
+    data: TaskStatusResponseData | None = Field(None)
+
+
+class OmniImageParamImage(BaseModel):
+    image: str = Field(...)
+
+
+class OmniProImageRequest(BaseModel):
+    model_name: str = Field(...)
+    resolution: str = Field(...)
+    aspect_ratio: str | None = Field(...)
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+    n: int | None = Field(1, le=9)
+    image_list: list[OmniImageParamImage] | None = Field(..., max_length=10)
+    result_type: str | None = Field(None, description="Set to 'series' for series generation")
+    series_amount: int | None = Field(None, ge=2, le=9, description="Number of images in a series")
+
+
+class TextToVideoWithAudioRequest(BaseModel):
+    model_name: str = Field(...)
+    aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
+    duration: str = Field(...)
+    prompt: str | None = Field(...)
+    negative_prompt: str | None = Field(None)
+    mode: str = Field("pro")
+    sound: str = Field(..., description="'on' or 'off'")
+    multi_shot: bool | None = Field(None)
+    multi_prompt: list[MultiPromptEntry] | None = Field(None)
+    shot_type: str | None = Field(None)
+
+
+class ImageToVideoWithAudioRequest(BaseModel):
+    model_name: str = Field(...)
+    image: str = Field(...)
+    image_tail: str | None = Field(None)
+    duration: str = Field(...)
+    prompt: str | None = Field(...)
+    negative_prompt: str | None = Field(None)
+    mode: str = Field("pro")
+    sound: str = Field(..., description="'on' or 'off'")
+    multi_shot: bool | None = Field(None)
+    multi_prompt: list[MultiPromptEntry] | None = Field(None)
+    shot_type: str | None = Field(None)
+
+
+class KlingAvatarRequest(BaseModel):
+    image: str = Field(...)
+    sound_file: str = Field(...)
+    prompt: str | None = Field(None)
+    mode: str = Field(...)
+
+
+class MotionControlRequest(BaseModel):
+    prompt: str = Field(...)
+    image_url: str = Field(...)
+    video_url: str = Field(...)
+    keep_original_sound: str = Field(...)
+    character_orientation: str = Field(...)
+    mode: str = Field(..., description="'pro' or 'std'")
+    model_name: str = Field(...)
+
+
+class Kling3TurboSettings(BaseModel):
+    resolution: str = Field("720p", description="'720p' or '1080p'")
+    aspect_ratio: str | None = Field(None, description="'16:9'/'9:16'/'1:1'; text-to-video only")
+    duration: int = Field(5, description="3-15 second")
+
+
+class Kling3TurboText2VideoRequest(BaseModel):
+    prompt: str = Field(..., description="<=3072 chars; may use multi-shot 'shot n, m, words; ...'")
+    settings: Kling3TurboSettings | None = Field(None)
+
+
+class Kling3TurboContent(BaseModel):
+    type: str = Field(..., description="'prompt' or 'first_frame'")
+    text: str | None = Field(None, description="for type=prompt; <=2500 chars")
+    url: str | None = Field(None, description="for type=first_frame")
+
+
+class Kling3TurboImage2VideoRequest(BaseModel):
+    contents: list[Kling3TurboContent] = Field(..., description="prompt + first_frame materials")
+    settings: Kling3TurboSettings | None = Field(None)
+
+
+class Kling3TurboCreateData(BaseModel):
+    id: str | None = Field(None, description="Task ID")
+    status: str | None = Field(None)
+    message: str | None = Field(None)
+
+
+class Kling3TurboCreateResponse(BaseModel):
+    code: int | None = Field(None)
+    message: str | None = Field(None)
+    request_id: str | None = Field(None)
+    data: Kling3TurboCreateData | None = Field(None)
+
+
+class Kling3TurboOutput(BaseModel):
+    type: str | None = Field(None, description="'video', 'image', 'audio', ...")
+    id: str | None = Field(None)
+    url: str | None = Field(None)
+    duration: str | None = Field(None)
+
+
+class Kling3TurboTaskData(BaseModel):
+    id: str | None = Field(None)
+    status: str | None = Field(None, description="submitted | processing | succeeded | failed")
+    message: str | None = Field(None)
+    outputs: list[Kling3TurboOutput] | None = Field(None)
+
+
+class Kling3TurboQueryResponse(BaseModel):
+    code: int | None = Field(None)
+    message: str | None = Field(None)
+    request_id: str | None = Field(None)
+    data: list[Kling3TurboTaskData] | None = Field(None)
diff --git a/comfy_api_nodes/apis/krea.py b/comfy_api_nodes/apis/krea.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8fd633190166c487984bf8b4dcca3358f499af1
--- /dev/null
+++ b/comfy_api_nodes/apis/krea.py
@@ -0,0 +1,46 @@
+"""Pydantic models for the Krea image-generation API."""
+
+from pydantic import BaseModel, Field
+
+
+class KreaMoodboard(BaseModel):
+    id: str = Field(...)
+    strength: float = Field(default=0.35, ge=-0.5, le=1.5)
+
+
+class KreaImageStyleReference(BaseModel):
+    strength: float = Field(..., ge=-2.0, le=2.0)
+    url: str | None = Field(default=None)
+
+
+class KreaGenerateImageRequest(BaseModel):
+    prompt: str = Field(...)
+    aspect_ratio: str = Field(...)
+    resolution: str = Field(...)
+    seed: int | None = Field(default=None)
+    creativity: str = Field(default="medium")
+    moodboards: list[KreaMoodboard] | None = Field(default=None)
+    image_style_references: list[KreaImageStyleReference] | None = Field(default=None)
+
+
+class KreaJobResult(BaseModel):
+    urls: list[str] | None = Field(default=None)
+    style_id: str | None = Field(default=None)
+
+
+class KreaJob(BaseModel):
+    job_id: str = Field(...)
+    status: str = Field(...)
+    created_at: str = Field(...)
+    completed_at: str | None = Field(default=None)
+    result: KreaJobResult | None = Field(default=None)
+
+
+class KreaAssetResponse(BaseModel):
+    id: str = Field(...)
+    image_url: str = Field(...)
+    uploaded_at: str = Field(...)
+    width: float | None = Field(default=None)
+    height: float | None = Field(default=None)
+    size_bytes: float | None = Field(default=None)
+    mime_type: str | None = Field(default=None)
diff --git a/comfy_api_nodes/apis/luma.py b/comfy_api_nodes/apis/luma.py
new file mode 100644
index 0000000000000000000000000000000000000000..74f175e9f47483b7d94ebd21c18030ee8db2c464
--- /dev/null
+++ b/comfy_api_nodes/apis/luma.py
@@ -0,0 +1,333 @@
+from __future__ import annotations
+
+from enum import Enum
+from typing import Optional, Union
+
+import torch
+from pydantic import BaseModel, Field, confloat
+
+
+class LumaIO:
+    LUMA_REF = "LUMA_REF"
+    LUMA_CONCEPTS = "LUMA_CONCEPTS"
+    LUMA_RAY32_KEYFRAME = "LUMA_RAY32_KEYFRAME"
+
+
+class LumaReference:
+    def __init__(self, image: torch.Tensor, weight: float):
+        self.image = image
+        self.weight = weight
+
+    def create_api_model(self, download_url: str):
+        return LumaImageRef(url=download_url, weight=self.weight)
+
+
+class LumaReferenceChain:
+    def __init__(self, first_ref: LumaReference = None):
+        self.refs: list[LumaReference] = []
+        if first_ref:
+            self.refs.append(first_ref)
+
+    def add(self, luma_ref: LumaReference = None):
+        self.refs.append(luma_ref)
+
+    def create_api_model(self, download_urls: list[str], max_refs=4):
+        if len(self.refs) == 0:
+            return None
+        api_refs: list[LumaImageRef] = []
+        for ref, url in zip(self.refs, download_urls):
+            api_ref = LumaImageRef(url=url, weight=ref.weight)
+            api_refs.append(api_ref)
+        return api_refs
+
+    def clone(self):
+        c = LumaReferenceChain()
+        for ref in self.refs:
+            c.add(ref)
+        return c
+
+
+class LumaConcept:
+    def __init__(self, key: str):
+        self.key = key
+
+
+class LumaConceptChain:
+    def __init__(self, str_list: list[str] = None):
+        self.concepts: list[LumaConcept] = []
+        if str_list is not None:
+            for c in str_list:
+                if c != "None":
+                    self.add(LumaConcept(key=c))
+
+    def add(self, concept: LumaConcept):
+        self.concepts.append(concept)
+
+    def create_api_model(self):
+        if len(self.concepts) == 0:
+            return None
+        api_concepts: list[LumaConceptObject] = []
+        for concept in self.concepts:
+            if concept.key == "None":
+                continue
+            api_concepts.append(LumaConceptObject(key=concept.key))
+        if len(api_concepts) == 0:
+            return None
+        return api_concepts
+
+    def clone(self):
+        c = LumaConceptChain()
+        for concept in self.concepts:
+            c.add(concept)
+        return c
+
+    def clone_and_merge(self, other: LumaConceptChain):
+        c = self.clone()
+        for concept in other.concepts:
+            c.add(concept)
+        return c
+
+
+def get_luma_concepts(include_none=False):
+    concepts = []
+    if include_none:
+        concepts.append("None")
+    return concepts + [
+        "truck_left",
+        "pan_right",
+        "pedestal_down",
+        "low_angle",
+        "pedestal_up",
+        "selfie",
+        "pan_left",
+        "roll_right",
+        "zoom_in",
+        "over_the_shoulder",
+        "orbit_right",
+        "orbit_left",
+        "static",
+        "tiny_planet",
+        "high_angle",
+        "bolt_cam",
+        "dolly_zoom",
+        "overhead",
+        "zoom_out",
+        "handheld",
+        "roll_left",
+        "pov",
+        "aerial_drone",
+        "push_in",
+        "crane_down",
+        "truck_right",
+        "tilt_down",
+        "elevator_doors",
+        "tilt_up",
+        "ground_level",
+        "pull_out",
+        "aerial",
+        "crane_up",
+        "eye_level",
+    ]
+
+
+class LumaImageModel(str, Enum):
+    photon_1 = "photon-1"
+    photon_flash_1 = "photon-flash-1"
+
+
+class LumaVideoModel(str, Enum):
+    ray_2 = "ray-2"
+    ray_flash_2 = "ray-flash-2"
+    ray_1_6 = "ray-1-6"
+
+
+class LumaAspectRatio(str, Enum):
+    ratio_1_1 = "1:1"
+    ratio_16_9 = "16:9"
+    ratio_9_16 = "9:16"
+    ratio_4_3 = "4:3"
+    ratio_3_4 = "3:4"
+    ratio_21_9 = "21:9"
+    ratio_9_21 = "9:21"
+
+
+class LumaVideoOutputResolution(str, Enum):
+    res_540p = "540p"
+    res_720p = "720p"
+    res_1080p = "1080p"
+    res_4k = "4k"
+
+
+class LumaVideoModelOutputDuration(str, Enum):
+    dur_5s = "5s"
+    dur_9s = "9s"
+
+
+class LumaGenerationType(str, Enum):
+    video = "video"
+    image = "image"
+
+
+class LumaState(str, Enum):
+    queued = "queued"
+    dreaming = "dreaming"
+    completed = "completed"
+    failed = "failed"
+
+
+class LumaAssets(BaseModel):
+    video: Optional[str] = Field(None, description="The URL of the video")
+    image: Optional[str] = Field(None, description="The URL of the image")
+    progress_video: Optional[str] = Field(None, description="The URL of the progress video")
+
+
+class LumaImageRef(BaseModel):
+    """Used for image gen"""
+
+    url: str = Field(..., description="The URL of the image reference")
+    weight: confloat(ge=0.0, le=1.0) = Field(..., description="The weight of the image reference")
+
+
+class LumaImageReference(BaseModel):
+    """Used for video gen"""
+
+    type: Optional[str] = Field("image", description="Input type, defaults to image")
+    url: str = Field(..., description="The URL of the image")
+
+
+class LumaModifyImageRef(BaseModel):
+    url: str = Field(..., description="The URL of the image reference")
+    weight: confloat(ge=0.0, le=1.0) = Field(..., description="The weight of the image reference")
+
+
+class LumaCharacterRef(BaseModel):
+    identity0: LumaImageIdentity = Field(..., description="The image identity object")
+
+
+class LumaImageIdentity(BaseModel):
+    images: list[str] = Field(..., description="The URLs of the image identity")
+
+
+class LumaGenerationReference(BaseModel):
+    type: str = Field("generation", description="Input type, defaults to generation")
+    id: str = Field(..., description="The ID of the generation")
+
+
+class LumaKeyframes(BaseModel):
+    frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description="")
+    frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description="")
+
+
+class LumaConceptObject(BaseModel):
+    key: str = Field(..., description="Camera Concept name")
+
+
+class LumaImageGenerationRequest(BaseModel):
+    prompt: str = Field(..., description="The prompt of the generation")
+    model: LumaImageModel = Field(LumaImageModel.photon_1, description="The image model used for the generation")
+    aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9)
+    image_ref: Optional[list[LumaImageRef]] = Field(None, description="List of image reference objects")
+    style_ref: Optional[list[LumaImageRef]] = Field(None, description="List of style reference objects")
+    character_ref: Optional[LumaCharacterRef] = Field(None, description="The image identity object")
+    modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description="The modify image reference object")
+
+
+class LumaGenerationRequest(BaseModel):
+    prompt: str = Field(..., description="The prompt of the generation")
+    model: LumaVideoModel = Field(LumaVideoModel.ray_2, description="The video model used for the generation")
+    duration: Optional[LumaVideoModelOutputDuration] = Field(None, description="The duration of the generation")
+    aspect_ratio: Optional[LumaAspectRatio] = Field(None, description="The aspect ratio of the generation")
+    resolution: Optional[LumaVideoOutputResolution] = Field(None, description="The resolution of the generation")
+    loop: Optional[bool] = Field(None, description="Whether to loop the video")
+    keyframes: Optional[LumaKeyframes] = Field(None, description="The keyframes of the generation")
+    concepts: Optional[list[LumaConceptObject]] = Field(None, description="Camera Concepts to apply to generation")
+
+
+class LumaGeneration(BaseModel):
+    id: str = Field(..., description="The ID of the generation")
+    generation_type: LumaGenerationType = Field(..., description="Generation type, image or video")
+    state: LumaState = Field(..., description="The state of the generation")
+    failure_reason: Optional[str] = Field(None, description="The reason for the state of the generation")
+    created_at: str = Field(..., description="The date and time when the generation was created")
+    assets: Optional[LumaAssets] = Field(None, description="The assets of the generation")
+    model: str = Field(..., description="The model used for the generation")
+    request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(...)
+
+
+class Luma2ImageRef(BaseModel):
+    url: str | None = None
+    data: str | None = None
+    media_type: str | None = None
+    generation_id: str | None = Field(None, description="reference a prior generation (extend / source reuse)")
+
+
+class Luma2VideoEdit(BaseModel):
+    """Edit controls for Ray 3.2 ``video_edit`` generations."""
+
+    auto_controls: bool | None = Field(None, description="derive a conditioning schedule from the source (recommended)")
+    strength: str | None = Field(None, description="'adhere_1' .. 'reimagine_3'; constrained by IO.Combo")
+
+
+class Luma2VideoOptions(BaseModel):
+    """Ray 3.2 ``video`` output settings (text / image / keyframe / edit / extend)."""
+
+    resolution: str | None = Field(None, description="360p | 540p | 720p | 1080p")
+    duration: str | None = Field(None, description="5s | 10s")
+    loop: bool | None = Field(None)
+    start_frame: Luma2ImageRef | None = Field(None)
+    end_frame: Luma2ImageRef | None = Field(None)
+    keyframes: list[Luma2ImageRef] | None = Field(None)
+    keyframe_indexes: list[int] | None = Field(None)
+    edit: Luma2VideoEdit | None = Field(None)
+
+
+class Luma2GenerationRequest(BaseModel):
+    prompt: str = Field(..., min_length=1, max_length=6000)
+    model: str | None = None
+    type: str | None = None
+    aspect_ratio: str | None = None
+    style: str | None = None
+    output_format: str | None = None
+    web_search: bool | None = None
+    image_ref: list[Luma2ImageRef] | None = None
+    source: Luma2ImageRef | None = None
+    video: Luma2VideoOptions | None = Field(None)
+
+
+class Luma2Generation(BaseModel):
+    id: str | None = None
+    type: str | None = None
+    state: str | None = None
+    model: str | None = None
+    created_at: str | None = None
+    output: list[LumaImageReference] | None = None
+    failure_reason: str | None = None
+    failure_code: str | None = None
+
+
+# --- Ray 3.2 multi-keyframe chain ---
+
+LUMA_KEYFRAME_MODE_FRACTION = "fraction"  # value in [0.0, 1.0] of the output video duration
+LUMA_KEYFRAME_MODE_SECONDS = "seconds"  # absolute time, in seconds, from the start of the output
+
+
+class LumaRay32KeyframeItem:
+    """One guide image anchored at a position on the Ray 3.2 output timeline."""
+
+    def __init__(self, image: torch.Tensor, mode: str, value: float):
+        self.image = image
+        self.mode = mode  # LUMA_KEYFRAME_MODE_FRACTION | LUMA_KEYFRAME_MODE_SECONDS
+        self.value = value
+
+
+class LumaRay32KeyframeChain:
+    def __init__(self):
+        self.items: list[LumaRay32KeyframeItem] = []
+
+    def add(self, item: LumaRay32KeyframeItem) -> None:
+        self.items.append(item)
+
+    def clone(self) -> "LumaRay32KeyframeChain":
+        c = LumaRay32KeyframeChain()
+        c.items = list(self.items)
+        return c
diff --git a/comfy_api_nodes/apis/magnific.py b/comfy_api_nodes/apis/magnific.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f2aa783c726233ad81e6a13761c4146647ca22f
--- /dev/null
+++ b/comfy_api_nodes/apis/magnific.py
@@ -0,0 +1,122 @@
+from typing import TypedDict
+
+from pydantic import AliasChoices, BaseModel, Field, model_validator
+
+
+class InputPortraitMode(TypedDict):
+    portrait_mode: str
+    portrait_style: str
+    portrait_beautifier: str
+
+
+class InputAdvancedSettings(TypedDict):
+    advanced_settings: str
+    whites: int
+    blacks: int
+    brightness: int
+    contrast: int
+    saturation: int
+    engine: str
+    transfer_light_a: str
+    transfer_light_b: str
+    fixed_generation: bool
+
+
+class InputSkinEnhancerMode(TypedDict):
+    mode: str
+    skin_detail: int
+    optimized_for: str
+
+
+class ImageUpscalerCreativeRequest(BaseModel):
+    image: str = Field(...)
+    scale_factor: str = Field(...)
+    optimized_for: str = Field(...)
+    prompt: str | None = Field(None)
+    creativity: int = Field(...)
+    hdr: int = Field(...)
+    resemblance: int = Field(...)
+    fractality: int = Field(...)
+    engine: str = Field(...)
+
+
+class ImageUpscalerPrecisionV2Request(BaseModel):
+    image: str = Field(...)
+    sharpen: int = Field(...)
+    smart_grain: int = Field(...)
+    ultra_detail: int = Field(...)
+    flavor: str = Field(...)
+    scale_factor: int = Field(...)
+
+
+class ImageRelightAdvancedSettingsRequest(BaseModel):
+    whites: int = Field(...)
+    blacks: int = Field(...)
+    brightness: int = Field(...)
+    contrast: int = Field(...)
+    saturation: int = Field(...)
+    engine: str = Field(...)
+    transfer_light_a: str = Field(...)
+    transfer_light_b: str = Field(...)
+    fixed_generation: bool = Field(...)
+
+
+class ImageRelightRequest(BaseModel):
+    image: str = Field(...)
+    prompt: str | None = Field(None)
+    transfer_light_from_reference_image: str | None = Field(None)
+    light_transfer_strength: int = Field(...)
+    interpolate_from_original: bool = Field(...)
+    change_background: bool = Field(...)
+    style: str = Field(...)
+    preserve_details: bool = Field(...)
+    advanced_settings: ImageRelightAdvancedSettingsRequest | None = Field(...)
+
+
+class ImageStyleTransferRequest(BaseModel):
+    image: str = Field(...)
+    reference_image: str = Field(...)
+    prompt: str | None = Field(None)
+    style_strength: int = Field(...)
+    structure_strength: int = Field(...)
+    is_portrait: bool = Field(...)
+    portrait_style: str | None = Field(...)
+    portrait_beautifier: str | None = Field(...)
+    flavor: str = Field(...)
+    engine: str = Field(...)
+    fixed_generation: bool = Field(...)
+
+
+class ImageSkinEnhancerCreativeRequest(BaseModel):
+    image: str = Field(...)
+    sharpen: int = Field(...)
+    smart_grain: int = Field(...)
+
+
+class ImageSkinEnhancerFaithfulRequest(BaseModel):
+    image: str = Field(...)
+    sharpen: int = Field(...)
+    smart_grain: int = Field(...)
+    skin_detail: int = Field(...)
+
+
+class ImageSkinEnhancerFlexibleRequest(BaseModel):
+    image: str = Field(...)
+    sharpen: int = Field(...)
+    smart_grain: int = Field(...)
+    optimized_for: str = Field(...)
+
+
+class TaskResponse(BaseModel):
+    """Unified response model that handles both wrapped and unwrapped API responses."""
+
+    task_id: str = Field(...)
+    status: str = Field(validation_alias=AliasChoices("status", "task_status"))
+    generated: list[str] | None = Field(None)
+
+    @model_validator(mode="before")
+    @classmethod
+    def unwrap_data(cls, values: dict) -> dict:
+        if "data" in values and isinstance(values["data"], dict):
+            return values["data"]
+        return values
diff --git a/comfy_api_nodes/apis/meshy.py b/comfy_api_nodes/apis/meshy.py
new file mode 100644
index 0000000000000000000000000000000000000000..94e904cc64184df56ba70821910a55f85492f68d
--- /dev/null
+++ b/comfy_api_nodes/apis/meshy.py
@@ -0,0 +1,173 @@
+from typing import TypedDict
+
+from pydantic import BaseModel, Field
+
+from comfy_api.latest import Input
+
+
+class InputShouldRemesh(TypedDict):
+    should_remesh: str
+    topology: str
+    target_polycount: int
+
+
+class InputShouldTexture(TypedDict):
+    should_texture: str
+    enable_pbr: bool
+    texture_resolution: str
+    texture_prompt: str
+    texture_image: Input.Image | None
+
+
+class MeshyTaskResponse(BaseModel):
+    result: str = Field(...)
+
+
+class MeshyTextToModelRequest(BaseModel):
+    mode: str = Field("preview")
+    prompt: str = Field(..., max_length=600)
+    art_style: str = Field(...)
+    ai_model: str = Field(...)
+    topology: str | None = Field(..., description="'quad' or 'triangle'")
+    target_polycount: int | None = Field(..., ge=100, le=300000)
+    should_remesh: bool = Field(
+        True,
+        description="False returns the original mesh, ignoring topology and polycount.",
+    )
+    symmetry_mode: str = Field(..., description="'auto', 'off' or 'on'")
+    pose_mode: str = Field(...)
+    ultra_mode: bool = Field(False)
+    seed: int = Field(...)
+    moderation: bool = Field(False)
+
+
+class MeshyRefineTask(BaseModel):
+    mode: str = Field("refine")
+    preview_task_id: str = Field(...)
+    enable_pbr: bool | None = Field(...)
+    texture_resolution: str = Field(...)
+    texture_prompt: str | None = Field(...)
+    texture_image_url: str | None = Field(...)
+    ai_model: str = Field(...)
+    moderation: bool = Field(False)
+
+
+class MeshyImageToModelRequest(BaseModel):
+    image_url: str = Field(...)
+    ai_model: str = Field(...)
+    topology: str | None = Field(..., description="'quad' or 'triangle'")
+    target_polycount: int | None = Field(..., ge=100, le=300000)
+    symmetry_mode: str = Field(..., description="'auto', 'off' or 'on'")
+    should_remesh: bool = Field(
+        True,
+        description="False returns the original mesh, ignoring topology and polycount.",
+    )
+    should_texture: bool = Field(...)
+    enable_pbr: bool | None = Field(...)
+    texture_resolution: str | None = Field(None)
+    pose_mode: str = Field(...)
+    ultra_mode: bool = Field(False)
+    texture_prompt: str | None = Field(None, max_length=600)
+    texture_image_url: str | None = Field(None)
+    seed: int = Field(...)
+    moderation: bool = Field(False)
+
+
+class MeshyMultiImageToModelRequest(BaseModel):
+    image_urls: list[str] = Field(...)
+    ai_model: str = Field(...)
+    topology: str | None = Field(..., description="'quad' or 'triangle'")
+    target_polycount: int | None = Field(..., ge=100, le=300000)
+    symmetry_mode: str = Field(..., description="'auto', 'off' or 'on'")
+    should_remesh: bool = Field(
+        True,
+        description="False returns the original mesh, ignoring topology and polycount.",
+    )
+    should_texture: bool = Field(...)
+    enable_pbr: bool | None = Field(...)
+    texture_resolution: str | None = Field(None)
+    pose_mode: str = Field(...)
+    texture_prompt: str | None = Field(None, max_length=600)
+    texture_image_url: str | None = Field(None)
+    seed: int = Field(...)
+    moderation: bool = Field(False)
+
+
+class MeshyRiggingRequest(BaseModel):
+    input_task_id: str = Field(...)
+    height_meters: float = Field(...)
+    texture_image_url: str | None = Field(...)
+
+
+class MeshyAnimationRequest(BaseModel):
+    rig_task_id: str = Field(...)
+    action_id: int = Field(...)
+
+
+class MeshyTextureRequest(BaseModel):
+    input_task_id: str = Field(...)
+    ai_model: str = Field(...)
+    enable_original_uv: bool = Field(...)
+    enable_pbr: bool = Field(...)
+    texture_resolution: str = Field(...)
+    text_style_prompt: str | None = Field(None)
+    image_style_url: str | None = Field(None)
+    multiview_image_urls: list[str] | None = Field(None)
+
+
+class MeshyModelsUrls(BaseModel):
+    glb: str = Field("")
+    fbx: str = Field("")
+    usdz: str = Field("")
+    obj: str = Field("")
+
+
+class MeshyRiggedModelsUrls(BaseModel):
+    rigged_character_glb_url: str = Field("")
+    rigged_character_fbx_url: str = Field("")
+
+
+class MeshyAnimatedModelsUrls(BaseModel):
+    animation_glb_url: str = Field("")
+    animation_fbx_url: str = Field("")
+
+
+class MeshyResultTextureUrls(BaseModel):
+    base_color: str = Field(...)
+    metallic: str | None = Field(None)
+    normal: str | None = Field(None)
+    roughness: str | None = Field(None)
+
+
+class MeshyTaskError(BaseModel):
+    message: str | None = Field(None)
+
+
+class MeshyModelResult(BaseModel):
+    id: str = Field(...)
+    type: str = Field(...)
+    model_urls: MeshyModelsUrls = Field(MeshyModelsUrls())
+    thumbnail_url: str = Field(...)
+    video_url: str | None = Field(None)
+    status: str = Field(...)
+    progress: int = Field(0)
+    texture_urls: list[MeshyResultTextureUrls] | None = Field([])
+    task_error: MeshyTaskError | None = Field(None)
+
+
+class MeshyRiggedResult(BaseModel):
+    id: str = Field(...)
+    type: str = Field(...)
+    status: str = Field(...)
+    progress: int = Field(0)
+    result: MeshyRiggedModelsUrls = Field(MeshyRiggedModelsUrls())
+    task_error: MeshyTaskError | None = Field(None)
+
+
+class MeshyAnimationResult(BaseModel):
+    id: str = Field(...)
+    type: str = Field(...)
+    status: str = Field(...)
+    progress: int = Field(0)
+    result: MeshyAnimatedModelsUrls = Field(MeshyAnimatedModelsUrls())
+    task_error: MeshyTaskError | None = Field(None)
diff --git a/comfy_api_nodes/apis/minimax.py b/comfy_api_nodes/apis/minimax.py
new file mode 100644
index 0000000000000000000000000000000000000000..85e4ce9524ea493b99e076e836cd37677757d27d
--- /dev/null
+++ b/comfy_api_nodes/apis/minimax.py
@@ -0,0 +1,217 @@
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class MinimaxBaseResponse(BaseModel):
+    status_code: int = Field(
+        ...,
+        description='Status code. 0 indicates success, other values indicate errors.',
+    )
+    status_msg: str = Field(
+        ..., description='Specific error details or success message.'
+    )
+
+
+class File(BaseModel):
+    bytes: Optional[int] = Field(None, description='File size in bytes')
+    created_at: Optional[int] = Field(
+        None, description='Unix timestamp when the file was created, in seconds'
+    )
+    download_url: Optional[str] = Field(
+        None, description='The URL to download the video'
+    )
+    backup_download_url: Optional[str] = Field(
+        None, description='The backup URL to download the video'
+    )
+
+    file_id: Optional[int] = Field(None, description='Unique identifier for the file')
+    filename: Optional[str] = Field(None, description='The name of the file')
+    purpose: Optional[str] = Field(None, description='The purpose of using the file')
+
+
+class MinimaxFileRetrieveResponse(BaseModel):
+    base_resp: MinimaxBaseResponse
+    file: File
+
+
+class MiniMaxModel(str, Enum):
+    T2V_01_Director = 'T2V-01-Director'
+    I2V_01_Director = 'I2V-01-Director'
+    S2V_01 = 'S2V-01'
+    I2V_01 = 'I2V-01'
+    I2V_01_live = 'I2V-01-live'
+    T2V_01 = 'T2V-01'
+    Hailuo_02 = 'MiniMax-Hailuo-02'
+
+
+class Status6(str, Enum):
+    Queueing = 'Queueing'
+    Preparing = 'Preparing'
+    Processing = 'Processing'
+    Success = 'Success'
+    Fail = 'Fail'
+
+
+class MinimaxTaskResultResponse(BaseModel):
+    base_resp: MinimaxBaseResponse
+    file_id: Optional[str] = Field(
+        None,
+        description='After the task status changes to Success, this field returns the file ID corresponding to the generated video.',
+    )
+    status: Status6 = Field(
+        ...,
+        description="Task status: 'Queueing' (in queue), 'Preparing' (task is preparing), 'Processing' (generating), 'Success' (task completed successfully), or 'Fail' (task failed).",
+    )
+    task_id: str = Field(..., description='The task ID being queried.')
+
+
+class SubjectReferenceItem(BaseModel):
+    image: Optional[str] = Field(
+        None, description='URL or base64 encoding of the subject reference image.'
+    )
+    mask: Optional[str] = Field(
+        None,
+        description='URL or base64 encoding of the mask for the subject reference image.',
+    )
+
+
+class MinimaxVideoGenerationRequest(BaseModel):
+    callback_url: Optional[str] = Field(
+        None,
+        description='Optional. URL to receive real-time status updates about the video generation task.',
+    )
+    first_frame_image: Optional[str] = Field(
+        None,
+        description='URL or base64 encoding of the first frame image. Required when model is I2V-01, I2V-01-Director, or I2V-01-live.',
+    )
+    model: MiniMaxModel = Field(
+        ...,
+        description='Required. ID of model. Options: T2V-01-Director, I2V-01-Director, S2V-01, I2V-01, I2V-01-live, T2V-01',
+    )
+    prompt: Optional[str] = Field(
+        None,
+        description='Description of the video. Should be less than 2000 characters. Supports camera movement instructions in [brackets].',
+        max_length=2000,
+    )
+    prompt_optimizer: Optional[bool] = Field(
+        True,
+        description='If true (default), the model will automatically optimize the prompt. Set to false for more precise control.',
+    )
+    subject_reference: Optional[list[SubjectReferenceItem]] = Field(
+        None,
+        description='Only available when model is S2V-01. The model will generate a video based on the subject uploaded through this parameter.',
+    )
+    duration: Optional[int] = Field(
+        None,
+        description="The length of the output video in seconds."
+    )
+    resolution: Optional[str] = Field(
+        None,
+        description="The dimensions of the video display. 1080p corresponds to 1920 x 1080 pixels, 768p corresponds to 1366 x 768 pixels."
+    )
+
+
+class MinimaxVideoGenerationResponse(BaseModel):
+    base_resp: MinimaxBaseResponse
+    task_id: str = Field(
+        ..., description='The task ID for the asynchronous video generation task.'
+    )
+
+
+class Hailuo03TextContent(BaseModel):
+    type: str = Field("text")
+    text: str = Field(...)
+
+
+class Hailuo03ImageContentUrl(BaseModel):
+    url: str = Field(...)
+
+
+class Hailuo03ImageContent(BaseModel):
+    type: str = Field("image_url")
+    image_url: Hailuo03ImageContentUrl = Field(...)
+    role: str = Field(...)
+
+
+class Hailuo03VideoContentUrl(BaseModel):
+    url: str = Field(...)
+
+
+class Hailuo03VideoContent(BaseModel):
+    type: str = Field("video_url")
+    video_url: Hailuo03VideoContentUrl = Field(...)
+    role: str = Field("reference_video")
+
+
+class Hailuo03AudioContentUrl(BaseModel):
+    url: str = Field(...)
+
+
+class Hailuo03AudioContent(BaseModel):
+    type: str = Field("audio_url")
+    audio_url: Hailuo03AudioContentUrl = Field(...)
+    role: str = Field("reference_audio")
+
+
+class Hailuo03TaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    content: list[Hailuo03TextContent | Hailuo03ImageContent | Hailuo03VideoContent | Hailuo03AudioContent] = Field(
+        ..., min_length=1
+    )
+    resolution: str = Field(...)
+    duration: int = Field(..., ge=4, le=15)
+    ratio: str | None = Field(None)
+    seed: int | None = Field(None, ge=0, le=4294967295)
+    aigc_watermark: bool | None = Field(None)
+
+
+class Hailuo03ContextIRRequest(BaseModel):
+    model: str = Field(...)
+    content: list[Hailuo03TextContent | Hailuo03ImageContent | Hailuo03VideoContent | Hailuo03AudioContent] = Field(
+        ..., min_length=1
+    )
+    duration: int = Field(..., ge=4, le=15)
+    ratio: str | None = Field(None)
+
+
+class Hailuo03RegenerationRequest(BaseModel):
+    model: str = Field(...)
+    content: list[Hailuo03TextContent | Hailuo03ImageContent | Hailuo03VideoContent | Hailuo03AudioContent] = Field(
+        ..., min_length=1
+    )
+    resolution: str = Field(...)
+    aigc_watermark: bool | None = Field(None)
+
+
+class Hailuo03TaskCreationResponse(BaseModel):
+    task_id: str = Field(...)
+
+
+class Hailuo03TaskError(BaseModel):
+    code: int | str | None = Field(None)
+    message: str | None = Field(None)
+
+
+class Hailuo03TaskContent(BaseModel):
+    url: str | None = Field(None)
+    prompt: str | None = Field(None)
+
+
+class Hailuo03TaskUsage(BaseModel):
+    total_seconds: float = Field(0)
+    input_seconds: float = Field(0)
+    output_seconds: float = Field(0)
+
+
+class Hailuo03Task(BaseModel):
+    id: str = Field(...)
+    status: str = Field(...)
+    error: Hailuo03TaskError | None = Field(None)
+    content: Hailuo03TaskContent | None = Field(None)
+    usage: Hailuo03TaskUsage | None = Field(None)
+
+
+class Hailuo03TaskQueryResponse(BaseModel):
+    task: Hailuo03Task = Field(...)
diff --git a/comfy_api_nodes/apis/openai.py b/comfy_api_nodes/apis/openai.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5eb136effbe45f1e20deae34256388dc81c9c1b
--- /dev/null
+++ b/comfy_api_nodes/apis/openai.py
@@ -0,0 +1,170 @@
+from pydantic import BaseModel, Field
+
+
+class Datum2(BaseModel):
+    b64_json: str | None = Field(None, description="Base64 encoded image data")
+    revised_prompt: str | None = Field(None, description="Revised prompt")
+    url: str | None = Field(None, description="URL of the image")
+
+
+class InputTokensDetails(BaseModel):
+    image_tokens: int | None = Field(None)
+    text_tokens: int | None = Field(None)
+
+
+class Usage(BaseModel):
+    input_tokens: int | None = Field(None)
+    input_tokens_details: InputTokensDetails | None = Field(None)
+    output_tokens: int | None = Field(None)
+    total_tokens: int | None = Field(None)
+
+
+class OpenAIImageGenerationResponse(BaseModel):
+    data: list[Datum2] | None = Field(None)
+    usage: Usage | None = Field(None)
+
+
+class OpenAIImageEditRequest(BaseModel):
+    background: str | None = Field(None, description="Background transparency")
+    model: str = Field(...)
+    moderation: str | None = Field(None)
+    n: int | None = Field(None, description="The number of images to generate")
+    output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
+    output_format: str | None = Field(None)
+    prompt: str = Field(...)
+    quality: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
+    size: str | None = Field(None, description="Size of the output image")
+
+
+class OpenAIImageGenerationRequest(BaseModel):
+    background: str | None = Field(None, description="Background transparency")
+    model: str | None = Field(None)
+    moderation: str | None = Field(None)
+    n: int | None = Field(
+        None,
+        description="The number of images to generate.",
+    )
+    output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
+    output_format: str | None = Field(None)
+    prompt: str = Field(...)
+    quality: str | None = Field(None, description="The quality of the generated image")
+    size: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
+    style: str | None = Field(None, description="Style of the image (only for dall-e-3)")
+
+
+class ModelResponseProperties(BaseModel):
+    instructions: str | None = Field(None)
+    max_output_tokens: int | None = Field(None)
+    model: str | None = Field(None)
+    temperature: float | None = Field(None, description="Controls randomness in the response", ge=0.0, le=2.0)
+    top_p: float | None = Field(
+        None,
+        description="Controls diversity of the response via nucleus sampling",
+        ge=0.0,
+        le=1.0,
+    )
+    truncation: str | None = Field(None, description="Allowed values: 'auto' or 'disabled'")
+
+
+class ResponseProperties(BaseModel):
+    instructions: str | None = Field(None)
+    max_output_tokens: int | None = Field(None)
+    model: str | None = Field(None)
+    previous_response_id: str | None = Field(None)
+    truncation: str | None = Field("disabled", description="Allowed values: 'auto' or 'disabled'")
+
+
+class ResponseError(BaseModel):
+    code: str = Field(...)
+    message: str = Field(...)
+
+
+class OutputTokensDetails(BaseModel):
+    reasoning_tokens: int = Field(..., description="The number of reasoning tokens.")
+
+
+class CachedTokensDetails(BaseModel):
+    cached_tokens: int = Field(
+        ...,
+        description="The number of tokens that were retrieved from the cache.",
+    )
+
+
+class ResponseUsage(BaseModel):
+    input_tokens: int = Field(..., description="The number of input tokens.")
+    input_tokens_details: CachedTokensDetails = Field(...)
+    output_tokens: int = Field(..., description="The number of output tokens.")
+    output_tokens_details: OutputTokensDetails = Field(...)
+    total_tokens: int = Field(..., description="The total number of tokens used.")
+
+
+class InputTextContent(BaseModel):
+    text: str = Field(..., description="The text input to the model.")
+    type: str = Field("input_text")
+
+
+class OutputContent(BaseModel):
+    type: str = Field(..., description="The type of output content")
+    text: str | None = Field(None, description="The text content")
+    data: str | None = Field(None, description="Base64-encoded audio data")
+    transcript: str | None = Field(None, description="Transcript of the audio")
+
+
+class OutputMessage(BaseModel):
+    type: str = Field(..., description="The type of output item")
+    content: list[OutputContent] | None = Field(None, description="The content of the message")
+    role: str | None = Field(None, description="The role of the message")
+
+
+class OpenAIResponse(ModelResponseProperties, ResponseProperties):
+    created_at: float | None = Field(
+        None,
+        description="Unix timestamp (in seconds) of when this Response was created.",
+    )
+    error: ResponseError | None = Field(None)
+    id: str | None = Field(None, description="Unique identifier for this Response.")
+    object: str | None = Field(None, description="The object type of this resource - always set to `response`.")
+    output: list[OutputMessage] | None = Field(None)
+    parallel_tool_calls: bool | None = Field(True)
+    status: str | None = Field(
+        None,
+        description="One of `completed`, `failed`, `in_progress`, `incomplete`, `queued`, or `cancelled`.",
+    )
+    usage: ResponseUsage | None = Field(None)
+
+
+class InputImageContent(BaseModel):
+    detail: str = Field(..., description="One of `high`, `low`, or `auto`. Defaults to `auto`.")
+    file_id: str | None = Field(None)
+    image_url: str | None = Field(None)
+    type: str = Field(..., description="The type of the input item. Always `input_image`.")
+
+
+class InputFileContent(BaseModel):
+    file_data: str | None = Field(None)
+    file_id: str | None = Field(None)
+    filename: str | None = Field(None, description="The name of the file to be sent to the model.")
+    type: str = Field(..., description="The type of the input item. Always `input_file`.")
+
+
+class InputMessage(BaseModel):
+    content: list[InputTextContent | InputImageContent | InputFileContent] = Field(
+        ...,
+        description="A list of one or many input items to the model, containing different content types.",
+    )
+    role: str | None = Field(None)
+    type: str | None = Field(None)
+
+
+class OpenAICreateResponse(ModelResponseProperties, ResponseProperties):
+    include: str | None = Field(None)
+    input: list[InputMessage] = Field(...)
+    parallel_tool_calls: bool | None = Field(
+        True, description="Whether to allow the model to run tool calls in parallel."
+    )
+    store: bool | None = Field(
+        True,
+        description="Whether to store the generated model response for later retrieval via API.",
+    )
+    stream: bool | None = Field(False)
+    usage: ResponseUsage | None = Field(None)
diff --git a/comfy_api_nodes/apis/openrouter.py b/comfy_api_nodes/apis/openrouter.py
new file mode 100644
index 0000000000000000000000000000000000000000..47f8b1adb7f32cdb5f65317b43482267faedc6f4
--- /dev/null
+++ b/comfy_api_nodes/apis/openrouter.py
@@ -0,0 +1,93 @@
+"""Pydantic models for the OpenRouter chat completions API.
+
+See: https://openrouter.ai/docs/api/api-reference/chat/send-chat-completion-request
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class OpenRouterTextContent(BaseModel):
+    type: Literal["text"] = "text"
+    text: str = Field(...)
+
+
+class OpenRouterImageUrl(BaseModel):
+    url: str = Field(...)
+
+
+class OpenRouterImageContent(BaseModel):
+    type: Literal["image_url"] = "image_url"
+    image_url: OpenRouterImageUrl = Field(...)
+
+
+class OpenRouterVideoUrl(BaseModel):
+    url: str = Field(...)
+
+
+class OpenRouterVideoContent(BaseModel):
+    type: Literal["video_url"] = "video_url"
+    video_url: OpenRouterVideoUrl = Field(...)
+
+
+OpenRouterContentBlock = OpenRouterTextContent | OpenRouterImageContent | OpenRouterVideoContent
+
+
+class OpenRouterMessage(BaseModel):
+    role: Literal["system", "user", "assistant"] = Field(...)
+    content: str | list[OpenRouterContentBlock] = Field(...)
+
+
+class OpenRouterReasoningConfig(BaseModel):
+    effort: str | None = Field(None)
+    exclude: bool | None = Field(None, description="If true, model reasons but reasoning is excluded from response.")
+
+
+class OpenRouterWebSearchOptions(BaseModel):
+    search_context_size: str | None = Field(None)
+
+
+class OpenRouterChatRequest(BaseModel):
+    model: str = Field(...)
+    messages: list[OpenRouterMessage] = Field(...)
+    seed: int | None = Field(None)
+    reasoning: OpenRouterReasoningConfig | None = Field(None)
+    web_search_options: OpenRouterWebSearchOptions | None = Field(None)
+    stream: bool = Field(False)
+
+
+class OpenRouterUsage(BaseModel):
+    prompt_tokens: int | None = Field(None)
+    completion_tokens: int | None = Field(None)
+    total_tokens: int | None = Field(None)
+    cost: float | None = Field(None, description="Server-side authoritative USD cost of the call.")
+
+
+class OpenRouterResponseMessage(BaseModel):
+    role: str | None = Field(None)
+    content: str | None = Field(None)
+    reasoning: str | None = Field(None)
+    refusal: str | None = Field(None)
+
+
+class OpenRouterChoice(BaseModel):
+    index: int | None = Field(None)
+    message: OpenRouterResponseMessage | None = Field(None)
+    finish_reason: str | None = Field(None)
+
+
+class OpenRouterError(BaseModel):
+    code: int | str | None = Field(None)
+    message: str | None = Field(None)
+    metadata: dict | None = Field(None)
+
+
+class OpenRouterChatResponse(BaseModel):
+    id: str | None = Field(None)
+    model: str | None = Field(None)
+    object: str | None = Field(None)
+    provider: str | None = Field(None)
+    choices: list[OpenRouterChoice] | None = Field(None)
+    usage: OpenRouterUsage | None = Field(None)
+    error: OpenRouterError | None = Field(None)
diff --git a/comfy_api_nodes/apis/pixverse.py b/comfy_api_nodes/apis/pixverse.py
new file mode 100644
index 0000000000000000000000000000000000000000..132e3dfbb2aebfd192108dc45d928657bc0a6f07
--- /dev/null
+++ b/comfy_api_nodes/apis/pixverse.py
@@ -0,0 +1,230 @@
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+
+pixverse_templates = {
+    "Microwave": 324641385496960,
+    "Suit Swagger": 328545151283968,
+    "Anything, Robot": 313358700761536,
+    "Subject 3 Fever": 327828816843648,
+    "kiss kiss": 315446315336768,
+}
+
+
+class PixverseIO:
+    TEMPLATE = "PIXVERSE_TEMPLATE"
+
+
+class PixverseStatus(int, Enum):
+    successful = 1
+    generating = 5
+    deleted = 6
+    contents_moderation = 7
+    failed = 8
+
+
+class PixverseAspectRatio(str, Enum):
+    ratio_16_9 = "16:9"
+    ratio_4_3 = "4:3"
+    ratio_1_1 = "1:1"
+    ratio_3_4 = "3:4"
+    ratio_9_16 = "9:16"
+
+
+class PixverseQuality(str, Enum):
+    res_360p = "360p"
+    res_540p = "540p"
+    res_720p = "720p"
+    res_1080p = "1080p"
+
+
+class PixverseDuration(int, Enum):
+    dur_5 = 5
+    dur_8 = 8
+
+
+class PixverseMotionMode(str, Enum):
+    normal = "normal"
+    fast = "fast"
+
+
+class PixverseStyle(str, Enum):
+    anime = "anime"
+    animation_3d = "3d_animation"
+    clay = "clay"
+    comic = "comic"
+    cyberpunk = "cyberpunk"
+
+
+class PixverseTextVideoRequest(BaseModel):
+    aspect_ratio: PixverseAspectRatio = Field(...)
+    quality: PixverseQuality = Field(...)
+    duration: PixverseDuration = Field(...)
+    model: str | None = Field("v3.5")
+    motion_mode: PixverseMotionMode | None = Field(PixverseMotionMode.normal)
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    seed: int | None = Field(None)
+    style: str | None = Field(None)
+    template_id: int | None = Field(None)
+    water_mark: bool | None = Field(None)
+
+
+class PixverseImageVideoRequest(BaseModel):
+    quality: PixverseQuality = Field(...)
+    duration: PixverseDuration = Field(...)
+    img_id: int = Field(...)
+    model: str | None = Field("v3.5")
+    motion_mode: PixverseMotionMode | None = Field(PixverseMotionMode.normal)
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    seed: int | None = Field(None)
+    style: str | None = Field(None)
+    template_id: int | None = Field(None)
+    water_mark: bool | None = Field(None)
+
+
+class PixverseTransitionVideoRequest(BaseModel):
+    quality: PixverseQuality = Field(...)
+    duration: PixverseDuration = Field(...)
+    first_frame_img: int = Field(...)
+    last_frame_img: int = Field(...)
+    model: str | None = Field("v3.5")
+    motion_mode: PixverseMotionMode | None = Field(PixverseMotionMode.normal)
+    prompt: str = Field(...)
+    seed: int | None = Field(None)
+
+
+class PixverseImgIdResponseObject(BaseModel):
+    img_id: int | None = None
+
+
+class PixverseImageUploadResponse(BaseModel):
+    ErrCode: int | None = None
+    ErrMsg: str | None = None
+    Resp: PixverseImgIdResponseObject | None = Field(None)
+
+
+class PixverseVideoIdResponseObject(BaseModel):
+    video_id: int = Field(...)
+    credits: int | None = Field(None)
+
+
+class PixverseVideoResponse(BaseModel):
+    ErrCode: int | None = Field(None)
+    ErrMsg: str | None = Field(None)
+    Resp: PixverseVideoIdResponseObject | None = Field(None)
+
+
+class PixverseGenerationStatusResponseObject(BaseModel):
+    create_time: str | None = Field(None)
+    id: int | None = Field(None)
+    modify_time: str | None = Field(None)
+    negative_prompt: str | None = Field(None)
+    outputHeight: int | None = Field(None)
+    outputWidth: int | None = Field(None)
+    prompt: str | None = Field(None)
+    resolution_ratio: int | None = Field(None)
+    seed: int | None = Field(None)
+    size: int | None = Field(None)
+    status: int | None = Field(None)
+    style: str | None = Field(None)
+    has_audio: bool | None = Field(None)
+    credits: int | None = Field(None)
+    url: str | None = Field(None)
+
+
+class PixverseGenerationStatusResponse(BaseModel):
+    ErrCode: int | None = Field(None)
+    ErrMsg: str | None = Field(None)
+    Resp: PixverseGenerationStatusResponseObject | None = Field(None)
+
+
+class PixverseV6AspectRatio(str, Enum):
+    ratio_16_9 = "16:9"
+    ratio_4_3 = "4:3"
+    ratio_1_1 = "1:1"
+    ratio_3_4 = "3:4"
+    ratio_9_16 = "9:16"
+    ratio_2_3 = "2:3"
+    ratio_3_2 = "3:2"
+    ratio_21_9 = "21:9"
+
+
+class PixverseV6Style(str, Enum):
+    none = "none"
+    anime = "anime"
+    animation_3d = "3d_animation"
+    clay = "clay"
+    comic = "comic"
+    cyberpunk = "cyberpunk"
+    realistic = "realistic"
+
+
+class PixverseReferenceType(str, Enum):
+    subject = "subject"
+    background = "background"
+
+
+class PixverseImageReference(BaseModel):
+    img_id: int = Field(...)
+    ref_name: str = Field(...)
+    type: PixverseReferenceType = Field(...)
+
+
+class PixverseVideoReference(BaseModel):
+    ref_name: str = Field(...)
+    video_media_id: int | None = Field(None)
+    source_video_id: int | None = Field(None)
+
+
+class PixverseV6BaseRequest(BaseModel):
+    model: str = Field("v6")
+    prompt: str = Field(...)
+    duration: int = Field(...)
+    quality: PixverseQuality = Field(...)
+    negative_prompt: str | None = Field(None)
+    seed: int | None = Field(None)
+    style: str | None = Field(None)
+    generate_audio_switch: bool | None = Field(None)
+
+
+class PixverseV6TextVideoRequest(PixverseV6BaseRequest):
+    aspect_ratio: PixverseV6AspectRatio = Field(...)
+    generate_multi_clip_switch: bool | None = Field(None)
+
+
+class PixverseV6ImageVideoRequest(PixverseV6BaseRequest):
+    img_id: int = Field(...)
+    generate_multi_clip_switch: bool | None = Field(None)
+
+
+class PixverseV6TransitionVideoRequest(PixverseV6BaseRequest):
+    first_frame_img: int = Field(...)
+    last_frame_img: int = Field(...)
+
+
+class PixverseV6ExtendVideoRequest(PixverseV6BaseRequest):
+    video_media_id: int = Field(...)
+
+
+class PixverseV6FusionVideoRequest(PixverseV6BaseRequest):
+    aspect_ratio: str = Field(...)
+    image_references: list[PixverseImageReference] | None = Field(None)
+    video_references: list[PixverseVideoReference] | None = Field(None)
+    reference_mode: str | None = Field(None)
+
+
+class PixverseMediaIdResponseObject(BaseModel):
+    media_id: int | None = Field(None)
+    media_type: str | None = Field(None)
+    url: str | None = Field(None)
+    width: int | None = Field(None)
+    height: int | None = Field(None)
+
+
+class PixverseMediaUploadResponse(BaseModel):
+    ErrCode: int | None = Field(None)
+    ErrMsg: str | None = Field(None)
+    Resp: PixverseMediaIdResponseObject | None = Field(None)
diff --git a/comfy_api_nodes/apis/quiver.py b/comfy_api_nodes/apis/quiver.py
new file mode 100644
index 0000000000000000000000000000000000000000..13e956e32466cb092b6b2497f2e59301f3dfc297
--- /dev/null
+++ b/comfy_api_nodes/apis/quiver.py
@@ -0,0 +1,43 @@
+from pydantic import BaseModel, Field
+
+
+class QuiverImageObject(BaseModel):
+    url: str = Field(...)
+
+
+class QuiverTextToSVGRequest(BaseModel):
+    model: str = Field(default="arrow-preview")
+    prompt: str = Field(...)
+    instructions: str | None = Field(default=None)
+    references: list[QuiverImageObject] | None = Field(default=None, max_length=4)
+    temperature: float | None = Field(default=None, ge=0, le=2)
+    top_p: float | None = Field(default=None, ge=0, le=1)
+    presence_penalty: float | None = Field(default=None, ge=-2, le=2)
+
+
+class QuiverImageToSVGRequest(BaseModel):
+    model: str = Field(default="arrow-preview")
+    image: QuiverImageObject = Field(...)
+    auto_crop: bool | None = Field(default=None)
+    target_size: int | None = Field(default=None, ge=128, le=4096)
+    temperature: float | None = Field(default=None, ge=0, le=2)
+    top_p: float | None = Field(default=None, ge=0, le=1)
+    presence_penalty: float | None = Field(default=None, ge=-2, le=2)
+
+
+class QuiverSVGResponseItem(BaseModel):
+    svg: str = Field(...)
+    mime_type: str | None = Field(default="image/svg+xml")
+
+
+class QuiverSVGUsage(BaseModel):
+    total_tokens: int | None = Field(default=None)
+    input_tokens: int | None = Field(default=None)
+    output_tokens: int | None = Field(default=None)
+
+
+class QuiverSVGResponse(BaseModel):
+    id: str | None = Field(default=None)
+    created: int | None = Field(default=None)
+    data: list[QuiverSVGResponseItem] = Field(...)
+    usage: QuiverSVGUsage | None = Field(default=None)
diff --git a/comfy_api_nodes/apis/qwen.py b/comfy_api_nodes/apis/qwen.py
new file mode 100644
index 0000000000000000000000000000000000000000..21fbfa0e9e996c5f0fe3772533a96b559edb4535
--- /dev/null
+++ b/comfy_api_nodes/apis/qwen.py
@@ -0,0 +1,46 @@
+from pydantic import BaseModel, Field
+
+
+class QwenImageContentItem(BaseModel):
+    image: str | None = Field(None)
+    text: str | None = Field(None)
+
+
+class QwenImageMessage(BaseModel):
+    role: str = Field("user")
+    content: list[QwenImageContentItem] = Field(...)
+
+
+class QwenImageInputField(BaseModel):
+    messages: list[QwenImageMessage] = Field(...)
+
+
+class QwenImageParametersField(BaseModel):
+    size: str | None = Field(None, description="Output resolution as 'width*height'; omit for the model default.")
+    n: int = Field(1, ge=1, le=6)
+    seed: int = Field(..., ge=0, le=2147483647)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+    negative_prompt: str | None = Field(None)
+
+
+class QwenImageGenerationRequest(BaseModel):
+    model: str = Field(...)
+    input: QwenImageInputField = Field(...)
+    parameters: QwenImageParametersField = Field(...)
+
+
+class QwenImageChoice(BaseModel):
+    finish_reason: str | None = Field(None)
+    message: QwenImageMessage | None = Field(None)
+
+
+class QwenImageOutputField(BaseModel):
+    choices: list[QwenImageChoice] = Field(default_factory=list)
+
+
+class QwenImageGenerationResponse(BaseModel):
+    output: QwenImageOutputField | None = Field(None)
+    request_id: str = Field(...)
+    code: str | None = Field(None, description="Error code for the failed request.")
+    message: str | None = Field(None, description="Details about the failed request.")
diff --git a/comfy_api_nodes/apis/recraft.py b/comfy_api_nodes/apis/recraft.py
new file mode 100644
index 0000000000000000000000000000000000000000..2160dadfeeb55127f91c18258ffa0a8aa7c766af
--- /dev/null
+++ b/comfy_api_nodes/apis/recraft.py
@@ -0,0 +1,320 @@
+from __future__ import annotations
+
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+
+class RecraftColor:
+    def __init__(self, r: int, g: int, b: int):
+        self.color = [r, g, b]
+
+    def create_api_model(self):
+        return RecraftColorObject(rgb=self.color)
+
+
+class RecraftColorChain:
+    def __init__(self):
+        self.colors: list[RecraftColor] = []
+
+    def get_first(self):
+        if len(self.colors) > 0:
+            return self.colors[0]
+        return None
+
+    def add(self, color: RecraftColor):
+        self.colors.append(color)
+
+    def create_api_model(self):
+        if not self.colors:
+            return None
+        colors_api = [x.create_api_model() for x in self.colors]
+        return colors_api
+
+    def clone(self):
+        c = RecraftColorChain()
+        for color in self.colors:
+            c.add(color)
+        return c
+
+    def clone_and_merge(self, other: RecraftColorChain):
+        c = self.clone()
+        for color in other.colors:
+            c.add(color)
+        return c
+
+
+class RecraftControls:
+    def __init__(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None,
+                 artistic_level: int=None, no_text: bool=None):
+        self.colors = colors
+        self.background_color = background_color
+        self.artistic_level = artistic_level
+        self.no_text = no_text
+
+    def create_api_model(self):
+        if self.colors is None and self.background_color is None and self.artistic_level is None and self.no_text is None:
+            return None
+        colors_api = None
+        background_color_api = None
+        if self.colors:
+            colors_api = self.colors.create_api_model()
+        if self.background_color:
+            first_background = self.background_color.get_first()
+            background_color_api = first_background.create_api_model() if first_background else None
+
+        return RecraftControlsObject(colors=colors_api, background_color=background_color_api,
+                                             artistic_level=self.artistic_level, no_text=self.no_text)
+
+
+class RecraftStyle:
+    def __init__(self, style: str=None, substyle: str=None, style_id: str=None):
+        self.style = style
+        if substyle == "None":
+            substyle = None
+        self.substyle = substyle
+        self.style_id = style_id
+
+
+class RecraftIO:
+    STYLEV3 = "RECRAFT_V3_STYLE"
+    COLOR = "RECRAFT_COLOR"
+    CONTROLS = "RECRAFT_CONTROLS"
+
+
+class RecraftStyleV3(str, Enum):
+    #any = 'any' NOTE: this does not work for some reason... why?
+    realistic_image = 'realistic_image'
+    digital_illustration = 'digital_illustration'
+    vector_illustration = 'vector_illustration'
+    logo_raster = 'logo_raster'
+
+
+def get_v3_substyles(style_v3: str, include_none=True) -> list[str]:
+    substyles: list[str] = []
+    if include_none:
+        substyles.append("None")
+    return substyles + dict_recraft_substyles_v3.get(style_v3, [])
+
+
+dict_recraft_substyles_v3 = {
+    RecraftStyleV3.realistic_image: [
+        "b_and_w",
+        "enterprise",
+        "evening_light",
+        "faded_nostalgia",
+        "forest_life",
+        "hard_flash",
+        "hdr",
+        "motion_blur",
+        "mystic_naturalism",
+        "natural_light",
+        "natural_tones",
+        "organic_calm",
+        "real_life_glow",
+        "retro_realism",
+        "retro_snapshot",
+        "studio_portrait",
+        "urban_drama",
+        "village_realism",
+        "warm_folk"
+    ],
+    RecraftStyleV3.digital_illustration: [
+        "2d_art_poster",
+        "2d_art_poster_2",
+        "antiquarian",
+        "bold_fantasy",
+        "child_book",
+        "child_books",
+        "cover",
+        "crosshatch",
+        "digital_engraving",
+        "engraving_color",
+        "expressionism",
+        "freehand_details",
+        "grain",
+        "grain_20",
+        "graphic_intensity",
+        "hand_drawn",
+        "hand_drawn_outline",
+        "handmade_3d",
+        "hard_comics",
+        "infantile_sketch",
+        "long_shadow",
+        "modern_folk",
+        "multicolor",
+        "neon_calm",
+        "noir",
+        "nostalgic_pastel",
+        "outline_details",
+        "pastel_gradient",
+        "pastel_sketch",
+        "pixel_art",
+        "plastic",
+        "pop_art",
+        "pop_renaissance",
+        "seamless",
+        "street_art",
+        "tablet_sketch",
+        "urban_glow",
+        "urban_sketching",
+        "vanilla_dreams",
+        "young_adult_book",
+        "young_adult_book_2"
+    ],
+    RecraftStyleV3.vector_illustration: [
+        "bold_stroke",
+        "chemistry",
+        "colored_stencil",
+        "contour_pop_art",
+        "cosmics",
+        "cutout",
+        "depressive",
+        "editorial",
+        "emotional_flat",
+        "engraving",
+        "infographical",
+        "line_art",
+        "line_circuit",
+        "linocut",
+        "marker_outline",
+        "mosaic",
+        "naivector",
+        "roundish_flat",
+        "seamless",
+        "segmented_colors",
+        "sharp_contrast",
+        "thin",
+        "vector_photo",
+        "vivid_shapes"
+    ],
+    RecraftStyleV3.logo_raster: [
+        "emblem_graffiti",
+        "emblem_pop_art",
+        "emblem_punk",
+        "emblem_stamp",
+        "emblem_vintage"
+    ],
+}
+
+
+class RecraftImageSize(str, Enum):
+    res_1024x1024 = '1024x1024'
+    res_1365x1024 = '1365x1024'
+    res_1024x1365 = '1024x1365'
+    res_1536x1024 = '1536x1024'
+    res_1024x1536 = '1024x1536'
+    res_1820x1024 = '1820x1024'
+    res_1024x1820 = '1024x1820'
+    res_1024x2048 = '1024x2048'
+    res_2048x1024 = '2048x1024'
+    res_1434x1024 = '1434x1024'
+    res_1024x1434 = '1024x1434'
+    res_1024x1280 = '1024x1280'
+    res_1280x1024 = '1280x1024'
+    res_1024x1707 = '1024x1707'
+    res_1707x1024 = '1707x1024'
+
+
+RECRAFT_V4_SIZES = [
+    "1024x1024",
+    "1536x768",
+    "768x1536",
+    "1280x832",
+    "832x1280",
+    "1216x896",
+    "896x1216",
+    "1152x896",
+    "896x1152",
+    "832x1344",
+    "1280x896",
+    "896x1280",
+    "1344x768",
+    "768x1344",
+]
+
+RECRAFT_V4_PRO_SIZES = [
+    "2048x2048",
+    "3072x1536",
+    "1536x3072",
+    "2560x1664",
+    "1664x2560",
+    "2432x1792",
+    "1792x2432",
+    "2304x1792",
+    "1792x2304",
+    "1664x2688",
+    "2560x1792",
+    "1792x2560",
+    "2688x1536",
+    "1536x2688",
+]
+
+RECRAFT_V4_STYLES_MODELS = frozenset(
+    {
+        "recraftv4_styles",
+        "recraftv4_styles_vector",
+        "recraftv4_styles_pro",
+        "recraftv4_styles_pro_vector",
+    }
+)
+
+RECRAFT_V4_VECTOR_MODEL_FOR_STYLE = {
+    "recraftv4": "recraftv4_vector",
+    "recraftv4_pro": "recraftv4_pro_vector",
+}
+
+RECRAFT_STYLE_MATCH_OPTIONS = ["precise", "flexible"]
+
+RECRAFT_STYLE_REFERENCES_MAX = 10
+
+RECRAFT_STYLE_REFERENCES_MAX_BYTES = 10 * 1024 * 1024
+
+
+class RecraftColorObject(BaseModel):
+    rgb: list[int] = Field(..., description='An array of 3 integer values in range of 0...255 defining RGB Color Model')
+
+
+class RecraftControlsObject(BaseModel):
+    colors: list[RecraftColorObject] | None = Field(None, description='An array of preferable colors')
+    background_color: RecraftColorObject | None = Field(None, description='Use given color as a desired background color')
+    no_text: bool | None = Field(None, description='Do not embed text layouts')
+    artistic_level: int | None = Field(None, description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity. The value should be in range [0..5].')
+
+
+class RecraftImageGenerationRequest(BaseModel):
+    prompt: str = Field(..., description='The text prompt describing the image to generate')
+    size: str | None = Field(None, description='The size of the generated image (e.g., "1024x1024")')
+    n: int = Field(..., description='The number of images to generate')
+    negative_prompt: str | None = Field(None, description='A text description of undesired elements on an image')
+    model: str = Field(...)
+    style: str | None = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")')
+    substyle: str | None = Field(None, description='The substyle to apply to the generated image, depending on the style input')
+    controls: RecraftControlsObject | None = Field(None, description='A set of custom parameters to tweak generation process')
+    style_id: str | None = Field(None, description='Use a previously uploaded style as a reference; UUID')
+    style_match: str | None = Field(None, description='How closely to follow the referenced style: "precise" or "flexible" for V4 models')
+    style_reference_urls: list[str] | None = Field(None, description='URLs or data URLs of style reference images; a private style is created from them and returned as style_id')
+    strength: float | None = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity')
+    random_seed: int | None = Field(None, description="Seed for video generation")
+
+
+class RecraftReturnedObject(BaseModel):
+    image_id: str = Field(..., description='Unique identifier for the generated image')
+    url: str = Field(..., description='URL to access the generated image')
+
+
+class RecraftImageGenerationResponse(BaseModel):
+    created: int = Field(..., description='Unix timestamp when the generation was created')
+    credits: int = Field(..., description='Number of credits used for the generation')
+    data: list[RecraftReturnedObject] | None = Field(None, description='Array of generated image information')
+    image: RecraftReturnedObject | None = Field(None, description='Single generated image')
+    style_id: str | None = Field(None, description='The style applied to the generation, including one auto-created from style references')
+
+
+class RecraftCreateStyleRequest(BaseModel):
+    style: str = Field(..., description="any, realistic_image, digital_illustration, vector_illustration, or icon")
+    model: str | None = Field(None, description="The model family the style is created for, e.g. recraftv4_styles")
+
+
+class RecraftCreateStyleResponse(BaseModel):
+    id: str = Field(..., description="UUID of the created style")
diff --git a/comfy_api_nodes/apis/reve.py b/comfy_api_nodes/apis/reve.py
new file mode 100644
index 0000000000000000000000000000000000000000..05c3f602d8fe49a80f0142b02df9c4294e96c14e
--- /dev/null
+++ b/comfy_api_nodes/apis/reve.py
@@ -0,0 +1,68 @@
+from pydantic import BaseModel, Field
+
+
+class RevePostprocessingOperation(BaseModel):
+    process: str = Field(..., description="The postprocessing operation: upscale or remove_background.")
+    upscale_factor: int | None = Field(
+        None,
+        description="Upscale factor (2, 3, or 4). Only used when process is upscale.",
+        ge=2,
+        le=4,
+    )
+
+
+class ReveImageCreateRequest(BaseModel):
+    prompt: str = Field(...)
+    aspect_ratio: str | None = Field(...)
+    version: str = Field(...)
+    test_time_scaling: int = Field(
+        ...,
+        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
+        ge=1,
+        le=15,
+    )
+    postprocessing: list[RevePostprocessingOperation] | None = Field(
+        None, description="Optional postprocessing operations to apply after generation."
+    )
+
+
+class ReveImageEditRequest(BaseModel):
+    edit_instruction: str = Field(...)
+    reference_image: str = Field(..., description="A base64 encoded image to use as reference for the edit.")
+    aspect_ratio: str | None = Field(...)
+    version: str = Field(...)
+    test_time_scaling: int | None = Field(
+        ...,
+        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
+        ge=1,
+        le=15,
+    )
+    postprocessing: list[RevePostprocessingOperation] | None = Field(
+        None, description="Optional postprocessing operations to apply after generation."
+    )
+
+
+class ReveImageRemixRequest(BaseModel):
+    prompt: str = Field(...)
+    reference_images: list[str] = Field(..., description="A list of 1-6 base64 encoded reference images.")
+    aspect_ratio: str | None = Field(...)
+    version: str = Field(...)
+    test_time_scaling: int | None = Field(
+        ...,
+        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
+        ge=1,
+        le=15,
+    )
+    postprocessing: list[RevePostprocessingOperation] | None = Field(
+        None, description="Optional postprocessing operations to apply after generation."
+    )
+
+
+class ReveImageResponse(BaseModel):
+    image: str | None = Field(None, description="The base64 encoded image data.")
+    request_id: str | None = Field(None, description="A unique id for the request.")
+    credits_used: float | None = Field(None, description="The number of credits used for this request.")
+    version: str | None = Field(None, description="The specific model version used.")
+    content_violation: bool | None = Field(
+        None, description="Indicates whether the generated image violates the content policy."
+    )
diff --git a/comfy_api_nodes/apis/rodin.py b/comfy_api_nodes/apis/rodin.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cd3a5ca9aa4aead2bd94d32b27c78704bf15402
--- /dev/null
+++ b/comfy_api_nodes/apis/rodin.py
@@ -0,0 +1,84 @@
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+
+class Rodin3DGenerateRequest(BaseModel):
+    seed: int = Field(..., description="seed_")
+    tier: str = Field(..., description="Tier of generation.")
+    material: str = Field(..., description="The material type.")
+    quality_override: int = Field(..., description="The poly count of the mesh.")
+    mesh_mode: str = Field(..., description="It controls the type of faces of generated models.")
+    TAPose: bool | None = Field(None, description="")
+
+
+class Rodin3DGen25Request(BaseModel):
+
+    tier: str = Field(..., description="Gen-2.5 tier (e.g. Gen-2.5-High).")
+    prompt: str | None = Field(None, description="Required for Text-to-3D; ignored otherwise.")
+    seed: int | None = Field(None, description="0-65535.")
+    material: str | None = Field(None, description="PBR | Shaded | All | None.")
+    geometry_file_format: str | None = Field(None, description="glb | usdz | fbx | obj | stl.")
+    texture_mode: str | None = Field(None, description="legacy | extreme-low | low | medium | high.")
+    mesh_mode: str | None = Field(None, description="Raw (triangular) | Quad.")
+    quality_override: int | None = Field(None, description="Mesh face count override.")
+    geometry_instruct_mode: str | None = Field(None, description="faithful | creative.")
+    bbox_condition: list[int] | None = Field(None, description="Bounding box [Width(Y), Height(Z), Length(X)] in cm.")
+    height: int | None = Field(None, description="Approximate model height in cm.")
+    TAPose: bool | None = Field(None, description="T/A pose for human-like models.")
+    hd_texture: bool | None = Field(None, description="Enhanced texture quality.")
+    texture_delight: bool | None = Field(None, description="Remove baked lighting from textures.")
+    is_micro: bool | None = Field(None, description="Micro detail (Extreme-High only).")
+    use_original_alpha: bool | None = Field(None, description="Preserve image transparency.")
+    preview_render: bool | None = Field(None, description="Generate high-quality preview render.")
+    addons: list[str] | None = Field(None, description='Optional addons, e.g. ["HighPack"].')
+
+
+class GenerateJobsData(BaseModel):
+    uuids: list[str] = Field(..., description="str LIST")
+    subscription_key: str = Field(..., description="subscription key")
+
+
+class Rodin3DGenerateResponse(BaseModel):
+    message: str | None = Field(None, description="Return message.")
+    prompt: str | None = Field(None, description="Generated Prompt from image.")
+    submit_time: str | None = Field(None, description="Submit Time")
+    uuid: str | None = Field(None, description="Task str")
+    jobs: GenerateJobsData | None = Field(None, description="Details of jobs")
+
+
+class JobStatus(str, Enum):
+    """
+    Status for jobs
+    """
+
+    Done = "Done"
+    Failed = "Failed"
+    Generating = "Generating"
+    Waiting = "Waiting"
+
+
+class Rodin3DCheckStatusRequest(BaseModel):
+    subscription_key: str = Field(..., description="subscription from generate endpoint")
+
+
+class JobItem(BaseModel):
+    uuid: str = Field(..., description="uuid")
+    status: JobStatus = Field(..., description="Status Currently")
+
+
+class Rodin3DCheckStatusResponse(BaseModel):
+    jobs: list[JobItem] = Field(..., description="Job status List")
+
+
+class Rodin3DDownloadRequest(BaseModel):
+    task_uuid: str = Field(..., description="Task str")
+
+
+class RodinResourceItem(BaseModel):
+    url: str = Field(..., description="Download Url")
+    name: str = Field(..., description="File name with ext")
+
+
+class Rodin3DDownloadResponse(BaseModel):
+    items: list[RodinResourceItem] = Field(..., alias="list", description="Source List")
diff --git a/comfy_api_nodes/apis/runway.py b/comfy_api_nodes/apis/runway.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd5a8f0073ffe3c05bde238d0855af529e282e5e
--- /dev/null
+++ b/comfy_api_nodes/apis/runway.py
@@ -0,0 +1,259 @@
+from enum import Enum
+from typing import Optional, List, Union
+from datetime import datetime
+
+from pydantic import BaseModel, Field, RootModel
+
+
+class RunwayAspectRatioEnum(str, Enum):
+    field_1280_720 = '1280:720'
+    field_720_1280 = '720:1280'
+    field_1104_832 = '1104:832'
+    field_832_1104 = '832:1104'
+    field_960_960 = '960:960'
+    field_1584_672 = '1584:672'
+    field_1280_768 = '1280:768'
+    field_768_1280 = '768:1280'
+
+
+class Position(str, Enum):
+    first = 'first'
+    last = 'last'
+
+
+class RunwayPromptImageDetailedObject(BaseModel):
+    position: Position = Field(
+        ...,
+        description="The position of the image in the output video. 'last' is currently supported for gen3a_turbo only.",
+    )
+    uri: str = Field(
+        ..., description='A HTTPS URL or data URI containing an encoded image.'
+    )
+
+
+class RunwayPromptImageObject(
+    RootModel[Union[str, List[RunwayPromptImageDetailedObject]]]
+):
+    root: Union[str, List[RunwayPromptImageDetailedObject]] = Field(
+        ...,
+        description='Image(s) to use for the video generation. Can be a single URI or an array of image objects with positions.',
+    )
+
+
+class RunwayModelEnum(str, Enum):
+    gen4_turbo = 'gen4_turbo'
+    gen3a_turbo = 'gen3a_turbo'
+
+
+class RunwayDurationEnum(int, Enum):
+    integer_5 = 5
+    integer_10 = 10
+
+
+class RunwayImageToVideoRequest(BaseModel):
+    duration: RunwayDurationEnum
+    model: RunwayModelEnum
+    promptImage: RunwayPromptImageObject
+    promptText: Optional[str] = Field(
+        None, description='Text prompt for the generation', max_length=1000
+    )
+    ratio: RunwayAspectRatioEnum
+    seed: int = Field(
+        ..., description='Random seed for generation', ge=0, le=4294967295
+    )
+
+
+class RunwayImageToVideoResponse(BaseModel):
+    id: Optional[str] = Field(None, description='Task ID')
+
+
+class RunwayTaskStatusResponse(BaseModel):
+    createdAt: datetime = Field(..., description='Task creation timestamp')
+    id: str = Field(..., description='Task ID')
+    output: Optional[List[str]] = Field(None, description='Array of output video URLs')
+    progress: Optional[float] = Field(
+        None,
+        description='Float value between 0 and 1 representing the progress of the task. Only available if status is RUNNING.',
+        ge=0.0,
+        le=1.0,
+    )
+    status: str = Field(..., description="SUCCEEDED, RUNNING, FAILED, PENDING, CANCELLED or THROTTLED")
+
+
+class Model4(str, Enum):
+    gen4_image = 'gen4_image'
+
+
+class ReferenceImage(BaseModel):
+    uri: Optional[str] = Field(
+        None, description='A HTTPS URL or data URI containing an encoded image'
+    )
+
+
+class RunwayTextToImageAspectRatioEnum(str, Enum):
+    field_1920_1080 = '1920:1080'
+    field_1080_1920 = '1080:1920'
+    field_1024_1024 = '1024:1024'
+    field_1360_768 = '1360:768'
+    field_1080_1080 = '1080:1080'
+    field_1168_880 = '1168:880'
+    field_1440_1080 = '1440:1080'
+    field_1080_1440 = '1080:1440'
+    field_1808_768 = '1808:768'
+    field_2112_912 = '2112:912'
+
+
+class RunwayTextToImageRequest(BaseModel):
+    model: Model4 = Field(..., description='Model to use for generation')
+    promptText: str = Field(
+        ..., description='Text prompt for the image generation', max_length=1000
+    )
+    ratio: RunwayTextToImageAspectRatioEnum
+    referenceImages: Optional[List[ReferenceImage]] = Field(
+        None, description='Array of reference images to guide the generation'
+    )
+
+
+class RunwayTextToImageResponse(BaseModel):
+    id: Optional[str] = Field(None, description='Task ID')
+
+
+class RunwayAleph2IO:
+    """Custom socket types for chaining Aleph2 guidance images."""
+
+    KEYFRAME = "RUNWAY_ALEPH2_KEYFRAME"
+    PROMPT_IMAGE = "RUNWAY_ALEPH2_PROMPT_IMAGE"
+
+
+# Keyframe timing modes (anchored to the INPUT video). Stored on the chain item and used to
+# choose the request model below. The values match the Aleph2 keyframe union field names.
+KEYFRAME_MODE_SECONDS = "seconds"  # absolute time, in seconds, from the start of the input video
+KEYFRAME_MODE_AT = "at"  # fraction [0.0, 1.0] of the input video duration
+
+# Prompt-image position modes (anchored to the OUTPUT video). Values match the Aleph2 position `type`.
+PROMPT_IMAGE_MODE_TIMESTAMP = "timestamp"  # absolute time, in seconds, from the start of the output video
+PROMPT_IMAGE_MODE_POSITION = "position"  # fraction [0.0, 1.0] of the output video duration
+
+
+class RunwayAleph2KeyframeItem:
+    """A guidance image anchored to a point of the INPUT video (one Aleph2 ``keyframe``)."""
+
+    def __init__(self, image, mode: str, value: float):
+        self.image = image
+        self.mode = mode  # KEYFRAME_MODE_SECONDS | KEYFRAME_MODE_AT
+        self.value = value
+
+
+class RunwayAleph2KeyframeChain:
+    """An ordered collection of keyframes, built by chaining Runway Aleph2 Keyframe nodes."""
+
+    def __init__(self):
+        self.items: list[RunwayAleph2KeyframeItem] = []
+
+    def add(self, item: RunwayAleph2KeyframeItem) -> None:
+        self.items.append(item)
+
+    def clone(self) -> "RunwayAleph2KeyframeChain":
+        c = RunwayAleph2KeyframeChain()
+        c.items = list(self.items)
+        return c
+
+
+class RunwayAleph2PromptImageItem:
+    """A guidance image anchored to a point of the OUTPUT video (one Aleph2 ``promptImage``)."""
+
+    def __init__(self, image, mode: str, value: float):
+        self.image = image
+        self.mode = mode  # PROMPT_IMAGE_MODE_TIMESTAMP | PROMPT_IMAGE_MODE_POSITION
+        self.value = value
+
+
+class RunwayAleph2PromptImageChain:
+    """An ordered collection of prompt images, built by chaining Runway Aleph2 Prompt Image nodes."""
+
+    def __init__(self):
+        self.items: list[RunwayAleph2PromptImageItem] = []
+
+    def add(self, item: RunwayAleph2PromptImageItem) -> None:
+        self.items.append(item)
+
+    def clone(self) -> "RunwayAleph2PromptImageChain":
+        c = RunwayAleph2PromptImageChain()
+        c.items = list(self.items)
+        return c
+
+
+class RunwayAleph2KeyframeSeconds(BaseModel):
+    seconds: float = Field(
+        ...,
+        description="Absolute timestamp in seconds from the start of the input video when this guidance image should apply.",
+        ge=0.0,
+    )
+    uri: str = Field(...)
+
+
+class RunwayAleph2KeyframeAt(BaseModel):
+    at: float = Field(
+        ...,
+        description="Position as a fraction [0.0, 1.0] of the input video duration.",
+        ge=0.0,
+        le=1.0,
+    )
+    uri: str = Field(...)
+
+
+class RunwayAleph2TimestampPosition(BaseModel):
+    type: str = Field(default="timestamp")
+    timestampSeconds: float = Field(
+        ...,
+        description="Absolute timestamp in seconds from the start of the output video.",
+        ge=0.0,
+    )
+
+
+class RunwayAleph2RelativePosition(BaseModel):
+    type: str = Field(default="position")
+    positionPercentage: float = Field(
+        ...,
+        description="Position as a fraction [0.0, 1.0] of the total output video duration.",
+        ge=0.0,
+        le=1.0,
+    )
+
+
+class RunwayAleph2PromptImage(BaseModel):
+    position: RunwayAleph2TimestampPosition | RunwayAleph2RelativePosition
+    uri: str = Field(...)
+
+
+class RunwayAleph2ContentModeration(BaseModel):
+    publicFigureThreshold: str = Field(
+        ...,
+        description='When set to "low", the content moderation system is less strict about '
+        'recognizable public figures. One of "auto" or "low".',
+    )
+
+
+class RunwayAleph2Request(BaseModel):
+    model: str = Field(default="aleph2")
+    promptText: str = Field(
+        ...,
+        description="A non-empty string describing what should appear in the output.",
+        min_length=1,
+        max_length=1000,
+    )
+    videoUri: str = Field(...)
+    seed: int = Field(..., description="Random seed for generation", ge=0, le=4294967295)
+    contentModeration: RunwayAleph2ContentModeration = Field(...)
+    keyframes: list[RunwayAleph2KeyframeSeconds | RunwayAleph2KeyframeAt] | None = Field(
+        None,
+        description="Timed guidance images placed at specific points in the input video. Up to 5.",
+    )
+    promptImage: list[RunwayAleph2PromptImage] | None = Field(
+        None,
+        description="Up to 5 image keyframes for guiding the edit at specific points in the output video.",
+    )
+
+
+class RunwayAleph2Response(BaseModel):
+    id: str | None = Field(None, description="Task ID")
diff --git a/comfy_api_nodes/apis/sync_so.py b/comfy_api_nodes/apis/sync_so.py
new file mode 100644
index 0000000000000000000000000000000000000000..cda813fcc08c550216cf7f2dc0a58279a0623a67
--- /dev/null
+++ b/comfy_api_nodes/apis/sync_so.py
@@ -0,0 +1,49 @@
+from pydantic import BaseModel, Field
+
+
+class SyncInputItem(BaseModel):
+    type: str = Field(..., description="Input kind: 'video', 'image' or 'audio'.")
+    url: str = Field(...)
+
+
+class SyncActiveSpeakerDetection(BaseModel):
+    auto_detect: bool | None = Field(
+        None, description="Detect the active speaker automatically. Video input only; rejected for images."
+    )
+    frame_number: int | None = Field(
+        None, description="Frame used for manual speaker selection. Must be 0 for image inputs."
+    )
+    coordinates: list[int] | None = Field(
+        None, description="Pixel [x, y] of the speaker's face in the frame selected by frame_number."
+    )
+
+
+class SyncGenerationOptions(BaseModel):
+    sync_mode: str | None = Field(
+        None,
+        description="How to resolve an audio/video duration mismatch: "
+        "cut_off, bounce, loop, silence or remap. Ignored for image inputs.",
+    )
+    i2v_prompt: str | None = Field(
+        None, description="Motion prompt for image-to-video generation. Image input only."
+    )
+    active_speaker_detection: SyncActiveSpeakerDetection | None = Field(None)
+
+
+class SyncGenerationRequest(BaseModel):
+    model: str = Field(..., description="Generation model, e.g. 'sync-3'.")
+    input: list[SyncInputItem] = Field(
+        ..., description="Exactly one visual input (video or image) plus one audio input."
+    )
+    options: SyncGenerationOptions | None = Field(None)
+
+
+class SyncGeneration(BaseModel):
+    """Subset of the Generation object returned by POST /v2/generate and GET /v2/generate/{id}."""
+
+    id: str = Field(...)
+    status: str = Field(..., description="PENDING | PROCESSING | COMPLETED | FAILED | REJECTED")
+    outputUrl: str | None = Field(None)
+    outputDuration: float | None = Field(None)
+    error: str | None = Field(None, description="Human-readable failure message.")
+    errorCode: str | None = Field(None, description="Stable machine-readable code from the GET /v2/errors catalog.")
diff --git a/comfy_api_nodes/apis/topaz.py b/comfy_api_nodes/apis/topaz.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f0558fb71d64bfa21a81dcc82ac64da805c91ab
--- /dev/null
+++ b/comfy_api_nodes/apis/topaz.py
@@ -0,0 +1,171 @@
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class ImageEnhanceRequest(BaseModel):
+    model: str = Field("Reimagine")
+    output_format: str = Field("jpeg")
+    subject_detection: str = Field("All")
+    face_enhancement: bool = Field(True)
+    face_enhancement_creativity: float = Field(0, description="Is ignored if face_enhancement is false")
+    face_enhancement_strength: float = Field(0.8, description="Is ignored if face_enhancement is false")
+    source_url: str = Field(...)
+    output_width: Optional[int] = Field(None)
+    output_height: Optional[int] = Field(None)
+    crop_to_fill: bool = Field(False)
+    prompt: Optional[str] = Field(None, description="Text prompt for creative upscaling guidance")
+    creativity: int = Field(3, description="Creativity settings range from 1 to 9")
+    face_preservation: str = Field("true", description="To preserve the identity of characters")
+    color_preservation: str = Field("true", description="To preserve the original color")
+
+
+class ImageEnhanceRequestV2(BaseModel):
+    model: str = Field(...)
+    output_format: str = Field("png")
+    source_url: str = Field(...)
+    output_width: Optional[int] = Field(None)
+    output_height: Optional[int] = Field(None)
+    crop_to_fill: Optional[bool] = Field(None, description="Available for Reimagine only")
+    prompt: Optional[str] = Field(None, description="Available for Reimagine and Bloom 2")
+    creativity: Optional[int] = Field(None, description="From 1 to 9; available for Reimagine and Bloom 2")
+    subject_detection: Optional[str] = Field(None, description="Available for Reimagine only")
+    face_enhancement: Optional[bool] = Field(None, description="Available for Reimagine only")
+    face_enhancement_creativity: Optional[float] = Field(None, description="Is ignored if face_enhancement is false")
+    face_enhancement_strength: Optional[float] = Field(None, description="Is ignored if face_enhancement is false")
+    face_preservation: Optional[str] = Field(
+        None, description='String "true" or "false"; available for Reimagine only'
+    )
+    color_preservation: Optional[str] = Field(
+        None, description='String "true" or "false"; available for Reimagine and Bloom 2'
+    )
+    autoprompt: Optional[str] = Field(
+        None, description='String "true" or "false"; auto-generate a prompt, available for Bloom 2 only'
+    )
+    seed: Optional[int] = Field(None, description="Available for Bloom 2 only")
+    enhancement_strength: Optional[str] = Field(
+        None, description="low, medium or high; available for Wonder 3.5 only"
+    )
+    grain: Optional[str] = Field(
+        None, description='String "true" or "false"; available for Bloom 2 and Wonder 3.5'
+    )
+    grain_model: Optional[str] = Field(None, description="silver, gaussian or grey")
+    grain_strength: Optional[float] = Field(None, description="From 0 to 1")
+    grain_size: Optional[float] = Field(None, description="From 1 to 5")
+    grain_density: Optional[float] = Field(None, description="From 0 to 1")
+
+
+class ImageAsyncTaskResponse(BaseModel):
+    process_id: str = Field(...)
+
+
+class ImageStatusResponse(BaseModel):
+    process_id: str = Field(...)
+    status: str = Field(...)
+    progress: Optional[int] = Field(None)
+    credits: int = Field(...)
+
+
+class ImageDownloadResponse(BaseModel):
+    download_url: str = Field(...)
+    expiry: int = Field(...)
+
+
+class Resolution(BaseModel):
+    width: int = Field(...)
+    height: int = Field(...)
+
+
+class CreateVideoRequestSource(BaseModel):
+    container: str = Field(...)
+    size: int = Field(..., description="Size of the video file in bytes")
+    duration: int = Field(..., description="Duration of the video file in seconds")
+    frameCount: int = Field(..., description="Total number of frames in the video")
+    frameRate: int = Field(...)
+    resolution: Resolution = Field(...)
+
+
+class VideoFrameInterpolationFilter(BaseModel):
+    model: str = Field(...)
+    slowmo: Optional[int] = Field(None)
+    fps: int = Field(...)
+    duplicate: bool = Field(...)
+    duplicate_threshold: float = Field(...)
+
+
+class VideoEnhancementFilter(BaseModel):
+    model: str = Field(...)
+    auto: Optional[str] = Field(None, description="Auto, Manual, Relative")
+    focusFixLevel: Optional[str] = Field(None, description="Downscales video input for correction of blurred subjects")
+    compression: Optional[float] = Field(None, description="Strength of compression recovery")
+    details: Optional[float] = Field(None, description="Amount of detail reconstruction")
+    prenoise: Optional[float] = Field(None, description="Amount of noise to add to input to reduce over-smoothing")
+    noise: Optional[float] = Field(None, description="Amount of noise reduction")
+    halo: Optional[float] = Field(None, description="Amount of halo reduction")
+    preblur: Optional[float] = Field(None, description="Anti-aliasing and deblurring strength")
+    blur: Optional[float] = Field(None, description="Amount of sharpness applied")
+    grain: Optional[float] = Field(None, description="Grain after AI model processing")
+    grainSize: Optional[float] = Field(None, description="Size of generated grain")
+    recoverOriginalDetailValue: Optional[float] = Field(None, description="Source details into the output video")
+    creativity: float | str | None = Field(None, description="slc-1/slp-2.5: enum (low/middle/high). ast-2: decimal 0.0-1.0.")
+    isOptimizedMode: Optional[bool] = Field(None, description="Set to true for Starlight Creative (slc-1) only")
+    prompt: str | None = Field(None, description="Descriptive scene prompt (ast-2 only)")
+    sharp: float | None = Field(None, description="ast-2 pre-enhance sharpness")
+    realism: float | None = Field(None, description="ast-2 realism control")
+
+
+class OutputInformationVideo(BaseModel):
+    resolution: Resolution = Field(...)
+    frameRate: int = Field(...)
+    audioCodec: Optional[str] = Field(..., description="Required if audioTransfer is Copy or Convert")
+    audioTransfer: str = Field(..., description="Copy, Convert, None")
+    dynamicCompressionLevel: str = Field(..., description="Low, Mid, High")
+
+
+class Overrides(BaseModel):
+    isPaidDiffusion: bool = Field(True)
+
+
+class CreateVideoRequest(BaseModel):
+    source: CreateVideoRequestSource = Field(...)
+    filters: list[VideoFrameInterpolationFilter | VideoEnhancementFilter] = Field(...)
+    output: OutputInformationVideo = Field(...)
+    overrides: Overrides = Field(Overrides(isPaidDiffusion=True))
+
+
+class CreateVideoResponse(BaseModel):
+    requestId: str = Field(...)
+
+
+class VideoAcceptResponse(BaseModel):
+    uploadId: str = Field(...)
+    urls: list[str] = Field(...)
+
+
+class VideoCompleteUploadRequestPart(BaseModel):
+    partNum: int = Field(...)
+    eTag: str = Field(...)
+
+
+class VideoCompleteUploadRequest(BaseModel):
+    uploadResults: list[VideoCompleteUploadRequestPart] = Field(...)
+
+
+class VideoCompleteUploadResponse(BaseModel):
+    message: str = Field(..., description="Confirmation message")
+
+
+class VideoStatusResponseEstimates(BaseModel):
+    cost: list[int] = Field(...)
+
+
+class VideoStatusResponseDownloadUrl(BaseModel):
+    url: str = Field(...)
+
+
+class VideoStatusResponse(BaseModel):
+    status: str = Field(...)
+    estimates: Optional[VideoStatusResponseEstimates] = Field(None)
+    progress: Optional[float] = Field(None)
+    message: Optional[str] = Field("")
+    download: Optional[VideoStatusResponseDownloadUrl] = Field(None)
diff --git a/comfy_api_nodes/apis/tripo.py b/comfy_api_nodes/apis/tripo.py
new file mode 100644
index 0000000000000000000000000000000000000000..4171b60bee8e0a948bfa5c2ac136b7ab0400c40a
--- /dev/null
+++ b/comfy_api_nodes/apis/tripo.py
@@ -0,0 +1,352 @@
+from enum import Enum
+from typing import Any
+
+from pydantic import BaseModel, Field, RootModel
+
+
+class TripoModelVersion(str, Enum):
+    v3_1_20260211 = "v3.1-20260211"
+    v3_0_20250812 = "v3.0-20250812"
+    v2_5_20250123 = "v2.5-20250123"
+    v1_4_20240625 = "v1.4-20240625"
+
+
+class TripoGeometryQuality(str, Enum):
+    standard = "standard"
+    detailed = "detailed"
+
+
+class TripoTextureQuality(str, Enum):
+    standard = "standard"
+    detailed = "detailed"
+
+
+class TripoStyle(str, Enum):
+    PERSON_TO_CARTOON = "person:person2cartoon"
+    ANIMAL_VENOM = "animal:venom"
+    OBJECT_CLAY = "object:clay"
+    OBJECT_STEAMPUNK = "object:steampunk"
+    OBJECT_CHRISTMAS = "object:christmas"
+    OBJECT_BARBIE = "object:barbie"
+    GOLD = "gold"
+    ANCIENT_BRONZE = "ancient_bronze"
+    NONE = "None"
+
+
+class TripoTaskType(str, Enum):
+    TEXT_TO_MODEL = "text_to_model"
+    IMAGE_TO_MODEL = "image_to_model"
+    MULTIVIEW_TO_MODEL = "multiview_to_model"
+    TEXTURE_MODEL = "texture_model"
+    ANIMATE_PRERIGCHECK = "animate_prerigcheck"
+    ANIMATE_RIG = "animate_rig"
+    ANIMATE_RETARGET = "animate_retarget"
+    STYLIZE_MODEL = "stylize_model"
+    CONVERT_MODEL = "convert_model"
+
+
+class TripoTextureAlignment(str, Enum):
+    ORIGINAL_IMAGE = "original_image"
+    GEOMETRY = "geometry"
+
+
+class TripoOrientation(str, Enum):
+    ALIGN_IMAGE = "align_image"
+    DEFAULT = "default"
+
+
+class TripoOutFormat(str, Enum):
+    GLB = "glb"
+    FBX = "fbx"
+
+
+class TripoSpec(str, Enum):
+    MIXAMO = "mixamo"
+    TRIPO = "tripo"
+
+
+class TripoAnimation(str, Enum):
+    IDLE = "preset:idle"
+    WALK = "preset:walk"
+    RUN = "preset:run"
+    DIVE = "preset:dive"
+    CLIMB = "preset:climb"
+    JUMP = "preset:jump"
+    SLASH = "preset:slash"
+    SHOOT = "preset:shoot"
+    HURT = "preset:hurt"
+    FALL = "preset:fall"
+    TURN = "preset:turn"
+    QUADRUPED_WALK = "preset:quadruped:walk"
+    HEXAPOD_WALK = "preset:hexapod:walk"
+    OCTOPOD_WALK = "preset:octopod:walk"
+    SERPENTINE_MARCH = "preset:serpentine:march"
+    AQUATIC_MARCH = "preset:aquatic:march"
+
+
+class TripoConvertFormat(str, Enum):
+    GLTF = "GLTF"
+    USDZ = "USDZ"
+    FBX = "FBX"
+    OBJ = "OBJ"
+    STL = "STL"
+    _3MF = "3MF"
+
+
+class TripoTextureFormat(str, Enum):
+    BMP = "BMP"
+    DPX = "DPX"
+    HDR = "HDR"
+    JPEG = "JPEG"
+    OPEN_EXR = "OPEN_EXR"
+    PNG = "PNG"
+    TARGA = "TARGA"
+    TIFF = "TIFF"
+    WEBP = "WEBP"
+
+
+class TripoTaskStatus(str, Enum):
+    QUEUED = "queued"
+    RUNNING = "running"
+    SUCCESS = "success"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+    UNKNOWN = "unknown"
+    BANNED = "banned"
+    EXPIRED = "expired"
+
+
+class TripoFbxPreset(str, Enum):
+    BLENDER = "blender"
+    MIXAMO = "mixamo"
+    _3DSMAX = "3dsmax"
+
+
+class TripoFileTokenReference(BaseModel):
+    type: str | None = Field(None, description="The type of the reference")
+    file_token: str
+
+
+class TripoUrlReference(BaseModel):
+    type: str | None = Field(None, description="The type of the reference")
+    url: str
+
+
+class TripoObjectStorage(BaseModel):
+    bucket: str
+    key: str
+
+
+class TripoObjectReference(BaseModel):
+    type: str
+    object: TripoObjectStorage
+
+
+class TripoFileEmptyReference(BaseModel):
+    pass
+
+
+class TripoFileReference(RootModel):
+    root: TripoFileTokenReference | TripoUrlReference | TripoObjectReference | TripoFileEmptyReference
+
+
+class TripoTextToModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.TEXT_TO_MODEL, description="Type of task")
+    prompt: str = Field(..., description="The text prompt describing the model to generate", max_length=1024)
+    negative_prompt: str | None = Field(None, description="The negative text prompt", max_length=1024)
+    model_version: TripoModelVersion | None = TripoModelVersion.v2_5_20250123
+    face_limit: int | None = Field(None, description="The number of faces to limit the generation to")
+    texture: bool | None = Field(True, description="Whether to apply texture to the generated model")
+    pbr: bool | None = Field(True, description="Whether to apply PBR to the generated model")
+    image_seed: int | None = Field(None, description="The seed for the text")
+    model_seed: int | None = Field(None, description="The seed for the model")
+    texture_seed: int | None = Field(None, description="The seed for the texture")
+    texture_quality: TripoTextureQuality | None = TripoTextureQuality.standard
+    geometry_quality: TripoGeometryQuality | None = TripoGeometryQuality.standard
+    style: TripoStyle | None = None
+    auto_size: bool | None = Field(False, description="Whether to auto-size the model")
+    quad: bool | None = Field(False, description="Whether to apply quad to the generated model")
+
+
+class TripoImageToModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.IMAGE_TO_MODEL, description="Type of task")
+    file: TripoFileReference = Field(..., description="The file reference to convert to a model")
+    model_version: TripoModelVersion | None = Field(None, description="The model version to use for generation")
+    face_limit: int | None = Field(None, description="The number of faces to limit the generation to")
+    texture: bool | None = Field(True, description="Whether to apply texture to the generated model")
+    pbr: bool | None = Field(True, description="Whether to apply PBR to the generated model")
+    model_seed: int | None = Field(None, description="The seed for the model")
+    texture_seed: int | None = Field(None, description="The seed for the texture")
+    texture_quality: TripoTextureQuality | None = TripoTextureQuality.standard
+    geometry_quality: TripoGeometryQuality | None = TripoGeometryQuality.standard
+    texture_alignment: TripoTextureAlignment | None = Field(
+        TripoTextureAlignment.ORIGINAL_IMAGE, description="The texture alignment method"
+    )
+    style: TripoStyle | None = Field(None, description="The style to apply to the generated model")
+    auto_size: bool | None = Field(False, description="Whether to auto-size the model")
+    orientation: TripoOrientation | None = TripoOrientation.DEFAULT
+    quad: bool | None = Field(False, description="Whether to apply quad to the generated model")
+
+
+class TripoMultiviewToModelRequest(BaseModel):
+    type: TripoTaskType = TripoTaskType.MULTIVIEW_TO_MODEL
+    files: list[TripoFileReference] = Field(..., description="The file references to convert to a model")
+    model_version: TripoModelVersion | None = Field(None, description="The model version to use for generation")
+    orthographic_projection: bool | None = Field(False, description="Whether to use orthographic projection")
+    face_limit: int | None = Field(None, description="The number of faces to limit the generation to")
+    texture: bool | None = Field(True, description="Whether to apply texture to the generated model")
+    pbr: bool | None = Field(True, description="Whether to apply PBR to the generated model")
+    model_seed: int | None = Field(None, description="The seed for the model")
+    texture_seed: int | None = Field(None, description="The seed for the texture")
+    texture_quality: TripoTextureQuality | None = TripoTextureQuality.standard
+    geometry_quality: TripoGeometryQuality | None = TripoGeometryQuality.standard
+    texture_alignment: TripoTextureAlignment | None = TripoTextureAlignment.ORIGINAL_IMAGE
+    auto_size: bool | None = Field(False, description="Whether to auto-size the model")
+    orientation: TripoOrientation | None = Field(TripoOrientation.DEFAULT, description="The orientation for the model")
+    quad: bool | None = Field(False, description="Whether to apply quad to the generated model")
+
+
+class TripoTexturePrompt(BaseModel):
+    text: str | None = Field(None, description="Text guidance for texture generation")
+
+
+class TripoTextureModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.TEXTURE_MODEL, description="Type of task")
+    original_model_task_id: str = Field(..., description="The task ID of the original model")
+    texture: bool | None = Field(True, description="Whether to apply texture to the model")
+    pbr: bool | None = Field(True, description="Whether to apply PBR to the model")
+    model_seed: int | None = Field(None, description="The seed for the model")
+    texture_seed: int | None = Field(None, description="The seed for the texture")
+    texture_quality: TripoTextureQuality | None = Field(None, description="The quality of the texture")
+    texture_alignment: TripoTextureAlignment | None = Field(
+        TripoTextureAlignment.ORIGINAL_IMAGE, description="The texture alignment method"
+    )
+    texture_prompt: TripoTexturePrompt | None = Field(
+        None,
+        description="Optional guidance for texturing. Required in practice for imported models, "
+        "which carry no source image to infer texture from.",
+    )
+
+
+class TripoAnimateRigRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.ANIMATE_RIG, description="Type of task")
+    original_model_task_id: str = Field(..., description="The task ID of the original model")
+    out_format: TripoOutFormat | None = Field(TripoOutFormat.GLB, description="The output format")
+    spec: TripoSpec | None = Field(TripoSpec.TRIPO, description="The specification for rigging")
+
+
+class TripoAnimateRetargetRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.ANIMATE_RETARGET, description="Type of task")
+    original_model_task_id: str = Field(..., description="The task ID of the original model")
+    animation: TripoAnimation = Field(..., description="The animation to apply")
+    out_format: TripoOutFormat | None = Field(TripoOutFormat.GLB, description="The output format")
+    bake_animation: bool | None = Field(True, description="Whether to bake the animation")
+
+
+class TripoConvertModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.CONVERT_MODEL, description="Type of task")
+    format: TripoConvertFormat = Field(..., description="The format to convert to")
+    original_model_task_id: str = Field(..., description="The task ID of the original model")
+    quad: bool | None = Field(None, description="Whether to apply quad to the model")
+    force_symmetry: bool | None = Field(None, description="Whether to force symmetry")
+    face_limit: int | None = Field(None, description="The number of faces to limit the conversion to")
+    flatten_bottom: bool | None = Field(None, description="Whether to flatten the bottom of the model")
+    flatten_bottom_threshold: float | None = Field(None, description="The threshold for flattening the bottom")
+    texture_size: int | None = Field(None, description="The size of the texture")
+    texture_format: TripoTextureFormat | None = Field(TripoTextureFormat.JPEG, description="The format of the texture")
+    pivot_to_center_bottom: bool | None = Field(None, description="Whether to pivot to the center bottom")
+    scale_factor: float | None = Field(None, description="The scale factor for the model")
+    with_animation: bool | None = Field(None, description="Whether to include animations")
+    pack_uv: bool | None = Field(None, description="Whether to pack the UVs")
+    bake: bool | None = Field(None, description="Whether to bake the model")
+    part_names: list[str] | None = Field(None, description="The names of the parts to include")
+    fbx_preset: TripoFbxPreset | None = Field(None, description="The preset for the FBX export")
+    export_vertex_colors: bool | None = Field(None, description="Whether to export the vertex colors")
+    export_orientation: TripoOrientation | None = Field(None, description="The orientation for the export")
+    animate_in_place: bool | None = Field(None, description="Whether to animate in place")
+
+
+class TripoP1CommonRequest(BaseModel):
+    """Fields supported by Tripo P1 across all input types."""
+
+    model_version: str = Field("P1-20260311")
+    model_seed: int | None = Field(None, description="Random seed for geometry generation")
+    face_limit: int | None = Field(None, ge=48, le=20000, description="Target face count (48-20000)")
+    texture: bool | None = Field(None, description="Enable texturing; pbr=True forces this true")
+    pbr: bool | None = Field(None, description="Enable PBR maps; when true, texture is also enabled")
+    texture_seed: int | None = Field(None, description="Random seed for texture generation")
+    texture_quality: str | None = Field(None, description='"standard" or "detailed"')
+    auto_size: bool | None = Field(None, description="Scale to real-world meters")
+    compress: str | None = Field(None, description='Only "geometry" is supported')
+    export_uv: bool | None = Field(None, description="Perform UV unwrapping during generation")
+
+
+class TripoP1TextToModelRequest(TripoP1CommonRequest):
+    type: str = "text_to_model"
+    prompt: str = Field(..., max_length=1024)
+    negative_prompt: str | None = Field(None, max_length=255)
+    image_seed: int | None = None
+
+
+class TripoP1ImageToModelRequest(TripoP1CommonRequest):
+    type: str = "image_to_model"
+    file: TripoFileReference
+    enable_image_autofix: bool | None = None
+    texture_alignment: str | None = Field(None, description='"original_image" or "geometry"')
+    orientation: str | None = Field(None, description='"default" or "align_image"; needs texture=true')
+
+
+class TripoP1MultiviewToModelRequest(TripoP1CommonRequest):
+    """P1 multiview generation.
+
+    Tripo requires `files` to be exactly four entries in [front, left, back, right] order with `{}`
+    (TripoFileEmptyReference) for omitted slots; front is required and at least two images total must be provided.
+    """
+
+    type: str = "multiview_to_model"
+    files: list[TripoFileReference]
+    texture_alignment: str | None = None
+    orientation: str | None = None
+
+
+class TripoImportModelRequest(BaseModel):
+    """Request for the comfy-api composite import endpoint (/proxy/tripo/v2/openapi/import).
+
+    The model file is uploaded to ComfyUI API storage first; the backend downloads it from
+    `url`, re-uploads it to Tripo's storage and creates the import_model task server-side.
+    """
+
+    url: str = Field(..., description="ComfyUI API storage download URL of the model file")
+    format: str = Field(..., description='File format: "glb", "fbx", "obj" or "stl"')
+
+
+class TripoTaskOutput(BaseModel):
+    model: str | None = Field(None, description="URL to the model")
+    base_model: str | None = Field(None, description="URL to the base model")
+    pbr_model: str | None = Field(None, description="URL to the PBR model")
+    rendered_image: str | None = Field(None, description="URL to the rendered image")
+    riggable: bool | None = Field(None, description="Whether the model is riggable")
+
+
+class TripoTask(BaseModel):
+    task_id: str = Field(..., description="The task ID")
+    type: str | None = Field(None, description="The type of task")
+    status: TripoTaskStatus | None = Field(None, description="The status of the task")
+    input: dict[str, Any] | None = Field(None, description="The input parameters for the task")
+    output: TripoTaskOutput | None = Field(None, description="The output of the task")
+    progress: int | None = Field(None, description="The progress of the task", ge=0, le=100)
+    create_time: int | None = Field(None, description="The creation time of the task")
+    running_left_time: int | None = Field(None, description="The estimated time left for the task")
+    queue_position: int | None = Field(None, description="The position in the queue")
+    consumed_credit: int | None = Field(None)
+
+
+class TripoTaskResponse(BaseModel):
+    code: int = Field(0, description="The response code")
+    data: TripoTask = Field(..., description="The task data")
+
+
+class TripoErrorResponse(BaseModel):
+    code: int = Field(..., description="The error code")
+    message: str = Field(..., description="The error message")
+    suggestion: str = Field(..., description="The suggestion for fixing the error")
diff --git a/comfy_api_nodes/apis/veo.py b/comfy_api_nodes/apis/veo.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec5a9b9e244e97f213aec50abdc5569a189164f5
--- /dev/null
+++ b/comfy_api_nodes/apis/veo.py
@@ -0,0 +1,99 @@
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class VeoRequestInstanceImage(BaseModel):
+    bytesBase64Encoded: str | None = Field(None)
+    gcsUri: str | None = Field(None)
+    mimeType: str | None = Field(None)
+
+
+class VeoRequestInstance(BaseModel):
+    image: VeoRequestInstanceImage | None = Field(None)
+    lastFrame: VeoRequestInstanceImage | None = Field(None)
+    prompt: str = Field(..., description='Text description of the video')
+
+
+class VeoRequestParameters(BaseModel):
+    aspectRatio: Optional[str] = Field(None, examples=['16:9'])
+    durationSeconds: Optional[int] = None
+    enhancePrompt: Optional[bool] = None
+    generateAudio: Optional[bool] = Field(
+        None,
+        description='Generate audio for the video. Only supported by veo 3 models.',
+    )
+    negativePrompt: Optional[str] = None
+    personGeneration: str | None = Field(None, description="ALLOW or BLOCK")
+    sampleCount: Optional[int] = None
+    seed: Optional[int] = None
+    storageUri: Optional[str] = Field(
+        None, description='Optional Cloud Storage URI to upload the video'
+    )
+    resolution: str | None = Field(None)
+
+
+class VeoGenVidRequest(BaseModel):
+    instances: list[VeoRequestInstance] | None = Field(None)
+    parameters: VeoRequestParameters | None = Field(None)
+
+
+class VeoGenVidResponse(BaseModel):
+    name: str = Field(
+        ...,
+        description='Operation resource name',
+        examples=[
+            'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8'
+        ],
+    )
+
+
+class VeoGenVidPollRequest(BaseModel):
+    operationName: str = Field(
+        ...,
+        description='Full operation name (from predict response)',
+        examples=[
+            'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID'
+        ],
+    )
+
+
+class Video(BaseModel):
+    bytesBase64Encoded: Optional[str] = Field(
+        None, description='Base64-encoded video content'
+    )
+    gcsUri: Optional[str] = Field(None, description='Cloud Storage URI of the video')
+    mimeType: Optional[str] = Field(None, description='Video MIME type')
+
+
+class Error1(BaseModel):
+    code: Optional[int] = Field(None, description='Error code')
+    message: Optional[str] = Field(None, description='Error message')
+
+
+class Response1(BaseModel):
+    field_type: Optional[str] = Field(
+        None,
+        alias='@type',
+        examples=[
+            'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse'
+        ],
+    )
+    raiMediaFilteredCount: Optional[int] = Field(
+        None, description='Count of media filtered by responsible AI policies'
+    )
+    raiMediaFilteredReasons: Optional[list[str]] = Field(
+        None, description='Reasons why media was filtered by responsible AI policies'
+    )
+    videos: Optional[list[Video]] = Field(None)
+
+
+class VeoGenVidPollResponse(BaseModel):
+    done: Optional[bool] = None
+    error: Optional[Error1] = Field(
+        None, description='Error details if operation failed'
+    )
+    name: Optional[str] = None
+    response: Optional[Response1] = Field(
+        None, description='The actual prediction response if done is true'
+    )
diff --git a/comfy_api_nodes/apis/vidu.py b/comfy_api_nodes/apis/vidu.py
new file mode 100644
index 0000000000000000000000000000000000000000..912c1ad3916f39f21a948e6f7641251bf24c017d
--- /dev/null
+++ b/comfy_api_nodes/apis/vidu.py
@@ -0,0 +1,65 @@
+from pydantic import BaseModel, Field
+
+
+class SubjectReference(BaseModel):
+    id: str = Field(...)
+    images: list[str] = Field(...)
+
+
+class FrameSetting(BaseModel):
+    prompt: str = Field(...)
+    key_image: str = Field(...)
+    duration: int = Field(...)
+
+
+class TaskMultiFrameCreationRequest(BaseModel):
+    model: str = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    resolution: str = Field(...)
+    start_image: str = Field(...)
+    image_settings: list[FrameSetting] = Field(...)
+
+
+class TaskExtendCreationRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(..., max_length=2000)
+    duration: int = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    resolution: str = Field(...)
+    images: list[str] | None = Field(None, description="Base64 encoded string or image URL")
+    video_url: str = Field(..., description="URL of the video to extend")
+
+
+class TaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(..., max_length=2000)
+    duration: int = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    aspect_ratio: str | None = Field(None)
+    resolution: str | None = Field(None)
+    movement_amplitude: str | None = Field(None)
+    images: list[str] | None = Field(None, description="Base64 encoded string or image URL")
+    subjects: list[SubjectReference] | None = Field(None)
+    bgm: bool | None = Field(None)
+    audio: bool | None = Field(None)
+
+
+class TaskCreationResponse(BaseModel):
+    task_id: str = Field(...)
+    state: str = Field(...)
+    created_at: str = Field(...)
+    code: int | None = Field(None, description="Error code")
+
+
+class TaskResult(BaseModel):
+    id: str = Field(..., description="Creation id")
+    url: str = Field(..., description="The URL of the generated results, valid for one hour")
+    cover_url: str = Field(..., description="The cover URL of the generated results, valid for one hour")
+
+
+class TaskStatusResponse(BaseModel):
+    state: str = Field(...)
+    err_code: str | None = Field(None)
+    progress: float | None = Field(None)
+    credits: int | None = Field(None)
+    creations: list[TaskResult] = Field(..., description="Generated results")
diff --git a/comfy_api_nodes/apis/wan.py b/comfy_api_nodes/apis/wan.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0efc5b3686e858f2243aadcff4b222a60179685
--- /dev/null
+++ b/comfy_api_nodes/apis/wan.py
@@ -0,0 +1,252 @@
+from pydantic import BaseModel, Field
+
+
+class Text2ImageInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+
+
+class Image2ImageInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    images: list[str] = Field(..., min_length=1, max_length=2)
+
+
+class Text2VideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    audio_url: str | None = Field(None)
+
+
+class Image2VideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    img_url: str = Field(...)
+    audio_url: str | None = Field(None)
+
+
+class Reference2VideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    reference_video_urls: list[str] = Field(...)
+
+
+class Txt2ImageParametersField(BaseModel):
+    size: str = Field(...)
+    n: int = Field(1, description="Number of images to generate.")  # we support only value=1
+    seed: int = Field(..., ge=0, le=2147483647)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+
+
+class Image2ImageParametersField(BaseModel):
+    size: str | None = Field(None)
+    n: int = Field(1, description="Number of images to generate.")  # we support only value=1
+    seed: int = Field(..., ge=0, le=2147483647)
+    watermark: bool = Field(False)
+
+
+class Text2VideoParametersField(BaseModel):
+    size: str = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    duration: int = Field(5, ge=5, le=15)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+    audio: bool = Field(False, description="Whether to generate audio automatically.")
+    shot_type: str = Field("single")
+
+
+class Image2VideoParametersField(BaseModel):
+    resolution: str = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    duration: int = Field(5, ge=5, le=15)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+    audio: bool = Field(False, description="Whether to generate audio automatically.")
+    shot_type: str = Field("single")
+
+
+class Reference2VideoParametersField(BaseModel):
+    size: str = Field(...)
+    duration: int = Field(5, ge=5, le=15)
+    shot_type: str = Field("single")
+    seed: int = Field(..., ge=0, le=2147483647)
+    watermark: bool = Field(False)
+
+
+class Text2ImageTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Text2ImageInputField = Field(...)
+    parameters: Txt2ImageParametersField = Field(...)
+
+
+class Image2ImageTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Image2ImageInputField = Field(...)
+    parameters: Image2ImageParametersField = Field(...)
+
+
+class Text2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Text2VideoInputField = Field(...)
+    parameters: Text2VideoParametersField = Field(...)
+
+
+class Image2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Image2VideoInputField = Field(...)
+    parameters: Image2VideoParametersField = Field(...)
+
+
+class Reference2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Reference2VideoInputField = Field(...)
+    parameters: Reference2VideoParametersField = Field(...)
+
+
+class Wan27MediaItem(BaseModel):
+    type: str = Field(...)
+    url: str = Field(...)
+
+
+class Wan27ReferenceVideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    media: list[Wan27MediaItem] = Field(...)
+
+
+class Wan27ReferenceVideoParametersField(BaseModel):
+    resolution: str = Field(...)
+    ratio: str | None = Field(None)
+    duration: int = Field(5, ge=2, le=15)
+    watermark: bool = Field(False)
+    seed: int = Field(..., ge=0, le=2147483647)
+
+
+class Wan27ReferenceVideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Wan27ReferenceVideoInputField = Field(...)
+    parameters: Wan27ReferenceVideoParametersField = Field(...)
+
+
+class Wan27ImageToVideoInputField(BaseModel):
+    prompt: str | None = Field(None)
+    negative_prompt: str | None = Field(None)
+    media: list[Wan27MediaItem] = Field(...)
+
+
+class Wan27ImageToVideoParametersField(BaseModel):
+    resolution: str = Field(...)
+    duration: int = Field(5, ge=2, le=15)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+    seed: int = Field(..., ge=0, le=2147483647)
+
+
+class Wan27ImageToVideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Wan27ImageToVideoInputField = Field(...)
+    parameters: Wan27ImageToVideoParametersField = Field(...)
+
+
+class Wan27VideoEditInputField(BaseModel):
+    prompt: str = Field(...)
+    media: list[Wan27MediaItem] = Field(...)
+
+
+class Wan27VideoEditParametersField(BaseModel):
+    resolution: str = Field(...)
+    ratio: str | None = Field(None)
+    duration: int | None = Field(0)
+    audio_setting: str = Field("auto")
+    watermark: bool = Field(False)
+    seed: int = Field(..., ge=0, le=2147483647)
+
+
+class Wan27VideoEditTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Wan27VideoEditInputField = Field(...)
+    parameters: Wan27VideoEditParametersField = Field(...)
+
+
+class Wan27Text2VideoParametersField(BaseModel):
+    resolution: str = Field(...)
+    ratio: str | None = Field(None)
+    duration: int = Field(5, ge=2, le=15)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+    seed: int = Field(..., ge=0, le=2147483647)
+
+
+class Wan27Text2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Text2VideoInputField = Field(...)
+    parameters: Wan27Text2VideoParametersField = Field(...)
+
+
+class Wan3MediaItem(BaseModel):
+    type: str = Field(...)
+    url: str = Field(...)
+
+
+class Wan3InputField(BaseModel):
+    prompt: str | None = Field(None)
+    media: list[Wan3MediaItem] | None = Field(None)
+
+
+class Wan3ParametersField(BaseModel):
+    resolution: str = Field(...)
+    ratio: str = Field(...)
+    duration: int = Field(..., ge=-1, le=30)
+    seed: int = Field(..., ge=0, le=2147483647)
+    audio: bool = Field(True)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+
+
+class Wan3TaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Wan3InputField = Field(...)
+    parameters: Wan3ParametersField = Field(...)
+
+
+class TaskCreationOutputField(BaseModel):
+    task_id: str = Field(...)
+    task_status: str = Field(...)
+
+
+class TaskCreationResponse(BaseModel):
+    output: TaskCreationOutputField | None = Field(None)
+    request_id: str = Field(...)
+    code: str | None = Field(None, description="Error code for the failed request.")
+    message: str | None = Field(None, description="Details about the failed request.")
+
+
+class TaskResult(BaseModel):
+    url: str | None = Field(None)
+    code: str | None = Field(None)
+    message: str | None = Field(None)
+
+
+class ImageTaskStatusOutputField(TaskCreationOutputField):
+    task_id: str = Field(...)
+    task_status: str = Field(...)
+    results: list[TaskResult] | None = Field(None)
+
+
+class VideoTaskStatusOutputField(TaskCreationOutputField):
+    task_id: str = Field(...)
+    task_status: str = Field(...)
+    video_url: str | None = Field(None)
+    code: str | None = Field(None)
+    message: str | None = Field(None)
+
+
+class ImageTaskStatusResponse(BaseModel):
+    output: ImageTaskStatusOutputField | None = Field(None)
+    request_id: str = Field(...)
+
+
+class VideoTaskStatusResponse(BaseModel):
+    output: VideoTaskStatusOutputField | None = Field(None)
+    request_id: str = Field(...)
diff --git a/comfy_api_nodes/apis/wavespeed.py b/comfy_api_nodes/apis/wavespeed.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3d9c628c33bbbab3268a9984b11dbe276f4ddaa
--- /dev/null
+++ b/comfy_api_nodes/apis/wavespeed.py
@@ -0,0 +1,35 @@
+from pydantic import BaseModel, Field
+
+
+class SeedVR2ImageRequest(BaseModel):
+    image: str = Field(...)
+    target_resolution: str = Field(...)
+    output_format: str = Field("png")
+    enable_sync_mode: bool = Field(False)
+
+
+class FlashVSRRequest(BaseModel):
+    target_resolution: str = Field(...)
+    video: str = Field(...)
+    duration: float = Field(...)
+
+
+class TaskCreatedDataResponse(BaseModel):
+    id: str = Field(...)
+
+
+class TaskCreatedResponse(BaseModel):
+    code: int = Field(...)
+    message: str = Field(...)
+    data: TaskCreatedDataResponse | None = Field(None)
+
+
+class TaskResultDataResponse(BaseModel):
+    status: str = Field(...)
+    outputs: list[str] = Field([])
+
+
+class TaskResultResponse(BaseModel):
+    code: int = Field(...)
+    message: str = Field(...)
+    data: TaskResultDataResponse | None = Field(None)
diff --git a/comfy_api_nodes/nodes_anthropic.py b/comfy_api_nodes/nodes_anthropic.py
new file mode 100644
index 0000000000000000000000000000000000000000..f472016d206c2291739ebdc9359c1232ca071c6a
--- /dev/null
+++ b/comfy_api_nodes/nodes_anthropic.py
@@ -0,0 +1,320 @@
+"""API Nodes for Anthropic Claude (Messages API). See: https://docs.anthropic.com/en/api/messages"""
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.anthropic import (
+    AnthropicImageContent,
+    AnthropicImageSourceUrl,
+    AnthropicMessage,
+    AnthropicMessagesRequest,
+    AnthropicMessagesResponse,
+    AnthropicOutputConfig,
+    AnthropicResponseTextBlock,
+    AnthropicRole,
+    AnthropicTextContent,
+    AnthropicThinkingConfig,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    get_number_of_images,
+    sync_op,
+    upload_images_to_comfyapi,
+    validate_string,
+)
+
+ANTHROPIC_MESSAGES_ENDPOINT = "/proxy/anthropic/v1/messages"
+ANTHROPIC_IMAGE_MAX_PIXELS = 1568 * 1568
+CLAUDE_MAX_IMAGES = 20
+
+CLAUDE_MODELS: dict[str, str] = {
+    "Opus 5": "claude-opus-5",
+    "Opus 4.8": "claude-opus-4-8",
+    "Fable 5": "claude-fable-5",
+    "Sonnet 5": "claude-sonnet-5",
+    "Opus 4.7": "claude-opus-4-7",
+    "Opus 4.6": "claude-opus-4-6",
+    "Sonnet 4.6": "claude-sonnet-4-6",
+    "Sonnet 4.5": "claude-sonnet-4-5-20250929",
+    "Haiku 4.5": "claude-haiku-4-5-20251001",
+}
+
+_THINKING_UNSUPPORTED = {"Haiku 4.5"}
+# Models that use the newer "adaptive" thinking mode (Opus 4.7+ require it; older models keep the explicit budget API).
+# Anthropic decides the actual budget when adaptive is used, based on the `output_config.effort` hint.
+_ADAPTIVE_THINKING_MODELS = {"Opus 4.8", "Sonnet 5", "Opus 4.7", "Opus 4.6", "Sonnet 4.6"}
+_ALWAYS_THINKING_MODELS = {"Opus 5", "Fable 5"}
+_EXPLICIT_THINKING_OFF_MODELS = {"Sonnet 5"}
+_NO_TEMPERATURE_MODELS = {"Opus 5", "Opus 4.8", "Fable 5", "Sonnet 5"}
+
+# Budget mode (Sonnet 4.5): effort -> reasoning budget in tokens. Must be < max_tokens.
+# Sized so even the "high" budget fits comfortably under the default max_tokens=32768.
+_REASONING_BUDGET: dict[str, int] = {
+    "low": 2048,
+    "medium": 8192,
+    "high": 16384,
+}
+_REASONING_EFFORTS = ["off", "low", "medium", "high"]
+
+
+def _claude_model_inputs(model_label: str):
+    inputs: list = [
+        IO.Int.Input(
+            "max_tokens",
+            default=32768,
+            min=4096,
+            max=64000,
+            tooltip="Maximum number of tokens to generate (includes reasoning tokens when enabled).",
+            advanced=True,
+        ),
+    ]
+    if model_label not in _NO_TEMPERATURE_MODELS:
+        inputs.append(
+            IO.Float.Input(
+                "temperature",
+                default=1.0,
+                min=0.0,
+                max=1.0,
+                step=0.01,
+                tooltip=(
+                    "Controls randomness. 0.0 is deterministic, 1.0 is most random. "
+                    "Ignored for Opus 4.7 and any model when reasoning_effort is set."
+                ),
+                advanced=True,
+            )
+        )
+    if model_label in _ALWAYS_THINKING_MODELS:
+        inputs.append(
+            IO.Combo.Input(
+                "reasoning_effort",
+                options=[e for e in _REASONING_EFFORTS if e != "off"],
+                default="high",
+                tooltip="Extended thinking effort. Reasoning is always enabled for this model.",
+                advanced=True,
+            )
+        )
+    elif model_label not in _THINKING_UNSUPPORTED:
+        inputs.append(
+            IO.Combo.Input(
+                "reasoning_effort",
+                options=_REASONING_EFFORTS,
+                default="off",
+                tooltip="Extended thinking effort. 'off' disables reasoning.",
+                advanced=True,
+            )
+        )
+    return inputs
+
+
+def _get_text_from_response(response: AnthropicMessagesResponse) -> str:
+    if not response.content:
+        return ""
+    # Thinking blocks are silently dropped — we never want reasoning in the output.
+    return "\n".join(
+        block.text for block in response.content
+        if isinstance(block, AnthropicResponseTextBlock) and block.text
+    )
+
+
+async def _build_image_content_blocks(
+    cls: type[IO.ComfyNode],
+    image_tensors: list[Input.Image],
+) -> list[AnthropicImageContent]:
+    urls = await upload_images_to_comfyapi(
+        cls,
+        image_tensors,
+        max_images=CLAUDE_MAX_IMAGES,
+        total_pixels=ANTHROPIC_IMAGE_MAX_PIXELS,
+        wait_label="Uploading reference images",
+    )
+    return [AnthropicImageContent(source=AnthropicImageSourceUrl(url=url)) for url in urls]
+
+
+class ClaudeNode(IO.ComfyNode):
+    """Generate text responses from an Anthropic Claude model."""
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ClaudeNode",
+            display_name="Anthropic Claude",
+            category="partner/text/Anthropic",
+            essentials_category="Text Generation",
+            description="Generate text responses with Anthropic's Claude models. "
+            "Provide a text prompt and optionally one or more images for multimodal context.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text input to the model.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(label, _claude_model_inputs(label))
+                        for label in CLAUDE_MODELS
+                    ],
+                    tooltip="The Claude model used to generate the response.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.Autogrow.Input(
+                    "images",
+                    template=IO.Autogrow.TemplateNames(
+                        IO.Image.Input("image"),
+                        names=[f"image_{i}" for i in range(1, CLAUDE_MAX_IMAGES + 1)],
+                        min=0,
+                    ),
+                    tooltip=f"Optional image(s) to use as context for the model. Up to {CLAUDE_MAX_IMAGES} images.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    advanced=True,
+                    tooltip="Foundational instructions that dictate the model's behavior.",
+                ),
+            ],
+            outputs=[IO.String.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $contains($m, "fable") ? {
+                    "type": "list_usd",
+                    "usd": [0.0143, 0.0715],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "opus 4.8") ? {
+                    "type": "list_usd",
+                    "usd": [0.00715, 0.03575],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "sonnet 5") ? {
+                    "type": "list_usd",
+                    "usd": [0.00286, 0.0143],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "opus 5") ? {
+                    "type": "list_usd",
+                    "usd": [0.00715, 0.03575],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "opus") ? {
+                    "type": "list_usd",
+                    "usd": [0.005, 0.025],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "sonnet") ? {
+                    "type": "list_usd",
+                    "usd": [0.003, 0.015],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "haiku") ? {
+                    "type": "list_usd",
+                    "usd": [0.001, 0.005],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : {"type":"text", "text":"Token-based"}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        images: dict | None = None,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_label = model["model"]
+        max_tokens = model.get("max_tokens", 32768)
+        reasoning_effort = model.get("reasoning_effort", "off")
+        always_thinking = model_label in _ALWAYS_THINKING_MODELS
+        thinking_enabled = always_thinking or (
+            reasoning_effort not in ("off", None) and model_label not in _THINKING_UNSUPPORTED
+        )
+
+        # Anthropic requires temperature to be unset (defaults to 1.0) when thinking is enabled.
+        # Opus 4.7 also rejects user-supplied temperature.
+        if model_label in _NO_TEMPERATURE_MODELS or thinking_enabled or model_label == "Opus 4.7":
+            temperature = None
+        else:
+            temperature = model.get("temperature", 1.0)
+
+        thinking_cfg: AnthropicThinkingConfig | None = None
+        output_cfg: AnthropicOutputConfig | None = None
+        if always_thinking:
+            output_cfg = AnthropicOutputConfig(effort=reasoning_effort)
+        elif thinking_enabled:
+            if model_label in _ADAPTIVE_THINKING_MODELS:
+                # Adaptive mode - Anthropic chooses the budget based on effort hint
+                thinking_cfg = AnthropicThinkingConfig(type="adaptive")
+                output_cfg = AnthropicOutputConfig(effort=reasoning_effort)
+            else:
+                # Budget mode (Sonnet 4.5). Leave at least 1024 tokens for the actual response
+                budget = _REASONING_BUDGET[reasoning_effort]
+                budget = min(budget, max(1024, max_tokens - 1024))
+                thinking_cfg = AnthropicThinkingConfig(type="enabled", budget_tokens=budget)
+        elif model_label in _EXPLICIT_THINKING_OFF_MODELS:
+            thinking_cfg = AnthropicThinkingConfig(type="disabled")
+
+        image_tensors: list[Input.Image] = [t for t in (images or {}).values() if t is not None]
+        if sum(get_number_of_images(t) for t in image_tensors) > CLAUDE_MAX_IMAGES:
+            raise ValueError(f"Up to {CLAUDE_MAX_IMAGES} images are supported per request.")
+
+        content: list[AnthropicTextContent | AnthropicImageContent] = []
+        if image_tensors:
+            content.extend(await _build_image_content_blocks(cls, image_tensors))
+        content.append(AnthropicTextContent(text=prompt))
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=ANTHROPIC_MESSAGES_ENDPOINT, method="POST"),
+            response_model=AnthropicMessagesResponse,
+            data=AnthropicMessagesRequest(
+                model=CLAUDE_MODELS[model_label],
+                max_tokens=max_tokens,
+                messages=[AnthropicMessage(role=AnthropicRole.user, content=content)],
+                system=system_prompt or None,
+                temperature=temperature,
+                thinking=thinking_cfg,
+                output_config=output_cfg,
+            ),
+        )
+        if response.stop_reason == "refusal":
+            raise ValueError(
+                "Claude declined to answer this request for safety reasons. "
+                "Rephrase the prompt or try a different model."
+            )
+        return IO.NodeOutput(_get_text_from_response(response) or "Empty response from Claude model.")
+
+
+class AnthropicExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [ClaudeNode]
+
+
+async def comfy_entrypoint() -> AnthropicExtension:
+    return AnthropicExtension()
diff --git a/comfy_api_nodes/nodes_beeble.py b/comfy_api_nodes/nodes_beeble.py
new file mode 100644
index 0000000000000000000000000000000000000000..723935de220aba8c1155294292b1cad801dd7a6c
--- /dev/null
+++ b/comfy_api_nodes/nodes_beeble.py
@@ -0,0 +1,404 @@
+from fractions import Fraction
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl, Types
+from comfy_api_nodes.apis.beeble import (
+    CreateSwitchXRequest,
+    SwitchXStatusResponse,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    bytesio_to_image_tensor,
+    convert_mask_to_image,
+    download_url_as_bytesio,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    downscale_image_tensor,
+    downscale_video_to_max_pixels,
+    poll_op,
+    sync_op,
+    upload_image_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+    validate_video_frame_count,
+)
+
+_MAX_PIXELS = 2_770_000
+_MAX_FRAMES = 240
+_MAX_PROMPT_LEN = 2000
+
+
+def _validate_inputs(prompt: str | None, reference_image: Input.Image | None) -> str | None:
+    """Beeble requires at least one of prompt or reference_image. Returns the cleaned prompt."""
+    cleaned = prompt.strip() if prompt else ""
+    if not cleaned and reference_image is None:
+        raise ValueError("At least one of 'prompt' or 'reference_image' must be provided.")
+    if cleaned:
+        validate_string(cleaned, strip_whitespace=False, max_length=_MAX_PROMPT_LEN)
+    return cleaned or None
+
+
+async def _upload_mask_as_image(
+    cls: type[IO.ComfyNode],
+    mask: Input.Image,
+    *,
+    wait_label: str,
+) -> str:
+    """Encode a single-frame MASK (H, W) or (1, H, W) as a PNG and upload."""
+    if mask.dim() == 2:
+        mask = mask.unsqueeze(0)
+    image = convert_mask_to_image(mask[:1])
+    return await upload_image_to_comfyapi(
+        cls,
+        image,
+        mime_type="image/png",
+        wait_label=wait_label,
+        total_pixels=_MAX_PIXELS,
+    )
+
+
+async def _upload_mask_batch_as_video(
+    cls: type[IO.ComfyNode],
+    mask: Input.Image,
+    *,
+    frame_rate: Fraction,
+    source_frame_count: int,
+    wait_label: str,
+) -> str:
+    """Encode a MASK batch (N, H, W) as a grayscale H.264 MP4 at frame_rate and upload.
+
+    The matte is always downscaled to the pixel budget so it stays within Beeble's limit and
+    keeps the same dimensions as the (similarly downscaled) source — both use the same algorithm
+    from the same starting dimensions, and downscaling is a no-op when already within budget.
+    """
+    if mask.dim() == 2:
+        mask = mask.unsqueeze(0)
+    if mask.shape[0] != source_frame_count:
+        raise ValueError(
+            f"Custom alpha video frame count ({mask.shape[0]}) does not match the "
+            f"source video frame count ({source_frame_count}). The Beeble API requires "
+            "one mask per source frame."
+        )
+    images = downscale_image_tensor(convert_mask_to_image(mask), _MAX_PIXELS)
+    alpha_video = InputImpl.VideoFromComponents(Types.VideoComponents(images=images, audio=None, frame_rate=frame_rate))
+    return await upload_video_to_comfyapi(cls, alpha_video, wait_label=wait_label)
+
+
+def _alpha_mode_input(*, video: bool) -> IO.DynamicCombo.Input:
+    """Build the alpha_mode DynamicCombo with mode-specific extra inputs."""
+    select_keyframe_tooltip = (
+        "First-frame keyframe mask. Beeble propagates this across the video." if video else "Grayscale keyframe mask."
+    )
+    custom_tooltip = (
+        "Per-frame grayscale mask covering the entire video. "
+        "Must have the same frame count as the source. "
+        "Connect a MASK output from SAM3_TrackToMask or similar."
+        if video
+        else "Grayscale mask to apply."
+    )
+    return IO.DynamicCombo.Input(
+        "alpha_mode",
+        tooltip=(
+            "Controls how SwitchX decides what to keep vs. regenerate. "
+            "'auto' isolates the main subject automatically. "
+            "'fill' regenerates the entire frame while preserving geometry. "
+            "'select' propagates a first-frame keyframe across the clip. "
+            "'custom' uses a per-frame alpha matte you provide."
+        ),
+        options=[
+            IO.DynamicCombo.Option("auto", []),
+            IO.DynamicCombo.Option("fill", []),
+            IO.DynamicCombo.Option(
+                "select",
+                [IO.Mask.Input("alpha_keyframe", tooltip=select_keyframe_tooltip)],
+            ),
+            IO.DynamicCombo.Option(
+                "custom",
+                [IO.Mask.Input("alpha_mask", tooltip=custom_tooltip)],
+            ),
+        ],
+    )
+
+
+def _common_inputs(*, source: IO.Input, video: bool) -> list[IO.Input]:
+    return [
+        source,
+        IO.String.Input(
+            "prompt",
+            multiline=True,
+            default="",
+            tooltip=(
+                "Text description of the desired output (max 2000 chars). "
+                "At least one of 'prompt' or 'reference_image' is required."
+            ),
+        ),
+        IO.Image.Input(
+            "reference_image",
+            optional=True,
+            tooltip=(
+                "Reference image whose look (background, lighting, costume) the result "
+                "should adopt. At least one of 'reference_image' or 'prompt' is required."
+            ),
+        ),
+        _alpha_mode_input(video=video),
+        IO.Combo.Input(
+            "max_resolution",
+            options=["1080p", "720p"],
+            default="1080p",
+            tooltip="Maximum output resolution.",
+        ),
+        IO.Int.Input(
+            "seed",
+            default=0,
+            min=0,
+            max=2147483647,
+            control_after_generate=True,
+            tooltip=(
+                "Seed controls whether the node should re-run; " "results are non-deterministic regardless of seed."
+            ),
+        ),
+    ]
+
+
+async def _submit_and_poll(
+    cls: type[IO.ComfyNode],
+    request: CreateSwitchXRequest,
+) -> SwitchXStatusResponse:
+    initial = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/beeble/v1/switchx/generations", method="POST"),
+        response_model=SwitchXStatusResponse,
+        data=request,
+    )
+    return await poll_op(
+        cls,
+        ApiEndpoint(path=f"/proxy/beeble/v1/switchx/generations/{initial.id}"),
+        response_model=SwitchXStatusResponse,
+        status_extractor=lambda r: r.status,
+        progress_extractor=lambda r: r.progress,
+    )
+
+
+def _require_output_url(response: SwitchXStatusResponse, name: str) -> str:
+    if response.output is None or getattr(response.output, name) is None:
+        raise RuntimeError(f"Beeble job {response.id} completed without a {name!r} output URL.")
+    return getattr(response.output, name)
+
+
+def _alpha_url(response: SwitchXStatusResponse, mode: str) -> str | None:
+    """URL of the alpha matte, or None when the mode produces no separate matte.
+
+    'fill' selects the whole frame, so Beeble writes no alpha asset even though the status
+    response still returns a (dangling) signed URL for it — fetching it 403s with S3
+    AccessDenied. The other three modes ('auto', 'custom', 'select') all produce a real,
+    downloadable matte.
+    """
+    if mode == "fill" or response.output is None:
+        return None
+    return response.output.alpha
+
+
+class BeebleSwitchXVideoEdit(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="BeebleSwitchXVideoEdit",
+            display_name="Beeble SwitchX Video Edit",
+            category="partner/video/Beeble",
+            description=(
+                "Edit a video with Beeble SwitchX. Switches anything in the scene (background, "
+                "lighting, costume) while preserving the original subject's pixels and motion. "
+                "Provide a reference image and/or text prompt to describe the new look. "
+                "Max 240 frames, max ~2.77MP per frame."
+            ),
+            inputs=_common_inputs(source=IO.Video.Input("video"), video=True),
+            outputs=[
+                IO.Video.Output(display_name="video"),
+                IO.Video.Output(
+                    display_name="alpha",
+                    tooltip="The alpha matte Beeble used. Empty for 'fill' mode, which has no separate matte.",
+                ),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["max_resolution"]),
+                expr="""
+                (
+                  $rate := widgets.max_resolution = "1080p" ? 0.429 : 0.143;
+                  {"type":"usd","usd": $rate, "format":{"suffix":"/30 frames"}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        prompt: str,
+        alpha_mode: dict,
+        max_resolution: str,
+        seed: int,
+        reference_image: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        cleaned_prompt = _validate_inputs(prompt, reference_image)
+
+        validate_video_frame_count(video, max_frame_count=_MAX_FRAMES)
+        video = downscale_video_to_max_pixels(video, _MAX_PIXELS)
+
+        mode = alpha_mode["alpha_mode"]
+        alpha_uri: str | None = None
+        if mode == "select":
+            alpha_uri = await _upload_mask_as_image(cls, alpha_mode["alpha_keyframe"], wait_label="Uploading keyframe")
+        elif mode == "custom":
+            alpha_uri = await _upload_mask_batch_as_video(
+                cls,
+                alpha_mode["alpha_mask"],
+                frame_rate=video.get_frame_rate(),
+                source_frame_count=video.get_frame_count(),
+                wait_label="Uploading alpha video",
+            )
+
+        source_uri = await upload_video_to_comfyapi(cls, video, wait_label="Uploading source")
+        reference_uri: str | None = None
+        if reference_image is not None:
+            reference_uri = await upload_image_to_comfyapi(
+                cls,
+                reference_image,
+                mime_type="image/png",
+                wait_label="Uploading reference",
+                total_pixels=_MAX_PIXELS,
+            )
+
+        request = CreateSwitchXRequest(
+            generation_type="video",
+            source_uri=source_uri,
+            alpha_mode=mode,
+            prompt=cleaned_prompt,
+            reference_image_uri=reference_uri,
+            alpha_uri=alpha_uri,
+            max_resolution=1080 if max_resolution == "1080p" else 720,
+        )
+        response = await _submit_and_poll(cls, request)
+
+        render = await download_url_to_video_output(_require_output_url(response, "render"))
+        alpha = None
+        if (alpha_url := _alpha_url(response, mode)) is not None:
+            alpha = await download_url_to_video_output(alpha_url)
+        return IO.NodeOutput(render, alpha)
+
+
+class BeebleSwitchXImageEdit(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="BeebleSwitchXImageEdit",
+            display_name="Beeble SwitchX Image Edit",
+            category="partner/image/Beeble",
+            description=(
+                "Edit a single image with Beeble SwitchX. Switches anything in the scene "
+                "(background, lighting, costume) while preserving the original subject's pixels. "
+                "Provide a reference image and/or text prompt to describe the new look. "
+                "Max ~2.77MP."
+            ),
+            inputs=_common_inputs(source=IO.Image.Input("image"), video=False),
+            outputs=[
+                IO.Image.Output(display_name="image"),
+                IO.Mask.Output(
+                    display_name="alpha",
+                    tooltip="The alpha matte Beeble used. Empty for 'fill' mode, which has no separate matte.",
+                ),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["max_resolution"]),
+                expr="""
+                (
+                  $rate := widgets.max_resolution = "1080p" ? 0.429 : 0.143;
+                  {"type":"usd","usd": $rate}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        prompt: str,
+        alpha_mode: dict,
+        max_resolution: str,
+        seed: int,
+        reference_image: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        cleaned_prompt = _validate_inputs(prompt, reference_image)
+
+        image = downscale_image_tensor(image, _MAX_PIXELS)
+
+        mode = alpha_mode["alpha_mode"]
+        alpha_uri: str | None = None
+        if mode == "select":
+            alpha_uri = await _upload_mask_as_image(cls, alpha_mode["alpha_keyframe"], wait_label="Uploading keyframe")
+        elif mode == "custom":
+            alpha_uri = await _upload_mask_as_image(cls, alpha_mode["alpha_mask"], wait_label="Uploading alpha")
+
+        source_uri = await upload_image_to_comfyapi(
+            cls,
+            image,
+            mime_type="image/png",
+            wait_label="Uploading source",
+            total_pixels=None,
+        )
+        reference_uri: str | None = None
+        if reference_image is not None:
+            reference_uri = await upload_image_to_comfyapi(
+                cls,
+                reference_image,
+                mime_type="image/png",
+                wait_label="Uploading reference",
+                total_pixels=_MAX_PIXELS,
+            )
+
+        request = CreateSwitchXRequest(
+            generation_type="image",
+            source_uri=source_uri,
+            alpha_mode=mode,
+            prompt=cleaned_prompt,
+            reference_image_uri=reference_uri,
+            alpha_uri=alpha_uri,
+            max_resolution=1080 if max_resolution == "1080p" else 720,
+        )
+        response = await _submit_and_poll(cls, request)
+
+        render = await download_url_to_image_tensor(_require_output_url(response, "render"))
+        alpha_mask = None
+        if (alpha_url := _alpha_url(response, mode)) is not None:
+            alpha_image = bytesio_to_image_tensor(await download_url_as_bytesio(alpha_url), mode="L")
+            alpha_mask = alpha_image.squeeze(-1) if alpha_image.dim() == 4 else alpha_image
+        return IO.NodeOutput(render, alpha_mask)
+
+
+class BeebleExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            BeebleSwitchXVideoEdit,
+            BeebleSwitchXImageEdit,
+        ]
+
+
+async def comfy_entrypoint() -> BeebleExtension:
+    return BeebleExtension()
diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py
new file mode 100644
index 0000000000000000000000000000000000000000..f84642eed3a26a216b9bbf6dd9012917bf5db148
--- /dev/null
+++ b/comfy_api_nodes/nodes_bfl.py
@@ -0,0 +1,1533 @@
+import math
+
+import torch
+from pydantic import BaseModel
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.bfl import (
+    BFLFluxEraseRequest,
+    BFLFluxExpandImageRequest,
+    BFLFluxFillImageRequest,
+    BFLFluxKontextProGenerateRequest,
+    BFLFluxProGenerateResponse,
+    BFLFluxProUltraGenerateRequest,
+    BFLFluxStatusResponse,
+    BFLFluxVideoUpscaleRequest,
+    BFLFluxVTORequest,
+    BFLStatus,
+    Flux2ProGenerateRequest,
+    Flux3ImageToVideoRequest,
+    Flux3TextToVideoRequest,
+    Flux3VideoContinuationRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    convert_mask_to_image,
+    download_url_to_image_tensor,
+    downscale_video_to_max_pixels,
+    download_url_to_video_output,
+    get_number_of_images,
+    poll_op,
+    resize_mask_to_image,
+    sync_op,
+    tensor_to_base64_string,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_aspect_ratio_string,
+    validate_image_dimensions,
+    validate_string,
+    validate_video_dimensions,
+    validate_video_duration,
+)
+
+
+class FluxProUltraImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FluxProUltraImageNode",
+            display_name="Flux 1.1 [pro] Ultra Image",
+            category="partner/image/BFL",
+            description="Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation",
+                ),
+                IO.Boolean.Input(
+                    "prompt_upsampling",
+                    default=False,
+                    tooltip="Whether to perform upsampling on the prompt. "
+                    "If active, automatically modifies the prompt for more creative generation, "
+                    "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                IO.String.Input(
+                    "aspect_ratio",
+                    default="16:9",
+                    tooltip="Aspect ratio of image; must be between 1:4 and 4:1.",
+                ),
+                IO.Boolean.Input(
+                    "raw",
+                    default=False,
+                    tooltip="When True, generate less processed, more natural-looking images.",
+                ),
+                IO.Image.Input(
+                    "image_prompt",
+                    optional=True,
+                ),
+                IO.Float.Input(
+                    "image_prompt_strength",
+                    default=0.1,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Blend between the prompt and the image prompt.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.06}""",
+            ),
+        )
+
+    @classmethod
+    def validate_inputs(cls, aspect_ratio: str):
+        validate_aspect_ratio_string(aspect_ratio, (1, 4), (4, 1))
+        return True
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        aspect_ratio: str,
+        prompt_upsampling: bool = False,
+        raw: bool = False,
+        seed: int = 0,
+        image_prompt: Input.Image | None = None,
+        image_prompt_strength: float = 0.1,
+    ) -> IO.NodeOutput:
+        if image_prompt is None:
+            validate_string(prompt, strip_whitespace=False)
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bfl/flux-pro-1.1-ultra/generate", method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=BFLFluxProUltraGenerateRequest(
+                prompt=prompt,
+                prompt_upsampling=prompt_upsampling,
+                seed=seed,
+                aspect_ratio=aspect_ratio,
+                raw=raw,
+                image_prompt=(image_prompt if image_prompt is None else tensor_to_base64_string(image_prompt)),
+                image_prompt_strength=(None if image_prompt is None else round(image_prompt_strength, 2)),
+            ),
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
+class FluxKontextProImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id=cls.NODE_ID,
+            display_name=cls.DISPLAY_NAME,
+            category="partner/image/BFL",
+            description="Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation - specify what and how to edit.",
+                ),
+                IO.String.Input(
+                    "aspect_ratio",
+                    default="16:9",
+                    tooltip="Aspect ratio of image; must be between 1:4 and 4:1.",
+                ),
+                IO.Float.Input(
+                    "guidance",
+                    default=3.0,
+                    min=0.1,
+                    max=99.0,
+                    step=0.1,
+                    tooltip="Guidance strength for the image generation process",
+                ),
+                IO.Int.Input(
+                    "steps",
+                    default=50,
+                    min=1,
+                    max=150,
+                    tooltip="Number of steps for the image generation process",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1234,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                IO.Boolean.Input(
+                    "prompt_upsampling",
+                    default=False,
+                    tooltip="Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
+                ),
+                IO.Image.Input(
+                    "input_image",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    BFL_PATH = "/proxy/bfl/flux-kontext-pro/generate"
+    NODE_ID = "FluxKontextProImageNode"
+    DISPLAY_NAME = "Flux.1 Kontext [pro] Image"
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        aspect_ratio: str,
+        guidance: float,
+        steps: int,
+        input_image: Input.Image | None = None,
+        seed=0,
+        prompt_upsampling=False,
+    ) -> IO.NodeOutput:
+        validate_aspect_ratio_string(aspect_ratio, (1, 4), (4, 1))
+        if input_image is None:
+            validate_string(prompt, strip_whitespace=False)
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=cls.BFL_PATH, method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=BFLFluxKontextProGenerateRequest(
+                prompt=prompt,
+                prompt_upsampling=prompt_upsampling,
+                guidance=round(guidance, 1),
+                steps=steps,
+                seed=seed,
+                aspect_ratio=aspect_ratio,
+                input_image=(input_image if input_image is None else tensor_to_base64_string(input_image)),
+            ),
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
+class FluxKontextMaxImageNode(FluxKontextProImageNode):
+
+    DESCRIPTION = "Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio."
+    BFL_PATH = "/proxy/bfl/flux-kontext-max/generate"
+    NODE_ID = "FluxKontextMaxImageNode"
+    DISPLAY_NAME = "Flux.1 Kontext [max] Image"
+
+
+class FluxProExpandNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FluxProExpandNode",
+            display_name="Flux.1 Expand Image",
+            category="partner/image/BFL",
+            description="Outpaints image based on prompt.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation",
+                ),
+                IO.Boolean.Input(
+                    "prompt_upsampling",
+                    default=False,
+                    tooltip="Whether to perform upsampling on the prompt. "
+                    "If active, automatically modifies the prompt for more creative generation, "
+                    "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "top",
+                    default=0,
+                    min=0,
+                    max=2048,
+                    tooltip="Number of pixels to expand at the top of the image",
+                ),
+                IO.Int.Input(
+                    "bottom",
+                    default=0,
+                    min=0,
+                    max=2048,
+                    tooltip="Number of pixels to expand at the bottom of the image",
+                ),
+                IO.Int.Input(
+                    "left",
+                    default=0,
+                    min=0,
+                    max=2048,
+                    tooltip="Number of pixels to expand at the left of the image",
+                ),
+                IO.Int.Input(
+                    "right",
+                    default=0,
+                    min=0,
+                    max=2048,
+                    tooltip="Number of pixels to expand at the right of the image",
+                ),
+                IO.Float.Input(
+                    "guidance",
+                    default=60,
+                    min=1.5,
+                    max=100,
+                    tooltip="Guidance strength for the image generation process",
+                ),
+                IO.Int.Input(
+                    "steps",
+                    default=50,
+                    min=15,
+                    max=50,
+                    tooltip="Number of steps for the image generation process",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.05}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        prompt: str,
+        prompt_upsampling: bool,
+        top: int,
+        bottom: int,
+        left: int,
+        right: int,
+        steps: int,
+        guidance: float,
+        seed=0,
+    ) -> IO.NodeOutput:
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bfl/flux-pro-1.0-expand/generate", method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=BFLFluxExpandImageRequest(
+                prompt=prompt,
+                prompt_upsampling=prompt_upsampling,
+                top=top,
+                bottom=bottom,
+                left=left,
+                right=right,
+                steps=steps,
+                guidance=guidance,
+                seed=seed,
+                image=tensor_to_base64_string(image),
+            ),
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
+class FluxProFillNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FluxProFillNode",
+            display_name="Flux.1 Fill Image",
+            category="partner/image/BFL",
+            description="Inpaints image based on mask and prompt.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Mask.Input("mask"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation",
+                ),
+                IO.Boolean.Input(
+                    "prompt_upsampling",
+                    default=False,
+                    tooltip="Whether to perform upsampling on the prompt. "
+                    "If active, automatically modifies the prompt for more creative generation, "
+                    "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "guidance",
+                    default=60,
+                    min=1.5,
+                    max=100,
+                    tooltip="Guidance strength for the image generation process",
+                ),
+                IO.Int.Input(
+                    "steps",
+                    default=50,
+                    min=15,
+                    max=50,
+                    tooltip="Number of steps for the image generation process",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.05}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        mask: Input.Image,
+        prompt: str,
+        prompt_upsampling: bool,
+        steps: int,
+        guidance: float,
+        seed=0,
+    ) -> IO.NodeOutput:
+        # prepare mask
+        mask = resize_mask_to_image(mask, image)
+        mask = tensor_to_base64_string(convert_mask_to_image(mask))
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bfl/flux-pro-1.0-fill/generate", method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=BFLFluxFillImageRequest(
+                prompt=prompt,
+                prompt_upsampling=prompt_upsampling,
+                steps=steps,
+                guidance=guidance,
+                seed=seed,
+                image=tensor_to_base64_string(image[:, :, :, :3]),  # make sure image will have alpha channel removed
+                mask=mask,
+            ),
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
+class FluxEraseNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FluxEraseNode",
+            display_name="Flux Erase Image",
+            category="partner/image/BFL",
+            description="Removes the masked object from an image and reconstructs the background. "
+            "Paint the mask over what you want to erase.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Mask.Input("mask", tooltip="White areas are removed; black areas are preserved."),
+                IO.Int.Input(
+                    "dilate_pixels",
+                    default=10,
+                    min=0,
+                    max=25,
+                    tooltip="Expands the mask boundaries to ensure clean coverage of the object's edges.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"range_usd","min_usd":0.03,"max_usd":0.06,"format":{"approximate":true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        mask: Input.Image,
+        dilate_pixels: int = 10,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        validate_image_dimensions(image, min_width=256, min_height=256)
+        mask = resize_mask_to_image(mask, image)
+        mask = tensor_to_base64_string(convert_mask_to_image(mask))
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bfl/v1/flux-tools/erase-v1", method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=BFLFluxEraseRequest(
+                image=tensor_to_base64_string(image[:, :, :, :3]),  # make sure image will have alpha channel removed
+                mask=mask,
+                dilate_pixels=dilate_pixels,
+                seed=seed,
+            ),
+        )
+
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
+class FluxVTONode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FluxVTONode",
+            display_name="Flux Virtual Try-On",
+            category="partner/image/BFL",
+            description="Virtual try-on: dresses the person in the provided garment.",
+            inputs=[
+                IO.Image.Input("person", tooltip="Image of the person to dress."),
+                IO.Image.Input("garment", tooltip="Image of the garment to apply."),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Optional natural-language styling instruction (e.g. how the garment should fit).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"range_usd","min_usd":0.0375,"max_usd":0.075,"format":{"approximate":true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        person: Input.Image,
+        garment: Input.Image,
+        prompt: str = "",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bfl/v1/flux-tools/vto-v1", method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=BFLFluxVTORequest(
+                prompt=prompt,
+                person=tensor_to_base64_string(person[:, :, :, :3]),
+                garment=tensor_to_base64_string(garment[:, :, :, :3]),
+                seed=seed,
+            ),
+        )
+
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
+class Flux2ProImageNode(IO.ComfyNode):
+
+    NODE_ID = "Flux2ProImageNode"
+    DISPLAY_NAME = "Flux.2 [pro] Image"
+    API_ENDPOINT = "/proxy/bfl/flux-2-pro/generate"
+    PRICE_BADGE_EXPR = """
+    (
+      $MP := 1024 * 1024;
+      $outMP := $max([1, $floor(((widgets.width * widgets.height) + $MP - 1) / $MP)]);
+      $outputCost := 0.03 + 0.015 * ($outMP - 1);
+      inputs.images.connected
+        ? {
+            "type":"range_usd",
+            "min_usd": $outputCost + 0.015,
+            "max_usd": $outputCost + 0.12,
+            "format": { "approximate": true }
+          }
+        : {"type":"usd","usd": $outputCost}
+    )
+    """
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id=cls.NODE_ID,
+            display_name=cls.DISPLAY_NAME,
+            category="partner/image/BFL",
+            description="Generates images synchronously based on prompt and resolution.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation or edit",
+                ),
+                IO.Int.Input(
+                    "width",
+                    default=1024,
+                    min=256,
+                    max=2048,
+                    step=32,
+                ),
+                IO.Int.Input(
+                    "height",
+                    default=768,
+                    min=256,
+                    max=2048,
+                    step=32,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                IO.Boolean.Input(
+                    "prompt_upsampling",
+                    default=True,
+                    tooltip="Whether to perform upsampling on the prompt. "
+                    "If active, automatically modifies the prompt for more creative generation.",
+                    advanced=True,
+                ),
+                IO.Image.Input("images", optional=True, tooltip="Up to 9 images to be used as references."),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["width", "height"], inputs=["images"]),
+                expr=cls.PRICE_BADGE_EXPR,
+            ),
+            is_deprecated=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        width: int,
+        height: int,
+        seed: int,
+        prompt_upsampling: bool,
+        images: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        reference_images = {}
+        if images is not None:
+            if get_number_of_images(images) > 9:
+                raise ValueError("The current maximum number of supported images is 9.")
+            for image_index in range(images.shape[0]):
+                key_name = f"input_image_{image_index + 1}" if image_index else "input_image"
+                reference_images[key_name] = tensor_to_base64_string(images[image_index], total_pixels=2048 * 2048)
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=cls.API_ENDPOINT, method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=Flux2ProGenerateRequest(
+                prompt=prompt,
+                width=width,
+                height=height,
+                seed=seed,
+                prompt_upsampling=prompt_upsampling,
+                **reference_images,
+            ),
+        )
+
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
+class Flux2MaxImageNode(Flux2ProImageNode):
+
+    NODE_ID = "Flux2MaxImageNode"
+    DISPLAY_NAME = "Flux.2 [max] Image"
+    API_ENDPOINT = "/proxy/bfl/flux-2-max/generate"
+    PRICE_BADGE_EXPR = """
+    (
+      $MP := 1024 * 1024;
+      $outMP := $max([1, $floor(((widgets.width * widgets.height) + $MP - 1) / $MP)]);
+      $outputCost := 0.07 + 0.03 * ($outMP - 1);
+
+      inputs.images.connected
+        ? {
+            "type":"range_usd",
+            "min_usd": $outputCost + 0.03,
+            "max_usd": $outputCost + 0.24,
+            "format": { "approximate": true }
+          }
+        : {"type":"usd","usd": $outputCost}
+    )
+    """
+
+
+_FLUX2_MODEL_ENDPOINTS = {
+    "Flux.2 [pro]": "/proxy/bfl/flux-2-pro/generate",
+    "Flux.2 [max]": "/proxy/bfl/flux-2-max/generate",
+}
+
+
+def _flux2_model_inputs():
+    return [
+        IO.Int.Input(
+            "width",
+            default=1024,
+            min=256,
+            max=2048,
+            step=32,
+        ),
+        IO.Int.Input(
+            "height",
+            default=768,
+            min=256,
+            max=2048,
+            step=32,
+        ),
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, 9)],
+                min=0,
+            ),
+            tooltip="Optional reference image(s) for image-to-image generation. Up to 8 images.",
+        ),
+    ]
+
+
+class Flux2ImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Flux2ImageNode",
+            display_name="Flux.2 Image",
+            category="partner/image/BFL",
+            description="Generate images via Flux.2 [pro] or Flux.2 [max] from a prompt and optional reference images.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation or edit",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Flux.2 [pro]", _flux2_model_inputs()),
+                        IO.DynamicCombo.Option("Flux.2 [max]", _flux2_model_inputs()),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "model.width", "model.height"],
+                    input_groups=["model.images"],
+                ),
+                expr="""
+                (
+                  $isMax := widgets.model = "flux.2 [max]";
+                  $MP := 1024 * 1024;
+                  $w := $lookup(widgets, "model.width");
+                  $h := $lookup(widgets, "model.height");
+                  $outMP := $max([1, $floor((($w * $h) + $MP - 1) / $MP)]);
+                  $outputCost := $isMax
+                    ? (0.07 + 0.03 * ($outMP - 1))
+                    : (0.03 + 0.015 * ($outMP - 1));
+                  $refMin := $isMax ? 0.03 : 0.015;
+                  $refMax := $isMax ? 0.24 : 0.12;
+                  $hasRefs := $lookup(inputGroups, "model.images") > 0;
+                  $hasRefs
+                    ? {
+                        "type": "range_usd",
+                        "min_usd": $outputCost + $refMin,
+                        "max_usd": $outputCost + $refMax,
+                        "format": { "approximate": true }
+                      }
+                    : {"type": "usd", "usd": $outputCost}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        model_choice = model["model"]
+        endpoint = _FLUX2_MODEL_ENDPOINTS[model_choice]
+        width = model["width"]
+        height = model["height"]
+        images_dict = model.get("images") or {}
+
+        image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
+        n_images = sum(get_number_of_images(t) for t in image_tensors)
+        if n_images > 8:
+            raise ValueError("The current maximum number of supported images is 8.")
+
+        flat_tensors: list[torch.Tensor] = []
+        for tensor in image_tensors:
+            if len(tensor.shape) == 4:
+                flat_tensors.extend(tensor[i] for i in range(tensor.shape[0]))
+            else:
+                flat_tensors.append(tensor)
+
+        reference_images: dict[str, str] = {}
+        for idx, tensor in enumerate(flat_tensors):
+            key_name = f"input_image_{idx + 1}" if idx else "input_image"
+            reference_images[key_name] = tensor_to_base64_string(tensor, total_pixels=2048 * 2048)
+
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=endpoint, method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=Flux2ProGenerateRequest(
+                prompt=prompt,
+                width=width,
+                height=height,
+                seed=seed,
+                **reference_images,
+            ),
+        )
+
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
+_FLUX3_ASPECT_RATIOS = ["auto", "21:9", "2:1", "16:9", "4:3", "1:1", "3:4", "9:16"]
+_FLUX3_MIN_DURATION = 5
+_FLUX3_MAX_DURATION = 20
+_FLUX3_DURATIONS = ["auto"] + [str(i) for i in range(_FLUX3_MIN_DURATION, _FLUX3_MAX_DURATION + 1)]
+_FLUX3_RESOLUTIONS = {"720p": "hd", "1080p": "fhd"}
+_FLUX3_MAX_IMAGES = 10
+_FLUX3_MIN_IMAGE_SIDE = 256
+_FLUX3_MAX_IMAGE_ASPECT = 64
+
+
+def _flux3_validate_image(image: torch.Tensor) -> None:
+    validate_image_dimensions(image, min_width=_FLUX3_MIN_IMAGE_SIDE, min_height=_FLUX3_MIN_IMAGE_SIDE)
+    height, width = image.shape[-3], image.shape[-2]
+    if max(width, height) > _FLUX3_MAX_IMAGE_ASPECT * min(width, height):
+        raise ValueError(
+            f"Image aspect ratio is too extreme ({width}x{height}); "
+            f"FLUX 3 accepts at most {_FLUX3_MAX_IMAGE_ASPECT}:1."
+        )
+
+
+def _flux3_collect_images(images: dict | None, field_name: str) -> list[torch.Tensor]:
+    """Flatten Autogrow slots (each possibly batched) into single images and validate them."""
+    flat: list[torch.Tensor] = []
+    for tensor in (images or {}).values():
+        if tensor is None:
+            continue
+        if tensor.ndim == 4:
+            flat.extend(tensor[i] for i in range(tensor.shape[0]))
+        else:
+            flat.append(tensor)
+    if len(flat) > _FLUX3_MAX_IMAGES:
+        raise ValueError(f"FLUX 3 supports at most {_FLUX3_MAX_IMAGES} {field_name}, got {len(flat)}.")
+    for tensor in flat:
+        _flux3_validate_image(tensor)
+    return flat
+
+
+def _flux3_parse_times(value: str, image_count: int, duration: int | str) -> list[float]:
+    """Parse one keyframe time in seconds per image: increasing, inside the clip."""
+    parts = [part.strip() for part in value.split(",") if part.strip()]
+    if len(parts) != image_count:
+        raise ValueError(
+            f"Give one time per keyframe image: got {len(parts)} time(s) for {image_count} image(s)."
+        )
+    try:
+        times = [float(part) for part in parts]
+    except ValueError as exc:
+        raise ValueError(f"Keyframe times must be numbers in seconds, comma-separated; got '{value}'.") from exc
+    if not all(math.isfinite(time) for time in times):
+        raise ValueError(f"Keyframe times must be finite numbers in seconds; got '{value}'.")
+    if any(later <= earlier for earlier, later in zip(times, times[1:])):
+        raise ValueError(f"Keyframe times must increase; got {times}.")
+    if times[0] < 0:
+        raise ValueError(f"Keyframe times cannot be negative; got {times[0]}.")
+    cap = _FLUX3_MAX_DURATION if duration == "auto" else int(duration)
+    if times[-1] > cap:
+        raise ValueError(f"Keyframe time {times[-1]}s is past the end of a {cap}s clip.")
+    return times
+
+
+class Flux3VideoNodeBase(IO.ComfyNode):
+    """Shared widgets, request plumbing and polling for the FLUX 3 generation modes."""
+
+    RATE_HD: float
+    RATE_FHD: float
+
+    @classmethod
+    def common_inputs(cls) -> list:
+        return [
+            IO.Combo.Input(
+                "aspect_ratio",
+                options=_FLUX3_ASPECT_RATIOS,
+                default="auto",
+                tooltip="Output aspect ratio. 'auto' picks one from the prompt and inputs.",
+            ),
+            IO.Combo.Input(
+                "duration",
+                options=_FLUX3_DURATIONS,
+                default="auto",
+                tooltip="Clip length in seconds. 'auto' fits the length to the content.",
+            ),
+            IO.Combo.Input(
+                "resolution",
+                options=list(_FLUX3_RESOLUTIONS),
+                default="720p",
+                tooltip="Output resolution.",
+            ),
+            IO.Boolean.Input(
+                "generate_audio",
+                default=True,
+                tooltip="Generate synchronized audio (ambient, speech, effects). "
+                "Off produces a video with no audio track.",
+            ),
+            IO.Int.Input(
+                "safety_tolerance",
+                default=2,
+                min=0,
+                max=4,
+                advanced=True,
+                tooltip="Moderation tolerance, 0 is the strictest. Requests that send images or "
+                "video are capped at 2 whatever you set here.",
+            ),
+            IO.Int.Input(
+                "seed",
+                default=42,
+                min=0,
+                max=0xFFFFFFFF,
+                control_after_generate=True,
+                tooltip="Seed to determine if node should re-run; FLUX 3 picks its own seed, so "
+                "actual results are nondeterministic regardless of this value.",
+            ),
+        ]
+
+    @classmethod
+    def common_fields(
+        cls,
+        prompt: str,
+        aspect_ratio: str,
+        duration: str,
+        resolution: str,
+        generate_audio: bool,
+        safety_tolerance: int,
+    ) -> dict:
+        validate_string(prompt, field_name="prompt", min_length=1)
+        return {
+            "prompt": prompt,
+            "aspect_ratio": aspect_ratio,
+            "duration": duration if duration == "auto" else int(duration),
+            "resolution": _FLUX3_RESOLUTIONS[resolution],
+            "generate_audio": generate_audio,
+            "safety_tolerance": safety_tolerance,
+        }
+
+    @classmethod
+    def price_badge(cls) -> IO.PriceBadge:
+        return IO.PriceBadge(
+            depends_on=IO.PriceBadgeDepends(widgets=["resolution", "duration"]),
+            expr=f"""
+                (
+                  $rate := widgets.resolution = "1080p" ? {cls.RATE_FHD} : {cls.RATE_HD};
+                  $type(widgets.duration) = "string" and widgets.duration != "auto"
+                    ? {{"type":"usd","usd": $rate * $number(widgets.duration)}}
+                    : {{"type":"usd","usd": $rate, "format": {{"suffix": "/second"}}}}
+                )
+                """,
+        )
+
+
+_FLUX3_VIDEO_ENDPOINT = ApiEndpoint(path="/proxy/bfl/v1/flux-3-video", method="POST")
+_FLUX_VIDEO_UPSCALE_ENDPOINT = ApiEndpoint(path="/proxy/bfl/v1/flux-tools/video-upscale-v1", method="POST")
+_BFL_POLL_PROXY_PATH = "/proxy/bfl/v1/get_result"
+
+
+async def _bfl_video_execute(
+    cls: type[IO.ComfyNode], endpoint: ApiEndpoint, request: BaseModel, poll_via_proxy: bool = False
+) -> IO.NodeOutput:
+    initial_response = await sync_op(cls, endpoint, response_model=BFLFluxProGenerateResponse, data=request)
+    poll_endpoint = (
+        ApiEndpoint(path=_BFL_POLL_PROXY_PATH, query_params={"polling_url": initial_response.polling_url})
+        if poll_via_proxy
+        else ApiEndpoint(initial_response.polling_url)
+    )
+    response = await poll_op(
+        cls,
+        poll_endpoint,
+        response_model=BFLFluxStatusResponse,
+        status_extractor=lambda r: r.status,
+        progress_extractor=lambda r: r.progress,
+        completed_statuses=[BFLStatus.ready],
+        failed_statuses=[
+            BFLStatus.request_moderated,
+            BFLStatus.content_moderated,
+            BFLStatus.error,
+            BFLStatus.task_not_found,
+        ],
+        queued_statuses=[BFLStatus.pending],
+        poll_interval=8.0,
+        # a failed task answers the poll with a retryable-class HTTP 5xx (500 and 503 observed);
+        # a small retry budget surfaces real failures quickly
+        max_retries_per_poll=3,
+    )
+    return IO.NodeOutput(await download_url_to_video_output(response.result["sample"]))
+
+
+class Flux3TextToVideoNode(Flux3VideoNodeBase):
+    RATE_HD = 0.2431
+    RATE_FHD = 0.4147
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Flux3TextToVideoNode",
+            display_name="Flux 3 Text to Video",
+            category="partner/video/BFL",
+            description="Generates a video with synchronized audio from a text prompt via FLUX 3.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="What you want, in plain language; the prompt is interpreted and expanded "
+                    "before generation. Describe ambient sound, music and speech separately for layered audio.",
+                ),
+                *cls.common_inputs(),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=cls.price_badge(),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        aspect_ratio: str,
+        duration: str,
+        resolution: str,
+        generate_audio: bool,
+        safety_tolerance: int,
+        seed: int,
+    ) -> IO.NodeOutput:
+        request = Flux3TextToVideoRequest(
+            **cls.common_fields(prompt, aspect_ratio, duration, resolution, generate_audio, safety_tolerance)
+        )
+        return await _bfl_video_execute(cls, _FLUX3_VIDEO_ENDPOINT, request)
+
+
+class Flux3ImageToVideoNode(Flux3VideoNodeBase):
+    RATE_HD = 0.2431
+    RATE_FHD = 0.4147
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Flux3ImageToVideoNode",
+            display_name="Flux 3 Image to Video",
+            category="partner/video/BFL",
+            description="Animates 1 to 10 images with FLUX 3. Each image becomes a frame of the clip: "
+            "one image opens it, two morph from the first to the second, and more are spread across it "
+            "or pinned to times you choose.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="How the scene should move and sound; the prompt is interpreted and "
+                    "expanded before generation.",
+                ),
+                IO.Autogrow.Input(
+                    "keyframes",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Image.Input("image", tooltip="Keyframe image."),
+                        prefix="image_",
+                        min=1,
+                        max=_FLUX3_MAX_IMAGES,
+                    ),
+                    tooltip="1 to 10 images, in playback order. Minimum 256x256 pixels each.",
+                ),
+                IO.DynamicCombo.Input(
+                    "placement",
+                    options=[
+                        IO.DynamicCombo.Option("spread across the clip", []),
+                        IO.DynamicCombo.Option(
+                            "at times",
+                            [
+                                IO.String.Input(
+                                    "times",
+                                    default="0",
+                                    tooltip="One time in seconds per image, comma-separated and "
+                                    "increasing, e.g. '0, 2.5, 5'.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="'spread across the clip' lets FLUX 3 place the images (one opens the clip, "
+                    "two become its start and end); 'at times' pins every image to a second you choose.",
+                ),
+                *cls.common_inputs(),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=cls.price_badge(),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        keyframes: IO.Autogrow.Type,
+        placement: dict,
+        aspect_ratio: str,
+        duration: str,
+        resolution: str,
+        generate_audio: bool,
+        safety_tolerance: int,
+        seed: int,
+    ) -> IO.NodeOutput:
+        fields = cls.common_fields(prompt, aspect_ratio, duration, resolution, generate_audio, safety_tolerance)
+        images = _flux3_collect_images(keyframes, "keyframes")
+        if not images:
+            raise ValueError("Connect at least one keyframe image.")
+        times = None
+        if placement["placement"] == "at times":
+            times = _flux3_parse_times(placement["times"], len(images), fields["duration"])
+        elif len(images) >= 3 and fields["duration"] == "auto":
+            # spread images land evenly between the first and last, which needs a known length
+            raise ValueError(
+                f"Spreading {len(images)} images across the clip needs an explicit duration: "
+                "set duration, or place the images yourself with 'at times'."
+            )
+        urls = await upload_images_to_comfyapi(
+            cls, images, max_images=_FLUX3_MAX_IMAGES, wait_label="Uploading keyframes"
+        )
+        request = Flux3ImageToVideoRequest(
+            keyframes=list(zip(times, urls)) if times is not None else urls,
+            **fields,
+        )
+        return await _bfl_video_execute(cls, _FLUX3_VIDEO_ENDPOINT, request)
+
+
+class Flux3VideoContinuationNode(Flux3VideoNodeBase):
+    RATE_HD = 0.5863
+    RATE_FHD = 0.7579
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Flux3VideoContinuationNode",
+            display_name="Flux 3 Video Continuation",
+            category="partner/video/BFL",
+            description="Continues a video with FLUX 3: the new clip carries on from the final frames "
+            "of the one you provide.",
+            inputs=[
+                IO.Video.Input("video", tooltip="The clip to continue."),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="What the continuation should show; the prompt is interpreted and expanded "
+                    "before generation.",
+                ),
+                *cls.common_inputs(),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=cls.price_badge(),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        prompt: str,
+        aspect_ratio: str,
+        duration: str,
+        resolution: str,
+        generate_audio: bool,
+        safety_tolerance: int,
+        seed: int,
+    ) -> IO.NodeOutput:
+        fields = cls.common_fields(prompt, aspect_ratio, duration, resolution, generate_audio, safety_tolerance)
+        url = await upload_video_to_comfyapi(cls, video, wait_label="Uploading source video")
+        request = Flux3VideoContinuationRequest(start_video=url, **fields)
+        return await _bfl_video_execute(cls, _FLUX3_VIDEO_ENDPOINT, request)
+
+
+_FLUX_VIDEO_UPSCALE_MODES = {"creative": 1, "precise": 0}
+_FLUX_VIDEO_UPSCALE_MAX_INPUT_PIXELS = 3840 * 2160
+_FLUX_VIDEO_UPSCALE_MAX_ASPECT_RATIO = 4.0
+
+
+class FluxVideoUpscaleNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FluxVideoUpscaleNode",
+            display_name="Flux Video Upscale",
+            category="partner/video/BFL",
+            description="Upscales a video 1.5 to 3 times with FLUX super-resolution, either preserving "
+            "the source precisely or creatively enhancing its detail.",
+            inputs=[
+                IO.Video.Input(
+                    "video",
+                    tooltip="Source clip of 1 to 20 seconds with an aspect ratio between 1:4 and 4:1. "
+                    "The output is rendered at 24 fps and capped at about 14.4 megapixels per frame.",
+                ),
+                IO.Float.Input(
+                    "upscale_factor",
+                    default=2.0,
+                    min=1.5,
+                    max=3.0,
+                    step=0.1,
+                    tooltip="Output size relative to the source. Very large sources are upscaled by "
+                    "less than the requested factor because of the per-frame cap.",
+                ),
+                IO.Combo.Input(
+                    "mode",
+                    options=list(_FLUX_VIDEO_UPSCALE_MODES),
+                    default="creative",
+                    tooltip="'creative' restores and invents fine detail, best for generated footage, "
+                    "textures and scenery. 'precise' sharpens the source without changing it, "
+                    "for faces, products and real footage.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Optional description of the clip that steers the enhanced detail. "
+                    "Leave empty for a neutral upscale.",
+                ),
+                IO.Boolean.Input(
+                    "auto_downscale",
+                    default=True,
+                    tooltip="Automatically downscale sources larger than 3840x2160 pixels in area to fit "
+                    "the input limit. Aspect ratio is preserved; smaller videos are untouched.",
+                ),
+                IO.Int.Input(
+                    "safety_tolerance",
+                    default=2,
+                    min=0,
+                    max=4,
+                    advanced=True,
+                    tooltip="Moderation tolerance, 0 is the strictest.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=0xFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; FLUX picks its own seed, so "
+                    "actual results are nondeterministic regardless of this value.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
+                expr="""
+                    (
+                      $precise := widgets.mode = "precise";
+                      {"type":"range_usd",
+                       "min_usd": $precise ? 0.212 : 0.297,
+                       "max_usd": $precise ? 0.848 : 1.188,
+                       "format": {"approximate": true, "suffix": "/s", "note": "(1080p-4K output)"}}
+                    )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        upscale_factor: float,
+        mode: str,
+        prompt: str,
+        auto_downscale: bool,
+        safety_tolerance: int,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, min_duration=1.0, max_duration=20.0)
+        validate_video_dimensions(video, min_width=64, min_height=64)
+        width, height = video.get_dimensions()
+        if max(width, height) > _FLUX_VIDEO_UPSCALE_MAX_ASPECT_RATIO * min(width, height):
+            raise ValueError(f"Video aspect ratio must be between 1:4 and 4:1, got {width}x{height}.")
+        if auto_downscale:
+            video = downscale_video_to_max_pixels(video, _FLUX_VIDEO_UPSCALE_MAX_INPUT_PIXELS)
+        elif width * height > _FLUX_VIDEO_UPSCALE_MAX_INPUT_PIXELS:
+            raise ValueError(
+                f"Video must be at most 3840x2160 pixels in area, got {width}x{height}. "
+                "Enable auto_downscale or use a smaller video."
+            )
+        url = await upload_video_to_comfyapi(cls, video, wait_label="Uploading source video")
+        request = BFLFluxVideoUpscaleRequest(
+            input_video=url,
+            upscale_factor=round(upscale_factor, 1),
+            creativity=_FLUX_VIDEO_UPSCALE_MODES[mode],
+            prompt=prompt.strip() or None,
+            safety_tolerance=safety_tolerance,
+        )
+        return await _bfl_video_execute(cls, _FLUX_VIDEO_UPSCALE_ENDPOINT, request, poll_via_proxy=True)
+
+
+class BFLExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            FluxProUltraImageNode,
+            FluxKontextProImageNode,
+            FluxKontextMaxImageNode,
+            FluxProExpandNode,
+            FluxProFillNode,
+            FluxEraseNode,
+            FluxVTONode,
+            Flux2ProImageNode,
+            Flux2MaxImageNode,
+            Flux2ImageNode,
+            Flux3TextToVideoNode,
+            Flux3ImageToVideoNode,
+            Flux3VideoContinuationNode,
+            FluxVideoUpscaleNode,
+        ]
+
+
+async def comfy_entrypoint() -> BFLExtension:
+    return BFLExtension()
diff --git a/comfy_api_nodes/nodes_bria.py b/comfy_api_nodes/nodes_bria.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b524690da37f40d39802c27b842270ab893c9c4
--- /dev/null
+++ b/comfy_api_nodes/nodes_bria.py
@@ -0,0 +1,1107 @@
+import av
+import torch
+from av.codec import CodecContext
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.bria import (
+    BriaEditImageRequest,
+    BriaEraseRequest,
+    BriaExpandRequest,
+    BriaExpandResponse,
+    BriaGenFillRequest,
+    BriaImageEditResponse,
+    BriaImageResultResponse,
+    BriaIncreaseResolutionRequest,
+    BriaRemoveBackgroundRequest,
+    BriaRemoveBackgroundResponse,
+    BriaRemoveVideoBackgroundRequest,
+    BriaRemoveVideoBackgroundResponse,
+    BriaStatusResponse,
+    BriaVideoGreenScreenRequest,
+    BriaVideoReplaceBackgroundRequest,
+    InputModerationSettings,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    convert_mask_to_image,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    downscale_image_tensor_by_max_side,
+    get_image_dimensions,
+    poll_op,
+    sync_op,
+    upload_image_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+    validate_video_duration,
+)
+
+BRIA_MAX_OUTPUT_SIDE = 8192
+BRIA_MIN_RATIO = 0.5
+BRIA_MAX_RATIO = 3.0
+BRIA_MIN_SHORT_SIDE = 224
+
+
+def _upscaled_output_side(height: int, width: int, multiplier: int) -> int:
+    prescale = max(1.0, BRIA_MIN_SHORT_SIDE / min(height, width))
+    return round(max(height, width) * prescale * multiplier)
+
+
+def _smallest_output_side(height: int, width: int, multiplier: int) -> int:
+    return round(max(height, width) / min(height, width) * BRIA_MIN_SHORT_SIDE * multiplier)
+
+
+class BriaImageEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaImageEditNode",
+            display_name="Bria FIBO Image Edit",
+            category="partner/image/Bria",
+            description="Edit images using Bria latest model",
+            inputs=[
+                IO.Combo.Input("model", options=["FIBO"]),
+                IO.Image.Input("image"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Instruction to edit image",
+                ),
+                IO.String.Input("negative_prompt", multiline=True, default=""),
+                IO.String.Input(
+                    "structured_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="A string containing the structured edit prompt in JSON format. "
+                    "Use this instead of usual prompt for precise, programmatic control.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=1,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.Float.Input(
+                    "guidance_scale",
+                    default=3,
+                    min=3,
+                    max=5,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Higher value makes the image follow the prompt more closely.",
+                ),
+                IO.Int.Input(
+                    "steps",
+                    default=50,
+                    min=20,
+                    max=50,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                ),
+                IO.DynamicCombo.Input(
+                    "moderation",
+                    options=[
+                        IO.DynamicCombo.Option("false", []),
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Boolean.Input("prompt_content_moderation", default=False),
+                                IO.Boolean.Input("visual_input_moderation", default=False),
+                                IO.Boolean.Input("visual_output_moderation", default=True),
+                            ],
+                        ),
+                    ],
+                    tooltip="Moderation settings",
+                ),
+                IO.Mask.Input(
+                    "mask",
+                    tooltip="If omitted, the edit applies to the entire image.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(display_name="structured_prompt"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.04}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        prompt: str,
+        negative_prompt: str,
+        structured_prompt: str,
+        seed: int,
+        guidance_scale: float,
+        steps: int,
+        moderation: InputModerationSettings,
+        mask: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        if not prompt and not structured_prompt:
+            raise ValueError("One of prompt or structured_prompt is required to be non-empty.")
+        mask_url = None
+        if mask is not None:
+            mask_url = await upload_image_to_comfyapi(cls, convert_mask_to_image(mask), wait_label="Uploading mask")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="proxy/bria/v2/image/edit", method="POST"),
+            data=BriaEditImageRequest(
+                instruction=prompt if prompt else None,
+                structured_instruction=structured_prompt if structured_prompt else None,
+                images=[await upload_image_to_comfyapi(cls, image, wait_label="Uploading image")],
+                mask=mask_url,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                guidance_scale=guidance_scale,
+                seed=seed,
+                model_version=model,
+                steps_num=steps,
+                prompt_content_moderation=moderation.get("prompt_content_moderation", False),
+                visual_input_content_moderation=moderation.get("visual_input_moderation", False),
+                visual_output_content_moderation=moderation.get("visual_output_moderation", False),
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaImageEditResponse,
+        )
+        return IO.NodeOutput(
+            await download_url_to_image_tensor(response.result.image_url),
+            response.result.structured_prompt,
+        )
+
+
+class BriaRemoveImageBackground(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaRemoveImageBackground",
+            display_name="Bria Remove Image Background",
+            category="partner/image/Bria",
+            description="Remove the background from an image using Bria RMBG 2.0.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.DynamicCombo.Input(
+                    "moderation",
+                    options=[
+                        IO.DynamicCombo.Option("false", []),
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Boolean.Input("visual_input_moderation", default=False),
+                                IO.Boolean.Input("visual_output_moderation", default=True),
+                            ],
+                        ),
+                    ],
+                    tooltip="Moderation settings",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.018}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        moderation: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/image/edit/remove_background", method="POST"),
+            data=BriaRemoveBackgroundRequest(
+                image=await upload_image_to_comfyapi(cls, image, wait_label="Uploading image"),
+                sync=False,
+                visual_input_content_moderation=moderation.get("visual_input_moderation", False),
+                visual_output_content_moderation=moderation.get("visual_output_moderation", False),
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveBackgroundResponse,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result.image_url))
+
+
+def _mask_to_binary_image(mask: Input.Image, action: str) -> torch.Tensor:
+    binary = (mask > 0.5).float()
+    if not binary.any():
+        raise ValueError(
+            f"The mask is empty, so there is nothing to {action}. Masks are binarized at 50%: "
+            f"areas painted at less than half opacity are ignored."
+        )
+    return convert_mask_to_image(binary)
+
+
+def _validate_mask_aspect_ratio(image: Input.Image, mask: Input.Image) -> None:
+    ih, iw = image.shape[1], image.shape[2]
+    mh, mw = mask.shape[-2], mask.shape[-1]
+    if abs(iw * mh - ih * mw) > 0.01 * ih * mw:
+        raise ValueError(f"Mask must have the same aspect ratio as the image: image is {iw}x{ih}, mask is {mw}x{mh}.")
+
+
+class BriaGenFill(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaGenFill",
+            display_name="Bria Generative Fill",
+            category="partner/image/Bria",
+            description="Generate objects or scenery inside a masked region of an image using Bria.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Mask.Input(
+                    "mask",
+                    tooltip="White areas are filled with generated content, black areas are preserved. "
+                    "The mask is binarized before sending, so partially painted areas count as white. "
+                    "Must have the same aspect ratio as the image.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Description of what to generate inside the masked region.",
+                ),
+                IO.String.Input("negative_prompt", multiline=True, default=""),
+                IO.Boolean.Input(
+                    "refine_prompt",
+                    default=True,
+                    tooltip="Automatically adjust the prompt for better results; "
+                    "disable to use the prompt exactly as written.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=1,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.DynamicCombo.Input(
+                    "moderation",
+                    options=[
+                        IO.DynamicCombo.Option("false", []),
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Boolean.Input("prompt_content_moderation", default=False),
+                                IO.Boolean.Input("visual_input_moderation", default=False),
+                                IO.Boolean.Input("visual_output_moderation", default=False),
+                            ],
+                        ),
+                    ],
+                    tooltip="Moderation settings",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.0429}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        mask: Input.Image,
+        prompt: str,
+        negative_prompt: str,
+        refine_prompt: bool,
+        seed: int,
+        moderation: InputModerationSettings,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1)
+        _validate_mask_aspect_ratio(image, mask)
+        mask_image = _mask_to_binary_image(mask, "fill")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/image/edit/gen_fill", method="POST"),
+            data=BriaGenFillRequest(
+                image=await upload_image_to_comfyapi(cls, image, total_pixels=None, wait_label="Uploading image"),
+                mask=await upload_image_to_comfyapi(
+                    cls, mask_image, total_pixels=None, wait_label="Uploading mask"
+                ),
+                prompt=prompt,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                refine_prompt=refine_prompt,
+                seed=seed,
+                prompt_content_moderation=moderation.get("prompt_content_moderation", False),
+                visual_input_content_moderation=moderation.get("visual_input_moderation", False),
+                visual_output_content_moderation=moderation.get("visual_output_moderation", False),
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaImageResultResponse,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result.image_url))
+
+
+class BriaEraser(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaEraser",
+            display_name="Bria Eraser",
+            category="partner/image/Bria",
+            description="Remove objects or areas outlined by a mask from an image using Bria.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Mask.Input(
+                    "mask",
+                    tooltip="White areas are erased, black areas are preserved. "
+                    "The mask is binarized before sending, so partially painted areas count as white. "
+                    "Must have the same aspect ratio as the image.",
+                ),
+                IO.Combo.Input(
+                    "mask_type",
+                    options=["manual", "automatic"],
+                    tooltip="manual for hand-drawn or brush masks, "
+                    "automatic for masks produced by segmentation models such as SAM.",
+                ),
+                IO.DynamicCombo.Input(
+                    "moderation",
+                    options=[
+                        IO.DynamicCombo.Option("false", []),
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Boolean.Input("visual_input_moderation", default=False),
+                                IO.Boolean.Input("visual_output_moderation", default=False),
+                            ],
+                        ),
+                    ],
+                    tooltip="Moderation settings",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.0286}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        mask: Input.Image,
+        mask_type: str,
+        moderation: dict,
+    ) -> IO.NodeOutput:
+        _validate_mask_aspect_ratio(image, mask)
+        mask_image = _mask_to_binary_image(mask, "erase")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/image/edit/erase", method="POST"),
+            data=BriaEraseRequest(
+                image=await upload_image_to_comfyapi(cls, image, total_pixels=None, wait_label="Uploading image"),
+                mask=await upload_image_to_comfyapi(
+                    cls, mask_image, total_pixels=None, wait_label="Uploading mask"
+                ),
+                mask_type=mask_type,
+                visual_input_content_moderation=moderation.get("visual_input_moderation", False),
+                visual_output_content_moderation=moderation.get("visual_output_moderation", False),
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaImageResultResponse,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result.image_url))
+
+
+class BriaExpandImage(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaExpandImage",
+            display_name="Bria Expand Image",
+            category="partner/image/Bria",
+            description="Expand an image beyond its borders with generated content using Bria.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.DynamicCombo.Input(
+                    "expand_mode",
+                    options=[
+                        *[IO.DynamicCombo.Option(ratio, []) for ratio in
+                          ["1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9"]],
+                        IO.DynamicCombo.Option(
+                            "custom_ratio",
+                            [
+                                IO.Int.Input(
+                                    "ratio_width",
+                                    default=21,
+                                    min=1,
+                                    max=100,
+                                    tooltip="Width side of the target ratio: 21 and 9 give 21:9.",
+                                ),
+                                IO.Int.Input(
+                                    "ratio_height",
+                                    default=9,
+                                    min=1,
+                                    max=100,
+                                    tooltip="Height side of the target ratio: 21 and 9 give 21:9. "
+                                    f"Bria only accepts width/height between {BRIA_MIN_RATIO} and "
+                                    f"{BRIA_MAX_RATIO}, so anything taller than 1:2 needs the manual mode.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "manual",
+                            [
+                                IO.Int.Input("canvas_width", default=1000, min=64, max=5000),
+                                IO.Int.Input("canvas_height", default=1000, min=64, max=5000),
+                                IO.Int.Input(
+                                    "image_width",
+                                    default=500,
+                                    min=1,
+                                    max=5000,
+                                    tooltip="Width of the original image inside the canvas.",
+                                ),
+                                IO.Int.Input(
+                                    "image_height",
+                                    default=500,
+                                    min=1,
+                                    max=5000,
+                                    tooltip="Height of the original image inside the canvas.",
+                                ),
+                                IO.Int.Input(
+                                    "image_x",
+                                    default=250,
+                                    min=-5000,
+                                    max=5000,
+                                    tooltip="X position of the image's top-left corner inside the canvas; "
+                                    "may fall outside the canvas, cropping the image.",
+                                ),
+                                IO.Int.Input(
+                                    "image_y",
+                                    default=250,
+                                    min=-5000,
+                                    max=5000,
+                                    tooltip="Y position of the image's top-left corner inside the canvas; "
+                                    "may fall outside the canvas, cropping the image.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Target shape of the expanded image: a preset aspect ratio, a custom ratio, "
+                    "or manual placement of the original image on a canvas. "
+                    "Manual is the only mode that can reach a canvas taller than 1:2.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Optional description of the expanded scene; "
+                    "when empty, Bria generates one from the image.",
+                ),
+                IO.String.Input("negative_prompt", multiline=True, default=""),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=1,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.DynamicCombo.Input(
+                    "moderation",
+                    options=[
+                        IO.DynamicCombo.Option("false", []),
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Boolean.Input("prompt_content_moderation", default=False),
+                                IO.Boolean.Input("visual_input_moderation", default=False),
+                                IO.Boolean.Input("visual_output_moderation", default=False),
+                            ],
+                        ),
+                    ],
+                    tooltip="Moderation settings",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(display_name="prompt", tooltip="The prompt used for the expansion; "
+                                 "auto-generated by Bria when the prompt input is empty."),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.0286}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        expand_mode: dict,
+        prompt: str,
+        negative_prompt: str,
+        seed: int,
+        moderation: InputModerationSettings,
+    ) -> IO.NodeOutput:
+        mode = expand_mode["expand_mode"]
+        aspect_ratio = canvas_size = original_image_size = original_image_location = None
+        if mode == "manual":
+            canvas_size = [expand_mode["canvas_width"], expand_mode["canvas_height"]]
+            original_image_size = [expand_mode["image_width"], expand_mode["image_height"]]
+            original_image_location = [expand_mode["image_x"], expand_mode["image_y"]]
+        elif mode == "custom_ratio":
+            ratio_width, ratio_height = expand_mode["ratio_width"], expand_mode["ratio_height"]
+            aspect_ratio = ratio_width / ratio_height
+            if not BRIA_MIN_RATIO <= aspect_ratio <= BRIA_MAX_RATIO:
+                raise ValueError(
+                    f"Bria accepts a width-to-height ratio between {BRIA_MIN_RATIO} and {BRIA_MAX_RATIO}: "
+                    f"{ratio_width}:{ratio_height} is {aspect_ratio:.4f}. "
+                    f"Use the manual expand mode to reach a canvas of any shape."
+                )
+        else:
+            aspect_ratio = mode
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/image/edit/expand", method="POST"),
+            data=BriaExpandRequest(
+                image=await upload_image_to_comfyapi(cls, image, total_pixels=None, wait_label="Uploading image"),
+                aspect_ratio=aspect_ratio,
+                canvas_size=canvas_size,
+                original_image_size=original_image_size,
+                original_image_location=original_image_location,
+                prompt=prompt if prompt else None,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                seed=seed,
+                prompt_content_moderation=moderation.get("prompt_content_moderation", False),
+                visual_input_content_moderation=moderation.get("visual_input_moderation", False),
+                visual_output_content_moderation=moderation.get("visual_output_moderation", False),
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaExpandResponse,
+        )
+        return IO.NodeOutput(
+            await download_url_to_image_tensor(response.result.image_url),
+            response.result.prompt or "",
+        )
+
+
+class BriaIncreaseResolution(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaIncreaseResolution",
+            display_name="Bria Increase Resolution",
+            category="partner/image/Bria",
+            description="Upscale an image by 2x or 4x using Bria, preserving the original content.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Combo.Input(
+                    "desired_increase",
+                    options=["2", "4"],
+                    tooltip="Resolution multiplier. The output must fit within 8192 pixels on each side.",
+                ),
+                IO.Boolean.Input(
+                    "auto_downscale",
+                    default=False,
+                    tooltip="Automatically lower the multiplier, and downscale the input image if that is "
+                    "still not enough, when the output would exceed the limit.",
+                ),
+                IO.DynamicCombo.Input(
+                    "moderation",
+                    options=[
+                        IO.DynamicCombo.Option("false", []),
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Boolean.Input("visual_input_moderation", default=False),
+                                IO.Boolean.Input("visual_output_moderation", default=False),
+                            ],
+                        ),
+                    ],
+                    tooltip="Moderation settings",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.0286}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        desired_increase: str,
+        auto_downscale: bool,
+        moderation: dict,
+    ) -> IO.NodeOutput:
+        multiplier = int(desired_increase)
+        height, width = get_image_dimensions(image)
+        if _upscaled_output_side(height, width, multiplier) > BRIA_MAX_OUTPUT_SIDE:
+            candidates = [c for c in (4, 2) if c <= multiplier]
+            if not auto_downscale:
+                predicted = _upscaled_output_side(height, width, multiplier)
+                raise ValueError(
+                    f"Bria can upscale up to a maximum output dimension of {BRIA_MAX_OUTPUT_SIDE} pixels: "
+                    f"input is {width}x{height}, x{multiplier} would be {predicted} pixels on the long side. "
+                    f"Enable auto_downscale, or use a smaller input image or a lower multiplier."
+                )
+            fitted = next(
+                (c for c in candidates if _upscaled_output_side(height, width, c) <= BRIA_MAX_OUTPUT_SIDE), None
+            )
+            if fitted is not None:
+                multiplier = fitted
+            else:
+                shrinkable = next((c for c in sorted(candidates) if _smallest_output_side(height, width, c)
+                                   <= BRIA_MAX_OUTPUT_SIDE), None)
+                if shrinkable is None:
+                    raise ValueError(
+                        f"This image cannot be upscaled by Bria at any multiplier: it is {width}x{height}, and "
+                        f"Bria first enlarges the short side to {BRIA_MIN_SHORT_SIDE} pixels, which pushes the "
+                        f"long side past the {BRIA_MAX_OUTPUT_SIDE} pixel limit. Crop it to a squarer shape first."
+                    )
+                multiplier = shrinkable
+                image = downscale_image_tensor_by_max_side(image, max_side=BRIA_MAX_OUTPUT_SIDE // multiplier)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/image/edit/increase_resolution", method="POST"),
+            data=BriaIncreaseResolutionRequest(
+                image=await upload_image_to_comfyapi(cls, image, total_pixels=None, wait_label="Uploading image"),
+                desired_increase=multiplier,
+                visual_input_content_moderation=moderation.get("visual_input_moderation", False),
+                visual_output_content_moderation=moderation.get("visual_output_moderation", False),
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaImageResultResponse,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result.image_url))
+
+
+class BriaRemoveVideoBackground(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaRemoveVideoBackground",
+            display_name="Bria Remove Video Background",
+            category="partner/video/Bria",
+            description="Remove the background from a video using Bria. ",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Combo.Input(
+                    "background_color",
+                    options=[
+                        "Black",
+                        "White",
+                        "Gray",
+                        "Red",
+                        "Green",
+                        "Blue",
+                        "Yellow",
+                        "Cyan",
+                        "Magenta",
+                        "Orange",
+                    ],
+                    tooltip="Background color for the output video.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.05,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        background_color: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, max_duration=60.0)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/remove_background", method="POST"),
+            data=BriaRemoveVideoBackgroundRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                background_color=background_color,
+                output_container_and_codec="mp4_h264",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
+
+
+class BriaVideoGreenScreen(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaVideoGreenScreen",
+            display_name="Bria Video Green Screen",
+            category="partner/video/Bria",
+            description="Replace a video's background with a solid chroma-key screen using Bria.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Combo.Input(
+                    "green_shade",
+                    options=["broadcast_green", "chroma_green", "blue_screen"],
+                    tooltip="Solid chroma-key shade applied behind the foreground: "
+                    "broadcast_green (#00B140), chroma_green (#00FF00), or blue_screen (#0000FF).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.05,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        green_shade: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, max_duration=60.0)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/green_screen", method="POST"),
+            data=BriaVideoGreenScreenRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                green_shade=green_shade,
+                output_container_and_codec="mp4_h264",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
+
+
+class BriaVideoReplaceBackground(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaVideoReplaceBackground",
+            display_name="Bria Video Replace Background",
+            category="partner/video/Bria",
+            description="Replace a video's background with a supplied image or video using Bria. "
+            "The output keeps the foreground's resolution and frame rate; a background with a "
+            "different aspect ratio is stretched to fit, so match it for undistorted results.",
+            inputs=[
+                IO.Video.Input("video", tooltip="Foreground video whose background is replaced."),
+                IO.Image.Input(
+                    "background_image",
+                    optional=True,
+                    tooltip="Background image to composite behind the foreground. "
+                    "Provide either a background image or a background video, not both.",
+                ),
+                IO.Video.Input(
+                    "background_video",
+                    optional=True,
+                    tooltip="Background video to composite behind the foreground. "
+                    "Provide either a background image or a background video, not both.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.05,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        seed: int,
+        background_image: Input.Image | None = None,
+        background_video: Input.Video | None = None,
+    ) -> IO.NodeOutput:
+        if (background_image is None) == (background_video is None):
+            raise ValueError("Provide either a background image or a background video, not both.")
+        validate_video_duration(video, max_duration=60.0)
+        if background_video is not None:
+            validate_video_duration(background_video, max_duration=60.0)
+            background_url = await upload_video_to_comfyapi(cls, background_video, wait_label="Uploading background")
+        else:
+            # Bria's replace_background 500s on RGBA, so drop the alpha channel before upload.
+            background_url = await upload_image_to_comfyapi(
+                cls, background_image[:, :, :, :3], wait_label="Uploading background"
+            )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/replace_background", method="POST"),
+            data=BriaVideoReplaceBackgroundRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                background_url=background_url,
+                output_container_and_codec="mp4_h264",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
+
+
+def _video_to_images_and_mask(video: Input.Video) -> tuple[Input.Image, Input.Mask]:
+    """Decode a transparent webm (VP9 + alpha) into image frames and an alpha mask.
+
+    VP9 keeps its alpha in a side layer that PyAV's default vp9 decoder drops, so the frames
+    are decoded with libvpx-vp9. Returns RGB images [B,H,W,3] in 0..1 and a mask [B,H,W]
+    following the Load Image convention (1 = transparent) for compositing or Save WEBM.
+    """
+    rgb_frames: list[torch.Tensor] = []
+    alpha_frames: list[torch.Tensor] = []
+    with av.open(video.get_stream_source(), mode="r") as container:
+        stream = container.streams.video[0]
+        decoder = CodecContext.create("libvpx-vp9", "r") if stream.codec_context.name == "vp9" else None
+        for packet in container.demux(stream):
+            for frame in (decoder.decode(packet) if decoder is not None else packet.decode()):
+                rgba = torch.from_numpy(frame.to_ndarray(format="rgba")).float() / 255.0
+                rgb_frames.append(rgba[..., :3])
+                alpha_frames.append(rgba[..., 3])
+    images = torch.stack(rgb_frames) if rgb_frames else torch.zeros(0, 0, 0, 3)
+    mask = (1.0 - torch.stack(alpha_frames)) if alpha_frames else torch.zeros((images.shape[0], 64, 64))
+    return images, mask
+
+
+class BriaTransparentVideoBackground(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaTransparentVideoBackground",
+            display_name="Bria Remove Video Background (Transparent)",
+            category="partner/video/Bria",
+            description="Remove the background from a video using Bria and return the cut-out frames "
+            "plus an alpha mask. Connect both to a compositing node, or feed them to Save WEBM to "
+            "write a transparent video.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(display_name="images"),
+                IO.Mask.Output(display_name="mask"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.05,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, max_duration=60.0)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/remove_background", method="POST"),
+            data=BriaRemoveVideoBackgroundRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                background_color="Transparent",
+                output_container_and_codec="webm_vp9",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        video_out = await download_url_to_video_output(response.result.video_url)
+        images, mask = _video_to_images_and_mask(video_out)
+        return IO.NodeOutput(images, mask)
+
+
+class BriaExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            BriaImageEditNode,
+            BriaRemoveImageBackground,
+            BriaGenFill,
+            BriaEraser,
+            BriaExpandImage,
+            BriaIncreaseResolution,
+            BriaRemoveVideoBackground,
+            BriaVideoGreenScreen,
+            BriaVideoReplaceBackground,
+            BriaTransparentVideoBackground,
+        ]
+
+
+async def comfy_entrypoint() -> BriaExtension:
+    return BriaExtension()
diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py
new file mode 100644
index 0000000000000000000000000000000000000000..aab7332eeb9154730cc378b921de3af3659ca73b
--- /dev/null
+++ b/comfy_api_nodes/nodes_bytedance.py
@@ -0,0 +1,3908 @@
+import asyncio
+import base64
+import hashlib
+import logging
+import math
+import re
+from io import BytesIO
+
+import torch
+from typing_extensions import override
+
+from comfy.utils import common_upscale
+from comfy_api.latest import IO, ComfyExtension, Input, Types
+from comfy_api_nodes.apis.bytedance import (
+    RECOMMENDED_PRESETS,
+    RECOMMENDED_PRESETS_SEEDREAM_4,
+    RECOMMENDED_PRESETS_SEEDREAM_4_0,
+    RECOMMENDED_PRESETS_SEEDREAM_4_5,
+    RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
+    RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
+    SEEDANCE2_REF_VIDEO_PIXEL_LIMITS,
+    VIDEO_TASKS_EXECUTION_TIME,
+    GetAssetResponse,
+    Image2VideoTaskCreationRequest,
+    ImageTaskCreationResponse,
+    MediaKitTaskCreateResponse,
+    MediaKitTaskResponse,
+    MediaKitVideoEnhanceRequest,
+    SeedAudioConfig,
+    SeedAudioReference,
+    SeedAudioRequest,
+    SeedAudioResponse,
+    Seedance2TaskCreationRequest,
+    SeedanceCreateAssetRequest,
+    SeedanceCreateAssetResponse,
+    SeedanceCreateVisualValidateSessionResponse,
+    SeedanceGetVisualValidateSessionResponse,
+    SeedanceVirtualLibraryCreateAssetRequest,
+    Seedream4Options,
+    Seedream4TaskCreationRequest,
+    Seedream5LayerOptimizePromptOptions,
+    Seedream5LayerSeparationRequest,
+    Seedream5OptimizePromptOptions,
+    TaskAudioContent,
+    TaskAudioContentUrl,
+    TaskCreationResponse,
+    TaskImageContent,
+    TaskImageContentUrl,
+    TaskStatusResponse,
+    TaskTextContent,
+    TaskVideoContent,
+    TaskVideoContentUrl,
+    Text2ImageTaskCreationRequest,
+    Text2VideoTaskCreationRequest,
+    seedance2_reference_limits,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_bytes_to_audio_input,
+    audio_input_to_mp3,
+    bytesio_to_image_tensor,
+    download_url_as_bytesio,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    downscale_image_tensor_by_max_side,
+    downscale_video_to_max_pixels,
+    get_number_of_images,
+    image_tensor_pair_to_batch,
+    poll_op,
+    sync_op,
+    tensor_to_base64_string,
+    upload_audio_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    upscale_image_tensor_to_min_pixels,
+    upscale_video_to_min_pixels,
+    validate_audio_duration,
+    validate_image_aspect_ratio,
+    validate_image_dimensions,
+    validate_string,
+    validate_video_dimensions,
+    validate_video_duration,
+)
+from comfy_api_nodes.util.common_exceptions import ProcessingInterrupted
+from server import PromptServer
+
+BYTEPLUS_IMAGE_ENDPOINT = "/proxy/byteplus/api/v3/images/generations"
+
+_VERIFICATION_POLL_TIMEOUT_SEC = 120
+_VERIFICATION_POLL_INTERVAL_SEC = 3
+
+SEEDREAM_MODELS = {
+    "seedream 5.0 pro": "seedream-5-0-pro-260628",
+    "seedream 5.0 lite": "seedream-5-0-260128",
+    "seedream-4-5-251128": "seedream-4-5-251128",
+    "seedream-4-0-250828": "seedream-4-0-250828",
+}
+
+SEEDREAM_PRESETS = {
+    "seedream-5-0-pro-260628": RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
+    "seedream-5-0-260128": RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
+    "seedream-4-5-251128": RECOMMENDED_PRESETS_SEEDREAM_4_5,
+    "seedream-4-0-250828": RECOMMENDED_PRESETS_SEEDREAM_4_0,
+}
+
+SEEDREAM_LAYER_SEPARATION_MODEL = "seedream-5-0-pro-260628"
+
+# Long-running tasks endpoints(e.g., video)
+BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"
+BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"  # + /{task_id}
+BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT = "/proxy/byteplus-seedance2/api/v3/contents/generations/tasks"  # + /{task_id}
+
+SEEDANCE_MODELS = {
+    "Seedance 2.5": "dreamina-seedance-2-5-260628",
+    "Seedance 2.0": "dreamina-seedance-2-0-260128",
+    "Seedance 2.0 Fast": "dreamina-seedance-2-0-fast-260128",
+    "Seedance 2.0 Mini": "dreamina-seedance-2-0-mini",
+}
+
+SEEDANCE_MODEL_TOOLTIP = (
+    "Seedance 2.5 for the newest model, videos up to 30 seconds and mp4/mov output; "
+    "Seedance 2.0 for maximum quality and 4k; Fast for speed optimization; "
+    "Mini for the fastest, lowest-cost generation."
+)
+
+DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-250428"}
+
+
+logger = logging.getLogger(__name__)
+
+
+def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: str, index: int) -> None:
+    """Validate reference video pixel count against Seedance 2.0 model limits for the selected resolution."""
+    model_limits = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id)
+    if not model_limits:
+        return
+    limits = model_limits.get(resolution)
+    if not limits:
+        return
+    try:
+        w, h = video.get_dimensions()
+    except Exception:
+        return
+    pixels = w * h
+    min_px = limits.get("min")
+    max_px = limits.get("max")
+    if min_px and pixels < min_px:
+        raise ValueError(
+            f"Reference video {index} is too small: {w}x{h} = {pixels:,} total pixels. "
+            f"Minimum for this model is {min_px:,} total pixels."
+        )
+    if max_px and pixels > max_px:
+        raise ValueError(
+            f"Reference video {index} is too large: {w}x{h} = {pixels:,} total pixels. "
+            f"Maximum for this model is {max_px:,} total pixels. Try downscaling the video."
+        )
+
+
+def _prepare_seedance_image(image: Input.Image) -> Input.Image:
+    """Auto-downscale a Seedance image input to the per-side limits, then validate it."""
+    validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+    image = downscale_image_tensor_by_max_side(image, max_side=6000)
+    validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000)
+    return image
+
+
+# Supported output aspect ratios, used to pre-size FLF frames to matching pixel pair to avoid the 1080p stretch jump.
+SEEDANCE2_RATIO_WH = {
+    "16:9": (16, 9),
+    "4:3": (4, 3),
+    "1:1": (1, 1),
+    "3:4": (3, 4),
+    "9:16": (9, 16),
+    "21:9": (21, 9),
+}
+SEEDANCE2_RES_SHORT_SIDE = {"480p": 480, "720p": 720, "1080p": 1080, "4k": 2160}
+
+
+def _seedance2_target_dims(resolution: str, ratio: str, image: torch.Tensor) -> tuple[int, int]:
+    """Exact supported output (width, height) for (resolution, ratio).
+
+    The shorter side equals the resolution number (e.g. 1080p 16:9 -> 1920x1080). For ratio
+    "adaptive" (or any unexpected value) the ratio is derived from the image's own aspect, snapped
+    to the nearest supported ratio, so the output keeps the frame's orientation.
+    """
+    short = SEEDANCE2_RES_SHORT_SIDE[resolution]
+    if ratio not in SEEDANCE2_RATIO_WH:
+        aspect = image.shape[-2] / image.shape[-3]  # W / H; tensor is (B, H, W, C)
+        ratio = min(SEEDANCE2_RATIO_WH, key=lambda k: abs(SEEDANCE2_RATIO_WH[k][0] / SEEDANCE2_RATIO_WH[k][1] - aspect))
+    rw, rh = SEEDANCE2_RATIO_WH[ratio]
+    if rw >= rh:  # landscape or square: shorter side is the height
+        out_w, out_h = round(short * rw / rh), short
+    else:  # portrait: shorter side is the width
+        out_w, out_h = short, round(short * rh / rw)
+    return out_w - out_w % 2, out_h - out_h % 2
+
+
+def _resize_to_exact(image: torch.Tensor, width: int, height: int) -> torch.Tensor:
+    """Center-crop to the target aspect and resize to exactly width x height (lanczos)."""
+    samples = image.movedim(-1, 1)  # (B, H, W, C) -> (B, C, H, W)
+    resized = common_upscale(samples, width, height, "lanczos", "center")
+    return resized.movedim(1, -1)
+
+
+async def _resolve_reference_assets(
+    cls: type[IO.ComfyNode],
+    asset_ids: list[str],
+) -> tuple[dict[str, str], dict[str, str], dict[str, str]]:
+    """Look up each asset, validate Active status, group by asset_type.
+
+    Returns (image_assets, video_assets, audio_assets), each mapping asset_id -> "asset://<asset_id>".
+    """
+    image_assets: dict[str, str] = {}
+    video_assets: dict[str, str] = {}
+    audio_assets: dict[str, str] = {}
+    for i, raw_id in enumerate(asset_ids, 1):
+        asset_id = (raw_id or "").strip()
+        if not asset_id:
+            continue
+        result = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/seedance/assets/{asset_id}"),
+            response_model=GetAssetResponse,
+        )
+        if result.status != "Active":
+            extra = f" {result.error.code}: {result.error.message}" if result.error else ""
+            raise ValueError(f"Reference asset {i} (Id={asset_id}) is not Active (Status={result.status}).{extra}")
+        asset_uri = f"asset://{asset_id}"
+        if result.asset_type == "Image":
+            image_assets[asset_id] = asset_uri
+        elif result.asset_type == "Video":
+            video_assets[asset_id] = asset_uri
+        elif result.asset_type == "Audio":
+            audio_assets[asset_id] = asset_uri
+    return image_assets, video_assets, audio_assets
+
+
+_ASSET_REF_RE = re.compile(r"\basset ?(\d{1,2})\b", re.IGNORECASE)
+
+
+def _build_asset_labels(
+    reference_assets: dict[str, str],
+    image_asset_uris: dict[str, str],
+    video_asset_uris: dict[str, str],
+    audio_asset_uris: dict[str, str],
+    n_reference_images: int,
+    n_reference_videos: int,
+    n_reference_audios: int,
+) -> dict[int, str]:
+    """Map asset slot number (from 'asset_N' keys) to its positional label.
+
+    Asset entries are appended to `content` after the reference_images/videos/audios,
+    so their 1-indexed labels continue from the count of existing same-type refs:
+    one reference_images entry + one Image-type asset -> asset labelled "Image 2".
+    """
+    image_n = n_reference_images
+    video_n = n_reference_videos
+    audio_n = n_reference_audios
+    labels: dict[int, str] = {}
+    for slot_key, raw_id in reference_assets.items():
+        asset_id = (raw_id or "").strip()
+        if not asset_id:
+            continue
+        try:
+            slot_num = int(slot_key.rsplit("_", 1)[-1])
+        except ValueError:
+            continue
+        if asset_id in image_asset_uris:
+            image_n += 1
+            labels[slot_num] = f"Image {image_n}"
+        elif asset_id in video_asset_uris:
+            video_n += 1
+            labels[slot_num] = f"Video {video_n}"
+        elif asset_id in audio_asset_uris:
+            audio_n += 1
+            labels[slot_num] = f"Audio {audio_n}"
+    return labels
+
+
+def _rewrite_asset_refs(prompt: str, labels: dict[int, str]) -> str:
+    """Case-insensitively replace 'assetNN' (1-2 digit) tokens with their labels."""
+    if not labels:
+        return prompt
+
+    def _sub(m: "re.Match[str]") -> str:
+        return labels.get(int(m.group(1)), m.group(0))
+
+    return _ASSET_REF_RE.sub(_sub, prompt)
+
+
+async def _obtain_group_id_via_h5_auth(cls: type[IO.ComfyNode]) -> str:
+    session = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/seedance/visual-validate/sessions", method="POST"),
+        response_model=SeedanceCreateVisualValidateSessionResponse,
+    )
+    logger.warning("Seedance authentication required. Open link: %s", session.h5_link)
+
+    h5_text = f"Open this link in your browser and complete face verification:\n\n{session.h5_link}"
+
+    result = await poll_op(
+        cls,
+        ApiEndpoint(path=f"/proxy/seedance/visual-validate/sessions/{session.session_id}"),
+        response_model=SeedanceGetVisualValidateSessionResponse,
+        status_extractor=lambda r: r.status,
+        completed_statuses=["completed"],
+        failed_statuses=["failed"],
+        poll_interval=_VERIFICATION_POLL_INTERVAL_SEC,
+        max_poll_attempts=(_VERIFICATION_POLL_TIMEOUT_SEC // _VERIFICATION_POLL_INTERVAL_SEC) - 1,
+        estimated_duration=_VERIFICATION_POLL_TIMEOUT_SEC - 1,
+        extra_text=h5_text,
+    )
+
+    if not result.group_id:
+        raise RuntimeError(f"Seedance session {session.session_id} completed without a group_id")
+
+    logger.warning("Seedance authentication complete. New GroupId: %s", result.group_id)
+    PromptServer.instance.send_progress_text(
+        f"Authentication complete. New GroupId: {result.group_id}", cls.hidden.unique_id
+    )
+    return result.group_id
+
+
+async def _resolve_group_id(cls: type[IO.ComfyNode], group_id: str) -> str:
+    if group_id and group_id.strip():
+        return group_id.strip()
+    return await _obtain_group_id_via_h5_auth(cls)
+
+
+async def _create_seedance_asset(
+    cls: type[IO.ComfyNode],
+    *,
+    group_id: str,
+    url: str,
+    name: str,
+    asset_type: str,
+) -> str:
+    req = SeedanceCreateAssetRequest(
+        group_id=group_id,
+        url=url,
+        asset_type=asset_type,
+        name=name or None,
+    )
+    result = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/seedance/assets", method="POST"),
+        response_model=SeedanceCreateAssetResponse,
+        data=req,
+    )
+    return result.asset_id
+
+
+async def _wait_for_asset_active(cls: type[IO.ComfyNode], asset_id: str, group_id: str) -> GetAssetResponse:
+    """Poll the newly created asset until its status becomes Active."""
+    return await poll_op(
+        cls,
+        ApiEndpoint(path=f"/proxy/seedance/assets/{asset_id}"),
+        response_model=GetAssetResponse,
+        status_extractor=lambda r: r.status,
+        completed_statuses=["Active"],
+        failed_statuses=["Failed"],
+        poll_interval=5,
+        max_poll_attempts=1200,
+        extra_text=f"Waiting for asset pre-processing...\n\nasset_id: {asset_id}\n\ngroup_id: {group_id}",
+    )
+
+
+async def _seedance_virtual_library_upload_image_asset(
+    cls: type[IO.ComfyNode],
+    image: torch.Tensor,
+    *,
+    wait_label: str = "Uploading image",
+) -> str:
+    """Upload an image into the caller's per-customer Seedance virtual library."""
+    public_url = await upload_image_to_comfyapi(cls, image, wait_label=wait_label)
+    normalized = image.detach().cpu().contiguous().to(torch.float32)
+    digest = hashlib.sha256()
+    digest.update(str(tuple(normalized.shape)).encode("utf-8"))
+    digest.update(b"\0")
+    digest.update(normalized.numpy().tobytes())
+    image_hash = digest.hexdigest()
+    create_resp = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/seedance/virtual-library/assets", method="POST"),
+        response_model=SeedanceCreateAssetResponse,
+        data=SeedanceVirtualLibraryCreateAssetRequest(url=public_url, hash=image_hash),
+    )
+    await _wait_for_asset_active(cls, create_resp.asset_id, group_id="virtual-library")
+    return f"asset://{create_resp.asset_id}"
+
+
+async def _seedance_virtual_library_upload_video_asset(
+    cls: type[IO.ComfyNode],
+    video: Input.Video,
+    *,
+    wait_label: str = "Uploading video",
+) -> str:
+    buf = BytesIO()
+    video.save_to(buf, format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264)
+    video_hash = hashlib.sha256(buf.getbuffer()).hexdigest()
+    public_url = await upload_video_to_comfyapi(cls, video, wait_label=wait_label)
+    create_resp = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/seedance/virtual-library/assets", method="POST"),
+        response_model=SeedanceCreateAssetResponse,
+        data=SeedanceVirtualLibraryCreateAssetRequest(url=public_url, hash=video_hash, asset_type="Video"),
+    )
+    await _wait_for_asset_active(cls, create_resp.asset_id, group_id="virtual-library")
+    return f"asset://{create_resp.asset_id}"
+
+
+def get_image_url_from_response(response: ImageTaskCreationResponse) -> str:
+    if response.error:
+        error_msg = f"ByteDance request failed. Code: {response.error['code']}, message: {response.error['message']}"
+        logging.info(error_msg)
+        raise RuntimeError(error_msg)
+    logging.info("ByteDance task succeeded, image URL: %s", response.data[0]["url"])
+    return response.data[0]["url"]
+
+
+class ByteDanceImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDanceImageNode",
+            display_name="ByteDance Image",
+            category="partner/image/ByteDance",
+            description="Generate images using ByteDance models via api based on prompt",
+            inputs=[
+                IO.Combo.Input("model", options=["seedream-3-0-t2i-250415"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="The text prompt used to generate the image",
+                ),
+                IO.Combo.Input(
+                    "size_preset",
+                    options=[label for label, _, _ in RECOMMENDED_PRESETS],
+                    tooltip="Pick a recommended size. Select Custom to use the width and height below",
+                ),
+                IO.Int.Input(
+                    "width",
+                    default=1024,
+                    min=512,
+                    max=2048,
+                    step=64,
+                    tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
+                ),
+                IO.Int.Input(
+                    "height",
+                    default=1024,
+                    min=512,
+                    max=2048,
+                    step=64,
+                    tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation",
+                    optional=True,
+                ),
+                IO.Float.Input(
+                    "guidance_scale",
+                    default=2.5,
+                    min=1.0,
+                    max=10.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Higher value makes the image follow the prompt more closely",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip='Whether to add an "AI generated" watermark to the image',
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.03}""",
+            ),
+            is_deprecated=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        size_preset: str,
+        width: int,
+        height: int,
+        seed: int,
+        guidance_scale: float,
+        watermark: bool,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        w = h = None
+        for label, tw, th in RECOMMENDED_PRESETS:
+            if label == size_preset:
+                w, h = tw, th
+                break
+
+        if w is None or h is None:
+            w, h = width, height
+            if not (512 <= w <= 2048) or not (512 <= h <= 2048):
+                raise ValueError(
+                    f"Custom size out of range: {w}x{h}. " "Both width and height must be between 512 and 2048 pixels."
+                )
+
+        payload = Text2ImageTaskCreationRequest(
+            model=model,
+            prompt=prompt,
+            size=f"{w}x{h}",
+            seed=seed,
+            guidance_scale=guidance_scale,
+            watermark=watermark,
+        )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
+            data=payload,
+            response_model=ImageTaskCreationResponse,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
+
+
+class ByteDanceSeedreamNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDanceSeedreamNode",
+            display_name="ByteDance Seedream 4.5 & 5.0",
+            category="partner/image/ByteDance",
+            description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=list(SEEDREAM_MODELS.keys()),
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for creating or editing an image.",
+                ),
+                IO.Image.Input(
+                    "image",
+                    tooltip="Input image(s) for image-to-image generation. "
+                    "Reference image(s) for single or multi-reference generation.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "size_preset",
+                    options=[label for label, _, _ in RECOMMENDED_PRESETS_SEEDREAM_4],
+                    tooltip="Pick a recommended size. Select Custom to use the width and height below.",
+                ),
+                IO.Int.Input(
+                    "width",
+                    default=2048,
+                    min=1024,
+                    max=6240,
+                    step=2,
+                    tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "height",
+                    default=2048,
+                    min=1024,
+                    max=4992,
+                    step=2,
+                    tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "sequential_image_generation",
+                    options=["disabled", "auto"],
+                    tooltip="Group image generation mode. "
+                    "'disabled' generates a single image. "
+                    "'auto' lets the model decide whether to generate multiple related images "
+                    "(e.g., story scenes, character variations).",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "max_images",
+                    default=1,
+                    min=1,
+                    max=15,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Maximum number of images to generate when sequential_image_generation='auto'. "
+                    "Total images (input + generated) cannot exceed 15.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip='Whether to add an "AI generated" watermark to the image.',
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "fail_on_partial",
+                    default=True,
+                    tooltip="If enabled, abort execution if any requested images are missing or return an error.",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $price := $contains(widgets.model, "5.0 lite") ? 0.035 :
+                            $contains(widgets.model, "4-5") ? 0.04 : 0.03;
+                  {
+                    "type":"usd",
+                    "usd": $price,
+                    "format": { "suffix":" x images/Run", "approximate": true }
+                  }
+                )
+                """,
+            ),
+            is_deprecated=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        image: Input.Image | None = None,
+        size_preset: str = RECOMMENDED_PRESETS_SEEDREAM_4[0][0],
+        width: int = 2048,
+        height: int = 2048,
+        sequential_image_generation: str = "disabled",
+        max_images: int = 1,
+        seed: int = 0,
+        watermark: bool = False,
+        fail_on_partial: bool = True,
+    ) -> IO.NodeOutput:
+        model = SEEDREAM_MODELS[model]
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        w = h = None
+        for label, tw, th in RECOMMENDED_PRESETS_SEEDREAM_4:
+            if label == size_preset:
+                w, h = tw, th
+                break
+
+        if w is None or h is None:
+            w, h = width, height
+
+        out_num_pixels = w * h
+        mp_provided = out_num_pixels / 1_000_000.0
+        if ("seedream-4-5" in model or "seedream-5-0" in model) and out_num_pixels < 3686400:
+            raise ValueError(
+                f"Minimum image resolution for the selected model is 3.68MP, " f"but {mp_provided:.2f}MP provided."
+            )
+        if "seedream-4-0" in model and out_num_pixels < 921600:
+            raise ValueError(
+                f"Minimum image resolution that the selected model can generate is 0.92MP, "
+                f"but {mp_provided:.2f}MP provided."
+            )
+        max_pixels = 10_404_496 if "seedream-5-0" in model else 16_777_216
+        if out_num_pixels > max_pixels:
+            raise ValueError(
+                f"Maximum image resolution for the selected model is {max_pixels / 1_000_000:.2f}MP, "
+                f"but {mp_provided:.2f}MP provided."
+            )
+        n_input_images = get_number_of_images(image) if image is not None else 0
+        max_num_of_images = 14 if model == "seedream-5-0-260128" else 10
+        if n_input_images > max_num_of_images:
+            raise ValueError(
+                f"Maximum of {max_num_of_images} reference images are supported, but {n_input_images} received."
+            )
+        if sequential_image_generation == "auto" and n_input_images + max_images > 15:
+            raise ValueError(
+                "The maximum number of generated images plus the number of reference images cannot exceed 15."
+            )
+        reference_images_urls = []
+        if n_input_images:
+            for i in image:
+                validate_image_aspect_ratio(i, (1, 3), (3, 1))
+            reference_images_urls = await upload_images_to_comfyapi(
+                cls,
+                image,
+                max_images=n_input_images,
+                mime_type="image/png",
+            )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
+            response_model=ImageTaskCreationResponse,
+            data=Seedream4TaskCreationRequest(
+                model=model,
+                prompt=prompt,
+                image=reference_images_urls,
+                size=f"{w}x{h}",
+                seed=seed,
+                sequential_image_generation=sequential_image_generation,
+                sequential_image_generation_options=Seedream4Options(max_images=max_images),
+                watermark=watermark,
+                output_format="png" if model == "seedream-5-0-260128" else None,
+            ),
+        )
+        if len(response.data) == 1:
+            return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
+        urls = [str(d["url"]) for d in response.data if isinstance(d, dict) and "url" in d]
+        if fail_on_partial and len(urls) < len(response.data):
+            raise RuntimeError(f"Only {len(urls)} of {len(response.data)} images were generated before error.")
+        return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls]))
+
+
+def _seedream_model_inputs(
+    *,
+    max_ref_images: int,
+    presets: list,
+    max_width: int = 6240,
+    max_height: int = 4992,
+    supports_batch: bool = True,
+    supports_fast: bool = False,
+    include_common: bool = False,
+):
+    inputs = [
+        IO.Combo.Input(
+            "size_preset",
+            options=[label for label, _, _ in presets],
+            tooltip="Pick a recommended size. Select Custom to use the width and height below.",
+        ),
+        IO.Int.Input(
+            "width",
+            default=2048,
+            min=1024,
+            max=max_width,
+            step=2,
+            tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
+        ),
+        IO.Int.Input(
+            "height",
+            default=2048,
+            min=1024,
+            max=max_height,
+            step=2,
+            tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
+        ),
+    ]
+    if supports_batch:
+        inputs.append(
+            IO.Int.Input(
+                "max_images",
+                default=1,
+                min=1,
+                max=max_ref_images,
+                step=1,
+                display_mode=IO.NumberDisplay.number,
+                tooltip="Maximum number of images to generate. With 1, exactly one image is produced. "
+                "With >1, the model generates between 1 and max_images related images "
+                "(e.g., story scenes, character variations). "
+                "Total images (input + generated) cannot exceed 15.",
+            )
+        )
+    inputs.append(
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, max_ref_images + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference image(s) for image-to-image or multi-reference generation. "
+            f"Up to {max_ref_images} images.",
+        )
+    )
+    if supports_batch:
+        inputs.append(
+            IO.Boolean.Input(
+                "fail_on_partial",
+                default=False,
+                tooltip="If enabled, abort execution if any requested images are missing or return an error.",
+                advanced=True,
+            )
+        )
+    if supports_fast:
+        inputs.append(
+            IO.Combo.Input(
+                "prompt_optimization",
+                options=["standard", "fast"],
+                default="standard",
+                tooltip="Prompt-optimization mode when reference images are provided: "
+                "'standard' gives higher quality, 'fast' shorter generation time.",
+                advanced=True,
+            )
+        )
+    if include_common:
+        inputs.extend(
+            [
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip='Whether to add an "AI generated" watermark to the image.',
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "thinking",
+                    default=True,
+                    tooltip=(
+                        "Enable the model's prompt-optimization reasoning ('thinking') for better adherence. "
+                        "Can substantially increase generation time — notably on Seedream 5.0 Pro. "
+                        "Can only be disabled for text-to-image (not when reference images are provided)."
+                    ),
+                    advanced=True,
+                ),
+            ]
+        )
+    return inputs
+
+
+class ByteDanceSeedreamNodeV3(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDanceSeedreamNodeV3",
+            display_name="ByteDance Seedream 4.5 & 5.0",
+            category="partner/image/ByteDance",
+            description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for creating or editing an image.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "seedream 5.0 pro",
+                            _seedream_model_inputs(
+                                max_ref_images=10,
+                                presets=RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
+                                max_width=3136,
+                                max_height=2496,
+                                supports_batch=False,
+                                supports_fast=True,
+                                include_common=True,
+                            ),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "seedream 5.0 lite",
+                            _seedream_model_inputs(
+                                max_ref_images=14,
+                                presets=RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
+                                include_common=True,
+                            ),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "seedream-4-5-251128",
+                            _seedream_model_inputs(
+                                max_ref_images=10,
+                                presets=RECOMMENDED_PRESETS_SEEDREAM_4_5,
+                                include_common=True,
+                            ),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "seedream-4-0-250828",
+                            _seedream_model_inputs(
+                                max_ref_images=10,
+                                presets=RECOMMENDED_PRESETS_SEEDREAM_4_0,
+                                include_common=True,
+                            ),
+                        ),
+                    ],
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "model.size_preset", "model.width", "model.height"],
+                    input_groups=["model.images"],
+                ),
+                expr="""
+                (
+                  $model := $string(widgets.model);
+                  $sp := $string($lookup(widgets, "model.size_preset"));
+                  $w := $lookup(widgets, "model.width");
+                  $h := $lookup(widgets, "model.height");
+                  $px := ($type($w) = "number" and $type($h) = "number") ? $w * $h : 0;
+                  $refs := $lookup(inputGroups, "model.images");
+                  $extra := ($type($refs) = "number" and $refs > 1) ? ($refs - 1) * 0.003 : 0;
+                  $isPro := $contains($model, "5.0 pro");
+                  $isCustom := $contains($sp, "custom");
+                  $sizeKnown := $isCustom ? $px > 0 : ($contains($sp, "1k") or $contains($sp, "2k"));
+                  $proPrice := $isCustom
+                                 ? ($px < 2610000 ? 0.045 : 0.09)
+                                 : ($contains($sp, "1k") ? 0.045 : 0.09);
+                  ($isPro and ($sizeKnown = false))
+                    ? {
+                        "type": "range_usd",
+                        "min_usd": 0.045 + $extra,
+                        "max_usd": 0.09 + $extra,
+                        "format": { "suffix": "/Image", "approximate": true }
+                      }
+                    : {
+                        "type": "usd",
+                        "usd": $isPro ? $proPrice + $extra
+                               : $contains($model, "5.0 lite") ? 0.035
+                               : $contains($model, "4-5") ? 0.04
+                               : 0.03,
+                        "format": { "suffix": $isPro ? "/Image" : " x images/Run", "approximate": true }
+                      }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int = 0,
+        watermark: bool = False,
+        thinking: bool = True,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_id = SEEDREAM_MODELS[model["model"]]
+        presets = SEEDREAM_PRESETS[model_id]
+        is_pro = "seedream-5-0-pro" in model_id
+
+        size_preset = model.get("size_preset", presets[0][0])
+        width = model.get("width", 2048)
+        height = model.get("height", 2048)
+        max_images = model.get("max_images", 1)
+        sequential_image_generation = "disabled" if max_images == 1 else "auto"
+        images_dict = model.get("images") or {}
+        fail_on_partial = model.get("fail_on_partial", False)
+        prompt_optimization = model.get("prompt_optimization", "standard")
+        seed = model.get("seed", seed)
+        watermark = model.get("watermark", watermark)
+        thinking = model.get("thinking", thinking)
+
+        w = h = None
+        for label, tw, th in presets:
+            if label == size_preset:
+                w, h = tw, th
+                break
+        if w is None or h is None:
+            w, h = width, height
+
+        out_num_pixels = w * h
+        mp_provided = out_num_pixels / 1_000_000.0
+        if is_pro:
+            if out_num_pixels < 921_600:
+                raise ValueError(
+                    f"Minimum image resolution for the selected model is 0.92MP, but {mp_provided:.2f}MP provided."
+                )
+            if out_num_pixels > 4_194_304:
+                raise ValueError(
+                    f"Maximum image resolution for the selected model is 4.19MP, but {mp_provided:.2f}MP provided."
+                )
+        else:
+            if ("seedream-4-5" in model_id or "seedream-5-0" in model_id) and out_num_pixels < 3_686_400:
+                raise ValueError(
+                    f"Minimum image resolution for the selected model is 3.68MP, but {mp_provided:.2f}MP provided."
+                )
+            if "seedream-4-0" in model_id and out_num_pixels < 921_600:
+                raise ValueError(
+                    f"Minimum image resolution that the selected model can generate is 0.92MP, "
+                    f"but {mp_provided:.2f}MP provided."
+                )
+            if out_num_pixels > 16_777_216:
+                raise ValueError(
+                    f"Maximum image resolution for the selected model is 16.78MP, but {mp_provided:.2f}MP provided."
+                )
+
+        image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
+        n_input_images = sum(get_number_of_images(t) for t in image_tensors)
+        max_num_of_images = 14 if model_id == "seedream-5-0-260128" else 10
+        if n_input_images > max_num_of_images:
+            raise ValueError(
+                f"Maximum of {max_num_of_images} reference images are supported, but {n_input_images} received."
+            )
+        if sequential_image_generation == "auto" and n_input_images + max_images > 15:
+            raise ValueError(
+                "The maximum number of generated images plus the number of reference images cannot exceed 15."
+            )
+        if not thinking and n_input_images > 0:
+            raise ValueError(
+                "'thinking' can only be disabled for text-to-image; enable it when using reference images."
+            )
+
+        reference_images_urls: list[str] = []
+        if image_tensors:
+            for tensor in image_tensors:
+                validate_image_aspect_ratio(tensor, (1, 3), (3, 1))
+            reference_images_urls = await upload_images_to_comfyapi(
+                cls,
+                image_tensors,
+                max_images=n_input_images,
+                mime_type="image/png",
+                wait_label="Uploading reference images",
+            )
+
+        optimize_prompt_options = None
+        if n_input_images == 0:
+            optimize_prompt_options = Seedream5OptimizePromptOptions(thinking="enabled" if thinking else "disabled")
+        elif prompt_optimization == "fast":
+            optimize_prompt_options = Seedream5OptimizePromptOptions(mode="fast")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
+            response_model=ImageTaskCreationResponse,
+            data=Seedream4TaskCreationRequest(
+                model=model_id,
+                prompt=prompt,
+                image=reference_images_urls,
+                size=f"{w}x{h}",
+                seed=seed,
+                sequential_image_generation=None if is_pro else sequential_image_generation,
+                sequential_image_generation_options=None if is_pro else Seedream4Options(max_images=max_images),
+                watermark=watermark,
+                optimize_prompt_options=optimize_prompt_options,
+            ),
+        )
+        if len(response.data) == 1:
+            return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
+        urls = [str(d["url"]) for d in response.data if isinstance(d, dict) and "url" in d]
+        if fail_on_partial and len(urls) < len(response.data):
+            raise RuntimeError(f"Only {len(urls)} of {len(response.data)} images were generated before error.")
+        return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls]))
+
+
+class ByteDanceSeedreamNodeV2(ByteDanceSeedreamNodeV3):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDanceSeedreamNodeV2",
+            display_name="ByteDance Seedream 4.5 & 5.0 (Legacy)",
+            category="partner/image/ByteDance",
+            description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for creating or editing an image.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "seedream 5.0 pro",
+                            _seedream_model_inputs(
+                                max_ref_images=10,
+                                presets=RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
+                                max_width=3136,
+                                max_height=2496,
+                                supports_batch=False,
+                            ),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "seedream 5.0 lite",
+                            _seedream_model_inputs(max_ref_images=14, presets=RECOMMENDED_PRESETS_SEEDREAM_5_LITE),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "seedream-4-5-251128",
+                            _seedream_model_inputs(max_ref_images=10, presets=RECOMMENDED_PRESETS_SEEDREAM_4_5),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "seedream-4-0-250828",
+                            _seedream_model_inputs(max_ref_images=10, presets=RECOMMENDED_PRESETS_SEEDREAM_4_0),
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip='Whether to add an "AI generated" watermark to the image.',
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "thinking",
+                    default=True,
+                    tooltip=(
+                        "Enable the model's prompt-optimization reasoning ('thinking') for better adherence. "
+                        "Can substantially increase generation time — notably on Seedream 5.0 Pro. "
+                        "Can only be disabled for text-to-image (not when reference images are provided)."
+                    ),
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_deprecated=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "model.size_preset", "model.width", "model.height"]
+                ),
+                expr="""
+                (
+                  $sp := $lookup(widgets, "model.size_preset");
+                  $px := $lookup(widgets, "model.width") * $lookup(widgets, "model.height");
+                  $isPro := $contains(widgets.model, "5.0 pro");
+                  $price := $isPro
+                              ? (
+                                  $contains($sp, "custom")
+                                    ? ($px <= 2360000 ? 0.045 : 0.09)
+                                    : ($contains($sp, "1k") ? 0.045 : 0.09)
+                                )
+                              : $contains(widgets.model, "5.0 lite") ? 0.035
+                              : $contains(widgets.model, "4-5") ? 0.04
+                              : 0.03;
+                  {
+                    "type": "usd",
+                    "usd": $price,
+                    "format": { "suffix": $isPro ? "/Image" : " x images/Run", "approximate": true }
+                  }
+                )
+                """,
+            ),
+        )
+
+class ByteDanceSeedreamLayerSeparationNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDanceSeedreamLayerSeparationNode",
+            display_name="ByteDance Seedream 5.0 Pro Layer Separation",
+            category="partner/image/ByteDance",
+            search_aliases=["layer separation", "split layers", "decompose", "cutout", "RGBA layers"],
+            description=(
+                "Decompose an image into a background plate plus up to 16 repositionable transparent layers, "
+                "each with stacking order, bounding box, name and description."
+            ),
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip=(
+                        "The image to separate. Exactly one image, at least 512x512 pixels, aspect ratio "
+                        "between 1:16 and 16:1. Inputs larger than about 4MP are downscaled before upload."
+                    ),
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip=(
+                        "How to separate the image. Leave empty to auto-detect and separate all major elements. "
+                        "Describe elements in natural language to control the separation, or target exact regions "
+                        "with <bbox>left top right bottom</bbox> tags (0-1000 per-mille coordinates)."
+                    ),
+                ),
+                IO.Combo.Input(
+                    "size",
+                    options=["auto", "1K", "1.5K", "2K"],
+                    default="auto",
+                    tooltip="Output resolution level. 'auto' follows the input image size (clamped to the 1K-2K range).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Combo.Input(
+                    "prompt_optimization",
+                    options=["standard", "fast"],
+                    default="standard",
+                    optional=True,
+                    advanced=True,
+                    tooltip="Prompt-optimization mode: 'standard' gives higher quality, 'fast' shorter generation time.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    optional=True,
+                    advanced=True,
+                    tooltip='Whether to add an "AI generated" watermark to the images.',
+                ),
+                IO.Boolean.Input(
+                    "crop_layers",
+                    default=False,
+                    optional=True,
+                    label_on="minimal size",
+                    label_off="full canvas",
+                    tooltip=(
+                        "Geometry of the layers/masks batch outputs (layer_stack is unaffected and always "
+                        "tight). Full canvas: each layer on a base-sized canvas at its bounding-box position - "
+                        "recompose directly with ImageCompositeMasked. Minimal size: each layer cropped to its "
+                        "bounding box (padded to the largest layer for batching) - much smaller tensors; "
+                        "rebuild placement with Layers From Bounding Boxes using the bboxes output."
+                    ),
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(
+                    display_name="base_image",
+                    tooltip="The base image (background plate) the layers stack onto.",
+                ),
+                IO.Mask.Output(
+                    display_name="base_mask",
+                    tooltip=(
+                        "Transparency of the base image (1 = transparent, LoadImage convention); currently "
+                        "always fully opaque."
+                    ),
+                ),
+                IO.Image.Output(
+                    display_name="layers",
+                    tooltip=(
+                        "Transparent layers ordered bottom to top. Full canvas mode: placed on a black "
+                        "base-sized canvas at their bounding-box position. Minimal size mode: cropped to "
+                        "their bounding box, anchored top-left, padded to the largest layer."
+                    ),
+                ),
+                IO.Mask.Output(
+                    display_name="masks",
+                    tooltip=(
+                        "Per-layer transparency, index-aligned with the layers batch (1 = transparent, "
+                        "LoadImage convention). For ImageCompositeMasked-style compositing, add InvertMask first."
+                    ),
+                ),
+                IO.BoundingBox.Output(
+                    display_name="bboxes",
+                    tooltip=(
+                        "One placement box per layer, index-aligned with the layers batch (feed both, plus "
+                        "masks, into Layers From Bounding Boxes to rebuild per-layer placement): {x, y, width, "
+                        "height, metadata: {name, desc, z_index, native_size, content_rect, flags}}. "
+                        "content_rect = [left, top, width, height] is the layer's content region within its "
+                        "own frame; it lands on the canvas at the box position plus that offset."
+                    ),
+                ),
+                IO.Layers.Output(
+                    display_name="layer_stack",
+                    tooltip=(
+                        "Ready-to-edit layer document for Create Layered Image: the base plate plus each "
+                        "element as its own named, tight-cropped layer at its true position and stacking "
+                        "order. Connect directly, or extend with Add Layer."
+                    ),
+                ),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["size"]),
+                expr="""
+                (
+                  widgets.size in ["1k", "1.5k"]
+                    ? {
+                        "type": "usd",
+                        "usd": 0.032,
+                        "format": { "suffix": " x images/Run", "approximate": true }
+                      }
+                    : {
+                        "type": "range_usd",
+                        "min_usd": 0.032,
+                        "max_usd": 0.064,
+                        "format": { "suffix": " x images/Run", "approximate": true }
+                      }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        prompt: str = "",
+        size: str = "auto",
+        seed: int = 0,
+        prompt_optimization: str = "standard",
+        watermark: bool = False,
+        crop_layers: bool = False,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Only a single input image is supported.")
+        validate_image_aspect_ratio(image, (1, 16), (16, 1), strict=False)
+        validate_image_dimensions(image, min_width=512, min_height=512)
+
+        request = Seedream5LayerSeparationRequest(
+            model=SEEDREAM_LAYER_SEPARATION_MODEL,
+            prompt=prompt.strip() or None,
+            image=await upload_image_to_comfyapi(cls, image),
+            size=size,
+            seed=seed,
+            watermark=watermark,
+            optimize_prompt_options=Seedream5LayerOptimizePromptOptions(mode=prompt_optimization),
+        )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
+            response_model=ImageTaskCreationResponse,
+            data=request,
+            wait_label="Separating layers",
+        )
+        if response.error:
+            raise RuntimeError(
+                f"ByteDance request failed. Code: {response.error['code']}, message: {response.error['message']}"
+            )
+
+        def z_index_of(d: dict) -> int:
+            v = d.get("z_index")
+            if isinstance(v, bool):
+                return 1_000_000
+            if isinstance(v, (int, float)):
+                return int(v)
+            if isinstance(v, str):
+                try:
+                    return int(v.strip())
+                except ValueError:
+                    return 1_000_000
+            return 1_000_000
+
+        data = [d for d in (response.data or []) if isinstance(d, dict)]
+        if not data or "url" not in data[0]:
+            raise RuntimeError("Unexpected response: no base image returned.")
+        base_item = data[0]
+        if base_item.get("bounding_box") is not None:
+            logging.warning(
+                "ByteDance layer separation: base item unexpectedly carries a bounding_box; ignoring it."
+            )
+        if z_index_of(base_item) not in (0, 1_000_000):
+            raise RuntimeError("Unexpected response: the first item is not the base image.")
+        layer_items = [d for d in data[1:] if "url" in d]
+        dropped = len(data) - 1 - len(layer_items)
+        if dropped > 0:
+            logging.warning(
+                "ByteDance layer separation: %d of %d returned elements had no 'url' and were dropped.",
+                dropped,
+                len(data) - 1,
+            )
+        if not layer_items:
+            raise RuntimeError("The model returned no layers. Try a different prompt or input image.")
+        layer_items.sort(key=z_index_of)
+
+        base_image = (await download_url_to_image_tensor(str(base_item["url"])))[..., :3].contiguous()
+        height, width = base_image.shape[1], base_image.shape[2]
+
+        specs = []
+        for item in layer_items:
+            flags = []
+            bbox = item.get("bounding_box")
+            absolute = bbox.get("absolute") if isinstance(bbox, dict) else None
+            if (
+                isinstance(absolute, (list, tuple))
+                and len(absolute) == 4
+                and all(isinstance(v, (int, float)) and not isinstance(v, bool) for v in absolute)
+            ):
+                left, top, right, bottom = (int(round(v)) for v in absolute)
+                rect_w, rect_h = right - left, bottom - top  # exclusive right/bottom
+                if rect_w > width or rect_h > height:
+                    rect_w, rect_h = min(rect_w, width), min(rect_h, height)
+                    flags.append("bbox_clamped")
+                if rect_w <= 0 or rect_h <= 0:
+                    flags.append("bbox_degenerate")
+            else:
+                flags.append("bbox_missing")
+                left, top, rect_w, rect_h = 0, 0, width, height
+            specs.append({"item": item, "flags": flags, "left": left, "top": top,
+                          "rect_w": rect_w, "rect_h": rect_h, "native_size": "", "stack_item": None})
+
+        if crop_layers:
+            canvas_w = max((s["rect_w"] for s in specs if "bbox_degenerate" not in s["flags"]), default=1)
+            canvas_h = max((s["rect_h"] for s in specs if "bbox_degenerate" not in s["flags"]), default=1)
+        else:
+            canvas_w, canvas_h = width, height
+        base_mask = torch.zeros((1, height, width))
+        layers = torch.zeros((len(specs), canvas_h, canvas_w, 3))
+        # Create Layered Image / LoadImage mask convention: 1 = transparent
+        masks = torch.ones((len(specs), canvas_h, canvas_w))
+
+        semaphore = asyncio.Semaphore(4)
+
+        async def fetch_and_place(i: int, spec: dict) -> None:
+            item, flags = spec["item"], spec["flags"]
+            left, top, rect_w, rect_h = spec["left"], spec["top"], spec["rect_w"], spec["rect_h"]
+            async with semaphore:
+                try:
+                    # the layer math below needs the alpha channel, and ByteDance encodes
+                    # alpha-less images as plain RGB (the base plate is one), so force RGBA
+                    rgba = bytesio_to_image_tensor(await download_url_as_bytesio(str(item["url"])), mode="RGBA")[0]
+                except ProcessingInterrupted:
+                    raise
+                except Exception as exc:
+                    raise RuntimeError(
+                        f"Failed to download layer {i + 1} of {len(specs)} (name={item.get('name')!r}): {exc} "
+                        "The generation completed and was billed; the response with all layer URLs "
+                        "is in ComfyUI/temp/api_logs/."
+                    ) from exc
+            spec["native_size"] = f"{rgba.shape[1]}x{rgba.shape[0]}"
+            if "bbox_degenerate" in flags:
+                return
+            if (rgba.shape[1], rgba.shape[0]) != (rect_w, rect_h):
+                # premultiply before resizing: interpolating straight alpha bleeds the undefined
+                # colors of transparent pixels into the anti-aliased edges
+                rgba = rgba.clone()
+                rgba[..., :3] *= rgba[..., 3:4]
+                rgba = (
+                    torch.nn.functional.interpolate(
+                        rgba.permute(2, 0, 1).unsqueeze(0),
+                        size=(rect_h, rect_w),
+                        mode="bilinear",
+                        antialias=True,
+                    )
+                    .squeeze(0)
+                    .permute(1, 2, 0)
+                )
+                alpha = rgba[..., 3:4]
+                rgba = torch.cat([rgba[..., :3] / alpha.clamp(min=1e-6), alpha], dim=-1).clamp(0, 1)
+                flags.append("resized_to_bbox")
+            # straight (unpremultiplied) RGB: downstream compositing applies the mask itself
+            if crop_layers:
+                layers[i, :rect_h, :rect_w] = rgba[..., :3]
+                masks[i, :rect_h, :rect_w] = 1.0 - rgba[..., 3]
+            else:
+                x0, y0 = max(left, 0), max(top, 0)
+                x1, y1 = min(left + rect_w, width), min(top + rect_h, height)
+                if x0 < x1 and y0 < y1:
+                    patch = rgba[y0 - top : y1 - top, x0 - left : x1 - left]
+                    layers[i, y0:y1, x0:x1] = patch[..., :3]
+                    masks[i, y0:y1, x0:x1] = 1.0 - patch[..., 3]
+                else:
+                    flags.append("bbox_out_of_canvas")
+            zi = z_index_of(item)
+            stack_item = {
+                "image": rgba[..., :3].unsqueeze(0).contiguous(),
+                "type": "raster",
+                "x": left,
+                "y": top,
+                "z_index": zi if zi != 1_000_000 else i + 1,
+                "mask": (1.0 - rgba[..., 3]).unsqueeze(0),
+            }
+            if isinstance(item.get("name"), str):
+                stack_item["name"] = item["name"]
+            spec["stack_item"] = stack_item
+
+        await asyncio.gather(*(fetch_and_place(i, s) for i, s in enumerate(specs)))
+
+        stack_items = [{"image": base_image, "type": "raster", "x": 0, "y": 0, "z_index": 0, "name": "background"}]
+        boxes = []
+        for i, s in enumerate(specs):
+            abnormal = [f for f in s["flags"] if f != "resized_to_bbox"]
+            if abnormal:
+                logging.warning(
+                    "ByteDance layer separation: layer %d (%r) flagged %s.",
+                    i + 1,
+                    s["item"].get("name"),
+                    ", ".join(abnormal),
+                )
+            if s["stack_item"] is not None:
+                stack_items.append(s["stack_item"])
+            zi = z_index_of(s["item"])
+            # placement box sized to this layer's tensor so Create Layered Image renders it 1:1;
+            # the true content rect travels in metadata, frame-relative
+            rect_x, rect_y = (0, 0) if crop_layers else (s["left"], s["top"])
+            boxes.append(
+                {
+                    "x": s["left"] if crop_layers else 0,
+                    "y": s["top"] if crop_layers else 0,
+                    "width": canvas_w,
+                    "height": canvas_h,
+                    "metadata": {
+                        "name": s["item"].get("name"),
+                        "desc": s["item"].get("description"),
+                        "z_index": zi if zi != 1_000_000 else None,
+                        "native_size": s["native_size"],
+                        "content_rect": [rect_x, rect_y, max(s["rect_w"], 0), max(s["rect_h"], 0)],
+                        "flags": s["flags"],
+                    },
+                }
+            )
+        # a single frame holding every box: the per-frame BOUNDING_BOX shape for boxes that
+        # annotate one image, as emitted and consumed by CreateBoundingBoxes
+        bboxes = [boxes]
+        layer_stack = {"version": 1, "canvas": (width, height), "layers": stack_items}
+        return IO.NodeOutput(base_image, base_mask, layers, masks, bboxes, layer_stack)
+
+
+class ByteDanceTextToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDanceTextToVideoNode",
+            display_name="ByteDance Text to Video",
+            category="partner/video/ByteDance",
+            description="Generate video using ByteDance models via api based on prompt",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=[
+                        "seedance-1-5-pro-251215",
+                        "seedance-1-0-pro-250528",
+                        "seedance-1-0-lite-t2v-250428",
+                        "seedance-1-0-pro-fast-251015",
+                    ],
+                    default="seedance-1-0-pro-fast-251015",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="The text prompt used to generate the video.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["480p", "720p", "1080p"],
+                    tooltip="The resolution of the output video.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["16:9", "4:3", "1:1", "3:4", "9:16", "21:9"],
+                    tooltip="The aspect ratio of the output video.",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=3,
+                    max=12,
+                    step=1,
+                    tooltip="The duration of the output video in seconds.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "camera_fixed",
+                    default=False,
+                    tooltip="Specifies whether to fix the camera. The platform appends an instruction "
+                    "to fix the camera to your prompt, but does not guarantee the actual effect.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip='Whether to add an "AI generated" watermark to the video.',
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        resolution: str,
+        aspect_ratio: str,
+        duration: int,
+        seed: int,
+        camera_fixed: bool,
+        watermark: bool,
+        generate_audio: bool = False,
+    ) -> IO.NodeOutput:
+        if model == "seedance-1-5-pro-251215" and duration < 4:
+            raise ValueError("Minimum supported duration for Seedance 1.5 Pro is 4 seconds.")
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"])
+
+        prompt = (
+            f"{prompt} "
+            f"--resolution {resolution} "
+            f"--ratio {aspect_ratio} "
+            f"--duration {duration} "
+            f"--seed {seed} "
+            f"--camerafixed {str(camera_fixed).lower()} "
+            f"--watermark {str(watermark).lower()}"
+        )
+        return await process_video_task(
+            cls,
+            payload=Text2VideoTaskCreationRequest(
+                model=model,
+                content=[TaskTextContent(text=prompt)],
+                generate_audio=generate_audio if model == "seedance-1-5-pro-251215" else None,
+            ),
+            estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
+        )
+
+
+class ByteDanceImageToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDanceImageToVideoNode",
+            display_name="ByteDance Image to Video",
+            category="partner/video/ByteDance",
+            description="Generate video using ByteDance models via api based on image and prompt",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=[
+                        "seedance-1-5-pro-251215",
+                        "seedance-1-0-pro-250528",
+                        "seedance-1-0-lite-i2v-250428",
+                        "seedance-1-0-pro-fast-251015",
+                    ],
+                    default="seedance-1-0-pro-fast-251015",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="The text prompt used to generate the video.",
+                ),
+                IO.Image.Input(
+                    "image",
+                    tooltip="First frame to be used for the video.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["480p", "720p", "1080p"],
+                    tooltip="The resolution of the output video.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["adaptive", "16:9", "4:3", "1:1", "3:4", "9:16", "21:9"],
+                    tooltip="The aspect ratio of the output video.",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=3,
+                    max=12,
+                    step=1,
+                    tooltip="The duration of the output video in seconds.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "camera_fixed",
+                    default=False,
+                    tooltip="Specifies whether to fix the camera. The platform appends an instruction "
+                    "to fix the camera to your prompt, but does not guarantee the actual effect.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip='Whether to add an "AI generated" watermark to the video.',
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        image: Input.Image,
+        resolution: str,
+        aspect_ratio: str,
+        duration: int,
+        seed: int,
+        camera_fixed: bool,
+        watermark: bool,
+        generate_audio: bool = False,
+    ) -> IO.NodeOutput:
+        if model == "seedance-1-5-pro-251215" and duration < 4:
+            raise ValueError("Minimum supported duration for Seedance 1.5 Pro is 4 seconds.")
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"])
+        validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000)
+        validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+
+        image_url = (await upload_images_to_comfyapi(cls, image, max_images=1))[0]
+        prompt = (
+            f"{prompt} "
+            f"--resolution {resolution} "
+            f"--ratio {aspect_ratio} "
+            f"--duration {duration} "
+            f"--seed {seed} "
+            f"--camerafixed {str(camera_fixed).lower()} "
+            f"--watermark {str(watermark).lower()}"
+        )
+
+        return await process_video_task(
+            cls,
+            payload=Image2VideoTaskCreationRequest(
+                model=model,
+                content=[TaskTextContent(text=prompt), TaskImageContent(image_url=TaskImageContentUrl(url=image_url))],
+                generate_audio=generate_audio if model == "seedance-1-5-pro-251215" else None,
+            ),
+            estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
+        )
+
+
+class ByteDanceFirstLastFrameNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDanceFirstLastFrameNode",
+            display_name="ByteDance First-Last-Frame to Video",
+            category="partner/video/ByteDance",
+            description="Generate video using prompt and first and last frames.",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["seedance-1-5-pro-251215", "seedance-1-0-pro-250528", "seedance-1-0-lite-i2v-250428"],
+                    default="seedance-1-0-lite-i2v-250428",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="The text prompt used to generate the video.",
+                ),
+                IO.Image.Input(
+                    "first_frame",
+                    tooltip="First frame to be used for the video.",
+                ),
+                IO.Image.Input(
+                    "last_frame",
+                    tooltip="Last frame to be used for the video.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["480p", "720p", "1080p"],
+                    tooltip="The resolution of the output video.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["adaptive", "16:9", "4:3", "1:1", "3:4", "9:16", "21:9"],
+                    tooltip="The aspect ratio of the output video.",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=3,
+                    max=12,
+                    step=1,
+                    tooltip="The duration of the output video in seconds.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "camera_fixed",
+                    default=False,
+                    tooltip="Specifies whether to fix the camera. The platform appends an instruction "
+                    "to fix the camera to your prompt, but does not guarantee the actual effect.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip='Whether to add an "AI generated" watermark to the video.',
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        first_frame: Input.Image,
+        last_frame: Input.Image,
+        resolution: str,
+        aspect_ratio: str,
+        duration: int,
+        seed: int,
+        camera_fixed: bool,
+        watermark: bool,
+        generate_audio: bool = False,
+    ) -> IO.NodeOutput:
+        if model == "seedance-1-5-pro-251215" and duration < 4:
+            raise ValueError("Minimum supported duration for Seedance 1.5 Pro is 4 seconds.")
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"])
+        for i in (first_frame, last_frame):
+            validate_image_dimensions(i, min_width=300, min_height=300, max_width=6000, max_height=6000)
+            validate_image_aspect_ratio(i, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+
+        download_urls = await upload_images_to_comfyapi(
+            cls,
+            image_tensor_pair_to_batch(first_frame, last_frame),
+            max_images=2,
+            mime_type="image/png",
+        )
+
+        prompt = (
+            f"{prompt} "
+            f"--resolution {resolution} "
+            f"--ratio {aspect_ratio} "
+            f"--duration {duration} "
+            f"--seed {seed} "
+            f"--camerafixed {str(camera_fixed).lower()} "
+            f"--watermark {str(watermark).lower()}"
+        )
+
+        return await process_video_task(
+            cls,
+            payload=Image2VideoTaskCreationRequest(
+                model=model,
+                content=[
+                    TaskTextContent(text=prompt),
+                    TaskImageContent(image_url=TaskImageContentUrl(url=str(download_urls[0])), role="first_frame"),
+                    TaskImageContent(image_url=TaskImageContentUrl(url=str(download_urls[1])), role="last_frame"),
+                ],
+                generate_audio=generate_audio if model == "seedance-1-5-pro-251215" else None,
+            ),
+            estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
+        )
+
+
+class ByteDanceImageReferenceNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDanceImageReferenceNode",
+            display_name="ByteDance Reference Images to Video",
+            category="partner/video/ByteDance",
+            description="Generate video using prompt and reference images.",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["seedance-1-0-pro-250528", "seedance-1-0-lite-i2v-250428"],
+                    default="seedance-1-0-lite-i2v-250428",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="The text prompt used to generate the video.",
+                ),
+                IO.Image.Input(
+                    "images",
+                    tooltip="One to four images.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["480p", "720p"],
+                    tooltip="The resolution of the output video.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["adaptive", "16:9", "4:3", "1:1", "3:4", "9:16", "21:9"],
+                    tooltip="The aspect ratio of the output video.",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=3,
+                    max=12,
+                    step=1,
+                    tooltip="The duration of the output video in seconds.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip='Whether to add an "AI generated" watermark to the video.',
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"]),
+                expr="""
+                (
+                  $priceByModel := {
+                    "seedance-1-0-pro": {
+                      "480p":[0.23,0.24],
+                      "720p":[0.51,0.56]
+                    },
+                    "seedance-1-0-lite": {
+                      "480p":[0.17,0.18],
+                      "720p":[0.37,0.41]
+                    }
+                  };
+                  $model := widgets.model;
+                  $modelKey :=
+                    $contains($model, "seedance-1-0-pro")  ? "seedance-1-0-pro" :
+                    "seedance-1-0-lite";
+                  $resolution := widgets.resolution;
+                  $resKey :=
+                    $contains($resolution, "720") ? "720p" :
+                    "480p";
+                  $modelPrices := $lookup($priceByModel, $modelKey);
+                  $baseRange := $lookup($modelPrices, $resKey);
+                  $min10s := $baseRange[0];
+                  $max10s := $baseRange[1];
+                  $scale := widgets.duration / 10;
+                  $minCost := $min10s * $scale;
+                  $maxCost := $max10s * $scale;
+                  ($minCost = $maxCost)
+                    ? {"type":"usd","usd": $minCost}
+                    : {"type":"range_usd","min_usd": $minCost, "max_usd": $maxCost}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        images: Input.Image,
+        resolution: str,
+        aspect_ratio: str,
+        duration: int,
+        seed: int,
+        watermark: bool,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "watermark"])
+        for image in images:
+            validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000)
+            validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+
+        image_urls = await upload_images_to_comfyapi(cls, images, max_images=4, mime_type="image/png")
+        prompt = (
+            f"{prompt} "
+            f"--resolution {resolution} "
+            f"--ratio {aspect_ratio} "
+            f"--duration {duration} "
+            f"--seed {seed} "
+            f"--watermark {str(watermark).lower()}"
+        )
+        x = [
+            TaskTextContent(text=prompt),
+            *[TaskImageContent(image_url=TaskImageContentUrl(url=str(i)), role="reference_image") for i in image_urls],
+        ]
+        return await process_video_task(
+            cls,
+            payload=Image2VideoTaskCreationRequest(model=model, content=x, generate_audio=None),
+            estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
+        )
+
+
+def raise_if_text_params(prompt: str, text_params: list[str]) -> None:
+    for i in text_params:
+        if f"--{i} " in prompt:
+            raise ValueError(
+                f"--{i} is not allowed in the prompt, use the appropriated widget input to change this value."
+            )
+
+
+PRICE_BADGE_VIDEO = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution", "generate_audio"]),
+    expr="""
+    (
+      $priceByModel := {
+        "seedance-1-5-pro": {
+          "480p":[0.12,0.12],
+          "720p":[0.26,0.26],
+          "1080p":[0.58,0.59]
+        },
+        "seedance-1-0-pro": {
+          "480p":[0.23,0.24],
+          "720p":[0.51,0.56],
+          "1080p":[1.18,1.22]
+        },
+        "seedance-1-0-pro-fast": {
+          "480p":[0.09,0.1],
+          "720p":[0.21,0.23],
+          "1080p":[0.47,0.49]
+        },
+        "seedance-1-0-lite": {
+          "480p":[0.17,0.18],
+          "720p":[0.37,0.41],
+          "1080p":[0.85,0.88]
+        }
+      };
+      $model := widgets.model;
+      $modelKey :=
+        $contains($model, "seedance-1-5-pro")      ? "seedance-1-5-pro" :
+        $contains($model, "seedance-1-0-pro-fast") ? "seedance-1-0-pro-fast" :
+        $contains($model, "seedance-1-0-pro")      ? "seedance-1-0-pro" :
+        "seedance-1-0-lite";
+      $resolution := widgets.resolution;
+      $resKey :=
+        $contains($resolution, "1080") ? "1080p" :
+        $contains($resolution, "720")  ? "720p" :
+        "480p";
+      $modelPrices := $lookup($priceByModel, $modelKey);
+      $baseRange := $lookup($modelPrices, $resKey);
+      $min10s := $baseRange[0];
+      $max10s := $baseRange[1];
+      $scale := widgets.duration / 10;
+      $audioMultiplier := ($modelKey = "seedance-1-5-pro" and widgets.generate_audio) ? 2 : 1;
+      $minCost := $min10s * $scale * $audioMultiplier;
+      $maxCost := $max10s * $scale * $audioMultiplier;
+      ($minCost = $maxCost)
+        ? {"type":"usd","usd": $minCost, "format": { "approximate": true }}
+        : {"type":"range_usd","min_usd": $minCost, "max_usd": $maxCost, "format": { "approximate": true }}
+    )
+    """,
+)
+
+
+def _seedance2_text_inputs(resolutions: list[str], default_ratio: str = "16:9"):
+    return [
+        IO.String.Input(
+            "prompt",
+            multiline=True,
+            default="",
+            tooltip="Text prompt for video generation.",
+        ),
+        IO.Combo.Input(
+            "resolution",
+            options=resolutions,
+            tooltip="Resolution of the output video.",
+        ),
+        IO.Combo.Input(
+            "ratio",
+            options=["16:9", "4:3", "1:1", "3:4", "9:16", "21:9", "adaptive"],
+            default=default_ratio,
+            tooltip="Aspect ratio of the output video.",
+        ),
+        IO.Int.Input(
+            "duration",
+            default=7,
+            min=4,
+            max=15,
+            step=1,
+            tooltip="Duration of the output video in seconds (4-15).",
+            display_mode=IO.NumberDisplay.slider,
+        ),
+        IO.Boolean.Input(
+            "generate_audio",
+            default=True,
+            tooltip="Enable audio generation for the output video.",
+        ),
+    ]
+
+
+def _seedance25_text_inputs(with_ratio: bool = True, with_video_editing: bool = False, with_task_type: bool = False):
+    return [
+        IO.String.Input(
+            "prompt",
+            multiline=True,
+            default="",
+            tooltip="Text prompt for video generation. Put spoken lines in double quotes to steer "
+            "the generated dialogue.",
+        ),
+        IO.Combo.Input(
+            "resolution",
+            options=["480p", "720p", "1080p"],
+            default="720p",
+            tooltip="Resolution of the output video.",
+        ),
+        *(
+            [
+                IO.Combo.Input(
+                    "ratio",
+                    options=["16:9", "4:3", "1:1", "3:4", "9:16", "21:9", "adaptive"],
+                    default="16:9",
+                    tooltip="Aspect ratio of the output video.",
+                )
+            ]
+            if with_ratio
+            else []
+        ),
+        IO.Int.Input(
+            "duration",
+            default=5,
+            min=4,
+            max=30,
+            step=1,
+            tooltip="Duration of the output video in seconds (4-30).",
+            display_mode=IO.NumberDisplay.slider,
+        ),
+        IO.Boolean.Input(
+            "generate_audio",
+            default=True,
+            tooltip="Enable audio generation for the output video.",
+        ),
+        *(
+            [
+                IO.Boolean.Input(
+                    "video_editing",
+                    default=False,
+                    tooltip="Enable when the prompt edits a connected reference video, for example "
+                    "replacing an object in it. The output then keeps the source clip's own length "
+                    "and aspect ratio, and the duration and ratio widgets are ignored. Leave "
+                    "disabled to generate a new video, or to extend one to the duration you set.",
+                )
+            ]
+            if with_video_editing
+            else []
+        ),
+        *(
+            [
+                IO.Combo.Input(
+                    "task_type",
+                    options=["auto", "reference", "edit", "extend"],
+                    default="auto",
+                    tooltip="What to do with the reference media. Every value except auto is "
+                    "validated when the task is submitted, so mismatched settings fail before "
+                    "generation starts. auto: the model infers the task from the prompt and "
+                    "inputs, and settings that conflict with its reading fail only after "
+                    "generation has started. reference: generate a new video guided by the "
+                    "reference images, videos, and audio. edit: change a connected reference "
+                    "video (add, remove, replace); the output keeps the source clip's own length "
+                    "and aspect ratio, and the duration and ratio widgets are ignored. extend: "
+                    "continue a connected reference video forward or backward; the prompt should "
+                    "say 'extend forward', 'extend backward', or 'continue', the aspect ratio "
+                    "follows the source clip, and the output contains only the newly generated "
+                    "segment of the duration you set, not the source clip.",
+                )
+            ]
+            if with_task_type
+            else []
+        ),
+        IO.Combo.Input(
+            "output_format",
+            options=["mp4"],
+            default="mp4",
+            tooltip="Container format of the output video.",
+        ),
+    ]
+
+
+def _seedance25_reference_inputs(with_video_editing: bool = False, with_task_type: bool = False):
+    return [
+        *_seedance25_text_inputs(with_video_editing=with_video_editing, with_task_type=with_task_type),
+        IO.Autogrow.Input(
+            "reference_images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("reference_image"),
+                names=[f"image_{i}" for i in range(1, 31)],
+                min=0,
+            ),
+        ),
+        IO.Autogrow.Input(
+            "reference_videos",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("reference_video"),
+                names=[f"video_{i}" for i in range(1, 11)],
+                min=0,
+            ),
+        ),
+        IO.Autogrow.Input(
+            "reference_audios",
+            template=IO.Autogrow.TemplateNames(
+                IO.Audio.Input("reference_audio"),
+                names=[f"audio_{i}" for i in range(1, 11)],
+                min=0,
+            ),
+        ),
+        IO.Boolean.Input(
+            "auto_downscale",
+            default=True,
+            optional=True,
+            tooltip="Automatically downscale reference videos that exceed the model's pixel budget "
+            "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.",
+        ),
+        IO.Boolean.Input(
+            "auto_upscale",
+            default=False,
+            advanced=True,
+            optional=True,
+            tooltip="Automatically upscale reference videos that are below the model's minimum pixel count "
+            "for the selected resolution. Aspect ratio is preserved; videos already meeting the minimum are "
+            "untouched. Note: upscaling a low-resolution source does not add real detail and may produce "
+            "lower-quality generations.",
+        ),
+        IO.Autogrow.Input(
+            "reference_assets",
+            template=IO.Autogrow.TemplateNames(
+                IO.String.Input("reference_asset"),
+                names=[f"asset_{i}" for i in range(1, 31)],
+                min=0,
+            ),
+        ),
+    ]
+
+
+def _seedance2_build_request(
+    model: dict,
+    model_id: str,
+    content: list,
+    seed: int,
+    watermark: bool,
+    ratio: str,
+) -> Seedance2TaskCreationRequest:
+    task_type = model.get("task_type", "auto")
+    duration = model["duration"]
+    if model.get("video_editing") or task_type == "edit":
+        ratio, duration = "adaptive", -1
+    elif task_type == "extend":
+        ratio = "adaptive"
+    return Seedance2TaskCreationRequest(
+        model=model_id,
+        content=content,
+        generate_audio=model["generate_audio"],
+        resolution=model["resolution"],
+        ratio=ratio,
+        duration=duration,
+        seed=seed,
+        watermark=watermark,
+        output_format=model.get("output_format"),
+        omni_reference_task_type=None if task_type == "auto" else task_type,
+    )
+
+
+_SEEDANCE2_PRICE_EXPR_TEMPLATE = """
+(
+  $m := widgets.model;
+  $res := $lookup(widgets, "model.resolution");
+  $ratio := $lookup(widgets, "model.ratio");
+  $dur := $lookup(widgets, "model.duration");
+  $auto := __IS_EDIT__;
+  $hasVideo := __HAS_VIDEO__;
+  $ready := $type($m) = "string" and $type($res) = "string" and ($auto or $type($dur) = "number");
+  $ready ? (
+    $contains($m, "2.5") ? (
+      $is480 := $res = "480p";
+      $is1080 := $res = "1080p";
+      $perFrame := $ratio = "1:1"  ? ($is480 ? 400      : $is1080 ? 2025      : 900) :
+                   $ratio = "4:3"  ? ($is480 ? 411.25   : $is1080 ? 2028      : 905.6719) :
+                   $ratio = "3:4"  ? ($is480 ? 411.25   : $is1080 ? 2028      : 905.6719) :
+                   $ratio = "21:9" ? ($is480 ? 418.5    : $is1080 ? 2037.9648 : 904.3945) :
+                                     ($is480 ? 400.3125 : $is1080 ? 2025      : 900);
+      $price := $is1080
+        ? ($hasVideo ? 0.01001 : 0.016731)
+        : ($hasVideo ? 0.009152 : 0.015301);
+      $costFor := function($d) { $floor($perFrame * (24 * $d + 1)) / 1000 * $price };
+      $lo := $costFor($auto ? 4 : $dur);
+      $hi := $costFor(($auto ? 30 : $dur) + ($hasVideo ? 30 : 0));
+      $lo = $hi
+        ? {"type": "usd", "usd": $lo, "format": {"approximate": true}}
+        : {"type": "range_usd", "min_usd": $lo, "max_usd": $hi, "format": {"approximate": true}}
+    ) : (
+      $rate := $res = "4k"    ? 195200 :
+               $res = "1080p" ? 48800 :
+               $res = "720p"  ? 21600 : 10044;
+      $noVideoPrice := $res = "4k"    ? 0.00572 :
+                       $res = "1080p" ? 0.011011 :
+                       $contains($m, "mini") ? 0.005005 :
+                       $contains($m, "fast") ? 0.008008 : 0.01001;
+      $videoPrice := $res = "4k"    ? 0.003432 :
+                     $res = "1080p" ? 0.006721 :
+                     $contains($m, "mini") ? 0.003003 :
+                     $contains($m, "fast") ? 0.004719 : 0.006149;
+      $hasVideo
+        ? {"type": "range_usd",
+           "min_usd": $ceil($dur * 5 / 3) * $rate * $videoPrice / 1000,
+           "max_usd": (15 + $dur) * $rate * $videoPrice / 1000,
+           "format": {"approximate": true}}
+        : {"type": "usd", "usd": $dur * $rate * $noVideoPrice / 1000,
+           "format": {"approximate": true}}
+    )
+  ) : undefined
+)
+"""
+
+
+_SEEDANCE_AUDIO_POLICY_CODE = "OutputAudioSensitiveContentDetected.PolicyViolation"
+_SEEDANCE_TASK_TYPE_CONSTRAINT_CODE = "InvalidParameter.TaskTypeConstraint"
+_SEEDANCE_TASK_TYPE_MISMATCH_CODE = "InvalidParameter.TaskTypeMismatch"
+
+
+async def _seedance2_poll_video_task(
+    cls: type[IO.ComfyNode],
+    task_id: str,
+    task_type: str | None = None,
+) -> TaskStatusResponse:
+    try:
+        return await poll_op(
+            cls,
+            ApiEndpoint(path=f"{BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT}/{task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: r.status,
+            poll_interval=9,
+        )
+    except Exception as exc:
+        if _SEEDANCE_AUDIO_POLICY_CODE in str(exc):
+            raise ValueError(
+                "The provider rejected the audio track this model generated for the video "
+                "(possible copyright match). The video itself was fine. Turn off generate_audio "
+                "to get a silent video, or adjust the prompt and try again."
+            ) from exc
+        if _SEEDANCE_TASK_TYPE_CONSTRAINT_CODE in str(exc):
+            if task_type is None:
+                raise ValueError(
+                    "Seedance read this prompt as editing the reference video, and an edit always "
+                    "takes its duration and aspect ratio from that video. Enable video_editing on "
+                    "this node and run again, or reword the prompt so it describes a new video "
+                    "rather than a change to the reference one."
+                ) from exc
+            if task_type == "edit":
+                raise ValueError(
+                    "The request does not satisfy the 'edit' constraints: the clip being edited "
+                    "must be 4 to 30 seconds long."
+                ) from exc
+            if task_type == "extend":
+                raise ValueError(
+                    "The request does not satisfy the 'extend' constraints: the clip being "
+                    "extended must be 1.9 to 30 seconds long."
+                ) from exc
+            raise ValueError(
+                "Seedance decided from the prompt that this task's duration or aspect ratio "
+                "must come from the reference video, and the current settings conflict with "
+                "that. Set task_type to the task you mean ('edit' or 'extend') and run again, "
+                "or reword the prompt so it describes a new video rather than a change to the "
+                "reference one."
+            ) from exc
+        if _SEEDANCE_TASK_TYPE_MISMATCH_CODE in str(exc):
+            raise ValueError(
+                f"Seedance read this prompt as a different task than the selected task_type "
+                f"'{task_type}'. Reword the prompt so it matches: an extend prompt should say "
+                "'extend forward', 'extend backward', or 'continue'; an edit prompt should use "
+                "words like add, remove, replace, or change. Or set task_type to auto."
+            ) from exc
+        raise
+
+
+def _seedance2_price_badge(with_reference_videos: bool, legacy_video_editing: bool = False) -> IO.PriceBadge:
+    widgets = ["model", "model.resolution", "model.ratio", "model.duration"]
+    if legacy_video_editing:
+        is_edit = '$lookup(widgets, "model.video_editing") = true'
+    else:
+        is_edit = '$lookup(widgets, "model.task_type") = "edit"'
+    if with_reference_videos:
+        widgets.append("model.video_editing" if legacy_video_editing else "model.task_type")
+    has_video = (
+        '$exists(inputGroups) and $lookup(inputGroups, "model.reference_videos") > 0'
+        if with_reference_videos
+        else "false"
+    )
+    return IO.PriceBadge(
+        depends_on=IO.PriceBadgeDepends(
+            widgets=widgets,
+            input_groups=["model.reference_videos"] if with_reference_videos else [],
+        ),
+        expr=_SEEDANCE2_PRICE_EXPR_TEMPLATE.replace("__HAS_VIDEO__", has_video).replace("__IS_EDIT__", is_edit),
+    )
+
+
+class ByteDance2TextToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDance2TextToVideoNode",
+            display_name="ByteDance Seedance 2.5 Text to Video",
+            category="partner/video/ByteDance",
+            description="Generate video using Seedance 2.5 or 2.0 models based on a text prompt.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Seedance 2.5", _seedance25_text_inputs()),
+                        IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs(["480p", "720p", "1080p", "4k"])),
+                        IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs(["480p", "720p"])),
+                        IO.DynamicCombo.Option("Seedance 2.0 Mini", _seedance2_text_inputs(["480p", "720p"])),
+                    ],
+                    tooltip=SEEDANCE_MODEL_TOOLTIP,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add a watermark to the video.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_seedance2_price_badge(with_reference_videos=False),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+    ) -> IO.NodeOutput:
+        validate_string(model["prompt"], strip_whitespace=True, min_length=1)
+        model_id = SEEDANCE_MODELS[model["model"]]
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"),
+            data=_seedance2_build_request(
+                model,
+                model_id,
+                [TaskTextContent(text=model["prompt"])],
+                seed,
+                watermark,
+                ratio=model["ratio"],
+            ),
+            response_model=TaskCreationResponse,
+        )
+        response = await _seedance2_poll_video_task(cls, initial_response.id)
+        return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
+
+
+class ByteDance2FirstLastFrameNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDance2FirstLastFrameNode",
+            display_name="ByteDance Seedance 2.5 First-Last-Frame to Video",
+            category="partner/video/ByteDance",
+            description="Generate video using Seedance 2.5 or 2.0 from a first frame image "
+            "and optional last frame image.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Seedance 2.5", _seedance25_text_inputs(with_ratio=False)),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0",
+                            _seedance2_text_inputs(["480p", "720p", "1080p", "4k"], default_ratio="adaptive"),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Fast",
+                            _seedance2_text_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Mini",
+                            _seedance2_text_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
+                    ],
+                    tooltip=SEEDANCE_MODEL_TOOLTIP,
+                ),
+                IO.Image.Input(
+                    "first_frame",
+                    tooltip="First frame image for the video.",
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "last_frame",
+                    tooltip="Last frame image for the video.",
+                    optional=True,
+                ),
+                IO.String.Input(
+                    "first_frame_asset_id",
+                    default="",
+                    tooltip="Seedance asset_id to use as the first frame. "
+                    "Mutually exclusive with the first_frame image input.",
+                    optional=True,
+                ),
+                IO.String.Input(
+                    "last_frame_asset_id",
+                    default="",
+                    tooltip="Seedance asset_id to use as the last frame. "
+                    "Mutually exclusive with the last_frame image input.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add a watermark to the video.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_seedance2_price_badge(with_reference_videos=False),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+        first_frame: Input.Image | None = None,
+        last_frame: Input.Image | None = None,
+        first_frame_asset_id: str = "",
+        last_frame_asset_id: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(model["prompt"], strip_whitespace=True, min_length=1)
+        model_id = SEEDANCE_MODELS[model["model"]]
+
+        first_frame_asset_id = first_frame_asset_id.strip()
+        last_frame_asset_id = last_frame_asset_id.strip()
+
+        if first_frame is not None and first_frame_asset_id:
+            raise ValueError("Provide only one of first_frame or first_frame_asset_id, not both.")
+        if first_frame is None and not first_frame_asset_id:
+            raise ValueError("Either first_frame or first_frame_asset_id is required.")
+        if last_frame is not None and last_frame_asset_id:
+            raise ValueError("Provide only one of last_frame or last_frame_asset_id, not both.")
+
+        if model_id == "dreamina-seedance-2-5-260628":
+            # 2.5 accepts ratio="adaptive" only here and keeps the first frame's own aspect
+            # (a 1920x1088 frame yields 850x482, not a grid ratio), so pre-sizing the frames to a
+            # supported pixel pair would crop framing the model would otherwise have preserved.
+            request_ratio = "adaptive"
+            if first_frame is not None:
+                first_frame = _prepare_seedance_image(first_frame)
+            if last_frame is not None:
+                last_frame = _prepare_seedance_image(last_frame)
+        elif first_frame_asset_id or last_frame_asset_id:
+            request_ratio = model["ratio"]
+            if first_frame is not None:
+                first_frame = _prepare_seedance_image(first_frame)
+            if last_frame is not None:
+                last_frame = _prepare_seedance_image(last_frame)
+        else:
+            # The 1080p FLF stretch fix (pre-size frames to a supported pixel pair + submit ratio="adaptive")
+            # only applies to local image inputs we can resize.
+            request_ratio = "adaptive"
+            target_dims: tuple[int, int] | None = None
+            if first_frame is not None:
+                validate_image_aspect_ratio(first_frame, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+                validate_image_dimensions(first_frame, min_width=300, min_height=300)
+                target_dims = _seedance2_target_dims(model["resolution"], model["ratio"], first_frame)
+                first_frame = _resize_to_exact(first_frame, *target_dims)
+            if last_frame is not None:
+                validate_image_aspect_ratio(last_frame, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+                validate_image_dimensions(last_frame, min_width=300, min_height=300)
+                if target_dims is None:
+                    target_dims = _seedance2_target_dims(model["resolution"], model["ratio"], last_frame)
+                last_frame = _resize_to_exact(last_frame, *target_dims)
+
+        asset_ids_to_resolve = [a for a in (first_frame_asset_id, last_frame_asset_id) if a]
+        image_assets: dict[str, str] = {}
+        if asset_ids_to_resolve:
+            image_assets, _, _ = await _resolve_reference_assets(cls, asset_ids_to_resolve)
+            for aid in asset_ids_to_resolve:
+                if aid not in image_assets:
+                    raise ValueError(f"Asset {aid} is not an Image asset.")
+
+        if first_frame_asset_id:
+            first_frame_url = image_assets[first_frame_asset_id]
+        else:
+            first_frame_url = await _seedance_virtual_library_upload_image_asset(
+                cls, first_frame, wait_label="Uploading first frame."
+            )
+
+        content: list[TaskTextContent | TaskImageContent] = [
+            TaskTextContent(text=model["prompt"]),
+            TaskImageContent(
+                image_url=TaskImageContentUrl(url=first_frame_url),
+                role="first_frame",
+            ),
+        ]
+        if last_frame_asset_id:
+            content.append(
+                TaskImageContent(
+                    image_url=TaskImageContentUrl(url=image_assets[last_frame_asset_id]),
+                    role="last_frame",
+                ),
+            )
+        elif last_frame is not None:
+            content.append(
+                TaskImageContent(
+                    image_url=TaskImageContentUrl(
+                        url=await _seedance_virtual_library_upload_image_asset(
+                            cls, last_frame, wait_label="Uploading last frame."
+                        )
+                    ),
+                    role="last_frame",
+                ),
+            )
+
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"),
+            data=_seedance2_build_request(model, model_id, content, seed, watermark, ratio=request_ratio),
+            response_model=TaskCreationResponse,
+        )
+        response = await _seedance2_poll_video_task(cls, initial_response.id)
+        return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
+
+
+def _seedance2_reference_inputs(resolutions: list[str], default_ratio: str = "16:9"):
+    return [
+        *_seedance2_text_inputs(resolutions, default_ratio=default_ratio),
+        IO.Autogrow.Input(
+            "reference_images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("reference_image"),
+                names=[
+                    "image_1",
+                    "image_2",
+                    "image_3",
+                    "image_4",
+                    "image_5",
+                    "image_6",
+                    "image_7",
+                    "image_8",
+                    "image_9",
+                ],
+                min=0,
+            ),
+        ),
+        IO.Autogrow.Input(
+            "reference_videos",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("reference_video"),
+                names=["video_1", "video_2", "video_3"],
+                min=0,
+            ),
+        ),
+        IO.Autogrow.Input(
+            "reference_audios",
+            template=IO.Autogrow.TemplateNames(
+                IO.Audio.Input("reference_audio"),
+                names=["audio_1", "audio_2", "audio_3"],
+                min=0,
+            ),
+        ),
+        IO.Boolean.Input(
+            "auto_downscale",
+            default=True,
+            optional=True,
+            tooltip="Automatically downscale reference videos that exceed the model's pixel budget "
+            "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.",
+        ),
+        IO.Boolean.Input(
+            "auto_upscale",
+            default=False,
+            advanced=True,
+            optional=True,
+            tooltip="Automatically upscale reference videos that are below the model's minimum pixel count "
+            "for the selected resolution. Aspect ratio is preserved; videos already meeting the minimum are "
+            "untouched. Note: upscaling a low-resolution source does not add real detail and may produce "
+            "lower-quality generations.",
+        ),
+        IO.Autogrow.Input(
+            "reference_assets",
+            template=IO.Autogrow.TemplateNames(
+                IO.String.Input("reference_asset"),
+                names=[
+                    "asset_1",
+                    "asset_2",
+                    "asset_3",
+                    "asset_4",
+                    "asset_5",
+                    "asset_6",
+                    "asset_7",
+                    "asset_8",
+                    "asset_9",
+                ],
+                min=0,
+            ),
+        ),
+    ]
+
+
+class ByteDance2ReferenceNodeV2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDance2ReferenceNodeV2",
+            display_name="ByteDance Seedance 2.5 Reference to Video",
+            category="partner/video/ByteDance",
+            description="Generate, edit, or extend video using Seedance 2.5 or 2.0 with reference "
+            "images, videos, and audio. Supports multimodal reference, video editing, and video extension.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Seedance 2.5", _seedance25_reference_inputs(with_task_type=True)),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0",
+                            _seedance2_reference_inputs(["480p", "720p", "1080p", "4k"], default_ratio="adaptive"),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Fast",
+                            _seedance2_reference_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Mini",
+                            _seedance2_reference_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
+                    ],
+                    tooltip=SEEDANCE_MODEL_TOOLTIP,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add a watermark to the video.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_seedance2_price_badge(with_reference_videos=True),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+    ) -> IO.NodeOutput:
+        validate_string(model["prompt"], strip_whitespace=True, min_length=1)
+
+        reference_images = model.get("reference_images", {})
+        reference_videos = model.get("reference_videos", {})
+        reference_audios = model.get("reference_audios", {})
+        reference_assets = model.get("reference_assets", {})
+
+        reference_image_assets, reference_video_assets, reference_audio_assets = await _resolve_reference_assets(
+            cls, list(reference_assets.values())
+        )
+
+        model_id = SEEDANCE_MODELS[model["model"]]
+        limits = seedance2_reference_limits(model_id)
+
+        if not reference_images and not reference_videos and not reference_image_assets and not reference_video_assets:
+            if model_id != "dreamina-seedance-2-5-260628" or not (reference_audios or reference_audio_assets):
+                raise ValueError("At least one reference image or video or asset is required.")
+
+        total_images = len(reference_images) + len(reference_image_assets)
+        if total_images > limits["max_images"]:
+            raise ValueError(
+                f"Too many reference images: {total_images} "
+                f"(images={len(reference_images)}, image assets={len(reference_image_assets)}). "
+                f"Maximum is {limits['max_images']}."
+            )
+        total_videos = len(reference_videos) + len(reference_video_assets)
+        if total_videos > limits["max_videos"]:
+            raise ValueError(
+                f"Too many reference videos: {total_videos} "
+                f"(videos={len(reference_videos)}, video assets={len(reference_video_assets)}). "
+                f"Maximum is {limits['max_videos']}."
+            )
+        task_type = model.get("task_type")
+        if task_type in ("edit", "extend") and total_videos == 0:
+            raise ValueError(
+                f"A '{task_type}' task needs at least one reference video. Connect the video "
+                f"you want to {'change' if task_type == 'edit' else 'continue'}, or set "
+                "task_type to 'reference' to generate a new video from the references you have."
+            )
+        total_audios = len(reference_audios) + len(reference_audio_assets)
+        if total_audios > limits["max_audios"]:
+            raise ValueError(
+                f"Too many reference audios: {total_audios} "
+                f"(audios={len(reference_audios)}, audio assets={len(reference_audio_assets)}). "
+                f"Maximum is {limits['max_audios']}."
+            )
+
+        for key in reference_images:
+            reference_images[key] = _prepare_seedance_image(reference_images[key])
+
+        if model.get("auto_downscale") and reference_videos:
+            max_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("max")
+            if max_px:
+                for key in reference_videos:
+                    reference_videos[key] = downscale_video_to_max_pixels(reference_videos[key], max_px)
+
+        if model.get("auto_upscale") and reference_videos:
+            min_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("min")
+            if min_px:
+                for key in reference_videos:
+                    reference_videos[key] = upscale_video_to_min_pixels(reference_videos[key], min_px)
+
+        total_video_duration = 0.0
+        for i, key in enumerate(reference_videos, 1):
+            video = reference_videos[key]
+            _validate_ref_video_pixels(video, model_id, model["resolution"], i)
+            try:
+                dur = video.get_duration()
+                if dur < 1.8:
+                    raise ValueError(f"Reference video {i} is too short: {dur:.1f}s. Minimum duration is 1.8 seconds.")
+                total_video_duration += dur
+            except ValueError:
+                raise
+            except Exception:
+                pass
+        if total_video_duration > limits["max_total_seconds"]:
+            raise ValueError(
+                f"Total reference video duration is {total_video_duration:.1f}s. "
+                f"Maximum is {limits['max_total_seconds']} seconds."
+            )
+
+        total_audio_duration = 0.0
+        for i, key in enumerate(reference_audios, 1):
+            audio = reference_audios[key]
+            dur = int(audio["waveform"].shape[-1]) / int(audio["sample_rate"])
+            if dur < 1.8:
+                raise ValueError(f"Reference audio {i} is too short: {dur:.1f}s. Minimum duration is 1.8 seconds.")
+            total_audio_duration += dur
+        if total_audio_duration > limits["max_total_seconds"]:
+            raise ValueError(
+                f"Total reference audio duration is {total_audio_duration:.1f}s. "
+                f"Maximum is {limits['max_total_seconds']} seconds."
+            )
+
+        asset_labels = _build_asset_labels(
+            reference_assets,
+            reference_image_assets,
+            reference_video_assets,
+            reference_audio_assets,
+            len(reference_images),
+            len(reference_videos),
+            len(reference_audios),
+        )
+        prompt_text = _rewrite_asset_refs(model["prompt"], asset_labels)
+
+        content: list[TaskTextContent | TaskImageContent | TaskVideoContent | TaskAudioContent] = [
+            TaskTextContent(text=prompt_text),
+        ]
+        for i, key in enumerate(reference_images, 1):
+            content.append(
+                TaskImageContent(
+                    image_url=TaskImageContentUrl(
+                        url=await _seedance_virtual_library_upload_image_asset(
+                            cls,
+                            reference_images[key],
+                            wait_label=f"Uploading image {i}",
+                        ),
+                    ),
+                    role="reference_image",
+                ),
+            )
+        for i, key in enumerate(reference_videos, 1):
+            content.append(
+                TaskVideoContent(
+                    video_url=TaskVideoContentUrl(
+                        url=await _seedance_virtual_library_upload_video_asset(
+                            cls,
+                            reference_videos[key],
+                            wait_label=f"Uploading video {i}",
+                        ),
+                    ),
+                ),
+            )
+        for key in reference_audios:
+            content.append(
+                TaskAudioContent(
+                    audio_url=TaskAudioContentUrl(
+                        url=await upload_audio_to_comfyapi(
+                            cls,
+                            reference_audios[key],
+                            container_format="mp3",
+                            codec_name="libmp3lame",
+                            mime_type="audio/mpeg",
+                        ),
+                    ),
+                ),
+            )
+        for url in reference_image_assets.values():
+            content.append(
+                TaskImageContent(
+                    image_url=TaskImageContentUrl(url=url),
+                    role="reference_image",
+                ),
+            )
+        for url in reference_video_assets.values():
+            content.append(
+                TaskVideoContent(video_url=TaskVideoContentUrl(url=url)),
+            )
+        for url in reference_audio_assets.values():
+            content.append(
+                TaskAudioContent(audio_url=TaskAudioContentUrl(url=url)),
+            )
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"),
+            data=_seedance2_build_request(model, model_id, content, seed, watermark, ratio=model["ratio"]),
+            response_model=TaskCreationResponse,
+        )
+        response = await _seedance2_poll_video_task(
+            cls,
+            initial_response.id,
+            task_type=task_type,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
+
+
+class ByteDance2ReferenceNode(ByteDance2ReferenceNodeV2):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDance2ReferenceNode",
+            display_name="ByteDance Seedance 2.5 Reference to Video (Legacy)",
+            category="partner/video/ByteDance",
+            description="Generate, edit, or extend video using Seedance 2.5 or 2.0 with reference "
+            "images, videos, and audio. Supports multimodal reference, video editing, and video extension.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Seedance 2.5", _seedance25_reference_inputs(with_video_editing=True)),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0",
+                            _seedance2_reference_inputs(["480p", "720p", "1080p", "4k"], default_ratio="adaptive"),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Fast",
+                            _seedance2_reference_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Mini",
+                            _seedance2_reference_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
+                    ],
+                    tooltip=SEEDANCE_MODEL_TOOLTIP,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add a watermark to the video.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_deprecated=True,
+            price_badge=_seedance2_price_badge(with_reference_videos=True, legacy_video_editing=True),
+        )
+
+
+async def process_video_task(
+    cls: type[IO.ComfyNode],
+    payload: Text2VideoTaskCreationRequest | Image2VideoTaskCreationRequest,
+    estimated_duration: int | None,
+) -> IO.NodeOutput:
+    if payload.model in DEPRECATED_MODELS:
+        logger.warning(
+            "Model '%s' is deprecated and will be deactivated on May 13, 2026. "
+            "Please switch to a newer model. Recommended: seedance-1-0-pro-fast-251015.",
+            payload.model,
+        )
+    initial_response = await sync_op(
+        cls,
+        ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"),
+        data=payload,
+        response_model=TaskCreationResponse,
+    )
+    response = await poll_op(
+        cls,
+        ApiEndpoint(path=f"{BYTEPLUS_TASK_STATUS_ENDPOINT}/{initial_response.id}"),
+        status_extractor=lambda r: r.status,
+        estimated_duration=estimated_duration,
+        response_model=TaskStatusResponse,
+    )
+    return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
+
+
+class ByteDanceCreateImageAsset(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ByteDanceCreateImageAsset",
+            display_name="ByteDance Create Image Asset",
+            category="partner/image/ByteDance",
+            description=(
+                "Create a Seedance 2.0 personal image asset. Uploads the input image and "
+                "registers it in the given asset group. If group_id is empty, runs a real-person "
+                "H5 authentication flow to create a new group before adding the asset."
+            ),
+            inputs=[
+                IO.Image.Input("image", tooltip="Image to register as a personal asset."),
+                IO.String.Input(
+                    "group_id",
+                    default="",
+                    tooltip="Reuse an existing Seedance asset group ID to skip repeated human verification for the "
+                    "same person. Leave empty to run real-person authentication in the browser and create a new group.",
+                ),
+                # IO.String.Input(
+                #     "name",
+                #     default="",
+                #     tooltip="Asset name (up to 64 characters).",
+                # ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="asset_id"),
+                IO.String.Output(display_name="group_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            # is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        group_id: str = "",
+        # name: str = "",
+    ) -> IO.NodeOutput:
+        # if len(name) > 64:
+        #     raise ValueError("Name of asset can not be greater then 64 symbols")
+        validate_image_dimensions(image, min_width=300, max_width=6000, min_height=300, max_height=6000)
+        validate_image_aspect_ratio(image, min_ratio=(0.4, 1), max_ratio=(2.5, 1))
+        resolved_group = await _resolve_group_id(cls, group_id)
+        asset_id = await _create_seedance_asset(
+            cls,
+            group_id=resolved_group,
+            url=await upload_image_to_comfyapi(cls, image),
+            name="",
+            asset_type="Image",
+        )
+        await _wait_for_asset_active(cls, asset_id, resolved_group)
+        PromptServer.instance.send_progress_text(
+            f"Please save the asset_id and group_id for reuse.\n\nasset_id: {asset_id}\n\n"
+            f"group_id: {resolved_group}",
+            cls.hidden.unique_id,
+        )
+        return IO.NodeOutput(asset_id, resolved_group)
+
+
+class ByteDanceCreateVideoAsset(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ByteDanceCreateVideoAsset",
+            display_name="ByteDance Create Video Asset",
+            category="partner/video/ByteDance",
+            description=(
+                "Create a Seedance 2.0 personal video asset. Uploads the input video and "
+                "registers it in the given asset group. If group_id is empty, runs a real-person "
+                "H5 authentication flow to create a new group before adding the asset."
+            ),
+            inputs=[
+                IO.Video.Input("video", tooltip="Video to register as a personal asset."),
+                IO.String.Input(
+                    "group_id",
+                    default="",
+                    tooltip="Reuse an existing Seedance asset group ID to skip repeated human verification for the "
+                    "same person. Leave empty to run real-person authentication in the browser and create a new group.",
+                ),
+                # IO.String.Input(
+                #     "name",
+                #     default="",
+                #     tooltip="Asset name (up to 64 characters).",
+                # ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="asset_id"),
+                IO.String.Output(display_name="group_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            # is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        group_id: str = "",
+        # name: str = "",
+    ) -> IO.NodeOutput:
+        # if len(name) > 64:
+        #     raise ValueError("Name of asset can not be greater then 64 symbols")
+        validate_video_duration(video, min_duration=2, max_duration=15)
+        validate_video_dimensions(video, min_width=300, max_width=6000, min_height=300, max_height=6000)
+
+        w, h = video.get_dimensions()
+        if h > 0:
+            ratio = w / h
+            if not (0.4 <= ratio <= 2.5):
+                raise ValueError(f"Asset video aspect ratio (W/H) must be in [0.4, 2.5], got {ratio:.3f} ({w}x{h}).")
+        pixels = w * h
+        if not (409_600 <= pixels <= 927_408):
+            raise ValueError(
+                f"Asset video total pixels (W×H) must be in [409600, 927408], " f"got {pixels:,} ({w}x{h})."
+            )
+
+        fps = float(video.get_frame_rate())
+        if not (24 <= fps <= 60):
+            raise ValueError(f"Asset video FPS must be in [24, 60], got {fps:.2f}.")
+
+        resolved_group = await _resolve_group_id(cls, group_id)
+        asset_id = await _create_seedance_asset(
+            cls,
+            group_id=resolved_group,
+            url=await upload_video_to_comfyapi(cls, video),
+            name="",
+            asset_type="Video",
+        )
+        await _wait_for_asset_active(cls, asset_id, resolved_group)
+        PromptServer.instance.send_progress_text(
+            f"Please save the asset_id and group_id for reuse.\n\nasset_id: {asset_id}\n\n"
+            f"group_id: {resolved_group}",
+            cls.hidden.unique_id,
+        )
+        return IO.NodeOutput(asset_id, resolved_group)
+
+
+MODE_TEXT = "text only"
+MODE_AUDIO = "audio reference"
+MODE_IMAGE = "image reference"
+MODE_SPEAKER = "preset voice"
+
+# (speaker_id, display_label) for built-in TTS 2.0 voices; resolvable ids are account-scoped.
+SEED_AUDIO_PRESET_VOICES: list[tuple[str, str]] = [
+    ("zh_female_vv_uranus_bigtts", "Vivi (Female, multilingual)"),
+    ("zh_female_xiaohe_uranus_bigtts", "Mindy (Female, multilingual)"),
+    ("en_female_stokie_uranus_bigtts", "Stokie (Female, English)"),
+    ("en_female_dacey_uranus_bigtts", "Dacey (Female, English)"),
+    ("en_male_tim_uranus_bigtts", "Tim (Male, English)"),
+    ("zh_male_m191_uranus_bigtts", "Kian (Male, multilingual)"),
+    ("zh_male_taocheng_uranus_bigtts", "Cedric (Male, multilingual)"),
+    ("zh_male_sophie_uranus_bigtts", "Sophie (Female, multilingual)"),
+    ("zh_female_yingyujiaoxue_uranus_bigtts", "Jean (Female, multilingual)"),
+    ("zh_male_dayi_uranus_bigtts", "Magnus (Male, multilingual)"),
+    ("zh_female_mizai_uranus_bigtts", "Mabel (Female, multilingual)"),
+    ("zh_female_jitangnv_uranus_bigtts", "Nadia (Female, multilingual)"),
+    ("zh_female_meilinvyou_uranus_bigtts", "Opal (Female, multilingual)"),
+    ("zh_female_liuchangnv_uranus_bigtts", "Pearl (Female, multilingual)"),
+    ("zh_male_ruyayichen_uranus_bigtts", "Quentin (Male, multilingual)"),
+    ("zh_female_vivo_uranus_bigtts", "Vienna (Female, multilingual)"),
+    ("zh_female_xiaoai_uranus_bigtts", "Alina (Female, multilingual)"),
+    ("zh_female_cancan_uranus_bigtts", "Corinne (Female, multilingual)"),
+    ("zh_female_tianmeixiaoyuan_uranus_bigtts", "Esther (Female, multilingual)"),
+    ("zh_female_tianmeitaozi_uranus_bigtts", "Freya (Female, multilingual)"),
+    ("zh_female_shuangkuaisisi_uranus_bigtts", "Gigi (Female, multilingual)"),
+    ("zh_female_peiqi_uranus_bigtts", "Holly (Female, multilingual)"),
+    ("zh_female_xiaoxue_uranus_bigtts", "Lyla (Female, multilingual)"),
+    ("zh_female_yuanqi_uranus_bigtts", "Daisy (Female, multilingual)"),
+    ("zh_female_kefunvsheng_uranus_bigtts", "Tracy (Female, multilingual)"),
+    ("zh_male_shaonianzixin_uranus_bigtts", "Jess (Male, multilingual)"),
+    ("zh_female_linjianvhai_uranus_bigtts", "Pinky (Female, multilingual)"),
+    ("zh_female_kiwi_uranus_bigtts", "Sweety (Female, multilingual)"),
+    ("zh_female_sajiaoxuemei_uranus_bigtts", "Sandy (Female, multilingual)"),
+    ("de_male_seven_uranus_bigtts", "Sven (Male, German)"),
+    ("jp_female_minimi_uranus_bigtts", "Minimi (Female, Japanese)"),
+    ("fr_male_usseau_uranus_bigtts", "Usseau (Male, French)"),
+    ("es_male_felipe_uranus_bigtts", "Felipe (Male, Spanish)"),
+    ("id_male_han_uranus_bigtts", "Han (Male, Indonesian)"),
+    ("pt_male_martins_uranus_bigtts", "Martins (Male, Portuguese)"),
+    ("it_male_enzo_uranus_bigtts", "Enzo (Male, Italian)"),
+    ("kr_male_shane_uranus_bigtts", "Shane (Male, Korean)"),
+    ("zh_male_liufei_uranus_bigtts", "Felix (Male, Chinese)"),
+    ("zh_female_qingxinnvsheng_uranus_bigtts", "Celeste (Female, Chinese)"),
+    ("zh_male_sunwukong_uranus_bigtts", "Monkey King (Male, Chinese)"),
+]
+SEED_AUDIO_VOICE_OPTIONS = [label for _, label in SEED_AUDIO_PRESET_VOICES]
+SEED_AUDIO_VOICE_MAP = {label: speaker_id for speaker_id, label in SEED_AUDIO_PRESET_VOICES}
+
+_AUDIO_TAG_RE = re.compile(r"@Audio(\d+)", re.IGNORECASE)
+
+
+def max_audio_tag(prompt: str) -> int:
+    """Highest N referenced as @AudioN in the prompt (0 if none)."""
+    nums = [int(m) for m in _AUDIO_TAG_RE.findall(prompt or "")]
+    return max(nums) if nums else 0
+
+
+def connected_audio_indices(reference_mode: dict) -> list[int]:
+    """Indices (1-based) of connected reference_audio sockets, in order."""
+    return [
+        i
+        for i in range(1, 3 + 1)
+        if reference_mode.get(f"reference_audio_{i}") is not None
+    ]
+
+
+def validate_seed_audio_inputs(
+    text_prompt: str,
+    mode: str,
+    audio_indices: list[int],
+    has_image: bool,
+    preset_voice: str | None = None,
+) -> None:
+    validate_string(text_prompt, field_name="text_prompt", min_length=1, max_length=3000)
+    max_tag = max_audio_tag(text_prompt)
+
+    if mode == MODE_TEXT:
+        if max_tag:
+            raise ValueError(
+                f"The prompt references @Audio{max_tag}, but reference mode is '{MODE_TEXT}'. "
+                f"Switch to '{MODE_AUDIO}' and connect the reference clip(s)."
+            )
+    elif mode == MODE_AUDIO:
+        if not audio_indices:
+            raise ValueError(
+                f"Reference mode '{MODE_AUDIO}' requires at least one reference_audio input "
+                f"(or switch to '{MODE_TEXT}')."
+            )
+        if audio_indices != list(range(1, len(audio_indices) + 1)):
+            raise ValueError(
+                "Connect reference_audio inputs in order without gaps: reference_audio_1, then _2, then _3."
+            )
+        if max_tag > len(audio_indices):
+            raise ValueError(
+                f"The prompt references @Audio{max_tag}, but only {len(audio_indices)} "
+                f"reference audio(s) are connected."
+            )
+    elif mode == MODE_IMAGE:
+        if not has_image:
+            raise ValueError(f"Reference mode '{MODE_IMAGE}' requires a reference_image input.")
+        if max_tag:
+            raise ValueError(
+                f"@AudioN tags are not used in '{MODE_IMAGE}' mode; the prompt should contain "
+                f"only the text to synthesize."
+            )
+    elif mode == MODE_SPEAKER:
+        if not preset_voice or preset_voice not in SEED_AUDIO_VOICE_MAP:
+            raise ValueError(f"Reference mode '{MODE_SPEAKER}' requires selecting a preset voice.")
+        if max_tag > 1:
+            raise ValueError(
+                f"'{MODE_SPEAKER}' mode uses a single voice, so @Audio{max_tag} is out of range. "
+                f"Remove the @AudioN tags — the whole prompt is read in the selected voice."
+            )
+    else:
+        raise ValueError(f"Unknown reference mode: {mode!r}")
+
+
+class ByteDanceSeedAudioNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ByteDanceSeedAudio",
+            display_name="ByteDance Seed Audio 1.0",
+            category="partner/audio/ByteDance",
+            description=(
+                "Generate speech, music, sound effects and multi-speaker dialogue from a single prompt "
+                "with ByteDance Seed Audio 1.0. Describe the voice(s), emotion, ambience, background music "
+                "and sound effects in the prompt, and include the lines to speak. Optionally pick a built-in "
+                "preset voice, clone voices from up to 3 reference clips (tagged @Audio1-3 in the prompt), "
+                "or derive a voice from a character image. Up to 2 minutes of audio per run. "
+                "The multilingual model supports 20 languages and timestamp-based timing control."
+            ),
+            inputs=[
+                IO.String.Input(
+                    "text_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip=(
+                        "Describe the voice(s), emotion, pacing, ambience, background music and sound "
+                        "effects, and include the lines to speak (name characters inline for dialogue). "
+                        "In 'audio reference' mode, refer to connected clips by order as @Audio1, @Audio2, "
+                        "@Audio3. With the multilingual model, a quoted line can start with a timestamp "
+                        'range that controls when and how long it is spoken, e.g. "[5.5s:8.0s] Wait for me!". '
+                        "Write the prompt in the same language as the lines to speak. Maximum 3000 characters."
+                    ),
+                ),
+                IO.DynamicCombo.Input(
+                    "reference_mode",
+                    options=[
+                        IO.DynamicCombo.Option(MODE_TEXT, []),
+                        IO.DynamicCombo.Option(
+                            MODE_AUDIO,
+                            [
+                                IO.Audio.Input(
+                                    "reference_audio_1",
+                                    optional=True,
+                                    tooltip="Reference clip for voice cloning, tagged @Audio1 in the prompt. "
+                                    "Up to 30s.",
+                                ),
+                                IO.Audio.Input(
+                                    "reference_audio_2",
+                                    optional=True,
+                                    tooltip="Reference clip tagged @Audio2 in the prompt. Up to 30s.",
+                                ),
+                                IO.Audio.Input(
+                                    "reference_audio_3",
+                                    optional=True,
+                                    tooltip="Reference clip tagged @Audio3 in the prompt. Up to 30s.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            MODE_IMAGE,
+                            [
+                                IO.Image.Input(
+                                    "reference_image",
+                                    optional=True,
+                                    tooltip="A single character image; the model derives a voice from it. "
+                                    "Cannot be combined with reference audio.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            MODE_SPEAKER,
+                            [
+                                IO.Combo.Input(
+                                    "preset_voice",
+                                    options=SEED_AUDIO_VOICE_OPTIONS,
+                                    default=SEED_AUDIO_VOICE_OPTIONS[0],
+                                    tooltip="A built-in TTS 2.0 voice that reads the prompt. No reference "
+                                    "clip needed, and @AudioN tags are not used in this mode.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip=(
+                        "How to condition the voice: 'text only' (describe everything in the prompt), "
+                        "'audio reference' (clone up to 3 voices, tagged @Audio1-3), 'image reference' "
+                        "(derive a voice from one character image), or 'preset voice' (pick a built-in "
+                        "named voice that reads the prompt)."
+                    ),
+                ),
+                IO.Combo.Input(
+                    "sample_rate",
+                    options=["8000", "16000", "24000", "32000", "44100", "48000"],
+                    default="24000",
+                    tooltip="Output sample rate in Hz.",
+                ),
+                IO.Int.Input(
+                    "speech_rate",
+                    default=0,
+                    min=-50,
+                    max=100,
+                    tooltip="Speaking speed. 0 = normal, 100 = 2.0x, -50 = 0.5x.",
+                ),
+                IO.Int.Input(
+                    "loudness_rate",
+                    default=0,
+                    min=-50,
+                    max=100,
+                    tooltip="Loudness. 0 = normal, 100 = 2.0x, -50 = 0.5x.",
+                ),
+                IO.Int.Input(
+                    "pitch_rate",
+                    default=0,
+                    min=-12,
+                    max=12,
+                    tooltip="Pitch shift in semitones (-12 to 12).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["seed-audio-1.0-multilingual", "seed-audio-1.0"],
+                    default="seed-audio-1.0-multilingual",
+                    optional=True,
+                    tooltip=(
+                        "seed-audio-1.0-multilingual: 20 languages (English, Chinese, Japanese, Korean, "
+                        "Mexican & Castilian Spanish, Indonesian, German, Brazilian Portuguese, French, "
+                        "Thai, Vietnamese, Malay, Filipino, Italian, Russian, Dutch, Polish, Turkish, "
+                        'Swedish) plus per-sentence timing control via "[5.5s:8.0s] ..." timestamps. '
+                        "seed-audio-1.0: English and Chinese only, no timing control."
+                    ),
+                ),
+            ],
+            outputs=[IO.Audio.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.2145, "format":{"suffix":"/minute","approximate":true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        text_prompt: str,
+        reference_mode: dict,
+        sample_rate: str,
+        speech_rate: int,
+        loudness_rate: int,
+        pitch_rate: int,
+        seed: int,
+        model: str = "seed-audio-1.0-multilingual",
+    ) -> IO.NodeOutput:
+        mode = reference_mode["reference_mode"]
+        audio_indices = connected_audio_indices(reference_mode)
+        image = reference_mode.get("reference_image")
+        preset_voice = reference_mode.get("preset_voice")
+        validate_seed_audio_inputs(text_prompt, mode, audio_indices, image is not None, preset_voice)
+
+        references: list[SeedAudioReference] | None = None
+        if mode == MODE_AUDIO:
+            references = []
+            for i in audio_indices:
+                clip = reference_mode[f"reference_audio_{i}"]
+                validate_audio_duration(clip, max_duration=30.0)
+                mp3_bytes = audio_input_to_mp3(clip).getvalue()
+                references.append(SeedAudioReference(audio_data=base64.b64encode(mp3_bytes).decode("utf-8")))
+        elif mode == MODE_IMAGE:
+            image = upscale_image_tensor_to_min_pixels(image, 160_000)
+            references = [SeedAudioReference(image_data=tensor_to_base64_string(image, mime_type="image/png"))]
+        elif mode == MODE_SPEAKER:
+            references = [SeedAudioReference(speaker=SEED_AUDIO_VOICE_MAP[preset_voice])]
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/byteplus/api/v3/tts/create", method="POST"),
+            response_model=SeedAudioResponse,
+            data=SeedAudioRequest(
+                model=model,
+                text_prompt=text_prompt,
+                references=references,
+                audio_config=SeedAudioConfig(
+                    sample_rate=int(sample_rate),
+                    speech_rate=speech_rate,
+                    loudness_rate=loudness_rate,
+                    pitch_rate=pitch_rate,
+                ),
+            ),
+        )
+        if not response.audio:
+            raise Exception(
+                f"Seed Audio returned no audio (code={response.code}): {response.message}"
+            )
+        return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response.audio)))
+
+
+_VCUBE_ENHANCE_VIDEO_ENDPOINT = ApiEndpoint(path="/proxy/byteplusmediakit/api/v1/tools/enhance-video", method="POST")
+_VCUBE_TASK_ENDPOINT_PREFIX = "/proxy/byteplusmediakit/api/v1/tasks/"
+
+_VCUBE_MAX_DURATION_SECONDS = 600
+_VCUBE_MIN_FPS = 15.0
+_VCUBE_MAX_FPS = 120.0
+_VCUBE_MIN_SHORT_SIDE = 128
+_VCUBE_MAX_SHORT_SIDE = 4320
+_VCUBE_MAX_INPUT_SHORT_SIDE = 1440
+_VCUBE_MAX_INPUT_LONG_SIDE = 2560
+_VCUBE_RESOLUTION_PRESETS = ["1080p", "720p", "2k", "4k", "8k"]
+_VCUBE_FPS_PRESETS = ["source", "24", "25", "30", "48", "50", "60", "120"]
+
+
+class ByteDanceVideoEnhanceNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ByteDanceVideoEnhanceNode",
+            display_name="ByteDance vCube Video Enhance",
+            category="partner/video/ByteDance",
+            description="Upscales and restores a video with ByteDance vCube: super-resolution up to 8K, "
+            "compression artifact and noise removal, colour and sharpness enhancement, "
+            "optional frame interpolation.",
+            inputs=[
+                IO.Video.Input(
+                    "video",
+                    tooltip="Video to enhance. The source resolution must be at most 2560x1440 (2K); "
+                    "the output size is set by the resolution input.",
+                ),
+                IO.DynamicCombo.Input(
+                    "tool_version",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "standard",
+                            [
+                                IO.Combo.Input(
+                                    "scene",
+                                    options=["aigc", "common", "ugc", "short_series", "old_film"],
+                                    default="aigc",
+                                    tooltip="Preset tuned to the content: 'aigc' for AI-generated footage, "
+                                    "'common' for general video, 'ugc' for compressed phone clips, "
+                                    "'short_series' for drama with faces, 'old_film' for scratched or "
+                                    "flickering archive footage.",
+                                ),
+                                IO.Combo.Input(
+                                    "enhance_style",
+                                    options=["hd", "natural"],
+                                    default="hd",
+                                    tooltip="'hd' applies a sharper enhancement; 'natural' reduces the strength "
+                                    "for a softer, less sharpened look.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "professional",
+                            [
+                                IO.Combo.Input(
+                                    "enhance_style",
+                                    options=["hd", "natural"],
+                                    default="hd",
+                                    tooltip="'hd' applies a sharper enhancement; 'natural' reduces the strength "
+                                    "for a softer, less sharpened look.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="'standard' balances speed and quality with 10+ enhancement algorithms. "
+                    "'professional' uses 30+ algorithms for cinema-grade restoration, takes about "
+                    "3x longer and costs 10x more.",
+                ),
+                IO.DynamicCombo.Input(
+                    "resolution",
+                    options=[
+                        *[IO.DynamicCombo.Option(preset, []) for preset in _VCUBE_RESOLUTION_PRESETS],
+                        IO.DynamicCombo.Option("source", []),
+                        IO.DynamicCombo.Option(
+                            "custom",
+                            [
+                                IO.Int.Input(
+                                    "short_side",
+                                    default=1080,
+                                    min=_VCUBE_MIN_SHORT_SIDE,
+                                    max=_VCUBE_MAX_SHORT_SIDE,
+                                    tooltip="Short side of the output in pixels; the long side follows "
+                                    "the source aspect ratio.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Output resolution. The short side is set to the chosen level and the long side "
+                    "follows the source aspect ratio. 'source' keeps the source size, 'custom' sets "
+                    "the short side in pixels. Sources wider or taller than about 2.2:1 are billed one "
+                    "resolution tier higher.",
+                ),
+                IO.Combo.Input(
+                    "fps",
+                    options=_VCUBE_FPS_PRESETS,
+                    default="source",
+                    tooltip="Output frame rate. A higher rate than the source enables AI frame interpolation; "
+                    "a lower one drops frames. 'source' keeps the source rate, up to 120 fps. "
+                    "Rates above 30 fps cost 2x, above 60 fps 4x.",
+                ),
+                IO.Combo.Input(
+                    "bitrate_level",
+                    options=["low", "medium", "high"],
+                    default="medium",
+                    advanced=True,
+                    tooltip="Target bitrate of the delivered file, scaled to the output resolution and frame rate.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["tool_version", "resolution", "resolution.short_side", "fps"],
+                ),
+                expr="""
+                (
+                  $tv := $lookup(widgets, "tool_version");
+                  $res := $lookup(widgets, "resolution");
+                  $fps := $lookup(widgets, "fps");
+                  $tiers := {"720p": 1, "1080p": 2, "2k": 4, "4k": 8, "8k": 32};
+                  $tier := $res = "custom"
+                    ? ($s := $number($lookup(widgets, "resolution.short_side"));
+                       $s < 1080 ? 1 : $s < 1440 ? 2 : $s < 2160 ? 4 : $s < 4320 ? 8 : 32)
+                    : $lookup($tiers, $res);
+                  $fpsMul := $fps = "source" ? 1 : ($number($fps) <= 30 ? 1 : ($number($fps) <= 60 ? 2 : 4));
+                  $base := 0.2066 * 1.43 / 60 * ($tv = "professional" ? 10 : 1);
+                  $min := $base * ($res = "source" ? 1 : $tier) * ($fps = "source" ? 1 : $fpsMul);
+                  $max := $base * ($res = "source" ? 4 : $tier) * ($fps = "source" ? 4 : $fpsMul);
+                  $min = $max
+                    ? {"type": "usd", "usd": $min, "format": {"suffix": "/second"}}
+                    : {"type": "range_usd", "min_usd": $min, "max_usd": $max,
+                       "format": {"approximate": true, "suffix": "/second",
+                                  "note": $res = "source"
+                                    ? ($fps = "source" ? "(by source size and frame rate)" : "(720p-2K, by source size)")
+                                    : "(by source frame rate)"}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        tool_version: dict,
+        resolution: dict,
+        fps: str,
+        bitrate_level: str,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, max_duration=_VCUBE_MAX_DURATION_SECONDS)
+        width, height = video.get_dimensions()
+        if min(width, height) > _VCUBE_MAX_INPUT_SHORT_SIDE or max(width, height) > _VCUBE_MAX_INPUT_LONG_SIDE:
+            raise ValueError(
+                f"Video resolution must be at most {_VCUBE_MAX_INPUT_LONG_SIDE}x{_VCUBE_MAX_INPUT_SHORT_SIDE} "
+                f"(2K), got {width}x{height}. Scale the video down before enhancing it."
+            )
+        if fps == "source":
+            source_fps = float(video.get_frame_rate())
+            output_fps = round(min(source_fps, _VCUBE_MAX_FPS), 3) if source_fps >= _VCUBE_MIN_FPS else None
+        else:
+            output_fps = float(fps)
+        target = resolution["resolution"]
+        resolution_preset = target if target in _VCUBE_RESOLUTION_PRESETS else None
+        short_side = None
+        if target == "custom":
+            short_side = resolution["short_side"]
+        elif target == "source" and min(width, height) >= _VCUBE_MIN_SHORT_SIDE:
+            short_side = min(width, height)
+        url = await upload_video_to_comfyapi(cls, video, wait_label="Uploading source video")
+        request = MediaKitVideoEnhanceRequest(
+            video_url=url,
+            tool_version=tool_version["tool_version"],
+            scene=tool_version.get("scene"),
+            enhance_style=tool_version.get("enhance_style"),
+            resolution=resolution_preset,
+            resolution_limit=short_side,
+            fps=output_fps,
+            bitrate_level=bitrate_level,
+        )
+        created = await sync_op(
+            cls, _VCUBE_ENHANCE_VIDEO_ENDPOINT, response_model=MediaKitTaskCreateResponse, data=request
+        )
+        if not created.success or not created.task_id:
+            error = created.error
+            raise ValueError(
+                f"{error.code}: {error.message}" if error and error.message else "Task submission failed."
+            )
+        task = await poll_op(
+            cls,
+            ApiEndpoint(path=_VCUBE_TASK_ENDPOINT_PREFIX + created.task_id, method="GET"),
+            response_model=MediaKitTaskResponse,
+            status_extractor=lambda r: r.status,
+            completed_statuses=["completed"],
+            failed_statuses=["failed"],
+            queued_statuses=[],
+            poll_interval=10.0,
+            max_poll_attempts=2000,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(task.result.video_url))
+
+
+class ByteDanceExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            ByteDanceImageNode,
+            ByteDanceSeedreamNode,
+            ByteDanceSeedreamNodeV2,
+            ByteDanceSeedreamNodeV3,
+            ByteDanceSeedreamLayerSeparationNode,
+            ByteDanceTextToVideoNode,
+            ByteDanceImageToVideoNode,
+            ByteDanceFirstLastFrameNode,
+            ByteDanceImageReferenceNode,
+            ByteDance2TextToVideoNode,
+            ByteDance2FirstLastFrameNode,
+            ByteDance2ReferenceNode,
+            ByteDance2ReferenceNodeV2,
+            ByteDanceCreateImageAsset,
+            ByteDanceCreateVideoAsset,
+            ByteDanceSeedAudioNode,
+            ByteDanceVideoEnhanceNode,
+        ]
+
+
+async def comfy_entrypoint() -> ByteDanceExtension:
+    return ByteDanceExtension()
diff --git a/comfy_api_nodes/nodes_bytedance_llm.py b/comfy_api_nodes/nodes_bytedance_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..b78729fdc8cc18d059ed300034ca842e496ca993
--- /dev/null
+++ b/comfy_api_nodes/nodes_bytedance_llm.py
@@ -0,0 +1,245 @@
+"""API Nodes for ByteDance Seed LLM via the BytePlus ModelArk Responses API.
+
+See: https://docs.byteplus.com/en/docs/ModelArk/1585128
+"""
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.bytedance_llm import (
+    BytePlusInputImage,
+    BytePlusInputMessage,
+    BytePlusInputText,
+    BytePlusInputVideo,
+    BytePlusMessageContent,
+    BytePlusResponseCreateRequest,
+    BytePlusResponseObject,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    get_number_of_images,
+    sync_op,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+)
+
+BYTEPLUS_RESPONSES_ENDPOINT = "/proxy/byteplus/api/v3/responses"
+SEED_MAX_IMAGES = 20
+SEED_MAX_VIDEOS = 4
+
+SEED_MODELS: dict[str, str] = {
+    "Seed 2.0 Pro": "seed-2-0-pro-260328",
+    "Seed 2.0 Lite": "seed-2-0-lite-260228",
+    "Seed 2.0 Mini": "seed-2-0-mini-260215",
+}
+
+
+def _seed_model_inputs(max_images: int = SEED_MAX_IMAGES, max_videos: int = SEED_MAX_VIDEOS):
+    return [
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, max_images + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional image(s) to use as context for the model. Up to {max_images} images.",
+        ),
+        IO.Autogrow.Input(
+            "videos",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("video"),
+                names=[f"video_{i}" for i in range(1, max_videos + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional video(s) to use as context for the model. Up to {max_videos} videos.",
+        ),
+        IO.Float.Input(
+            "temperature",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            step=0.01,
+            tooltip="Controls randomness. 0.0 is deterministic, higher values are more random.",
+            advanced=True,
+        ),
+    ]
+
+
+def _get_text_from_response(response: BytePlusResponseObject) -> str:
+    """Extract concatenated text from all assistant message output_text blocks."""
+    if not response.output:
+        return ""
+    chunks: list[str] = []
+    for item in response.output:
+        if item.type != "message" or not item.content:
+            continue
+        for block in item.content:
+            if block.type == "output_text" and block.text:
+                chunks.append(block.text)
+            elif block.type == "refusal" and block.refusal:
+                raise ValueError(f"Model refused to respond: {block.refusal}")
+    return "\n".join(chunks)
+
+
+async def _build_image_content_blocks(
+    cls: type[IO.ComfyNode],
+    image_tensors: list[Input.Image],
+) -> list[BytePlusInputImage]:
+    urls = await upload_images_to_comfyapi(
+        cls,
+        image_tensors,
+        max_images=SEED_MAX_IMAGES,
+        wait_label="Uploading reference images",
+    )
+    return [BytePlusInputImage(image_url=url) for url in urls]
+
+
+async def _build_video_content_blocks(
+    cls: type[IO.ComfyNode],
+    videos: list[Input.Video],
+) -> list[BytePlusInputVideo]:
+    blocks: list[BytePlusInputVideo] = []
+    total = len(videos)
+    for idx, video in enumerate(videos):
+        label = "Uploading reference video"
+        if total > 1:
+            label = f"{label} ({idx + 1}/{total})"
+        url = await upload_video_to_comfyapi(cls, video, wait_label=label)
+        blocks.append(BytePlusInputVideo(video_url=url))
+    return blocks
+
+
+class ByteDanceSeedNode(IO.ComfyNode):
+    """Generate text responses from a ByteDance Seed 2.0 model."""
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ByteDanceSeedNode",
+            display_name="ByteDance Seed",
+            category="partner/text/ByteDance",
+            essentials_category="Text Generation",
+            description="Generate text responses with ByteDance's Seed 2.0 models. "
+            "Provide a text prompt and optionally one or more images or videos for multimodal context.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text input to the model.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[IO.DynamicCombo.Option(label, _seed_model_inputs()) for label in SEED_MODELS],
+                    tooltip="The Seed model used to generate the response.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    advanced=True,
+                    tooltip="Foundational instructions that dictate the model's behavior.",
+                ),
+            ],
+            outputs=[IO.String.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $contains($m, "mini") ? {
+                    "type": "list_usd",
+                    "usd": [0.00025, 0.0009],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "lite") ? {
+                    "type": "list_usd",
+                    "usd": [0.0003, 0.002],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "pro") ? {
+                    "type": "list_usd",
+                    "usd": [0.0005, 0.003],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : {"type":"text", "text":"Token-based"}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_label = model["model"]
+        temperature = model["temperature"]
+        model_id = SEED_MODELS[model_label]
+
+        image_tensors: list[Input.Image] = [t for t in (model.get("images") or {}).values() if t is not None]
+        if sum(get_number_of_images(t) for t in image_tensors) > SEED_MAX_IMAGES:
+            raise ValueError(f"Up to {SEED_MAX_IMAGES} images are supported per request.")
+
+        video_inputs: list[Input.Video] = [v for v in (model.get("videos") or {}).values() if v is not None]
+        if len(video_inputs) > SEED_MAX_VIDEOS:
+            raise ValueError(f"Up to {SEED_MAX_VIDEOS} videos are supported per request.")
+
+        content: list[BytePlusMessageContent] = []
+        if image_tensors:
+            content.extend(await _build_image_content_blocks(cls, image_tensors))
+        if video_inputs:
+            content.extend(await _build_video_content_blocks(cls, video_inputs))
+        content.append(BytePlusInputText(text=prompt))
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=BYTEPLUS_RESPONSES_ENDPOINT, method="POST"),
+            response_model=BytePlusResponseObject,
+            data=BytePlusResponseCreateRequest(
+                model=model_id,
+                input=[BytePlusInputMessage(role="user", content=content)],
+                instructions=system_prompt or None,
+                temperature=temperature,
+                store=False,
+                stream=False,
+            ),
+        )
+        if response.error:
+            raise ValueError(f"Seed API error ({response.error.code}): {response.error.message}")
+        result = _get_text_from_response(response)
+        if not result:
+            raise ValueError("Empty response from Seed model.")
+        return IO.NodeOutput(result)
+
+
+class ByteDanceLLMExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [ByteDanceSeedNode]
+
+
+async def comfy_entrypoint() -> ByteDanceLLMExtension:
+    return ByteDanceLLMExtension()
diff --git a/comfy_api_nodes/nodes_elevenlabs.py b/comfy_api_nodes/nodes_elevenlabs.py
new file mode 100644
index 0000000000000000000000000000000000000000..823dea0f247e12ce6f69810f994ecd731f0cef31
--- /dev/null
+++ b/comfy_api_nodes/nodes_elevenlabs.py
@@ -0,0 +1,924 @@
+import json
+import uuid
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.elevenlabs import (
+    AddVoiceRequest,
+    AddVoiceResponse,
+    DialogueInput,
+    DialogueSettings,
+    SpeechToSpeechRequest,
+    SpeechToTextRequest,
+    SpeechToTextResponse,
+    TextToDialogueRequest,
+    TextToSoundEffectsRequest,
+    TextToSpeechRequest,
+    TextToSpeechVoiceSettings,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_bytes_to_audio_input,
+    audio_ndarray_to_bytesio,
+    audio_tensor_to_contiguous_ndarray,
+    sync_op,
+    sync_op_raw,
+    upload_audio_to_comfyapi,
+    validate_string,
+)
+
+ELEVENLABS_MUSIC_SECTIONS = "ELEVENLABS_MUSIC_SECTIONS"  # Custom type for music sections
+ELEVENLABS_COMPOSITION_PLAN = "ELEVENLABS_COMPOSITION_PLAN"  # Custom type for composition plan
+ELEVENLABS_VOICE = "ELEVENLABS_VOICE"  # Custom type for voice selection
+
+# Predefined ElevenLabs voices: (voice_id, display_name, gender, accent)
+ELEVENLABS_VOICES = [
+    ("CwhRBWXzGAHq8TQ4Fs17", "Roger", "male", "american"),
+    ("EXAVITQu4vr4xnSDxMaL", "Sarah", "female", "american"),
+    ("FGY2WhTYpPnrIDTdsKH5", "Laura", "female", "american"),
+    ("IKne3meq5aSn9XLyUdCD", "Charlie", "male", "australian"),
+    ("JBFqnCBsd6RMkjVDRZzb", "George", "male", "british"),
+    ("N2lVS1w4EtoT3dr4eOWO", "Callum", "male", "american"),
+    ("SAz9YHcvj6GT2YYXdXww", "River", "neutral", "american"),
+    ("SOYHLrjzK2X1ezoPC6cr", "Harry", "male", "american"),
+    ("TX3LPaxmHKxFdv7VOQHJ", "Liam", "male", "american"),
+    ("Xb7hH8MSUJpSbSDYk0k2", "Alice", "female", "british"),
+    ("XrExE9yKIg1WjnnlVkGX", "Matilda", "female", "american"),
+    ("bIHbv24MWmeRgasZH58o", "Will", "male", "american"),
+    ("cgSgspJ2msm6clMCkdW9", "Jessica", "female", "american"),
+    ("cjVigY5qzO86Huf0OWal", "Eric", "male", "american"),
+    ("hpp4J3VqNfWAUOO0d1Us", "Bella", "female", "american"),
+    ("iP95p4xoKVk53GoZ742B", "Chris", "male", "american"),
+    ("nPczCjzI2devNBz1zQrb", "Brian", "male", "american"),
+    ("onwK4e9ZLuTAKqWW03F9", "Daniel", "male", "british"),
+    ("pFZP5JQG7iQjIQuC4Bku", "Lily", "female", "british"),
+    ("pNInz6obpgDQGcFmaJgB", "Adam", "male", "american"),
+    ("pqHfZKP75CvOlQylNhV4", "Bill", "male", "american"),
+]
+
+ELEVENLABS_VOICE_OPTIONS = [f"{name} ({gender}, {accent})" for _, name, gender, accent in ELEVENLABS_VOICES]
+ELEVENLABS_VOICE_MAP = {
+    f"{name} ({gender}, {accent})": voice_id for voice_id, name, gender, accent in ELEVENLABS_VOICES
+}
+
+
+class ElevenLabsSpeechToText(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsSpeechToText",
+            display_name="ElevenLabs Speech to Text",
+            category="partner/audio/ElevenLabs",
+            description="Transcribe audio to text. "
+            "Supports automatic language detection, speaker diarization, and audio event tagging.",
+            inputs=[
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Audio to transcribe.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "scribe_v2",
+                            [
+                                IO.Boolean.Input(
+                                    "tag_audio_events",
+                                    default=False,
+                                    tooltip="Annotate sounds like (laughter), (music), etc. in transcript.",
+                                ),
+                                IO.Boolean.Input(
+                                    "diarize",
+                                    default=False,
+                                    tooltip="Annotate which speaker is talking.",
+                                ),
+                                IO.Float.Input(
+                                    "diarization_threshold",
+                                    default=0.22,
+                                    min=0.1,
+                                    max=0.4,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Speaker separation sensitivity. "
+                                    "Lower values are more sensitive to speaker changes.",
+                                ),
+                                IO.Float.Input(
+                                    "temperature",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=2.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Randomness control. "
+                                    "0.0 uses model default. Higher values increase randomness.",
+                                ),
+                                IO.Combo.Input(
+                                    "timestamps_granularity",
+                                    options=["word", "character", "none"],
+                                    default="word",
+                                    tooltip="Timing precision for transcript words.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for transcription.",
+                ),
+                IO.String.Input(
+                    "language_code",
+                    default="",
+                    tooltip="ISO-639-1 or ISO-639-3 language code (e.g., 'en', 'es', 'fra'). "
+                    "Leave empty for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "num_speakers",
+                    default=0,
+                    min=0,
+                    max=32,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Maximum number of speakers to predict. Set to 0 for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    tooltip="Seed for reproducibility (determinism not guaranteed).",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="text"),
+                IO.String.Output(display_name="language_code"),
+                IO.String.Output(display_name="words_json"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.0073,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        audio: Input.Audio,
+        model: dict,
+        language_code: str,
+        num_speakers: int,
+        seed: int,
+    ) -> IO.NodeOutput:
+        if model["diarize"] and num_speakers:
+            raise ValueError(
+                "Number of speakers cannot be specified when diarization is enabled. "
+                "Either disable diarization or set num_speakers to 0."
+            )
+        request = SpeechToTextRequest(
+            model_id=model["model"],
+            cloud_storage_url=await upload_audio_to_comfyapi(
+                cls, audio, container_format="mp4", codec_name="aac", mime_type="audio/mp4"
+            ),
+            language_code=language_code if language_code.strip() else None,
+            tag_audio_events=model["tag_audio_events"],
+            num_speakers=num_speakers if num_speakers > 0 else None,
+            timestamps_granularity=model["timestamps_granularity"],
+            diarize=model["diarize"],
+            diarization_threshold=model["diarization_threshold"] if model["diarize"] else None,
+            seed=seed,
+            temperature=model["temperature"],
+        )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/elevenlabs/v1/speech-to-text", method="POST"),
+            response_model=SpeechToTextResponse,
+            data=request,
+            content_type="multipart/form-data",
+        )
+        words_json = json.dumps(
+            [w.model_dump(exclude_none=True) for w in response.words] if response.words else [],
+            indent=2,
+        )
+        return IO.NodeOutput(response.text, response.language_code, words_json)
+
+
+class ElevenLabsVoiceSelector(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsVoiceSelector",
+            display_name="ElevenLabs Voice Selector",
+            category="partner/audio/ElevenLabs",
+            description="Select a predefined ElevenLabs voice for text-to-speech generation.",
+            inputs=[
+                IO.Combo.Input(
+                    "voice",
+                    options=ELEVENLABS_VOICE_OPTIONS,
+                    tooltip="Choose a voice from the predefined ElevenLabs voices.",
+                ),
+            ],
+            outputs=[
+                IO.Custom(ELEVENLABS_VOICE).Output(display_name="voice"),
+            ],
+            is_api_node=False,
+        )
+
+    @classmethod
+    def execute(cls, voice: str) -> IO.NodeOutput:
+        voice_id = ELEVENLABS_VOICE_MAP.get(voice)
+        if not voice_id:
+            raise ValueError(f"Unknown voice: {voice}")
+        return IO.NodeOutput(voice_id)
+
+
+class ElevenLabsTextToSpeech(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsTextToSpeech",
+            display_name="ElevenLabs Text to Speech",
+            category="partner/audio/ElevenLabs",
+            description="Convert text to speech.",
+            inputs=[
+                IO.Custom(ELEVENLABS_VOICE).Input(
+                    "voice",
+                    tooltip="Voice to use for speech synthesis. Connect from Voice Selector or Instant Voice Clone.",
+                ),
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="The text to convert to speech.",
+                ),
+                IO.Float.Input(
+                    "stability",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Voice stability. Lower values give broader emotional range, "
+                    "higher values produce more consistent but potentially monotonous speech.",
+                ),
+                IO.Combo.Input(
+                    "apply_text_normalization",
+                    options=["auto", "on", "off"],
+                    tooltip="Text normalization mode. 'auto' lets the system decide, "
+                    "'on' always applies normalization, 'off' skips it.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "eleven_multilingual_v2",
+                            [
+                                IO.Float.Input(
+                                    "speed",
+                                    default=1.0,
+                                    min=0.7,
+                                    max=1.3,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Speech speed. 1.0 is normal, <1.0 slower, >1.0 faster.",
+                                ),
+                                IO.Float.Input(
+                                    "similarity_boost",
+                                    default=0.75,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Similarity boost. Higher values make the voice more similar to the original.",
+                                ),
+                                IO.Boolean.Input(
+                                    "use_speaker_boost",
+                                    default=False,
+                                    tooltip="Boost similarity to the original speaker voice.",
+                                ),
+                                IO.Float.Input(
+                                    "style",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=0.2,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Style exaggeration. Higher values increase stylistic expression "
+                                    "but may reduce stability.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "eleven_v3",
+                            [
+                                IO.Float.Input(
+                                    "speed",
+                                    default=1.0,
+                                    min=0.7,
+                                    max=1.3,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Speech speed. 1.0 is normal, <1.0 slower, >1.0 faster.",
+                                ),
+                                IO.Float.Input(
+                                    "similarity_boost",
+                                    default=0.75,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Similarity boost. Higher values make the voice more similar to the original.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for text-to-speech.",
+                ),
+                IO.String.Input(
+                    "language_code",
+                    default="",
+                    tooltip="ISO-639-1 or ISO-639-3 language code (e.g., 'en', 'es', 'fra'). "
+                    "Leave empty for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    tooltip="Seed for reproducibility (determinism not guaranteed).",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/1K chars"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        voice: str,
+        text: str,
+        stability: float,
+        apply_text_normalization: str,
+        model: dict,
+        language_code: str,
+        seed: int,
+        output_format: str,
+    ) -> IO.NodeOutput:
+        validate_string(text, min_length=1)
+        request = TextToSpeechRequest(
+            text=text,
+            model_id=model["model"],
+            language_code=language_code if language_code.strip() else None,
+            voice_settings=TextToSpeechVoiceSettings(
+                stability=stability,
+                similarity_boost=model["similarity_boost"],
+                speed=model["speed"],
+                use_speaker_boost=model.get("use_speaker_boost", None),
+                style=model.get("style", None),
+            ),
+            seed=seed,
+            apply_text_normalization=apply_text_normalization,
+        )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/elevenlabs/v1/text-to-speech/{voice}",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=request,
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsAudioIsolation(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsAudioIsolation",
+            display_name="ElevenLabs Voice Isolation",
+            category="partner/audio/ElevenLabs",
+            description="Remove background noise from audio, isolating vocals or speech.",
+            inputs=[
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Audio to process for background noise removal.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        audio: Input.Audio,
+    ) -> IO.NodeOutput:
+        audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+        audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, audio["sample_rate"], "mp4", "aac")
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(path="/proxy/elevenlabs/v1/audio-isolation", method="POST"),
+            files={"audio": ("audio.mp4", audio_bytes_io, "audio/mp4")},
+            content_type="multipart/form-data",
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsTextToSoundEffects(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsTextToSoundEffects",
+            display_name="ElevenLabs Text to Sound Effects",
+            category="partner/audio/ElevenLabs",
+            description="Generate sound effects from text descriptions.",
+            inputs=[
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the sound effect to generate.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "eleven_sfx_v2",
+                            [
+                                IO.Float.Input(
+                                    "duration",
+                                    default=5.0,
+                                    min=0.5,
+                                    max=30.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of generated sound in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "loop",
+                                    default=False,
+                                    tooltip="Create a smoothly looping sound effect.",
+                                ),
+                                IO.Float.Input(
+                                    "prompt_influence",
+                                    default=0.3,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="How closely generation follows the prompt. "
+                                    "Higher values make the sound follow the text more closely.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for sound effect generation.",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.14,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        text: str,
+        model: dict,
+        output_format: str,
+    ) -> IO.NodeOutput:
+        validate_string(text, min_length=1)
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/elevenlabs/v1/sound-generation",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=TextToSoundEffectsRequest(
+                text=text,
+                duration_seconds=model["duration"],
+                prompt_influence=model["prompt_influence"],
+                loop=model.get("loop", None),
+            ),
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsInstantVoiceClone(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsInstantVoiceClone",
+            display_name="ElevenLabs Instant Voice Clone",
+            category="partner/audio/ElevenLabs",
+            description="Create a cloned voice from audio samples. "
+            "Provide 1-8 audio recordings of the voice to clone.",
+            inputs=[
+                IO.Autogrow.Input(
+                    "files",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Audio.Input("audio"),
+                        prefix="audio",
+                        min=1,
+                        max=8,
+                    ),
+                    tooltip="Audio recordings for voice cloning.",
+                ),
+                IO.Boolean.Input(
+                    "remove_background_noise",
+                    default=False,
+                    tooltip="Remove background noise from voice samples using audio isolation.",
+                ),
+            ],
+            outputs=[
+                IO.Custom(ELEVENLABS_VOICE).Output(display_name="voice"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr="""{"type":"usd","usd":0.15}"""),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        files: IO.Autogrow.Type,
+        remove_background_noise: bool,
+    ) -> IO.NodeOutput:
+        file_tuples: list[tuple[str, tuple[str, bytes, str]]] = []
+        for key in files:
+            audio = files[key]
+            sample_rate: int = audio["sample_rate"]
+            waveform = audio["waveform"]
+            audio_data_np = audio_tensor_to_contiguous_ndarray(waveform)
+            audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, sample_rate, "mp4", "aac")
+            file_tuples.append(("files", (f"{key}.mp4", audio_bytes_io.getvalue(), "audio/mp4")))
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/elevenlabs/v1/voices/add", method="POST"),
+            response_model=AddVoiceResponse,
+            data=AddVoiceRequest(
+                name=str(uuid.uuid4()),
+                remove_background_noise=remove_background_noise,
+            ),
+            files=file_tuples,
+            content_type="multipart/form-data",
+        )
+        return IO.NodeOutput(response.voice_id)
+
+
+ELEVENLABS_STS_VOICE_SETTINGS = [
+    IO.Float.Input(
+        "speed",
+        default=1.0,
+        min=0.7,
+        max=1.3,
+        step=0.01,
+        display_mode=IO.NumberDisplay.slider,
+        tooltip="Speech speed. 1.0 is normal, <1.0 slower, >1.0 faster.",
+    ),
+    IO.Float.Input(
+        "similarity_boost",
+        default=0.75,
+        min=0.0,
+        max=1.0,
+        step=0.01,
+        display_mode=IO.NumberDisplay.slider,
+        tooltip="Similarity boost. Higher values make the voice more similar to the original.",
+    ),
+    IO.Boolean.Input(
+        "use_speaker_boost",
+        default=False,
+        tooltip="Boost similarity to the original speaker voice.",
+    ),
+    IO.Float.Input(
+        "style",
+        default=0.0,
+        min=0.0,
+        max=0.2,
+        step=0.01,
+        display_mode=IO.NumberDisplay.slider,
+        tooltip="Style exaggeration. Higher values increase stylistic expression but may reduce stability.",
+    ),
+]
+
+
+class ElevenLabsSpeechToSpeech(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsSpeechToSpeech",
+            display_name="ElevenLabs Speech to Speech",
+            category="partner/audio/ElevenLabs",
+            description="Transform speech from one voice to another while preserving the original content and emotion.",
+            inputs=[
+                IO.Custom(ELEVENLABS_VOICE).Input(
+                    "voice",
+                    tooltip="Target voice for the transformation. "
+                    "Connect from Voice Selector or Instant Voice Clone.",
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Source audio to transform.",
+                ),
+                IO.Float.Input(
+                    "stability",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Voice stability. Lower values give broader emotional range, "
+                    "higher values produce more consistent but potentially monotonous speech.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "eleven_multilingual_sts_v2",
+                            ELEVENLABS_STS_VOICE_SETTINGS,
+                        ),
+                        IO.DynamicCombo.Option(
+                            "eleven_english_sts_v2",
+                            ELEVENLABS_STS_VOICE_SETTINGS,
+                        ),
+                    ],
+                    tooltip="Model to use for speech-to-speech transformation.",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    tooltip="Seed for reproducibility.",
+                ),
+                IO.Boolean.Input(
+                    "remove_background_noise",
+                    default=False,
+                    tooltip="Remove background noise from input audio using audio isolation.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        voice: str,
+        audio: Input.Audio,
+        stability: float,
+        model: dict,
+        output_format: str,
+        seed: int,
+        remove_background_noise: bool,
+    ) -> IO.NodeOutput:
+        audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+        audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, audio["sample_rate"], "mp4", "aac")
+        voice_settings = TextToSpeechVoiceSettings(
+            stability=stability,
+            similarity_boost=model["similarity_boost"],
+            style=model["style"],
+            use_speaker_boost=model["use_speaker_boost"],
+            speed=model["speed"],
+        )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/elevenlabs/v1/speech-to-speech/{voice}",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=SpeechToSpeechRequest(
+                model_id=model["model"],
+                voice_settings=voice_settings.model_dump_json(exclude_none=True),
+                seed=seed,
+                remove_background_noise=remove_background_noise,
+            ),
+            files={"audio": ("audio.mp4", audio_bytes_io.getvalue(), "audio/mp4")},
+            content_type="multipart/form-data",
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+def _generate_dialogue_inputs(count: int) -> list:
+    """Generate input widgets for a given number of dialogue entries."""
+    inputs = []
+    for i in range(1, count + 1):
+        inputs.extend(
+            [
+                IO.String.Input(
+                    f"text{i}",
+                    multiline=True,
+                    default="",
+                    tooltip=f"Text content for dialogue entry {i}.",
+                ),
+                IO.Custom(ELEVENLABS_VOICE).Input(
+                    f"voice{i}",
+                    tooltip=f"Voice for dialogue entry {i}. Connect from Voice Selector or Instant Voice Clone.",
+                ),
+            ]
+        )
+    return inputs
+
+
+class ElevenLabsTextToDialogue(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsTextToDialogue",
+            display_name="ElevenLabs Text to Dialogue",
+            category="partner/audio/ElevenLabs",
+            description="Generate multi-speaker dialogue from text. Each dialogue entry has its own text and voice.",
+            inputs=[
+                IO.Float.Input(
+                    "stability",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.5,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Voice stability. Lower values give broader emotional range, "
+                    "higher values produce more consistent but potentially monotonous speech.",
+                ),
+                IO.Combo.Input(
+                    "apply_text_normalization",
+                    options=["auto", "on", "off"],
+                    tooltip="Text normalization mode. 'auto' lets the system decide, "
+                    "'on' always applies normalization, 'off' skips it.",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["eleven_v3"],
+                    tooltip="Model to use for dialogue generation.",
+                ),
+                IO.DynamicCombo.Input(
+                    "inputs",
+                    options=[
+                        IO.DynamicCombo.Option("1", _generate_dialogue_inputs(1)),
+                        IO.DynamicCombo.Option("2", _generate_dialogue_inputs(2)),
+                        IO.DynamicCombo.Option("3", _generate_dialogue_inputs(3)),
+                        IO.DynamicCombo.Option("4", _generate_dialogue_inputs(4)),
+                        IO.DynamicCombo.Option("5", _generate_dialogue_inputs(5)),
+                        IO.DynamicCombo.Option("6", _generate_dialogue_inputs(6)),
+                        IO.DynamicCombo.Option("7", _generate_dialogue_inputs(7)),
+                        IO.DynamicCombo.Option("8", _generate_dialogue_inputs(8)),
+                        IO.DynamicCombo.Option("9", _generate_dialogue_inputs(9)),
+                        IO.DynamicCombo.Option("10", _generate_dialogue_inputs(10)),
+                    ],
+                    tooltip="Number of dialogue entries.",
+                ),
+                IO.String.Input(
+                    "language_code",
+                    default="",
+                    tooltip="ISO-639-1 or ISO-639-3 language code (e.g., 'en', 'es', 'fra'). "
+                    "Leave empty for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=4294967295,
+                    tooltip="Seed for reproducibility.",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/1K chars"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        stability: float,
+        apply_text_normalization: str,
+        model: str,
+        inputs: dict,
+        language_code: str,
+        seed: int,
+        output_format: str,
+    ) -> IO.NodeOutput:
+        num_entries = int(inputs["inputs"])
+        dialogue_inputs: list[DialogueInput] = []
+        for i in range(1, num_entries + 1):
+            text = inputs[f"text{i}"]
+            voice_id = inputs[f"voice{i}"]
+            validate_string(text, min_length=1)
+            dialogue_inputs.append(DialogueInput(text=text, voice_id=voice_id))
+        request = TextToDialogueRequest(
+            inputs=dialogue_inputs,
+            model_id=model,
+            language_code=language_code if language_code.strip() else None,
+            settings=DialogueSettings(stability=stability),
+            seed=seed,
+            apply_text_normalization=apply_text_normalization,
+        )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/elevenlabs/v1/text-to-dialogue",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=request,
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            ElevenLabsSpeechToText,
+            ElevenLabsVoiceSelector,
+            ElevenLabsTextToSpeech,
+            ElevenLabsAudioIsolation,
+            ElevenLabsTextToSoundEffects,
+            ElevenLabsInstantVoiceClone,
+            ElevenLabsSpeechToSpeech,
+            ElevenLabsTextToDialogue,
+        ]
+
+
+async def comfy_entrypoint() -> ElevenLabsExtension:
+    return ElevenLabsExtension()
diff --git a/comfy_api_nodes/nodes_fishaudio.py b/comfy_api_nodes/nodes_fishaudio.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7e237427c1099c864b6e1f5bacd3f4999f198fa
--- /dev/null
+++ b/comfy_api_nodes/nodes_fishaudio.py
@@ -0,0 +1,454 @@
+import json
+import re
+import uuid
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.fishaudio import (
+    FishAudioASRRequest,
+    FishAudioASRResponse,
+    FishAudioCreateModelRequest,
+    FishAudioCreateModelResponse,
+    FishAudioProsody,
+    FishAudioTTSRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_bytes_to_audio_input,
+    audio_ndarray_to_bytesio,
+    audio_tensor_to_contiguous_ndarray,
+    sync_op,
+    sync_op_raw,
+    validate_string,
+)
+
+FISHAUDIO_VOICE = "FISHAUDIO_VOICE"
+
+FISHAUDIO_VOICES = [
+    ("802e3bc2b27e49c2995d23ef70e6ac89", "Energetic Male (en)"),
+    ("b545c585f631496c914815291da4e893", "Friendly Women (en)"),
+    ("933563129e564b19a115bedd57b7406a", "Sarah (en)"),
+    ("8d21b053e2804e2a890e1cf62f267b6f", "Verity (en)"),
+    ("f48d143a59a946ab87c0130fd081f349", "Polo (en)"),
+    ("bf322df2096a46f18c579d0baa36f41d", "Adrian (en)"),
+    ("98655a12fa944e26b274c535e5e03842", "E-girl (en)"),
+    ("0327fdb5da9e4fd782899a8058c8ae2b", "Narrator (en)"),
+    ("5212eb29e500460391d03af42af6552e", "Warm Conversational Voice (en)"),
+    ("5c8dc6a69c0b4edfb32634db6384bf34", "Warm Storyteller (en)"),
+    ("7a18a1851d2649108c48ec9f2c80eb2c", "Dramatic Character Male (en)"),
+    ("59cb5986671546eaa6ca8ae6f29f6d22", "News Narrator (zh)"),
+    ("bf6c479f5a384b8d857310030035824b", "Lively Female (zh)"),
+    ("faccba1a8ac54016bcfc02761285e67f", "Gentle Female (zh)"),
+    ("5161d41404314212af1254556477c17d", "Energetic Female (ja)"),
+    ("0089dce5fefb4c6ba9b9f2f0debe1ddc", "Calm Female (ja)"),
+    ("45c5d3723c9c42f598e4776dcfd5f02d", "Calm Male (ja)"),
+]
+
+FISHAUDIO_VOICE_MAP = {label: voice_id for voice_id, label in FISHAUDIO_VOICES}
+
+MAX_REFERENCE_AUDIO_SECONDS = 270
+
+
+def _rewrite_voice_tags(text: str, voice_count: int) -> tuple[str, set[int]]:
+    referenced: set[int] = set()
+
+    def repl(match: re.Match) -> str:
+        index = int(match.group(1))
+        if index < 1 or index > voice_count:
+            raise ValueError(
+                f"@Voice{index} does not match any connected voice ({voice_count} connected)."
+            )
+        referenced.add(index)
+        return f"<|speaker:{index - 1}|>"
+
+    rewritten = re.sub(r"(?<!\S)@voice([0-9]+)\b", repl, text, flags=re.IGNORECASE)
+    return rewritten, referenced
+
+
+def _tts_option_inputs() -> list:
+    return [
+        IO.Float.Input(
+            "temperature",
+            default=0.7,
+            min=0.0,
+            max=1.0,
+            step=0.01,
+            display_mode=IO.NumberDisplay.slider,
+            tooltip="Expressiveness. Higher values are more varied, lower values are more consistent.",
+        ),
+        IO.Float.Input(
+            "top_p",
+            default=0.7,
+            min=0.01,
+            max=1.0,
+            step=0.01,
+            display_mode=IO.NumberDisplay.slider,
+            tooltip="Diversity via nucleus sampling.",
+        ),
+        IO.Float.Input(
+            "speed",
+            default=1.0,
+            min=0.5,
+            max=2.0,
+            step=0.01,
+            display_mode=IO.NumberDisplay.slider,
+            tooltip="Speaking rate. 1.0 is normal, <1.0 slower, >1.0 faster.",
+        ),
+        IO.Float.Input(
+            "volume",
+            default=0.0,
+            min=-10.0,
+            max=10.0,
+            step=0.5,
+            display_mode=IO.NumberDisplay.slider,
+            tooltip="Volume adjustment in decibels. 0 is no change.",
+        ),
+        IO.Boolean.Input(
+            "normalize",
+            default=True,
+            tooltip="Normalize numbers and text for English and Chinese, "
+            "improving stability for numbers and dates.",
+        ),
+    ]
+
+
+def _multi_speaker_inputs() -> list:
+    return [
+        IO.Autogrow.Input(
+            "voices",
+            template=IO.Autogrow.TemplatePrefix(
+                IO.Custom(FISHAUDIO_VOICE).Input("voice"),
+                prefix="voice",
+                min=0,
+                max=5,
+            ),
+            tooltip="Voices for synthesis. Leave empty for the default voice. "
+            "With two or more voices, mark speaker changes in the text with @Voice1, @Voice2, etc.",
+        ),
+        *_tts_option_inputs(),
+    ]
+
+
+class FishAudioVoiceSelector(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FishAudioVoiceSelector",
+            display_name="Fish Audio Voice Selector",
+            category="partner/audio/Fish Audio",
+            description="Select a voice from the Fish Audio library for text-to-speech generation.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "voice",
+                    options=[
+                        *(IO.DynamicCombo.Option(label, []) for _, label in FISHAUDIO_VOICES),
+                        IO.DynamicCombo.Option(
+                            "custom",
+                            [
+                                IO.String.Input(
+                                    "voice_id",
+                                    default="",
+                                    tooltip="Voice model ID from fish.audio, e.g. the ID in "
+                                    "https://fish.audio/m/<id>/.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Choose a voice, or 'custom' to enter any fish.audio voice model ID.",
+                ),
+            ],
+            outputs=[
+                IO.Custom(FISHAUDIO_VOICE).Output(display_name="voice"),
+            ],
+            is_api_node=False,
+        )
+
+    @classmethod
+    def execute(cls, voice: dict) -> IO.NodeOutput:
+        selected = voice["voice"]
+        if selected == "custom":
+            voice_id = voice["voice_id"].strip()
+            if not voice_id:
+                raise ValueError("Custom voice ID is empty.")
+            return IO.NodeOutput(voice_id)
+        voice_id = FISHAUDIO_VOICE_MAP.get(selected)
+        if not voice_id:
+            raise ValueError(f"Unknown voice: {selected}")
+        return IO.NodeOutput(voice_id)
+
+
+class FishAudioTextToSpeech(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FishAudioTextToSpeech",
+            display_name="Fish Audio Text to Speech",
+            category="partner/audio/Fish Audio",
+            description="Convert text to speech. Supports emotion cues in the text "
+            "([happy], [whispering] on s2.1-pro; (happy) on s1) and multi-speaker dialogue "
+            "via @Voice1/@Voice2 tags with multiple connected voices.",
+            inputs=[
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="The text to convert to speech. With two or more voices connected, "
+                    "mark speaker changes with @Voice1, @Voice2, etc.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("s2.1-pro", _multi_speaker_inputs()),
+                        IO.DynamicCombo.Option(
+                            "s1",
+                            [
+                                IO.Custom(FISHAUDIO_VOICE).Input(
+                                    "voice",
+                                    optional=True,
+                                    tooltip="Voice for synthesis. Leave unconnected for the default voice.",
+                                ),
+                                *_tts_option_inputs(),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for text-to-speech.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["text"]),
+                expr="""
+                (
+                  $t := widgets.text;
+                  $type($t) = "string"
+                    ? (
+                        $bytes := $length($t) + 2 * $count($match($t, /[^\\x00-\\x7F]/));
+                        {"type":"usd","usd": $bytes * 21.45 / 1000000, "format":{"approximate":true}}
+                      )
+                    : {"type":"usd","usd": 0.02145, "format":{"approximate":true, "suffix":"/1K bytes"}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        text: str,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(text, field_name="text", min_length=1)
+        model_name = model["model"]
+        if model_name == "s1":
+            voices = [model["voice"]] if model.get("voice") else []
+        else:
+            voices = [model["voices"][key] for key in model["voices"]]
+        rewritten, referenced = _rewrite_voice_tags(text, len(voices))
+        if len(voices) >= 2:
+            missing = [i for i in range(1, len(voices) + 1) if i not in referenced]
+            if missing:
+                raise ValueError(
+                    "With multiple voices, the text must mark speaker changes with tags for "
+                    "each connected voice; missing: " + ", ".join(f"@Voice{i}" for i in missing)
+                )
+        reference_id: str | list[str] | None = None
+        if len(voices) == 1:
+            reference_id = voices[0]
+        elif voices:
+            reference_id = voices
+        request = FishAudioTTSRequest(
+            text=rewritten,
+            reference_id=reference_id,
+            temperature=model["temperature"],
+            top_p=model["top_p"],
+            prosody=FishAudioProsody(speed=model["speed"], volume=model["volume"]),
+            normalize=model["normalize"],
+        )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/fishaudio/v1/tts",
+                method="POST",
+                headers={"model": model_name},
+            ),
+            data=request,
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class FishAudioSpeechToText(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FishAudioSpeechToText",
+            display_name="Fish Audio Speech to Text",
+            category="partner/audio/Fish Audio",
+            description="Transcribe audio to text with automatic language detection.",
+            inputs=[
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Audio to transcribe.",
+                ),
+                IO.String.Input(
+                    "language",
+                    default="",
+                    tooltip="ISO 639-1 language hint (e.g. 'en', 'zh'). "
+                    "The language is auto-detected regardless.",
+                ),
+                IO.Boolean.Input(
+                    "precise_timestamps",
+                    default=False,
+                    tooltip="Return word-level timestamped segments.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(id="text", display_name="text"),
+                IO.String.Output(id="language_code", display_name="language_code"),
+                IO.String.Output(id="segments_json", display_name="segments_json"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.00858,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        audio: Input.Audio,
+        language: str,
+        precise_timestamps: bool,
+    ) -> IO.NodeOutput:
+        audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+        audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, audio["sample_rate"], "mp4", "aac")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/fishaudio/v1/asr", method="POST"),
+            response_model=FishAudioASRResponse,
+            data=FishAudioASRRequest(
+                language=language.strip() or None,
+                ignore_timestamps=not precise_timestamps,
+            ),
+            files={"audio": ("audio.mp4", audio_bytes_io, "audio/mp4")},
+            content_type="multipart/form-data",
+        )
+        segments_json = json.dumps(
+            [s.model_dump(exclude_none=True) for s in (response.segments or [])],
+            indent=2,
+        )
+        return IO.NodeOutput(response.text or "", response.language_code or "", segments_json)
+
+
+class FishAudioInstantVoiceClone(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FishAudioInstantVoiceClone",
+            display_name="Fish Audio Instant Voice Clone",
+            category="partner/audio/Fish Audio",
+            description="Create a private cloned voice from audio samples, instantly usable "
+            "for text-to-speech. Provide 1-20 recordings, 10-30 seconds each recommended, "
+            "under 270 seconds in total.",
+            inputs=[
+                IO.Autogrow.Input(
+                    "files",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Audio.Input("audio"),
+                        prefix="audio",
+                        min=1,
+                        max=20,
+                    ),
+                    tooltip="Audio recordings for voice cloning.",
+                ),
+                IO.Boolean.Input(
+                    "enhance_audio_quality",
+                    default=True,
+                    tooltip="Enhance reference audio quality before training.",
+                ),
+            ],
+            outputs=[
+                IO.Custom(FISHAUDIO_VOICE).Output(display_name="voice"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr="""{"type":"usd","usd":0}"""),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        files: IO.Autogrow.Type,
+        enhance_audio_quality: bool,
+    ) -> IO.NodeOutput:
+        total_seconds = 0.0
+        for key in files:
+            audio = files[key]
+            total_seconds += audio["waveform"].shape[-1] / audio["sample_rate"]
+        if total_seconds >= MAX_REFERENCE_AUDIO_SECONDS:
+            raise ValueError(
+                f"Total reference audio is {total_seconds:.0f} seconds; "
+                f"it must be under {MAX_REFERENCE_AUDIO_SECONDS} seconds."
+            )
+        file_tuples: list[tuple[str, tuple[str, bytes, str]]] = []
+        for key in files:
+            audio = files[key]
+            audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+            audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, audio["sample_rate"], "mp4", "aac")
+            file_tuples.append(("voices", (f"{key}.mp4", audio_bytes_io.getvalue(), "audio/mp4")))
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/fishaudio/model", method="POST"),
+            response_model=FishAudioCreateModelResponse,
+            data=FishAudioCreateModelRequest(
+                title=str(uuid.uuid4()),
+                enhance_audio_quality=enhance_audio_quality,
+            ),
+            files=file_tuples,
+            content_type="multipart/form-data",
+        )
+        return IO.NodeOutput(response.id)
+
+
+class FishAudioExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            FishAudioVoiceSelector,
+            FishAudioTextToSpeech,
+            FishAudioSpeechToText,
+            FishAudioInstantVoiceClone,
+        ]
+
+
+async def comfy_entrypoint() -> FishAudioExtension:
+    return FishAudioExtension()
diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py
new file mode 100644
index 0000000000000000000000000000000000000000..c187731fc988a74ca9c65131836a6d410152fb78
--- /dev/null
+++ b/comfy_api_nodes/nodes_gemini.py
@@ -0,0 +1,2019 @@
+"""
+API Nodes for Gemini Multimodal LLM Usage via Remote API
+See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+"""
+
+import base64
+import os
+from fnmatch import fnmatch
+from io import BytesIO
+from typing import Any, Literal
+
+import torch
+from typing_extensions import override
+
+import folder_paths
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl, Types
+from comfy_api_nodes.apis.gemini import (
+    GeminiContent,
+    GeminiFile,
+    GeminiFileData,
+    GeminiGenerateContentRequest,
+    GeminiGenerationConfig,
+    GeminiGenerateContentResponse,
+    GeminiImageConfig,
+    GeminiImageGenerateContentRequest,
+    GeminiImageGenerationConfig,
+    GeminiInlineData,
+    GeminiInteraction,
+    GeminiInteractionGenerationConfig,
+    GeminiInteractionMediaPart,
+    GeminiInteractionRequest,
+    GeminiInteractionResponseFormat,
+    GeminiInteractionTextPart,
+    GeminiInteractionVideoConfig,
+    GeminiMimeType,
+    GeminiPart,
+    GeminiRole,
+    GeminiSystemInstructionContent,
+    GeminiTextPart,
+    GeminiThinkingConfig,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_to_base64_string,
+    bytesio_to_image_tensor,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    get_number_of_images,
+    pad_images_to_common_channels,
+    poll_op,
+    sync_op,
+    sync_op_raw,
+    tensor_to_base64_string,
+    upload_audio_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+    validate_video_duration,
+    video_to_base64_string,
+)
+
+GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
+GEMINI_INTERACTIONS_ENDPOINT = "/proxy/gemini-interactions"
+GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024  # 20 MB
+GEMINI_URL_INPUT_BUDGET = 10
+GEMINI_MAX_INLINE_BYTES = 18 * 1024 * 1024
+GEMINI_INTERACTIONS_MAX_INLINE_BYTES = 90 * 1024 * 1024  # the Interactions API rejects requests over ~100MiB
+GEMINI_IMAGE_SYS_PROMPT = (
+    "You are an expert image-generation engine. You must ALWAYS produce an image.\n"
+    "Interpret all user input—regardless of "
+    "format, intent, or abstraction—as literal visual directives for image composition.\n"
+    "If a prompt is conversational or lacks specific visual details, "
+    "you must creatively invent a concrete visual scenario that depicts the concept.\n"
+    "Prioritize generating the visual representation above any text, formatting, or conversational requests."
+)
+
+GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["model", "resolution"]),
+    expr="""
+    (
+      $m := widgets.model;
+      $r := widgets.resolution;
+      $isFlash := $contains($m, "nano banana 2");
+      $flashPrices := {"1k": 0.0835, "2k": 0.1217, "4k": 0.1848};
+      $proPrices := {"1k": 0.1608, "2k": 0.1608, "4k": 0.288};
+      $prices := $isFlash ? $flashPrices : $proPrices;
+      {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
+    )
+    """,
+)
+
+
+async def create_image_parts(
+    cls: type[IO.ComfyNode],
+    images: Input.Image | list[Input.Image],
+    image_limit: int = 0,
+) -> list[GeminiPart]:
+    image_parts: list[GeminiPart] = []
+    if image_limit < 0:
+        raise ValueError("image_limit must be greater than or equal to 0 when creating Gemini image parts.")
+
+    # Accept either a single (possibly-batched) tensor or a list of them; share URL budget across all.
+    images_list: list[Input.Image] = images if isinstance(images, list) else [images]
+    total_images = sum(get_number_of_images(img) for img in images_list)
+    if total_images <= 0:
+        raise ValueError("No images provided to create_image_parts; at least one image is required.")
+
+    # If image_limit == 0 --> use all images; otherwise clamp to image_limit.
+    effective_max = total_images if image_limit == 0 else min(total_images, image_limit)
+
+    # Number of images we'll send as URLs (fileData)
+    num_url_images = min(effective_max, 10)  # Vertex API max number of image links
+    upload_kwargs: dict = {"wait_label": "Uploading reference images"}
+    if effective_max > num_url_images:
+        # Split path (e.g. 11+ images): suppress per-image counter to avoid a confusing dual-fraction label.
+        upload_kwargs = {
+            "wait_label": f"Uploading reference images ({num_url_images}+)",
+            "show_batch_index": False,
+        }
+    reference_images_urls = await upload_images_to_comfyapi(
+        cls,
+        images_list,
+        max_images=num_url_images,
+        **upload_kwargs,
+    )
+    for reference_image_url in reference_images_urls:
+        image_parts.append(
+            GeminiPart(
+                fileData=GeminiFileData(
+                    mimeType=GeminiMimeType.image_png,
+                    fileUri=reference_image_url,
+                )
+            )
+        )
+    if effective_max > num_url_images:
+        flat: list[torch.Tensor] = []
+        for tensor in images_list:
+            if len(tensor.shape) == 4:
+                flat.extend(tensor[i] for i in range(tensor.shape[0]))
+            else:
+                flat.append(tensor)
+        for idx in range(num_url_images, effective_max):
+            image_parts.append(
+                GeminiPart(
+                    inlineData=GeminiInlineData(
+                        mimeType=GeminiMimeType.image_png,
+                        data=tensor_to_base64_string(flat[idx]),
+                    )
+                )
+            )
+    return image_parts
+
+
+def _mime_matches(mime: GeminiMimeType | None, pattern: str) -> bool:
+    """Check if a MIME type matches a pattern. Supports fnmatch globs (e.g. 'image/*')."""
+    if mime is None:
+        return False
+    return fnmatch(mime.value, pattern)
+
+
+def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Literal["text"] | str) -> list[GeminiPart]:
+    """
+    Filter response parts by their type.
+
+    Args:
+        response: The API response from Gemini.
+        part_type: Type of parts to extract ("text" or a MIME type).
+
+    Returns:
+        List of response parts matching the requested type.
+    """
+    if not response.candidates:
+        if response.promptFeedback and response.promptFeedback.blockReason:
+            feedback = response.promptFeedback
+            raise ValueError(
+                f"Gemini API blocked the request. Reason: {feedback.blockReason} ({feedback.blockReasonMessage})"
+            )
+        raise ValueError(
+            "Gemini API returned no response candidates. If you are using the `IMAGE` modality, "
+            "try changing it to `IMAGE+TEXT` to view the model's reasoning and understand why image generation failed."
+        )
+    parts = []
+    blocked_reasons = []
+    for candidate in response.candidates:
+        if candidate.finishReason and candidate.finishReason.upper() == "IMAGE_PROHIBITED_CONTENT":
+            blocked_reasons.append(candidate.finishReason)
+            continue
+        if candidate.content is None or candidate.content.parts is None:
+            continue
+        for part in candidate.content.parts:
+            if part_type == "text" and part.text:
+                parts.append(part)
+            elif part.inlineData and _mime_matches(part.inlineData.mimeType, part_type):
+                parts.append(part)
+            elif part.fileData and _mime_matches(part.fileData.mimeType, part_type):
+                parts.append(part)
+
+    if not parts and blocked_reasons:
+        raise ValueError(f"Gemini API blocked the request. Reasons: {blocked_reasons}")
+
+    return parts
+
+
+def get_text_from_response(response: GeminiGenerateContentResponse) -> str:
+    """
+    Extract and concatenate all text parts from the response.
+
+    Args:
+        response: The API response from Gemini.
+
+    Returns:
+        Combined text from all text parts in the response.
+    """
+    parts = get_parts_by_type(response, "text")
+    return "\n".join([part.text for part in parts])
+
+
+async def get_image_from_response(response: GeminiGenerateContentResponse, thought: bool = False) -> Input.Image:
+    image_tensors: list[Input.Image] = []
+    parts = get_parts_by_type(response, "image/*")
+    for part in parts:
+        if (part.thought is True) != thought:
+            continue
+        if part.inlineData:
+            image_data = base64.b64decode(part.inlineData.data)
+            returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+        else:
+            returned_image = await download_url_to_image_tensor(part.fileData.fileUri)
+        image_tensors.append(returned_image)
+    if len(image_tensors) == 0:
+        if not thought:
+            # No images generated --> extract text response for a meaningful error
+            model_message = get_text_from_response(response).strip()
+            if model_message:
+                raise ValueError(f"Gemini did not generate an image. Model response: {model_message}")
+            raise ValueError(
+                "Gemini did not generate an image. "
+                "Try rephrasing your prompt or changing the response modality to 'IMAGE+TEXT' "
+                "to see the model's reasoning."
+            )
+        return torch.zeros((1, 1024, 1024, 3))
+    return torch.cat(pad_images_to_common_channels(image_tensors), dim=0)
+
+
+def get_text_from_interaction(interaction: GeminiInteraction) -> str:
+    """Extract and concatenate all model output text from an Interactions API response."""
+    texts = []
+    for step in interaction.steps or []:
+        if step.type != "model_output":
+            continue
+        for content in step.content or []:
+            if content.type == "text" and content.text:
+                texts.append(content.text)
+    return "\n".join(texts)
+
+
+async def get_video_from_interaction(
+    interaction: GeminiInteraction, cls: type[IO.ComfyNode] | None = None
+) -> InputImpl.VideoFromFile:
+    for step in interaction.steps or []:
+        if step.type != "model_output":
+            continue
+        for content in step.content or []:
+            if content.type != "video":
+                continue
+            if content.data:
+                return InputImpl.VideoFromFile(BytesIO(base64.b64decode(content.data)))
+            if content.uri:
+                return await download_interaction_video(content.uri, cls=cls)
+    model_message = get_text_from_interaction(interaction).strip()
+    if model_message:
+        raise ValueError(f"Gemini did not generate a video. Model response: {model_message}")
+    raise ValueError(
+        "Gemini did not generate a video. Try rephrasing your prompt, "
+        "shortening the requested duration, or reducing the number of input images/videos."
+    )
+
+
+async def download_interaction_video(uri: str, cls: type[IO.ComfyNode] | None = None) -> InputImpl.VideoFromFile:
+    if "/files/" not in uri:
+        return await download_url_to_video_output(uri, cls=cls)
+    name = uri.split("?", 1)[0].rsplit("/files/", 1)[-1].split(":", 1)[0]
+    await poll_op(
+        cls,
+        ApiEndpoint(path=f"{GEMINI_INTERACTIONS_ENDPOINT}/files/{name}"),
+        response_model=GeminiFile,
+        status_extractor=lambda file: file.state,
+        completed_statuses=["ACTIVE"],
+        failed_statuses=["FAILED"],
+        queued_statuses=["PROCESSING"],
+        poll_interval=3.0,
+        max_poll_attempts=200,
+    )
+    video_bytes = await sync_op_raw(
+        cls,
+        ApiEndpoint(path=f"{GEMINI_INTERACTIONS_ENDPOINT}/files/{name}:download", query_params={"alt": "media"}),
+        as_binary=True,
+        wait_label="Downloading video",
+    )
+    return InputImpl.VideoFromFile(BytesIO(video_bytes))
+
+
+def create_video_parts(video_input: Input.Video) -> list[GeminiPart]:
+    """Convert a single video input to Gemini API compatible parts (inline MP4/H.264)."""
+    base_64_string = video_to_base64_string(
+        video_input, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
+    )
+    return [
+        GeminiPart(
+            inlineData=GeminiInlineData(
+                mimeType=GeminiMimeType.video_mp4,
+                data=base_64_string,
+            )
+        )
+    ]
+
+
+def create_audio_parts(audio_input: Input.Audio) -> list[GeminiPart]:
+    """Convert an audio input to Gemini API compatible parts (one inline MP3 part per batch item)."""
+    audio_parts: list[GeminiPart] = []
+    for batch_index in range(audio_input["waveform"].shape[0]):
+        # Recreate an IO.AUDIO object for the given batch dimension index
+        audio_at_index = Input.Audio(
+            waveform=audio_input["waveform"][batch_index].unsqueeze(0),
+            sample_rate=audio_input["sample_rate"],
+        )
+        # Convert to MP3 format for compatibility with Gemini API
+        audio_bytes = audio_to_base64_string(
+            audio_at_index,
+            container_format="mp3",
+            codec_name="libmp3lame",
+        )
+        audio_parts.append(
+            GeminiPart(
+                inlineData=GeminiInlineData(
+                    mimeType=GeminiMimeType.audio_mp3,
+                    data=audio_bytes,
+                )
+            )
+        )
+    return audio_parts
+
+
+def _flatten_images(images: list[Input.Image]) -> list[torch.Tensor]:
+    """Expand any batched image tensors into individual (H, W, C) frames, preserving order."""
+    frames: list[torch.Tensor] = []
+    for img in images:
+        if len(img.shape) == 4:
+            frames.extend(img[i] for i in range(img.shape[0]))
+        else:
+            frames.append(img)
+    return frames
+
+
+def _flatten_audio(audios: list[Input.Audio]) -> list[Input.Audio]:
+    """Expand any batched audio inputs into individual single-clip audio inputs, preserving order."""
+    clips: list[Input.Audio] = []
+    for audio in audios:
+        waveform = audio["waveform"]
+        for i in range(waveform.shape[0]):
+            clips.append(Input.Audio(waveform=waveform[i].unsqueeze(0), sample_rate=audio["sample_rate"]))
+    return clips
+
+
+async def _media_url_part(cls: type[IO.ComfyNode], kind: str, payload: Any) -> GeminiPart:
+    """Upload a single media unit to ComfyAPI storage and return a fileData (URL) part."""
+    if kind == "image":
+        url = await upload_image_to_comfyapi(cls, payload, mime_type="image/png", wait_label="Uploading image")
+        return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.image_png, fileUri=url))
+    if kind == "audio":
+        url = await upload_audio_to_comfyapi(
+            cls, payload, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mp3"
+        )
+        return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.audio_mp3, fileUri=url))
+    url = await upload_video_to_comfyapi(cls, payload, wait_label="Uploading video")
+    return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.video_mp4, fileUri=url))
+
+
+def _media_inline_part(kind: str, payload: Any) -> tuple[GeminiPart, int]:
+    """Encode a single media unit as an inline base64 part; returns (part, base64_length)."""
+    if kind == "image":
+        data = tensor_to_base64_string(payload, mime_type="image/webp")
+        mime = GeminiMimeType.image_webp
+    elif kind == "audio":
+        data = audio_to_base64_string(payload, container_format="mp3", codec_name="libmp3lame")
+        mime = GeminiMimeType.audio_mp3
+    else:
+        data = video_to_base64_string(
+            payload, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
+        )
+        mime = GeminiMimeType.video_mp4
+    return GeminiPart(inlineData=GeminiInlineData(mimeType=mime, data=data)), len(data)
+
+
+async def build_gemini_media_parts(
+    cls: type[IO.ComfyNode],
+    images: list[Input.Image],
+    audios: list[Input.Audio],
+    videos: list[Input.Video],
+    *,
+    url_budget: int = GEMINI_URL_INPUT_BUDGET,
+    max_inline_bytes: int = GEMINI_MAX_INLINE_BYTES,
+) -> list[GeminiPart]:
+    """Build Gemini parts for multimodal inputs (images, audio, video).
+
+    fileData URLs are preferred for every media type: the upload is fetched directly by the
+    model, keeping the request body tiny regardless of media size. The URL budget is shared
+    across all media and assigned largest-first (video, then audio, then images), so that if it
+    is ever exhausted the inline-base64 overflow is limited to the smallest items. Total inline
+    payload is capped by `max_inline_bytes`.
+    """
+    units: list[tuple[str, Any]] = (
+        [("video", v) for v in videos]
+        + [("audio", a) for a in _flatten_audio(audios)]
+        + [("image", f) for f in _flatten_images(images)]
+    )
+
+    parts: list[GeminiPart] = []
+    url_used = 0
+    inline_bytes = 0
+    for kind, payload in units:
+        if url_used < url_budget:
+            parts.append(await _media_url_part(cls, kind, payload))
+            url_used += 1
+            continue
+        part, nbytes = _media_inline_part(kind, payload)
+        inline_bytes += nbytes
+        if inline_bytes > max_inline_bytes:
+            detail = f" after the first {url_budget} inputs are uploaded as URLs" if url_budget else ""
+            raise ValueError(
+                f"Too much media to send inline (over {max_inline_bytes // (1024 * 1024)}MB{detail}). "
+                "Reduce the number or size of attached media."
+            )
+        parts.append(part)
+    return parts
+
+
+def to_interaction_media_part(part: GeminiPart) -> GeminiInteractionMediaPart:
+    """Convert a fileData/inlineData GeminiPart into an Interactions API media part."""
+    if part.fileData:
+        mime = part.fileData.mimeType.value
+        return GeminiInteractionMediaPart(type=mime.split("/")[0], uri=part.fileData.fileUri, mime_type=mime)
+    mime = part.inlineData.mimeType.value
+    return GeminiInteractionMediaPart(type=mime.split("/")[0], data=part.inlineData.data, mime_type=mime)
+
+
+class GeminiNode(IO.ComfyNode):
+    """
+    Node to generate text responses from a Gemini model.
+
+    This node allows users to interact with Google's Gemini AI models, providing
+    multimodal inputs (text, images, audio, video, files) to generate coherent
+    text responses. The node works with the latest Gemini models, handling the
+    API communication and response parsing.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiNode",
+            display_name="Google Gemini",
+            category="partner/text/Gemini",
+            description="Generate text responses with Google's Gemini AI model. "
+            "You can provide multiple types of inputs (text, images, audio, video) "
+            "as context for generating more relevant and meaningful responses.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text inputs to the model, used to generate a response. "
+                    "You can include detailed instructions, questions, or context for the model.",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=[
+                        "gemini-2.5-pro",
+                        "gemini-2.5-flash",
+                        "gemini-3-pro-preview",
+                        "gemini-3-1-pro",
+                        "gemini-3-1-flash-lite",
+                    ],
+                    default="gemini-3-1-pro",
+                    tooltip="The Gemini model to use for generating responses.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="When seed is fixed to a specific value, the model makes a best effort to provide "
+                    "the same response for repeated requests. Deterministic output isn't guaranteed. "
+                    "Also, changing the model or parameter settings, such as the temperature, "
+                    "can cause variations in the response even when you use the same seed value. "
+                    "By default, a random seed value is used.",
+                ),
+                IO.Image.Input(
+                    "images",
+                    optional=True,
+                    tooltip="Optional image(s) to use as context for the model. "
+                    "To include multiple images, you can use the Batch Images node.",
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    optional=True,
+                    tooltip="Optional audio to use as context for the model.",
+                ),
+                IO.Video.Input(
+                    "video",
+                    optional=True,
+                    tooltip="Optional video to use as context for the model.",
+                ),
+                IO.Custom("GEMINI_INPUT_FILES").Input(
+                    "files",
+                    optional=True,
+                    tooltip="Optional file(s) to use as context for the model. "
+                    "Accepts inputs from the Gemini Generate Content Input Files node.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $contains($m, "gemini-2.5-flash") ? {
+                    "type": "list_usd",
+                    "usd": [0.0003, 0.0025],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens"}
+                  }
+                  : $contains($m, "gemini-2.5-pro") ? {
+                    "type": "list_usd",
+                    "usd": [0.00125, 0.01],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : ($contains($m, "gemini-3-pro-preview") or $contains($m, "gemini-3-1-pro")) ? {
+                    "type": "list_usd",
+                    "usd": [0.002, 0.012],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gemini-3-1-flash-lite") ? {
+                    "type": "list_usd",
+                    "usd": [0.00025, 0.0015],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : {"type":"text", "text":"Token-based"}
+                )
+                """,
+            ),
+            is_deprecated=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: str,
+        seed: int,
+        images: Input.Image | None = None,
+        audio: Input.Audio | None = None,
+        video: Input.Video | None = None,
+        files: list[GeminiPart] | None = None,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        if model == "gemini-3-pro-preview":
+            model = "gemini-3.1-pro-preview"  # model "gemini-3-pro-preview" will be soon deprecated by Google
+        elif model == "gemini-3-1-pro":
+            model = "gemini-3.1-pro-preview"
+        elif model == "gemini-3-1-flash-lite":
+            model = "gemini-3.1-flash-lite-preview"
+
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        if images is not None:
+            parts.extend(await create_image_parts(cls, images))
+        if audio is not None:
+            parts.extend(create_audio_parts(audio))
+        if video is not None:
+            parts.extend(create_video_parts(video))
+        if files is not None:
+            parts.extend(files)
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"),
+            data=GeminiGenerateContentRequest(
+                contents=[
+                    GeminiContent(
+                        role=GeminiRole.user,
+                        parts=parts,
+                    )
+                ],
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+        )
+
+        output_text = get_text_from_response(response)
+        return IO.NodeOutput(output_text or "Empty response from Gemini model...")
+
+
+GEMINI_V2_MODELS: dict[str, str] = {
+    "Gemini 3.7 Flash": "gemini-3.7-flash",
+    "Gemini 3.1 Pro": "gemini-3.1-pro-preview",
+    "Gemini 3.5 Flash": "gemini-3.5-flash",
+    "Gemini 3.1 Flash-Lite": "gemini-3.1-flash-lite-preview",
+}
+
+
+def _gemini_text_model_inputs(thinking_default: str, thinking_options: list[str] | None = None) -> list[Input]:
+    """Per-model inputs revealed by the model DynamicCombo (shared media + sampling controls)."""
+    return [
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, 17)],
+                min=0,
+            ),
+            tooltip="Optional image(s) to use as context for the model. Up to 16 images.",
+        ),
+        IO.Autogrow.Input(
+            "audio",
+            template=IO.Autogrow.TemplateNames(
+                IO.Audio.Input("audio"),
+                names=["audio_1"],
+                min=0,
+            ),
+            tooltip="Optional audio clip to use as context for the model.",
+        ),
+        IO.Autogrow.Input(
+            "video",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("video"),
+                names=["video_1"],
+                min=0,
+            ),
+            tooltip="Optional video clip to use as context for the model.",
+        ),
+        IO.Custom("GEMINI_INPUT_FILES").Input(
+            "files",
+            optional=True,
+            tooltip="Optional file(s) to use as context for the model. "
+            "Accepts inputs from the Gemini Input Files node.",
+        ),
+        IO.Combo.Input(
+            "thinking_level",
+            options=thinking_options or ["LOW", "HIGH"],
+            default=thinking_default,
+            tooltip="How hard the model reasons internally before answering. "
+            "HIGH improves quality on difficult tasks but costs more (thinking) tokens and is slower.",
+        ),
+        IO.Float.Input(
+            "temperature",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            step=0.01,
+            tooltip="Controls randomness. Lower is more focused/deterministic, higher is more creative.",
+            advanced=True,
+        ),
+        IO.Float.Input(
+            "top_p",
+            default=0.95,
+            min=0.0,
+            max=1.0,
+            step=0.01,
+            tooltip="Nucleus sampling: sample from the smallest token set whose cumulative probability reaches top_p.",
+            advanced=True,
+        ),
+        IO.Int.Input(
+            "max_output_tokens",
+            default=32768,
+            min=16,
+            max=65536,
+            tooltip="Maximum tokens to generate, including the model's internal thinking. "
+            "With thinking_level HIGH, a low value can leave no room for the answer; raise this if "
+            "responses come back empty or truncated. The model stops early when finished, so a higher "
+            "cap costs nothing extra for short replies.",
+            advanced=True,
+        ),
+    ]
+
+
+class GeminiNodeV2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiNodeV2",
+            display_name="Google Gemini",
+            category="partner/text/Gemini",
+            essentials_category="Text Generation",
+            description="Generate text responses with Google's Gemini models. Provide a text prompt and, "
+            "optionally, one or more images, audio clips, videos, or files as multimodal context.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text input to the model. Include detailed instructions, questions, or context.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Gemini 3.7 Flash",
+                            _gemini_text_model_inputs("MEDIUM", ["LOW", "MEDIUM", "HIGH"]),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Gemini 3.5 Flash",
+                            _gemini_text_model_inputs("MEDIUM", ["MINIMAL", "LOW", "MEDIUM", "HIGH"]),
+                        ),
+                        IO.DynamicCombo.Option("Gemini 3.1 Pro", _gemini_text_model_inputs("HIGH")),
+                        IO.DynamicCombo.Option("Gemini 3.1 Flash-Lite", _gemini_text_model_inputs("LOW")),
+                    ],
+                    tooltip="The Gemini model used to generate the response.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for sampling. Set to 0 for a random seed. Deterministic output isn't guaranteed.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    advanced=True,
+                    tooltip="Foundational instructions that dictate the model's behavior.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $contains($m, "lite") ? {
+                    "type": "list_usd",
+                    "usd": [0.00025, 0.0015],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "3.7 flash") ? {
+                    "type": "list_usd",
+                    "usd": [0.00215, 0.01073],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "3.5 flash") ? {
+                    "type": "list_usd",
+                    "usd": [0.0015, 0.009],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : {
+                    "type": "list_usd",
+                    "usd": [0.002, 0.012],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_id = GEMINI_V2_MODELS[model["model"]]
+
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        images = [t for t in (model.get("images") or {}).values() if t is not None]
+        audios = [a for a in (model.get("audio") or {}).values() if a is not None]
+        videos = [v for v in (model.get("video") or {}).values() if v is not None]
+        if images or audios or videos:
+            parts.extend(await build_gemini_media_parts(cls, images, audios, videos))
+        files = model.get("files")
+        if files is not None:
+            parts.extend(files)
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model_id}", method="POST"),
+            data=GeminiGenerateContentRequest(
+                contents=[
+                    GeminiContent(
+                        role=GeminiRole.user,
+                        parts=parts,
+                    )
+                ],
+                generationConfig=GeminiGenerationConfig(
+                    temperature=model["temperature"],
+                    topP=model["top_p"],
+                    maxOutputTokens=model["max_output_tokens"],
+                    seed=seed if seed > 0 else None,
+                    thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]),
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+        )
+
+        output_text = get_text_from_response(response)
+        return IO.NodeOutput(output_text or "Empty response from Gemini model...")
+
+
+class GeminiInputFiles(IO.ComfyNode):
+    """
+    Loads and formats input files for use with the Gemini API.
+
+    This node allows users to include text (.txt) and PDF (.pdf) files as input
+    context for the Gemini model. Files are converted to the appropriate format
+    required by the API and can be chained together to include multiple files
+    in a single request.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        """
+        For details about the supported file input types, see:
+        https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+        """
+        input_dir = folder_paths.get_input_directory()
+        input_files = [
+            f
+            for f in os.scandir(input_dir)
+            if f.is_file()
+            and (f.name.endswith(".txt") or f.name.endswith(".pdf"))
+            and f.stat().st_size < GEMINI_MAX_INPUT_FILE_SIZE
+        ]
+        input_files = sorted(input_files, key=lambda x: x.name)
+        input_files = [f.name for f in input_files]
+        return IO.Schema(
+            node_id="GeminiInputFiles",
+            display_name="Gemini Input Files",
+            category="partner/text/Gemini",
+            description="Loads and prepares input files to include as inputs for Gemini LLM nodes. "
+            "The files will be read by the Gemini model when generating a response. "
+            "The contents of the text file count toward the token limit. "
+            "🛈 TIP: Can be chained together with other Gemini Input File nodes.",
+            inputs=[
+                IO.Combo.Input(
+                    "file",
+                    options=input_files,
+                    default=input_files[0] if input_files else None,
+                    tooltip="Input files to include as context for the model. "
+                    "Only accepts text (.txt) and PDF (.pdf) files for now.",
+                ),
+                IO.Custom("GEMINI_INPUT_FILES").Input(
+                    "GEMINI_INPUT_FILES",
+                    optional=True,
+                    tooltip="An optional additional file(s) to batch together with the file loaded from this node. "
+                    "Allows chaining of input files so that a single message can include multiple input files.",
+                ),
+            ],
+            outputs=[
+                IO.Custom("GEMINI_INPUT_FILES").Output(),
+            ],
+        )
+
+    @classmethod
+    def create_file_part(cls, file_path: str) -> GeminiPart:
+        mime_type = GeminiMimeType.application_pdf if file_path.endswith(".pdf") else GeminiMimeType.text_plain
+        # Use base64 string directly, not the data URI
+        with open(file_path, "rb") as f:
+            file_content = f.read()
+        base64_str = base64.b64encode(file_content).decode("utf-8")
+
+        return GeminiPart(
+            inlineData=GeminiInlineData(
+                mimeType=mime_type,
+                data=base64_str,
+            )
+        )
+
+    @classmethod
+    def execute(cls, file: str, GEMINI_INPUT_FILES: list[GeminiPart] | None = None) -> IO.NodeOutput:
+        """Loads and formats input files for Gemini API."""
+        if GEMINI_INPUT_FILES is None:
+            GEMINI_INPUT_FILES = []
+        file_path = folder_paths.get_annotated_filepath(file)
+        input_file_content = cls.create_file_part(file_path)
+        return IO.NodeOutput([input_file_content] + GEMINI_INPUT_FILES)
+
+
+class GeminiImage(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiImageNode",
+            display_name="Nano Banana (Google Gemini Image)",
+            category="partner/image/Gemini",
+            description="Edit images synchronously via Google API.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text prompt for generation",
+                    default="",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["gemini-2.5-flash-image"],
+                    tooltip="The Gemini model to use for generating responses.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="When seed is fixed to a specific value, the model makes a best effort to provide "
+                    "the same response for repeated requests. Deterministic output isn't guaranteed. "
+                    "Also, changing the model or parameter settings, such as the temperature, "
+                    "can cause variations in the response even when you use the same seed value. "
+                    "By default, a random seed value is used.",
+                ),
+                IO.Image.Input(
+                    "images",
+                    optional=True,
+                    tooltip="Optional image(s) to use as context for the model. "
+                    "To include multiple images, you can use the Batch Images node.",
+                ),
+                IO.Custom("GEMINI_INPUT_FILES").Input(
+                    "files",
+                    optional=True,
+                    tooltip="Optional file(s) to use as context for the model. "
+                    "Accepts inputs from the Gemini Generate Content Input Files node.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["auto", "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9"],
+                    default="auto",
+                    tooltip="Defaults to matching the output image size to that of your input image, "
+                    "or otherwise generates 1:1 squares.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "response_modalities",
+                    options=["IMAGE+TEXT", "IMAGE"],
+                    tooltip="Choose 'IMAGE' for image-only output, or "
+                    "'IMAGE+TEXT' to return both the generated image and a text response.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default=GEMINI_IMAGE_SYS_PROMPT,
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.039,"format":{"suffix":"/Image (1K)","approximate":true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: str,
+        seed: int,
+        images: Input.Image | None = None,
+        files: list[GeminiPart] | None = None,
+        aspect_ratio: str = "auto",
+        response_modalities: str = "IMAGE+TEXT",
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+
+        if not aspect_ratio:
+            aspect_ratio = "auto"  # for backward compatability with old workflows; to-do remove this in December
+        image_config = GeminiImageConfig() if aspect_ratio == "auto" else GeminiImageConfig(aspectRatio=aspect_ratio)
+
+        if images is not None:
+            parts.extend(await create_image_parts(cls, images))
+        if files is not None:
+            parts.extend(files)
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/vertexai/gemini/{model}", method="POST"),
+            data=GeminiImageGenerateContentRequest(
+                contents=[
+                    GeminiContent(role=GeminiRole.user, parts=parts),
+                ],
+                generationConfig=GeminiImageGenerationConfig(
+                    responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
+                    imageConfig=image_config,
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+        )
+        return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response))
+
+
+class GeminiImage2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiImage2Node",
+            display_name="Nano Banana Pro (Google Gemini Image)",
+            category="partner/image/Gemini",
+            description="Generate or edit images synchronously via Google Vertex API.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text prompt describing the image to generate or the edits to apply. "
+                    "Include any constraints, styles, or details the model should follow.",
+                    default="",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["gemini-3-pro-image-preview", "Nano Banana 2 (Gemini 3.1 Flash Image)"],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="When the seed is fixed to a specific value, the model makes a best effort to provide "
+                    "the same response for repeated requests. Deterministic output isn't guaranteed. "
+                    "Also, changing the model or parameter settings, such as the temperature, "
+                    "can cause variations in the response even when you use the same seed value. "
+                    "By default, a random seed value is used.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["auto", "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9"],
+                    default="auto",
+                    tooltip="If set to 'auto', matches your input image's aspect ratio; "
+                    "if no image is provided, a 16:9 square is usually generated.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["1K", "2K", "4K"],
+                    tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
+                ),
+                IO.Combo.Input(
+                    "response_modalities",
+                    options=["IMAGE+TEXT", "IMAGE"],
+                    tooltip="Choose 'IMAGE' for image-only output, or "
+                    "'IMAGE+TEXT' to return both the generated image and a text response.",
+                    advanced=True,
+                ),
+                IO.Image.Input(
+                    "images",
+                    optional=True,
+                    tooltip="Optional reference image(s). "
+                    "To include multiple images, use the Batch Images node (up to 14).",
+                ),
+                IO.Custom("GEMINI_INPUT_FILES").Input(
+                    "files",
+                    optional=True,
+                    tooltip="Optional file(s) to use as context for the model. "
+                    "Accepts inputs from the Gemini Generate Content Input Files node.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default=GEMINI_IMAGE_SYS_PROMPT,
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=GEMINI_IMAGE_2_PRICE_BADGE,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: str,
+        seed: int,
+        aspect_ratio: str,
+        resolution: str,
+        response_modalities: str,
+        images: Input.Image | None = None,
+        files: list[GeminiPart] | None = None,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
+            model = "gemini-3.1-flash-image"
+        elif model == "gemini-3-pro-image-preview":
+            model = "gemini-3-pro-image"
+
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        if images is not None:
+            if get_number_of_images(images) > 14:
+                raise ValueError("The current maximum number of supported images is 14.")
+            parts.extend(await create_image_parts(cls, images))
+        if files is not None:
+            parts.extend(files)
+
+        image_config = GeminiImageConfig(imageSize=resolution)
+        if aspect_ratio != "auto":
+            image_config.aspectRatio = aspect_ratio
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/vertexai/gemini/{model}", method="POST"),
+            data=GeminiImageGenerateContentRequest(
+                contents=[
+                    GeminiContent(role=GeminiRole.user, parts=parts),
+                ],
+                generationConfig=GeminiImageGenerationConfig(
+                    responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
+                    imageConfig=image_config,
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+        )
+        return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response))
+
+
+class GeminiNanoBanana2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiNanoBanana2",
+            display_name="Nano Banana 2",
+            category="partner/image/Gemini",
+            description="Generate or edit images synchronously via Google Vertex API.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text prompt describing the image to generate or the edits to apply. "
+                    "Include any constraints, styles, or details the model should follow.",
+                    default="",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["Nano Banana 2 (Gemini 3.1 Flash Image)"],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="When the seed is fixed to a specific value, the model makes a best effort to provide "
+                    "the same response for repeated requests. Deterministic output isn't guaranteed. "
+                    "Also, changing the model or parameter settings, such as the temperature, "
+                    "can cause variations in the response even when you use the same seed value. "
+                    "By default, a random seed value is used.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=[
+                        "auto",
+                        "1:1",
+                        "2:3",
+                        "3:2",
+                        "3:4",
+                        "4:3",
+                        "4:5",
+                        "5:4",
+                        "9:16",
+                        "16:9",
+                        "21:9",
+                    ],
+                    default="auto",
+                    tooltip="If set to 'auto', matches your input image's aspect ratio; "
+                    "if no image is provided, a 16:9 square is usually generated.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["1K", "2K", "4K"],
+                    tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
+                ),
+                IO.Combo.Input(
+                    "response_modalities",
+                    options=["IMAGE", "IMAGE+TEXT"],
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "thinking_level",
+                    options=["MINIMAL", "HIGH"],
+                ),
+                IO.Image.Input(
+                    "images",
+                    optional=True,
+                    tooltip="Optional reference image(s). "
+                    "To include multiple images, use the Batch Images node (up to 14).",
+                ),
+                IO.Custom("GEMINI_INPUT_FILES").Input(
+                    "files",
+                    optional=True,
+                    tooltip="Optional file(s) to use as context for the model. "
+                    "Accepts inputs from the Gemini Generate Content Input Files node.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default=GEMINI_IMAGE_SYS_PROMPT,
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(),
+                IO.Image.Output(
+                    display_name="thought_image",
+                    tooltip="First image from the model's thinking process. "
+                    "Only available with thinking_level HIGH and IMAGE+TEXT modality.",
+                ),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=GEMINI_IMAGE_2_PRICE_BADGE,
+            is_deprecated=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: str,
+        seed: int,
+        aspect_ratio: str,
+        resolution: str,
+        response_modalities: str,
+        thinking_level: str,
+        images: Input.Image | None = None,
+        files: list[GeminiPart] | None = None,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
+            model = "gemini-3.1-flash-image-preview"
+
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        if images is not None:
+            if get_number_of_images(images) > 14:
+                raise ValueError("The current maximum number of supported images is 14.")
+            parts.extend(await create_image_parts(cls, images))
+        if files is not None:
+            parts.extend(files)
+
+        image_config = GeminiImageConfig(imageSize=resolution)
+        if aspect_ratio != "auto":
+            image_config.aspectRatio = aspect_ratio
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/vertexai/gemini/{model}", method="POST"),
+            data=GeminiImageGenerateContentRequest(
+                contents=[
+                    GeminiContent(role=GeminiRole.user, parts=parts),
+                ],
+                generationConfig=GeminiImageGenerationConfig(
+                    responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
+                    imageConfig=image_config,
+                    thinkingConfig=GeminiThinkingConfig(thinkingLevel=thinking_level),
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+        )
+        return IO.NodeOutput(
+            await get_image_from_response(response),
+            get_text_from_response(response),
+            await get_image_from_response(response, thought=True),
+        )
+
+
+def _nano_banana_2_v2_model_inputs(resolutions: list[str]):
+    return [
+        IO.Combo.Input(
+            "aspect_ratio",
+            options=[
+                "auto",
+                "1:1",
+                "2:3",
+                "3:2",
+                "3:4",
+                "4:3",
+                "4:5",
+                "5:4",
+                "9:16",
+                "16:9",
+                "21:9",
+                "1:4",
+                "4:1",
+                "8:1",
+                "1:8",
+            ],
+            default="auto",
+            tooltip="If set to 'auto', matches your input image's aspect ratio; "
+            "if no image is provided, a 16:9 square is usually generated.",
+        ),
+        IO.Combo.Input(
+            "resolution",
+            options=resolutions,
+            tooltip="Target output resolution.",
+        ),
+        IO.Combo.Input(
+            "thinking_level",
+            options=["MINIMAL", "HIGH"],
+        ),
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, 15)],
+                min=0,
+            ),
+            tooltip="Optional reference image(s). Up to 14 images total.",
+        ),
+        IO.Custom("GEMINI_INPUT_FILES").Input(
+            "files",
+            optional=True,
+            tooltip="Optional file(s) to use as context for the model. "
+                    "Accepts inputs from the Gemini Generate Content Input Files node.",
+        ),
+    ]
+
+
+class GeminiNanoBanana2V2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiNanoBanana2V2",
+            display_name="Nano Banana 2",
+            category="partner/image/Gemini",
+            description="Generate or edit images synchronously via Google Vertex API.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text prompt describing the image to generate or the edits to apply. "
+                    "Include any constraints, styles, or details the model should follow.",
+                    default="",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Nano Banana 2 (Gemini 3.1 Flash Image)",
+                            _nano_banana_2_v2_model_inputs(resolutions=["1K", "2K", "4K"]),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Nano Banana 2 Lite",
+                            _nano_banana_2_v2_model_inputs(resolutions=["1K"]),
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="When the seed is fixed to a specific value, the model makes a best effort to provide "
+                    "the same response for repeated requests. Deterministic output isn't guaranteed. "
+                    "Also, changing the model or parameter settings, such as the temperature, "
+                    "can cause variations in the response even when you use the same seed value. "
+                    "By default, a random seed value is used.",
+                ),
+                IO.Combo.Input(
+                    "response_modalities",
+                    options=["IMAGE", "IMAGE+TEXT"],
+                    advanced=True,
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default=GEMINI_IMAGE_SYS_PROMPT,
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "temperature",
+                    default=1.0,
+                    min=0.0,
+                    max=2.0,
+                    step=0.01,
+                    optional=True,
+                    tooltip="Controls randomness in generation. Lower is more focused/deterministic.",
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "top_p",
+                    default=0.95,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    optional=True,
+                    tooltip="Nucleus sampling threshold. Lower is more focused, higher more diverse.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(),
+                IO.Image.Output(
+                    display_name="thought_image",
+                    tooltip="First image from the model's thinking process. "
+                    "Only available with thinking_level HIGH and IMAGE+TEXT modality.",
+                ),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
+                expr="""
+                (
+                  $contains(widgets.model, "lite")
+                    ? {"type":"usd","usd": 0.0408, "format":{"suffix":"/Image","approximate":true}}
+                    : (
+                        $r := $lookup(widgets, "model.resolution");
+                        $prices := {"1k": 0.0835, "2k": 0.1217, "4k": 0.1848};
+                        {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
+                      )
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        response_modalities: str,
+        system_prompt: str = "",
+        temperature: float = 1.0,
+        top_p: float = 0.95,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_choice = model["model"]
+        if model_choice == "Nano Banana 2 (Gemini 3.1 Flash Image)":
+            model_id = "gemini-3.1-flash-image"
+        elif model_choice == "Nano Banana 2 Lite":
+            model_id = "gemini-3.1-flash-lite-image"
+        else:
+            model_id = model_choice
+
+        images = model.get("images") or {}
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        if images:
+            image_tensors: list[Input.Image] = [t for t in images.values() if t is not None]
+            if image_tensors:
+                if sum(get_number_of_images(t) for t in image_tensors) > 14:
+                    raise ValueError("The current maximum number of supported images is 14.")
+                parts.extend(await create_image_parts(cls, image_tensors))
+        files = model.get("files")
+        if files is not None:
+            parts.extend(files)
+
+        image_config = GeminiImageConfig(imageSize=model["resolution"])
+        if model["aspect_ratio"] != "auto":
+            image_config.aspectRatio = model["aspect_ratio"]
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/vertexai/gemini/{model_id}", method="POST"),
+            data=GeminiImageGenerateContentRequest(
+                contents=[
+                    GeminiContent(role=GeminiRole.user, parts=parts),
+                ],
+                generationConfig=GeminiImageGenerationConfig(
+                    responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
+                    imageConfig=image_config,
+                    thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]),
+                    temperature=temperature,
+                    topP=top_p,
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+        )
+        return IO.NodeOutput(
+            await get_image_from_response(response),
+            get_text_from_response(response),
+            await get_image_from_response(response, thought=True),
+        )
+
+
+OMNI_MAX_IMAGES = 14
+OMNI_MAX_VIDEOS = 3
+OMNI_URI_DELIVERY_RESOLUTIONS = ("1080p", "4k")
+
+OMNI_MODELS: dict[str, str] = {
+    "Omni Flash": "gemini-omni-flash-preview",
+    "Omni Flash 1.1": "gemini-omni-1.1-flash",
+}
+
+
+def _omni_flash_inputs() -> list[Input]:
+    """Per-model inputs for the Omni video DynamicCombo (prompt + reference media + sampling)."""
+    return [
+        IO.String.Input(
+            "prompt",
+            multiline=True,
+            default="",
+            tooltip="Describe the video to generate. Specify the length and aspect ratio directly in the "
+            'prompt, e.g. "a 6-second clip in 16:9". Length may be 3-10 seconds; the aspect ratio must be '
+            "16:9 (landscape) or 9:16 (portrait). The output is 720p, 24 FPS, with audio.",
+        ),
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, OMNI_MAX_IMAGES + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference image(s) to guide or animate the video. Up to {OMNI_MAX_IMAGES} images.",
+        ),
+        IO.Autogrow.Input(
+            "videos",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("video"),
+                names=[f"video_{i}" for i in range(1, OMNI_MAX_VIDEOS + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference video(s) to guide or edit. Up to {OMNI_MAX_VIDEOS} videos, "
+            f"each up to 10 seconds long.",
+        ),
+        IO.Float.Input(
+            "temperature",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            step=0.01,
+            tooltip="Controls randomness. Lower is more focused/deterministic, higher is more varied.",
+            advanced=True,
+        ),
+        IO.Float.Input(
+            "top_p",
+            default=0.95,
+            min=0.0,
+            max=1.0,
+            step=0.01,
+            tooltip="Nucleus sampling: sample from the smallest token set whose cumulative probability reaches top_p.",
+            advanced=True,
+        ),
+    ]
+
+
+class GeminiVideoOmni(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiVideoOmni",
+            display_name="Google Gemini Omni (Video)",
+            category="partner/video/Gemini",
+            essentials_category="Video Generation",
+            description="Generate a video with audio from a text prompt using Google's Gemini Omni Flash model. "
+            "Optionally provide reference images and/or videos to guide or edit the result. Describe the desired "
+            "length (3-10s) and aspect ratio (16:9 or 9:16) directly in the prompt.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Omni Flash", _omni_flash_inputs()),
+                    ],
+                    tooltip="The Gemini video model used to generate the video.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_deprecated=True,
+            price_badge=IO.PriceBadge(
+                expr='{"type":"usd","usd":0.1449,"format":{"suffix":"/second","approximate":true}}'
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, model: dict, seed: int) -> IO.NodeOutput:
+        prompt = model.get("prompt") or ""
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_id = OMNI_MODELS[model["model"]]
+
+        images = [t for t in (model.get("images") or {}).values() if t is not None]
+        videos = [v for v in (model.get("videos") or {}).values() if v is not None]
+        if sum(get_number_of_images(t) for t in images) > OMNI_MAX_IMAGES:
+            raise ValueError(f"The current maximum number of supported images is {OMNI_MAX_IMAGES}.")
+        if len(videos) > OMNI_MAX_VIDEOS:
+            raise ValueError(f"The current maximum number of supported videos is {OMNI_MAX_VIDEOS}.")
+        for video in videos:
+            validate_video_duration(video, max_duration=10)
+
+        parts: list[GeminiInteractionTextPart | GeminiInteractionMediaPart] = []
+        if images or videos:
+            # The Interactions API accepts video only inline or as a Files API URI, not as an HTTP URL.
+            media_parts = await build_gemini_media_parts(
+                cls, [], [], videos, url_budget=0, max_inline_bytes=GEMINI_INTERACTIONS_MAX_INLINE_BYTES
+            )
+            video_inline_bytes = sum(len(p.inlineData.data) for p in media_parts)
+            media_parts += await build_gemini_media_parts(
+                cls, images, [], [], max_inline_bytes=GEMINI_INTERACTIONS_MAX_INLINE_BYTES - video_inline_bytes
+            )
+            parts.extend(to_interaction_media_part(p) for p in media_parts)
+        parts.append(GeminiInteractionTextPart(text=prompt))
+        interaction = await sync_op(
+            cls,
+            ApiEndpoint(path=GEMINI_INTERACTIONS_ENDPOINT, method="POST"),
+            data=GeminiInteractionRequest(
+                model=model_id,
+                input=parts,
+                generation_config=GeminiInteractionGenerationConfig(
+                    temperature=model.get("temperature", 1.0),
+                    top_p=model.get("top_p", 0.95),
+                ),
+            ),
+            response_model=GeminiInteraction,
+        )
+        if interaction.status != "completed":
+            model_message = get_text_from_interaction(interaction).strip()
+            raise ValueError(
+                f"Gemini interaction did not complete (status: {interaction.status})."
+                + (f" Model response: {model_message}" if model_message else "")
+            )
+        return IO.NodeOutput(
+            await get_video_from_interaction(interaction, cls=cls),
+            get_text_from_interaction(interaction),
+        )
+
+
+def _omni_task_input(with_extend: bool) -> Input:
+    return IO.Combo.Input(
+        "task_type",
+        options=["auto", "text_to_video", "image_to_video", "reference_to_video", "edit"]
+        + (["extend"] if with_extend else []),
+        default="auto",
+        tooltip="What to do with the prompt and the attached media. With 'auto' the model decides. "
+        "'text_to_video' generates from the prompt alone and rejects attached media. 'image_to_video' "
+        "animates one image, or interpolates from a starting frame to an ending frame when two are "
+        "attached. 'reference_to_video' treats the attached media as subject references. "
+        "'edit' rewrites exactly one attached video"
+        + (", and 'extend' appends new footage to it, so the output starts with the input video." if with_extend else "."),
+    )
+
+
+def _omni_seed_input() -> Input:
+    return IO.Int.Input(
+        "seed",
+        default=42,
+        min=0,
+        max=2147483647,
+        control_after_generate=True,
+        tooltip="Seed controls whether the node should re-run; results are non-deterministic regardless of seed.",
+    )
+
+
+def _omni_v2_flash_inputs() -> list[Input]:
+    return [
+        IO.String.Input(
+            "prompt",
+            multiline=True,
+            default="",
+            tooltip="Describe the video to generate, or the edit to apply to an attached video. Specify the "
+            'length directly in the prompt, e.g. "a 6-second clip"; length may be 3-10 seconds. '
+            "The output is 720p, 24 FPS, with audio.",
+        ),
+        IO.Combo.Input(
+            "aspect_ratio",
+            options=["16:9", "9:16"],
+            default="16:9",
+            tooltip="Output aspect ratio: 16:9 (landscape) or 9:16 (portrait). "
+            "The 'edit' task keeps the aspect ratio of the input video instead.",
+        ),
+        _omni_task_input(with_extend=False),
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, OMNI_MAX_IMAGES + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference image(s) to guide or animate the video. Up to {OMNI_MAX_IMAGES} images.",
+        ),
+        IO.Autogrow.Input(
+            "videos",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("video"),
+                names=[f"video_{i}" for i in range(1, OMNI_MAX_VIDEOS + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference video(s) to guide or edit. Up to {OMNI_MAX_VIDEOS} videos, "
+            f"each up to 10 seconds long.",
+        ),
+        IO.Float.Input(
+            "temperature",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            step=0.01,
+            tooltip="Controls randomness. Lower is more focused/deterministic, higher is more varied.",
+            advanced=True,
+        ),
+        IO.Float.Input(
+            "top_p",
+            default=0.95,
+            min=0.0,
+            max=1.0,
+            step=0.01,
+            tooltip="Nucleus sampling: sample from the smallest token set whose cumulative probability reaches top_p.",
+            advanced=True,
+        ),
+        _omni_seed_input(),
+    ]
+
+
+def _omni_v2_flash_1_1_inputs() -> list[Input]:
+    return [
+        IO.String.Input(
+            "prompt",
+            multiline=True,
+            default="",
+            tooltip="Describe the video to generate, or the edit to apply to an attached video. Specify the "
+            'length directly in the prompt, e.g. "a 6-second clip" or, for the \'extend\' task, "extend by 5 seconds"; '
+            "the generated length may be 3-10 seconds and defaults to 10. The output has audio.",
+        ),
+        IO.Combo.Input(
+            "resolution",
+            options=["360p", "720p", "1080p", "4k"],
+            default="720p",
+            tooltip="Output resolution.",
+        ),
+        IO.Combo.Input(
+            "aspect_ratio",
+            options=["16:9", "9:16"],
+            default="16:9",
+            tooltip="Output aspect ratio: 16:9 (landscape) or 9:16 (portrait). "
+            "The 'edit' and 'extend' tasks keep the aspect ratio of the input video instead.",
+        ),
+        _omni_task_input(with_extend=True),
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, OMNI_MAX_IMAGES + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference image(s) to guide or animate the video. Up to {OMNI_MAX_IMAGES} images; "
+            "with the 'image_to_video' task the first one is the starting frame and an optional second one "
+            "is the ending frame.",
+        ),
+        IO.Autogrow.Input(
+            "videos",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("video"),
+                names=[f"video_{i}" for i in range(1, OMNI_MAX_VIDEOS + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference video(s) to guide or edit. Up to {OMNI_MAX_VIDEOS} videos, "
+            f"each up to 10 seconds long.",
+        ),
+        _omni_seed_input(),
+    ]
+
+
+
+class GeminiVideoOmniV2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiVideoOmniV2",
+            display_name="Google Gemini Omni (Video)",
+            category="partner/video/Gemini",
+            essentials_category="Video Generation",
+            description="Generate a video with audio from a text prompt using Google's Gemini Omni Flash models. "
+            "Optionally provide reference images and/or videos to guide or edit the result. Describe the desired "
+            "length (3-10s) directly in the prompt.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Omni Flash 1.1", _omni_v2_flash_1_1_inputs()),
+                        IO.DynamicCombo.Option("Omni Flash", _omni_v2_flash_inputs()),
+                    ],
+                    tooltip="The Gemini video model used to generate the video.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
+                expr="""
+                (
+                  $prices := {"360p": 0.0483, "720p": 0.1449, "1080p": 0.2174, "4k": 0.4349};
+                  $r := $lookup(widgets, "model.resolution");
+                  {"type":"usd","usd": $r ? $lookup($prices, $r) : 0.1449,
+                   "format":{"suffix":"/second","approximate":true}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, model: dict) -> IO.NodeOutput:
+        prompt = model.get("prompt") or ""
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_id = OMNI_MODELS[model["model"]]
+        task = model["task_type"]
+
+        images = [t for t in (model.get("images") or {}).values() if t is not None]
+        videos = [v for v in (model.get("videos") or {}).values() if v is not None]
+        total_images = sum(get_number_of_images(t) for t in images)
+        if total_images > OMNI_MAX_IMAGES:
+            raise ValueError(f"The current maximum number of supported images is {OMNI_MAX_IMAGES}.")
+        if len(videos) > OMNI_MAX_VIDEOS:
+            raise ValueError(f"The current maximum number of supported videos is {OMNI_MAX_VIDEOS}.")
+        for video in videos:
+            validate_video_duration(video, max_duration=10.1)
+        if task == "text_to_video" and (images or videos):
+            raise ValueError("The 'text_to_video' task generates from the prompt alone; detach the reference media.")
+        if task == "image_to_video" and videos:
+            raise ValueError(
+                "The 'image_to_video' task takes images only; detach the video(s) or use 'reference_to_video'."
+            )
+        if task == "image_to_video" and not 1 <= total_images <= 2:
+            raise ValueError(
+                "The 'image_to_video' task takes one image as the starting frame, "
+                "and an optional second one as the ending frame."
+            )
+        if task in ("edit", "extend") and len(videos) != 1:
+            raise ValueError(f"The '{task}' task requires exactly one input video.")
+
+        parts: list[GeminiInteractionTextPart | GeminiInteractionMediaPart] = []
+        if images or videos:
+            # The Interactions API accepts video only inline or as a Files API URI, not as an HTTP URL.
+            media_parts = await build_gemini_media_parts(
+                cls, [], [], videos, url_budget=0, max_inline_bytes=GEMINI_INTERACTIONS_MAX_INLINE_BYTES
+            )
+            video_inline_bytes = sum(len(p.inlineData.data) for p in media_parts)
+            media_parts += await build_gemini_media_parts(
+                cls, images, [], [], max_inline_bytes=GEMINI_INTERACTIONS_MAX_INLINE_BYTES - video_inline_bytes
+            )
+            parts.extend(to_interaction_media_part(p) for p in media_parts)
+        parts.append(GeminiInteractionTextPart(text=prompt))
+
+        resolution = model.get("resolution")
+        response_format = GeminiInteractionResponseFormat(
+            resolution=resolution,
+            aspect_ratio=None if task in ("edit", "extend") else model["aspect_ratio"],
+            delivery="uri" if resolution in OMNI_URI_DELIVERY_RESOLUTIONS else None,
+        )
+        generation_config = None
+        if task != "auto" or "temperature" in model:
+            generation_config = GeminiInteractionGenerationConfig(
+                temperature=model.get("temperature"),
+                top_p=model.get("top_p"),
+                video_config=GeminiInteractionVideoConfig(task=task) if task != "auto" else None,
+            )
+
+        interaction = await sync_op(
+            cls,
+            ApiEndpoint(path=GEMINI_INTERACTIONS_ENDPOINT, method="POST"),
+            data=GeminiInteractionRequest(
+                model=model_id,
+                input=parts,
+                generation_config=generation_config,
+                response_format=response_format,
+            ),
+            response_model=GeminiInteraction,
+        )
+        if interaction.status != "completed":
+            model_message = get_text_from_interaction(interaction).strip()
+            raise ValueError(
+                f"Gemini interaction did not complete (status: {interaction.status})."
+                + (f" Model response: {model_message}" if model_message else "")
+            )
+        return IO.NodeOutput(
+            await get_video_from_interaction(interaction, cls=cls),
+            get_text_from_interaction(interaction),
+        )
+
+
+class GeminiExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            GeminiNode,
+            GeminiNodeV2,
+            GeminiImage,
+            GeminiImage2,
+            GeminiNanoBanana2,
+            GeminiNanoBanana2V2,
+            GeminiVideoOmni,
+            GeminiVideoOmniV2,
+            GeminiInputFiles,
+        ]
+
+
+async def comfy_entrypoint() -> GeminiExtension:
+    return GeminiExtension()
diff --git a/comfy_api_nodes/nodes_grok.py b/comfy_api_nodes/nodes_grok.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdf728f57d505bff800f9f95a08bd265e784ed37
--- /dev/null
+++ b/comfy_api_nodes/nodes_grok.py
@@ -0,0 +1,1133 @@
+import re
+
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.grok import (
+    ImageEditRequest,
+    ImageGenerationRequest,
+    ImageGenerationResponse,
+    InputUrlObject,
+    VideoEditRequest,
+    VideoExtensionRequest,
+    VideoGenerationRequest,
+    VideoGenerationResponse,
+    VideoStatusResponse,
+    VoiceReferenceObject,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    get_fs_object_size,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    tensor_to_base64_string,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+    validate_video_duration,
+)
+
+
+_GROK_VIDEO_MODEL_API_IDS = {
+    "grok-imagine-video-1.5": "grok-imagine-video-1.5",
+}
+
+_GROK_IMAGE_MODEL_API_IDS = {
+    "grok-imagine-image-2.0": "grok-imagine-image-2.0",
+}
+
+_GROK_IMAGE_QUALITY_MODELS = {"grok-imagine-image-2.0"}
+
+_GROK_IMAGE_QUALITY_OPTIONS = ["medium", "low"]
+
+_GROK_IMAGE_EDIT_MAX_IMAGES = {
+    "grok-imagine-image-2.0": 3,
+    "grok-imagine-image-pro": 1,
+    "grok-imagine-image-quality": 3,
+    "grok-imagine-image": 3,
+}
+
+_GROK_IMAGE_EDIT_ASPECT_RATIO_NEEDS_MULTIPLE = {
+    "grok-imagine-image-quality",
+    "grok-imagine-image",
+}
+
+_GROK_VOICE_OPTIONS = [
+    "none",
+    "ara",
+    "eve",
+    "leo",
+    "rex",
+    "sal",
+    "carina",
+    "zagan",
+    "helix",
+    "orion",
+    "luna",
+    "iris",
+    "altair",
+    "zenith",
+    "perseus",
+    "helios",
+    "lux",
+    "kepler",
+    "rigel",
+    "cosmo",
+    "celeste",
+    "ursa",
+    "sirius",
+    "lumen",
+    "castor",
+    "naksh",
+    "atlas",
+]
+
+
+_GROK_REF_TAG_RE = re.compile(r"(?<!\w)@(image|audio)(?P<idx>\d*)(?!\w)", re.IGNORECASE | re.ASCII)
+
+
+def _normalize_grok_reference_prompt(prompt: str, total_images: int, voices: list[str]) -> str:
+    """Rewrite @Image1/@Audio1 style references (1-based, shared partner-node syntax)
+    into Grok's native <IMAGE_0>/<AUDIO_0> tags; an unnumbered @image/@audio means the first one.
+    Native tags pass through untouched. @ImageN refers to the Nth reference image overall, in
+    input order — a batched input contributes one number per image. @AudioN refers to the
+    'voice_N' widget; the API only accepts compact arrays, so voices are remapped to array
+    positions and 'none' slots between selected voices are harmless. Substitution repeats until
+    stable so adjacent tags like '@Image1@Image2' all resolve."""
+    audio_indices: dict[int, int] = {}
+    for slot, voice in enumerate(voices, start=1):
+        if voice != "none":
+            audio_indices[slot] = len(audio_indices)
+
+    def repl(match: re.Match) -> str:
+        kind = match.group(1).lower()
+        idx = int(match.group("idx") or 1)
+        if kind == "image":
+            if not 1 <= idx <= total_images:
+                raise ValueError(
+                    f"The prompt references @Image{idx}, but only {total_images} "
+                    f"reference images are connected (a batched input counts once per image)."
+                )
+            return f"<IMAGE_{idx - 1}>"
+        if idx not in audio_indices:
+            if 1 <= idx <= len(voices):
+                raise ValueError(f"The prompt references @Audio{idx}, but 'voice_{idx}' is set to 'none'.")
+            raise ValueError(f"The prompt references @Audio{idx}, but only voices 1..{len(voices)} exist.")
+        return f"<AUDIO_{audio_indices[idx]}>"
+
+    prev = None
+    while prev != prompt:
+        prev = prompt
+        prompt = _GROK_REF_TAG_RE.sub(repl, prompt)
+    return prompt
+
+
+class GrokImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrokImageNode",
+            display_name="Grok Image",
+            category="partner/image/Grok",
+            description="Generate images using Grok based on a text prompt",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=[
+                        "grok-imagine-image-2.0",
+                        "grok-imagine-image-quality",
+                        "grok-imagine-image-pro",
+                        "grok-imagine-image",
+                    ],
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="The text prompt used to generate the image",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=[
+                        "1:1",
+                        "2:3",
+                        "3:2",
+                        "3:4",
+                        "4:3",
+                        "9:16",
+                        "16:9",
+                        "9:19.5",
+                        "19.5:9",
+                        "9:20",
+                        "20:9",
+                        "1:2",
+                        "2:1",
+                    ],
+                ),
+                IO.Int.Input(
+                    "number_of_images",
+                    default=1,
+                    min=1,
+                    max=10,
+                    step=1,
+                    tooltip="Number of images to generate",
+                    display_mode=IO.NumberDisplay.number,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Combo.Input("resolution", options=["1K", "2K"], optional=True),
+                IO.Combo.Input(
+                    "quality",
+                    options=_GROK_IMAGE_QUALITY_OPTIONS,
+                    optional=True,
+                    tooltip="Quality level, supported only by the grok-imagine-image-2.0 model.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "number_of_images", "resolution", "quality"]),
+                expr="""
+                (
+                  $is1k := widgets.resolution = "1k";
+                  $rate := widgets.model = "grok-imagine-image-2.0"
+                    ? (widgets.quality = "low" ? ($is1k ? 0.04 : 0.06) : ($is1k ? 0.06 : 0.08))
+                    : (widgets.model = "grok-imagine-image-quality"
+                        ? ($is1k ? 0.05 : 0.07)
+                        : ($contains(widgets.model, "pro") ? 0.07 : 0.02));
+                  {"type":"usd","usd": $rate * widgets.number_of_images}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        aspect_ratio: str,
+        number_of_images: int,
+        seed: int,
+        resolution: str = "1K",
+        quality: str = "medium",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/xai/v1/images/generations", method="POST"),
+            data=ImageGenerationRequest(
+                model=_GROK_IMAGE_MODEL_API_IDS.get(model, model),
+                prompt=prompt,
+                aspect_ratio=aspect_ratio,
+                n=number_of_images,
+                seed=seed,
+                resolution=resolution.lower(),
+                quality=quality if model in _GROK_IMAGE_QUALITY_MODELS else None,
+            ),
+            response_model=ImageGenerationResponse,
+        )
+        if len(response.data) == 1:
+            return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
+        return IO.NodeOutput(
+            torch.cat(
+                [await download_url_to_image_tensor(i) for i in [str(d.url) for d in response.data if d.url]],
+            )
+        )
+
+
+_GROK_IMAGE_EDIT_ASPECT_RATIO_OPTIONS = [
+    "auto",
+    "1:1",
+    "2:3",
+    "3:2",
+    "3:4",
+    "4:3",
+    "9:16",
+    "16:9",
+    "9:19.5",
+    "19.5:9",
+    "9:20",
+    "20:9",
+    "1:2",
+    "2:1",
+]
+
+
+def _grok_image_edit_model_inputs(
+    *, max_ref_images: int, with_aspect_ratio: bool, with_quality: bool = False, aspect_ratio_needs_multiple: bool = True
+):
+    inputs = [
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, max_ref_images + 1)],
+                min=1,
+            ),
+            tooltip=(
+                "Reference image to edit."
+                if max_ref_images == 1
+                else f"Reference image(s) to edit. Up to {max_ref_images} images."
+            ),
+        ),
+        IO.Combo.Input("resolution", options=["1K", "2K"]),
+        IO.Int.Input(
+            "number_of_images",
+            default=1,
+            min=1,
+            max=10,
+            step=1,
+            tooltip="Number of edited images to generate",
+            display_mode=IO.NumberDisplay.number,
+        ),
+    ]
+    if with_quality:
+        inputs.append(IO.Combo.Input("quality", options=_GROK_IMAGE_QUALITY_OPTIONS))
+    if with_aspect_ratio:
+        inputs.append(
+            IO.Combo.Input(
+                "aspect_ratio",
+                options=_GROK_IMAGE_EDIT_ASPECT_RATIO_OPTIONS,
+                tooltip=(
+                    "Only allowed when multiple images are connected."
+                    if aspect_ratio_needs_multiple
+                    else "Aspect ratio of the edited image."
+                ),
+            )
+        )
+    return inputs
+
+
+class GrokImageEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrokImageEditNode",
+            display_name="Grok Image Edit",
+            category="partner/image/Grok",
+            description="Modify an existing image based on a text prompt",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=[
+                        "grok-imagine-image-quality",
+                        "grok-imagine-image-pro",
+                        "grok-imagine-image",
+                    ],
+                ),
+                IO.Image.Input("image", display_name="images"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="The text prompt used to generate the image",
+                ),
+                IO.Combo.Input("resolution", options=["1K", "2K"]),
+                IO.Int.Input(
+                    "number_of_images",
+                    default=1,
+                    min=1,
+                    max=10,
+                    step=1,
+                    tooltip="Number of edited images to generate",
+                    display_mode=IO.NumberDisplay.number,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=[
+                        "auto",
+                        "1:1",
+                        "2:3",
+                        "3:2",
+                        "3:4",
+                        "4:3",
+                        "9:16",
+                        "16:9",
+                        "9:19.5",
+                        "19.5:9",
+                        "9:20",
+                        "20:9",
+                        "1:2",
+                        "2:1",
+                    ],
+                    optional=True,
+                    tooltip="Only allowed when multiple images are connected to the image input.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "number_of_images", "resolution"]),
+                expr="""
+                (
+                  $isQualityModel := widgets.model = "grok-imagine-image-quality";
+                  $isPro := $contains(widgets.model, "pro");
+                  $rate := $isQualityModel
+                    ? (widgets.resolution = "1k" ? 0.05 : 0.07)
+                    : ($isPro ? 0.07 : 0.02);
+                  $base := $isQualityModel ? 0.01 : 0.002;
+                  $output := $rate * widgets.number_of_images;
+                  $isPro
+                    ? {"type":"usd","usd": $base + $output}
+                    : {"type":"range_usd","min_usd": $base + $output, "max_usd": 3 * $base + $output}
+                )
+                """,
+            ),
+            is_deprecated=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        prompt: str,
+        resolution: str,
+        number_of_images: int,
+        seed: int,
+        aspect_ratio: str = "auto",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        if model == "grok-imagine-image-pro":
+            if get_number_of_images(image) > 1:
+                raise ValueError("The pro model supports only 1 input image.")
+        elif get_number_of_images(image) > 3:
+            raise ValueError("A maximum of 3 input images is supported.")
+        if aspect_ratio != "auto" and get_number_of_images(image) == 1:
+            raise ValueError(
+                "Custom aspect ratio is only allowed when multiple images are connected to the image input."
+            )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/xai/v1/images/edits", method="POST"),
+            data=ImageEditRequest(
+                model=model,
+                images=[InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(i)}") for i in image],
+                prompt=prompt,
+                resolution=resolution.lower(),
+                n=number_of_images,
+                seed=seed,
+                aspect_ratio=None if aspect_ratio == "auto" else aspect_ratio,
+            ),
+            response_model=ImageGenerationResponse,
+        )
+        if len(response.data) == 1:
+            return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
+        return IO.NodeOutput(
+            torch.cat(
+                [await download_url_to_image_tensor(i) for i in [str(d.url) for d in response.data if d.url]],
+            )
+        )
+
+
+class GrokImageEditNodeV2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrokImageEditNodeV2",
+            display_name="Grok Image Edit",
+            category="partner/image/Grok",
+            description="Modify an existing image based on a text prompt",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="The text prompt used to generate the image",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "grok-imagine-image-2.0",
+                            _grok_image_edit_model_inputs(
+                                max_ref_images=3,
+                                with_aspect_ratio=True,
+                                with_quality=True,
+                                aspect_ratio_needs_multiple=False,
+                            ),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "grok-imagine-image-quality",
+                            _grok_image_edit_model_inputs(max_ref_images=3, with_aspect_ratio=True),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "grok-imagine-image-pro",
+                            _grok_image_edit_model_inputs(max_ref_images=1, with_aspect_ratio=False),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "grok-imagine-image",
+                            _grok_image_edit_model_inputs(max_ref_images=3, with_aspect_ratio=True),
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "model.resolution", "model.number_of_images", "model.quality"],
+                ),
+                expr="""
+                (
+                  $is20 := widgets.model = "grok-imagine-image-2.0";
+                  $isPro := $contains(widgets.model, "pro");
+                  $res := $lookup(widgets, "model.resolution");
+                  $n := $lookup(widgets, "model.number_of_images");
+                  $is1k := $res = "1k";
+                  $rate := $is20
+                    ? ($lookup(widgets, "model.quality") = "low"
+                        ? ($is1k ? 0.04 : 0.06)
+                        : ($is1k ? 0.06 : 0.08))
+                    : (widgets.model = "grok-imagine-image-quality"
+                        ? ($is1k ? 0.05 : 0.07)
+                        : ($isPro ? 0.07 : 0.02));
+                  $base := ($is20 or widgets.model = "grok-imagine-image-quality") ? 0.01 : 0.002;
+                  $output := $rate * $n;
+                  $isPro
+                    ? {"type":"usd","usd": $base + $output}
+                    : {"type":"range_usd","min_usd": $base + $output, "max_usd": 3 * $base + $output}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_id = model["model"]
+        resolution = model["resolution"]
+        number_of_images = model["number_of_images"]
+        images_dict = model.get("images") or {}
+        aspect_ratio = model.get("aspect_ratio", "auto")
+
+        image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
+        n_images = sum(get_number_of_images(t) for t in image_tensors)
+        max_images = _GROK_IMAGE_EDIT_MAX_IMAGES.get(model_id, 3)
+        if n_images < 1:
+            raise ValueError("At least one image is required for editing.")
+        if n_images > max_images:
+            raise ValueError(
+                f"The {model_id} model supports at most {max_images} input "
+                f"image{'s' if max_images > 1 else ''}; {n_images} are connected."
+            )
+        if aspect_ratio != "auto" and model_id in _GROK_IMAGE_EDIT_ASPECT_RATIO_NEEDS_MULTIPLE and n_images == 1:
+            raise ValueError(
+                "Custom aspect ratio is only allowed when multiple images are connected to the image input."
+            )
+
+        flat_tensors: list[torch.Tensor] = []
+        for tensor in image_tensors:
+            if len(tensor.shape) == 4:
+                flat_tensors.extend(tensor[i] for i in range(tensor.shape[0]))
+            else:
+                flat_tensors.append(tensor)
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/xai/v1/images/edits", method="POST"),
+            data=ImageEditRequest(
+                model=_GROK_IMAGE_MODEL_API_IDS.get(model_id, model_id),
+                images=[
+                    InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(i)}") for i in flat_tensors
+                ],
+                prompt=prompt,
+                resolution=resolution.lower(),
+                n=number_of_images,
+                seed=seed,
+                aspect_ratio=None if aspect_ratio == "auto" else aspect_ratio,
+                quality=model.get("quality") if model_id in _GROK_IMAGE_QUALITY_MODELS else None,
+            ),
+            response_model=ImageGenerationResponse,
+        )
+        if len(response.data) == 1:
+            return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
+        return IO.NodeOutput(
+            torch.cat(
+                [await download_url_to_image_tensor(i) for i in [str(d.url) for d in response.data if d.url]],
+            )
+        )
+
+
+class GrokVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrokVideoNode",
+            display_name="Grok Video",
+            category="partner/video/Grok",
+            description="Generate video from a prompt or an image",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["grok-imagine-video", "grok-imagine-video-1.5"],
+                    tooltip="The model to use for video generation.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text description of the desired video. "
+                    "Optional for grok-imagine-video-1.5 when an input image is provided.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["480p", "720p", "1080p"],
+                    tooltip="The resolution of the output video. 1080p is only available for grok-imagine-video-1.5.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["auto", "16:9", "4:3", "3:2", "1:1", "2:3", "3:4", "9:16"],
+                    tooltip="The aspect ratio of the output video.",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=6,
+                    min=1,
+                    max=15,
+                    step=1,
+                    tooltip="The duration of the output video in seconds.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Image.Input(
+                    "image",
+                    optional=True,
+                    tooltip="Optional starting image. If omitted, the video is generated from the text prompt alone.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"], inputs=["image"]),
+                expr="""
+                (
+                  $is15 := $contains(widgets.model, "1.5");
+                  $rate := $is15
+                    ? (widgets.resolution = "1080p" ? 0.25 : (widgets.resolution = "720p" ? 0.14 : 0.08))
+                    : (widgets.resolution = "720p" ? 0.07 : 0.05);
+                  $imgCost := $is15 ? 0.01 : 0.002;
+                  $base := $rate * widgets.duration;
+                  $total := inputs.image.connected ? $base + $imgCost : $base;
+                  {"type":"usd","usd": $is15 ? $total * 1.43 : $total}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        resolution: str,
+        aspect_ratio: str,
+        duration: int,
+        seed: int,
+        image: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        if resolution == "1080p" and model != "grok-imagine-video-1.5":
+            raise ValueError(f"1080p resolution is only available for grok-imagine-video-1.5, not '{model}'.")
+        image_url = None
+        if image is not None:
+            if get_number_of_images(image) != 1:
+                raise ValueError("Only one input image is supported.")
+            image_url = InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(image)}")
+        if image is None or model != "grok-imagine-video-1.5":
+            validate_string(prompt, strip_whitespace=True, min_length=1)
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/xai/v1/videos/generations", method="POST"),
+            data=VideoGenerationRequest(
+                model=_GROK_VIDEO_MODEL_API_IDS.get(model, model),
+                image=image_url,
+                prompt=prompt,
+                resolution=resolution,
+                duration=duration,
+                aspect_ratio=None if aspect_ratio == "auto" else aspect_ratio,
+                seed=seed,
+            ),
+            response_model=VideoGenerationResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
+            status_extractor=lambda r: r.status if r.status is not None else "complete",
+            response_model=VideoStatusResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.video.url))
+
+
+class GrokVideoEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrokVideoEditNode",
+            display_name="Grok Video Edit",
+            category="partner/video/Grok",
+            description="Edit an existing video based on a text prompt.",
+            inputs=[
+                IO.Combo.Input("model", options=["grok-imagine-video"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text description of the desired video.",
+                ),
+                IO.Video.Input("video", tooltip="Maximum supported duration is 8.7 seconds and 50MB file size."),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.06, "format": {"suffix": "/sec", "approximate": true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        video: Input.Video,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        validate_video_duration(video, min_duration=1, max_duration=8.7)
+        video_stream = video.get_stream_source()
+        video_size = get_fs_object_size(video_stream)
+        if video_size > 50 * 1024 * 1024:
+            raise ValueError(f"Video size ({video_size / 1024 / 1024:.1f}MB) exceeds 50MB limit.")
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/xai/v1/videos/edits", method="POST"),
+            data=VideoEditRequest(
+                model=model,
+                video=InputUrlObject(url=await upload_video_to_comfyapi(cls, video)),
+                prompt=prompt,
+                seed=seed,
+            ),
+            response_model=VideoGenerationResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
+            status_extractor=lambda r: r.status if r.status is not None else "complete",
+            response_model=VideoStatusResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.video.url))
+
+
+class GrokVideoReferenceNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrokVideoReferenceNode",
+            display_name="Grok Reference-to-Video",
+            category="partner/video/Grok",
+            description="Generate video guided by reference images, with optional preset voice references.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text description of the desired video.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "grok-imagine-video-1.5",
+                            [
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("image"),
+                                        names=[f"reference_{i}" for i in range(1, 8)],
+                                        min=1,
+                                    ),
+                                    tooltip="Up to 7 reference images to guide the video generation. "
+                                    "Refer to them in the prompt as @Image1 ... @Image7, numbered "
+                                    "in input order; a batched input counts once per image.",
+                                ),
+                                IO.Combo.Input(
+                                    "voice_1",
+                                    options=_GROK_VOICE_OPTIONS,
+                                    tooltip="Optional preset voice reference; refer to it in the prompt as @Audio1. "
+                                    "The API supports only these preset voices, not custom audio.",
+                                ),
+                                IO.Combo.Input(
+                                    "voice_2",
+                                    options=_GROK_VOICE_OPTIONS,
+                                    tooltip="Optional second voice reference; @Audio2 in the prompt.",
+                                ),
+                                IO.Combo.Input(
+                                    "voice_3",
+                                    options=_GROK_VOICE_OPTIONS,
+                                    tooltip="Optional third voice reference; @Audio3 in the prompt.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["480p", "720p"],
+                                    tooltip="The resolution of the output video.",
+                                ),
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=["16:9", "4:3", "3:2", "1:1", "2:3", "3:4", "9:16"],
+                                    tooltip="The aspect ratio of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=6,
+                                    min=1,
+                                    max=15,
+                                    step=1,
+                                    tooltip="The duration of the output video in seconds.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "grok-imagine-video",
+                            [
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplatePrefix(
+                                        IO.Image.Input("image"),
+                                        prefix="reference_",
+                                        min=1,
+                                        max=7,
+                                    ),
+                                    tooltip="Up to 7 reference images to guide the video generation.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["480p", "720p"],
+                                    tooltip="The resolution of the output video.",
+                                ),
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=["16:9", "4:3", "3:2", "1:1", "2:3", "3:4", "9:16"],
+                                    tooltip="The aspect ratio of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=6,
+                                    min=2,
+                                    max=10,
+                                    step=1,
+                                    tooltip="The duration of the output video in seconds.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="The model to use for video generation.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "model.duration", "model.resolution"],
+                    input_groups=["model.reference_images"],
+                ),
+                expr="""
+                (
+                  $is15 := $contains(widgets.model, "1.5");
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $refs := $lookup(inputGroups, "model.reference_images");
+                  $rate := $is15
+                    ? ($res = "720p" ? 0.14 : 0.08)
+                    : ($res = "720p" ? 0.07 : 0.05);
+                  $imgCost := $is15 ? 0.01 : 0.002;
+                  $price := ($rate * $dur + $imgCost * $refs) * 1.43;
+                  {"type":"usd","usd": $price}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        total_images = sum(get_number_of_images(t) for t in model["reference_images"].values())
+        if total_images > 7:
+            raise ValueError(f"A maximum of 7 reference images is supported; {total_images} are connected.")
+        reference_audios = None
+        if model["model"] == "grok-imagine-video-1.5":
+            voices = [model.get(f"voice_{i}", "none") for i in range(1, 4)]
+            reference_audios = [VoiceReferenceObject(voice_id=v) for v in voices if v != "none"] or None
+            prompt = _normalize_grok_reference_prompt(prompt, total_images=total_images, voices=voices)
+        ref_image_urls = await upload_images_to_comfyapi(
+            cls,
+            list(model["reference_images"].values()),
+            mime_type="image/png",
+            wait_label="Uploading base images",
+            max_images=7,
+        )
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/xai/v1/videos/generations", method="POST"),
+            data=VideoGenerationRequest(
+                model=_GROK_VIDEO_MODEL_API_IDS.get(model["model"], model["model"]),
+                reference_images=[InputUrlObject(url=i) for i in ref_image_urls],
+                reference_audios=reference_audios,
+                prompt=prompt,
+                resolution=model["resolution"],
+                duration=model["duration"],
+                aspect_ratio=model["aspect_ratio"],
+                seed=seed,
+            ),
+            response_model=VideoGenerationResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
+            status_extractor=lambda r: r.status if r.status is not None else "complete",
+            response_model=VideoStatusResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.video.url))
+
+
+class GrokVideoExtendNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrokVideoExtendNode",
+            display_name="Grok Video Extend",
+            category="partner/video/Grok",
+            description="Extend an existing video with a seamless continuation based on a text prompt.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text description of what should happen next in the video.",
+                ),
+                IO.Video.Input("video", tooltip="Source video to extend. MP4 format, 2-15 seconds."),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "grok-imagine-video",
+                            [
+                                IO.Int.Input(
+                                    "duration",
+                                    default=8,
+                                    min=2,
+                                    max=10,
+                                    step=1,
+                                    tooltip="Length of the extension in seconds.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="The model to use for video extension.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model.duration"]),
+                expr="""
+                (
+                  $dur := $lookup(widgets, "model.duration");
+                  {
+                    "type": "range_usd",
+                    "min_usd": (0.02 + 0.05 * $dur) * 1.43,
+                    "max_usd": (0.15 + 0.05 * $dur) * 1.43
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        video: Input.Video,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        validate_video_duration(video, min_duration=2, max_duration=15)
+        video_size = get_fs_object_size(video.get_stream_source())
+        if video_size > 50 * 1024 * 1024:
+            raise ValueError(f"Video size ({video_size / 1024 / 1024:.1f}MB) exceeds 50MB limit.")
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/xai/v1/videos/extensions", method="POST"),
+            data=VideoExtensionRequest(
+                prompt=prompt,
+                video=InputUrlObject(url=await upload_video_to_comfyapi(cls, video)),
+                duration=model["duration"],
+            ),
+            response_model=VideoGenerationResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
+            status_extractor=lambda r: r.status if r.status is not None else "complete",
+            response_model=VideoStatusResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.video.url))
+
+
+class GrokExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            GrokImageNode,
+            GrokImageEditNode,
+            GrokImageEditNodeV2,
+            GrokVideoNode,
+            GrokVideoReferenceNode,
+            GrokVideoEditNode,
+            GrokVideoExtendNode,
+        ]
+
+
+async def comfy_entrypoint() -> GrokExtension:
+    return GrokExtension()
diff --git a/comfy_api_nodes/nodes_heygen.py b/comfy_api_nodes/nodes_heygen.py
new file mode 100644
index 0000000000000000000000000000000000000000..00cafc4a51f5f6367bb5ec031be46028a3bfbc36
--- /dev/null
+++ b/comfy_api_nodes/nodes_heygen.py
@@ -0,0 +1,801 @@
+import uuid
+
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.heygen import (
+    HEYGEN_AVATAR_MAP,
+    HEYGEN_AVATAR_OPTIONS,
+    HEYGEN_TRANSLATE_LANGUAGES,
+    HEYGEN_VOICE_GENERAL_MAP,
+    HEYGEN_VOICE_GENERAL_OPTIONS,
+    HEYGEN_VOICE_TTS_MAP,
+    HEYGEN_VOICE_TTS_OPTIONS,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_bytes_to_audio_input,
+    download_url_as_bytesio,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    downscale_image_tensor_by_max_side,
+    get_number_of_images,
+    poll_op_raw,
+    sync_op_raw,
+    upload_audio_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+)
+from server import PromptServer
+
+_VIDEOS_PATH = "/proxy/heygen/v3/videos"
+_TRANSLATIONS_PATH = "/proxy/heygen/v3/video-translations"
+_SPEECH_PATH = "/proxy/heygen/v3/voices/speech"
+_AVATARS_PATH = "/proxy/heygen/v3/avatars"
+_LOOKS_PATH = "/proxy/heygen/v3/avatars/looks"
+
+_DEFAULT_VOICE_OPTION = "(avatar's default voice)"
+
+_AVATARS_BY_ENGINE = {
+    e: [label for label, (_aid, _atype, engines) in HEYGEN_AVATAR_MAP.items() if e in engines]
+    for e in ("avatar_iv", "avatar_iii", "avatar_v")
+}
+
+
+async def _apply_speech_source(cls: type[IO.ComfyNode], payload: dict, speech: dict, require_voice: bool) -> None:
+    """Fill script/audio speech fields of a /v3/videos payload from the DynamicCombo dict."""
+    if speech["speech"] == "audio":
+        payload["audio_url"] = await upload_audio_to_comfyapi(
+            cls, speech["audio"], container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg"
+        )
+    elif speech["speech"] == "script":
+        validate_string(speech["text"], strip_whitespace=True, min_length=1, max_length=5000)
+        payload["script"] = speech["text"]
+        voice_id = speech.get("custom_voice_id", "").strip()
+        if not voice_id and speech["voice"] != _DEFAULT_VOICE_OPTION:
+            voice_id = HEYGEN_VOICE_GENERAL_MAP[speech["voice"]]
+        if voice_id:
+            payload["voice_id"] = voice_id
+        elif require_voice:
+            raise ValueError("A voice is required when driving the video with a text script.")
+        speed = speech.get("voice_speed", 1.0)
+        if speed != 1.0:
+            payload["voice_settings"] = {"speed": round(speed, 2)}
+
+
+async def _create_and_poll_video(cls: type[IO.ComfyNode], payload: dict) -> dict:
+    """POST a /v3/videos payload, poll until terminal, and return the final video data."""
+    created = await sync_op_raw(
+        cls,
+        ApiEndpoint(path=_VIDEOS_PATH, method="POST", headers={"Idempotency-Key": uuid.uuid4().hex}),
+        data=payload,
+    )
+    video_id = (created.get("data") or {}).get("video_id")
+    if not video_id:
+        raise ValueError(f"HeyGen did not return a video_id: {created}")
+    final = await poll_op_raw(
+        cls,
+        ApiEndpoint(path=f"{_VIDEOS_PATH}/{video_id}"),
+        status_extractor=lambda r: (r.get("data") or {}).get("status"),
+        queued_statuses=["pending", "waiting"],
+        poll_interval=5.0,
+    )
+    data = final["data"]
+    if not data.get("video_url"):
+        raise ValueError(f"HeyGen returned no video_url for video {video_id}.")
+    return data
+
+
+async def _resolve_avatar(
+    cls: type[IO.ComfyNode], avatar_label: str, custom_avatar_id: str, engine_choice: str
+) -> tuple[str, str | None]:
+    """Resolve (avatar_id, engine_type) from the combo/override + engine widgets."""
+    custom_avatar_id = custom_avatar_id.strip()
+    if custom_avatar_id:
+        look = (
+            await sync_op_raw(
+                cls,
+                ApiEndpoint(path=f"{_LOOKS_PATH}/{custom_avatar_id}"),
+                final_label_on_success=None,
+            )
+        ).get("data") or {}
+        avatar_id = custom_avatar_id
+        avatar_label = look.get("name") or custom_avatar_id
+        supported = look.get("supported_api_engines") or []
+    else:
+        avatar_id, avatar_type, supported = HEYGEN_AVATAR_MAP[avatar_label]
+
+    if engine_choice == "auto":
+        engine = next((e for e in ("avatar_iv", "avatar_iii", "avatar_v") if e in supported), None)
+    else:
+        engine = engine_choice
+        if supported and engine not in supported:
+            raise ValueError(
+                f"Avatar '{avatar_label}' does not support the {engine} engine "
+                f"(supported: {', '.join(supported)}). Set engine to 'auto' to pick "
+                "a compatible engine automatically."
+            )
+    return avatar_id, engine
+
+
+class HeyGenTalkingPhotoNode(IO.ComfyNode):
+    """Animate a still image of a person into a lip-synced talking video."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="HeyGenTalkingPhotoNode",
+            display_name="HeyGen Talking Photo",
+            category="partner/video/HeyGen",
+            description="Animate any image of a person into a lip-synced talking video "
+            "(HeyGen Avatar IV). Drive it with a text script or your own audio.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="Image of a person to animate. Downscaled automatically if larger than 2K.",
+                ),
+                IO.DynamicCombo.Input(
+                    "speech",
+                    display_name="speech source",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "script",
+                            [
+                                IO.String.Input(
+                                    "text",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Text for the avatar to speak (up to 5000 characters). "
+                                    "The generated speech must be at least 1 second long.",
+                                ),
+                                IO.Combo.Input(
+                                    "voice",
+                                    options=HEYGEN_VOICE_GENERAL_OPTIONS,
+                                    tooltip="Voice for the script (HeyGen's most popular voices).",
+                                ),
+                                IO.String.Input(
+                                    "custom_voice_id",
+                                    default="",
+                                    optional=True,
+                                    tooltip="Optional HeyGen voice ID. When set, overrides the voice selected above. "
+                                    "Any voice from HeyGen's library (2000+) can be used.",
+                                ),
+                                IO.Float.Input(
+                                    "voice_speed",
+                                    default=1.0,
+                                    min=0.5,
+                                    max=1.5,
+                                    step=0.05,
+                                    optional=True,
+                                    tooltip="Speech speed multiplier.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "audio",
+                            [
+                                IO.Audio.Input(
+                                    "audio",
+                                    tooltip="Audio for the avatar to lip-sync, up to 10 minutes.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Drive the avatar with a text script (HeyGen text-to-speech) or your own audio.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["720p", "1080p"],
+                    default="1080p",
+                    optional=True,
+                    tooltip="Output video resolution.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["auto", "16:9", "9:16", "1:1", "4:5", "5:4"],
+                    default="auto",
+                    optional=True,
+                    tooltip="Output aspect ratio. 'auto' follows the input image.",
+                ),
+                IO.Combo.Input(
+                    "expressiveness",
+                    options=["low", "medium", "high"],
+                    default="low",
+                    optional=True,
+                    tooltip="How expressive the animated face and gestures are.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    optional=True,
+                    tooltip="Not sent to HeyGen; change it to force a re-run.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.0715,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        speech: dict,
+        resolution: str = "1080p",
+        aspect_ratio: str = "auto",
+        expressiveness: str = "low",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        image = downscale_image_tensor_by_max_side(image, max_side=2000)
+        image_url = await upload_image_to_comfyapi(cls, image, mime_type="image/png", total_pixels=None)
+        payload = {
+            "type": "image",
+            "image": {"type": "url", "url": image_url},
+            "resolution": resolution,
+            "aspect_ratio": aspect_ratio,
+            "expressiveness": expressiveness,
+            "title": "ComfyUI Talking Photo",
+        }
+        await _apply_speech_source(cls, payload, speech, require_voice=True)
+        video = await _create_and_poll_video(cls, payload)
+        return IO.NodeOutput(await download_url_to_video_output(video["video_url"]))
+
+
+class HeyGenAvatarVideoNode(IO.ComfyNode):
+    """Generate a presenter video from a HeyGen avatar look."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="HeyGenAvatarVideoNode",
+            display_name="HeyGen Avatar Video",
+            category="partner/video/HeyGen",
+            description="Generate a talking-presenter video from a HeyGen avatar. "
+            "Includes HeyGen's most popular public avatars; any look ID can be supplied as an override.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "engine",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "auto",
+                            [
+                                IO.Combo.Input(
+                                    "avatar",
+                                    options=HEYGEN_AVATAR_OPTIONS,
+                                    tooltip="Avatar look to present the video (curated from HeyGen's "
+                                    "public library). The best engine the look supports is chosen "
+                                    "automatically.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "avatar_iv",
+                            [
+                                IO.Combo.Input(
+                                    "avatar",
+                                    options=_AVATARS_BY_ENGINE["avatar_iv"],
+                                    tooltip="Avatar looks that support the Avatar IV engine.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "avatar_iii",
+                            [
+                                IO.Combo.Input(
+                                    "avatar",
+                                    options=_AVATARS_BY_ENGINE["avatar_iii"],
+                                    tooltip="Avatar looks that support the Avatar III engine.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "avatar_v",
+                            [
+                                IO.Combo.Input(
+                                    "avatar",
+                                    options=_AVATARS_BY_ENGINE["avatar_v"],
+                                    tooltip="Avatar looks that support the Avatar V engine.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Rendering engine; each choice lists only the avatars that support it. "
+                    "'auto' offers every avatar and picks its best engine (Avatar IV preferred). "
+                    "Avatar V is highest fidelity, Avatar III is the most affordable.",
+                ),
+                IO.String.Input(
+                    "custom_avatar_id",
+                    default="",
+                    optional=True,
+                    tooltip="Optional HeyGen avatar look ID. When set, overrides the avatar selected above. "
+                    "Any of HeyGen's 3000+ public looks (or your private avatars) can be used.",
+                ),
+                IO.DynamicCombo.Input(
+                    "speech",
+                    display_name="speech source",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "script",
+                            [
+                                IO.String.Input(
+                                    "text",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Text for the avatar to speak (up to 5000 characters). "
+                                    "The generated speech must be at least 1 second long.",
+                                ),
+                                IO.Combo.Input(
+                                    "voice",
+                                    options=[_DEFAULT_VOICE_OPTION] + HEYGEN_VOICE_GENERAL_OPTIONS,
+                                    tooltip="Voice for the script. The default option uses the voice HeyGen assigned to the avatar.",
+                                ),
+                                IO.String.Input(
+                                    "custom_voice_id",
+                                    default="",
+                                    optional=True,
+                                    tooltip="Optional HeyGen voice ID. When set, overrides the voice selected above. "
+                                    "Any voice from HeyGen's library (2000+) can be used.",
+                                ),
+                                IO.Float.Input(
+                                    "voice_speed",
+                                    default=1.0,
+                                    min=0.5,
+                                    max=1.5,
+                                    step=0.05,
+                                    optional=True,
+                                    tooltip="Speech speed multiplier.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "audio",
+                            [
+                                IO.Audio.Input(
+                                    "audio",
+                                    tooltip="Audio for the avatar to lip-sync, up to 10 minutes.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Drive the avatar with a text script (HeyGen text-to-speech) or your own audio.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["720p", "1080p"],
+                    default="1080p",
+                    optional=True,
+                    tooltip="Output video resolution.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["auto", "16:9", "9:16", "1:1", "4:5", "5:4"],
+                    default="auto",
+                    optional=True,
+                    tooltip="Output aspect ratio. 'auto' follows the avatar's source footage.",
+                ),
+                IO.String.Input(
+                    "background_color",
+                    default="",
+                    optional=True,
+                    tooltip="Optional solid background color as a hex code (e.g. '#00ff00'). "
+                    "Leave empty for the avatar's own background.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    optional=True,
+                    tooltip="Not sent to HeyGen; change it to force a re-run.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["engine"]),
+                expr="""
+                widgets.engine = "avatar_iii"
+                  ? {"type":"range_usd","min_usd":0.0143,"max_usd":0.023595,"format":{"suffix":"/second"}}
+                  : widgets.engine = "avatar_v"
+                  ? {"type":"usd","usd":0.1716,"format":{"suffix":"/second"}}
+                  : widgets.engine = "avatar_iv"
+                  ? {"type":"range_usd","min_usd":0.055055,"max_usd":0.115115,"format":{"suffix":"/second"}}
+                  : {"type":"range_usd","min_usd":0.0143,"max_usd":0.1716,"format":{"suffix":"/second"}}
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        engine: dict,
+        speech: dict,
+        custom_avatar_id: str = "",
+        resolution: str = "1080p",
+        aspect_ratio: str = "auto",
+        background_color: str = "",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        avatar_id, engine_type = await _resolve_avatar(cls, engine["avatar"], custom_avatar_id, engine["engine"])
+        payload = {
+            "type": "avatar",
+            "avatar_id": avatar_id,
+            "resolution": resolution,
+            "aspect_ratio": aspect_ratio,
+            "title": "ComfyUI Avatar Video",
+        }
+        if engine_type:
+            payload["engine"] = {"type": engine_type}
+        background_color = background_color.strip()
+        if background_color:
+            if not background_color.startswith("#"):
+                raise ValueError("background_color must be a hex color code like '#00ff00'.")
+            payload["background"] = {"type": "color", "value": background_color}
+        await _apply_speech_source(cls, payload, speech, require_voice=False)
+        video = await _create_and_poll_video(cls, payload)
+        return IO.NodeOutput(await download_url_to_video_output(video["video_url"]))
+
+
+class HeyGenCreateAvatarNode(IO.ComfyNode):
+    """Create a reusable HeyGen avatar from a photo or a text prompt."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="HeyGenCreateAvatarNode",
+            display_name="HeyGen Create Avatar",
+            category="partner/video/HeyGen",
+            description="Create your own reusable HeyGen avatar from a photo of a person or "
+            "from a text prompt (a generated character). Feed the resulting avatar_id into "
+            "HeyGen Avatar Video's custom_avatar_id — and save the ID somewhere to reuse the "
+            "avatar in future workflows.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "source",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "prompt",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Description of the avatar to generate (up to 1000 characters).",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("ref_image"),
+                                        names=[f"ref_image_{i}" for i in range(1, 4)],
+                                        min=0,
+                                    ),
+                                    tooltip="Up to 3 reference images guiding the generated look.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "photo",
+                            [
+                                IO.Image.Input(
+                                    "identity_photo",
+                                    tooltip="Photo of the person to turn into an avatar. "
+                                    "Downscaled automatically if larger than 2K.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Generate a new character from a text prompt, or create the avatar "
+                    "from a connected photo of a person.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(
+                    display_name="avatar_id",
+                    tooltip="Avatar look ID. Pass it to HeyGen Avatar Video's custom_avatar_id; "
+                    "save it to reuse the avatar later.",
+                ),
+                IO.Image.Output(display_name="preview"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["source"]),
+                expr="""{"type":"usd","usd": widgets.source = "photo" ? 1.8876 : 1.43}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        source: dict,
+    ) -> IO.NodeOutput:
+        payload: dict = {"name": "ComfyUI Avatar"}
+        if source["source"] == "photo":
+            image = downscale_image_tensor_by_max_side(source["identity_photo"], max_side=2000)
+            image_url = await upload_image_to_comfyapi(cls, image, mime_type="image/png", total_pixels=None)
+            payload["type"] = "photo"
+            payload["file"] = {"type": "url", "url": image_url}
+        else:
+            validate_string(source["prompt"], strip_whitespace=True, min_length=1, max_length=1000)
+            payload["type"] = "prompt"
+            payload["prompt"] = source["prompt"]
+            ref_tensors = [t for t in (source.get("reference_images") or {}).values() if t is not None]
+            if ref_tensors:
+                n_images = sum(get_number_of_images(t) for t in ref_tensors)
+                if n_images > 3:
+                    raise ValueError(f"HeyGen accepts at most 3 reference images; got {n_images}.")
+                scaled = [downscale_image_tensor_by_max_side(t, max_side=2000) for t in ref_tensors]
+                ref_urls = await upload_images_to_comfyapi(
+                    cls, scaled, max_images=3, mime_type="image/png", total_pixels=None
+                )
+                payload["reference_images"] = [{"type": "url", "url": u} for u in ref_urls]
+        created = await sync_op_raw(
+            cls,
+            ApiEndpoint(path=_AVATARS_PATH, method="POST"),
+            data=payload,
+        )
+        look_id = ((created.get("data") or {}).get("avatar_item") or {}).get("id")
+        if not look_id:
+            raise ValueError(f"HeyGen did not return an avatar: {created}")
+        final = await poll_op_raw(
+            cls,
+            ApiEndpoint(path=f"{_LOOKS_PATH}/{look_id}"),
+            # A missing status means the look needed no training and is ready.
+            status_extractor=lambda r: (r.get("data") or {}).get("status") or "completed",
+            failed_statuses=["failed", "pending_consent"],
+            poll_interval=5.0,
+        )
+        data = final["data"]
+        if data.get("preview_image_url"):
+            preview = await download_url_to_image_tensor(data["preview_image_url"])
+        else:
+            preview = torch.zeros(1, 64, 64, 3)
+        PromptServer.instance.send_progress_text(
+            f"Please save the avatar_id for reuse.\n\navatar_id: {look_id}",
+            cls.hidden.unique_id,
+        )
+        return IO.NodeOutput(look_id, preview)
+
+
+class HeyGenVideoTranslateNode(IO.ComfyNode):
+    """Translate a spoken video into another language with voice cloning and lip sync."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="HeyGenVideoTranslateNode",
+            display_name="HeyGen Video Translate",
+            category="partner/video/HeyGen",
+            description="Translate a spoken video into another language. Clones the original "
+            "speaker's voice and re-animates the mouth to match the translated speech.",
+            inputs=[
+                IO.Video.Input(
+                    "video",
+                    tooltip="Video with speech to translate.",
+                ),
+                IO.Combo.Input(
+                    "output_language",
+                    options=HEYGEN_TRANSLATE_LANGUAGES,
+                    tooltip="Target language for the translated video.",
+                ),
+                IO.Combo.Input(
+                    "mode",
+                    options=["speed", "precision"],
+                    default="speed",
+                    tooltip="'speed' is faster; 'precision' produces higher-quality lip sync at a higher price.",
+                ),
+                IO.Boolean.Input(
+                    "translate_audio_only",
+                    default=False,
+                    optional=True,
+                    tooltip="Only swap the audio track, keeping the original mouth movements (no lip sync).",
+                ),
+                IO.Int.Input(
+                    "speaker_count",
+                    default=0,
+                    min=0,
+                    max=10,
+                    optional=True,
+                    tooltip="Number of speakers in the video. 0 = detect automatically.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    optional=True,
+                    tooltip="Not sent to HeyGen; change it to force a re-run.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode", "translate_audio_only"]),
+                expr="""{"type":"usd","usd": widgets.mode = "precision" ? 0.03575 """
+                """: widgets.translate_audio_only = true ? 0.013585 : 0.019305,"""
+                """"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        output_language: str,
+        mode: str,
+        translate_audio_only: bool = False,
+        speaker_count: int = 0,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        video_url = await upload_video_to_comfyapi(cls, video)
+        payload = {
+            "video": {"type": "url", "url": video_url},
+            "output_languages": [output_language],
+            "mode": mode,
+            "translate_audio_only": translate_audio_only,
+            "title": "ComfyUI Video Translate",
+        }
+        if speaker_count > 0:
+            payload["speaker_num"] = speaker_count
+        created = await sync_op_raw(
+            cls,
+            ApiEndpoint(path=_TRANSLATIONS_PATH, method="POST"),
+            data=payload,
+        )
+        translation_ids = (created.get("data") or {}).get("video_translation_ids") or []
+        if not translation_ids:
+            raise ValueError(f"HeyGen did not return a translation ID: {created}")
+        final = await poll_op_raw(
+            cls,
+            ApiEndpoint(path=f"{_TRANSLATIONS_PATH}/{translation_ids[0]}"),
+            status_extractor=lambda r: (r.get("data") or {}).get("status"),
+            queued_statuses=["pending"],
+            poll_interval=5.0,
+        )
+        data = final["data"]
+        if not data.get("video_url"):
+            raise ValueError(f"HeyGen returned no video_url for translation {translation_ids[0]}.")
+        return IO.NodeOutput(await download_url_to_video_output(data["video_url"]))
+
+
+class HeyGenTextToSpeechNode(IO.ComfyNode):
+    """Synthesize speech audio from text with HeyGen's Starfish TTS engine."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="HeyGenTextToSpeechNode",
+            display_name="HeyGen Text to Speech",
+            category="partner/audio/HeyGen",
+            description="Generate speech audio from text using HeyGen's Starfish TTS engine. "
+            "Includes HeyGen's most popular voices across 17 languages.",
+            inputs=[
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text to synthesize (up to 5000 characters). The generated speech "
+                    "must be at least 1 second long.",
+                ),
+                IO.Combo.Input(
+                    "voice",
+                    options=HEYGEN_VOICE_TTS_OPTIONS,
+                    tooltip="Voice to use (curated from HeyGen's most popular Starfish-compatible voices).",
+                ),
+                IO.String.Input(
+                    "custom_voice_id",
+                    default="",
+                    optional=True,
+                    tooltip="Optional HeyGen voice ID. When set, overrides the voice selected above. "
+                    "The voice must support the Starfish engine.",
+                ),
+                IO.Float.Input(
+                    "speed",
+                    default=1.0,
+                    min=0.5,
+                    max=2.0,
+                    step=0.05,
+                    optional=True,
+                    tooltip="Speech speed multiplier.",
+                ),
+                IO.Boolean.Input(
+                    "ssml",
+                    default=False,
+                    optional=True,
+                    tooltip="Treat the text as SSML markup (for pauses, emphasis, and pronunciation control).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    optional=True,
+                    tooltip="Not sent to HeyGen; change it to force a re-run.",
+                ),
+            ],
+            outputs=[IO.Audio.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.00095381,"format":{"approximate":true,"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        text: str,
+        voice: str,
+        custom_voice_id: str = "",
+        speed: float = 1.0,
+        ssml: bool = False,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        validate_string(text, strip_whitespace=True, min_length=1, max_length=5000)
+        payload = {
+            "text": text,
+            "voice_id": custom_voice_id.strip() or HEYGEN_VOICE_TTS_MAP[voice],
+            "speed": round(speed, 2),
+        }
+        if ssml:
+            payload["input_type"] = "ssml"
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(path=_SPEECH_PATH, method="POST"),
+            data=payload,
+        )
+        audio_url = (response.get("data") or {}).get("audio_url")
+        if not audio_url:
+            raise ValueError(f"HeyGen did not return an audio_url: {response}")
+        audio_bytes = await download_url_as_bytesio(audio_url)
+        return IO.NodeOutput(audio_bytes_to_audio_input(audio_bytes.getvalue()))
+
+
+class HeyGenExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            HeyGenTalkingPhotoNode,
+            HeyGenAvatarVideoNode,
+            HeyGenCreateAvatarNode,
+            HeyGenVideoTranslateNode,
+            HeyGenTextToSpeechNode,
+        ]
+
+
+async def comfy_entrypoint() -> HeyGenExtension:
+    return HeyGenExtension()
diff --git a/comfy_api_nodes/nodes_hitpaw.py b/comfy_api_nodes/nodes_hitpaw.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2b983d032229d8ad5be6648fddf0c05a6b9973c
--- /dev/null
+++ b/comfy_api_nodes/nodes_hitpaw.py
@@ -0,0 +1,336 @@
+import math
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.hitpaw import (
+    ImageEnhanceTaskCreateRequest,
+    InputVideoModel,
+    TaskCreateDataResponse,
+    TaskCreateResponse,
+    TaskStatusPollRequest,
+    TaskStatusResponse,
+    VideoEnhanceTaskCreateRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    downscale_image_tensor,
+    get_image_dimensions,
+    poll_op,
+    sync_op,
+    upload_image_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_video_duration,
+)
+
+VIDEO_MODELS_MODELS_MAP = {
+    "Portrait Restore Model (1x)": "portrait_restore_1x",
+    "Portrait Restore Model (2x)": "portrait_restore_2x",
+    "General Restore Model (1x)": "general_restore_1x",
+    "General Restore Model (2x)": "general_restore_2x",
+    "General Restore Model (4x)": "general_restore_4x",
+    "Ultra HD Model (2x)": "ultrahd_restore_2x",
+    "Generative Model (1x)": "generative_1x",
+}
+
+# Resolution name to target dimension (shorter side) in pixels
+RESOLUTION_TARGET_MAP = {
+    "720p": 720,
+    "1080p": 1080,
+    "2K/QHD": 1440,
+    "4K/UHD": 2160,
+    "8K": 4320,
+}
+
+# Square (1:1) resolutions use standard square dimensions
+RESOLUTION_SQUARE_MAP = {
+    "720p": 720,
+    "1080p": 1080,
+    "2K/QHD": 1440,
+    "4K/UHD": 2048,  # DCI 4K square
+    "8K": 4096,  # DCI 8K square
+}
+
+# Models with limited resolution support (no 8K)
+LIMITED_RESOLUTION_MODELS = {"Generative Model (1x)"}
+
+# Resolution options for different model types
+RESOLUTIONS_LIMITED = ["original", "720p", "1080p", "2K/QHD", "4K/UHD"]
+RESOLUTIONS_FULL = ["original", "720p", "1080p", "2K/QHD", "4K/UHD", "8K"]
+
+# Maximum output resolution in pixels
+MAX_PIXELS_GENERATIVE = 32_000_000
+MAX_MP_GENERATIVE = MAX_PIXELS_GENERATIVE // 1_000_000
+
+
+class HitPawGeneralImageEnhance(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HitPawGeneralImageEnhance",
+            display_name="HitPaw General Image Enhance",
+            category="partner/image/HitPaw",
+            description="Upscale low-resolution images to super-resolution, eliminate artifacts and noise. "
+            f"Maximum output: {MAX_MP_GENERATIVE} megapixels.",
+            inputs=[
+                IO.Combo.Input("model", options=["generative_portrait", "generative"]),
+                IO.Image.Input("image"),
+                IO.Combo.Input("upscale_factor", options=[1, 2, 4]),
+                IO.Boolean.Input(
+                    "auto_downscale",
+                    default=False,
+                    tooltip="Automatically downscale input image if output would exceed the limit.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $prices := {
+                    "generative_portrait": {"min": 0.02, "max": 0.06},
+                    "generative": {"min": 0.05, "max": 0.15}
+                  };
+                  $price := $lookup($prices, widgets.model);
+                  {
+                    "type": "range_usd",
+                    "min_usd": $price.min,
+                    "max_usd": $price.max
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        upscale_factor: int,
+        auto_downscale: bool,
+    ) -> IO.NodeOutput:
+        height, width = get_image_dimensions(image)
+        requested_scale = upscale_factor
+        output_pixels = height * width * requested_scale * requested_scale
+        if output_pixels > MAX_PIXELS_GENERATIVE:
+            if auto_downscale:
+                input_pixels = width * height
+                scale = 1
+                max_input_pixels = MAX_PIXELS_GENERATIVE
+
+                for candidate in [4, 2, 1]:
+                    if candidate > requested_scale:
+                        continue
+                    scale_output_pixels = input_pixels * candidate * candidate
+                    if scale_output_pixels <= MAX_PIXELS_GENERATIVE:
+                        scale = candidate
+                        max_input_pixels = None
+                        break
+                    # Check if we can downscale input by at most 2x to fit
+                    downscale_ratio = math.sqrt(scale_output_pixels / MAX_PIXELS_GENERATIVE)
+                    if downscale_ratio <= 2.0:
+                        scale = candidate
+                        max_input_pixels = MAX_PIXELS_GENERATIVE // (candidate * candidate)
+                        break
+
+                if max_input_pixels is not None:
+                    image = downscale_image_tensor(image, total_pixels=max_input_pixels)
+                upscale_factor = scale
+            else:
+                output_width = width * requested_scale
+                output_height = height * requested_scale
+                raise ValueError(
+                    f"Output size ({output_width}x{output_height} = {output_pixels:,} pixels) "
+                    f"exceeds maximum allowed size of {MAX_PIXELS_GENERATIVE:,} pixels ({MAX_MP_GENERATIVE}MP). "
+                    f"Enable auto_downscale or use a smaller input image or a lower upscale factor."
+                )
+
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/hitpaw/api/photo-enhancer", method="POST"),
+            response_model=TaskCreateResponse,
+            data=ImageEnhanceTaskCreateRequest(
+                model_name=f"{model}_{upscale_factor}x",
+                img_url=await upload_image_to_comfyapi(cls, image, total_pixels=None),
+            ),
+            wait_label="Creating task",
+            final_label_on_success="Task created",
+        )
+        if initial_res.code != 200:
+            raise ValueError(f"Task creation failed with code {initial_res.code}: {initial_res.message}")
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/hitpaw/api/task-status", method="POST"),
+            data=TaskCreateDataResponse(job_id=initial_res.data.job_id),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda x: x.data.status,
+            poll_interval=10.0,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(final_response.data.res_url))
+
+
+class HitPawVideoEnhance(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        model_options = []
+        for model_name in VIDEO_MODELS_MODELS_MAP:
+            if model_name in LIMITED_RESOLUTION_MODELS:
+                resolutions = RESOLUTIONS_LIMITED
+            else:
+                resolutions = RESOLUTIONS_FULL
+            model_options.append(
+                IO.DynamicCombo.Option(
+                    model_name,
+                    [IO.Combo.Input("resolution", options=resolutions)],
+                )
+            )
+
+        return IO.Schema(
+            node_id="HitPawVideoEnhance",
+            display_name="HitPaw Video Enhance",
+            category="partner/video/HitPaw",
+            description="Upscale low-resolution videos to high resolution, eliminate artifacts and noise. "
+            "Prices shown are per second of video.",
+            inputs=[
+                IO.DynamicCombo.Input("model", options=model_options),
+                IO.Video.Input("video"),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
+                expr="""
+                (
+                  $m := $lookup(widgets, "model");
+                  $res := $lookup(widgets, "model.resolution");
+                  $standard_model_prices := {
+                    "original": {"min": 0.01, "max": 0.198},
+                    "720p": {"min": 0.01, "max": 0.06},
+                    "1080p": {"min": 0.015, "max": 0.09},
+                    "2k/qhd": {"min": 0.02, "max": 0.117},
+                    "4k/uhd": {"min": 0.025, "max": 0.152},
+                    "8k": {"min": 0.033, "max": 0.198}
+                  };
+                  $ultra_hd_model_prices := {
+                    "original": {"min": 0.015, "max": 0.264},
+                    "720p": {"min": 0.015, "max": 0.092},
+                    "1080p": {"min": 0.02, "max": 0.12},
+                    "2k/qhd": {"min": 0.026, "max": 0.156},
+                    "4k/uhd": {"min": 0.034, "max": 0.203},
+                    "8k": {"min": 0.044, "max": 0.264}
+                  };
+                  $generative_model_prices := {
+                    "original": {"min": 0.015, "max": 0.338},
+                    "720p": {"min": 0.008, "max": 0.090},
+                    "1080p": {"min": 0.05, "max": 0.15},
+                    "2k/qhd": {"min": 0.038, "max": 0.225},
+                    "4k/uhd": {"min": 0.056, "max": 0.338}
+                  };
+                  $prices := $contains($m, "ultra hd") ? $ultra_hd_model_prices :
+                             $contains($m, "generative") ? $generative_model_prices :
+                             $standard_model_prices;
+                  $price := $lookup($prices, $res);
+                  {
+                    "type": "range_usd",
+                    "min_usd": $price.min,
+                    "max_usd": $price.max,
+                    "format": {"approximate": true, "suffix": "/second"}
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: InputVideoModel,
+        video: Input.Video,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, min_duration=0.5, max_duration=60 * 60)
+        resolution = model["resolution"]
+        src_width, src_height = video.get_dimensions()
+
+        if resolution == "original":
+            output_width = src_width
+            output_height = src_height
+        else:
+            if src_width == src_height:
+                target_size = RESOLUTION_SQUARE_MAP[resolution]
+                if target_size < src_width:
+                    raise ValueError(
+                        f"Selected resolution {resolution} ({target_size}x{target_size}) is smaller than "
+                        f"the input video ({src_width}x{src_height}). Please select a higher resolution or 'original'."
+                    )
+                output_width = target_size
+                output_height = target_size
+            else:
+                min_dimension = min(src_width, src_height)
+                target_size = RESOLUTION_TARGET_MAP[resolution]
+                if target_size < min_dimension:
+                    raise ValueError(
+                        f"Selected resolution {resolution} ({target_size}p) is smaller than "
+                        f"the input video's shorter dimension ({min_dimension}p). "
+                        f"Please select a higher resolution or 'original'."
+                    )
+                if src_width > src_height:
+                    output_height = target_size
+                    output_width = int(target_size * (src_width / src_height))
+                else:
+                    output_width = target_size
+                    output_height = int(target_size * (src_height / src_width))
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/hitpaw/api/video-enhancer", method="POST"),
+            response_model=TaskCreateResponse,
+            data=VideoEnhanceTaskCreateRequest(
+                video_url=await upload_video_to_comfyapi(cls, video),
+                resolution=[output_width, output_height],
+                original_resolution=[src_width, src_height],
+                model_name=VIDEO_MODELS_MODELS_MAP[model["model"]],
+            ),
+            wait_label="Creating task",
+            final_label_on_success="Task created",
+        )
+        if initial_res.code != 200:
+            raise ValueError(f"Task creation failed with code {initial_res.code}: {initial_res.message}")
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/hitpaw/api/task-status", method="POST"),
+            data=TaskStatusPollRequest(job_id=initial_res.data.job_id),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda x: x.data.status,
+            poll_interval=10.0,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.res_url))
+
+
+class HitPawExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            HitPawGeneralImageEnhance,
+            HitPawVideoEnhance,
+        ]
+
+
+async def comfy_entrypoint() -> HitPawExtension:
+    return HitPawExtension()
diff --git a/comfy_api_nodes/nodes_hunyuan3d.py b/comfy_api_nodes/nodes_hunyuan3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..de98e466b5b29c096af4e21e51a3b1c967f93730
--- /dev/null
+++ b/comfy_api_nodes/nodes_hunyuan3d.py
@@ -0,0 +1,768 @@
+import zipfile
+from io import BytesIO
+
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input, Types
+from comfy_api_nodes.apis.hunyuan3d import (
+    Hunyuan3DViewImage,
+    InputGenerateType,
+    ResultFile3D,
+    SmartTopologyRequest,
+    TaskFile3DInput,
+    TextureEditTaskRequest,
+    To3DPartTaskRequest,
+    To3DProTaskCreateResponse,
+    To3DProTaskQueryRequest,
+    To3DProTaskRequest,
+    To3DProTaskResultResponse,
+    To3DUVTaskRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    bytesio_to_image_tensor,
+    download_url_to_bytesio,
+    download_url_to_file_3d,
+    download_url_to_image_tensor,
+    downscale_image_tensor_by_max_side,
+    poll_op,
+    sync_op,
+    upload_3d_model_to_comfyapi,
+    upload_image_to_comfyapi,
+    validate_image_dimensions,
+    validate_string,
+)
+
+
+def _is_tencent_rate_limited(status: int, body: object) -> bool:
+    return (
+        status == 400
+        and isinstance(body, dict)
+        and "RequestLimitExceeded" in str(body.get("Response", {}).get("Error", {}).get("Code", ""))
+    )
+
+
+class ObjZipResult:
+    __slots__ = ("obj", "texture", "metallic", "normal", "roughness")
+
+    def __init__(
+        self,
+        obj: Types.File3D,
+        texture: Input.Image | None = None,
+        metallic: Input.Image | None = None,
+        normal: Input.Image | None = None,
+        roughness: Input.Image | None = None,
+    ):
+        self.obj = obj
+        self.texture = texture
+        self.metallic = metallic
+        self.normal = normal
+        self.roughness = roughness
+
+
+async def download_and_extract_obj_zip(url: str) -> ObjZipResult:
+    """The Tencent API returns OBJ results as ZIP archives containing the .obj mesh, and texture images.
+
+    When PBR is enabled, the ZIP may contain additional metallic, normal, and roughness maps
+    identified by their filename suffixes.
+    """
+    data = BytesIO()
+    await download_url_to_bytesio(url, data)
+    data.seek(0)
+    if not zipfile.is_zipfile(data):
+        data.seek(0)
+        return ObjZipResult(obj=Types.File3D(source=data, file_format="obj"))
+    data.seek(0)
+    obj_bytes = None
+    textures: dict[str, Input.Image] = {}
+    with zipfile.ZipFile(data) as zf:
+        for name in zf.namelist():
+            lower = name.lower()
+            if lower.endswith(".obj"):
+                obj_bytes = zf.read(name)
+            elif any(lower.endswith(ext) for ext in (".png", ".jpg", ".jpeg", ".bmp", ".tiff", ".webp")):
+                stem = lower.rsplit(".", 1)[0]
+                tensor = bytesio_to_image_tensor(BytesIO(zf.read(name)), mode="RGB")
+                matched_key = "texture"
+                for suffix, key in {
+                    "_metallic": "metallic",
+                    "_normal": "normal",
+                    "_roughness": "roughness",
+                }.items():
+                    if stem.endswith(suffix):
+                        matched_key = key
+                        break
+                textures[matched_key] = tensor
+    if obj_bytes is None:
+        raise ValueError("ZIP archive does not contain an OBJ file.")
+    return ObjZipResult(
+        obj=Types.File3D(source=BytesIO(obj_bytes), file_format="obj"),
+        texture=textures.get("texture"),
+        metallic=textures.get("metallic"),
+        normal=textures.get("normal"),
+        roughness=textures.get("roughness"),
+    )
+
+
+def get_file_from_response(
+    response_objs: list[ResultFile3D], file_type: str, raise_if_not_found: bool = True
+) -> ResultFile3D | None:
+    for i in response_objs:
+        if i.Type.lower() == file_type.lower():
+            return i
+    if raise_if_not_found:
+        raise ValueError(f"'{file_type}' file type is not found in the response.")
+    return None
+
+
+class TencentTextToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentTextToModelNode",
+            display_name="Hunyuan3D: Text to Model",
+            category="partner/3d/Tencent",
+            essentials_category="3D",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["3.0", "3.1"],
+                    tooltip="The LowPoly option is unavailable for the `3.1` model.",
+                ),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Supports up to 1024 characters."),
+                IO.Int.Input("face_count", default=500000, min=3000, max=1500000),
+                IO.DynamicCombo.Input(
+                    "generate_type",
+                    options=[
+                        IO.DynamicCombo.Option("Normal", [IO.Boolean.Input("pbr", default=False)]),
+                        IO.DynamicCombo.Option(
+                            "LowPoly",
+                            [
+                                IO.Combo.Input("polygon_type", options=["triangle", "quadrilateral"]),
+                                IO.Boolean.Input("pbr", default=False),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("Geometry", []),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["generate_type", "generate_type.pbr", "face_count"]),
+                expr="""
+                (
+                  $base := widgets.generate_type = "normal" ? 25 : widgets.generate_type = "lowpoly" ? 30 : 15;
+                  $pbr := $lookup(widgets, "generate_type.pbr") ? 10 : 0;
+                  $face := widgets.face_count != 500000 ? 10 : 0;
+                  {"type":"usd","usd": ($base + $pbr + $face) * 0.02}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        face_count: int,
+        generate_type: InputGenerateType,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        validate_string(prompt, field_name="prompt", min_length=1, max_length=1024)
+        if model == "3.1" and generate_type["generate_type"].lower() == "lowpoly":
+            raise ValueError("The LowPoly option is currently unavailable for the 3.1 model.")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DProTaskRequest(
+                Model=model,
+                Prompt=prompt,
+                FaceCount=face_count,
+                GenerateType=generate_type["generate_type"],
+                EnablePBR=generate_type.get("pbr", None),
+                PolygonType=generate_type.get("polygon_type", None),
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        task_id = response.JobId
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=task_id),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        obj_file_response = get_file_from_response(result.ResultFile3Ds, "obj", raise_if_not_found=False)
+        obj_result = None
+        if obj_file_response:
+            obj_result = await download_and_extract_obj_zip(obj_file_response.Url)
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
+            ),
+            obj_result.obj if obj_result else None,
+            obj_result.texture if obj_result else None,
+        )
+
+
+class TencentImageToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentImageToModelNode",
+            display_name="Hunyuan3D: Image(s) to Model",
+            category="partner/3d/Tencent",
+            essentials_category="3D",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["3.0", "3.1"],
+                    tooltip="The LowPoly option is unavailable for the `3.1` model.",
+                ),
+                IO.Image.Input("image"),
+                IO.Image.Input("image_left", optional=True),
+                IO.Image.Input("image_right", optional=True),
+                IO.Image.Input("image_back", optional=True),
+                IO.Int.Input("face_count", default=500000, min=3000, max=1500000),
+                IO.DynamicCombo.Input(
+                    "generate_type",
+                    options=[
+                        IO.DynamicCombo.Option("Normal", [IO.Boolean.Input("pbr", default=False)]),
+                        IO.DynamicCombo.Option(
+                            "LowPoly",
+                            [
+                                IO.Combo.Input("polygon_type", options=["triangle", "quadrilateral"]),
+                                IO.Boolean.Input("pbr", default=False),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("Geometry", []),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
+                IO.Image.Output(display_name="optional_metallic"),
+                IO.Image.Output(display_name="optional_normal"),
+                IO.Image.Output(display_name="optional_roughness"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["generate_type", "generate_type.pbr", "face_count"],
+                    inputs=["image_left", "image_right", "image_back"],
+                ),
+                expr="""
+                (
+                  $base := widgets.generate_type = "normal" ? 25 : widgets.generate_type = "lowpoly" ? 30 : 15;
+                  $multiview := (
+                    inputs.image_left.connected or inputs.image_right.connected or inputs.image_back.connected
+                  ) ? 10 : 0;
+                  $pbr := $lookup(widgets, "generate_type.pbr") ? 10 : 0;
+                  $face := widgets.face_count != 500000 ? 10 : 0;
+                  {"type":"usd","usd": ($base + $multiview + $pbr + $face) * 0.02}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        face_count: int,
+        generate_type: InputGenerateType,
+        seed: int,
+        image_left: Input.Image | None = None,
+        image_right: Input.Image | None = None,
+        image_back: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        _ = seed
+        if model == "3.1" and generate_type["generate_type"].lower() == "lowpoly":
+            raise ValueError("The LowPoly option is currently unavailable for the 3.1 model.")
+        validate_image_dimensions(image, min_width=128, min_height=128)
+        multiview_images = []
+        for k, v in {
+            "left": image_left,
+            "right": image_right,
+            "back": image_back,
+        }.items():
+            if v is None:
+                continue
+            validate_image_dimensions(v, min_width=128, min_height=128)
+            multiview_images.append(
+                Hunyuan3DViewImage(
+                    ViewType=k,
+                    ViewImageUrl=await upload_image_to_comfyapi(
+                        cls,
+                        downscale_image_tensor_by_max_side(v, max_side=4900),
+                        mime_type="image/webp",
+                        total_pixels=24_010_000,
+                    ),
+                )
+            )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DProTaskRequest(
+                Model=model,
+                FaceCount=face_count,
+                GenerateType=generate_type["generate_type"],
+                ImageUrl=await upload_image_to_comfyapi(
+                    cls,
+                    downscale_image_tensor_by_max_side(image, max_side=4900),
+                    mime_type="image/webp",
+                    total_pixels=24_010_000,
+                ),
+                MultiViewImages=multiview_images if multiview_images else None,
+                EnablePBR=generate_type.get("pbr", None),
+                PolygonType=generate_type.get("polygon_type", None),
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        task_id = response.JobId
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=task_id),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        obj_file_response = get_file_from_response(result.ResultFile3Ds, "obj", raise_if_not_found=False)
+        if obj_file_response:
+            obj_result = await download_and_extract_obj_zip(obj_file_response.Url)
+            return IO.NodeOutput(
+                f"{task_id}.glb",
+                await download_url_to_file_3d(
+                    get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
+                ),
+                obj_result.obj,
+                obj_result.texture,
+                obj_result.metallic if obj_result.metallic is not None else torch.zeros(1, 1, 1, 3),
+                obj_result.normal if obj_result.normal is not None else torch.zeros(1, 1, 1, 3),
+                obj_result.roughness if obj_result.roughness is not None else torch.zeros(1, 1, 1, 3),
+            )
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
+            ),
+            None,
+            None,
+            None,
+            None,
+            None,
+        )
+
+
+class TencentModelTo3DUVNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentModelTo3DUVNode",
+            display_name="Hunyuan3D: Model to UV",
+            category="partner/3d/Tencent",
+            description="Perform UV unfolding on a 3D model to generate UV texture. "
+            "Input model must have less than 30000 faces.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DGLB, IO.File3DOBJ, IO.File3DFBX, IO.File3DAny],
+                    tooltip="Input 3D model (GLB, OBJ, or FBX)",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.File3DFBX.Output(display_name="FBX"),
+                IO.Image.Output(display_name="uv_image"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr='{"type":"usd","usd":0.2}'),
+        )
+
+    SUPPORTED_FORMATS = {"glb", "obj", "fbx"}
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format not in cls.SUPPORTED_FORMATS:
+            raise ValueError(
+                f"Unsupported file format: '{file_format}'. "
+                f"Supported formats: {', '.join(sorted(cls.SUPPORTED_FORMATS))}."
+            )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-uv", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DUVTaskRequest(
+                File=TaskFile3DInput(
+                    Type=file_format.upper(),
+                    Url=await upload_3d_model_to_comfyapi(cls, model_3d, file_format),
+                )
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-uv/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        uv_image_file = get_file_from_response(result.ResultFile3Ds, "uv_image", raise_if_not_found=False)
+        uv_image = (
+            await download_url_to_image_tensor(uv_image_file.Url)
+            if uv_image_file is not None
+            else torch.zeros(1, 1, 1, 3)
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
+            uv_image,
+        )
+
+
+class Tencent3DTextureEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Tencent3DTextureEditNode",
+            display_name="Hunyuan3D: 3D Texture Edit",
+            category="partner/3d/Tencent",
+            description="After inputting the 3D model, perform 3D model texture redrawing.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DFBX, IO.File3DAny],
+                    tooltip="3D model in FBX format. Model should have less than 100000 faces.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Describes texture editing. Supports up to 1024 UTF-8 characters.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.6}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        prompt: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format != "fbx":
+            raise ValueError(f"Unsupported file format: '{file_format}'. Only FBX format is supported.")
+        validate_string(prompt, field_name="prompt", min_length=1, max_length=1024)
+        model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-texture-edit", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=TextureEditTaskRequest(
+                File3D=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+                Prompt=prompt,
+                EnablePBR=True,
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-texture-edit/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb"),
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
+            await download_url_to_image_tensor(get_file_from_response(result.ResultFile3Ds, "texture_image").Url),
+        )
+
+
+class Tencent3DPartNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Tencent3DPartNode",
+            display_name="Hunyuan3D: 3D Part",
+            category="partner/3d/Tencent",
+            description="Automatically perform component identification and generation based on the model structure.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DFBX, IO.File3DAny],
+                    tooltip="3D model in FBX format. Model should have less than 30000 faces.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr='{"type":"usd","usd":0.6}'),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format != "fbx":
+            raise ValueError(f"Unsupported file format: '{file_format}'. Only FBX format is supported.")
+        model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-part", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DPartTaskRequest(
+                File=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+                EnableStagedGeneration=True,
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-part/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
+        )
+
+
+class TencentSmartTopologyNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentSmartTopologyNode",
+            display_name="Hunyuan3D: Smart Topology",
+            category="partner/3d/Tencent",
+            description="Perform smart retopology on a 3D model. "
+            "Supports GLB/OBJ formats; max 200MB; recommended for high-poly models.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DGLB, IO.File3DOBJ, IO.File3DAny],
+                    tooltip="Input 3D model (GLB or OBJ)",
+                ),
+                IO.Combo.Input(
+                    "polygon_type",
+                    options=["triangle", "quadrilateral"],
+                    tooltip="Surface composition type.",
+                ),
+                IO.Combo.Input(
+                    "face_level",
+                    options=["medium", "high", "low"],
+                    tooltip="Polygon reduction level.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DOBJ.Output(display_name="OBJ"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr='{"type":"usd","usd":1.0}'),
+        )
+
+    SUPPORTED_FORMATS = {"glb", "obj"}
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        polygon_type: str,
+        face_level: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format not in cls.SUPPORTED_FORMATS:
+            raise ValueError(
+                f"Unsupported file format: '{file_format}'. " f"Supported: {', '.join(sorted(cls.SUPPORTED_FORMATS))}."
+            )
+        model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-smart-topology", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=SmartTopologyRequest(
+                File3D=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+                PolygonType=polygon_type,
+                FaceLevel=face_level,
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed: [{response.Error.Code}] {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-smart-topology/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
+        )
+
+
+class TencentHunyuan3DExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TencentTextToModelNode,
+            TencentImageToModelNode,
+            TencentModelTo3DUVNode,
+            Tencent3DTextureEditNode,
+            Tencent3DPartNode,
+            TencentSmartTopologyNode,
+        ]
+
+
+async def comfy_entrypoint() -> TencentHunyuan3DExtension:
+    return TencentHunyuan3DExtension()
diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1ab8bd1400cb44aa2ec945ce8128b054ffc8d66
--- /dev/null
+++ b/comfy_api_nodes/nodes_ideogram.py
@@ -0,0 +1,681 @@
+from io import BytesIO
+from typing_extensions import override
+from comfy_api.latest import IO, ComfyExtension
+from PIL import Image
+import numpy as np
+import torch
+from comfy_api_nodes.apis.ideogram import (
+    IdeogramGenerateResponse,
+    IdeogramPImageRequest,
+    IdeogramV3Request,
+    IdeogramV3EditRequest,
+    IdeogramV4Request,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    bytesio_to_image_tensor,
+    download_url_as_bytesio,
+    resize_mask_to_image,
+    sync_op,
+    validate_string,
+)
+
+
+V3_RATIO_MAP = {
+    "1:3":"1x3",
+    "3:1":"3x1",
+    "1:2":"1x2",
+    "2:1":"2x1",
+    "9:16":"9x16",
+    "16:9":"16x9",
+    "10:16":"10x16",
+    "16:10":"16x10",
+    "2:3":"2x3",
+    "3:2":"3x2",
+    "3:4":"3x4",
+    "4:3":"4x3",
+    "4:5":"4x5",
+    "5:4":"5x4",
+    "1:1":"1x1",
+}
+
+V3_RESOLUTIONS= [
+    "Auto",
+    "512x1536",
+    "576x1408",
+    "576x1472",
+    "576x1536",
+    "640x1344",
+    "640x1408",
+    "640x1472",
+    "640x1536",
+    "704x1152",
+    "704x1216",
+    "704x1280",
+    "704x1344",
+    "704x1408",
+    "704x1472",
+    "736x1312",
+    "768x1088",
+    "768x1216",
+    "768x1280",
+    "768x1344",
+    "800x1280",
+    "832x960",
+    "832x1024",
+    "832x1088",
+    "832x1152",
+    "832x1216",
+    "832x1248",
+    "864x1152",
+    "896x960",
+    "896x1024",
+    "896x1088",
+    "896x1120",
+    "896x1152",
+    "960x832",
+    "960x896",
+    "960x1024",
+    "960x1088",
+    "1024x832",
+    "1024x896",
+    "1024x960",
+    "1024x1024",
+    "1088x768",
+    "1088x832",
+    "1088x896",
+    "1088x960",
+    "1120x896",
+    "1152x704",
+    "1152x832",
+    "1152x864",
+    "1152x896",
+    "1216x704",
+    "1216x768",
+    "1216x832",
+    "1248x832",
+    "1280x704",
+    "1280x768",
+    "1280x800",
+    "1312x736",
+    "1344x640",
+    "1344x704",
+    "1344x768",
+    "1408x576",
+    "1408x640",
+    "1408x704",
+    "1472x576",
+    "1472x640",
+    "1472x704",
+    "1536x512",
+    "1536x576",
+    "1536x640"
+]
+
+async def download_and_process_images(image_urls):
+    """Helper function to download and process multiple images from URLs"""
+
+    # Initialize list to store image tensors
+    image_tensors = []
+
+    for image_url in image_urls:
+        # Using functions from apinode_utils.py to handle downloading and processing
+        image_bytesio = await download_url_as_bytesio(image_url)  # Download image content to BytesIO
+        img_tensor = bytesio_to_image_tensor(image_bytesio, mode="RGB")  # Convert to torch.Tensor with RGB mode
+        image_tensors.append(img_tensor)
+
+    # Stack tensors to match (N, width, height, channels)
+    if image_tensors:
+        stacked_tensors = torch.cat(image_tensors, dim=0)
+    else:
+        raise Exception("No valid images were processed")
+
+    return stacked_tensors
+
+
+class IdeogramV3(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="IdeogramV3",
+            display_name="Ideogram V3",
+            category="partner/image/Ideogram",
+            description="Generates images using the Ideogram V3 model. "
+                        "Supports both regular image generation from text prompts and image editing with mask.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation or editing",
+                ),
+                IO.Image.Input(
+                    "image",
+                    tooltip="Optional reference image for image editing.",
+                    optional=True,
+                ),
+                IO.Mask.Input(
+                    "mask",
+                    tooltip="Optional mask for inpainting (white areas will be replaced)",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=list(V3_RATIO_MAP.keys()),
+                    default="1:1",
+                    tooltip="The aspect ratio for image generation. Ignored if resolution is not set to Auto.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=V3_RESOLUTIONS,
+                    default="Auto",
+                    tooltip="The resolution for image generation. "
+                            "If not set to Auto, this overrides the aspect_ratio setting.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "magic_prompt_option",
+                    options=["AUTO", "ON", "OFF"],
+                    default="AUTO",
+                    tooltip="Determine if MagicPrompt should be used in generation",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    control_after_generate=True,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "num_images",
+                    default=1,
+                    min=1,
+                    max=8,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "rendering_speed",
+                    options=["DEFAULT", "TURBO", "QUALITY"],
+                    default="DEFAULT",
+                    tooltip="Controls the trade-off between generation speed and quality",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Image.Input(
+                    "character_image",
+                    tooltip="Image to use as character reference.",
+                    optional=True,
+                ),
+                IO.Mask.Input(
+                    "character_mask",
+                    tooltip="Optional mask for character reference image.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["rendering_speed", "num_images"], inputs=["character_image"]),
+                expr="""
+                (
+                  $n := widgets.num_images;
+                  $speed := widgets.rendering_speed;
+                  $hasChar := inputs.character_image.connected;
+                  $base :=
+                    $contains($speed,"quality") ? ($hasChar ? 0.286 : 0.1287) :
+                    $contains($speed,"default") ? ($hasChar ? 0.2145 : 0.0858) :
+                    $contains($speed,"turbo") ? ($hasChar ? 0.143 : 0.0429) :
+                    0.0858;
+                  {"type":"usd","usd": $round($base * $n, 2)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt,
+        image=None,
+        mask=None,
+        resolution="Auto",
+        aspect_ratio="1:1",
+        magic_prompt_option="AUTO",
+        seed=0,
+        num_images=1,
+        rendering_speed="DEFAULT",
+        character_image=None,
+        character_mask=None,
+    ):
+        if rendering_speed == "BALANCED":  # for backward compatibility
+            rendering_speed = "DEFAULT"
+
+        character_img_binary = None
+        character_mask_binary = None
+
+        if character_image is not None:
+            input_tensor = character_image.squeeze().cpu()
+            if character_mask is not None:
+                character_mask = resize_mask_to_image(character_mask, character_image, allow_gradient=False)
+                character_mask = 1.0 - character_mask
+                if character_mask.shape[1:] != character_image.shape[1:-1]:
+                    raise Exception("Character mask and image must be the same size")
+
+                mask_np = (character_mask.squeeze().cpu().numpy() * 255).astype(np.uint8)
+                mask_img = Image.fromarray(mask_np)
+                mask_byte_arr = BytesIO()
+                mask_img.save(mask_byte_arr, format="PNG")
+                mask_byte_arr.seek(0)
+                character_mask_binary = mask_byte_arr
+                character_mask_binary.name = "mask.png"
+
+            img_np = (input_tensor.numpy() * 255).astype(np.uint8)
+            img = Image.fromarray(img_np)
+            img_byte_arr = BytesIO()
+            img.save(img_byte_arr, format="PNG")
+            img_byte_arr.seek(0)
+            character_img_binary = img_byte_arr
+            character_img_binary.name = "image.png"
+        elif character_mask is not None:
+            raise Exception("Character mask requires character image to be present")
+
+        # Check if both image and mask are provided for editing mode
+        if image is not None and mask is not None:
+            # Process image and mask
+            input_tensor = image.squeeze().cpu()
+            # Resize mask to match image dimension
+            mask = resize_mask_to_image(mask, image, allow_gradient=False)
+            # Invert mask, as Ideogram API will edit black areas instead of white areas (opposite of convention).
+            mask = 1.0 - mask
+
+            # Validate mask dimensions match image
+            if mask.shape[1:] != image.shape[1:-1]:
+                raise Exception("Mask and Image must be the same size")
+
+            # Process image
+            img_np = (input_tensor.numpy() * 255).astype(np.uint8)
+            img = Image.fromarray(img_np)
+            img_byte_arr = BytesIO()
+            img.save(img_byte_arr, format="PNG")
+            img_byte_arr.seek(0)
+            img_binary = img_byte_arr
+            img_binary.name = "image.png"
+
+            # Process mask - white areas will be replaced
+            mask_np = (mask.squeeze().cpu().numpy() * 255).astype(np.uint8)
+            mask_img = Image.fromarray(mask_np)
+            mask_byte_arr = BytesIO()
+            mask_img.save(mask_byte_arr, format="PNG")
+            mask_byte_arr.seek(0)
+            mask_binary = mask_byte_arr
+            mask_binary.name = "mask.png"
+
+            # Create edit request
+            edit_request = IdeogramV3EditRequest(
+                prompt=prompt,
+                rendering_speed=rendering_speed,
+            )
+
+            # Add optional parameters
+            if magic_prompt_option != "AUTO":
+                edit_request.magic_prompt = magic_prompt_option
+            if seed != 0:
+                edit_request.seed = seed
+            if num_images > 1:
+                edit_request.num_images = num_images
+
+            files = {
+                "image": img_binary,
+                "mask": mask_binary,
+            }
+            if character_img_binary:
+                files["character_reference_images"] = character_img_binary
+            if character_mask_binary:
+                files["character_mask_binary"] = character_mask_binary
+
+            response = await sync_op(
+                cls,
+                ApiEndpoint(path="/proxy/ideogram/ideogram-v3/edit", method="POST"),
+                response_model=IdeogramGenerateResponse,
+                data=edit_request,
+                files=files,
+                content_type="multipart/form-data",
+                max_retries=1,
+            )
+
+        elif image is not None or mask is not None:
+            # If only one of image or mask is provided, raise an error
+            raise Exception("Ideogram V3 image editing requires both an image AND a mask")
+        else:
+            # Create generation request
+            gen_request = IdeogramV3Request(
+                prompt=prompt,
+                rendering_speed=rendering_speed,
+            )
+
+            # Handle resolution vs aspect ratio
+            if resolution != "Auto":
+                gen_request.resolution = resolution
+            elif aspect_ratio != "1:1":
+                v3_aspect = V3_RATIO_MAP.get(aspect_ratio)
+                if v3_aspect:
+                    gen_request.aspect_ratio = v3_aspect
+
+            # Add optional parameters
+            if magic_prompt_option != "AUTO":
+                gen_request.magic_prompt = magic_prompt_option
+            if seed != 0:
+                gen_request.seed = seed
+            if num_images > 1:
+                gen_request.num_images = num_images
+
+            files = {}
+            if character_img_binary:
+                files["character_reference_images"] = character_img_binary
+            if character_mask_binary:
+                files["character_mask_binary"] = character_mask_binary
+            if files:
+                gen_request.style_type = "AUTO"
+
+            response = await sync_op(
+                cls,
+                endpoint=ApiEndpoint(path="/proxy/ideogram/ideogram-v3/generate", method="POST"),
+                response_model=IdeogramGenerateResponse,
+                data=gen_request,
+                files=files if files else None,
+                content_type="multipart/form-data",
+                max_retries=1,
+            )
+
+        if not response.data or len(response.data) == 0:
+            raise Exception("No images were generated in the response")
+
+        image_urls = [image_data.url for image_data in response.data if image_data.url]
+        if not image_urls:
+            raise Exception("No image URLs were generated in the response")
+        return IO.NodeOutput(await download_and_process_images(image_urls))
+
+
+class IdeogramV4(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="IdeogramV4",
+            display_name="Ideogram V4",
+            category="partner/image/Ideogram",
+            description="Generates images using the Ideogram 4.0 model from a text prompt.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the image generation.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        "Auto",
+                        "2048x2048 (1:1)",
+                        "1440x2880 (1:2)",
+                        "2880x1440 (2:1)",
+                        "1664x2496 (2:3)",
+                        "2496x1664 (3:2)",
+                        "1792x2240 (4:5)",
+                        "2240x1792 (5:4)",
+                        "1440x2560 (9:16)",
+                        "2560x1440 (16:9)",
+                        "1600x2560 (5:8)",
+                        "2560x1600 (8:5)",
+                        "1728x2304 (3:4)",
+                        "2304x1728 (4:3)",
+                        "1296x3168 (9:22)",
+                        "3168x1296 (22:9)",
+                        "1152x2944 (9:23)",
+                        "2944x1152 (23:9)",
+                        "1248x3328 (3:8)",
+                        "3328x1248 (8:3)",
+                        "1280x3072 (5:12)",
+                        "3072x1280 (12:5)",
+                    ],
+                    default="Auto",
+                ),
+                IO.Combo.Input(
+                    "rendering_speed",
+                    options=["DEFAULT", "TURBO", "QUALITY"],
+                    default="DEFAULT",
+                    tooltip="Controls the trade-off between generation speed and quality.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    control_after_generate=True,
+                    display_mode=IO.NumberDisplay.number,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["rendering_speed"]),
+                expr="""
+                (
+                  $speed := widgets.rendering_speed;
+                  $price :=
+                    $contains($speed,"turbo") ? 0.0429 :
+                    $contains($speed,"quality") ? 0.143 :
+                    0.0858;
+                  {"type":"usd","usd": $price}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        resolution: str,
+        rendering_speed: str,
+        seed: int,
+    ):
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/ideogram/ideogram-v4/generate", method="POST"),
+            response_model=IdeogramGenerateResponse,
+            data=IdeogramV4Request(
+                text_prompt=prompt,
+                resolution=resolution.split(" ")[0] if resolution != "Auto" else None,
+                rendering_speed=rendering_speed,
+            ),
+            max_retries=1,
+        )
+
+        if not response.data or len(response.data) == 0:
+            raise Exception("No images were generated in the response")
+        image_urls = [image_data.url for image_data in response.data if image_data.url]
+        if not image_urls:
+            raise Exception("No image URLs were generated in the response")
+        return IO.NodeOutput(await download_and_process_images(image_urls))
+
+
+class IdeogramPImage(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="IdeogramPImage",
+            display_name="Ideogram & Pruna P-Image",
+            category="partner/image/Ideogram",
+            description="Generates images using P-Image, Ideogram's fast text-to-image model. "
+                        "Strong typography and photorealism; "
+                        "supports Ideogram 4.0 structured JSON captions for exact text, "
+                        "colors and layout.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt. Also accepts an Ideogram 4.0 structured JSON caption "
+                            "(exact colors as #RRGGBB hexes, exact text strings, bounding-box "
+                            "layout) — set prompt_upsampling to OFF to use it verbatim.",
+                ),
+                IO.Combo.Input(
+                    "quality",
+                    options=["VERY_LOW", "LOW", "MEDIUM", "HIGH"],
+                    default="MEDIUM",
+                    tooltip="Speed/price/quality tier. MEDIUM is the everyday default; HIGH for "
+                            "complex prompts, fine detail and difficult text; VERY_LOW/LOW for "
+                            "drafts at scale. Difficult text renders poorly below MEDIUM.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["1K", "2K"],
+                    default="1K",
+                    tooltip="Output size class (exact pixels follow the aspect ratio, e.g. "
+                            "16:9 gives 1280x720 at 1K and 2560x1440 at 2K). "
+                            "Prefer HIGH + 2K for crisp typography.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=list(V3_RATIO_MAP.keys()),
+                    default="1:1",
+                    tooltip="The aspect ratio for image generation.",
+                ),
+                IO.Combo.Input(
+                    "prompt_upsampling",
+                    options=["AUTO", "ON", "OFF"],
+                    default="AUTO",
+                    tooltip="Expands short prompts into a detailed structured caption before "
+                            "generation (the rewritten prompt is returned as final_prompt). "
+                            "Set OFF when supplying your own JSON caption or exact wording.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    control_after_generate=True,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Seed for reproducible generation. With prompt_upsampling OFF, "
+                            "the same seed and settings return the same image; with ON/AUTO "
+                            "the prompt rewrite varies per run — reproduce a result by reusing "
+                            "its final_prompt output with prompt_upsampling OFF and the same "
+                            "seed.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(
+                    "final_prompt",
+                    tooltip="The prompt the image was actually generated from (the rewritten "
+                            "structured caption when prompt_upsampling ran, else your prompt). "
+                            "Feed it back with prompt_upsampling OFF and the same seed to "
+                            "reproduce this image.",
+                ),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["quality", "resolution"]),
+                expr="""
+                (
+                  $q := widgets.quality;
+                  $is2k := $contains(widgets.resolution, "2k");
+                  $usd :=
+                    $contains($q, "very_low") ? ($is2k ? 0.00858 : 0.00429) :
+                    $contains($q, "high")     ? ($is2k ? 0.0429  : 0.02145) :
+                    $contains($q, "medium")   ? ($is2k ? 0.0286  : 0.0143)  :
+                                                ($is2k ? 0.02145 : 0.010725);
+                  {"type": "usd", "usd": $usd}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        quality: str = "MEDIUM",
+        resolution: str = "1K",
+        aspect_ratio: str = "1:1",
+        prompt_upsampling: str = "AUTO",
+        seed: int = 42,
+    ):
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        request = IdeogramPImageRequest(
+            prompt=prompt,
+            quality=quality,
+            resolution=resolution,
+            aspect_ratio=V3_RATIO_MAP[aspect_ratio],
+            prompt_upsampling=prompt_upsampling,
+            seed=seed,
+        )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/ideogram/text-to-image/p-image-ideogram", method="POST"),
+            response_model=IdeogramGenerateResponse,
+            data=request,
+            max_retries=1,
+        )
+        if not response.data:
+            raise Exception("No images were generated in the response")
+        image_urls = [image_data.url for image_data in response.data if image_data.url]
+        if not image_urls:
+            if any(image_data.is_image_safe is False for image_data in response.data):
+                raise Exception(
+                    "The generation was blocked by Ideogram's content safety filter. "
+                    "Adjust the prompt and try again."
+                )
+            raise Exception("No image URLs were generated in the response")
+        return IO.NodeOutput(
+            await download_and_process_images(image_urls),
+            response.data[0].prompt or prompt,
+        )
+
+
+class IdeogramExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            IdeogramV3,
+            IdeogramV4,
+            IdeogramPImage,
+        ]
+
+
+async def comfy_entrypoint() -> IdeogramExtension:
+    return IdeogramExtension()
diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py
new file mode 100644
index 0000000000000000000000000000000000000000..85dca01770c5e88714e79fc71bda30939960bf27
--- /dev/null
+++ b/comfy_api_nodes/nodes_kling.py
@@ -0,0 +1,2763 @@
+"""Kling API Nodes
+
+For source of truth on the allowed permutations of request fields, please reference:
+- [Compatibility Table](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
+"""
+
+import logging
+import re
+
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis import (
+    KlingVideoGenDuration,
+    KlingVideoGenMode,
+    KlingVideoGenAspectRatio,
+    KlingVideoGenModelName,
+    KlingText2VideoRequest,
+    KlingText2VideoResponse,
+    KlingImage2VideoRequest,
+    KlingImage2VideoResponse,
+    KlingVideoExtendRequest,
+    KlingVideoExtendResponse,
+    KlingLipSyncVoiceLanguage,
+    KlingLipSyncInputObject,
+    KlingLipSyncRequest,
+    KlingLipSyncResponse,
+    KlingVideoResult,
+    KlingImageResult,
+    KlingImageGenerationsRequest,
+    KlingImageGenerationsResponse,
+    KlingImageGenImageReferenceType,
+    KlingImageGenAspectRatio,
+)
+from comfy_api_nodes.apis.kling import (
+    ImageToVideoWithAudioRequest,
+    KlingAvatarRequest,
+    MotionControlRequest,
+    MultiPromptEntry,
+    OmniImageParamImage,
+    OmniParamImage,
+    OmniParamVideo,
+    OmniProFirstLastFrameRequest,
+    OmniProImageRequest,
+    OmniProReferences2VideoRequest,
+    OmniProText2VideoRequest,
+    Kling3TurboSettings,
+    Kling3TurboText2VideoRequest,
+    Kling3TurboContent,
+    Kling3TurboImage2VideoRequest,
+    Kling3TurboCreateResponse,
+    Kling3TurboQueryResponse,
+    TaskStatusResponse,
+    TextToVideoWithAudioRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    tensor_to_base64_string,
+    upload_audio_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_audio_duration,
+    validate_image_aspect_ratio,
+    validate_image_dimensions,
+    validate_string,
+    validate_video_dimensions,
+    validate_video_duration,
+)
+
+
+def _generate_storyboard_inputs(count: int) -> list:
+    inputs = []
+    for i in range(1, count + 1):
+        inputs.extend(
+            [
+                IO.String.Input(
+                    f"storyboard_{i}_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip=f"Prompt for storyboard segment {i}. Max 512 characters.",
+                ),
+                IO.Int.Input(
+                    f"storyboard_{i}_duration",
+                    default=4,
+                    min=1,
+                    max=15,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip=f"Duration for storyboard segment {i} in seconds.",
+                ),
+            ]
+        )
+    return inputs
+
+
+KLING_API_VERSION = "v1"
+PATH_TEXT_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/text2video"
+PATH_IMAGE_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/image2video"
+PATH_VIDEO_EXTEND = f"/proxy/kling/{KLING_API_VERSION}/videos/video-extend"
+PATH_LIP_SYNC = f"/proxy/kling/{KLING_API_VERSION}/videos/lip-sync"
+PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations"
+
+MAX_PROMPT_LENGTH_T2V = 2500
+MAX_PROMPT_LENGTH_I2V = 500
+MAX_PROMPT_LENGTH_IMAGE_GEN = 500
+MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200
+MAX_PROMPT_LENGTH_LIP_SYNC = 120
+
+AVERAGE_DURATION_T2V = 319
+AVERAGE_DURATION_I2V = 164
+AVERAGE_DURATION_LIP_SYNC = 455
+AVERAGE_DURATION_IMAGE_GEN = 32
+AVERAGE_DURATION_VIDEO_EXTEND = 320
+
+
+MODE_TEXT2VIDEO = {
+    "pro mode / 5s duration / kling-v2-5-turbo": ("pro", "5", "kling-v2-5-turbo"),
+    "pro mode / 10s duration / kling-v2-5-turbo": ("pro", "10", "kling-v2-5-turbo"),
+}
+"""
+Mapping of mode strings to their corresponding (mode, duration, model_name) tuples.
+Only includes config combos that support the `image_tail` request field.
+
+See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
+"""
+
+
+MODE_START_END_FRAME = {
+    "pro mode / 5s duration / kling-v2-5-turbo": ("pro", "5", "kling-v2-5-turbo"),
+    "pro mode / 10s duration / kling-v2-5-turbo": ("pro", "10", "kling-v2-5-turbo"),
+}
+"""
+Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples.
+Only includes config combos that support the `image_tail` request field.
+
+See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
+"""
+
+
+VOICES_CONFIG = {
+    # English voices
+    "Melody": ("girlfriend_4_speech02", "en"),
+    "Sunny": ("genshin_vindi2", "en"),
+    "Sage": ("zhinen_xuesheng", "en"),
+    "Ace": ("AOT", "en"),
+    "Blossom": ("ai_shatang", "en"),
+    "Peppy": ("genshin_klee2", "en"),
+    "Dove": ("genshin_kirara", "en"),
+    "Shine": ("ai_kaiya", "en"),
+    "Anchor": ("oversea_male1", "en"),
+    "Lyric": ("ai_chenjiahao_712", "en"),
+    "Tender": ("chat1_female_new-3", "en"),
+    "Siren": ("chat_0407_5-1", "en"),
+    "Zippy": ("cartoon-boy-07", "en"),
+    "Bud": ("uk_boy1", "en"),
+    "Sprite": ("cartoon-girl-01", "en"),
+    "Candy": ("PeppaPig_platform", "en"),
+    "Beacon": ("ai_huangzhong_712", "en"),
+    "Rock": ("ai_huangyaoshi_712", "en"),
+    "Titan": ("ai_laoguowang_712", "en"),
+    "Grace": ("chengshu_jiejie", "en"),
+    "Helen": ("you_pingjing", "en"),
+    "Lore": ("calm_story1", "en"),
+    "Crag": ("uk_man2", "en"),
+    "Prattle": ("laopopo_speech02", "en"),
+    "Hearth": ("heainainai_speech02", "en"),
+    "The Reader": ("reader_en_m-v1", "en"),
+    "Commercial Lady": ("commercial_lady_en_f-v1", "en"),
+    # Chinese voices
+    "阳光少年": ("genshin_vindi2", "zh"),
+    "懂事小弟": ("zhinen_xuesheng", "zh"),
+    "运动少年": ("tiyuxi_xuedi", "zh"),
+    "青春少女": ("ai_shatang", "zh"),
+    "温柔小妹": ("genshin_klee2", "zh"),
+    "元气少女": ("genshin_kirara", "zh"),
+    "阳光男生": ("ai_kaiya", "zh"),
+    "幽默小哥": ("tiexin_nanyou", "zh"),
+    "文艺小哥": ("ai_chenjiahao_712", "zh"),
+    "甜美邻家": ("girlfriend_1_speech02", "zh"),
+    "温柔姐姐": ("chat1_female_new-3", "zh"),
+    "职场女青": ("girlfriend_2_speech02", "zh"),
+    "活泼男童": ("cartoon-boy-07", "zh"),
+    "俏皮女童": ("cartoon-girl-01", "zh"),
+    "稳重老爸": ("ai_huangyaoshi_712", "zh"),
+    "温柔妈妈": ("you_pingjing", "zh"),
+    "严肃上司": ("ai_laoguowang_712", "zh"),
+    "优雅贵妇": ("chengshu_jiejie", "zh"),
+    "慈祥爷爷": ("zhuxi_speech02", "zh"),
+    "唠叨爷爷": ("uk_oldman3", "zh"),
+    "唠叨奶奶": ("laopopo_speech02", "zh"),
+    "和蔼奶奶": ("heainainai_speech02", "zh"),
+    "东北老铁": ("dongbeilaotie_speech02", "zh"),
+    "重庆小伙": ("chongqingxiaohuo_speech02", "zh"),
+    "四川妹子": ("chuanmeizi_speech02", "zh"),
+    "潮汕大叔": ("chaoshandashu_speech02", "zh"),
+    "台湾男生": ("ai_taiwan_man2_speech02", "zh"),
+    "西安掌柜": ("xianzhanggui_speech02", "zh"),
+    "天津姐姐": ("tianjinjiejie_speech02", "zh"),
+    "新闻播报男": ("diyinnansang_DB_CN_M_04-v2", "zh"),
+    "译制片男": ("yizhipiannan-v1", "zh"),
+    "撒娇女友": ("tianmeixuemei-v1", "zh"),
+    "刀片烟嗓": ("daopianyansang-v1", "zh"),
+    "乖巧正太": ("mengwa-v1", "zh"),
+}
+
+
+def normalize_omni_prompt_references(prompt: str) -> str:
+    """
+    Rewrites Kling Omni-style placeholders used in the app, like:
+
+        @image, @image1, @image2, ... @imageN
+        @video, @video1, @video2, ... @videoN
+
+    into the API-compatible form:
+
+        <<<image_1>>>, <<<image_2>>>, ...
+        <<<video_1>>>, <<<video_2>>>, ...
+
+    This is a UX shim for ComfyUI so users can type the same syntax as in the Kling app.
+    """
+    if not prompt:
+        return prompt
+
+    def _image_repl(match):
+        return f"<<<image_{match.group('idx') or '1'}>>>"
+
+    def _video_repl(match):
+        return f"<<<video_{match.group('idx') or '1'}>>>"
+
+    # (?<!\w) avoids matching e.g. "test@image.com"
+    # (?!\w) makes sure we only match @image / @image<digits> and not @imageFoo
+    prompt = re.sub(r"(?<!\w)@image(?P<idx>\d*)(?!\w)", _image_repl, prompt)
+    return re.sub(r"(?<!\w)@video(?P<idx>\d*)(?!\w)", _video_repl, prompt)
+
+
+async def finish_omni_video_task(cls: type[IO.ComfyNode], response: TaskStatusResponse) -> IO.NodeOutput:
+    if response.code:
+        raise RuntimeError(
+            f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+        )
+    final_response = await poll_op(
+        cls,
+        ApiEndpoint(path=f"/proxy/kling/v1/videos/omni-video/{response.data.task_id}"),
+        response_model=TaskStatusResponse,
+        status_extractor=lambda r: (r.data.task_status if r.data else None),
+    )
+    return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
+def is_valid_task_creation_response(response: KlingText2VideoResponse) -> bool:
+    """Verifies that the initial response contains a task ID."""
+    return bool(response.data.task_id)
+
+
+def is_valid_video_response(response: KlingText2VideoResponse) -> bool:
+    """Verifies that the response contains a task result with at least one video."""
+    return (
+        response.data is not None
+        and response.data.task_result is not None
+        and response.data.task_result.videos is not None
+        and len(response.data.task_result.videos) > 0
+    )
+
+
+def is_valid_image_response(response: KlingImageGenerationsResponse) -> bool:
+    """Verifies that the response contains a task result with at least one image."""
+    return (
+        response.data is not None
+        and response.data.task_result is not None
+        and response.data.task_result.images is not None
+        and len(response.data.task_result.images) > 0
+    )
+
+
+def validate_prompts(prompt: str, negative_prompt: str, max_length: int) -> bool:
+    """Verifies that the positive prompt is not empty and that neither promt is too long."""
+    if not prompt:
+        raise ValueError("Positive prompt is empty")
+    if len(prompt) > max_length:
+        raise ValueError(f"Positive prompt is too long: {len(prompt)} characters")
+    if negative_prompt and len(negative_prompt) > max_length:
+        raise ValueError(
+            f"Negative prompt is too long: {len(negative_prompt)} characters"
+        )
+    return True
+
+
+def validate_task_creation_response(response) -> None:
+    """Validates that the Kling task creation request was successful."""
+    if not is_valid_task_creation_response(response):
+        error_msg = f"Kling initial request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+        logging.error(error_msg)
+        raise Exception(error_msg)
+
+
+def validate_video_result_response(response) -> None:
+    """Validates that the Kling task result contains a video."""
+    if not is_valid_video_response(response):
+        error_msg = f"Kling task {response.data.task_id} succeeded but no video data found in response."
+        logging.error("Error: %s.\nResponse: %s", error_msg, response)
+        raise Exception(error_msg)
+
+
+def validate_image_result_response(response) -> None:
+    """Validates that the Kling task result contains an image."""
+    if not is_valid_image_response(response):
+        error_msg = f"Kling task {response.data.task_id} succeeded but no image data found in response."
+        logging.error("Error: %s.\nResponse: %s", error_msg, response)
+        raise Exception(error_msg)
+
+
+def validate_input_image(image: torch.Tensor) -> None:
+    """
+    Validates the input image adheres to the expectations of the Kling API:
+    - The image resolution should not be less than 300*300px
+    - The aspect ratio of the image should be between 1:2.5 ~ 2.5:1
+
+    See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo
+    """
+    validate_image_dimensions(image, min_width=300, min_height=300)
+    validate_image_aspect_ratio(image, (1, 2.5), (2.5, 1))
+
+
+def get_video_from_response(response) -> KlingVideoResult:
+    """Returns the first video object from the Kling video generation task result.
+    Will raise an error if the response is not valid.
+    """
+    video = response.data.task_result.videos[0]
+    logging.info(
+        "Kling task %s succeeded. Video URL: %s", response.data.task_id, video.url
+    )
+    return video
+
+
+def get_video_url_from_response(response) -> str | None:
+    """Returns the first video url from the Kling video generation task result.
+    Will not raise an error if the response is not valid.
+    """
+    if response and is_valid_video_response(response):
+        return str(get_video_from_response(response).url)
+    else:
+        return None
+
+
+def get_images_from_response(response) -> list[KlingImageResult]:
+    """Returns the list of image objects from the Kling image generation task result.
+    Will raise an error if the response is not valid.
+    """
+    images = response.data.task_result.images
+    logging.info("Kling task %s succeeded. Images: %s", response.data.task_id, images)
+    return images
+
+
+def get_images_urls_from_response(response) -> str | None:
+    """Returns the list of image urls from the Kling image generation task result.
+    Will not raise an error if the response is not valid. If there is only one image, returns the url as a string. If there are multiple images, returns a list of urls.
+    """
+    if response and is_valid_image_response(response):
+        images = get_images_from_response(response)
+        image_urls = [str(image.url) for image in images]
+        return "\n".join(image_urls)
+    else:
+        return None
+
+
+async def image_result_to_node_output(
+    images: list[KlingImageResult],
+) -> torch.Tensor:
+    """
+    Converts a KlingImageResult to a tuple containing a [B, H, W, C] tensor.
+    If multiple images are returned, they will be stacked along the batch dimension.
+    """
+    if len(images) == 1:
+        return await download_url_to_image_tensor(str(images[0].url))
+    else:
+        return torch.cat([await download_url_to_image_tensor(str(image.url)) for image in images])
+
+
+async def execute_text2video(
+    cls: type[IO.ComfyNode],
+    prompt: str,
+    negative_prompt: str,
+    cfg_scale: float,
+    model_name: str,
+    model_mode: str,
+    duration: str,
+    aspect_ratio: str,
+) -> IO.NodeOutput:
+    validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
+    task_creation_response = await sync_op(
+        cls,
+        ApiEndpoint(path=PATH_TEXT_TO_VIDEO, method="POST"),
+        response_model=KlingText2VideoResponse,
+        data=KlingText2VideoRequest(
+            prompt=prompt if prompt else None,
+            negative_prompt=negative_prompt if negative_prompt else None,
+            duration=KlingVideoGenDuration(duration),
+            mode=KlingVideoGenMode(model_mode),
+            model_name=model_name,
+            cfg_scale=cfg_scale,
+            aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
+        ),
+    )
+
+    validate_task_creation_response(task_creation_response)
+
+    task_id = task_creation_response.data.task_id
+    final_response = await poll_op(
+        cls,
+        ApiEndpoint(path=f"{PATH_TEXT_TO_VIDEO}/{task_id}"),
+        response_model=KlingText2VideoResponse,
+        estimated_duration=AVERAGE_DURATION_T2V,
+        status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
+    )
+    validate_video_result_response(final_response)
+
+    video = get_video_from_response(final_response)
+    return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration))
+
+
+async def execute_image2video(
+    cls: type[IO.ComfyNode],
+    start_frame: torch.Tensor,
+    prompt: str,
+    negative_prompt: str,
+    model_name: str,
+    cfg_scale: float,
+    model_mode: str,
+    aspect_ratio: str,
+    duration: str,
+    end_frame: torch.Tensor | None = None,
+) -> IO.NodeOutput:
+    validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V)
+    validate_input_image(start_frame)
+
+    task_creation_response = await sync_op(
+        cls,
+        ApiEndpoint(path=PATH_IMAGE_TO_VIDEO, method="POST"),
+        response_model=KlingImage2VideoResponse,
+        data=KlingImage2VideoRequest(
+            model_name=KlingVideoGenModelName(model_name),
+            image=tensor_to_base64_string(start_frame),
+            image_tail=(
+                tensor_to_base64_string(end_frame)
+                if end_frame is not None
+                else None
+            ),
+            prompt=prompt,
+            negative_prompt=negative_prompt if negative_prompt else None,
+            cfg_scale=cfg_scale,
+            mode=KlingVideoGenMode(model_mode),
+            duration=KlingVideoGenDuration(duration),
+        ),
+    )
+
+    validate_task_creation_response(task_creation_response)
+    task_id = task_creation_response.data.task_id
+
+    final_response = await poll_op(
+        cls,
+        ApiEndpoint(path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}"),
+        response_model=KlingImage2VideoResponse,
+        estimated_duration=AVERAGE_DURATION_I2V,
+        status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
+    )
+    validate_video_result_response(final_response)
+
+    video = get_video_from_response(final_response)
+    return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration))
+
+
+async def execute_lipsync(
+    cls: type[IO.ComfyNode],
+    video: Input.Video,
+    audio: Input.Audio | None = None,
+    voice_language: str | None = None,
+    model_mode: str | None = None,
+    text: str | None = None,
+    voice_speed: float | None = None,
+    voice_id: str | None = None,
+) -> IO.NodeOutput:
+    if text:
+        validate_string(text, field_name="Text", max_length=MAX_PROMPT_LENGTH_LIP_SYNC)
+    validate_video_dimensions(video, 720, 1920)
+    validate_video_duration(video, 2, 10)
+
+    # Upload video to Comfy API and get download URL
+    video_url = await upload_video_to_comfyapi(cls, video)
+    logging.info("Uploaded video to Comfy API. URL: %s", video_url)
+
+    # Upload the audio file to Comfy API and get download URL
+    if audio:
+        audio_url = await upload_audio_to_comfyapi(
+            cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg"
+        )
+        logging.info("Uploaded audio to Comfy API. URL: %s", audio_url)
+    else:
+        audio_url = None
+
+    task_creation_response = await sync_op(
+        cls,
+        ApiEndpoint(PATH_LIP_SYNC, "POST"),
+        response_model=KlingLipSyncResponse,
+        data=KlingLipSyncRequest(
+            input=KlingLipSyncInputObject(
+                video_url=video_url,
+                mode=model_mode,
+                text=text,
+                voice_language=voice_language,
+                voice_speed=voice_speed,
+                audio_type="url",
+                audio_url=audio_url,
+                voice_id=voice_id,
+            ),
+        ),
+    )
+
+    validate_task_creation_response(task_creation_response)
+    task_id = task_creation_response.data.task_id
+
+    final_response = await poll_op(
+        cls,
+        ApiEndpoint(path=f"{PATH_LIP_SYNC}/{task_id}"),
+        response_model=KlingLipSyncResponse,
+        estimated_duration=AVERAGE_DURATION_LIP_SYNC,
+        status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
+    )
+    validate_video_result_response(final_response)
+
+    video = get_video_from_response(final_response)
+    return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration))
+
+
+class KlingTextToVideoNode(IO.ComfyNode):
+    """Kling Text to Video Node"""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        modes = list(MODE_TEXT2VIDEO.keys())
+        return IO.Schema(
+            node_id="KlingTextToVideoNode",
+            display_name="Kling Text to Video",
+            category="partner/video/Kling",
+            description="Kling Text to Video Node",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
+                IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
+                IO.Float.Input("cfg_scale", default=1.0, min=0.0, max=1.0),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=KlingVideoGenAspectRatio,
+                    default="16:9",
+                ),
+                IO.Combo.Input(
+                    "mode",
+                    options=modes,
+                    default=modes[0],
+                    tooltip="The configuration to use for the video generation following the format: mode / duration / model_name.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="video_id"),
+                IO.String.Output(display_name="duration"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
+                expr="""
+                (
+                  $m := widgets.mode;
+                  $contains($m,"10") ? {"type":"usd","usd":0.7} : {"type":"usd","usd":0.35}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str,
+        cfg_scale: float,
+        mode: str,
+        aspect_ratio: str,
+    ) -> IO.NodeOutput:
+        model_mode, duration, model_name = MODE_TEXT2VIDEO[mode]
+        return await execute_text2video(
+            cls,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            cfg_scale=cfg_scale,
+            model_mode=model_mode,
+            aspect_ratio=aspect_ratio,
+            model_name=model_name,
+            duration=duration,
+        )
+
+
+class OmniProTextToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProTextToVideoNode",
+            display_name="Kling 3.0 Omni Text to Video",
+            category="partner/video/Kling",
+            description="Use text prompts to generate videos with the latest Kling model.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the video content. "
+                    "This can include both positive and negative descriptions. "
+                    "Ignored when storyboards are enabled.",
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+                IO.Int.Input("duration", default=5, min=3, max=15, display_mode=IO.NumberDisplay.slider),
+                IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p", optional=True),
+                IO.DynamicCombo.Input(
+                    "storyboards",
+                    options=[
+                        IO.DynamicCombo.Option("disabled", []),
+                        IO.DynamicCombo.Option("1 storyboard", _generate_storyboard_inputs(1)),
+                        IO.DynamicCombo.Option("2 storyboards", _generate_storyboard_inputs(2)),
+                        IO.DynamicCombo.Option("3 storyboards", _generate_storyboard_inputs(3)),
+                        IO.DynamicCombo.Option("4 storyboards", _generate_storyboard_inputs(4)),
+                        IO.DynamicCombo.Option("5 storyboards", _generate_storyboard_inputs(5)),
+                        IO.DynamicCombo.Option("6 storyboards", _generate_storyboard_inputs(6)),
+                    ],
+                    tooltip="Generate a series of video segments with individual prompts and durations. "
+                    "Ignored for o1 model.",
+                    optional=True,
+                ),
+                IO.Boolean.Input("generate_audio", default=False, optional=True),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution", "model_name", "generate_audio"]),
+                expr="""
+                (
+                  $res := widgets.resolution;
+                  $mode := $res = "4k" ? "4k" : ($res = "720p" ? "std" : "pro");
+                  $isV3 := $contains(widgets.model_name, "v3");
+                  $audio := $isV3 and widgets.generate_audio;
+                  $rates := $audio
+                    ? {"std": 0.112, "pro": 0.14, "4k": 0.42}
+                    : {"std": 0.084, "pro": 0.112, "4k": 0.42};
+                  {"type":"usd","usd": $lookup($rates, $mode) * widgets.duration}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        aspect_ratio: str,
+        duration: int,
+        resolution: str = "1080p",
+        storyboards: dict | None = None,
+        generate_audio: bool = False,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        _ = seed
+        if model_name == "kling-video-o1":
+            if duration not in (5, 10):
+                raise ValueError("kling-video-o1 only supports durations of 5 or 10 seconds.")
+            if generate_audio:
+                raise ValueError("kling-video-o1 does not support audio generation.")
+            if resolution == "4k":
+                raise ValueError("kling-video-o1 does not support 4k resolution.")
+        stories_enabled = storyboards is not None and storyboards["storyboards"] != "disabled"
+        if stories_enabled and model_name == "kling-video-o1":
+            raise ValueError("kling-video-o1 does not support storyboards.")
+        validate_string(prompt, strip_whitespace=True, min_length=0 if stories_enabled else 1, max_length=2500)
+
+        multi_shot = None
+        multi_prompt_list = None
+        if stories_enabled:
+            count = int(storyboards["storyboards"].split()[0])
+            multi_shot = True
+            multi_prompt_list = []
+            for i in range(1, count + 1):
+                sb_prompt = storyboards[f"storyboard_{i}_prompt"]
+                sb_duration = storyboards[f"storyboard_{i}_duration"]
+                validate_string(sb_prompt, field_name=f"storyboard_{i}_prompt", min_length=1, max_length=512)
+                multi_prompt_list.append(
+                    MultiPromptEntry(
+                        index=i,
+                        prompt=sb_prompt,
+                        duration=str(sb_duration),
+                    )
+                )
+            total_storyboard_duration = sum(int(e.duration) for e in multi_prompt_list)
+            if total_storyboard_duration != duration:
+                raise ValueError(
+                    f"Total storyboard duration ({total_storyboard_duration}s) "
+                    f"must equal the global duration ({duration}s)."
+                )
+
+        if resolution == "4k":
+            mode = "4k"
+        elif resolution == "1080p":
+            mode = "pro"
+        else:
+            mode = "std"
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProText2VideoRequest(
+                model_name=model_name,
+                prompt=prompt,
+                aspect_ratio=aspect_ratio,
+                duration=str(duration),
+                mode=mode,
+                multi_shot=multi_shot,
+                multi_prompt=multi_prompt_list,
+                shot_type="customize" if multi_shot else None,
+                sound="on" if generate_audio else "off",
+            ),
+        )
+        return await finish_omni_video_task(cls, response)
+
+
+class OmniProFirstLastFrameNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProFirstLastFrameNode",
+            display_name="Kling 3.0 Omni First-Last-Frame to Video",
+            category="partner/video/Kling",
+            description="Use a start frame, an optional end frame, or reference images with the latest Kling model.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the video content. "
+                    "This can include both positive and negative descriptions. "
+                    "Ignored when storyboards are enabled.",
+                ),
+                IO.Int.Input("duration", default=5, min=3, max=15, display_mode=IO.NumberDisplay.slider),
+                IO.Image.Input("first_frame"),
+                IO.Image.Input(
+                    "end_frame",
+                    optional=True,
+                    tooltip="An optional end frame for the video. "
+                    "This cannot be used simultaneously with 'reference_images'. "
+                    "Does not work with storyboards.",
+                ),
+                IO.Image.Input(
+                    "reference_images",
+                    optional=True,
+                    tooltip="Up to 6 additional reference images.",
+                ),
+                IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p", optional=True),
+                IO.DynamicCombo.Input(
+                    "storyboards",
+                    options=[
+                        IO.DynamicCombo.Option("disabled", []),
+                        IO.DynamicCombo.Option("1 storyboard", _generate_storyboard_inputs(1)),
+                        IO.DynamicCombo.Option("2 storyboards", _generate_storyboard_inputs(2)),
+                        IO.DynamicCombo.Option("3 storyboards", _generate_storyboard_inputs(3)),
+                        IO.DynamicCombo.Option("4 storyboards", _generate_storyboard_inputs(4)),
+                        IO.DynamicCombo.Option("5 storyboards", _generate_storyboard_inputs(5)),
+                        IO.DynamicCombo.Option("6 storyboards", _generate_storyboard_inputs(6)),
+                    ],
+                    tooltip="Generate a series of video segments with individual prompts and durations. "
+                    "Only supported for kling-v3-omni.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="Generate audio for the video. Only supported for kling-v3-omni.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution", "model_name", "generate_audio"]),
+                expr="""
+                (
+                  $res := widgets.resolution;
+                  $mode := $res = "4k" ? "4k" : ($res = "720p" ? "std" : "pro");
+                  $isV3 := $contains(widgets.model_name, "v3");
+                  $audio := $isV3 and widgets.generate_audio;
+                  $rates := $audio
+                    ? {"std": 0.112, "pro": 0.14, "4k": 0.42}
+                    : {"std": 0.084, "pro": 0.112, "4k": 0.42};
+                  {"type":"usd","usd": $lookup($rates, $mode) * widgets.duration}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        duration: int,
+        first_frame: Input.Image,
+        end_frame: Input.Image | None = None,
+        reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
+        storyboards: dict | None = None,
+        generate_audio: bool = False,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        _ = seed
+        if model_name == "kling-video-o1":
+            if duration > 10:
+                raise ValueError("kling-video-o1 does not support durations greater than 10 seconds.")
+            if generate_audio:
+                raise ValueError("kling-video-o1 does not support audio generation.")
+            if resolution == "4k":
+                raise ValueError("kling-video-o1 does not support 4k resolution.")
+        stories_enabled = storyboards is not None and storyboards["storyboards"] != "disabled"
+        if stories_enabled and model_name == "kling-video-o1":
+            raise ValueError("kling-video-o1 does not support storyboards.")
+        prompt = normalize_omni_prompt_references(prompt)
+        validate_string(prompt, strip_whitespace=True, min_length=0 if stories_enabled else 1, max_length=2500)
+        if end_frame is not None and reference_images is not None:
+            raise ValueError("The 'end_frame' input cannot be used simultaneously with 'reference_images'.")
+        if end_frame is not None and stories_enabled:
+            raise ValueError("The 'end_frame' input cannot be used simultaneously with storyboards.")
+        if (
+            model_name == "kling-video-o1"
+            and duration not in (5, 10)
+            and end_frame is None
+            and reference_images is None
+        ):
+            raise ValueError(
+                "Duration is only supported for 5 or 10 seconds if there is no end frame or reference images."
+            )
+
+        multi_shot = None
+        multi_prompt_list = None
+        if stories_enabled:
+            count = int(storyboards["storyboards"].split()[0])
+            multi_shot = True
+            multi_prompt_list = []
+            for i in range(1, count + 1):
+                sb_prompt = storyboards[f"storyboard_{i}_prompt"]
+                sb_duration = storyboards[f"storyboard_{i}_duration"]
+                validate_string(sb_prompt, field_name=f"storyboard_{i}_prompt", min_length=1, max_length=512)
+                multi_prompt_list.append(
+                    MultiPromptEntry(
+                        index=i,
+                        prompt=sb_prompt,
+                        duration=str(sb_duration),
+                    )
+                )
+            total_storyboard_duration = sum(int(e.duration) for e in multi_prompt_list)
+            if total_storyboard_duration != duration:
+                raise ValueError(
+                    f"Total storyboard duration ({total_storyboard_duration}s) "
+                    f"must equal the global duration ({duration}s)."
+                )
+
+        validate_image_dimensions(first_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(first_frame, (1, 2.5), (2.5, 1))
+        image_list: list[OmniParamImage] = [
+            OmniParamImage(
+                image_url=(await upload_images_to_comfyapi(cls, first_frame, wait_label="Uploading first frame"))[0],
+                type="first_frame",
+            )
+        ]
+        if end_frame is not None:
+            validate_image_dimensions(end_frame, min_width=300, min_height=300)
+            validate_image_aspect_ratio(end_frame, (1, 2.5), (2.5, 1))
+            image_list.append(
+                OmniParamImage(
+                    image_url=(await upload_images_to_comfyapi(cls, end_frame, wait_label="Uploading end frame"))[0],
+                    type="end_frame",
+                )
+            )
+        if reference_images is not None:
+            if get_number_of_images(reference_images) > 6:
+                raise ValueError("The maximum number of reference images allowed is 6.")
+            for i in reference_images:
+                validate_image_dimensions(i, min_width=300, min_height=300)
+                validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+            for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference frame(s)"):
+                image_list.append(OmniParamImage(image_url=i))
+        if resolution == "4k":
+            mode = "4k"
+        elif resolution == "1080p":
+            mode = "pro"
+        else:
+            mode = "std"
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProFirstLastFrameRequest(
+                model_name=model_name,
+                prompt=prompt,
+                duration=str(duration),
+                image_list=image_list,
+                mode=mode,
+                sound="on" if generate_audio else "off",
+                multi_shot=multi_shot,
+                multi_prompt=multi_prompt_list,
+                shot_type="customize" if multi_shot else None,
+            ),
+        )
+        return await finish_omni_video_task(cls, response)
+
+
+class OmniProImageToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProImageToVideoNode",
+            display_name="Kling 3.0 Omni Image to Video",
+            category="partner/video/Kling",
+            description="Use up to 7 reference images to generate a video with the latest Kling model.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the video content. "
+                    "This can include both positive and negative descriptions. "
+                    "Ignored when storyboards are enabled.",
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+                IO.Int.Input("duration", default=5, min=3, max=15, display_mode=IO.NumberDisplay.slider),
+                IO.Image.Input(
+                    "reference_images",
+                    tooltip="Up to 7 reference images.",
+                ),
+                IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p", optional=True),
+                IO.DynamicCombo.Input(
+                    "storyboards",
+                    options=[
+                        IO.DynamicCombo.Option("disabled", []),
+                        IO.DynamicCombo.Option("1 storyboard", _generate_storyboard_inputs(1)),
+                        IO.DynamicCombo.Option("2 storyboards", _generate_storyboard_inputs(2)),
+                        IO.DynamicCombo.Option("3 storyboards", _generate_storyboard_inputs(3)),
+                        IO.DynamicCombo.Option("4 storyboards", _generate_storyboard_inputs(4)),
+                        IO.DynamicCombo.Option("5 storyboards", _generate_storyboard_inputs(5)),
+                        IO.DynamicCombo.Option("6 storyboards", _generate_storyboard_inputs(6)),
+                    ],
+                    tooltip="Generate a series of video segments with individual prompts and durations. "
+                    "Only supported for kling-v3-omni.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="Generate audio for the video. Only supported for kling-v3-omni.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution", "model_name", "generate_audio"]),
+                expr="""
+                (
+                  $res := widgets.resolution;
+                  $mode := $res = "4k" ? "4k" : ($res = "720p" ? "std" : "pro");
+                  $isV3 := $contains(widgets.model_name, "v3");
+                  $audio := $isV3 and widgets.generate_audio;
+                  $rates := $audio
+                    ? {"std": 0.112, "pro": 0.14, "4k": 0.42}
+                    : {"std": 0.084, "pro": 0.112, "4k": 0.42};
+                  {"type":"usd","usd": $lookup($rates, $mode) * widgets.duration}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        aspect_ratio: str,
+        duration: int,
+        reference_images: Input.Image,
+        resolution: str = "1080p",
+        storyboards: dict | None = None,
+        generate_audio: bool = False,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        _ = seed
+        if model_name == "kling-video-o1":
+            if duration > 10:
+                raise ValueError("kling-video-o1 does not support durations greater than 10 seconds.")
+            if generate_audio:
+                raise ValueError("kling-video-o1 does not support audio generation.")
+            if resolution == "4k":
+                raise ValueError("kling-video-o1 does not support 4k resolution.")
+        stories_enabled = storyboards is not None and storyboards["storyboards"] != "disabled"
+        if stories_enabled and model_name == "kling-video-o1":
+            raise ValueError("kling-video-o1 does not support storyboards.")
+        prompt = normalize_omni_prompt_references(prompt)
+        validate_string(prompt, strip_whitespace=True, min_length=0 if stories_enabled else 1, max_length=2500)
+
+        multi_shot = None
+        multi_prompt_list = None
+        if stories_enabled:
+            count = int(storyboards["storyboards"].split()[0])
+            multi_shot = True
+            multi_prompt_list = []
+            for i in range(1, count + 1):
+                sb_prompt = storyboards[f"storyboard_{i}_prompt"]
+                sb_duration = storyboards[f"storyboard_{i}_duration"]
+                validate_string(sb_prompt, field_name=f"storyboard_{i}_prompt", min_length=1, max_length=512)
+                multi_prompt_list.append(
+                    MultiPromptEntry(
+                        index=i,
+                        prompt=sb_prompt,
+                        duration=str(sb_duration),
+                    )
+                )
+            total_storyboard_duration = sum(int(e.duration) for e in multi_prompt_list)
+            if total_storyboard_duration != duration:
+                raise ValueError(
+                    f"Total storyboard duration ({total_storyboard_duration}s) "
+                    f"must equal the global duration ({duration}s)."
+                )
+
+        if get_number_of_images(reference_images) > 7:
+            raise ValueError("The maximum number of reference images is 7.")
+        for i in reference_images:
+            validate_image_dimensions(i, min_width=300, min_height=300)
+            validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+        image_list: list[OmniParamImage] = []
+        for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+            image_list.append(OmniParamImage(image_url=i))
+        if resolution == "4k":
+            mode = "4k"
+        elif resolution == "1080p":
+            mode = "pro"
+        else:
+            mode = "std"
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProReferences2VideoRequest(
+                model_name=model_name,
+                prompt=prompt,
+                aspect_ratio=aspect_ratio,
+                duration=str(duration),
+                image_list=image_list,
+                mode=mode,
+                sound="on" if generate_audio else "off",
+                multi_shot=multi_shot,
+                multi_prompt=multi_prompt_list,
+                shot_type="customize" if multi_shot else None,
+            ),
+        )
+        return await finish_omni_video_task(cls, response)
+
+
+class OmniProVideoToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProVideoToVideoNode",
+            display_name="Kling 3.0 Omni Video to Video",
+            category="partner/video/Kling",
+            description="Use a video and up to 4 reference images to generate a video with the latest Kling model.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the video content. "
+                    "This can include both positive and negative descriptions.",
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+                IO.Int.Input("duration", default=3, min=3, max=10, display_mode=IO.NumberDisplay.slider),
+                IO.Video.Input("reference_video", tooltip="Video to use as a reference."),
+                IO.Boolean.Input("keep_original_sound", default=True),
+                IO.Image.Input(
+                    "reference_images",
+                    tooltip="Up to 4 additional reference images.",
+                    optional=True,
+                ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution"]),
+                expr="""
+                (
+                  $mode := (widgets.resolution = "720p") ? "std" : "pro";
+                  $rates := {"std": 0.126, "pro": 0.168};
+                  {"type":"usd","usd": $lookup($rates, $mode) * widgets.duration}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        aspect_ratio: str,
+        duration: int,
+        reference_video: Input.Video,
+        keep_original_sound: bool,
+        reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        _ = seed
+        prompt = normalize_omni_prompt_references(prompt)
+        validate_string(prompt, min_length=1, max_length=2500)
+        validate_video_duration(reference_video, min_duration=3.0, max_duration=10.05)
+        validate_video_dimensions(reference_video, min_width=720, min_height=720, max_width=2160, max_height=2160)
+        image_list: list[OmniParamImage] = []
+        if reference_images is not None:
+            if get_number_of_images(reference_images) > 4:
+                raise ValueError("The maximum number of reference images allowed with a video input is 4.")
+            for i in reference_images:
+                validate_image_dimensions(i, min_width=300, min_height=300)
+                validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+            for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+                image_list.append(OmniParamImage(image_url=i))
+        video_list = [
+            OmniParamVideo(
+                video_url=await upload_video_to_comfyapi(cls, reference_video, wait_label="Uploading reference video"),
+                refer_type="feature",
+                keep_original_sound="yes" if keep_original_sound else "no",
+            )
+        ]
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProReferences2VideoRequest(
+                model_name=model_name,
+                prompt=prompt,
+                aspect_ratio=aspect_ratio,
+                duration=str(duration),
+                image_list=image_list if image_list else None,
+                video_list=video_list,
+                mode="pro" if resolution == "1080p" else "std",
+            ),
+        )
+        return await finish_omni_video_task(cls, response)
+
+
+class OmniProEditVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProEditVideoNode",
+            display_name="Kling 3.0 Omni Edit Video",
+            category="partner/video/Kling",
+            essentials_category="Video Generation",
+            description="Edit an existing video with the latest model from Kling.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the video content. "
+                    "This can include both positive and negative descriptions.",
+                ),
+                IO.Video.Input("video", tooltip="Video for editing. The output video length will be the same."),
+                IO.Boolean.Input("keep_original_sound", default=True),
+                IO.Image.Input(
+                    "reference_images",
+                    tooltip="Up to 4 additional reference images.",
+                    optional=True,
+                ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
+                expr="""
+                (
+                  $mode := (widgets.resolution = "720p") ? "std" : "pro";
+                  $rates := {"std": 0.126, "pro": 0.168};
+                  {"type":"usd","usd": $lookup($rates, $mode), "format":{"suffix":"/second"}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        video: Input.Video,
+        keep_original_sound: bool,
+        reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        _ = seed
+        prompt = normalize_omni_prompt_references(prompt)
+        validate_string(prompt, min_length=1, max_length=2500)
+        validate_video_duration(video, min_duration=3.0, max_duration=10.05)
+        validate_video_dimensions(video, min_width=720, min_height=720, max_width=2160, max_height=2160)
+        image_list: list[OmniParamImage] = []
+        if reference_images is not None:
+            if get_number_of_images(reference_images) > 4:
+                raise ValueError("The maximum number of reference images allowed with a video input is 4.")
+            for i in reference_images:
+                validate_image_dimensions(i, min_width=300, min_height=300)
+                validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+            for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+                image_list.append(OmniParamImage(image_url=i))
+        video_list = [
+            OmniParamVideo(
+                video_url=await upload_video_to_comfyapi(cls, video, wait_label="Uploading base video"),
+                refer_type="base",
+                keep_original_sound="yes" if keep_original_sound else "no",
+            )
+        ]
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProReferences2VideoRequest(
+                model_name=model_name,
+                prompt=prompt,
+                aspect_ratio=None,
+                duration=None,
+                image_list=image_list if image_list else None,
+                video_list=video_list,
+                mode="pro" if resolution == "1080p" else "std",
+            ),
+        )
+        return await finish_omni_video_task(cls, response)
+
+
+class OmniProImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProImageNode",
+            display_name="Kling 3.0 Omni Image",
+            category="partner/image/Kling",
+            description="Create or edit images with the latest model from Kling.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-image-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the image content. "
+                    "This can include both positive and negative descriptions.",
+                ),
+                IO.Combo.Input("resolution", options=["1K", "2K", "4K"]),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["16:9", "9:16", "1:1", "4:3", "3:4", "3:2", "2:3", "21:9"],
+                ),
+                IO.Combo.Input(
+                    "series_amount",
+                    options=["disabled", "2", "3", "4", "5", "6", "7", "8", "9"],
+                    tooltip="Generate a series of images. Not supported for kling-image-o1.",
+                ),
+                IO.Image.Input(
+                    "reference_images",
+                    tooltip="Up to 10 additional reference images.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["resolution", "series_amount", "model_name"]),
+                expr="""
+                (
+                  $prices := {"1k": 0.028, "2k": 0.028, "4k": 0.056};
+                  $base := $lookup($prices, widgets.resolution);
+                  $isO1 := widgets.model_name = "kling-image-o1";
+                  $mult := ($isO1 or widgets.series_amount = "disabled") ? 1 : $number(widgets.series_amount);
+                  {"type":"usd","usd": $base * $mult}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        resolution: str,
+        aspect_ratio: str,
+        series_amount: str = "disabled",
+        reference_images: Input.Image | None = None,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        _ = seed
+        if model_name == "kling-image-o1" and resolution == "4K":
+            raise ValueError("4K resolution is not supported for kling-image-o1 model.")
+        prompt = normalize_omni_prompt_references(prompt)
+        validate_string(prompt, min_length=1, max_length=2500)
+        image_list: list[OmniImageParamImage] = []
+        if reference_images is not None:
+            if get_number_of_images(reference_images) > 10:
+                raise ValueError("The maximum number of reference images is 10.")
+            for i in reference_images:
+                validate_image_dimensions(i, min_width=300, min_height=300)
+                validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+            for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+                image_list.append(OmniImageParamImage(image=i))
+        use_series = series_amount != "disabled"
+        if use_series and model_name == "kling-image-o1":
+            raise ValueError("kling-image-o1 does not support series generation.")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/images/omni-image", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProImageRequest(
+                model_name=model_name,
+                prompt=prompt,
+                resolution=resolution.lower(),
+                aspect_ratio=aspect_ratio,
+                image_list=image_list if image_list else None,
+                result_type="series" if use_series else None,
+                series_amount=int(series_amount) if use_series else None,
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/images/omni-image/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        images = final_response.data.task_result.series_images or final_response.data.task_result.images
+        tensors = [await download_url_to_image_tensor(img.url) for img in images]
+        return IO.NodeOutput(torch.cat(tensors, dim=0))
+
+
+class KlingImage2VideoNode(IO.ComfyNode):
+    """Kling Image to Video Node"""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingImage2VideoNode",
+            display_name="Kling Image(First Frame) to Video",
+            category="partner/video/Kling",
+            inputs=[
+                IO.Image.Input("start_frame", tooltip="The reference image used to generate the video."),
+                IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
+                IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
+                IO.Combo.Input(
+                    "model_name",
+                    options=["kling-v2-5-turbo"],
+                ),
+                IO.Float.Input("cfg_scale", default=0.8, min=0.0, max=1.0),
+                IO.Combo.Input("mode", options=["pro"]),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=KlingVideoGenAspectRatio,
+                    default=KlingVideoGenAspectRatio.field_16_9,
+                ),
+                IO.Combo.Input("duration", options=KlingVideoGenDuration, default=KlingVideoGenDuration.field_5),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="video_id"),
+                IO.String.Output(display_name="duration"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration"]),
+                expr="""
+                (
+                  $contains(widgets.duration,"10") ? {"type":"usd","usd":0.7} : {"type":"usd","usd":0.35}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        start_frame: torch.Tensor,
+        prompt: str,
+        negative_prompt: str,
+        model_name: str,
+        cfg_scale: float,
+        mode: str,
+        aspect_ratio: str,
+        duration: str,
+    ) -> IO.NodeOutput:
+        return await execute_image2video(
+            cls,
+            start_frame=start_frame,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            cfg_scale=cfg_scale,
+            model_name=model_name,
+            aspect_ratio=aspect_ratio,
+            model_mode=mode,
+            duration=duration,
+        )
+
+
+class KlingStartEndFrameNode(IO.ComfyNode):
+    """
+    Kling First Last Frame Node. This node allows creation of a video from a first and last frame. It calls the normal image to video endpoint, but only allows the subset of input options that support the `image_tail` request field.
+    """
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        modes = list(MODE_START_END_FRAME.keys())
+        return IO.Schema(
+            node_id="KlingStartEndFrameNode",
+            display_name="Kling Start-End Frame to Video",
+            category="partner/video/Kling",
+            description="Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last.",
+            inputs=[
+                IO.Image.Input(
+                    "start_frame",
+                    tooltip="Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.",
+                ),
+                IO.Image.Input(
+                    "end_frame",
+                    tooltip="Reference Image - End frame control. URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px. Base64 should not include data:image prefix.",
+                ),
+                IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
+                IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
+                IO.Float.Input("cfg_scale", default=0.5, min=0.0, max=1.0),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+                IO.Combo.Input(
+                    "mode",
+                    options=modes,
+                    default=modes[0],
+                    tooltip="The configuration to use for the video generation following the format: mode / duration / model_name.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="video_id"),
+                IO.String.Output(display_name="duration"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
+                expr="""
+                (
+                  $m := widgets.mode;
+                  $contains($m,"10") ? {"type":"usd","usd":0.7} : {"type":"usd","usd":0.35}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        start_frame: torch.Tensor,
+        end_frame: torch.Tensor,
+        prompt: str,
+        negative_prompt: str,
+        cfg_scale: float,
+        aspect_ratio: str,
+        mode: str,
+    ) -> IO.NodeOutput:
+        mode, duration, model_name = MODE_START_END_FRAME[mode]
+        return await execute_image2video(
+            cls,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            model_name=model_name,
+            start_frame=start_frame,
+            cfg_scale=cfg_scale,
+            model_mode=mode,
+            aspect_ratio=aspect_ratio,
+            duration=duration,
+            end_frame=end_frame,
+        )
+
+
+class KlingVideoExtendNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingVideoExtendNode",
+            display_name="Kling Video Extend",
+            category="partner/video/Kling",
+            description="Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Positive text prompt for guiding the video extension",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    tooltip="Negative text prompt for elements to avoid in the extended video",
+                ),
+                IO.Float.Input("cfg_scale", default=0.5, min=0.0, max=1.0),
+                IO.String.Input(
+                    "video_id",
+                    force_input=True,
+                    tooltip="The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="video_id"),
+                IO.String.Output(display_name="duration"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.28}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str,
+        cfg_scale: float,
+        video_id: str,
+    ) -> IO.NodeOutput:
+        validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
+        task_creation_response = await sync_op(
+            cls,
+            ApiEndpoint(path=PATH_VIDEO_EXTEND, method="POST"),
+            response_model=KlingVideoExtendResponse,
+            data=KlingVideoExtendRequest(
+                prompt=prompt if prompt else None,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                cfg_scale=cfg_scale,
+                video_id=video_id,
+            ),
+        )
+
+        validate_task_creation_response(task_creation_response)
+        task_id = task_creation_response.data.task_id
+
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"{PATH_VIDEO_EXTEND}/{task_id}"),
+            response_model=KlingVideoExtendResponse,
+            estimated_duration=AVERAGE_DURATION_VIDEO_EXTEND,
+            status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
+        )
+        validate_video_result_response(final_response)
+
+        video = get_video_from_response(final_response)
+        return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration))
+
+
+class KlingLipSyncAudioToVideoNode(IO.ComfyNode):
+    """Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingLipSyncAudioToVideoNode",
+            display_name="Kling Lip Sync Video with Audio",
+            category="partner/video/Kling",
+            essentials_category="Video Generation",
+            description="Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Audio.Input("audio"),
+                IO.Combo.Input(
+                    "voice_language",
+                    options=[i.value for i in KlingLipSyncVoiceLanguage],
+                    default="en",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="video_id"),
+                IO.String.Output(display_name="duration"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.1,"format":{"approximate":true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        audio: Input.Audio,
+        voice_language: str,
+    ) -> IO.NodeOutput:
+        return await execute_lipsync(
+            cls,
+            video=video,
+            audio=audio,
+            voice_language=voice_language,
+            model_mode="audio2video",
+        )
+
+
+class KlingLipSyncTextToVideoNode(IO.ComfyNode):
+    """Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingLipSyncTextToVideoNode",
+            display_name="Kling Lip Sync Video with Text",
+            category="partner/video/Kling",
+            description="Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    tooltip="Text Content for Lip-Sync Video Generation. Required when mode is text2video. Maximum length is 120 characters.",
+                ),
+                IO.Combo.Input(
+                    "voice",
+                    options=list(VOICES_CONFIG.keys()),
+                    default="Melody",
+                ),
+                IO.Float.Input(
+                    "voice_speed",
+                    default=1,
+                    min=0.8,
+                    max=2.0,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="video_id"),
+                IO.String.Output(display_name="duration"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.1,"format":{"approximate":true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        text: str,
+        voice: str,
+        voice_speed: float,
+    ) -> IO.NodeOutput:
+        voice_id, voice_language = VOICES_CONFIG[voice]
+        return await execute_lipsync(
+            cls,
+            video=video,
+            text=text,
+            voice_language=voice_language,
+            voice_id=voice_id,
+            voice_speed=voice_speed,
+            model_mode="text2video",
+        )
+
+
+class KlingImageGenerationNode(IO.ComfyNode):
+    """Kling Image Generation Node. Generate an image from a text prompt with an optional reference image."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingImageGenerationNode",
+            display_name="Kling 3.0 Image",
+            category="partner/image/Kling",
+            description="Kling Image Generation Node. Generate an image from a text prompt with an optional reference image.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
+                IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
+                IO.Combo.Input(
+                    "image_type",
+                    options=[i.value for i in KlingImageGenImageReferenceType],
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "image_fidelity",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Reference intensity for user-uploaded images",
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "human_fidelity",
+                    default=0.45,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Subject reference similarity",
+                    advanced=True,
+                ),
+                IO.Combo.Input("model_name", options=["kling-v3"]),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=[i.value for i in KlingImageGenAspectRatio],
+                    default="16:9",
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=9,
+                    tooltip="Number of generated images",
+                ),
+                IO.Image.Input("image", optional=True),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["n"]),
+                expr="""{"type":"usd","usd": 0.028 * widgets.n}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        negative_prompt: str,
+        image_type: KlingImageGenImageReferenceType,
+        image_fidelity: float,
+        human_fidelity: float,
+        n: int,
+        aspect_ratio: KlingImageGenAspectRatio,
+        image: torch.Tensor | None = None,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        _ = seed
+        validate_string(prompt, field_name="prompt", min_length=1, max_length=MAX_PROMPT_LENGTH_IMAGE_GEN)
+        validate_string(negative_prompt, field_name="negative_prompt", max_length=MAX_PROMPT_LENGTH_IMAGE_GEN)
+        task_creation_response = await sync_op(
+            cls,
+            ApiEndpoint(path=PATH_IMAGE_GENERATIONS, method="POST"),
+            response_model=KlingImageGenerationsResponse,
+            data=KlingImageGenerationsRequest(
+                model_name=model_name,
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                image=tensor_to_base64_string(image) if image is not None else None,
+                image_reference=image_type if image is not None else None,
+                image_fidelity=image_fidelity,
+                human_fidelity=human_fidelity,
+                n=n,
+                aspect_ratio=aspect_ratio,
+            ),
+        )
+
+        validate_task_creation_response(task_creation_response)
+        task_id = task_creation_response.data.task_id
+
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"{PATH_IMAGE_GENERATIONS}/{task_id}"),
+            response_model=KlingImageGenerationsResponse,
+            estimated_duration=AVERAGE_DURATION_IMAGE_GEN,
+            status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
+        )
+        validate_image_result_response(final_response)
+
+        images = get_images_from_response(final_response)
+        return IO.NodeOutput(await image_result_to_node_output(images))
+
+
+class TextToVideoWithAudio(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingTextToVideoWithAudio",
+            display_name="Kling 2.6 Text to Video with Audio",
+            category="partner/video/Kling",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v2-6"]),
+                IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
+                IO.Combo.Input("mode", options=["pro"]),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+                IO.Combo.Input("duration", options=[5, 10]),
+                IO.Boolean.Input("generate_audio", default=True, advanced=True),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration", "generate_audio"]),
+                expr="""{"type":"usd","usd": 0.07 * widgets.duration * (widgets.generate_audio ? 2 : 1)}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        mode: str,
+        aspect_ratio: str,
+        duration: int,
+        generate_audio: bool,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2500)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/text2video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=TextToVideoWithAudioRequest(
+                model_name=model_name,
+                prompt=prompt,
+                mode=mode,
+                aspect_ratio=aspect_ratio,
+                duration=str(duration),
+                sound="on" if generate_audio else "off",
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/text2video/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
+class ImageToVideoWithAudio(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingImageToVideoWithAudio",
+            display_name="Kling 2.6 Image(First Frame) to Video with Audio",
+            category="partner/video/Kling",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v2-6"]),
+                IO.Image.Input("start_frame"),
+                IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
+                IO.Combo.Input("mode", options=["pro"]),
+                IO.Combo.Input("duration", options=[5, 10]),
+                IO.Boolean.Input("generate_audio", default=True, advanced=True),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration", "generate_audio"]),
+                expr="""{"type":"usd","usd": 0.07 * widgets.duration * (widgets.generate_audio ? 2 : 1)}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        start_frame: Input.Image,
+        prompt: str,
+        mode: str,
+        duration: int,
+        generate_audio: bool,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2500)
+        validate_image_dimensions(start_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1))
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/image2video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=ImageToVideoWithAudioRequest(
+                model_name=model_name,
+                image=(await upload_images_to_comfyapi(cls, start_frame))[0],
+                prompt=prompt,
+                mode=mode,
+                duration=str(duration),
+                sound="on" if generate_audio else "off",
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/image2video/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
+class MotionControl(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingMotionControl",
+            display_name="Kling Motion Control",
+            category="partner/video/Kling",
+            inputs=[
+                IO.String.Input("prompt", multiline=True),
+                IO.Image.Input("reference_image"),
+                IO.Video.Input(
+                    "reference_video",
+                    tooltip="Motion reference video used to drive movement/expression.\n"
+                    "Duration limits depend on character_orientation:\n"
+                    " - image: 3–10s (max 10s)\n"
+                    " - video: 3–30s (max 30s)",
+                ),
+                IO.Boolean.Input("keep_original_sound", default=True),
+                IO.Combo.Input(
+                    "character_orientation",
+                    options=["video", "image"],
+                    tooltip="Controls where the character's facing/orientation comes from.\n"
+                    "video: movements, expressions, camera moves, and orientation "
+                    "follow the motion reference video (other details via prompt).\n"
+                    "image: movements and expressions still follow the motion reference video, "
+                    "but the character orientation matches the reference image (camera/other details via prompt).",
+                ),
+                IO.Combo.Input("mode", options=["pro", "std"]),
+                IO.Combo.Input("model", options=["kling-v3", "kling-v2-6"], optional=True),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode", "model"]),
+                expr="""
+                (
+                  $prices := {
+                    "kling-v3": {"std": 0.126, "pro": 0.168},
+                    "kling-v2-6": {"std": 0.07, "pro": 0.112}
+                  };
+                  $modelPrices := $lookup($prices, widgets.model);
+                  {"type":"usd","usd": $lookup($modelPrices, widgets.mode), "format":{"suffix":"/second"}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        reference_image: Input.Image,
+        reference_video: Input.Video,
+        keep_original_sound: bool,
+        character_orientation: str,
+        mode: str,
+        model: str = "kling-v2-6",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, max_length=2500)
+        validate_image_dimensions(reference_image, min_width=340, min_height=340)
+        validate_image_aspect_ratio(reference_image, (1, 2.5), (2.5, 1))
+        if character_orientation == "image":
+            validate_video_duration(reference_video, min_duration=3, max_duration=10)
+        else:
+            validate_video_duration(reference_video, min_duration=3, max_duration=30)
+        validate_video_dimensions(reference_video, min_width=340, min_height=340, max_width=3850, max_height=3850)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/motion-control", method="POST"),
+            response_model=TaskStatusResponse,
+            data=MotionControlRequest(
+                prompt=prompt,
+                image_url=(await upload_images_to_comfyapi(cls, reference_image))[0],
+                video_url=await upload_video_to_comfyapi(cls, reference_video),
+                keep_original_sound="yes" if keep_original_sound else "no",
+                character_orientation=character_orientation,
+                mode=mode,
+                model_name=model,
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/motion-control/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
+def build_turbo_shot_prompt(multi_prompt: list[MultiPromptEntry]) -> str:
+    """Render storyboard entries into the Turbo multi-shot prompt 'shot n, m, words; ...'."""
+    return "; ".join(f"shot {i}, {int(e.duration)}, {e.prompt}" for i, e in enumerate(multi_prompt, 1)) + ";"
+
+
+def _turbo_video_url(response: Kling3TurboQueryResponse) -> str:
+    """Extract the result video URL from a /tasks response (data[].outputs[] where type == 'video')."""
+    task = response.data[0] if response.data else None
+    if task and task.outputs:
+        for output in task.outputs:
+            if output.type == "video" and output.url:
+                return output.url
+    raise RuntimeError(f"Kling 3.0 Turbo task finished without a video output: {response.model_dump()}")
+
+
+async def execute_kling_turbo(
+    cls: type[IO.ComfyNode],
+    *,
+    prompt: str,
+    resolution: str,
+    aspect_ratio: str,
+    duration: int,
+    start_frame: torch.Tensor | None,
+) -> IO.NodeOutput:
+    """Create + poll a Kling 3.0 Turbo task. Image-to-video when start_frame is given, else text-to-video."""
+    if start_frame is not None:
+        validate_image_dimensions(start_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1))
+        contents = [Kling3TurboContent(type="first_frame", url=tensor_to_base64_string(start_frame))]
+        if prompt:
+            contents.insert(0, Kling3TurboContent(type="prompt", text=prompt))
+        create = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/image-to-video/kling-3.0-turbo", method="POST"),
+            response_model=Kling3TurboCreateResponse,
+            data=Kling3TurboImage2VideoRequest(
+                contents=contents,
+                settings=Kling3TurboSettings(resolution=resolution, duration=duration),  # i2v: no aspect_ratio
+            ),
+        )
+    else:
+        create = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/text-to-video/kling-3.0-turbo", method="POST"),
+            response_model=Kling3TurboCreateResponse,
+            data=Kling3TurboText2VideoRequest(
+                prompt=prompt,
+                settings=Kling3TurboSettings(resolution=resolution, aspect_ratio=aspect_ratio, duration=duration),
+            ),
+        )
+    if not (create.data and create.data.id):
+        raise RuntimeError(f"Kling 3.0 Turbo create failed. Code: {create.code}, Message: {create.message}")
+    final_response = await poll_op(
+        cls,
+        ApiEndpoint(path="/proxy/kling/tasks", query_params={"task_ids": create.data.id}),
+        response_model=Kling3TurboQueryResponse,
+        status_extractor=lambda r: (r.data[0].status if r.data else None),
+    )
+    return IO.NodeOutput(await download_url_to_video_output(_turbo_video_url(final_response)))
+
+
+class KlingVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingVideoNode",
+            display_name="Kling 3.0 Video",
+            category="partner/video/Kling",
+            description="Generate videos with Kling V3. "
+            "Supports text-to-video and image-to-video with optional storyboard multi-prompt and audio generation.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "multi_shot",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "disabled",
+                            [
+                                IO.String.Input("prompt", multiline=True, default=""),
+                                IO.String.Input("negative_prompt", multiline=True, default=""),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=3,
+                                    max=15,
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("1 storyboard", _generate_storyboard_inputs(1)),
+                        IO.DynamicCombo.Option("2 storyboards", _generate_storyboard_inputs(2)),
+                        IO.DynamicCombo.Option("3 storyboards", _generate_storyboard_inputs(3)),
+                        IO.DynamicCombo.Option("4 storyboards", _generate_storyboard_inputs(4)),
+                        IO.DynamicCombo.Option("5 storyboards", _generate_storyboard_inputs(5)),
+                        IO.DynamicCombo.Option("6 storyboards", _generate_storyboard_inputs(6)),
+                    ],
+                    tooltip="Generate a series of video segments with individual prompts and durations.",
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=True,
+                    tooltip="'kling-3.0-turbo' always generates native audio, so the audio toggle is ignored.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "kling-v3",
+                            [
+                                IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p"),
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=["16:9", "9:16", "1:1"],
+                                    tooltip="Ignored in image-to-video mode.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "kling-3.0-turbo",
+                            [
+                                IO.Combo.Input("resolution", options=["1080p", "720p"], default="720p"),
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=["16:9", "9:16", "1:1"],
+                                    tooltip="Ignored in image-to-video mode.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model and generation settings.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                            "results are non-deterministic regardless of seed.",
+                ),
+                IO.Image.Input(
+                    "start_frame",
+                    optional=True,
+                    tooltip="Optional start frame image. When connected, switches to image-to-video mode.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=[
+                        "model",
+                        "model.resolution",
+                        "generate_audio",
+                        "multi_shot",
+                        "multi_shot.duration",
+                        "multi_shot.storyboard_1_duration",
+                        "multi_shot.storyboard_2_duration",
+                        "multi_shot.storyboard_3_duration",
+                        "multi_shot.storyboard_4_duration",
+                        "multi_shot.storyboard_5_duration",
+                        "multi_shot.storyboard_6_duration",
+                    ],
+                ),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $ms := widgets.multi_shot;
+                  $isSb := $ms != "disabled";
+                  $n := $isSb ? $number($substring($ms, 0, 1)) : 0;
+                  $d1 := $lookup(widgets, "multi_shot.storyboard_1_duration");
+                  $d2 := $n >= 2 ? $lookup(widgets, "multi_shot.storyboard_2_duration") : 0;
+                  $d3 := $n >= 3 ? $lookup(widgets, "multi_shot.storyboard_3_duration") : 0;
+                  $d4 := $n >= 4 ? $lookup(widgets, "multi_shot.storyboard_4_duration") : 0;
+                  $d5 := $n >= 5 ? $lookup(widgets, "multi_shot.storyboard_5_duration") : 0;
+                  $d6 := $n >= 6 ? $lookup(widgets, "multi_shot.storyboard_6_duration") : 0;
+                  $dur := $isSb ? $d1 + $d2 + $d3 + $d4 + $d5 + $d6 : $lookup(widgets, "multi_shot.duration");
+                  widgets.model = "kling-3.0-turbo"
+                    ? {"type":"usd","usd": ($res = "1080p" ? 0.14 : 0.112) * $dur}
+                    : (
+                        $rates := {
+                          "4k": {"off": 0.42, "on": 0.42},
+                          "1080p": {"off": 0.112, "on": 0.168},
+                          "720p": {"off": 0.084, "on": 0.126}
+                        };
+                        $audio := widgets.generate_audio ? "on" : "off";
+                        $rate := $lookup($lookup($rates, $res), $audio);
+                        {"type":"usd","usd": $rate * $dur}
+                      )
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        multi_shot: dict,
+        generate_audio: bool,
+        model: dict,
+        seed: int,
+        start_frame: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        _ = seed
+        if model["resolution"] == "4k":
+            mode = "4k"
+        elif model["resolution"] == "1080p":
+            mode = "pro"
+        else:
+            mode = "std"
+        custom_multi_shot = False
+        if multi_shot["multi_shot"] == "disabled":
+            shot_type = None
+        else:
+            shot_type = "customize"
+            custom_multi_shot = True
+
+        multi_prompt_list = None
+        if shot_type == "customize":
+            count = int(multi_shot["multi_shot"].split()[0])
+            multi_prompt_list = []
+            for i in range(1, count + 1):
+                sb_prompt = multi_shot[f"storyboard_{i}_prompt"]
+                sb_duration = multi_shot[f"storyboard_{i}_duration"]
+                validate_string(sb_prompt, field_name=f"storyboard_{i}_prompt", min_length=1, max_length=512)
+                multi_prompt_list.append(
+                    MultiPromptEntry(
+                        index=i,
+                        prompt=sb_prompt,
+                        duration=str(sb_duration),
+                    )
+                )
+            duration = sum(int(e.duration) for e in multi_prompt_list)
+            if duration < 3 or duration > 15:
+                raise ValueError(
+                    f"Total storyboard duration ({duration}s) must be between 3 and 15 seconds."
+                )
+        else:
+            duration = multi_shot["duration"]
+            validate_string(multi_shot["prompt"], min_length=1, max_length=2500)
+
+        if model["model"] == "kling-3.0-turbo":
+            turbo_prompt = build_turbo_shot_prompt(multi_prompt_list) if custom_multi_shot else multi_shot["prompt"]
+            return await execute_kling_turbo(
+                cls,
+                prompt=turbo_prompt,
+                resolution=model["resolution"],
+                aspect_ratio=model["aspect_ratio"],
+                duration=duration,
+                start_frame=start_frame,
+            )
+
+        if start_frame is not None:
+            validate_image_dimensions(start_frame, min_width=300, min_height=300)
+            validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1))
+            image_url = await upload_image_to_comfyapi(cls, start_frame, wait_label="Uploading start frame")
+            response = await sync_op(
+                cls,
+                ApiEndpoint(path="/proxy/kling/v1/videos/image2video", method="POST"),
+                response_model=TaskStatusResponse,
+                data=ImageToVideoWithAudioRequest(
+                    model_name=model["model"],
+                    image=image_url,
+                    prompt=None if custom_multi_shot else multi_shot["prompt"],
+                    negative_prompt=None if custom_multi_shot else multi_shot["negative_prompt"],
+                    mode=mode,
+                    duration=str(duration),
+                    sound="on" if generate_audio else "off",
+                    multi_shot=True if shot_type else None,
+                    multi_prompt=multi_prompt_list,
+                    shot_type=shot_type,
+                ),
+            )
+            poll_path = f"/proxy/kling/v1/videos/image2video/{response.data.task_id}"
+        else:
+            response = await sync_op(
+                cls,
+                ApiEndpoint(path="/proxy/kling/v1/videos/text2video", method="POST"),
+                response_model=TaskStatusResponse,
+                data=TextToVideoWithAudioRequest(
+                    model_name=model["model"],
+                    aspect_ratio=model["aspect_ratio"],
+                    prompt=None if custom_multi_shot else multi_shot["prompt"],
+                    negative_prompt=None if custom_multi_shot else multi_shot["negative_prompt"],
+                    mode=mode,
+                    duration=str(duration),
+                    sound="on" if generate_audio else "off",
+                    multi_shot=True if shot_type else None,
+                    multi_prompt=multi_prompt_list,
+                    shot_type=shot_type,
+                ),
+            )
+            poll_path = f"/proxy/kling/v1/videos/text2video/{response.data.task_id}"
+
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=poll_path),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
+class KlingFirstLastFrameNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingFirstLastFrameNode",
+            display_name="Kling 3.0 First-Last-Frame to Video",
+            category="partner/video/Kling",
+            description="Generate videos with Kling V3 using first and last frames.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default=""),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=3,
+                    max=15,
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Image.Input("first_frame"),
+                IO.Image.Input("end_frame"),
+                IO.Boolean.Input("generate_audio", default=True),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "kling-v3",
+                            [
+                                IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p"),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model and generation settings.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model.resolution", "generate_audio", "duration"],
+                ),
+                expr="""
+                (
+                  $rates := {
+                    "4k": {"off": 0.42, "on": 0.42},
+                    "1080p": {"off": 0.112, "on": 0.168},
+                    "720p": {"off": 0.084, "on": 0.126}
+                  };
+                  $res := $lookup(widgets, "model.resolution");
+                  $audio := widgets.generate_audio ? "on" : "off";
+                  $rate := $lookup($lookup($rates, $res), $audio);
+                  {"type":"usd","usd": $rate * widgets.duration}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        duration: int,
+        first_frame: Input.Image,
+        end_frame: Input.Image,
+        generate_audio: bool,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        validate_string(prompt, min_length=1, max_length=2500)
+        validate_image_dimensions(first_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(first_frame, (1, 2.5), (2.5, 1))
+        validate_image_dimensions(end_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(end_frame, (1, 2.5), (2.5, 1))
+        image_url = await upload_image_to_comfyapi(cls, first_frame, wait_label="Uploading first frame")
+        image_tail_url = await upload_image_to_comfyapi(cls, end_frame, wait_label="Uploading end frame")
+        if model["resolution"] == "4k":
+            mode = "4k"
+        elif model["resolution"] == "1080p":
+            mode = "pro"
+        else:
+            mode = "std"
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/image2video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=ImageToVideoWithAudioRequest(
+                model_name=model["model"],
+                image=image_url,
+                image_tail=image_tail_url,
+                prompt=prompt,
+                mode=mode,
+                duration=str(duration),
+                sound="on" if generate_audio else "off",
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/image2video/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
+class KlingAvatarNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingAvatarNode",
+            display_name="Kling Avatar 2.0",
+            category="partner/video/Kling",
+            description="Generate broadcast-style digital human videos from a single photo and an audio file.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="Avatar reference image. "
+                    "Width and height must be at least 300px. Aspect ratio must be between 1:2.5 and 2.5:1.",
+                ),
+                IO.Audio.Input(
+                    "sound_file",
+                    tooltip="Audio input. Must be between 2 and 300 seconds in duration.",
+                ),
+                IO.Combo.Input("mode", options=["std", "pro"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    tooltip="Optional prompt to define avatar actions, emotions, and camera movements.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
+                expr="""
+                (
+                  $prices := {"std": 0.056, "pro": 0.112};
+                  {"type":"usd","usd": $lookup($prices, widgets.mode), "format":{"suffix":"/second"}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        sound_file: Input.Audio,
+        mode: str,
+        seed: int,
+        prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_image_dimensions(image, min_width=300, min_height=300)
+        validate_image_aspect_ratio(image, (1, 2.5), (2.5, 1))
+        validate_audio_duration(sound_file, min_duration=2, max_duration=300)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/avatar/image2video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=KlingAvatarRequest(
+                image=await upload_image_to_comfyapi(cls, image),
+                sound_file=await upload_audio_to_comfyapi(
+                    cls, sound_file, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg"
+                ),
+                prompt=prompt or None,
+                mode=mode,
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/avatar/image2video/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+            max_poll_attempts=800,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
+class KlingExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            KlingTextToVideoNode,
+            KlingImage2VideoNode,
+            KlingStartEndFrameNode,
+            KlingVideoExtendNode,
+            KlingLipSyncAudioToVideoNode,
+            KlingLipSyncTextToVideoNode,
+            KlingImageGenerationNode,
+            OmniProTextToVideoNode,
+            OmniProFirstLastFrameNode,
+            OmniProImageToVideoNode,
+            OmniProVideoToVideoNode,
+            OmniProEditVideoNode,
+            OmniProImageNode,
+            TextToVideoWithAudio,
+            ImageToVideoWithAudio,
+            MotionControl,
+            KlingVideoNode,
+            KlingFirstLastFrameNode,
+            KlingAvatarNode,
+        ]
+
+
+async def comfy_entrypoint() -> KlingExtension:
+    return KlingExtension()
diff --git a/comfy_api_nodes/nodes_krea.py b/comfy_api_nodes/nodes_krea.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc22e9b3944b076fbae23bb5c9e25b0a98afcd7b
--- /dev/null
+++ b/comfy_api_nodes/nodes_krea.py
@@ -0,0 +1,294 @@
+"""Krea image-generation nodes."""
+
+import re
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.krea import (
+    KreaAssetResponse,
+    KreaGenerateImageRequest,
+    KreaImageStyleReference,
+    KreaJob,
+    KreaMoodboard,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_image_tensor,
+    poll_op,
+    sync_op,
+    tensor_to_bytesio,
+    validate_string,
+)
+
+
+class KreaIO:
+    STYLE_REF = "KREA_STYLE_REF"
+
+
+async def _upload_image_to_krea_assets(cls: type[IO.ComfyNode], image: Input.Image) -> str:
+    """Upload an image to Krea's /assets endpoint and return the Krea-hosted image URL."""
+    img_io = tensor_to_bytesio(image, total_pixels=2048 * 2048, mime_type="image/png")
+    response = await sync_op(
+        cls,
+        endpoint=ApiEndpoint(path="/proxy/krea/assets", method="POST"),
+        response_model=KreaAssetResponse,
+        files=[("file", (img_io.name, img_io, "image/png"))],
+        content_type="multipart/form-data",
+        max_retries=1,
+        wait_label="Uploading reference",
+    )
+    return response.image_url
+
+
+_MODEL_MEDIUM = "Krea 2 Medium"
+_MODEL_MEDIUM_TURBO = "Krea 2 Medium Turbo"
+_MODEL_LARGE = "Krea 2 Large"
+_MODEL_ENDPOINTS: dict[str, str] = {
+    _MODEL_MEDIUM: "/proxy/krea/generate/image/krea/krea-2/medium",
+    _MODEL_MEDIUM_TURBO: "/proxy/krea/generate/image/krea/krea-2/medium-turbo",
+    _MODEL_LARGE: "/proxy/krea/generate/image/krea/krea-2/large",
+}
+
+_ASPECT_RATIOS = ["1:1", "4:3", "3:2", "16:9", "2.35:1", "4:5", "2:3", "9:16"]
+_RESOLUTIONS = ["1K"]
+_CREATIVITY_LEVELS = ["raw", "low", "medium", "high"]
+_KREA_QUEUED_STATUSES = ["backlogged", "queued", "scheduled"]
+
+_UUID_RE = re.compile(r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$")
+
+
+def _krea_model_inputs() -> list:
+    """Nested inputs shared by Krea 2 Medium, Medium Turbo and Large under the DynamicCombo."""
+    return [
+        IO.Combo.Input(
+            "aspect_ratio",
+            options=_ASPECT_RATIOS,
+            tooltip="Output aspect ratio.",
+        ),
+        IO.Combo.Input(
+            "resolution",
+            options=_RESOLUTIONS,
+            tooltip="Resolution scale.",
+        ),
+        IO.Combo.Input(
+            "creativity",
+            options=_CREATIVITY_LEVELS,
+            default="medium",
+            tooltip="Prompt interpretation strength: raw stays closest to the prompt; high is most creative.",
+        ),
+        IO.String.Input(
+            "moodboard_id",
+            default="",
+            tooltip="Optional Krea moodboard UUID (e.g. from the Krea website). "
+            "Leave empty to disable. Only one moodboard is supported per request.",
+            optional=True,
+        ),
+        IO.Float.Input(
+            "moodboard_strength",
+            default=0.35,
+            min=-0.5,
+            max=1.5,
+            step=0.05,
+            tooltip="Moodboard influence; ignored when moodboard_id is empty.",
+            optional=True,
+        ),
+        IO.Custom(KreaIO.STYLE_REF).Input(
+            "style_reference",
+            optional=True,
+            tooltip="Optional chain of style references (max 10) from Krea 2 Style Reference nodes.",
+        ),
+    ]
+
+
+class Krea2ImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Krea2ImageNode",
+            display_name="Krea 2 Image",
+            category="partner/image/Krea",
+            description=(
+                "Generate images via Krea 2 — pick Medium (expressive illustrations) or "
+                "Large (expressive photorealism). Supports an optional moodboard and up "
+                "to 10 chained image style references."
+            ),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the image.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(_MODEL_MEDIUM, _krea_model_inputs()),
+                        IO.DynamicCombo.Option(_MODEL_MEDIUM_TURBO, _krea_model_inputs()),
+                        IO.DynamicCombo.Option(_MODEL_LARGE, _krea_model_inputs()),
+                    ],
+                    tooltip="Krea 2 Medium is best for expressive illustrations; "
+                    "Krea 2 Large is best for expressive photorealism.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Random seed for reproducibility.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "model.moodboard_id"],
+                    inputs=["model.style_reference"],
+                ),
+                expr="""
+                (
+                  $rates := {
+                    "krea 2 medium turbo": {"text": 0.015, "style": 0.0175, "moodboard": 0.02},
+                    "krea 2 medium": {"text": 0.03, "style": 0.035, "moodboard": 0.04},
+                    "krea 2 large": {"text": 0.06, "style": 0.065, "moodboard": 0.07}
+                  };
+                  $r := $lookup($rates, widgets.model);
+                  $hasMoodboard := $length($lookup(widgets, "model.moodboard_id")) > 0;
+                  $hasStyle := $lookup(inputs, "model.style_reference").connected;
+                  $usd := $hasMoodboard ? $r.moodboard : ($hasStyle ? $r.style : $r.text);
+                  {"type":"usd","usd": $usd}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1)
+
+        model_choice = model["model"]
+        endpoint_path = _MODEL_ENDPOINTS.get(model_choice)
+        if endpoint_path is None:
+            raise ValueError(f"Unknown Krea 2 model: {model_choice!r}")
+
+        moodboards: list[KreaMoodboard] | None = None
+        mb_id = (model.get("moodboard_id") or "").strip()
+        if mb_id:
+            if not _UUID_RE.match(mb_id):
+                raise ValueError(f"moodboard_id must be a UUID (received {mb_id!r}); copy it from the Krea website.")
+            mb_strength = model.get("moodboard_strength")
+            moodboards = [KreaMoodboard(id=mb_id, strength=0.35 if mb_strength is None else float(mb_strength))]
+
+        style_reference = model.get("style_reference")
+        image_style_references: list[KreaImageStyleReference] | None = None
+        if style_reference:
+            if len(style_reference) > 10:
+                raise ValueError(f"Krea 2 accepts at most 10 image_style_references; received {len(style_reference)}.")
+            image_style_references = [
+                KreaImageStyleReference(url=ref["url"], strength=float(ref["strength"])) for ref in style_reference
+            ]
+        initial = await sync_op(
+            cls,
+            ApiEndpoint(path=endpoint_path, method="POST"),
+            response_model=KreaJob,
+            data=KreaGenerateImageRequest(
+                prompt=prompt,
+                aspect_ratio=model["aspect_ratio"],
+                resolution=model["resolution"],
+                seed=seed,
+                creativity=model["creativity"],
+                moodboards=moodboards,
+                image_style_references=image_style_references,
+            ),
+        )
+        job = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/krea/jobs/{initial.job_id}", method="GET"),
+            response_model=KreaJob,
+            status_extractor=lambda r: r.status,
+            queued_statuses=_KREA_QUEUED_STATUSES,
+        )
+        if not job.result or not job.result.urls:
+            raise RuntimeError(f"Krea 2 job {job.job_id} completed without any image URLs.")
+        image = await download_url_to_image_tensor(job.result.urls[0])
+        return IO.NodeOutput(image)
+
+
+class Krea2StyleReferenceNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Krea2StyleReferenceNode",
+            display_name="Krea 2 Style Reference",
+            category="partner/image/Krea",
+            description=(
+                "Add an image style reference to a Krea 2 generation. Chain multiple Krea 2 "
+                "Style Reference nodes (max 10) and feed the final `style_reference` output "
+                "into Krea 2 Image. Each image is uploaded to ComfyAPI storage and passed as URL."
+            ),
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="Reference image whose style influences the generation.",
+                ),
+                IO.Float.Input(
+                    "strength",
+                    default=1.0,
+                    min=-2.0,
+                    max=2.0,
+                    step=0.05,
+                    tooltip="Reference strength; negative values invert the style influence.",
+                ),
+                IO.Custom(KreaIO.STYLE_REF).Input(
+                    "style_reference",
+                    optional=True,
+                    tooltip="Optional incoming chain of style references; this node appends one more.",
+                ),
+            ],
+            outputs=[IO.Custom(KreaIO.STYLE_REF).Output(display_name="style_reference")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        strength: float,
+        style_reference: list[dict] | None = None,
+    ) -> IO.NodeOutput:
+        chain: list[dict] = list(style_reference) if style_reference else []
+        if len(chain) >= 10:
+            raise ValueError("Krea 2 accepts at most 10 image_style_references in one generation.")
+        url = await _upload_image_to_krea_assets(cls, image)
+        chain.append({"url": url, "strength": float(strength)})
+        return IO.NodeOutput(chain)
+
+
+class KreaExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            Krea2ImageNode,
+            Krea2StyleReferenceNode,
+        ]
+
+
+async def comfy_entrypoint() -> KreaExtension:
+    return KreaExtension()
diff --git a/comfy_api_nodes/nodes_ltxv.py b/comfy_api_nodes/nodes_ltxv.py
new file mode 100644
index 0000000000000000000000000000000000000000..36a0ef7e24b72d0bf2e61818d1ed52477178781a
--- /dev/null
+++ b/comfy_api_nodes/nodes_ltxv.py
@@ -0,0 +1,596 @@
+from io import BytesIO
+
+from pydantic import BaseModel, Field
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    sync_op_raw,
+    upload_audio_to_comfyapi,
+    upload_images_to_comfyapi,
+    validate_string,
+)
+
+MODELS_MAP = {
+    "LTX-2 (Pro)": "ltx-2-pro",
+    "LTX-2 (Fast)": "ltx-2-fast",
+}
+
+V25_MODELS_MAP = {
+    "LTX-2.5 (Fast)": "ltx-2-5-fast",
+    "LTX-2.5 (Pro)": "ltx-2-5-pro",
+}
+
+
+class ExecuteTaskRequest(BaseModel):
+    prompt: str = Field(...)
+    model: str = Field(...)
+    duration: int = Field(...)
+    resolution: str = Field(...)
+    fps: int | None = Field(25)
+    generate_audio: bool | None = Field(True)
+    image_uri: str | None = Field(None)
+    last_frame_uri: str | None = Field(None)
+
+
+class AudioToVideoRequest(BaseModel):
+    prompt: str = Field(...)
+    model: str = Field(...)
+    resolution: str = Field(...)
+    audio_uri: str = Field(...)
+    image_uri: str | None = Field(None)
+
+
+class Ltx25SubmitResponse(BaseModel):
+    id: str = Field(...)
+
+
+class Ltx25JobResult(BaseModel):
+    video_url: str | None = Field(None)
+
+
+class Ltx25JobStatusResponse(BaseModel):
+    id: str = Field(...)
+    status: str = Field(...)
+    result: Ltx25JobResult | None = Field(None)
+
+
+async def _v25_submit_and_poll(cls: type[IO.ComfyNode], route: str, data: BaseModel) -> IO.NodeOutput:
+    submit = await sync_op(
+        cls,
+        ApiEndpoint(f"/proxy/ltx/v2/{route}", "POST"),
+        response_model=Ltx25SubmitResponse,
+        data=data,
+        max_retries=1,
+    )
+    job = await poll_op(
+        cls,
+        ApiEndpoint(f"/proxy/ltx/v2/{route}/{submit.id}"),
+        response_model=Ltx25JobStatusResponse,
+        status_extractor=lambda r: r.status,
+    )
+    if not job.result or not job.result.video_url:
+        raise RuntimeError(f"LTX job {job.id} completed without a video URL.")
+    return IO.NodeOutput(await download_url_to_video_output(job.result.video_url, cls=cls))
+
+
+PRICE_BADGE = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"]),
+    expr="""
+    (
+      $prices := {
+        "ltx-2 (pro)": {"1920x1080":0.06,"2560x1440":0.12,"3840x2160":0.24},
+        "ltx-2 (fast)": {"1920x1080":0.04,"2560x1440":0.08,"3840x2160":0.16}
+      };
+      $modelPrices := $lookup($prices, $lowercase(widgets.model));
+      $pps := $lookup($modelPrices, widgets.resolution);
+      {"type":"usd","usd": $pps * widgets.duration}
+    )
+    """,
+)
+
+V25_PRICE_BADGE = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
+    expr="""
+    (
+      $prices := {
+        "ltx-2.5 (fast)": {
+          "1280x720":0.1287,"720x1280":0.1287,
+          "1920x1080":0.1859,"1080x1920":0.1859,
+          "2560x1440":0.2717,"1440x2560":0.2717,
+          "3840x2160":0.429,"2160x3840":0.429
+        },
+        "ltx-2.5 (pro)": {
+          "1280x720":0.1716,"720x1280":0.1716,
+          "1920x1080":0.2431,"1080x1920":0.2431
+        }
+      };
+      $model := $lookup(widgets, "model");
+      $table := $type($model) = "string" ? $lookup($prices, $model) : undefined;
+      $res := $lookup(widgets, "model.resolution");
+      $pps := $type($table) = "object" and $type($res) = "string" ? $lookup($table, $res) : undefined;
+      $durRaw := $lookup(widgets, "model.duration");
+      $dur := $type($durRaw) in ["string", "number"] ? $number($durRaw) : undefined;
+      $type($pps) = "number" and $type($dur) = "number"
+        ? {"type":"usd","usd": $pps * $dur}
+        : undefined
+    )
+    """,
+)
+
+V25_A2V_PRICE_BADGE = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+    expr="""
+    (
+      $rates := {"ltx-2.5 (fast)":0.1859, "ltx-2.5 (pro)":0.2431};
+      $model := $lookup(widgets, "model");
+      $rate := $type($model) = "string" ? $lookup($rates, $model) : undefined;
+      $type($rate) = "number"
+        ? {"type":"usd","usd": $rate, "format":{"suffix":"/second"}}
+        : undefined
+    )
+    """,
+)
+
+
+def _v25_generation_inputs(
+    durations: list[str], resolutions: list[str], fps_options: list[str], tooltip: str | None
+) -> list:
+    return [
+        IO.Combo.Input(
+            "duration",
+            options=durations,
+            default="8",
+            tooltip=tooltip,
+        ),
+        IO.Combo.Input(
+            "resolution",
+            options=resolutions,
+            default="1920x1080",
+        ),
+        IO.Combo.Input("fps", options=fps_options, default="25"),
+        IO.Boolean.Input(
+            "generate_audio",
+            default=True,
+            tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+            advanced=True,
+        ),
+    ]
+
+
+def _v25_model_combo() -> IO.DynamicCombo.Input:
+    return IO.DynamicCombo.Input(
+        "model",
+        options=[
+            IO.DynamicCombo.Option(
+                "LTX-2.5 (Fast)",
+                _v25_generation_inputs(
+                    ["2", "3", "4", "5", "6", "8", "10", "12", "14", "16", "18", "20"],
+                    [
+                        "1280x720",
+                        "720x1280",
+                        "1920x1080",
+                        "1080x1920",
+                        "2560x1440",
+                        "1440x2560",
+                        "3840x2160",
+                        "2160x3840",
+                    ],
+                    ["24", "25", "48", "50"],
+                    "Video duration in seconds. Durations over 10s require a 720p/1080p resolution and 24/25 FPS.",
+                ),
+            ),
+            IO.DynamicCombo.Option(
+                "LTX-2.5 (Pro)",
+                _v25_generation_inputs(
+                    ["2", "3", "4", "5", "6", "8", "10"],
+                    ["1280x720", "720x1280", "1920x1080", "1080x1920"],
+                    ["24", "25", "50"],
+                    "Video duration in seconds.",
+                ),
+            ),
+        ],
+    )
+
+
+def _v25_seed_input() -> IO.Int.Input:
+    return IO.Int.Input(
+        "seed",
+        default=42,
+        min=0,
+        max=0xFFFFFFFF,
+        control_after_generate=True,
+        tooltip="Seed to determine if node should re-run; "
+        "actual results are nondeterministic regardless of seed.",
+    )
+
+
+def _v25_validate_settings(model: dict) -> None:
+    if int(model["duration"]) > 10 and (
+        int(model["fps"]) > 25 or model["resolution"] in ("2560x1440", "1440x2560", "3840x2160", "2160x3840")
+    ):
+        raise ValueError("Durations over 10s require a 720p or 1080p resolution and 24/25 FPS.")
+
+
+class TextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LtxvApiTextToVideo",
+            display_name="LTXV Text To Video",
+            category="partner/video/LTXV",
+            description="Professional-quality videos with customizable duration and resolution.",
+            inputs=[
+                IO.Combo.Input("model", options=list(MODELS_MAP.keys())),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                ),
+                IO.Combo.Input("duration", options=[6, 8, 10, 12, 14, 16, 18, 20], default=8),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        "1920x1080",
+                        "2560x1440",
+                        "3840x2160",
+                    ],
+                ),
+                IO.Combo.Input("fps", options=[25, 50], default=25),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_deprecated=True,
+            price_badge=PRICE_BADGE,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        duration: int,
+        resolution: str,
+        fps: int = 25,
+        generate_audio: bool = False,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=10000)
+        if duration > 10 and (model != "LTX-2 (Fast)" or resolution != "1920x1080" or fps != 25):
+            raise ValueError(
+                "Durations over 10s are only available for the Fast model at 1920x1080 resolution and 25 FPS."
+            )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint("/proxy/ltx/v1/text-to-video", "POST"),
+            data=ExecuteTaskRequest(
+                prompt=prompt,
+                model=MODELS_MAP[model],
+                duration=duration,
+                resolution=resolution,
+                fps=fps,
+                generate_audio=generate_audio,
+            ),
+            as_binary=True,
+            max_retries=1,
+        )
+        return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(response)))
+
+
+class ImageToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LtxvApiImageToVideo",
+            display_name="LTXV Image To Video",
+            category="partner/video/LTXV",
+            description="Professional-quality videos with customizable duration and resolution based on start image.",
+            inputs=[
+                IO.Image.Input("image", tooltip="First frame to be used for the video."),
+                IO.Combo.Input("model", options=list(MODELS_MAP.keys())),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                ),
+                IO.Combo.Input("duration", options=[6, 8, 10, 12, 14, 16, 18, 20], default=8),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        "1920x1080",
+                        "2560x1440",
+                        "3840x2160",
+                    ],
+                ),
+                IO.Combo.Input("fps", options=[25, 50], default=25),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_deprecated=True,
+            price_badge=PRICE_BADGE,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        model: str,
+        prompt: str,
+        duration: int,
+        resolution: str,
+        fps: int = 25,
+        generate_audio: bool = False,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=10000)
+        if duration > 10 and (model != "LTX-2 (Fast)" or resolution != "1920x1080" or fps != 25):
+            raise ValueError(
+                "Durations over 10s are only available for the Fast model at 1920x1080 resolution and 25 FPS."
+            )
+        if get_number_of_images(image) != 1:
+            raise ValueError("Currently only one input image is supported.")
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint("/proxy/ltx/v1/image-to-video", "POST"),
+            data=ExecuteTaskRequest(
+                image_uri=(await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png"))[0],
+                prompt=prompt,
+                model=MODELS_MAP[model],
+                duration=duration,
+                resolution=resolution,
+                fps=fps,
+                generate_audio=generate_audio,
+            ),
+            as_binary=True,
+            max_retries=1,
+        )
+        return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(response)))
+
+
+class Ltx25TextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LtxApi25TextToVideo",
+            display_name="LTX 2.5 Text To Video",
+            category="partner/video/LTXV",
+            description="Professional-quality videos with customizable duration and resolution.",
+            inputs=[
+                _v25_model_combo(),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                ),
+                _v25_seed_input(),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=V25_PRICE_BADGE,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        prompt: str,
+        seed: int = 42,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=10000)
+        _v25_validate_settings(model)
+        return await _v25_submit_and_poll(
+            cls,
+            "text-to-video",
+            ExecuteTaskRequest(
+                prompt=prompt,
+                model=V25_MODELS_MAP[model["model"]],
+                duration=int(model["duration"]),
+                resolution=model["resolution"],
+                fps=int(model["fps"]),
+                generate_audio=model["generate_audio"],
+            ),
+        )
+
+
+class Ltx25ImageToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LtxApi25ImageToVideo",
+            display_name="LTX 2.5 Image To Video",
+            category="partner/video/LTXV",
+            description="Professional-quality videos with customizable duration and resolution based on start image.",
+            inputs=[
+                IO.Image.Input("image", tooltip="First frame to be used for the video."),
+                _v25_model_combo(),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                ),
+                _v25_seed_input(),
+                IO.Image.Input(
+                    "last_frame",
+                    optional=True,
+                    tooltip="Last frame to be used for the video.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=V25_PRICE_BADGE,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        model: dict,
+        prompt: str,
+        seed: int = 42,
+        last_frame: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=10000)
+        _v25_validate_settings(model)
+        if get_number_of_images(image) != 1:
+            raise ValueError("Currently only one input image is supported.")
+        last_frame_uri = None
+        if last_frame is not None:
+            if get_number_of_images(last_frame) != 1:
+                raise ValueError("Currently only one last frame image is supported.")
+            last_frame_uri = (await upload_images_to_comfyapi(cls, last_frame, max_images=1, mime_type="image/png"))[0]
+        return await _v25_submit_and_poll(
+            cls,
+            "image-to-video",
+            ExecuteTaskRequest(
+                image_uri=(await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png"))[0],
+                last_frame_uri=last_frame_uri,
+                prompt=prompt,
+                model=V25_MODELS_MAP[model["model"]],
+                duration=int(model["duration"]),
+                resolution=model["resolution"],
+                fps=int(model["fps"]),
+                generate_audio=model["generate_audio"],
+            ),
+        )
+
+
+class Ltx25AudioToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LtxApi25AudioToVideo",
+            display_name="LTX 2.5 Audio To Video",
+            category="partner/video/LTXV",
+            description="Generate a video driven by an audio track, with an optional first frame image.",
+            inputs=[
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Audio track driving the video. Its length (2-20 seconds) sets the video duration.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "LTX-2.5 (Fast)",
+                            [IO.Combo.Input("resolution", options=["1920x1080", "1080x1920"])],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "LTX-2.5 (Pro)",
+                            [IO.Combo.Input("resolution", options=["1920x1080", "1080x1920"])],
+                        ),
+                    ],
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                ),
+                _v25_seed_input(),
+                IO.Image.Input(
+                    "image",
+                    optional=True,
+                    tooltip="Optional first frame to be used for the video.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=V25_A2V_PRICE_BADGE,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        audio: Input.Audio,
+        model: dict,
+        prompt: str,
+        seed: int = 42,
+        image: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=10000)
+        audio_duration = audio["waveform"].shape[-1] / audio["sample_rate"]
+        if not 2 <= audio_duration <= 20:
+            raise ValueError(f"Audio duration must be between 2 and 20 seconds, got {audio_duration:.1f}s.")
+        image_uri = None
+        if image is not None:
+            if get_number_of_images(image) != 1:
+                raise ValueError("Currently only one input image is supported.")
+            image_uri = (await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png"))[0]
+        return await _v25_submit_and_poll(
+            cls,
+            "audio-to-video",
+            AudioToVideoRequest(
+                prompt=prompt,
+                model=V25_MODELS_MAP[model["model"]],
+                resolution=model["resolution"],
+                audio_uri=await upload_audio_to_comfyapi(cls, audio),
+                image_uri=image_uri,
+            ),
+        )
+
+
+class LtxvApiExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TextToVideoNode,
+            ImageToVideoNode,
+            Ltx25TextToVideoNode,
+            Ltx25ImageToVideoNode,
+            Ltx25AudioToVideoNode,
+        ]
+
+
+async def comfy_entrypoint() -> LtxvApiExtension:
+    return LtxvApiExtension()
diff --git a/comfy_api_nodes/nodes_luma.py b/comfy_api_nodes/nodes_luma.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f466008eaa19e8ec5a7db17d7b93ca45e0bba26
--- /dev/null
+++ b/comfy_api_nodes/nodes_luma.py
@@ -0,0 +1,1495 @@
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.luma import (
+    LUMA_KEYFRAME_MODE_FRACTION,
+    LUMA_KEYFRAME_MODE_SECONDS,
+    Luma2Generation,
+    Luma2GenerationRequest,
+    Luma2ImageRef,
+    Luma2VideoEdit,
+    Luma2VideoOptions,
+    LumaAspectRatio,
+    LumaCharacterRef,
+    LumaConceptChain,
+    LumaGeneration,
+    LumaGenerationRequest,
+    LumaImageGenerationRequest,
+    LumaImageIdentity,
+    LumaImageModel,
+    LumaImageReference,
+    LumaIO,
+    LumaKeyframes,
+    LumaModifyImageRef,
+    LumaRay32KeyframeChain,
+    LumaRay32KeyframeItem,
+    LumaReference,
+    LumaReferenceChain,
+    LumaVideoModel,
+    LumaVideoModelOutputDuration,
+    LumaVideoOutputResolution,
+    get_luma_concepts,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    poll_op,
+    sync_op,
+    upload_image_to_comfyapi,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+)
+
+LUMA_T2V_AVERAGE_DURATION = 105
+LUMA_I2V_AVERAGE_DURATION = 100
+
+
+class LumaReferenceNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaReferenceNode",
+            display_name="Luma Reference",
+            category="partner/image/Luma",
+            description="Holds an image and weight for use with Luma Generate Image node.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="Image to use as reference.",
+                ),
+                IO.Float.Input(
+                    "weight",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Weight of image reference.",
+                ),
+                IO.Custom(LumaIO.LUMA_REF).Input(
+                    "luma_ref",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Custom(LumaIO.LUMA_REF).Output(display_name="luma_ref")],
+        )
+
+    @classmethod
+    def execute(cls, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None) -> IO.NodeOutput:
+        if luma_ref is not None:
+            luma_ref = luma_ref.clone()
+        else:
+            luma_ref = LumaReferenceChain()
+        luma_ref.add(LumaReference(image=image, weight=round(weight, 2)))
+        return IO.NodeOutput(luma_ref)
+
+
+class LumaConceptsNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaConceptsNode",
+            display_name="Luma Concepts",
+            category="partner/video/Luma",
+            description="Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.",
+            inputs=[
+                IO.Combo.Input(
+                    "concept1",
+                    options=get_luma_concepts(include_none=True),
+                ),
+                IO.Combo.Input(
+                    "concept2",
+                    options=get_luma_concepts(include_none=True),
+                ),
+                IO.Combo.Input(
+                    "concept3",
+                    options=get_luma_concepts(include_none=True),
+                ),
+                IO.Combo.Input(
+                    "concept4",
+                    options=get_luma_concepts(include_none=True),
+                ),
+                IO.Custom(LumaIO.LUMA_CONCEPTS).Input(
+                    "luma_concepts",
+                    tooltip="Optional Camera Concepts to add to the ones chosen here.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Custom(LumaIO.LUMA_CONCEPTS).Output(display_name="luma_concepts")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        concept1: str,
+        concept2: str,
+        concept3: str,
+        concept4: str,
+        luma_concepts: LumaConceptChain = None,
+    ) -> IO.NodeOutput:
+        chain = LumaConceptChain(str_list=[concept1, concept2, concept3, concept4])
+        if luma_concepts is not None:
+            chain = luma_concepts.clone_and_merge(chain)
+        return IO.NodeOutput(chain)
+
+
+class LumaImageGenerationNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaImageNode",
+            display_name="Luma Text to Image",
+            category="partner/image/Luma",
+            description="Generates images synchronously based on prompt and aspect ratio.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=LumaImageModel,
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=LumaAspectRatio,
+                    default=LumaAspectRatio.ratio_16_9,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Float.Input(
+                    "style_image_weight",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Weight of style image. Ignored if no style_image provided.",
+                ),
+                IO.Custom(LumaIO.LUMA_REF).Input(
+                    "image_luma_ref",
+                    tooltip="Luma Reference node connection to influence generation with input images; up to 4 images can be considered.",
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "style_image",
+                    tooltip="Style reference image; only 1 image will be used.",
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "character_image",
+                    tooltip="Character reference images; can be a batch of multiple, up to 4 images can be considered.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $contains($m,"photon-flash-1")
+                    ? {"type":"usd","usd":0.0027}
+                    : $contains($m,"photon-1")
+                      ? {"type":"usd","usd":0.0104}
+                      : {"type":"usd","usd":0.0246}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: str,
+        aspect_ratio: str,
+        seed,
+        style_image_weight: float,
+        image_luma_ref: LumaReferenceChain | None = None,
+        style_image: torch.Tensor | None = None,
+        character_image: torch.Tensor | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=3)
+        # handle image_luma_ref
+        api_image_ref = None
+        if image_luma_ref is not None:
+            api_image_ref = await cls._convert_luma_refs(image_luma_ref, max_refs=4)
+        # handle style_luma_ref
+        api_style_ref = None
+        if style_image is not None:
+            api_style_ref = await cls._convert_style_image(style_image, weight=style_image_weight)
+        # handle character_ref images
+        character_ref = None
+        if character_image is not None:
+            download_urls = await upload_images_to_comfyapi(cls, character_image, max_images=4)
+            character_ref = LumaCharacterRef(identity0=LumaImageIdentity(images=download_urls))
+
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/luma/generations/image", method="POST"),
+            response_model=LumaGeneration,
+            data=LumaImageGenerationRequest(
+                prompt=prompt,
+                model=model,
+                aspect_ratio=aspect_ratio,
+                image_ref=api_image_ref,
+                style_ref=api_style_ref,
+                character_ref=character_ref,
+            ),
+        )
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
+            response_model=LumaGeneration,
+            status_extractor=lambda x: x.state,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response_poll.assets.image))
+
+    @classmethod
+    async def _convert_luma_refs(cls, luma_ref: LumaReferenceChain, max_refs: int):
+        luma_urls = []
+        ref_count = 0
+        for ref in luma_ref.refs:
+            download_urls = await upload_images_to_comfyapi(cls, ref.image, max_images=1)
+            luma_urls.append(download_urls[0])
+            ref_count += 1
+            if ref_count >= max_refs:
+                break
+        return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs)
+
+    @classmethod
+    async def _convert_style_image(cls, style_image: torch.Tensor, weight: float):
+        chain = LumaReferenceChain(first_ref=LumaReference(image=style_image, weight=weight))
+        return await cls._convert_luma_refs(chain, max_refs=1)
+
+
+class LumaImageModifyNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaImageModifyNode",
+            display_name="Luma Image to Image",
+            category="partner/image/Luma",
+            description="Modifies images synchronously based on prompt and aspect ratio.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation",
+                ),
+                IO.Float.Input(
+                    "image_weight",
+                    default=0.1,
+                    min=0.0,
+                    max=0.98,
+                    step=0.01,
+                    tooltip="Weight of the image; the closer to 1.0, the less the image will be modified.",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=LumaImageModel,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $contains($m,"photon-flash-1")
+                    ? {"type":"usd","usd":0.0027}
+                    : $contains($m,"photon-1")
+                      ? {"type":"usd","usd":0.0104}
+                      : {"type":"usd","usd":0.0246}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: str,
+        image: torch.Tensor,
+        image_weight: float,
+        seed,
+    ) -> IO.NodeOutput:
+        download_urls = await upload_images_to_comfyapi(cls, image, max_images=1)
+        image_url = download_urls[0]
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/luma/generations/image", method="POST"),
+            response_model=LumaGeneration,
+            data=LumaImageGenerationRequest(
+                prompt=prompt,
+                model=model,
+                modify_image_ref=LumaModifyImageRef(
+                    url=image_url, weight=round(max(min(1.0 - image_weight, 0.98), 0.0), 2)
+                ),
+            ),
+        )
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
+            response_model=LumaGeneration,
+            status_extractor=lambda x: x.state,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response_poll.assets.image))
+
+
+class LumaTextToVideoGenerationNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaVideoNode",
+            display_name="Luma Text to Video",
+            category="partner/video/Luma",
+            description="Generates videos synchronously based on prompt and output_size.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=LumaVideoModel,
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=LumaAspectRatio,
+                    default=LumaAspectRatio.ratio_16_9,
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=LumaVideoOutputResolution,
+                    default=LumaVideoOutputResolution.res_540p,
+                ),
+                IO.Combo.Input(
+                    "duration",
+                    options=LumaVideoModelOutputDuration,
+                ),
+                IO.Boolean.Input(
+                    "loop",
+                    default=False,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(LumaIO.LUMA_CONCEPTS).Input(
+                    "luma_concepts",
+                    tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: str,
+        aspect_ratio: str,
+        resolution: str,
+        duration: str,
+        loop: bool,
+        seed,
+        luma_concepts: LumaConceptChain | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=3)
+        duration = duration if model != LumaVideoModel.ray_1_6 else None
+        resolution = resolution if model != LumaVideoModel.ray_1_6 else None
+
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/luma/generations", method="POST"),
+            response_model=LumaGeneration,
+            data=LumaGenerationRequest(
+                prompt=prompt,
+                model=model,
+                resolution=resolution,
+                aspect_ratio=aspect_ratio,
+                duration=duration,
+                loop=loop,
+                concepts=luma_concepts.create_api_model() if luma_concepts else None,
+            ),
+        )
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
+            response_model=LumaGeneration,
+            status_extractor=lambda x: x.state,
+            estimated_duration=LUMA_T2V_AVERAGE_DURATION,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response_poll.assets.video))
+
+
+class LumaImageToVideoGenerationNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaImageToVideoNode",
+            display_name="Luma Image to Video",
+            category="partner/video/Luma",
+            description="Generates videos synchronously based on prompt, input images, and output_size.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=LumaVideoModel,
+                ),
+                # IO.Combo.Input(
+                #     "aspect_ratio",
+                #     options=[ratio.value for ratio in LumaAspectRatio],
+                #     default=LumaAspectRatio.ratio_16_9,
+                # ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=LumaVideoOutputResolution,
+                    default=LumaVideoOutputResolution.res_540p,
+                ),
+                IO.Combo.Input(
+                    "duration",
+                    options=[dur.value for dur in LumaVideoModelOutputDuration],
+                ),
+                IO.Boolean.Input(
+                    "loop",
+                    default=False,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Image.Input(
+                    "first_image",
+                    tooltip="First frame of generated video.",
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "last_image",
+                    tooltip="Last frame of generated video.",
+                    optional=True,
+                ),
+                IO.Custom(LumaIO.LUMA_CONCEPTS).Input(
+                    "luma_concepts",
+                    tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: str,
+        resolution: str,
+        duration: str,
+        loop: bool,
+        seed,
+        first_image: torch.Tensor = None,
+        last_image: torch.Tensor = None,
+        luma_concepts: LumaConceptChain = None,
+    ) -> IO.NodeOutput:
+        if first_image is None and last_image is None:
+            raise Exception("At least one of first_image and last_image requires an input.")
+        keyframes = await cls._convert_to_keyframes(first_image, last_image)
+        duration = duration if model != LumaVideoModel.ray_1_6 else None
+        resolution = resolution if model != LumaVideoModel.ray_1_6 else None
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/luma/generations", method="POST"),
+            response_model=LumaGeneration,
+            data=LumaGenerationRequest(
+                prompt=prompt,
+                model=model,
+                aspect_ratio=LumaAspectRatio.ratio_16_9,  # ignored, but still needed by the API for some reason
+                resolution=resolution,
+                duration=duration,
+                loop=loop,
+                keyframes=keyframes,
+                concepts=luma_concepts.create_api_model() if luma_concepts else None,
+            ),
+        )
+        response_poll = await poll_op(
+            cls,
+            poll_endpoint=ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
+            response_model=LumaGeneration,
+            status_extractor=lambda x: x.state,
+            estimated_duration=LUMA_I2V_AVERAGE_DURATION,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response_poll.assets.video))
+
+    @classmethod
+    async def _convert_to_keyframes(
+        cls,
+        first_image: torch.Tensor = None,
+        last_image: torch.Tensor = None,
+    ):
+        if first_image is None and last_image is None:
+            return None
+        frame0 = None
+        frame1 = None
+        if first_image is not None:
+            download_urls = await upload_images_to_comfyapi(cls, first_image, max_images=1)
+            frame0 = LumaImageReference(type="image", url=download_urls[0])
+        if last_image is not None:
+            download_urls = await upload_images_to_comfyapi(cls, last_image, max_images=1)
+            frame1 = LumaImageReference(type="image", url=download_urls[0])
+        return LumaKeyframes(frame0=frame0, frame1=frame1)
+
+
+PRICE_BADGE_VIDEO = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["model", "resolution", "duration"]),
+    expr="""
+    (
+      $p := {
+        "ray-flash-2": {
+          "5s": {"4k":3.13,"1080p":0.79,"720p":0.34,"540p":0.2},
+          "9s": {"4k":5.65,"1080p":1.42,"720p":0.61,"540p":0.36}
+        },
+        "ray-2": {
+          "5s": {"4k":9.11,"1080p":2.27,"720p":1.02,"540p":0.57},
+          "9s": {"4k":16.4,"1080p":4.1,"720p":1.83,"540p":1.03}
+        }
+      };
+
+      $m := widgets.model;
+      $d := widgets.duration;
+      $r := widgets.resolution;
+
+      $modelKey :=
+        $contains($m,"ray-flash-2") ? "ray-flash-2" :
+        $contains($m,"ray-2") ? "ray-2" :
+        $contains($m,"ray-1-6") ? "ray-1-6" :
+        "other";
+
+      $durKey := $contains($d,"5s") ? "5s" : $contains($d,"9s") ? "9s" : "";
+      $resKey :=
+        $contains($r,"4k") ? "4k" :
+        $contains($r,"1080p") ? "1080p" :
+        $contains($r,"720p") ? "720p" :
+        $contains($r,"540p") ? "540p" : "";
+
+      $modelPrices := $lookup($p, $modelKey);
+      $durPrices := $lookup($modelPrices, $durKey);
+      $v := $lookup($durPrices, $resKey);
+
+      $price :=
+        ($modelKey = "ray-1-6") ? 0.5 :
+        ($modelKey = "other") ? 0.79 :
+        ($exists($v) ? $v : 0.79);
+
+      {"type":"usd","usd": $price}
+    )
+    """,
+)
+
+
+def _luma2_uni1_common_inputs(max_image_refs: int) -> list:
+    return [
+        IO.Combo.Input(
+            "style",
+            options=["auto", "manga"],
+            default="auto",
+            tooltip="Style preset. 'auto' picks based on the prompt; "
+            "'manga' applies a manga/anime aesthetic and requires a portrait "
+            "aspect ratio (2:3, 9:16, 1:2, 1:3).",
+        ),
+        IO.Boolean.Input(
+            "web_search",
+            default=False,
+            tooltip="Search the web for visual references before generating.",
+        ),
+        IO.Autogrow.Input(
+            "image_ref",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, max_image_refs + 1)],
+                min=0,
+            ),
+            optional=True,
+            tooltip=f"Up to {max_image_refs} reference images for style/content guidance.",
+        ),
+    ]
+
+
+async def _luma2_upload_image_refs(
+    cls: type[IO.ComfyNode],
+    refs: dict | None,
+    max_count: int,
+) -> list[Luma2ImageRef] | None:
+    if not refs:
+        return None
+    out: list[Luma2ImageRef] = []
+    for key in refs:
+        url = await upload_image_to_comfyapi(cls, refs[key])
+        out.append(Luma2ImageRef(url=url))
+    if len(out) > max_count:
+        raise ValueError(f"Maximum {max_count} reference images are allowed.")
+    return out or None
+
+
+async def _luma2_submit_and_poll(
+    cls: type[IO.ComfyNode],
+    request: Luma2GenerationRequest,
+    *,
+    estimated_duration: int | None = None,
+) -> Luma2Generation:
+    """Submit a Luma Agents generation and poll until done; returns the completed generation."""
+    initial = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/luma_2/generations", method="POST"),
+        response_model=Luma2Generation,
+        data=request,
+    )
+    if not initial.id:
+        raise RuntimeError("Luma API did not return a generation id.")
+    final = await poll_op(
+        cls,
+        ApiEndpoint(path=f"/proxy/luma_2/generations/{initial.id}", method="GET"),
+        response_model=Luma2Generation,
+        status_extractor=lambda r: r.state,
+        progress_extractor=lambda r: None,
+        estimated_duration=estimated_duration,
+    )
+    if not final.output or not final.output[0].url:
+        msg = final.failure_reason or "no output returned"
+        if final.failure_code:
+            msg = f"{msg} [{final.failure_code}]"
+        raise RuntimeError(f"Luma generation failed: {msg}")
+    return final
+
+
+class LumaImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaImageNode2",
+            display_name="Luma UNI-1 Image",
+            category="partner/image/Luma",
+            description="Generate images from text using the Luma UNI-1 model.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the desired image. 1–6000 characters.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "uni-1",
+                            [
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=[
+                                        "auto",
+                                        "3:1",
+                                        "2:1",
+                                        "16:9",
+                                        "3:2",
+                                        "1:1",
+                                        "2:3",
+                                        "9:16",
+                                        "1:2",
+                                        "1:3",
+                                    ],
+                                    default="auto",
+                                    tooltip="Output image aspect ratio. 'auto' lets "
+                                    "the model pick based on the prompt.",
+                                ),
+                                *_luma2_uni1_common_inputs(max_image_refs=9),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "uni-1-max",
+                            [
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=[
+                                        "auto",
+                                        "3:1",
+                                        "2:1",
+                                        "16:9",
+                                        "3:2",
+                                        "1:1",
+                                        "2:3",
+                                        "9:16",
+                                        "1:2",
+                                        "1:3",
+                                    ],
+                                    default="auto",
+                                    tooltip="Output image aspect ratio. 'auto' lets "
+                                    "the model pick based on the prompt.",
+                                ),
+                                *_luma2_uni1_common_inputs(max_image_refs=9),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for generation.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"], input_groups=["model.image_ref"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $refs := $lookup(inputGroups, "model.image_ref");
+                  $base := $m = "uni-1-max" ? 0.1 : 0.0404;
+                  {"type":"usd","usd": $round($base + 0.003 * $refs, 4)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=6000)
+        aspect_ratio = model["aspect_ratio"]
+        style = model["style"]
+        allowed_manga_ratios = {"2:3", "9:16", "1:2", "1:3"}
+        if style == "manga" and aspect_ratio != "auto" and aspect_ratio not in allowed_manga_ratios:
+            raise ValueError(
+                f"'manga' style requires a portrait aspect ratio "
+                f"({', '.join(sorted(allowed_manga_ratios))}) or 'auto'; got '{aspect_ratio}'."
+            )
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model=model["model"],
+            type="image",
+            aspect_ratio=aspect_ratio if aspect_ratio != "auto" else None,
+            style=style if style != "auto" else None,
+            output_format="png",
+            web_search=model["web_search"],
+            image_ref=await _luma2_upload_image_refs(cls, model.get("image_ref"), max_count=9),
+        )
+        final = await _luma2_submit_and_poll(cls, request)
+        return IO.NodeOutput(await download_url_to_image_tensor(final.output[0].url))
+
+
+class LumaImageEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaImageEditNode2",
+            display_name="Luma UNI-1 Image Edit",
+            category="partner/image/Luma",
+            description="Edit an existing image with a text prompt using the Luma UNI-1 model.",
+            inputs=[
+                IO.Image.Input(
+                    "source",
+                    tooltip="Source image to edit.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Description of the desired edit. 1–6000 characters.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "uni-1",
+                            _luma2_uni1_common_inputs(max_image_refs=8),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "uni-1-max",
+                            _luma2_uni1_common_inputs(max_image_refs=8),
+                        ),
+                    ],
+                    tooltip="Model to use for editing.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"], input_groups=["model.image_ref"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $refs := $lookup(inputGroups, "model.image_ref");
+                  $base := $m = "uni-1-max" ? 0.103 : 0.0434;
+                  {"type":"usd","usd": $round($base + 0.003 * $refs, 4)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        source: Input.Image,
+        prompt: str,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=6000)
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model=model["model"],
+            type="image_edit",
+            source=Luma2ImageRef(url=await upload_image_to_comfyapi(cls, source)),
+            style=model["style"] if model["style"] != "auto" else None,
+            output_format="png",
+            web_search=model["web_search"],
+            image_ref=await _luma2_upload_image_refs(cls, model.get("image_ref"), max_count=8),
+        )
+        final = await _luma2_submit_and_poll(cls, request)
+        return IO.NodeOutput(await download_url_to_image_tensor(final.output[0].url))
+
+
+_BADGE_RAY32_VIDEO = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution", "duration"]),
+    expr="""
+    (
+      $p := {
+        "360p": {"5s": 0.06, "10s": 0.18},
+        "540p": {"5s": 0.15, "10s": 0.45},
+        "720p": {"5s": 0.3, "10s": 0.9},
+        "1080p": {"5s": 1.2, "10s": 3.6}
+      };
+      {"type": "usd", "usd": $lookup($lookup($p, widgets.resolution), widgets.duration)}
+    )
+    """,
+)
+
+_BADGE_RAY32_VIDEO_5S = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
+    expr="""
+    (
+      $p := {"360p": 0.06, "540p": 0.15, "720p": 0.3, "1080p": 1.2};
+      {"type": "usd", "usd": $lookup($p, widgets.resolution)}
+    )
+    """,
+)
+
+_BADGE_RAY32_EDIT = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
+    expr="""
+    (
+      $p := {
+        "360p": {"min": 0.54, "max": 1.08},
+        "540p": {"min": 0.72, "max": 1.44},
+        "720p": {"min": 1.08, "max": 2.16},
+        "1080p": {"min": 2.16, "max": 4.32}
+      };
+      $r := $lookup($p, widgets.resolution);
+      {"type": "range_usd", "min_usd": $r.min, "max_usd": $r.max, "format": {"note": "(by source length)"}}
+    )
+    """,
+)
+
+_BADGE_RAY32_REFRAME = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
+    expr="""
+    (
+      $p := {"360p": 0.03, "540p": 0.06, "720p": 0.12, "1080p": 0.36};
+      {"type": "usd", "usd": $lookup($p, widgets.resolution), "format": {"suffix": "/second"}}
+    )
+    """,
+)
+
+
+def _ray32_seed_input() -> IO.Input:
+    return IO.Int.Input(
+        "seed",
+        default=0,
+        min=0,
+        max=0xFFFFFFFFFFFFFFFF,
+        control_after_generate=True,
+        tooltip="Seed to determine if node should re-run; results are nondeterministic regardless of seed.",
+    )
+
+
+async def _ray32_generate(cls: type[IO.ComfyNode], request: Luma2GenerationRequest) -> IO.NodeOutput:
+    """Run a ray-3.2 generation and return (video, generation_id)."""
+    final = await _luma2_submit_and_poll(cls, request, estimated_duration=120)
+    video = await download_url_to_video_output(final.output[0].url)
+    return IO.NodeOutput(video, final.id or "")
+
+
+class LumaRay32TextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32TextToVideoNode",
+            display_name="Luma Ray 3.2 Text to Video",
+            category="partner/video/Luma",
+            description="Generate a video from a text prompt using Luma's Ray 3.2 model.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1", "4:3", "3:4", "21:9"]),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Combo.Input("duration", options=["5s", "10s"]),
+                IO.Boolean.Input(
+                    "loop",
+                    default=False,
+                    tooltip="Make the video loop seamlessly. Only available with 5s duration.",
+                ),
+                _ray32_seed_input(),
+            ],
+            outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls, prompt: str, aspect_ratio: str, resolution: str, duration: str, loop: bool, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        if loop and duration == "10s":
+            raise ValueError("Looping is only available with 5s duration on Ray 3.2.")
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video",
+            aspect_ratio=aspect_ratio,
+            video=Luma2VideoOptions(resolution=resolution, duration=duration, loop=loop or None),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32ImageToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32ImageToVideoNode",
+            display_name="Luma Ray 3.2 Image to Video",
+            category="partner/video/Luma",
+            description="Generate a video from a start and/or end frame using Luma's Ray 3.2 model. "
+            "Image-anchored generations are always 5 seconds.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Boolean.Input(
+                    "loop",
+                    default=False,
+                    tooltip="Make the video loop seamlessly. Not available when an end_frame is set.",
+                ),
+                _ray32_seed_input(),
+                IO.Image.Input("start_frame", optional=True, tooltip="First frame of the generated video."),
+                IO.Image.Input("end_frame", optional=True, tooltip="Last frame of the generated video."),
+            ],
+            outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO_5S,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        resolution: str,
+        loop: bool,
+        seed: int,
+        start_frame: torch.Tensor | None = None,
+        end_frame: torch.Tensor | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        if start_frame is None and end_frame is None:
+            raise ValueError("Provide at least one of start_frame / end_frame.")
+        if loop and end_frame is not None:
+            raise ValueError("Looping is not available when an end_frame is set.")
+        video = Luma2VideoOptions(resolution=resolution, duration="5s", loop=loop or None)
+        if start_frame is not None:
+            url = await upload_image_to_comfyapi(cls, start_frame, mime_type="image/png")
+            video.start_frame = Luma2ImageRef(url=url)
+        if end_frame is not None:
+            url = await upload_image_to_comfyapi(cls, end_frame, mime_type="image/png")
+            video.end_frame = Luma2ImageRef(url=url)
+        request = Luma2GenerationRequest(prompt=prompt, model="ray-3.2", type="video", video=video)
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32KeyframeNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32KeyframeNode",
+            display_name="Luma Ray 3.2 Keyframe",
+            category="partner/video/Luma",
+            description="Anchor a guide image to a position on the Ray 3.2 output video timeline. Connect this to "
+            "the 'keyframes' input of the Luma Ray 3.2 Keyframes to Video node; chain several together via the "
+            "optional 'keyframes' input below.",
+            inputs=[
+                IO.Image.Input("image", tooltip="Guide image to place at the chosen moment of the output video."),
+                IO.DynamicCombo.Input(
+                    "position",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Fraction of duration (0.0-1.0)",
+                            [
+                                IO.Float.Input(
+                                    "fraction",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Where in the output video this image applies " "(0.0 = start, 1.0 = end).",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Absolute time (seconds)",
+                            [
+                                IO.Float.Input(
+                                    "seconds",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=10.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Time in seconds from the start of the output video where this "
+                                    "image applies.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="How to place this image on the output video's timeline.",
+                ),
+                IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Input(
+                    "keyframes",
+                    optional=True,
+                    tooltip="Optional earlier keyframes to chain with this one.",
+                ),
+            ],
+            outputs=[IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Output(display_name="keyframes")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image: torch.Tensor,
+        position: dict,
+        keyframes: LumaRay32KeyframeChain | None = None,
+    ) -> IO.NodeOutput:
+        chain = keyframes.clone() if keyframes is not None else LumaRay32KeyframeChain()
+        if position["position"] == "Absolute time (seconds)":
+            mode, value = LUMA_KEYFRAME_MODE_SECONDS, float(position["seconds"])
+        else:
+            mode, value = LUMA_KEYFRAME_MODE_FRACTION, float(position["fraction"])
+        chain.add(LumaRay32KeyframeItem(image=image, mode=mode, value=value))
+        return IO.NodeOutput(chain)
+
+
+class LumaRay32KeyframesToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32KeyframesToVideoNode",
+            display_name="Luma Ray 3.2 Keyframes to Video",
+            category="partner/video/Luma",
+            description="Generate a video that interpolates through a sequence of guide images, each anchored to a "
+            "position on the timeline, using Luma Ray 3.2. Build the sequence with Luma Ray 3.2 Keyframe nodes "
+            "(at least 2).",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Combo.Input("duration", options=["5s", "10s"]),
+                _ray32_seed_input(),
+                IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Input(
+                    "keyframes",
+                    tooltip="Keyframe sequence from Luma Ray 3.2 Keyframe nodes (at least 2).",
+                ),
+            ],
+            outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        resolution: str,
+        duration: str,
+        seed: int,
+        keyframes: LumaRay32KeyframeChain | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        items = keyframes.items if keyframes is not None else []
+        if len(items) < 2:
+            raise ValueError(
+                "Connect at least 2 Luma Ray 3.2 Keyframe nodes "
+                "(use Luma Ray 3.2 Image to Video for a single start/end frame)."
+            )
+        if len(items) > 64:
+            raise ValueError(f"Ray 3.2 supports at most 64 keyframes; got {len(items)}.")
+        maxframe = 120 if duration == "5s" else 240
+        duration_seconds = maxframe / 24  # 5.0 or 10.0
+        # Resolve each keyframe to an output-frame index, then order by position
+        # (so the user can chain keyframes in any order — the position is what places them)
+        placed: list[tuple[int, torch.Tensor]] = []
+        for item in items:
+            if item.mode == LUMA_KEYFRAME_MODE_SECONDS:
+                if item.value > duration_seconds:
+                    raise ValueError(
+                        f"Keyframe position {item.value:g}s is past the end of the {duration} video; "
+                        f"use 0-{duration_seconds:g}s (or switch the keyframe to fraction mode)."
+                    )
+                idx = round(item.value * 24)
+            else:
+                idx = round(item.value * maxframe)
+            placed.append((max(0, min(maxframe, idx)), item.image))
+        placed.sort(key=lambda p: p[0])
+        indexes = [idx for idx, _ in placed]
+        for a, b in zip(indexes, indexes[1:]):
+            if a == b:
+                raise ValueError(
+                    f"Two keyframes resolve to the same output frame ({a}) for a {duration} video "
+                    f"(valid range 0-{maxframe}); give each keyframe a distinct position."
+                )
+        refs: list[Luma2ImageRef] = []
+        for _, image in placed:
+            url = await upload_image_to_comfyapi(cls, image, mime_type="image/png")
+            refs.append(Luma2ImageRef(url=url))
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video",
+            video=Luma2VideoOptions(resolution=resolution, duration=duration, keyframes=refs, keyframe_indexes=indexes),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32VideoEditNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32VideoEditNode",
+            display_name="Luma Ray 3.2 Video Edit",
+            category="partner/video/Luma",
+            description="Re-render an existing video under a new prompt using Luma Ray 3.2 (restyle, relight, add "
+            "or remove elements) while keeping the original motion. Source video up to 18 seconds; the edited "
+            "video keeps the source's length.",
+            inputs=[
+                IO.Video.Input("video", tooltip="Source video to edit. Up to 18 seconds."),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Describes the desired edit."),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                IO.Combo.Input(
+                    "strength",
+                    options=[
+                        "auto",
+                        "adhere_1",
+                        "adhere_2",
+                        "adhere_3",
+                        "flex_1",
+                        "flex_2",
+                        "flex_3",
+                        "reimagine_1",
+                        "reimagine_2",
+                        "reimagine_3",
+                    ],
+                    default="auto",
+                    tooltip="How strongly to preserve vs. reimagine the source. 'auto' lets Ray 3.2 choose; "
+                    "adhere_* preserves the most, flex_* is balanced, reimagine_* changes the most.",
+                ),
+                _ray32_seed_input(),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="generation_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_EDIT,
+        )
+
+    @classmethod
+    async def execute(
+        cls, video: Input.Video, prompt: str, resolution: str, strength: str, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
+        try:
+            duration = "5s" if video.get_duration() <= 5.0 else "10s"
+        except Exception:
+            duration = "10s"
+        source_url = await upload_video_to_comfyapi(cls, video, max_duration=18)
+        edit = Luma2VideoEdit(auto_controls=True) if strength == "auto" else Luma2VideoEdit(strength=strength)
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video_edit",
+            source=Luma2ImageRef(url=source_url, media_type="video/mp4"),
+            video=Luma2VideoOptions(resolution=resolution, duration=duration, edit=edit),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32VideoReframeNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32VideoReframeNode",
+            display_name="Luma Ray 3.2 Video Reframe",
+            category="partner/video/Luma",
+            description="Change the aspect ratio of an existing video, using Luma Ray 3.2 to fill the newly "
+            "exposed canvas areas. Source video up to 30 seconds. Billed per second of output.",
+            inputs=[
+                IO.Video.Input("video", tooltip="Source video to reframe. Up to 30 seconds."),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Describes how the newly exposed canvas areas should be filled.",
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1", "4:3", "3:4", "21:9"]),
+                IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
+                _ray32_seed_input(),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="generation_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_REFRAME,
+        )
+
+    @classmethod
+    async def execute(
+        cls, video: Input.Video, prompt: str, aspect_ratio: str, resolution: str, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=6000)
+        if resolution == "1080p" and aspect_ratio in {"9:16", "3:4"}:
+            raise ValueError("1080p is not available for vertical aspect ratios (9:16, 3:4) when reframing.")
+        source_url = await upload_video_to_comfyapi(cls, video, max_duration=30)
+        request = Luma2GenerationRequest(
+            prompt=prompt,
+            model="ray-3.2",
+            type="video_reframe",
+            aspect_ratio=aspect_ratio,
+            source=Luma2ImageRef(url=source_url, media_type="video/mp4"),
+            video=Luma2VideoOptions(resolution=resolution),
+        )
+        return await _ray32_generate(cls, request)
+
+
+class LumaRay32ExtendVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="LumaRay32ExtendVideoNode",
+            display_name="Luma Ray 3.2 Extend Video",
+            category="partner/video/Luma",
+            description="Extend a previous Ray 3.2 generation forward (continue after it) or backward (lead-in "
+            "before it). Connect the generation_id output of a prior Luma Ray 3.2 node."
+            " Extensions are always 5 seconds.",
+            inputs=[
+                IO.String.Input(
+                    "source_generation_id",
+                    default="",
+                    tooltip="generation_id of the prior Ray 3.2 video to extend."
+                    " Connect the generation_id output of another Luma Ray 3.2 node.",
+                ),
+                IO.DynamicCombo.Input(
+                    "direction",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Forward (continue after)",
+                            [
+                                IO.Boolean.Input(
+                                    "loop",
+                                    default=False,
+                                    tooltip="Loop the extended video seamlessly (forward extend only).",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("Backward (lead-in before)", []),
+                    ],
+                    tooltip="Forward continues after the prior clip; backward is prepended before it.",
+                ),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the new content."),
+                IO.Combo.Input("resolution", options=["540p", "720p", "1080p"], default="720p"),
+                _ray32_seed_input(),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(display_name="generation_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=_BADGE_RAY32_VIDEO_5S,
+        )
+
+    @classmethod
+    async def execute(
+        cls, source_generation_id: str, direction: dict, prompt: str, resolution: str, seed: int
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=6000)
+        gen_id = (source_generation_id or "").strip()
+        if not gen_id:
+            raise ValueError(
+                "source_generation_id is required (connect the generation_id output of a prior Luma Ray 3.2 node)."
+            )
+        video = Luma2VideoOptions(resolution=resolution, duration="5s")
+        ref = Luma2ImageRef(generation_id=gen_id)
+        if direction["direction"] == "Forward (continue after)":
+            video.start_frame = ref
+            if direction.get("loop"):
+                video.loop = True
+        else:
+            video.end_frame = ref
+        request = Luma2GenerationRequest(prompt=prompt, model="ray-3.2", type="video", video=video)
+        return await _ray32_generate(cls, request)
+
+
+class LumaExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            LumaImageGenerationNode,
+            LumaImageModifyNode,
+            LumaTextToVideoGenerationNode,
+            LumaImageToVideoGenerationNode,
+            LumaReferenceNode,
+            LumaConceptsNode,
+            LumaImageNode,
+            LumaImageEditNode,
+            LumaRay32TextToVideoNode,
+            LumaRay32ImageToVideoNode,
+            LumaRay32KeyframeNode,
+            LumaRay32KeyframesToVideoNode,
+            LumaRay32VideoEditNode,
+            LumaRay32VideoReframeNode,
+            LumaRay32ExtendVideoNode,
+        ]
+
+
+async def comfy_entrypoint() -> LumaExtension:
+    return LumaExtension()
diff --git a/comfy_api_nodes/nodes_magnific.py b/comfy_api_nodes/nodes_magnific.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ba6324552ab897f62c5a71e6eeeaee74653f9b2
--- /dev/null
+++ b/comfy_api_nodes/nodes_magnific.py
@@ -0,0 +1,906 @@
+import math
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.magnific import (
+    ImageRelightAdvancedSettingsRequest,
+    ImageRelightRequest,
+    ImageSkinEnhancerCreativeRequest,
+    ImageSkinEnhancerFaithfulRequest,
+    ImageSkinEnhancerFlexibleRequest,
+    ImageStyleTransferRequest,
+    ImageUpscalerCreativeRequest,
+    ImageUpscalerPrecisionV2Request,
+    InputAdvancedSettings,
+    InputPortraitMode,
+    InputSkinEnhancerMode,
+    TaskResponse,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_image_tensor,
+    downscale_image_tensor,
+    get_image_dimensions,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    upload_images_to_comfyapi,
+    validate_image_aspect_ratio,
+    validate_image_dimensions,
+)
+
+class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MagnificImageUpscalerCreativeNode",
+            display_name="Magnific Image Upscale (Creative)",
+            category="partner/image/Magnific",
+            description="Prompt‑guided enhancement, stylization, and 2x/4x/8x/16x upscaling. "
+            "Maximum output: 25.3 megapixels.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.String.Input("prompt", multiline=True, default=""),
+                IO.Combo.Input("scale_factor", options=["2x", "4x", "8x", "16x"]),
+                IO.Combo.Input(
+                    "optimized_for",
+                    options=[
+                        "standard",
+                        "soft_portraits",
+                        "hard_portraits",
+                        "art_n_illustration",
+                        "videogame_assets",
+                        "nature_n_landscapes",
+                        "films_n_photography",
+                        "3d_renders",
+                        "science_fiction_n_horror",
+                    ],
+                ),
+                IO.Int.Input("creativity", min=-10, max=10, default=0, display_mode=IO.NumberDisplay.slider),
+                IO.Int.Input(
+                    "hdr",
+                    min=-10,
+                    max=10,
+                    default=0,
+                    tooltip="The level of definition and detail.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "resemblance",
+                    min=-10,
+                    max=10,
+                    default=0,
+                    tooltip="The level of resemblance to the original image.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "fractality",
+                    min=-10,
+                    max=10,
+                    default=0,
+                    tooltip="The strength of the prompt and intricacy per square pixel.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Combo.Input(
+                    "engine",
+                    options=["automatic", "magnific_illusio", "magnific_sharpy", "magnific_sparkle"],
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "auto_downscale",
+                    default=False,
+                    tooltip="Automatically downscale input image if output would exceed maximum pixel limit.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["scale_factor", "auto_downscale"]),
+                expr="""
+                (
+                  $ad := widgets.auto_downscale;
+                  $mins := $ad
+                    ? {"2x": 0.172, "4x": 0.343, "8x": 0.515, "16x": 0.515}
+                    : {"2x": 0.172, "4x": 0.343, "8x": 0.515, "16x": 0.844};
+                  $maxs := {"2x": 0.515, "4x": 0.844, "8x": 1.015, "16x": 1.187};
+                  {
+                    "type": "range_usd",
+                    "min_usd": $lookup($mins, widgets.scale_factor),
+                    "max_usd": $lookup($maxs, widgets.scale_factor),
+                    "format": { "approximate": true }
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        prompt: str,
+        scale_factor: str,
+        optimized_for: str,
+        creativity: int,
+        hdr: int,
+        resemblance: int,
+        fractality: int,
+        engine: str,
+        auto_downscale: bool,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        validate_image_aspect_ratio(image, (1, 3), (3, 1), strict=False)
+        validate_image_dimensions(image, min_height=160, min_width=160)
+
+        max_output_pixels = 25_300_000
+        height, width = get_image_dimensions(image)
+        requested_scale = int(scale_factor.rstrip("x"))
+        output_pixels = height * width * requested_scale * requested_scale
+
+        if output_pixels > max_output_pixels:
+            if auto_downscale:
+                # Find optimal scale factor that doesn't require >2x downscale.
+                # Server upscales in 2x steps, so aggressive downscaling degrades quality.
+                input_pixels = width * height
+                scale = 2
+                max_input_pixels = max_output_pixels // 4
+                for candidate in [16, 8, 4, 2]:
+                    if candidate > requested_scale:
+                        continue
+                    scale_output_pixels = input_pixels * candidate * candidate
+                    if scale_output_pixels <= max_output_pixels:
+                        scale = candidate
+                        max_input_pixels = None
+                        break
+                    downscale_ratio = math.sqrt(scale_output_pixels / max_output_pixels)
+                    if downscale_ratio <= 2.0:
+                        scale = candidate
+                        max_input_pixels = max_output_pixels // (candidate * candidate)
+                        break
+
+                if max_input_pixels is not None:
+                    image = downscale_image_tensor(image, total_pixels=max_input_pixels)
+                scale_factor = f"{scale}x"
+            else:
+                raise ValueError(
+                    f"Output size ({width * requested_scale}x{height * requested_scale} = {output_pixels:,} pixels) "
+                    f"exceeds maximum allowed size of {max_output_pixels:,} pixels. "
+                    f"Use a smaller input image or lower scale factor."
+                )
+
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/freepik/v1/ai/image-upscaler", method="POST"),
+            response_model=TaskResponse,
+            data=ImageUpscalerCreativeRequest(
+                image=(await upload_images_to_comfyapi(cls, image, max_images=1, total_pixels=None))[0],
+                scale_factor=scale_factor,
+                optimized_for=optimized_for,
+                creativity=creativity,
+                hdr=hdr,
+                resemblance=resemblance,
+                fractality=fractality,
+                engine=engine,
+                prompt=prompt if prompt else None,
+            ),
+        )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-upscaler/{initial_res.task_id}"),
+            response_model=TaskResponse,
+            status_extractor=lambda x: x.status,
+            poll_interval=10.0,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
+
+
+class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MagnificImageUpscalerPreciseV2Node",
+            display_name="Magnific Image Upscale (Precise V2)",
+            category="partner/image/Magnific",
+            description="High-fidelity upscaling with fine control over sharpness, grain, and detail. "
+            "Maximum output: 10060×10060 pixels.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Combo.Input("scale_factor", options=["2x", "4x", "8x", "16x"]),
+                IO.Combo.Input(
+                    "flavor",
+                    options=["sublime", "photo", "photo_denoiser"],
+                    tooltip="Processing style: "
+                    "sublime for general use, photo for photographs, photo_denoiser for noisy photos.",
+                ),
+                IO.Int.Input(
+                    "sharpen",
+                    min=0,
+                    max=100,
+                    default=7,
+                    tooltip="Image sharpness intensity. Higher values increase edge definition and clarity.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "smart_grain",
+                    min=0,
+                    max=100,
+                    default=7,
+                    tooltip="Intelligent grain/texture enhancement to prevent the image from "
+                    "looking too smooth or artificial.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "ultra_detail",
+                    min=0,
+                    max=100,
+                    default=30,
+                    tooltip="Controls fine detail, textures, and micro-details added during upscaling.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Boolean.Input(
+                    "auto_downscale",
+                    default=False,
+                    tooltip="Automatically downscale input image if output would exceed maximum resolution.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["scale_factor"]),
+                expr="""
+                (
+                  $mins := {"2x": 0.172, "4x": 0.343, "8x": 0.515, "16x": 0.844};
+                  $maxs := {"2x": 2.045, "4x": 2.545, "8x": 2.889, "16x": 3.06};
+                  {
+                    "type": "range_usd",
+                    "min_usd": $lookup($mins, widgets.scale_factor),
+                    "max_usd": $lookup($maxs, widgets.scale_factor),
+                    "format": { "approximate": true }
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        scale_factor: str,
+        flavor: str,
+        sharpen: int,
+        smart_grain: int,
+        ultra_detail: int,
+        auto_downscale: bool,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        validate_image_aspect_ratio(image, (1, 3), (3, 1), strict=False)
+        validate_image_dimensions(image, min_height=160, min_width=160)
+
+        max_output_dimension = 10060
+        height, width = get_image_dimensions(image)
+        requested_scale = int(scale_factor.strip("x"))
+        output_width = width * requested_scale
+        output_height = height * requested_scale
+
+        if output_width > max_output_dimension or output_height > max_output_dimension:
+            if auto_downscale:
+                # Find optimal scale factor that doesn't require >2x downscale.
+                # Server upscales in 2x steps, so aggressive downscaling degrades quality.
+                max_dim = max(width, height)
+                scale = 2
+                max_input_dim = max_output_dimension // 2
+                scale_ratio = max_input_dim / max_dim
+                max_input_pixels = int(width * height * scale_ratio * scale_ratio)
+                for candidate in [16, 8, 4, 2]:
+                    if candidate > requested_scale:
+                        continue
+                    output_dim = max_dim * candidate
+                    if output_dim <= max_output_dimension:
+                        scale = candidate
+                        max_input_pixels = None
+                        break
+                    downscale_ratio = output_dim / max_output_dimension
+                    if downscale_ratio <= 2.0:
+                        scale = candidate
+                        max_input_dim = max_output_dimension // candidate
+                        scale_ratio = max_input_dim / max_dim
+                        max_input_pixels = int(width * height * scale_ratio * scale_ratio)
+                        break
+
+                if max_input_pixels is not None:
+                    image = downscale_image_tensor(image, total_pixels=max_input_pixels)
+                requested_scale = scale
+            else:
+                raise ValueError(
+                    f"Output dimensions ({output_width}x{output_height}) exceed maximum allowed "
+                    f"resolution of {max_output_dimension}x{max_output_dimension} pixels. "
+                    f"Use a smaller input image or lower scale factor."
+                )
+
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/freepik/v1/ai/image-upscaler-precision-v2", method="POST"),
+            response_model=TaskResponse,
+            data=ImageUpscalerPrecisionV2Request(
+                image=(await upload_images_to_comfyapi(cls, image, max_images=1, total_pixels=None))[0],
+                scale_factor=requested_scale,
+                flavor=flavor,
+                sharpen=sharpen,
+                smart_grain=smart_grain,
+                ultra_detail=ultra_detail,
+            ),
+        )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-upscaler-precision-v2/{initial_res.task_id}"),
+            response_model=TaskResponse,
+            status_extractor=lambda x: x.status,
+            poll_interval=10.0,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
+
+
+class MagnificImageStyleTransferNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MagnificImageStyleTransferNode",
+            display_name="Magnific Image Style Transfer",
+            category="partner/image/Magnific",
+            description="Transfer the style from a reference image to your input image.",
+            inputs=[
+                IO.Image.Input("image", tooltip="The image to apply style transfer to."),
+                IO.Image.Input("reference_image", tooltip="The reference image to extract style from."),
+                IO.String.Input("prompt", multiline=True, default=""),
+                IO.Int.Input(
+                    "style_strength",
+                    min=0,
+                    max=100,
+                    default=100,
+                    tooltip="Percentage of style strength.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "structure_strength",
+                    min=0,
+                    max=100,
+                    default=50,
+                    tooltip="Maintains the structure of the original image.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Combo.Input(
+                    "flavor",
+                    options=["faithful", "gen_z", "psychedelia", "detaily", "clear", "donotstyle", "donotstyle_sharp"],
+                    tooltip="Style transfer flavor.",
+                ),
+                IO.Combo.Input(
+                    "engine",
+                    options=[
+                        "balanced",
+                        "definio",
+                        "illusio",
+                        "3d_cartoon",
+                        "colorful_anime",
+                        "caricature",
+                        "real",
+                        "super_real",
+                        "softy",
+                    ],
+                    tooltip="Processing engine selection.",
+                    advanced=True,
+                ),
+                IO.DynamicCombo.Input(
+                    "portrait_mode",
+                    options=[
+                        IO.DynamicCombo.Option("disabled", []),
+                        IO.DynamicCombo.Option(
+                            "enabled",
+                            [
+                                IO.Combo.Input(
+                                    "portrait_style",
+                                    options=["standard", "pop", "super_pop"],
+                                    tooltip="Visual style applied to portrait images.",
+                                ),
+                                IO.Combo.Input(
+                                    "portrait_beautifier",
+                                    options=["none", "beautify_face", "beautify_face_max"],
+                                    tooltip="Facial beautification intensity on portraits.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Enable portrait mode for facial enhancements.",
+                ),
+                IO.Boolean.Input(
+                    "fixed_generation",
+                    default=True,
+                    tooltip="When disabled, expect each generation to introduce a degree of randomness, "
+                    "leading to more diverse outcomes.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.11}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        reference_image: Input.Image,
+        prompt: str,
+        style_strength: int,
+        structure_strength: int,
+        flavor: str,
+        engine: str,
+        portrait_mode: InputPortraitMode,
+        fixed_generation: bool,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        if get_number_of_images(reference_image) != 1:
+            raise ValueError("Exactly one reference image is required.")
+        validate_image_aspect_ratio(image, (1, 3), (3, 1), strict=False)
+        validate_image_aspect_ratio(reference_image, (1, 3), (3, 1), strict=False)
+        validate_image_dimensions(image, min_height=160, min_width=160)
+        validate_image_dimensions(reference_image, min_height=160, min_width=160)
+
+        is_portrait = portrait_mode["portrait_mode"] == "enabled"
+        portrait_style = portrait_mode.get("portrait_style", "standard")
+        portrait_beautifier = portrait_mode.get("portrait_beautifier", "none")
+
+        uploaded_urls = await upload_images_to_comfyapi(cls, [image, reference_image], max_images=2)
+
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/freepik/v1/ai/image-style-transfer", method="POST"),
+            response_model=TaskResponse,
+            data=ImageStyleTransferRequest(
+                image=uploaded_urls[0],
+                reference_image=uploaded_urls[1],
+                prompt=prompt if prompt else None,
+                style_strength=style_strength,
+                structure_strength=structure_strength,
+                is_portrait=is_portrait,
+                portrait_style=portrait_style if is_portrait else None,
+                portrait_beautifier=portrait_beautifier if is_portrait and portrait_beautifier != "none" else None,
+                flavor=flavor,
+                engine=engine,
+                fixed_generation=fixed_generation,
+            ),
+        )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-style-transfer/{initial_res.task_id}"),
+            response_model=TaskResponse,
+            status_extractor=lambda x: x.status,
+            poll_interval=10.0,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
+
+
+class MagnificImageRelightNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MagnificImageRelightNode",
+            display_name="Magnific Image Relight",
+            category="partner/image/Magnific",
+            description="Relight an image with lighting adjustments and optional reference-based light transfer.",
+            inputs=[
+                IO.Image.Input("image", tooltip="The image to relight."),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Descriptive guidance for lighting. Supports emphasis notation (1-1.4).",
+                ),
+                IO.Int.Input(
+                    "light_transfer_strength",
+                    min=0,
+                    max=100,
+                    default=100,
+                    tooltip="Intensity of light transfer application.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Combo.Input(
+                    "style",
+                    options=[
+                        "standard",
+                        "darker_but_realistic",
+                        "clean",
+                        "smooth",
+                        "brighter",
+                        "contrasted_n_hdr",
+                        "just_composition",
+                    ],
+                    tooltip="Stylistic output preference.",
+                ),
+                IO.Boolean.Input(
+                    "interpolate_from_original",
+                    default=False,
+                    tooltip="Restricts generation freedom to match original more closely.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "change_background",
+                    default=True,
+                    tooltip="Modifies background based on prompt/reference.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "preserve_details",
+                    default=True,
+                    tooltip="Maintains texture and fine details from original.",
+                    advanced=True,
+                ),
+                IO.DynamicCombo.Input(
+                    "advanced_settings",
+                    options=[
+                        IO.DynamicCombo.Option("disabled", []),
+                        IO.DynamicCombo.Option(
+                            "enabled",
+                            [
+                                IO.Int.Input(
+                                    "whites",
+                                    min=0,
+                                    max=100,
+                                    default=50,
+                                    tooltip="Adjusts the brightest tones in the image.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                                IO.Int.Input(
+                                    "blacks",
+                                    min=0,
+                                    max=100,
+                                    default=50,
+                                    tooltip="Adjusts the darkest tones in the image.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                                IO.Int.Input(
+                                    "brightness",
+                                    min=0,
+                                    max=100,
+                                    default=50,
+                                    tooltip="Overall brightness adjustment.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                                IO.Int.Input(
+                                    "contrast",
+                                    min=0,
+                                    max=100,
+                                    default=50,
+                                    tooltip="Contrast adjustment.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                                IO.Int.Input(
+                                    "saturation",
+                                    min=0,
+                                    max=100,
+                                    default=50,
+                                    tooltip="Color saturation adjustment.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                                IO.Combo.Input(
+                                    "engine",
+                                    options=[
+                                        "automatic",
+                                        "balanced",
+                                        "cool",
+                                        "real",
+                                        "illusio",
+                                        "fairy",
+                                        "colorful_anime",
+                                        "hard_transform",
+                                        "softy",
+                                    ],
+                                    tooltip="Processing engine selection.",
+                                ),
+                                IO.Combo.Input(
+                                    "transfer_light_a",
+                                    options=["automatic", "low", "medium", "normal", "high", "high_on_faces"],
+                                    tooltip="The intensity of light transfer.",
+                                ),
+                                IO.Combo.Input(
+                                    "transfer_light_b",
+                                    options=[
+                                        "automatic",
+                                        "composition",
+                                        "straight",
+                                        "smooth_in",
+                                        "smooth_out",
+                                        "smooth_both",
+                                        "reverse_both",
+                                        "soft_in",
+                                        "soft_out",
+                                        "soft_mid",
+                                        # "strong_mid",  # Commented out because requests fail when this is set.
+                                        "style_shift",
+                                        "strong_shift",
+                                    ],
+                                    tooltip="Also modifies light transfer intensity. "
+                                    "Can be combined with the previous control for varied effects.",
+                                ),
+                                IO.Boolean.Input(
+                                    "fixed_generation",
+                                    default=True,
+                                    tooltip="Ensures consistent output with the same settings.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Fine-tuning options for advanced lighting control.",
+                ),
+                IO.Image.Input(
+                    "reference_image",
+                    optional=True,
+                    tooltip="Optional reference image to transfer lighting from.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.11}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        prompt: str,
+        light_transfer_strength: int,
+        style: str,
+        interpolate_from_original: bool,
+        change_background: bool,
+        preserve_details: bool,
+        advanced_settings: InputAdvancedSettings,
+        reference_image: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        if reference_image is not None and get_number_of_images(reference_image) != 1:
+            raise ValueError("Exactly one reference image is required.")
+        validate_image_aspect_ratio(image, (1, 3), (3, 1), strict=False)
+        validate_image_dimensions(image, min_height=160, min_width=160)
+        if reference_image is not None:
+            validate_image_aspect_ratio(reference_image, (1, 3), (3, 1), strict=False)
+            validate_image_dimensions(reference_image, min_height=160, min_width=160)
+
+        image_url = (await upload_images_to_comfyapi(cls, image, max_images=1))[0]
+        reference_url = None
+        if reference_image is not None:
+            reference_url = (await upload_images_to_comfyapi(cls, reference_image, max_images=1))[0]
+
+        adv_settings = None
+        if advanced_settings["advanced_settings"] == "enabled":
+            adv_settings = ImageRelightAdvancedSettingsRequest(
+                whites=advanced_settings["whites"],
+                blacks=advanced_settings["blacks"],
+                brightness=advanced_settings["brightness"],
+                contrast=advanced_settings["contrast"],
+                saturation=advanced_settings["saturation"],
+                engine=advanced_settings["engine"],
+                transfer_light_a=advanced_settings["transfer_light_a"],
+                transfer_light_b=advanced_settings["transfer_light_b"],
+                fixed_generation=advanced_settings["fixed_generation"],
+            )
+
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/freepik/v1/ai/image-relight", method="POST"),
+            response_model=TaskResponse,
+            data=ImageRelightRequest(
+                image=image_url,
+                prompt=prompt if prompt else None,
+                transfer_light_from_reference_image=reference_url,
+                light_transfer_strength=light_transfer_strength,
+                interpolate_from_original=interpolate_from_original,
+                change_background=change_background,
+                style=style,
+                preserve_details=preserve_details,
+                advanced_settings=adv_settings,
+            ),
+        )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-relight/{initial_res.task_id}"),
+            response_model=TaskResponse,
+            status_extractor=lambda x: x.status,
+            poll_interval=10.0,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
+
+
+class MagnificImageSkinEnhancerNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MagnificImageSkinEnhancerNode",
+            display_name="Magnific Image Skin Enhancer",
+            category="partner/image/Magnific",
+            description="Skin enhancement for portraits with multiple processing modes.",
+            inputs=[
+                IO.Image.Input("image", tooltip="The portrait image to enhance."),
+                IO.Int.Input(
+                    "sharpen",
+                    min=0,
+                    max=100,
+                    default=0,
+                    tooltip="Sharpening intensity level.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "smart_grain",
+                    min=0,
+                    max=100,
+                    default=2,
+                    tooltip="Smart grain intensity level.",
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.DynamicCombo.Input(
+                    "mode",
+                    options=[
+                        IO.DynamicCombo.Option("creative", []),
+                        IO.DynamicCombo.Option(
+                            "faithful",
+                            [
+                                IO.Int.Input(
+                                    "skin_detail",
+                                    min=0,
+                                    max=100,
+                                    default=80,
+                                    tooltip="Skin detail enhancement level.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "flexible",
+                            [
+                                IO.Combo.Input(
+                                    "optimized_for",
+                                    options=[
+                                        "enhance_skin",
+                                        "improve_lighting",
+                                        "enhance_everything",
+                                        "transform_to_real",
+                                        "no_make_up",
+                                    ],
+                                    tooltip="Enhancement optimization target.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Processing mode: creative for artistic enhancement, "
+                    "faithful for preserving original appearance, "
+                    "flexible for targeted optimization.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
+                expr="""
+                (
+                  $rates := {"creative": 0.29, "faithful": 0.37, "flexible": 0.45};
+                  {"type":"usd","usd": $lookup($rates, widgets.mode)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        sharpen: int,
+        smart_grain: int,
+        mode: InputSkinEnhancerMode,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        validate_image_aspect_ratio(image, (1, 3), (3, 1), strict=False)
+        validate_image_dimensions(image, min_height=160, min_width=160)
+
+        image_url = (await upload_images_to_comfyapi(cls, image, max_images=1, total_pixels=4096 * 4096))[0]
+        selected_mode = mode["mode"]
+
+        if selected_mode == "creative":
+            endpoint = "creative"
+            data = ImageSkinEnhancerCreativeRequest(
+                image=image_url,
+                sharpen=sharpen,
+                smart_grain=smart_grain,
+            )
+        elif selected_mode == "faithful":
+            endpoint = "faithful"
+            data = ImageSkinEnhancerFaithfulRequest(
+                image=image_url,
+                sharpen=sharpen,
+                smart_grain=smart_grain,
+                skin_detail=mode["skin_detail"],
+            )
+        else:  # flexible
+            endpoint = "flexible"
+            data = ImageSkinEnhancerFlexibleRequest(
+                image=image_url,
+                sharpen=sharpen,
+                smart_grain=smart_grain,
+                optimized_for=mode["optimized_for"],
+            )
+
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/freepik/v1/ai/skin-enhancer/{endpoint}", method="POST"),
+            response_model=TaskResponse,
+            data=data,
+        )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/freepik/v1/ai/skin-enhancer/{initial_res.task_id}"),
+            response_model=TaskResponse,
+            status_extractor=lambda x: x.status,
+            poll_interval=10.0,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
+
+
+class MagnificExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            MagnificImageUpscalerCreativeNode,
+            MagnificImageUpscalerPreciseV2Node,
+            MagnificImageStyleTransferNode,
+            MagnificImageRelightNode,
+            MagnificImageSkinEnhancerNode,
+        ]
+
+
+async def comfy_entrypoint() -> MagnificExtension:
+    return MagnificExtension()
diff --git a/comfy_api_nodes/nodes_meshy.py b/comfy_api_nodes/nodes_meshy.py
new file mode 100644
index 0000000000000000000000000000000000000000..037130f7c91a12f8b5c95ac60d85fb39a1a5b82e
--- /dev/null
+++ b/comfy_api_nodes/nodes_meshy.py
@@ -0,0 +1,1006 @@
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.meshy import (
+    InputShouldRemesh,
+    InputShouldTexture,
+    MeshyAnimationRequest,
+    MeshyAnimationResult,
+    MeshyImageToModelRequest,
+    MeshyModelResult,
+    MeshyMultiImageToModelRequest,
+    MeshyRefineTask,
+    MeshyRiggedResult,
+    MeshyRiggingRequest,
+    MeshyTaskResponse,
+    MeshyTextToModelRequest,
+    MeshyTextureRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_file_3d,
+    poll_op,
+    sync_op,
+    upload_images_to_comfyapi,
+    validate_string,
+)
+
+
+class MeshyTextToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MeshyTextToModelNode",
+            display_name="Meshy: Text to Model",
+            category="partner/3d/Meshy",
+            inputs=[
+                IO.Combo.Input("model", options=["meshy-7", "meshy-6", "latest"]),
+                IO.String.Input("prompt", multiline=True, default=""),
+                IO.Combo.Input("style", options=["realistic"]),
+                IO.DynamicCombo.Input(
+                    "should_remesh",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Combo.Input("topology", options=["triangle", "quad"]),
+                                IO.Int.Input(
+                                    "target_polycount",
+                                    default=300000,
+                                    min=100,
+                                    max=300000,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("false", []),
+                    ],
+                    tooltip="When set to false, returns an unprocessed triangular mesh.",
+                ),
+                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"], advanced=True),
+                IO.Combo.Input(
+                    "pose_mode",
+                    options=["", "A-pose", "T-pose"],
+                    tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.Boolean.Input(
+                    "ultra_mode",
+                    default=False,
+                    tooltip="Run an extra refinement pass for higher-fidelity geometry with finer surface detail.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MESHY_TASK_ID").Output(display_name="meshy_task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "ultra_mode"]),
+                expr="""
+                (
+                  $credits := 20 + ((widgets.ultra_mode and widgets.model in ["meshy-7", "latest"]) ? 5 : 0);
+                  {"type":"usd","usd": $round($credits * 0.0572, 4)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        style: str,
+        should_remesh: InputShouldRemesh,
+        symmetry_mode: str,
+        pose_mode: str,
+        seed: int,
+        ultra_mode: bool,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, field_name="prompt", min_length=1, max_length=600)
+        if ultra_mode and model not in ("meshy-7", "latest"):
+            raise ValueError("ultra_mode requires the meshy-7 or latest model")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/meshy/openapi/v2/text-to-3d", method="POST"),
+            response_model=MeshyTaskResponse,
+            data=MeshyTextToModelRequest(
+                prompt=prompt,
+                art_style=style,
+                ai_model=model,
+                topology=should_remesh.get("topology", None),
+                target_polycount=should_remesh.get("target_polycount", None),
+                should_remesh=should_remesh["should_remesh"] == "true",
+                symmetry_mode=symmetry_mode,
+                pose_mode=pose_mode.lower(),
+                ultra_mode=ultra_mode,
+                seed=seed,
+            ),
+        )
+        task_id = response.result
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/meshy/openapi/v2/text-to-3d/{task_id}"),
+            response_model=MeshyModelResult,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+        )
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            task_id,
+            await download_url_to_file_3d(result.model_urls.glb, "glb", task_id=task_id),
+            await download_url_to_file_3d(result.model_urls.fbx, "fbx", task_id=task_id),
+        )
+
+
+class MeshyRefineNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MeshyRefineNode",
+            display_name="Meshy: Refine Draft Model",
+            category="partner/3d/Meshy",
+            description="Refine a previously created draft model.",
+            inputs=[
+                IO.Combo.Input("model", options=["meshy-7", "meshy-6", "latest"]),
+                IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),
+                IO.Boolean.Input(
+                    "enable_pbr",
+                    default=False,
+                    tooltip="Generate PBR Maps (metallic, roughness, normal) in addition to the base color.",
+                    advanced=True,
+                ),
+                IO.String.Input(
+                    "texture_prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Provide a text prompt to guide the texturing process. "
+                    "Maximum 600 characters. Cannot be used at the same time as 'texture_image'.",
+                ),
+                IO.Image.Input(
+                    "texture_image",
+                    tooltip="Only one of 'texture_image' or 'texture_prompt' may be used at the same time.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "texture_resolution",
+                    options=["2k", "4k", "8k"],
+                    tooltip="Base color texture resolution. Higher resolutions capture more surface detail.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MESHY_TASK_ID").Output(display_name="meshy_task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["texture_resolution"]),
+                expr="""
+                (
+                  $credits := widgets.texture_resolution = "8k" ? 15 : 10;
+                  {"type":"usd","usd": $round($credits * 0.0572, 4)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        meshy_task_id: str,
+        enable_pbr: bool,
+        texture_prompt: str,
+        texture_resolution: str,
+        texture_image: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        if texture_prompt and texture_image is not None:
+            raise ValueError("texture_prompt and texture_image cannot be used at the same time")
+        texture_image_url = None
+        if texture_prompt:
+            validate_string(texture_prompt, field_name="texture_prompt", max_length=600)
+        if texture_image is not None:
+            texture_image_url = (await upload_images_to_comfyapi(cls, texture_image, wait_label="Uploading texture"))[0]
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/meshy/openapi/v2/text-to-3d", method="POST"),
+            response_model=MeshyTaskResponse,
+            data=MeshyRefineTask(
+                preview_task_id=meshy_task_id,
+                enable_pbr=enable_pbr,
+                texture_resolution=texture_resolution,
+                texture_prompt=texture_prompt if texture_prompt else None,
+                texture_image_url=texture_image_url,
+                ai_model=model,
+            ),
+        )
+        task_id = response.result
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/meshy/openapi/v2/text-to-3d/{task_id}"),
+            response_model=MeshyModelResult,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+        )
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            task_id,
+            await download_url_to_file_3d(result.model_urls.glb, "glb", task_id=task_id),
+            await download_url_to_file_3d(result.model_urls.fbx, "fbx", task_id=task_id),
+        )
+
+
+class MeshyImageToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MeshyImageToModelNode",
+            display_name="Meshy: Image to Model",
+            category="partner/3d/Meshy",
+            inputs=[
+                IO.Combo.Input("model", options=["meshy-7", "meshy-6", "latest"]),
+                IO.Image.Input("image"),
+                IO.DynamicCombo.Input(
+                    "should_remesh",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Combo.Input("topology", options=["triangle", "quad"]),
+                                IO.Int.Input(
+                                    "target_polycount",
+                                    default=300000,
+                                    min=100,
+                                    max=300000,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("false", []),
+                    ],
+                    tooltip="When set to false, returns an unprocessed triangular mesh.",
+                ),
+                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"]),
+                IO.DynamicCombo.Input(
+                    "should_texture",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Boolean.Input(
+                                    "enable_pbr",
+                                    default=False,
+                                    tooltip="Generate PBR Maps (metallic, roughness, normal) "
+                                    "in addition to the base color.",
+                                ),
+                                IO.String.Input(
+                                    "texture_prompt",
+                                    default="",
+                                    multiline=True,
+                                    tooltip="Provide a text prompt to guide the texturing process. "
+                                    "Maximum 600 characters. Cannot be used at the same time as 'texture_image'.",
+                                ),
+                                IO.Image.Input(
+                                    "texture_image",
+                                    tooltip="Only one of 'texture_image' or 'texture_prompt' "
+                                    "may be used at the same time.",
+                                    optional=True,
+                                ),
+                                IO.Combo.Input(
+                                    "texture_resolution",
+                                    options=["2k", "4k", "8k"],
+                                    tooltip="Base color texture resolution. "
+                                    "Higher resolutions capture more surface detail.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("false", []),
+                    ],
+                    tooltip="Determines whether textures are generated. "
+                    "Setting it to false skips the texture phase and returns a mesh without textures.",
+                ),
+                IO.Combo.Input(
+                    "pose_mode",
+                    options=["", "A-pose", "T-pose"],
+                    tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.Boolean.Input(
+                    "ultra_mode",
+                    default=False,
+                    tooltip="Run an extra refinement pass for higher-fidelity geometry with finer surface detail.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MESHY_TASK_ID").Output(display_name="meshy_task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "should_texture", "should_texture.texture_resolution", "ultra_mode"],
+                ),
+                expr="""
+                (
+                  $textured := widgets.should_texture = "true";
+                  $resolution := $textured ? $lookup(widgets, "should_texture.texture_resolution") : "2k";
+                  $credits := ($textured ? 30 : 20)
+                    + ($resolution = "8k" ? 5 : 0)
+                    + ((widgets.ultra_mode and widgets.model in ["meshy-7", "latest"]) ? 5 : 0);
+                  {"type":"usd","usd": $round($credits * 0.0572, 4)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        should_remesh: InputShouldRemesh,
+        symmetry_mode: str,
+        should_texture: InputShouldTexture,
+        pose_mode: str,
+        seed: int,
+        ultra_mode: bool,
+    ) -> IO.NodeOutput:
+        if ultra_mode and model not in ("meshy-7", "latest"):
+            raise ValueError("ultra_mode requires the meshy-7 or latest model")
+        texture = should_texture["should_texture"] == "true"
+        texture_image_url = texture_prompt = None
+        if texture:
+            if should_texture["texture_prompt"] and should_texture["texture_image"] is not None:
+                raise ValueError("texture_prompt and texture_image cannot be used at the same time")
+            if should_texture["texture_prompt"]:
+                validate_string(should_texture["texture_prompt"], field_name="texture_prompt", max_length=600)
+                texture_prompt = should_texture["texture_prompt"]
+            if should_texture["texture_image"] is not None:
+                texture_image_url = (
+                    await upload_images_to_comfyapi(
+                        cls, should_texture["texture_image"], wait_label="Uploading texture"
+                    )
+                )[0]
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/meshy/openapi/v1/image-to-3d", method="POST"),
+            response_model=MeshyTaskResponse,
+            data=MeshyImageToModelRequest(
+                image_url=(await upload_images_to_comfyapi(cls, image, wait_label="Uploading base image"))[0],
+                ai_model=model,
+                topology=should_remesh.get("topology", None),
+                target_polycount=should_remesh.get("target_polycount", None),
+                symmetry_mode=symmetry_mode,
+                should_remesh=should_remesh["should_remesh"] == "true",
+                should_texture=texture,
+                enable_pbr=should_texture.get("enable_pbr", None),
+                texture_resolution=should_texture.get("texture_resolution", None),
+                pose_mode=pose_mode.lower(),
+                ultra_mode=ultra_mode,
+                texture_prompt=texture_prompt,
+                texture_image_url=texture_image_url,
+                seed=seed,
+            ),
+        )
+        task_id = response.result
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/meshy/openapi/v1/image-to-3d/{task_id}"),
+            response_model=MeshyModelResult,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+        )
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            task_id,
+            await download_url_to_file_3d(result.model_urls.glb, "glb", task_id=task_id),
+            await download_url_to_file_3d(result.model_urls.fbx, "fbx", task_id=task_id),
+        )
+
+
+class MeshyMultiImageToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MeshyMultiImageToModelNode",
+            display_name="Meshy: Multi-Image to Model",
+            category="partner/3d/Meshy",
+            inputs=[
+                IO.Combo.Input("model", options=["meshy-7", "meshy-6", "latest"]),
+                IO.Autogrow.Input(
+                    "images",
+                    template=IO.Autogrow.TemplatePrefix(IO.Image.Input("image"), prefix="image", min=2, max=4),
+                ),
+                IO.DynamicCombo.Input(
+                    "should_remesh",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Combo.Input("topology", options=["triangle", "quad"]),
+                                IO.Int.Input(
+                                    "target_polycount",
+                                    default=300000,
+                                    min=100,
+                                    max=300000,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("false", []),
+                    ],
+                    tooltip="When set to false, returns an unprocessed triangular mesh.",
+                ),
+                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"], advanced=True),
+                IO.DynamicCombo.Input(
+                    "should_texture",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Boolean.Input(
+                                    "enable_pbr",
+                                    default=False,
+                                    tooltip="Generate PBR Maps (metallic, roughness, normal) "
+                                    "in addition to the base color.",
+                                ),
+                                IO.String.Input(
+                                    "texture_prompt",
+                                    default="",
+                                    multiline=True,
+                                    tooltip="Provide a text prompt to guide the texturing process. "
+                                    "Maximum 600 characters. Cannot be used at the same time as 'texture_image'.",
+                                ),
+                                IO.Image.Input(
+                                    "texture_image",
+                                    tooltip="Only one of 'texture_image' or 'texture_prompt' "
+                                    "may be used at the same time.",
+                                    optional=True,
+                                ),
+                                IO.Combo.Input(
+                                    "texture_resolution",
+                                    options=["2k", "4k", "8k"],
+                                    tooltip="Base color texture resolution. "
+                                    "Higher resolutions capture more surface detail.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("false", []),
+                    ],
+                    tooltip="Determines whether textures are generated. "
+                    "Setting it to false skips the texture phase and returns a mesh without textures.",
+                ),
+                IO.Combo.Input(
+                    "pose_mode",
+                    options=["", "A-pose", "T-pose"],
+                    tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MESHY_TASK_ID").Output(display_name="meshy_task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["should_texture", "should_texture.texture_resolution"],
+                ),
+                expr="""
+                (
+                  $textured := widgets.should_texture = "true";
+                  $resolution := $textured ? $lookup(widgets, "should_texture.texture_resolution") : "2k";
+                  $credits := ($textured ? 30 : 20) + ($resolution = "8k" ? 5 : 0);
+                  {"type":"usd","usd": $round($credits * 0.0572, 4)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        images: IO.Autogrow.Type,
+        should_remesh: InputShouldRemesh,
+        symmetry_mode: str,
+        should_texture: InputShouldTexture,
+        pose_mode: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        texture = should_texture["should_texture"] == "true"
+        texture_image_url = texture_prompt = None
+        if texture:
+            if should_texture["texture_prompt"] and should_texture["texture_image"] is not None:
+                raise ValueError("texture_prompt and texture_image cannot be used at the same time")
+            if should_texture["texture_prompt"]:
+                validate_string(should_texture["texture_prompt"], field_name="texture_prompt", max_length=600)
+                texture_prompt = should_texture["texture_prompt"]
+            if should_texture["texture_image"] is not None:
+                texture_image_url = (
+                    await upload_images_to_comfyapi(
+                        cls, should_texture["texture_image"], wait_label="Uploading texture"
+                    )
+                )[0]
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/meshy/openapi/v1/multi-image-to-3d", method="POST"),
+            response_model=MeshyTaskResponse,
+            data=MeshyMultiImageToModelRequest(
+                image_urls=await upload_images_to_comfyapi(
+                    cls, list(images.values()), wait_label="Uploading base images"
+                ),
+                ai_model=model,
+                topology=should_remesh.get("topology", None),
+                target_polycount=should_remesh.get("target_polycount", None),
+                symmetry_mode=symmetry_mode,
+                should_remesh=should_remesh["should_remesh"] == "true",
+                should_texture=texture,
+                enable_pbr=should_texture.get("enable_pbr", None),
+                texture_resolution=should_texture.get("texture_resolution", None),
+                pose_mode=pose_mode.lower(),
+                texture_prompt=texture_prompt,
+                texture_image_url=texture_image_url,
+                seed=seed,
+            ),
+        )
+        task_id = response.result
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/meshy/openapi/v1/multi-image-to-3d/{task_id}"),
+            response_model=MeshyModelResult,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+        )
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            task_id,
+            await download_url_to_file_3d(result.model_urls.glb, "glb", task_id=task_id),
+            await download_url_to_file_3d(result.model_urls.fbx, "fbx", task_id=task_id),
+        )
+
+
+class MeshyRigModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MeshyRigModelNode",
+            display_name="Meshy: Rig Model",
+            category="partner/3d/Meshy",
+            description="Provides a rigged character in standard formats. "
+            "Auto-rigging is currently not suitable for untextured meshes, non-humanoid assets, "
+            "or humanoid assets with unclear limb and body structure.",
+            inputs=[
+                IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),
+                IO.Float.Input(
+                    "height_meters",
+                    min=0.1,
+                    max=15.0,
+                    default=1.7,
+                    tooltip="The approximate height of the character model in meters. "
+                    "This aids in scaling and rigging accuracy.",
+                ),
+                IO.Image.Input(
+                    "texture_image",
+                    tooltip="The model's UV-unwrapped base color texture image.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MESHY_RIGGED_TASK_ID").Output(display_name="rig_task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.286}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        meshy_task_id: str,
+        height_meters: float,
+        texture_image: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        texture_image_url = None
+        if texture_image is not None:
+            texture_image_url = (await upload_images_to_comfyapi(cls, texture_image, wait_label="Uploading texture"))[0]
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/meshy/openapi/v1/rigging", method="POST"),
+            response_model=MeshyTaskResponse,
+            data=MeshyRiggingRequest(
+                input_task_id=meshy_task_id,
+                height_meters=height_meters,
+                texture_image_url=texture_image_url,
+            ),
+        )
+        task_id = response.result
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/meshy/openapi/v1/rigging/{task_id}"),
+            response_model=MeshyRiggedResult,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+        )
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            task_id,
+            await download_url_to_file_3d(result.result.rigged_character_glb_url, "glb", task_id=task_id),
+            await download_url_to_file_3d(result.result.rigged_character_fbx_url, "fbx", task_id=task_id),
+        )
+
+
+class MeshyAnimateModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MeshyAnimateModelNode",
+            display_name="Meshy: Animate Model",
+            category="partner/3d/Meshy",
+            description="Apply a specific animation action to a previously rigged character.",
+            inputs=[
+                IO.Custom("MESHY_RIGGED_TASK_ID").Input("rig_task_id"),
+                IO.Int.Input(
+                    "action_id",
+                    default=0,
+                    min=0,
+                    max=696,
+                    tooltip="Visit https://docs.meshy.ai/en/api/animation-library for a list of available values.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.1716}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        rig_task_id: str,
+        action_id: int,
+    ) -> IO.NodeOutput:
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/meshy/openapi/v1/animations", method="POST"),
+            response_model=MeshyTaskResponse,
+            data=MeshyAnimationRequest(
+                rig_task_id=rig_task_id,
+                action_id=action_id,
+            ),
+        )
+        task_id = response.result
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/meshy/openapi/v1/animations/{task_id}"),
+            response_model=MeshyAnimationResult,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+        )
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            await download_url_to_file_3d(result.result.animation_glb_url, "glb", task_id=task_id),
+            await download_url_to_file_3d(result.result.animation_fbx_url, "fbx", task_id=task_id),
+        )
+
+
+class MeshyTextureNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MeshyTextureNode",
+            display_name="Meshy: Texture Model",
+            category="partner/3d/Meshy",
+            inputs=[
+                IO.Combo.Input("model", options=["meshy-7", "meshy-6", "latest"]),
+                IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),
+                IO.Boolean.Input(
+                    "enable_original_uv",
+                    default=True,
+                    tooltip="Use the original UV of the model instead of generating new UVs. "
+                    "When enabled, Meshy preserves existing textures from the uploaded model. "
+                    "If the model has no original UV, the quality of the output might not be as good.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input("pbr", default=False, advanced=True),
+                IO.String.Input(
+                    "text_style_prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Describe your desired texture style of the object using text. Maximum 600 characters."
+                    "Maximum 600 characters. Cannot be used at the same time as 'image_style'.",
+                ),
+                IO.Image.Input(
+                    "image_style",
+                    optional=True,
+                    tooltip="A 2d image to guide the texturing process. "
+                    "Can not be used at the same time with 'text_style_prompt'.",
+                ),
+                IO.Combo.Input(
+                    "texture_resolution",
+                    options=["2k", "4k", "8k"],
+                    tooltip="Base color texture resolution. Higher resolutions capture more surface detail.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MESHY_TASK_ID").Output(display_name="meshy_task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["texture_resolution"]),
+                expr="""
+                (
+                  $credits := widgets.texture_resolution = "8k" ? 15 : 10;
+                  {"type":"usd","usd": $round($credits * 0.0572, 4)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        meshy_task_id: str,
+        enable_original_uv: bool,
+        pbr: bool,
+        text_style_prompt: str,
+        texture_resolution: str,
+        image_style: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        if text_style_prompt and image_style is not None:
+            raise ValueError("text_style_prompt and image_style cannot be used at the same time")
+        if not text_style_prompt and image_style is None:
+            raise ValueError("Either text_style_prompt or image_style is required")
+        image_style_url = None
+        if image_style is not None:
+            image_style_url = (await upload_images_to_comfyapi(cls, image_style, wait_label="Uploading style"))[0]
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/meshy/openapi/v1/retexture", method="POST"),
+            response_model=MeshyTaskResponse,
+            data=MeshyTextureRequest(
+                input_task_id=meshy_task_id,
+                ai_model=model,
+                enable_original_uv=enable_original_uv,
+                enable_pbr=pbr,
+                texture_resolution=texture_resolution,
+                text_style_prompt=text_style_prompt if text_style_prompt else None,
+                image_style_url=image_style_url,
+            ),
+        )
+        task_id = response.result
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/meshy/openapi/v1/retexture/{task_id}"),
+            response_model=MeshyModelResult,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+        )
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            task_id,
+            await download_url_to_file_3d(result.model_urls.glb, "glb", task_id=task_id),
+            await download_url_to_file_3d(result.model_urls.fbx, "fbx", task_id=task_id),
+        )
+
+
+class MeshyTextureMultiViewNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MeshyTextureMultiViewNode",
+            display_name="Meshy: Texture Model (Multi-View)",
+            category="partner/3d/Meshy",
+            description="Texture a previously created model using 1 to 4 reference views of the same object.",
+            inputs=[
+                IO.Combo.Input("model", options=["meshy-7"]),
+                IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),
+                IO.Autogrow.Input(
+                    "multiview_images",
+                    template=IO.Autogrow.TemplatePrefix(IO.Image.Input("image"), prefix="image", min=1, max=4),
+                    tooltip="Reference views of the same object. The first image is the primary (front) view; "
+                    "the order of the remaining views does not matter.",
+                ),
+                IO.Boolean.Input(
+                    "enable_original_uv",
+                    default=True,
+                    tooltip="Use the original UV of the model instead of generating new UVs. "
+                    "When enabled, Meshy preserves existing textures from the uploaded model. "
+                    "If the model has no original UV, the quality of the output might not be as good.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input("pbr", default=False, advanced=True),
+                IO.Combo.Input(
+                    "texture_resolution",
+                    options=["2k", "4k", "8k"],
+                    tooltip="Base color texture resolution. Higher resolutions capture more surface detail.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MESHY_TASK_ID").Output(display_name="meshy_task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["texture_resolution"]),
+                expr="""
+                (
+                  $credits := widgets.texture_resolution = "8k" ? 15 : 10;
+                  {"type":"usd","usd": $round($credits * 0.0572, 4)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        meshy_task_id: str,
+        multiview_images: IO.Autogrow.Type,
+        enable_original_uv: bool,
+        pbr: bool,
+        texture_resolution: str,
+    ) -> IO.NodeOutput:
+        reference_views = list(multiview_images.values())
+        view_count = sum(v.shape[0] if len(v.shape) > 3 else 1 for v in reference_views)
+        if not 1 <= view_count <= 4:
+            raise ValueError("multiview_images must contain 1 to 4 images")
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/meshy/openapi/v1/retexture", method="POST"),
+            response_model=MeshyTaskResponse,
+            data=MeshyTextureRequest(
+                input_task_id=meshy_task_id,
+                ai_model=model,
+                enable_original_uv=enable_original_uv,
+                enable_pbr=pbr,
+                texture_resolution=texture_resolution,
+                multiview_image_urls=await upload_images_to_comfyapi(
+                    cls, reference_views, max_images=4, wait_label="Uploading reference views"
+                ),
+            ),
+        )
+        task_id = response.result
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/meshy/openapi/v1/retexture/{task_id}"),
+            response_model=MeshyModelResult,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+        )
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            task_id,
+            await download_url_to_file_3d(result.model_urls.glb, "glb", task_id=task_id),
+            await download_url_to_file_3d(result.model_urls.fbx, "fbx", task_id=task_id),
+        )
+
+
+class MeshyExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            MeshyTextToModelNode,
+            MeshyRefineNode,
+            MeshyImageToModelNode,
+            MeshyMultiImageToModelNode,
+            MeshyRigModelNode,
+            MeshyAnimateModelNode,
+            MeshyTextureNode,
+            MeshyTextureMultiViewNode,
+        ]
+
+
+async def comfy_entrypoint() -> MeshyExtension:
+    return MeshyExtension()
diff --git a/comfy_api_nodes/nodes_minimax.py b/comfy_api_nodes/nodes_minimax.py
new file mode 100644
index 0000000000000000000000000000000000000000..c64c311f014d079e0621ef9466a2455107735155
--- /dev/null
+++ b/comfy_api_nodes/nodes_minimax.py
@@ -0,0 +1,1550 @@
+from typing import Optional
+
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.minimax import (
+    Hailuo03AudioContent,
+    Hailuo03AudioContentUrl,
+    Hailuo03ContextIRRequest,
+    Hailuo03ImageContent,
+    Hailuo03ImageContentUrl,
+    Hailuo03RegenerationRequest,
+    Hailuo03TaskCreationRequest,
+    Hailuo03TaskCreationResponse,
+    Hailuo03TaskQueryResponse,
+    Hailuo03TextContent,
+    Hailuo03VideoContent,
+    Hailuo03VideoContentUrl,
+    MinimaxFileRetrieveResponse,
+    MiniMaxModel,
+    MinimaxTaskResultResponse,
+    MinimaxVideoGenerationRequest,
+    MinimaxVideoGenerationResponse,
+    SubjectReferenceItem,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    poll_op,
+    sync_op,
+    upload_audio_to_comfyapi,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_image_aspect_ratio,
+    validate_image_dimensions,
+    validate_string,
+)
+
+I2V_AVERAGE_DURATION = 114
+T2V_AVERAGE_DURATION = 234
+
+
+async def _generate_mm_video(
+    cls: type[IO.ComfyNode],
+    *,
+    prompt_text: str,
+    seed: int,
+    model: str,
+    image: Optional[torch.Tensor] = None,  # used for ImageToVideo
+    subject: Optional[torch.Tensor] = None,  # used for SubjectToVideo
+    average_duration: Optional[int] = None,
+) -> IO.NodeOutput:
+    if image is None:
+        validate_string(prompt_text, field_name="prompt_text")
+    image_url = None
+    if image is not None:
+        image_url = (await upload_images_to_comfyapi(cls, image, max_images=1))[0]
+
+    # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model
+    subject_reference = None
+    if subject is not None:
+        subject_url = (await upload_images_to_comfyapi(cls, subject, max_images=1))[0]
+        subject_reference = [SubjectReferenceItem(image=subject_url)]
+
+    response = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/minimax/video_generation", method="POST"),
+        response_model=MinimaxVideoGenerationResponse,
+        data=MinimaxVideoGenerationRequest(
+            model=MiniMaxModel(model),
+            prompt=prompt_text,
+            callback_url=None,
+            first_frame_image=image_url,
+            subject_reference=subject_reference,
+            prompt_optimizer=None,
+        ),
+    )
+
+    task_id = response.task_id
+    if not task_id:
+        raise Exception(f"MiniMax generation failed: {response.base_resp}")
+
+    task_result = await poll_op(
+        cls,
+        ApiEndpoint(path="/proxy/minimax/query/video_generation", query_params={"task_id": task_id}),
+        response_model=MinimaxTaskResultResponse,
+        status_extractor=lambda x: x.status.value,
+        estimated_duration=average_duration,
+    )
+
+    file_id = task_result.file_id
+    if file_id is None:
+        raise Exception("Request was not successful. Missing file ID.")
+    file_result = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/minimax/files/retrieve", query_params={"file_id": int(file_id)}),
+        response_model=MinimaxFileRetrieveResponse,
+    )
+
+    file_url = file_result.file.download_url
+    if file_url is None:
+        raise Exception(f"No video was found in the response. Full response: {file_result.model_dump()}")
+    if file_result.file.backup_download_url:
+        try:
+            return IO.NodeOutput(await download_url_to_video_output(file_url, timeout=10, max_retries=2))
+        except Exception:  # if we have a second URL to retrieve the result, try again using that one
+            return IO.NodeOutput(
+                await download_url_to_video_output(file_result.file.backup_download_url, max_retries=3)
+            )
+    return IO.NodeOutput(await download_url_to_video_output(file_url))
+
+
+class MinimaxTextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="MinimaxTextToVideoNode",
+            display_name="MiniMax Text to Video",
+            category="partner/video/MiniMax",
+            description="Generates videos synchronously based on a prompt, and optional parameters.",
+            inputs=[
+                IO.String.Input(
+                    "prompt_text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt to guide the video generation",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["T2V-01", "T2V-01-Director"],
+                    default="T2V-01",
+                    tooltip="Model to use for video generation",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    step=1,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.43}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt_text: str,
+        model: str = "T2V-01",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        return await _generate_mm_video(
+            cls,
+            prompt_text=prompt_text,
+            seed=seed,
+            model=model,
+            image=None,
+            subject=None,
+            average_duration=T2V_AVERAGE_DURATION,
+        )
+
+
+class MinimaxImageToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="MinimaxImageToVideoNode",
+            display_name="MiniMax Image to Video",
+            category="partner/video/MiniMax",
+            description="Generates videos synchronously based on an image and prompt, and optional parameters.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="Image to use as first frame of video generation",
+                ),
+                IO.String.Input(
+                    "prompt_text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt to guide the video generation",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["I2V-01-Director", "I2V-01", "I2V-01-live"],
+                    default="I2V-01",
+                    tooltip="Model to use for video generation",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    step=1,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.43}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        prompt_text: str,
+        model: str = "I2V-01",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        return await _generate_mm_video(
+            cls,
+            prompt_text=prompt_text,
+            seed=seed,
+            model=model,
+            image=image,
+            subject=None,
+            average_duration=I2V_AVERAGE_DURATION,
+        )
+
+
+class MinimaxSubjectToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="MinimaxSubjectToVideoNode",
+            display_name="MiniMax Subject to Video",
+            category="partner/video/MiniMax",
+            description="Generates videos synchronously based on an image and prompt, and optional parameters.",
+            inputs=[
+                IO.Image.Input(
+                    "subject",
+                    tooltip="Image of subject to reference for video generation",
+                ),
+                IO.String.Input(
+                    "prompt_text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt to guide the video generation",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["S2V-01"],
+                    default="S2V-01",
+                    tooltip="Model to use for video generation",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    step=1,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        subject: torch.Tensor,
+        prompt_text: str,
+        model: str = "S2V-01",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        return await _generate_mm_video(
+            cls,
+            prompt_text=prompt_text,
+            seed=seed,
+            model=model,
+            image=None,
+            subject=subject,
+            average_duration=T2V_AVERAGE_DURATION,
+        )
+
+
+class MinimaxHailuoVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="MinimaxHailuoVideoNode",
+            display_name="MiniMax Hailuo 02 Video",
+            category="partner/video/MiniMax",
+            description="Generates videos from prompt, with optional start frame using the MiniMax Hailuo-02 model.",
+            inputs=[
+                IO.String.Input(
+                    "prompt_text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt to guide the video generation.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    step=1,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "first_frame_image",
+                    tooltip="Optional image to use as the first frame to generate a video.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "prompt_optimizer",
+                    default=True,
+                    tooltip="Optimize prompt to improve generation quality when needed.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "duration",
+                    options=[6, 10],
+                    default=6,
+                    tooltip="The length of the output video in seconds.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["768P", "1080P"],
+                    default="768P",
+                    tooltip="The dimensions of the video display. 1080p is 1920x1080, 768p is 1366x768.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["resolution", "duration"]),
+                expr="""
+                (
+                  $prices := {
+                    "768p": {"6": 0.28, "10": 0.56},
+                    "1080p": {"6": 0.49}
+                  };
+                  $resPrices := $lookup($prices, $lowercase(widgets.resolution));
+                  $price := $lookup($resPrices, $string(widgets.duration));
+                  {"type":"usd","usd": $price ? $price : 0.43}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt_text: str,
+        seed: int = 0,
+        first_frame_image: Optional[torch.Tensor] = None,  # used for ImageToVideo
+        prompt_optimizer: bool = True,
+        duration: int = 6,
+        resolution: str = "768P",
+        model: str = "MiniMax-Hailuo-02",
+    ) -> IO.NodeOutput:
+        if first_frame_image is None:
+            validate_string(prompt_text, field_name="prompt_text")
+
+        if model == "MiniMax-Hailuo-02" and resolution.upper() == "1080P" and duration != 6:
+            raise Exception(
+                "When model is MiniMax-Hailuo-02 and resolution is 1080P, duration is limited to 6 seconds."
+            )
+
+        # upload image, if passed in
+        image_url = None
+        if first_frame_image is not None:
+            image_url = (await upload_images_to_comfyapi(cls, first_frame_image, max_images=1))[0]
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/minimax/video_generation", method="POST"),
+            response_model=MinimaxVideoGenerationResponse,
+            data=MinimaxVideoGenerationRequest(
+                model=MiniMaxModel(model),
+                prompt=prompt_text,
+                callback_url=None,
+                first_frame_image=image_url,
+                prompt_optimizer=prompt_optimizer,
+                duration=duration,
+                resolution=resolution,
+            ),
+        )
+
+        task_id = response.task_id
+        if not task_id:
+            raise Exception(f"MiniMax generation failed: {response.base_resp}")
+
+        average_duration = 120 if resolution == "768P" else 240
+        task_result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/minimax/query/video_generation", query_params={"task_id": task_id}),
+            response_model=MinimaxTaskResultResponse,
+            status_extractor=lambda x: x.status.value,
+            estimated_duration=average_duration,
+        )
+
+        file_id = task_result.file_id
+        if file_id is None:
+            raise Exception("Request was not successful. Missing file ID.")
+        file_result = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/minimax/files/retrieve", query_params={"file_id": int(file_id)}),
+            response_model=MinimaxFileRetrieveResponse,
+        )
+
+        file_url = file_result.file.download_url
+        if file_url is None:
+            raise Exception(f"No video was found in the response. Full response: {file_result.model_dump()}")
+
+        if file_result.file.backup_download_url:
+            try:
+                return IO.NodeOutput(await download_url_to_video_output(file_url, timeout=10, max_retries=2))
+            except Exception:  # if we have a second URL to retrieve the result, try again using that one
+                return IO.NodeOutput(
+                    await download_url_to_video_output(file_result.file.backup_download_url, max_retries=3)
+                )
+        return IO.NodeOutput(await download_url_to_video_output(file_url))
+
+
+HAILUO_03_CREATE_ENDPOINT = "/proxy/minimax/v2/video_generation"
+HAILUO_03_QUERY_ENDPOINT = "/proxy/minimax/v2/query/video_generation"  # + /{task_id}
+HAILUO_03_MODELS = {"MiniMax H3": "MiniMax-H3"}
+HAILUO_03_FAILED_STATUSES = ["failed", "cancelled", "expired"]
+
+HAILUO_03_CONTEXT_IR_ENDPOINT = "/proxy/minimax/v2/h3_context_ir"
+HAILUO_03_REGENERATION_ENDPOINT = "/proxy/minimax/v2/video_regeneration"
+
+
+def _hailuo03_model_inputs(include_ratio: bool = True, allow_adaptive: bool = True):
+    inputs = [
+        IO.String.Input(
+            "prompt",
+            multiline=True,
+            default="",
+            tooltip="Text prompt for video generation.",
+        ),
+        IO.Combo.Input(
+            "resolution",
+            options=["768P", "2K"],
+            tooltip="Resolution of the output video.",
+        ),
+    ]
+    if include_ratio:
+        ratio_options = ["16:9", "4:3", "1:1", "3:4", "9:16", "21:9"]
+        if allow_adaptive:
+            ratio_options.insert(0, "adaptive")
+        inputs.append(
+            IO.Combo.Input(
+                "ratio",
+                options=ratio_options,
+                default=ratio_options[0],
+                tooltip="Aspect ratio of the output video.",
+            )
+        )
+    inputs.append(
+        IO.Int.Input(
+            "duration",
+            default=5,
+            min=4,
+            max=15,
+            step=1,
+            tooltip="Duration of the output video in seconds (4-15).",
+            display_mode=IO.NumberDisplay.slider,
+        )
+    )
+    return inputs
+
+
+async def _hailuo03_run_task(
+    cls: type[IO.ComfyNode],
+    *,
+    model_id: str,
+    content: list,
+    resolution: str,
+    duration: int,
+    ratio: str | None,
+    seed: int,
+    watermark: bool,
+) -> IO.NodeOutput:
+    response = await sync_op(
+        cls,
+        ApiEndpoint(path=HAILUO_03_CREATE_ENDPOINT, method="POST"),
+        response_model=Hailuo03TaskCreationResponse,
+        data=Hailuo03TaskCreationRequest(
+            model=model_id,
+            content=content,
+            resolution=resolution,
+            duration=duration,
+            ratio=ratio,
+            seed=seed,
+            aigc_watermark=watermark,
+        ),
+    )
+    task_result = await poll_op(
+        cls,
+        ApiEndpoint(path=f"{HAILUO_03_QUERY_ENDPOINT}/{response.task_id}"),
+        response_model=Hailuo03TaskQueryResponse,
+        status_extractor=lambda r: r.task.status,
+        failed_statuses=HAILUO_03_FAILED_STATUSES,
+        poll_interval=15,
+    )
+    video_url = task_result.task.content.url if task_result.task.content else None
+    if not video_url:
+        raise Exception(f"No video URL in the response: {task_result.model_dump()}")
+    return IO.NodeOutput(await download_url_to_video_output(video_url))
+
+
+class MinimaxHailuo03TextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MinimaxHailuo03TextToVideoNode",
+            display_name="MiniMax H3 Text to Video",
+            category="partner/video/MiniMax",
+            description="Generate video from a text prompt using the MiniMax H3 model.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[IO.DynamicCombo.Option("MiniMax H3", _hailuo03_model_inputs(allow_adaptive=False))],
+                    tooltip="Model to use for video generation.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Random seed. The same request with the same seed gives similar, "
+                    "but not guaranteed identical, results.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AIGC watermark to the video.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $dur := $lookup(widgets, "model.duration");
+                  $rate := $lookup(widgets, "model.resolution") = "768p" ? 0.1287 : 0.1859;
+                  {"type": "usd", "usd": $dur * $rate}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+    ) -> IO.NodeOutput:
+        validate_string(model["prompt"], strip_whitespace=True, min_length=1)
+        return await _hailuo03_run_task(
+            cls,
+            model_id=HAILUO_03_MODELS[model["model"]],
+            content=[Hailuo03TextContent(text=model["prompt"])],
+            resolution=model["resolution"],
+            duration=model["duration"],
+            ratio=model["ratio"],
+            seed=seed,
+            watermark=watermark,
+        )
+
+
+class MinimaxHailuo03FirstLastFrameNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MinimaxHailuo03FirstLastFrameNode",
+            display_name="MiniMax H3 First-Last-Frame to Video",
+            category="partner/video/MiniMax",
+            description="Generate video from a first frame image and an optional last frame image "
+            "using the MiniMax H3 model. The aspect ratio of the video follows the supplied images.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[IO.DynamicCombo.Option("MiniMax H3", _hailuo03_model_inputs(include_ratio=False))],
+                    tooltip="Model to use for video generation.",
+                ),
+                IO.Image.Input(
+                    "first_frame",
+                    tooltip="First frame image for the video.",
+                ),
+                IO.Image.Input(
+                    "last_frame",
+                    tooltip="Optional last frame image for the video.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Random seed. The same request with the same seed gives similar, "
+                    "but not guaranteed identical, results.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AIGC watermark to the video.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $dur := $lookup(widgets, "model.duration");
+                  $rate := $lookup(widgets, "model.resolution") = "768p" ? 0.1287 : 0.1859;
+                  {"type": "usd", "usd": $dur * $rate}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        first_frame: torch.Tensor,
+        seed: int,
+        watermark: bool,
+        last_frame: torch.Tensor | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(model["prompt"], strip_whitespace=True, min_length=1)
+        for frame in (first_frame, last_frame):
+            if frame is not None:
+                validate_image_aspect_ratio(frame, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+                validate_image_dimensions(frame, min_width=256, min_height=256)
+
+        content: list = [
+            Hailuo03TextContent(text=model["prompt"]),
+            Hailuo03ImageContent(
+                image_url=Hailuo03ImageContentUrl(
+                    url=(
+                        await upload_images_to_comfyapi(
+                            cls, first_frame, max_images=1, wait_label="Uploading first frame"
+                        )
+                    )[0],
+                ),
+                role="first_frame",
+            ),
+        ]
+        if last_frame is not None:
+            content.append(
+                Hailuo03ImageContent(
+                    image_url=Hailuo03ImageContentUrl(
+                        url=(
+                            await upload_images_to_comfyapi(
+                                cls, last_frame, max_images=1, wait_label="Uploading last frame"
+                            )
+                        )[0],
+                    ),
+                    role="last_frame",
+                )
+            )
+        return await _hailuo03_run_task(
+            cls,
+            model_id=HAILUO_03_MODELS[model["model"]],
+            content=content,
+            resolution=model["resolution"],
+            duration=model["duration"],
+            ratio=None,
+            seed=seed,
+            watermark=watermark,
+        )
+
+
+class MinimaxHailuo03ReferenceNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MinimaxHailuo03ReferenceNode",
+            display_name="MiniMax H3 Reference to Video",
+            category="partner/video/MiniMax",
+            description="Generate video conditioned on reference images, videos, and audio using the "
+            "MiniMax H3 model. Refer to the references in the prompt by their order: "
+            "'Image 1', 'Image 2', 'Video 1', 'Audio 1', and so on.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "MiniMax H3",
+                            [
+                                *_hailuo03_model_inputs(),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[
+                                            "image_1",
+                                            "image_2",
+                                            "image_3",
+                                            "image_4",
+                                            "image_5",
+                                            "image_6",
+                                            "image_7",
+                                            "image_8",
+                                            "image_9",
+                                        ],
+                                        min=0,
+                                    ),
+                                    tooltip="Subject or style reference images, referred to in the prompt "
+                                    "as 'Image 1'..'Image 9' in connection order. Up to 9 images.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_videos",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Video.Input("reference_video"),
+                                        names=["video_1", "video_2", "video_3"],
+                                        min=0,
+                                    ),
+                                    tooltip="Motion or scene reference videos, referred to in the prompt "
+                                    "as 'Video 1'..'Video 3' in connection order. Up to 3 videos, "
+                                    "2-15 seconds each, 15 seconds in total.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_audios",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Audio.Input("reference_audio"),
+                                        names=["audio_1", "audio_2", "audio_3"],
+                                        min=0,
+                                    ),
+                                    tooltip="Audio references, referred to in the prompt as "
+                                    "'Audio 1'..'Audio 3' in connection order. Up to 3 clips, "
+                                    "2-15 seconds each, 15 seconds in total. Cannot be used without "
+                                    "a reference image or video.",
+                                ),
+                            ],
+                        )
+                    ],
+                    tooltip="Model to use for video generation.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Random seed. The same request with the same seed gives similar, "
+                    "but not guaranteed identical, results.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AIGC watermark to the video.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model.resolution", "model.duration"],
+                    input_groups=["model.reference_images", "model.reference_videos"],
+                ),
+                expr="""
+                (
+                  $dur := $lookup(widgets, "model.duration");
+                  $rate := $lookup(widgets, "model.resolution") = "768p" ? 0.1287 : 0.1859;
+                  $imgsRaw := $lookup(inputGroups, "model.reference_images");
+                  $imgs := $imgsRaw ? $imgsRaw : 0;
+                  $vidsRaw := $lookup(inputGroups, "model.reference_videos");
+                  $vids := $vidsRaw ? $vidsRaw : 0;
+                  $base := $dur * $rate + ($imgs > 5 ? ($imgs - 5) * 0.0572 : 0);
+                  $vids > 0
+                    ? {"type": "range_usd", "min_usd": $base + $vids * 2 * $rate,
+                       "max_usd": $base + 15 * $rate, "format": {"approximate": true}}
+                    : {"type": "usd", "usd": $base}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+    ) -> IO.NodeOutput:
+        validate_string(model["prompt"], strip_whitespace=True, min_length=1)
+
+        reference_images = model.get("reference_images", {})
+        reference_videos = model.get("reference_videos", {})
+        reference_audios = model.get("reference_audios", {})
+        if not reference_images and not reference_videos:
+            raise ValueError("At least one reference image or video is required.")
+
+        for image in reference_images.values():
+            validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+            validate_image_dimensions(image, min_width=256, min_height=256)
+
+        total_video_duration = 0.0
+        for i, video in enumerate(reference_videos.values(), 1):
+            try:
+                fps = float(video.get_frame_rate())
+            except Exception:
+                fps = 0.0
+            if fps and not (23.9 <= fps <= 60.5):
+                raise ValueError(f"Reference video {i} is {fps:.2f} FPS. Supported range is 23.976-60 FPS.")
+            try:
+                dur = video.get_duration()
+            except Exception:
+                continue
+            if dur < 1.8:
+                raise ValueError(f"Reference video {i} is too short: {dur:.1f}s. Minimum duration is 2 seconds.")
+            total_video_duration += dur
+        if total_video_duration > 15.1:
+            raise ValueError(
+                f"Total reference video duration is {total_video_duration:.1f}s. Maximum is 15 seconds."
+            )
+
+        total_audio_duration = 0.0
+        for i, audio in enumerate(reference_audios.values(), 1):
+            dur = int(audio["waveform"].shape[-1]) / int(audio["sample_rate"])
+            if dur < 1.8:
+                raise ValueError(f"Reference audio {i} is too short: {dur:.1f}s. Minimum duration is 2 seconds.")
+            total_audio_duration += dur
+        if total_audio_duration > 15.1:
+            raise ValueError(
+                f"Total reference audio duration is {total_audio_duration:.1f}s. Maximum is 15 seconds."
+            )
+
+        content: list = [Hailuo03TextContent(text=model["prompt"])]
+        for i, image in enumerate(reference_images.values(), 1):
+            content.append(
+                Hailuo03ImageContent(
+                    image_url=Hailuo03ImageContentUrl(
+                        url=(
+                            await upload_images_to_comfyapi(
+                                cls, image, max_images=1, wait_label=f"Uploading image {i}"
+                            )
+                        )[0],
+                    ),
+                    role="reference_image",
+                )
+            )
+        for i, video in enumerate(reference_videos.values(), 1):
+            content.append(
+                Hailuo03VideoContent(
+                    video_url=Hailuo03VideoContentUrl(
+                        url=await upload_video_to_comfyapi(cls, video, wait_label=f"Uploading video {i}"),
+                    ),
+                )
+            )
+        for audio in reference_audios.values():
+            content.append(
+                Hailuo03AudioContent(
+                    audio_url=Hailuo03AudioContentUrl(
+                        url=await upload_audio_to_comfyapi(
+                            cls,
+                            audio,
+                            container_format="mp3",
+                            codec_name="libmp3lame",
+                            mime_type="audio/mpeg",
+                        ),
+                    ),
+                )
+            )
+        return await _hailuo03_run_task(
+            cls,
+            model_id=HAILUO_03_MODELS[model["model"]],
+            content=content,
+            resolution=model["resolution"],
+            duration=model["duration"],
+            ratio=model["ratio"],
+            seed=seed,
+            watermark=watermark,
+        )
+
+
+class MinimaxHailuo03ContextIRNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MinimaxHailuo03ContextIRNode",
+            display_name="MiniMax H3 Context IR (Prompt Enhancer)",
+            category="partner/video/MiniMax",
+            description="Analyze text and media context with MiniMax H3 Context IR and produce an enhanced, "
+            "structured video prompt. Feed the output into the prompt of a MiniMax H3 video node and attach "
+            "the same media there in the same order, because the enhanced prompt refers to the attached "
+            "media by position.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "MiniMax H3",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Description of the video you intend to generate.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=4,
+                                    max=15,
+                                    step=1,
+                                    tooltip="Duration of the video you intend to generate, in seconds (4-15).",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["adaptive", "16:9", "4:3", "1:1", "3:4", "9:16", "21:9"],
+                                    default="adaptive",
+                                    tooltip="Aspect ratio of the video you intend to generate. 'adaptive' "
+                                    "requires at least one image, video, or audio input.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[
+                                            "image_1",
+                                            "image_2",
+                                            "image_3",
+                                            "image_4",
+                                            "image_5",
+                                            "image_6",
+                                            "image_7",
+                                            "image_8",
+                                            "image_9",
+                                        ],
+                                        min=0,
+                                    ),
+                                    tooltip="Subject or style reference images, referred to in the prompt "
+                                    "as 'Image 1'..'Image 9' in connection order. Up to 9 images.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_videos",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Video.Input("reference_video"),
+                                        names=["video_1", "video_2", "video_3"],
+                                        min=0,
+                                    ),
+                                    tooltip="Motion or scene reference videos, referred to in the prompt "
+                                    "as 'Video 1'..'Video 3' in connection order. Up to 3 videos, "
+                                    "2-15 seconds each, 15 seconds in total.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_audios",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Audio.Input("reference_audio"),
+                                        names=["audio_1", "audio_2", "audio_3"],
+                                        min=0,
+                                    ),
+                                    tooltip="Audio references, referred to in the prompt as "
+                                    "'Audio 1'..'Audio 3' in connection order. Up to 3 clips, "
+                                    "2-15 seconds each, 15 seconds in total. Cannot be used without "
+                                    "a reference image or video.",
+                                ),
+                            ],
+                        )
+                    ],
+                    tooltip="Model to use for prompt enhancement.",
+                ),
+                IO.Image.Input(
+                    "first_frame",
+                    tooltip="First frame of the video you intend to generate. Cannot be combined with "
+                    "reference media.",
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "last_frame",
+                    tooltip="Last frame of the video you intend to generate. Cannot be combined with "
+                    "reference media.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    inputs=["first_frame", "last_frame"],
+                    input_groups=["model.reference_images", "model.reference_videos", "model.reference_audios"],
+                ),
+                expr="""
+                (
+                  $imgsRaw := $lookup(inputGroups, "model.reference_images");
+                  $imgs := $imgsRaw ? $imgsRaw : 0;
+                  $vidsRaw := $lookup(inputGroups, "model.reference_videos");
+                  $vids := $vidsRaw ? $vidsRaw : 0;
+                  $audsRaw := $lookup(inputGroups, "model.reference_audios");
+                  $auds := $audsRaw ? $audsRaw : 0;
+                  $frames := (inputs.first_frame.connected ? 1 : 0) + (inputs.last_frame.connected ? 1 : 0);
+                  ($imgs + $vids + $auds) > 0
+                    ? {"type": "range_usd", "min_usd": 0.06, "max_usd": 0.11, "format": {"approximate": true}}
+                    : $frames > 0
+                      ? {"type": "usd", "usd": 0.05, "format": {"approximate": true}}
+                      : {"type": "usd", "usd": 0.02, "format": {"approximate": true}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        first_frame: torch.Tensor | None = None,
+        last_frame: torch.Tensor | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(model["prompt"], strip_whitespace=True, min_length=1)
+
+        reference_images = {k: v for k, v in (model.get("reference_images") or {}).items() if v is not None}
+        reference_videos = {k: v for k, v in (model.get("reference_videos") or {}).items() if v is not None}
+        reference_audios = {k: v for k, v in (model.get("reference_audios") or {}).items() if v is not None}
+        has_frames = first_frame is not None or last_frame is not None
+        has_references = bool(reference_images) or bool(reference_videos) or bool(reference_audios)
+        if has_frames and has_references:
+            raise ValueError(
+                "First/last frame and reference media are mutually exclusive. Use frames for an "
+                "image-to-video prompt, or reference media for a reference-to-video prompt."
+            )
+        if reference_audios and not reference_images and not reference_videos:
+            raise ValueError("Reference audio cannot be used without a reference image or video.")
+        if not has_frames and not has_references and model["ratio"] == "adaptive":
+            raise ValueError(
+                "Ratio 'adaptive' is not supported for text-only requests; select an explicit aspect ratio."
+            )
+
+        for frame in (first_frame, last_frame):
+            if frame is not None:
+                validate_image_aspect_ratio(frame, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+                validate_image_dimensions(frame, min_width=256, min_height=256)
+        for image in reference_images.values():
+            validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+            validate_image_dimensions(image, min_width=256, min_height=256)
+
+        total_video_duration = 0.0
+        for i, video in enumerate(reference_videos.values(), 1):
+            try:
+                fps = float(video.get_frame_rate())
+            except Exception:
+                fps = 0.0
+            if fps and not (23.9 <= fps <= 60.5):
+                raise ValueError(f"Reference video {i} is {fps:.2f} FPS. Supported range is 23.976-60 FPS.")
+            try:
+                dur = video.get_duration()
+            except Exception:
+                continue
+            if dur < 1.8:
+                raise ValueError(f"Reference video {i} is too short: {dur:.1f}s. Minimum duration is 2 seconds.")
+            total_video_duration += dur
+        if total_video_duration > 15.1:
+            raise ValueError(
+                f"Total reference video duration is {total_video_duration:.1f}s. Maximum is 15 seconds."
+            )
+
+        total_audio_duration = 0.0
+        for i, audio in enumerate(reference_audios.values(), 1):
+            dur = int(audio["waveform"].shape[-1]) / int(audio["sample_rate"])
+            if dur < 1.8:
+                raise ValueError(f"Reference audio {i} is too short: {dur:.1f}s. Minimum duration is 2 seconds.")
+            total_audio_duration += dur
+        if total_audio_duration > 15.1:
+            raise ValueError(
+                f"Total reference audio duration is {total_audio_duration:.1f}s. Maximum is 15 seconds."
+            )
+
+        content: list = [Hailuo03TextContent(text=model["prompt"])]
+        if first_frame is not None:
+            content.append(
+                Hailuo03ImageContent(
+                    image_url=Hailuo03ImageContentUrl(
+                        url=(
+                            await upload_images_to_comfyapi(
+                                cls, first_frame, max_images=1, wait_label="Uploading first frame"
+                            )
+                        )[0],
+                    ),
+                    role="first_frame",
+                )
+            )
+        if last_frame is not None:
+            content.append(
+                Hailuo03ImageContent(
+                    image_url=Hailuo03ImageContentUrl(
+                        url=(
+                            await upload_images_to_comfyapi(
+                                cls, last_frame, max_images=1, wait_label="Uploading last frame"
+                            )
+                        )[0],
+                    ),
+                    role="last_frame",
+                )
+            )
+        for i, image in enumerate(reference_images.values(), 1):
+            content.append(
+                Hailuo03ImageContent(
+                    image_url=Hailuo03ImageContentUrl(
+                        url=(
+                            await upload_images_to_comfyapi(
+                                cls, image, max_images=1, wait_label=f"Uploading image {i}"
+                            )
+                        )[0],
+                    ),
+                    role="reference_image",
+                )
+            )
+        for i, video in enumerate(reference_videos.values(), 1):
+            content.append(
+                Hailuo03VideoContent(
+                    video_url=Hailuo03VideoContentUrl(
+                        url=await upload_video_to_comfyapi(cls, video, wait_label=f"Uploading video {i}"),
+                    ),
+                )
+            )
+        for audio in reference_audios.values():
+            content.append(
+                Hailuo03AudioContent(
+                    audio_url=Hailuo03AudioContentUrl(
+                        url=await upload_audio_to_comfyapi(
+                            cls,
+                            audio,
+                            container_format="mp3",
+                            codec_name="libmp3lame",
+                            mime_type="audio/mpeg",
+                        ),
+                    ),
+                )
+            )
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=HAILUO_03_CONTEXT_IR_ENDPOINT, method="POST"),
+            response_model=Hailuo03TaskCreationResponse,
+            data=Hailuo03ContextIRRequest(
+                model=HAILUO_03_MODELS[model["model"]],
+                content=content,
+                duration=model["duration"],
+                ratio=None if model["ratio"] == "adaptive" else model["ratio"],
+            ),
+        )
+        task_result = await poll_op(
+            cls,
+            ApiEndpoint(path=f"{HAILUO_03_QUERY_ENDPOINT}/{response.task_id}"),
+            response_model=Hailuo03TaskQueryResponse,
+            status_extractor=lambda r: r.task.status,
+            failed_statuses=HAILUO_03_FAILED_STATUSES,
+            poll_interval=5,
+        )
+        prompt = task_result.task.content.prompt if task_result.task.content else None
+        if not prompt:
+            raise Exception(f"No enhanced prompt in the response: {task_result.model_dump()}")
+        return IO.NodeOutput(prompt)
+
+
+class MinimaxHailuo03RegenerateNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MinimaxHailuo03RegenerateNode",
+            display_name="MiniMax H3 Regenerate to 2K",
+            category="partner/video/MiniMax",
+            description="Re-render a MiniMax H3 768P output at 2K resolution. Connect the unmodified 768P "
+            "video and the exact prompt used to generate it; if the original generation used first/last "
+            "frames or reference media, attach the same inputs.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "MiniMax H3",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="The exact prompt used to generate the source video.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["2K"],
+                                    tooltip="Resolution to re-render the source video at.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[
+                                            "image_1",
+                                            "image_2",
+                                            "image_3",
+                                            "image_4",
+                                            "image_5",
+                                            "image_6",
+                                            "image_7",
+                                            "image_8",
+                                            "image_9",
+                                        ],
+                                        min=0,
+                                    ),
+                                    tooltip="Reference images from the original generation, in the same "
+                                    "order. Up to 9 images.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_videos",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Video.Input("reference_video"),
+                                        names=["video_1", "video_2", "video_3"],
+                                        min=0,
+                                    ),
+                                    tooltip="Reference videos from the original generation, in the same "
+                                    "order. Up to 3 videos, 2-15 seconds each, 15 seconds in total.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_audios",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Audio.Input("reference_audio"),
+                                        names=["audio_1", "audio_2", "audio_3"],
+                                        min=0,
+                                    ),
+                                    tooltip="Audio references from the original generation, in the same "
+                                    "order. Up to 3 clips, 2-15 seconds each, 15 seconds in total. "
+                                    "Cannot be used without a reference image or video.",
+                                ),
+                            ],
+                        )
+                    ],
+                    tooltip="Model to use for video regeneration.",
+                ),
+                IO.Video.Input(
+                    "video",
+                    tooltip="The MiniMax H3 768P output video to re-render. Connect the unmodified output "
+                    "of a MiniMax H3 video node (24 FPS, 4-15 seconds). 2K outputs cannot be used.",
+                ),
+                IO.Image.Input(
+                    "first_frame",
+                    tooltip="First frame image from the original generation, if one was used.",
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "last_frame",
+                    tooltip="Last frame image from the original generation, if one was used.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AIGC watermark to the video.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type": "usd", "usd": 0.0715, "format": {"suffix": "/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        video: Input.Video,
+        watermark: bool,
+        first_frame: torch.Tensor | None = None,
+        last_frame: torch.Tensor | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(model["prompt"], strip_whitespace=True, min_length=1)
+
+        try:
+            fps = float(video.get_frame_rate())
+        except Exception:
+            fps = 0.0
+        if fps and not (23.9 <= fps <= 24.1):
+            raise ValueError(
+                f"The source video is {fps:.2f} FPS. Regeneration accepts unmodified MiniMax H3 768P "
+                "outputs, which are 24 FPS."
+            )
+        try:
+            width, height = video.get_dimensions()
+        except Exception:
+            width = height = 0
+        if width and height and (width % 32 or height % 32 or width * height > 1_032_192):
+            raise ValueError(
+                f"The source video is {width}x{height}. Regeneration accepts MiniMax H3 768P outputs "
+                "(width and height divisible by 32, at most 1,032,192 total pixels); 2K outputs cannot "
+                "be used as a source."
+            )
+        try:
+            frame_count = video.get_frame_count()
+        except Exception:
+            frame_count = 0
+        if frame_count and (frame_count < 107 or frame_count > 362 or (frame_count - 107) % 17):
+            raise ValueError(
+                f"The source video has {frame_count} frames. Regeneration accepts unmodified "
+                "MiniMax H3 outputs, whose length is 107 to 362 frames in steps of 17 "
+                "(4 to 15 seconds at 24 FPS)."
+            )
+
+        reference_images = {k: v for k, v in (model.get("reference_images") or {}).items() if v is not None}
+        reference_videos = {k: v for k, v in (model.get("reference_videos") or {}).items() if v is not None}
+        reference_audios = {k: v for k, v in (model.get("reference_audios") or {}).items() if v is not None}
+        if (first_frame is not None or last_frame is not None) and (
+            reference_images or reference_videos or reference_audios
+        ):
+            raise ValueError(
+                "First/last frame and reference media are mutually exclusive. Use frames for an "
+                "image-to-video prompt, or reference media for a reference-to-video prompt."
+            )
+        if reference_audios and not reference_images and not reference_videos:
+            raise ValueError("Reference audio cannot be used without a reference image or video.")
+
+        for frame in (first_frame, last_frame):
+            if frame is not None:
+                validate_image_aspect_ratio(frame, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+                validate_image_dimensions(frame, min_width=256, min_height=256)
+        for image in reference_images.values():
+            validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+            validate_image_dimensions(image, min_width=256, min_height=256)
+
+        total_video_duration = 0.0
+        for i, ref_video in enumerate(reference_videos.values(), 1):
+            try:
+                ref_fps = float(ref_video.get_frame_rate())
+            except Exception:
+                ref_fps = 0.0
+            if ref_fps and not (23.9 <= ref_fps <= 60.5):
+                raise ValueError(f"Reference video {i} is {ref_fps:.2f} FPS. Supported range is 23.976-60 FPS.")
+            try:
+                dur = ref_video.get_duration()
+            except Exception:
+                continue
+            if dur < 1.8:
+                raise ValueError(f"Reference video {i} is too short: {dur:.1f}s. Minimum duration is 2 seconds.")
+            total_video_duration += dur
+        if total_video_duration > 15.1:
+            raise ValueError(
+                f"Total reference video duration is {total_video_duration:.1f}s. Maximum is 15 seconds."
+            )
+
+        total_audio_duration = 0.0
+        for i, audio in enumerate(reference_audios.values(), 1):
+            dur = int(audio["waveform"].shape[-1]) / int(audio["sample_rate"])
+            if dur < 1.8:
+                raise ValueError(f"Reference audio {i} is too short: {dur:.1f}s. Minimum duration is 2 seconds.")
+            total_audio_duration += dur
+        if total_audio_duration > 15.1:
+            raise ValueError(
+                f"Total reference audio duration is {total_audio_duration:.1f}s. Maximum is 15 seconds."
+            )
+
+        content: list = [
+            Hailuo03VideoContent(
+                video_url=Hailuo03VideoContentUrl(
+                    url=await upload_video_to_comfyapi(cls, video, wait_label="Uploading source video"),
+                ),
+                role="base_video",
+            ),
+            Hailuo03TextContent(text=model["prompt"]),
+        ]
+        if first_frame is not None:
+            content.append(
+                Hailuo03ImageContent(
+                    image_url=Hailuo03ImageContentUrl(
+                        url=(
+                            await upload_images_to_comfyapi(
+                                cls, first_frame, max_images=1, wait_label="Uploading first frame"
+                            )
+                        )[0],
+                    ),
+                    role="first_frame",
+                )
+            )
+        if last_frame is not None:
+            content.append(
+                Hailuo03ImageContent(
+                    image_url=Hailuo03ImageContentUrl(
+                        url=(
+                            await upload_images_to_comfyapi(
+                                cls, last_frame, max_images=1, wait_label="Uploading last frame"
+                            )
+                        )[0],
+                    ),
+                    role="last_frame",
+                )
+            )
+        for i, image in enumerate(reference_images.values(), 1):
+            content.append(
+                Hailuo03ImageContent(
+                    image_url=Hailuo03ImageContentUrl(
+                        url=(
+                            await upload_images_to_comfyapi(
+                                cls, image, max_images=1, wait_label=f"Uploading image {i}"
+                            )
+                        )[0],
+                    ),
+                    role="reference_image",
+                )
+            )
+        for i, ref_video in enumerate(reference_videos.values(), 1):
+            content.append(
+                Hailuo03VideoContent(
+                    video_url=Hailuo03VideoContentUrl(
+                        url=await upload_video_to_comfyapi(cls, ref_video, wait_label=f"Uploading video {i}"),
+                    ),
+                )
+            )
+        for audio in reference_audios.values():
+            content.append(
+                Hailuo03AudioContent(
+                    audio_url=Hailuo03AudioContentUrl(
+                        url=await upload_audio_to_comfyapi(
+                            cls,
+                            audio,
+                            container_format="mp3",
+                            codec_name="libmp3lame",
+                            mime_type="audio/mpeg",
+                        ),
+                    ),
+                )
+            )
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=HAILUO_03_REGENERATION_ENDPOINT, method="POST"),
+            response_model=Hailuo03TaskCreationResponse,
+            data=Hailuo03RegenerationRequest(
+                model=HAILUO_03_MODELS[model["model"]],
+                content=content,
+                resolution=model["resolution"],
+                aigc_watermark=watermark,
+            ),
+        )
+        task_result = await poll_op(
+            cls,
+            ApiEndpoint(path=f"{HAILUO_03_QUERY_ENDPOINT}/{response.task_id}"),
+            response_model=Hailuo03TaskQueryResponse,
+            status_extractor=lambda r: r.task.status,
+            failed_statuses=HAILUO_03_FAILED_STATUSES,
+            poll_interval=10,
+        )
+        video_url = task_result.task.content.url if task_result.task.content else None
+        if not video_url:
+            raise Exception(f"No video URL in the response: {task_result.model_dump()}")
+        return IO.NodeOutput(await download_url_to_video_output(video_url))
+
+
+class MinimaxExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            MinimaxTextToVideoNode,
+            MinimaxImageToVideoNode,
+            # MinimaxSubjectToVideoNode,
+            MinimaxHailuoVideoNode,
+            MinimaxHailuo03TextToVideoNode,
+            MinimaxHailuo03FirstLastFrameNode,
+            MinimaxHailuo03ReferenceNode,
+            MinimaxHailuo03ContextIRNode,
+            MinimaxHailuo03RegenerateNode,
+        ]
+
+
+async def comfy_entrypoint() -> MinimaxExtension:
+    return MinimaxExtension()
diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f2036b10e7434513813ab2bb9c1cdf5ef5a88ab
--- /dev/null
+++ b/comfy_api_nodes/nodes_openai.py
@@ -0,0 +1,1322 @@
+import base64
+import os
+from enum import Enum
+from io import BytesIO
+
+import numpy as np
+import torch
+from PIL import Image
+from typing_extensions import override
+
+import folder_paths
+from comfy.utils import common_upscale
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.openai import (
+    InputFileContent,
+    InputImageContent,
+    InputMessage,
+    InputTextContent,
+    ModelResponseProperties,
+    OpenAICreateResponse,
+    OpenAIImageEditRequest,
+    OpenAIImageGenerationRequest,
+    OpenAIImageGenerationResponse,
+    OpenAIResponse,
+    OutputContent,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_bytesio,
+    downscale_image_tensor,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    tensor_to_base64_string,
+    text_filepath_to_data_uri,
+    validate_string,
+)
+
+RESPONSES_ENDPOINT = "/proxy/openai/v1/responses"
+STARTING_POINT_ID_PATTERN = r"<starting_point_id:(.*)>"
+
+
+class SupportedOpenAIModel(str, Enum):
+    gpt_5_6_sol = "gpt-5.6-sol"
+    gpt_5_6_terra = "gpt-5.6-terra"
+    gpt_5_6_luna = "gpt-5.6-luna"
+    gpt_5_5_pro = "gpt-5.5-pro"
+    gpt_5_5 = "gpt-5.5"
+    gpt_5 = "gpt-5"
+    gpt_5_mini = "gpt-5-mini"
+    gpt_5_nano = "gpt-5-nano"
+    gpt_4_1 = "gpt-4.1"
+    gpt_4_1_mini = "gpt-4.1-mini"
+    gpt_4_1_nano = "gpt-4.1-nano"
+    o4_mini = "o4-mini"
+    o3 = "o3"
+    o1_pro = "o1-pro"
+    o1 = "o1"
+
+
+async def validate_and_cast_response(response, timeout: int = None) -> torch.Tensor:
+    """Validates and casts a response to a torch.Tensor.
+
+    Args:
+        response: The response to validate and cast.
+        timeout: Request timeout in seconds. Defaults to None (no timeout).
+
+    Returns:
+        A torch.Tensor of shape (N, H, W, C) with all returned images; images whose
+        dimensions differ from the first image's are resized to match it.
+
+    Raises:
+        ValueError: If the response is not valid.
+    """
+    # validate raw JSON response
+    data = response.data
+    if not data or len(data) == 0:
+        raise ValueError("No images returned from API endpoint")
+
+    # Initialize list to store image tensors
+    image_tensors: list[torch.Tensor] = []
+
+    # Process each image in the data array
+    for img_data in data:
+        if img_data.b64_json:
+            img_io = BytesIO(base64.b64decode(img_data.b64_json))
+        elif img_data.url:
+            img_io = BytesIO()
+            await download_url_to_bytesio(img_data.url, img_io, timeout=timeout)
+        else:
+            raise ValueError("Invalid image payload – neither URL nor base64 data present.")
+
+        pil_img = Image.open(img_io).convert("RGBA")
+        arr = np.asarray(pil_img).astype(np.float32) / 255.0
+        image_tensors.append(torch.from_numpy(arr))
+
+    # With size="auto" the API can return images whose dimensions differ by a few pixels within a single response
+    # resize them to the first image's dimensions so they can be stacked into one batch.
+    ref_h, ref_w = image_tensors[0].shape[:2]
+    for i, t in enumerate(image_tensors):
+        if t.shape[:2] != (ref_h, ref_w):
+            samples = t.unsqueeze(0).movedim(-1, 1)
+            samples = common_upscale(samples, ref_w, ref_h, "bilinear", "center")
+            image_tensors[i] = samples.movedim(1, -1).squeeze(0)
+    return torch.stack(image_tensors, dim=0)
+
+
+class OpenAIDalle2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIDalle2",
+            display_name="OpenAI DALL·E 2",
+            category="partner/image/OpenAI",
+            description="Generates images synchronously via OpenAI's DALL·E 2 endpoint.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Text prompt for DALL·E",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2**31 - 1,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="not implemented yet in backend",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "size",
+                    default="1024x1024",
+                    options=["256x256", "512x512", "1024x1024"],
+                    tooltip="Image size",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=8,
+                    step=1,
+                    tooltip="How many images to generate",
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "image",
+                    tooltip="Optional reference image for image editing.",
+                    optional=True,
+                ),
+                IO.Mask.Input(
+                    "mask",
+                    tooltip="Optional mask for inpainting (white areas will be replaced)",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["size", "n"]),
+                expr="""
+                (
+                  $size := widgets.size;
+                  $nRaw := widgets.n;
+                  $n := ($nRaw != null and $nRaw != 0) ? $nRaw : 1;
+
+                  $base :=
+                    $contains($size, "256x256") ? 0.016 :
+                    $contains($size, "512x512") ? 0.018 :
+                    0.02;
+
+                  {"type":"usd","usd": $round($base * $n, 3)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt,
+        seed=0,
+        image=None,
+        mask=None,
+        n=1,
+        size="1024x1024",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+        model = "dall-e-2"
+        path = "/proxy/openai/images/generations"
+        content_type = "application/json"
+        request_class = OpenAIImageGenerationRequest
+        img_binary = None
+
+        if image is not None and mask is not None:
+            path = "/proxy/openai/images/edits"
+            content_type = "multipart/form-data"
+            request_class = OpenAIImageEditRequest
+
+            input_tensor = image.squeeze().cpu()
+            height, width, channels = input_tensor.shape
+            rgba_tensor = torch.ones(height, width, 4, device="cpu")
+            rgba_tensor[:, :, :channels] = input_tensor
+
+            if mask.shape[1:] != image.shape[1:-1]:
+                raise Exception("Mask and Image must be the same size")
+            rgba_tensor[:, :, 3] = 1 - mask.squeeze().cpu()
+
+            rgba_tensor = downscale_image_tensor(rgba_tensor.unsqueeze(0)).squeeze()
+
+            image_np = (rgba_tensor.numpy() * 255).astype(np.uint8)
+            img = Image.fromarray(image_np)
+            img_byte_arr = BytesIO()
+            img.save(img_byte_arr, format="PNG")
+            img_byte_arr.seek(0)
+            img_binary = img_byte_arr  # .getvalue()
+            img_binary.name = "image.png"
+        elif image is not None or mask is not None:
+            raise Exception("Dall-E 2 image editing requires an image AND a mask")
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=path, method="POST"),
+            response_model=OpenAIImageGenerationResponse,
+            data=request_class(
+                model=model,
+                prompt=prompt,
+                n=n,
+                size=size,
+                seed=seed,
+            ),
+            files=(
+                {
+                    "image": ("image.png", img_binary, "image/png"),
+                }
+                if img_binary
+                else None
+            ),
+            content_type=content_type,
+        )
+
+        return IO.NodeOutput(await validate_and_cast_response(response))
+
+
+class OpenAIDalle3(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIDalle3",
+            display_name="OpenAI DALL·E 3",
+            category="partner/image/OpenAI",
+            description="Generates images synchronously via OpenAI's DALL·E 3 endpoint.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Text prompt for DALL·E",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2**31 - 1,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="not implemented yet in backend",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "quality",
+                    default="standard",
+                    options=["standard", "hd"],
+                    tooltip="Image quality",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "style",
+                    default="natural",
+                    options=["natural", "vivid"],
+                    tooltip="Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "size",
+                    default="1024x1024",
+                    options=["1024x1024", "1024x1792", "1792x1024"],
+                    tooltip="Image size",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["size", "quality"]),
+                expr="""
+                (
+                  $size := widgets.size;
+                  $q := widgets.quality;
+                  $hd := $contains($q, "hd");
+
+                  $price :=
+                    $contains($size, "1024x1024")
+                      ? ($hd ? 0.08 : 0.04)
+                      : (($contains($size, "1792x1024") or $contains($size, "1024x1792"))
+                          ? ($hd ? 0.12 : 0.08)
+                          : 0.04);
+
+                  {"type":"usd","usd": $price}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt,
+        seed=0,
+        style="natural",
+        quality="standard",
+        size="1024x1024",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+        model = "dall-e-3"
+
+        # build the operation
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/openai/images/generations", method="POST"),
+            response_model=OpenAIImageGenerationResponse,
+            data=OpenAIImageGenerationRequest(
+                model=model,
+                prompt=prompt,
+                quality=quality,
+                size=size,
+                style=style,
+                seed=seed,
+            ),
+        )
+
+        return IO.NodeOutput(await validate_and_cast_response(response))
+
+
+class OpenAIGPTImage1(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIGPTImage1",
+            display_name="OpenAI GPT Image 2",
+            category="partner/image/OpenAI",
+            description="Generates images synchronously via OpenAI's GPT Image endpoint.",
+            is_deprecated=True,
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Text prompt for GPT Image",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2**31 - 1,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="not implemented yet in backend",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "quality",
+                    default="low",
+                    options=["low", "medium", "high"],
+                    tooltip="Image quality, affects cost and generation time.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "background",
+                    default="auto",
+                    options=["auto", "opaque", "transparent"],
+                    tooltip="Return image with or without background",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "size",
+                    default="auto",
+                    options=[
+                        "auto",
+                        "1024x1024",
+                        "1024x1536",
+                        "1536x1024",
+                        "2048x2048",
+                        "2048x1152",
+                        "1152x2048",
+                        "3840x2160",
+                        "2160x3840",
+                        "Custom",
+                    ],
+                    tooltip="Image size. Select 'Custom' to use the custom width and height (GPT Image 2 only).",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=8,
+                    step=1,
+                    tooltip="How many images to generate",
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "image",
+                    tooltip="Optional reference image for image editing.",
+                    optional=True,
+                ),
+                IO.Mask.Input(
+                    "mask",
+                    tooltip="Optional mask for inpainting (white areas will be replaced)",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["gpt-image-1", "gpt-image-1.5", "gpt-image-2"],
+                    default="gpt-image-2",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "custom_width",
+                    default=1024,
+                    min=1024,
+                    max=3840,
+                    step=16,
+                    tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16 (GPT Image 2 only).",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "custom_height",
+                    default=1024,
+                    min=1024,
+                    max=3840,
+                    step=16,
+                    tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16 (GPT Image 2 only).",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["quality", "n", "model"]),
+                expr="""
+                (
+                  $ranges := {
+                    "gpt-image-1": {
+                      "low":    [0.011, 0.02],
+                      "medium": [0.042, 0.07],
+                      "high":   [0.167, 0.25]
+                    },
+                    "gpt-image-1.5": {
+                      "low":    [0.009, 0.02],
+                      "medium": [0.034, 0.062],
+                      "high":   [0.133, 0.22]
+                    },
+                    "gpt-image-2": {
+                      "low":    [0.0058, 0.0228],
+                      "medium": [0.0492, 0.2016],
+                      "high":   [0.198, 0.804]
+                    }
+                  };
+                  $range := $lookup($lookup($ranges, widgets.model), widgets.quality);
+                  $nRaw := widgets.n;
+                  $n := ($nRaw != null and $nRaw != 0) ? $nRaw : 1;
+                  ($n = 1)
+                    ? {"type":"range_usd","min_usd": $range[0], "max_usd": $range[1], "format": {"approximate": true}}
+                    : {
+                        "type":"range_usd",
+                        "min_usd": $range[0] * $n,
+                        "max_usd": $range[1] * $n,
+                        "format": { "suffix": "/Run", "approximate": true }
+                      }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        seed: int = 0,
+        quality: str = "low",
+        background: str = "opaque",
+        image: Input.Image | None = None,
+        mask: Input.Image | None = None,
+        n: int = 1,
+        size: str = "1024x1024",
+        custom_width: int = 1024,
+        custom_height: int = 1024,
+        model: str = "gpt-image-1",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+
+        if mask is not None and image is None:
+            raise ValueError("Cannot use a mask without an input image")
+
+        if size == "Custom":
+            if model != "gpt-image-2":
+                raise ValueError("Custom resolution is only supported by GPT Image 2 model")
+            if custom_width % 16 != 0 or custom_height % 16 != 0:
+                raise ValueError(f"Custom width and height must be multiples of 16, got {custom_width}x{custom_height}")
+            if max(custom_width, custom_height) > 3840:
+                raise ValueError(f"Custom resolution max edge must be <= 3840, got {custom_width}x{custom_height}")
+            ratio = max(custom_width, custom_height) / min(custom_width, custom_height)
+            if ratio > 3:
+                raise ValueError(
+                    f"Custom resolution aspect ratio must not exceed 3:1, got {custom_width}x{custom_height}"
+                )
+            total_pixels = custom_width * custom_height
+            if not 655_360 <= total_pixels <= 8_294_400:
+                raise ValueError(
+                    f"Custom resolution total pixels must be between 655,360 and 8,294,400, got {total_pixels}"
+                )
+            size = f"{custom_width}x{custom_height}"
+        elif model in ("gpt-image-1", "gpt-image-1.5"):
+            if size not in ("auto", "1024x1024", "1024x1536", "1536x1024"):
+                raise ValueError(f"Resolution {size} is only supported by GPT Image 2 model")
+
+        if model == "gpt-image-2":
+            if background == "transparent":
+                raise ValueError("Transparent background is not supported for GPT Image 2 model")
+        elif model not in ("gpt-image-1", "gpt-image-1.5"):
+            raise ValueError(f"Unknown model: {model}")
+
+        if image is not None:
+            files = []
+            batch_size = image.shape[0]
+            for i in range(batch_size):
+                single_image = image[i : i + 1]
+                scaled_image = downscale_image_tensor(single_image, total_pixels=2048 * 2048).squeeze()
+
+                image_np = (scaled_image.numpy() * 255).astype(np.uint8)
+                img = Image.fromarray(image_np)
+                img_byte_arr = BytesIO()
+                img.save(img_byte_arr, format="PNG")
+                img_byte_arr.seek(0)
+
+                if batch_size == 1:
+                    files.append(("image", (f"image_{i}.png", img_byte_arr, "image/png")))
+                else:
+                    files.append(("image[]", (f"image_{i}.png", img_byte_arr, "image/png")))
+
+            if mask is not None:
+                if image.shape[0] != 1:
+                    raise Exception("Cannot use a mask with multiple image")
+                if mask.shape[1:] != image.shape[1:-1]:
+                    raise Exception("Mask and Image must be the same size")
+                _, height, width = mask.shape
+                rgba_mask = torch.zeros(height, width, 4, device="cpu")
+                rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu()
+
+                scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0), total_pixels=2048 * 2048).squeeze()
+
+                mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
+                mask_img = Image.fromarray(mask_np)
+                mask_img_byte_arr = BytesIO()
+                mask_img.save(mask_img_byte_arr, format="PNG")
+                mask_img_byte_arr.seek(0)
+                files.append(("mask", ("mask.png", mask_img_byte_arr, "image/png")))
+
+            response = await sync_op(
+                cls,
+                ApiEndpoint(path="/proxy/openai/images/edits", method="POST"),
+                response_model=OpenAIImageGenerationResponse,
+                data=OpenAIImageEditRequest(
+                    model=model,
+                    prompt=prompt,
+                    quality=quality,
+                    background=background,
+                    n=n,
+                    seed=seed,
+                    size=size,
+                    moderation="low",
+                ),
+                content_type="multipart/form-data",
+                files=files,
+            )
+        else:
+            response = await sync_op(
+                cls,
+                ApiEndpoint(path="/proxy/openai/images/generations", method="POST"),
+                response_model=OpenAIImageGenerationResponse,
+                data=OpenAIImageGenerationRequest(
+                    model=model,
+                    prompt=prompt,
+                    quality=quality,
+                    background=background,
+                    n=n,
+                    seed=seed,
+                    size=size,
+                    moderation="low",
+                ),
+            )
+        return IO.NodeOutput(await validate_and_cast_response(response))
+
+
+def _gpt_image_shared_inputs():
+    """Inputs shared by all GPT Image models (quality + reference images + mask)."""
+    return [
+        IO.Combo.Input(
+            "quality",
+            default="low",
+            options=["low", "medium", "high"],
+            tooltip="Image quality, affects cost and generation time.",
+        ),
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, 17)],
+                min=0,
+            ),
+            tooltip="Optional reference image(s) for image editing. Up to 16 images.",
+        ),
+        IO.Mask.Input(
+            "mask",
+            optional=True,
+            tooltip="Optional mask for inpainting (white areas will be replaced). "
+            "Requires exactly one reference image.",
+        ),
+    ]
+
+
+def _gpt_image_legacy_model_inputs():
+    """Per-model widget set for legacy gpt-image-1 / gpt-image-1.5 (4 base sizes, transparent bg allowed)."""
+    return [
+        IO.Combo.Input(
+            "size",
+            default="auto",
+            options=["auto", "1024x1024", "1024x1536", "1536x1024"],
+            tooltip="Image size.",
+        ),
+        IO.Combo.Input(
+            "background",
+            default="auto",
+            options=["auto", "opaque", "transparent"],
+            tooltip="Return image with or without background.",
+        ),
+        *_gpt_image_shared_inputs(),
+    ]
+
+
+class OpenAIGPTImageNodeV2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIGPTImageNodeV2",
+            display_name="OpenAI GPT Image 2",
+            category="partner/image/OpenAI",
+            description="Generates images via OpenAI's GPT Image endpoint.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Text prompt for GPT Image",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "gpt-image-2",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    default="auto",
+                                    options=[
+                                        "auto",
+                                        "1024x1024",
+                                        "1024x1536",
+                                        "1536x1024",
+                                        "2048x2048",
+                                        "2048x1152",
+                                        "1152x2048",
+                                        "3840x2160",
+                                        "2160x3840",
+                                        "Custom",
+                                    ],
+                                    tooltip="Image size. Select 'Custom' to use the custom width and height.",
+                                ),
+                                IO.Int.Input(
+                                    "custom_width",
+                                    default=1024,
+                                    min=1024,
+                                    max=3840,
+                                    step=16,
+                                    tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16.",
+                                ),
+                                IO.Int.Input(
+                                    "custom_height",
+                                    default=1024,
+                                    min=1024,
+                                    max=3840,
+                                    step=16,
+                                    tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16.",
+                                ),
+                                IO.Combo.Input(
+                                    "background",
+                                    default="auto",
+                                    options=["auto", "opaque"],
+                                    tooltip="Return image with or without background.",
+                                ),
+                                *_gpt_image_shared_inputs(),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("gpt-image-1.5", _gpt_image_legacy_model_inputs()),
+                        IO.DynamicCombo.Option("gpt-image-1", _gpt_image_legacy_model_inputs()),
+                    ],
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=8,
+                    step=1,
+                    tooltip="How many images to generate",
+                    display_mode=IO.NumberDisplay.number,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="not implemented yet in backend",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.quality", "n"]),
+                expr="""
+                (
+                  $ranges := {
+                    "gpt-image-1": {
+                      "low":    [0.011, 0.02],
+                      "medium": [0.042, 0.07],
+                      "high":   [0.167, 0.25]
+                    },
+                    "gpt-image-1.5": {
+                      "low":    [0.009, 0.02],
+                      "medium": [0.034, 0.062],
+                      "high":   [0.133, 0.22]
+                    },
+                    "gpt-image-2": {
+                      "low":    [0.0058, 0.0228],
+                      "medium": [0.0492, 0.2016],
+                      "high":   [0.198, 0.804]
+                    }
+                  };
+                  $range := $lookup($lookup($ranges, widgets.model), $lookup(widgets, "model.quality"));
+                  $nRaw := widgets.n;
+                  $n := ($nRaw != null and $nRaw != 0) ? $nRaw : 1;
+                  ($n = 1)
+                    ? {"type":"range_usd","min_usd": $range[0], "max_usd": $range[1], "format": {"approximate": true}}
+                    : {
+                        "type":"range_usd",
+                        "min_usd": $range[0] * $n,
+                        "max_usd": $range[1] * $n,
+                        "format": { "suffix": "/Run", "approximate": true }
+                      }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        n: int,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+
+        model_id = model["model"]
+        size = model["size"]
+        background = model["background"]
+        quality = model["quality"]
+        custom_width = model.get("custom_width", 1024)
+        custom_height = model.get("custom_height", 1024)
+
+        images_dict = model.get("images") or {}
+        image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
+        n_images = sum(get_number_of_images(t) for t in image_tensors)
+        mask = model.get("mask")
+
+        if mask is not None and n_images == 0:
+            raise ValueError("Cannot use a mask without an input image")
+
+        if size == "Custom":
+            if custom_width % 16 != 0 or custom_height % 16 != 0:
+                raise ValueError(
+                    f"Custom width and height must be multiples of 16, got {custom_width}x{custom_height}"
+                )
+            if max(custom_width, custom_height) > 3840:
+                raise ValueError(
+                    f"Custom resolution max edge must be <= 3840, got {custom_width}x{custom_height}"
+                )
+            ratio = max(custom_width, custom_height) / min(custom_width, custom_height)
+            if ratio > 3:
+                raise ValueError(
+                    f"Custom resolution aspect ratio must not exceed 3:1, got {custom_width}x{custom_height}"
+                )
+            total_pixels = custom_width * custom_height
+            if not 655_360 <= total_pixels <= 8_294_400:
+                raise ValueError(
+                    f"Custom resolution total pixels must be between 655,360 and 8,294,400, got {total_pixels}"
+                )
+            size = f"{custom_width}x{custom_height}"
+
+        if model_id not in ("gpt-image-1", "gpt-image-1.5", "gpt-image-2"):
+            raise ValueError(f"Unknown model: {model_id}")
+
+        if image_tensors:
+            flat: list[torch.Tensor] = []
+            for tensor in image_tensors:
+                if len(tensor.shape) == 4:
+                    flat.extend(tensor[i : i + 1] for i in range(tensor.shape[0]))
+                else:
+                    flat.append(tensor.unsqueeze(0))
+
+            files = []
+            for i, single_image in enumerate(flat):
+                scaled_image = downscale_image_tensor(single_image, total_pixels=2048 * 2048).squeeze()
+                image_np = (scaled_image.numpy() * 255).astype(np.uint8)
+                img = Image.fromarray(image_np)
+                img_byte_arr = BytesIO()
+                img.save(img_byte_arr, format="PNG")
+                img_byte_arr.seek(0)
+
+                if len(flat) == 1:
+                    files.append(("image", (f"image_{i}.png", img_byte_arr, "image/png")))
+                else:
+                    files.append(("image[]", (f"image_{i}.png", img_byte_arr, "image/png")))
+
+            if mask is not None:
+                if len(flat) != 1:
+                    raise Exception("Cannot use a mask with multiple image")
+                ref_image = flat[0]
+                if mask.shape[1:] != ref_image.shape[1:-1]:
+                    raise Exception("Mask and Image must be the same size")
+                _, height, width = mask.shape
+                rgba_mask = torch.zeros(height, width, 4, device="cpu")
+                rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu()
+                scaled_mask = downscale_image_tensor(
+                    rgba_mask.unsqueeze(0), total_pixels=2048 * 2048
+                ).squeeze()
+                mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
+                mask_img = Image.fromarray(mask_np)
+                mask_img_byte_arr = BytesIO()
+                mask_img.save(mask_img_byte_arr, format="PNG")
+                mask_img_byte_arr.seek(0)
+                files.append(("mask", ("mask.png", mask_img_byte_arr, "image/png")))
+
+            response = await sync_op(
+                cls,
+                ApiEndpoint(path="/proxy/openai/images/edits", method="POST"),
+                response_model=OpenAIImageGenerationResponse,
+                data=OpenAIImageEditRequest(
+                    model=model_id,
+                    prompt=prompt,
+                    quality=quality,
+                    background=background,
+                    n=n,
+                    size=size,
+                    moderation="low",
+                ),
+                content_type="multipart/form-data",
+                files=files,
+            )
+        else:
+            response = await sync_op(
+                cls,
+                ApiEndpoint(path="/proxy/openai/images/generations", method="POST"),
+                response_model=OpenAIImageGenerationResponse,
+                data=OpenAIImageGenerationRequest(
+                    model=model_id,
+                    prompt=prompt,
+                    quality=quality,
+                    background=background,
+                    n=n,
+                    size=size,
+                    moderation="low",
+                ),
+            )
+        return IO.NodeOutput(await validate_and_cast_response(response))
+
+
+class OpenAIChatNode(IO.ComfyNode):
+    """
+    Node to generate text responses from an OpenAI model.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIChatNode",
+            display_name="OpenAI ChatGPT",
+            category="partner/text/OpenAI",
+            essentials_category="Text Generation",
+            description="Generate text responses from an OpenAI model.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Text inputs to the model, used to generate a response.",
+                ),
+                IO.Boolean.Input(
+                    "persist_context",
+                    default=False,
+                    tooltip="This parameter is deprecated and has no effect.",
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=SupportedOpenAIModel,
+                    tooltip="The model used to generate the response",
+                ),
+                IO.Image.Input(
+                    "images",
+                    tooltip="Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.",
+                    optional=True,
+                ),
+                IO.Custom("OPENAI_INPUT_FILES").Input(
+                    "files",
+                    optional=True,
+                    tooltip="Optional file(s) to use as context for the model. Accepts inputs from the OpenAI Chat Input Files node.",
+                ),
+                IO.Custom("OPENAI_CHAT_CONFIG").Input(
+                    "advanced_options",
+                    optional=True,
+                    tooltip="Optional configuration for the model. Accepts inputs from the OpenAI Chat Advanced Options node.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $contains($m, "o4-mini") ? {
+                    "type": "list_usd",
+                    "usd": [0.0011, 0.0044],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "o1-pro") ? {
+                    "type": "list_usd",
+                    "usd": [0.15, 0.6],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "o1") ? {
+                    "type": "list_usd",
+                    "usd": [0.015, 0.06],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "o3-mini") ? {
+                    "type": "list_usd",
+                    "usd": [0.0011, 0.0044],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "o3") ? {
+                    "type": "list_usd",
+                    "usd": [0.01, 0.04],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-4.1-nano") ? {
+                    "type": "list_usd",
+                    "usd": [0.0001, 0.0004],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-4.1-mini") ? {
+                    "type": "list_usd",
+                    "usd": [0.0004, 0.0016],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-4.1") ? {
+                    "type": "list_usd",
+                    "usd": [0.002, 0.008],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5.6-terra") ? {
+                    "type": "list_usd",
+                    "usd": [0.0025, 0.015],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5.6-luna") ? {
+                    "type": "list_usd",
+                    "usd": [0.001, 0.006],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5.6") ? {
+                    "type": "list_usd",
+                    "usd": [0.005, 0.03],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5.5-pro") ? {
+                    "type": "list_usd",
+                    "usd": [0.03, 0.18],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5.5") ? {
+                    "type": "list_usd",
+                    "usd": [0.005, 0.03],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5-nano") ? {
+                    "type": "list_usd",
+                    "usd": [0.00005, 0.0004],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5-mini") ? {
+                    "type": "list_usd",
+                    "usd": [0.00025, 0.002],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5") ? {
+                    "type": "list_usd",
+                    "usd": [0.00125, 0.01],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : {"type": "text", "text": "Token-based"}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    def get_message_content_from_response(cls, response: OpenAIResponse) -> list[OutputContent]:
+        """Extract message content from the API response."""
+        for output in response.output:
+            if output.type == "message":
+                return output.content
+        raise TypeError("No output message found in response")
+
+    @classmethod
+    def get_text_from_message_content(cls, message_content: list[OutputContent]) -> str:
+        """Extract text content from message content."""
+        for content_item in message_content:
+            if content_item.type == "output_text":
+                return str(content_item.text)
+        return "No text output found in response"
+
+    @classmethod
+    def tensor_to_input_image_content(cls, image: torch.Tensor, detail_level: str = "auto") -> InputImageContent:
+        """Convert a tensor to an input image content object."""
+        return InputImageContent(
+            detail=detail_level,
+            image_url=f"data:image/png;base64,{tensor_to_base64_string(image)}",
+            type="input_image",
+        )
+
+    @classmethod
+    def create_input_message_contents(
+        cls,
+        prompt: str,
+        image: torch.Tensor | None = None,
+        files: list[InputFileContent] | None = None,
+    ) -> list[InputTextContent | InputImageContent | InputFileContent]:
+        """Create a list of input message contents from prompt and optional image."""
+        content_list: list[InputTextContent | InputImageContent | InputFileContent] = [
+            InputTextContent(text=prompt, type="input_text"),
+        ]
+        if image is not None:
+            for i in range(image.shape[0]):
+                content_list.append(
+                    InputImageContent(
+                        detail="auto",
+                        image_url=f"data:image/png;base64,{tensor_to_base64_string(image[i].unsqueeze(0))}",
+                        type="input_image",
+                    )
+                )
+        if files is not None:
+            content_list.extend(files)
+        return content_list
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        persist_context: bool = False,
+        model: SupportedOpenAIModel = SupportedOpenAIModel.gpt_5.value,
+        images: torch.Tensor | None = None,
+        files: list[InputFileContent] | None = None,
+        advanced_options: ModelResponseProperties | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+
+        # Create response
+        create_response = await sync_op(
+            cls,
+            ApiEndpoint(path=RESPONSES_ENDPOINT, method="POST"),
+            response_model=OpenAIResponse,
+            data=OpenAICreateResponse(
+                input=[
+                    InputMessage(
+                        content=cls.create_input_message_contents(prompt, images, files),
+                        role="user",
+                    ),
+                ],
+                store=True,
+                stream=False,
+                model=model,
+                previous_response_id=None,
+                **(advanced_options.model_dump(exclude_none=True) if advanced_options else {}),
+            ),
+        )
+        response_id = create_response.id
+
+        # Get result output
+        result_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"{RESPONSES_ENDPOINT}/{response_id}"),
+            response_model=OpenAIResponse,
+            status_extractor=lambda response: response.status,
+            completed_statuses=["incomplete", "completed"],
+        )
+        return IO.NodeOutput(cls.get_text_from_message_content(cls.get_message_content_from_response(result_response)))
+
+
+class OpenAIInputFiles(IO.ComfyNode):
+    """
+    Loads and formats input files for OpenAI API.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        """
+        For details about the supported file input types, see:
+        https://platform.openai.com/docs/guides/pdf-files?api-mode=responses
+        """
+        input_dir = folder_paths.get_input_directory()
+        input_files = [
+            f
+            for f in os.scandir(input_dir)
+            if f.is_file()
+            and (f.name.endswith(".txt") or f.name.endswith(".pdf"))
+            and f.stat().st_size < 32 * 1024 * 1024
+        ]
+        input_files = sorted(input_files, key=lambda x: x.name)
+        input_files = [f.name for f in input_files]
+        return IO.Schema(
+            node_id="OpenAIInputFiles",
+            display_name="OpenAI ChatGPT Input Files",
+            category="partner/text/OpenAI",
+            description="Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes.",
+            inputs=[
+                IO.Combo.Input(
+                    "file",
+                    options=input_files,
+                    default=input_files[0] if input_files else None,
+                    tooltip="Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.",
+                ),
+                IO.Custom("OPENAI_INPUT_FILES").Input(
+                    "OPENAI_INPUT_FILES",
+                    tooltip="An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Custom("OPENAI_INPUT_FILES").Output(),
+            ],
+        )
+
+    @classmethod
+    def create_input_file_content(cls, file_path: str) -> InputFileContent:
+        return InputFileContent(
+            file_data=text_filepath_to_data_uri(file_path),
+            filename=os.path.basename(file_path),
+            type="input_file",
+        )
+
+    @classmethod
+    def execute(cls, file: str, OPENAI_INPUT_FILES: list[InputFileContent] = []) -> IO.NodeOutput:
+        """
+        Loads and formats input files for OpenAI API.
+        """
+        file_path = folder_paths.get_annotated_filepath(file)
+        input_file_content = cls.create_input_file_content(file_path)
+        files = [input_file_content] + OPENAI_INPUT_FILES
+        return IO.NodeOutput(files)
+
+
+class OpenAIChatConfig(IO.ComfyNode):
+    """Allows setting additional configuration for the OpenAI Chat Node."""
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIChatConfig",
+            display_name="OpenAI ChatGPT Advanced Options",
+            category="partner/text/OpenAI",
+            description="Allows specifying advanced configuration options for the OpenAI Chat Nodes.",
+            inputs=[
+                IO.Combo.Input(
+                    "truncation",
+                    options=["auto", "disabled"],
+                    default="auto",
+                    tooltip="The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "max_output_tokens",
+                    min=16,
+                    default=4096,
+                    max=16384,
+                    tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.String.Input(
+                    "instructions",
+                    multiline=True,
+                    optional=True,
+                    tooltip="Instructions for the model on how to generate the response",
+                ),
+            ],
+            outputs=[
+                IO.Custom("OPENAI_CHAT_CONFIG").Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        truncation: bool,
+        instructions: str | None = None,
+        max_output_tokens: int | None = None,
+    ) -> IO.NodeOutput:
+        """
+        Configure advanced options for the OpenAI Chat Node.
+
+        Note:
+            While `top_p` and `temperature` are listed as properties in the
+            spec, they are not supported for all models (e.g., o4-mini).
+            They are not exposed as inputs at all to avoid having to manually
+            remove depending on model choice.
+        """
+        return IO.NodeOutput(
+            ModelResponseProperties(
+                instructions=instructions,
+                truncation=truncation,
+                max_output_tokens=max_output_tokens,
+            )
+        )
+
+
+class OpenAIExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            OpenAIDalle2,
+            OpenAIDalle3,
+            OpenAIGPTImage1,
+            OpenAIGPTImageNodeV2,
+            OpenAIChatNode,
+            OpenAIInputFiles,
+            OpenAIChatConfig,
+        ]
+
+
+async def comfy_entrypoint() -> OpenAIExtension:
+    return OpenAIExtension()
diff --git a/comfy_api_nodes/nodes_openrouter.py b/comfy_api_nodes/nodes_openrouter.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d4cf52e0c41f9bf72932b934643c749bcada147
--- /dev/null
+++ b/comfy_api_nodes/nodes_openrouter.py
@@ -0,0 +1,380 @@
+"""API Nodes for OpenRouter LLM chat completions."""
+
+from dataclasses import dataclass
+from typing import Literal
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.openrouter import (
+    OpenRouterChatRequest,
+    OpenRouterChatResponse,
+    OpenRouterContentBlock,
+    OpenRouterImageContent,
+    OpenRouterImageUrl,
+    OpenRouterMessage,
+    OpenRouterReasoningConfig,
+    OpenRouterTextContent,
+    OpenRouterVideoContent,
+    OpenRouterVideoUrl,
+    OpenRouterWebSearchOptions,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    get_number_of_images,
+    sync_op,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+)
+
+OPENROUTER_CHAT_ENDPOINT = "/proxy/openrouter/api/v1/chat/completions"
+
+
+Profile = Literal["standard", "reasoning", "frontier_reasoning", "perplexity", "perplexity_reasoning"]
+
+
+@dataclass(frozen=True)
+class _ModelSpec:
+    slug: str  # exact OpenRouter model id
+    profile: Profile
+    price_in: float  # USD per token (prompt)
+    price_out: float  # USD per token (completion)
+    max_images: int = 0  # 0 = no image input; otherwise max URL-passed images supported
+    max_videos: int = 0  # 0 = no video input; otherwise max URL-passed videos supported
+
+
+MODELS: list[_ModelSpec] = [
+    _ModelSpec("anthropic/claude-opus-5", "frontier_reasoning", 0.00000715, 0.00003575, max_images=20),
+    _ModelSpec("anthropic/claude-opus-4.8", "frontier_reasoning", 0.00000715, 0.00003575, max_images=20),
+    _ModelSpec("anthropic/claude-opus-4.7", "frontier_reasoning", 0.00000715, 0.00003575, max_images=20),
+    _ModelSpec("anthropic/claude-fable-5", "frontier_reasoning", 0.0000143, 0.0000715, max_images=20),
+    _ModelSpec("anthropic/claude-sonnet-5", "frontier_reasoning", 0.00000286, 0.0000143, max_images=20),
+    _ModelSpec("anthropic/claude-haiku-4.5", "frontier_reasoning", 0.00000143, 0.00000715, max_images=20),
+    _ModelSpec("openai/gpt-5.6-sol-pro", "frontier_reasoning", 0.00000715, 0.0000429, max_images=20),
+    _ModelSpec("openai/gpt-5.6-sol", "frontier_reasoning", 0.00000715, 0.0000429, max_images=20),
+    _ModelSpec("openai/gpt-5.6-terra-pro", "frontier_reasoning", 0.000003575, 0.00002145, max_images=20),
+    _ModelSpec("openai/gpt-5.6-terra", "frontier_reasoning", 0.000003575, 0.00002145, max_images=20),
+    _ModelSpec("openai/gpt-5.6-luna-pro", "frontier_reasoning", 0.00000143, 0.00000858, max_images=20),
+    _ModelSpec("openai/gpt-5.6-luna", "frontier_reasoning", 0.00000143, 0.00000858, max_images=20),
+    _ModelSpec("openai/gpt-5.5-pro", "frontier_reasoning", 0.0000429, 0.0002574, max_images=20),
+    _ModelSpec("openai/gpt-5.5", "frontier_reasoning", 0.00000715, 0.0000429, max_images=20),
+    _ModelSpec("google/gemini-3.5-flash", "reasoning", 0.000002145, 0.00001287, max_images=20, max_videos=4),
+    _ModelSpec("x-ai/grok-4.5", "reasoning", 0.00000286, 0.00000858, max_images=20),
+    _ModelSpec("x-ai/grok-4.20", "reasoning", 0.0000017875, 0.000003575, max_images=20),
+    _ModelSpec("x-ai/grok-4.3", "reasoning", 0.0000017875, 0.000003575, max_images=20),
+    _ModelSpec("deepseek/deepseek-v4-pro", "reasoning", 0.00000062205, 0.0000012441),
+    _ModelSpec("deepseek/deepseek-v4-flash", "reasoning", 0.00000016016, 0.00000032032),
+    _ModelSpec("deepseek/deepseek-v3.2", "reasoning", 0.00000036036, 0.00000054054),
+    _ModelSpec("qwen/qwen3.6-max-preview", "reasoning", 0.0000014872, 0.0000089232),
+    _ModelSpec("qwen/qwen3.6-plus", "reasoning", 0.00000046475, 0.0000027885, max_images=10, max_videos=4),
+    _ModelSpec("qwen/qwen3.6-flash", "reasoning", 0.000000268125, 0.00000160875, max_images=10, max_videos=4),
+    _ModelSpec("mistralai/mistral-large-2512", "standard", 0.000000715, 0.000002145, max_images=8),
+    _ModelSpec("mistralai/mistral-medium-3-5", "reasoning", 0.000002145, 0.000010725, max_images=8),
+    _ModelSpec("z-ai/glm-4.6", "reasoning", 0.0000006149, 0.0000024882),
+    _ModelSpec("z-ai/glm-5", "reasoning", 0.000000858, 0.0000027456),
+    _ModelSpec("moonshotai/kimi-k3", "reasoning", 0.00000429, 0.00002145, max_images=10),
+    _ModelSpec("moonshotai/kimi-k2.6", "reasoning", 0.0000010439, 0.0000049907, max_images=10),
+    _ModelSpec("moonshotai/kimi-k2-thinking", "reasoning", 0.000000858, 0.000003575),
+    _ModelSpec("perplexity/sonar-pro", "perplexity", 0.00000429, 0.00002145),
+    _ModelSpec("perplexity/sonar-reasoning-pro", "perplexity_reasoning", 0.00000286, 0.00001144),
+    _ModelSpec("perplexity/sonar-deep-research", "perplexity_reasoning", 0.00000286, 0.00001144),
+]
+
+_MODELS_BY_SLUG: dict[str, _ModelSpec] = {m.slug: m for m in MODELS}
+_REASONING_EFFORTS = ["off", "low", "medium", "high"]
+_SEARCH_CONTEXT_SIZES = ["low", "medium", "high"]
+
+
+def _reasoning_extra_inputs() -> list:
+    return [
+        IO.Combo.Input(
+            "reasoning_effort",
+            options=_REASONING_EFFORTS,
+            default="off",
+            tooltip="Reasoning effort. 'off' disables reasoning entirely.",
+            advanced=True,
+        ),
+    ]
+
+
+def _perplexity_extra_inputs() -> list:
+    return [
+        IO.Combo.Input(
+            "search_context_size",
+            options=_SEARCH_CONTEXT_SIZES,
+            default="medium",
+            tooltip="How much web search context to retrieve. Larger = more grounded but slower/pricier.",
+            advanced=True,
+        ),
+    ]
+
+
+def _profile_inputs(profile: Profile) -> list:
+    if profile == "standard":
+        return []
+    if profile in ("reasoning", "frontier_reasoning"):
+        return _reasoning_extra_inputs()
+    if profile == "perplexity":
+        return _perplexity_extra_inputs()
+    if profile == "perplexity_reasoning":
+        return _perplexity_extra_inputs() + _reasoning_extra_inputs()
+    raise ValueError(f"Unknown profile: {profile}")
+
+
+def _media_inputs(spec: _ModelSpec) -> list:
+    extras: list = []
+    if spec.max_images > 0:
+        extras.append(
+            IO.Autogrow.Input(
+                "images",
+                template=IO.Autogrow.TemplateNames(
+                    IO.Image.Input("image"),
+                    names=[f"image_{i}" for i in range(1, spec.max_images + 1)],
+                    min=0,
+                ),
+                tooltip=f"Optional reference image(s) — up to {spec.max_images}. Sent as URLs.",
+            )
+        )
+    if spec.max_videos > 0:
+        extras.append(
+            IO.Autogrow.Input(
+                "videos",
+                template=IO.Autogrow.TemplateNames(
+                    IO.Video.Input("video"),
+                    names=[f"video_{i}" for i in range(1, spec.max_videos + 1)],
+                    min=0,
+                ),
+                tooltip=f"Optional reference video(s) — up to {spec.max_videos}. Sent as URLs.",
+            )
+        )
+    return extras
+
+
+def _inputs_for_model(spec: _ModelSpec) -> list:
+    return _profile_inputs(spec.profile) + _media_inputs(spec)
+
+
+def _build_model_options() -> list[IO.DynamicCombo.Option]:
+    return [IO.DynamicCombo.Option(spec.slug, _inputs_for_model(spec)) for spec in MODELS]
+
+
+def _price_badge_jsonata() -> str:
+    rates_pairs = []
+    for spec in MODELS:
+        prompt_per_1k = spec.price_in * 1000
+        completion_per_1k = spec.price_out * 1000
+        rates_pairs.append(f'  "{spec.slug}": [{prompt_per_1k:.8g}, {completion_per_1k:.8g}]')
+    rates_block = ",\n".join(rates_pairs)
+    return (
+        "(\n"
+        "  $rates := {\n"
+        f"{rates_block}\n"
+        "  };\n"
+        "  $r := $lookup($rates, widgets.model);\n"
+        "  $r ? {\n"
+        '    "type": "list_usd",\n'
+        '    "usd": $r,\n'
+        '    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }\n'
+        '  } : {"type": "text", "text": "Token-based"}\n'
+        ")"
+    )
+
+
+async def _build_image_blocks(
+    cls: type[IO.ComfyNode], spec: _ModelSpec, images: list[Input.Image]
+) -> list[OpenRouterImageContent]:
+    urls = await upload_images_to_comfyapi(
+        cls,
+        images,
+        max_images=spec.max_images,
+        total_pixels=2048 * 2048,
+        mime_type="image/png",
+        wait_label="Uploading reference images",
+    )
+    return [OpenRouterImageContent(image_url=OpenRouterImageUrl(url=url)) for url in urls]
+
+
+async def _build_video_blocks(cls: type[IO.ComfyNode], videos: list[Input.Video]) -> list[OpenRouterVideoContent]:
+    blocks: list[OpenRouterVideoContent] = []
+    total = len(videos)
+    for idx, video in enumerate(videos):
+        label = "Uploading reference video"
+        if total > 1:
+            label = f"{label} ({idx + 1}/{total})"
+        url = await upload_video_to_comfyapi(cls, video, wait_label=label)
+        blocks.append(OpenRouterVideoContent(video_url=OpenRouterVideoUrl(url=url)))
+    return blocks
+
+
+def _user_message(prompt: str, media_blocks: list[OpenRouterContentBlock]) -> OpenRouterMessage:
+    if not media_blocks:
+        return OpenRouterMessage(role="user", content=prompt)
+    blocks: list[OpenRouterContentBlock] = list(media_blocks)
+    blocks.append(OpenRouterTextContent(text=prompt))
+    return OpenRouterMessage(role="user", content=blocks)
+
+
+def _build_messages(
+    system_prompt: str, prompt: str, media_blocks: list[OpenRouterContentBlock]
+) -> list[OpenRouterMessage]:
+    messages: list[OpenRouterMessage] = []
+    if system_prompt:
+        messages.append(OpenRouterMessage(role="system", content=system_prompt))
+    messages.append(_user_message(prompt, media_blocks))
+    return messages
+
+
+def _build_request(
+    slug: str,
+    system_prompt: str,
+    prompt: str,
+    media_blocks: list[OpenRouterContentBlock],
+    *,
+    seed: int,
+    reasoning_effort: str | None,
+    search_context_size: str | None,
+) -> OpenRouterChatRequest:
+    reasoning_cfg: OpenRouterReasoningConfig | None = None
+    if reasoning_effort and reasoning_effort != "off":
+        # exclude=True asks providers to reason internally but not return the trace
+        reasoning_cfg = OpenRouterReasoningConfig(effort=reasoning_effort, exclude=True)
+    web_search_cfg: OpenRouterWebSearchOptions | None = None
+    if search_context_size:
+        web_search_cfg = OpenRouterWebSearchOptions(search_context_size=search_context_size)
+    return OpenRouterChatRequest(
+        model=slug,
+        messages=_build_messages(system_prompt, prompt, media_blocks),
+        seed=seed if seed > 0 else None,
+        reasoning=reasoning_cfg,
+        web_search_options=web_search_cfg,
+    )
+
+
+def _extract_text(response: OpenRouterChatResponse) -> str:
+    if response.error:
+        code = response.error.code if response.error.code is not None else "unknown"
+        raise ValueError(f"OpenRouter error ({code}): {response.error.message or 'no message'}")
+    if not response.choices:
+        raise ValueError("Empty response from OpenRouter (no choices).")
+    message = response.choices[0].message
+    if not message:
+        raise ValueError("Empty response from OpenRouter (no message).")
+    if message.refusal:
+        raise ValueError(f"Model refused to respond: {message.refusal}")
+    return message.content or ""
+
+
+class OpenRouterLLMNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenRouterLLMNode",
+            display_name="OpenRouter LLM",
+            category="partner/text/OpenRouter",
+            essentials_category="Text Generation",
+            description=(
+                "Generate text responses through OpenRouter. Routes to a curated set of popular "
+                "models from Anthropic (Claude), OpenAI (GPT), Google (Gemini), xAI (Grok), "
+                "DeepSeek, Qwen, Mistral, Z.AI (GLM), Moonshot (Kimi), and Perplexity Sonar."
+            ),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text input to the model.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=_build_model_options(),
+                    tooltip="The OpenRouter model used to generate the response.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for sampling. Set to 0 to omit. Most models treat this as a hint only.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    advanced=True,
+                    tooltip="Foundational instructions that dictate the model's behavior.",
+                ),
+            ],
+            outputs=[IO.String.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr=_price_badge_jsonata(),
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        slug: str = model["model"]
+        spec = _MODELS_BY_SLUG.get(slug)
+        if spec is None:
+            raise ValueError(f"Unknown OpenRouter model: {slug}")
+
+        reasoning_effort: str | None = model.get("reasoning_effort")
+        search_context_size: str | None = model.get("search_context_size")
+
+        image_tensors: list[Input.Image] = [t for t in (model.get("images") or {}).values() if t is not None]
+        if image_tensors and sum(get_number_of_images(t) for t in image_tensors) > spec.max_images:
+            raise ValueError(f"Up to {spec.max_images} images are supported for {slug}.")
+        video_inputs: list[Input.Video] = [v for v in (model.get("videos") or {}).values() if v is not None]
+        if video_inputs and len(video_inputs) > spec.max_videos:
+            raise ValueError(f"Up to {spec.max_videos} videos are supported for {slug}.")
+
+        media_blocks: list[OpenRouterContentBlock] = []
+        if image_tensors:
+            media_blocks.extend(await _build_image_blocks(cls, spec, image_tensors))
+        if video_inputs:
+            media_blocks.extend(await _build_video_blocks(cls, video_inputs))
+
+        request = _build_request(
+            slug,
+            system_prompt,
+            prompt,
+            media_blocks,
+            seed=seed,
+            reasoning_effort=reasoning_effort,
+            search_context_size=search_context_size,
+        )
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=OPENROUTER_CHAT_ENDPOINT, method="POST"),
+            response_model=OpenRouterChatResponse,
+            data=request,
+        )
+        return IO.NodeOutput(_extract_text(response))
+
+
+class OpenRouterExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [OpenRouterLLMNode]
+
+
+async def comfy_entrypoint() -> OpenRouterExtension:
+    return OpenRouterExtension()
diff --git a/comfy_api_nodes/nodes_pixverse.py b/comfy_api_nodes/nodes_pixverse.py
new file mode 100644
index 0000000000000000000000000000000000000000..52e43ff4ecdf12a693b85b6afb89f61e8a34e213
--- /dev/null
+++ b/comfy_api_nodes/nodes_pixverse.py
@@ -0,0 +1,1043 @@
+import re
+
+import torch
+from typing_extensions import override
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.pixverse import (
+    PixverseTextVideoRequest,
+    PixverseImageVideoRequest,
+    PixverseTransitionVideoRequest,
+    PixverseImageUploadResponse,
+    PixverseMediaUploadResponse,
+    PixverseVideoResponse,
+    PixverseGenerationStatusResponse,
+    PixverseAspectRatio,
+    PixverseImageReference,
+    PixverseQuality,
+    PixverseDuration,
+    PixverseMotionMode,
+    PixverseReferenceType,
+    PixverseStatus,
+    PixverseV6AspectRatio,
+    PixverseV6ExtendVideoRequest,
+    PixverseV6FusionVideoRequest,
+    PixverseV6ImageVideoRequest,
+    PixverseV6Style,
+    PixverseV6TextVideoRequest,
+    PixverseV6TransitionVideoRequest,
+    PixverseVideoReference,
+    PixverseIO,
+    pixverse_templates,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    poll_op,
+    sync_op,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+    validate_video_dimensions,
+    validate_video_duration,
+)
+
+AVERAGE_DURATION_T2V = 32
+AVERAGE_DURATION_I2V = 30
+AVERAGE_DURATION_T2T = 52
+
+V6_MAX_PROMPT_LENGTH = 5000
+V6_MAX_NEGATIVE_PROMPT_LENGTH = 2048
+V6_MIN_DURATION = 1
+V6_MAX_DURATION = 15
+V6_MAX_SUBJECTS = 8
+V6_MAX_BACKGROUNDS = 2
+V6_MAX_REFERENCE_IMAGES_OMNI = 10
+V6_MAX_REFERENCE_IMAGES_PLAIN = 7
+V6_MAX_REFERENCE_VIDEOS = 2
+V6_MAX_REFERENCE_VIDEO_SECONDS = 15
+V6_EXTEND_SOURCE_SECONDS_LIMIT = 40
+V6_MAX_SOURCE_VIDEO_SIDE = 1920
+
+PIXVERSE_STATUS_LABELS = {
+    PixverseStatus.successful: "completed",
+    PixverseStatus.generating: "generating",
+    PixverseStatus.deleted: "deleted",
+    PixverseStatus.contents_moderation: "content moderation failed",
+    PixverseStatus.failed: "generation failed",
+}
+PIXVERSE_COMPLETED_STATUSES = ["completed"]
+PIXVERSE_FAILED_STATUSES = ["deleted", "content moderation failed", "generation failed"]
+
+
+def _pixverse_status(response) -> str:
+    resp = getattr(response, "Resp", None)
+    status = getattr(resp, "status", None)
+    return PIXVERSE_STATUS_LABELS.get(status, f"unknown status {status}")
+
+
+async def upload_video_to_pixverse(cls: type[IO.ComfyNode], video: Input.Video) -> int:
+    response_upload = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/pixverse/media/upload", method="POST"),
+        response_model=PixverseMediaUploadResponse,
+        data={"file_url": await upload_video_to_comfyapi(cls, video)},
+        content_type="multipart/form-data",
+    )
+    if response_upload.Resp is None or response_upload.Resp.media_id is None:
+        raise Exception(f"PixVerse video upload request failed: '{response_upload.ErrMsg}'")
+    return response_upload.Resp.media_id
+
+
+async def upload_image_to_pixverse(cls: type[IO.ComfyNode], image: torch.Tensor) -> int:
+    image_urls = await upload_images_to_comfyapi(cls, image, max_images=1)
+    response_upload = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/pixverse/image/upload", method="POST"),
+        response_model=PixverseImageUploadResponse,
+        data={"image_url": image_urls[0]},
+        content_type="multipart/form-data",
+    )
+    if response_upload.Resp is None or response_upload.Resp.img_id is None:
+        raise Exception(f"PixVerse image upload request failed: '{response_upload.ErrMsg}'")
+    return response_upload.Resp.img_id
+
+
+class PixverseTemplateNode(IO.ComfyNode):
+    """
+    Select template for PixVerse Video generation.
+    """
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="PixverseTemplateNode",
+            display_name="PixVerse Template",
+            category="partner/video/PixVerse",
+            inputs=[
+                IO.Combo.Input("template", options=list(pixverse_templates.keys())),
+            ],
+            outputs=[IO.Custom(PixverseIO.TEMPLATE).Output(display_name="pixverse_template")],
+        )
+
+    @classmethod
+    def execute(cls, template: str) -> IO.NodeOutput:
+        template_id = pixverse_templates.get(template, None)
+        if template_id is None:
+            raise Exception(f"Template '{template}' is not recognized.")
+        return IO.NodeOutput(template_id)
+
+
+class PixverseTextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="PixverseTextToVideoNode",
+            display_name="PixVerse Text to Video",
+            category="partner/video/PixVerse",
+            description="Generates videos based on prompt and output_size.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=PixverseAspectRatio,
+                ),
+                IO.Combo.Input(
+                    "quality",
+                    options=PixverseQuality,
+                    default=PixverseQuality.res_540p,
+                ),
+                IO.Combo.Input(
+                    "duration_seconds",
+                    options=PixverseDuration,
+                ),
+                IO.Combo.Input(
+                    "motion_mode",
+                    options=PixverseMotionMode,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+                IO.Custom(PixverseIO.TEMPLATE).Input(
+                    "pixverse_template",
+                    tooltip="An optional template to influence style of generation, created by the PixVerse Template node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        aspect_ratio: str,
+        quality: str,
+        duration_seconds: int,
+        motion_mode: str,
+        seed,
+        negative_prompt: str = None,
+        pixverse_template: int = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1)
+        # 1080p is limited to 5 seconds duration
+        # only normal motion_mode supported for 1080p or for non-5 second duration
+        if quality == PixverseQuality.res_1080p:
+            motion_mode = PixverseMotionMode.normal
+            duration_seconds = PixverseDuration.dur_5
+        elif duration_seconds != PixverseDuration.dur_5:
+            motion_mode = PixverseMotionMode.normal
+
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/pixverse/video/text/generate", method="POST"),
+            response_model=PixverseVideoResponse,
+            data=PixverseTextVideoRequest(
+                prompt=prompt,
+                aspect_ratio=aspect_ratio,
+                quality=quality,
+                duration=duration_seconds,
+                motion_mode=motion_mode,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                template_id=pixverse_template,
+                seed=seed,
+            ),
+        )
+        if response_api.Resp is None:
+            raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'")
+
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}"),
+            response_model=PixverseGenerationStatusResponse,
+            completed_statuses=PIXVERSE_COMPLETED_STATUSES,
+            failed_statuses=PIXVERSE_FAILED_STATUSES,
+            status_extractor=_pixverse_status,
+            estimated_duration=AVERAGE_DURATION_T2V,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response_poll.Resp.url))
+
+
+class PixverseImageToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="PixverseImageToVideoNode",
+            display_name="PixVerse Image to Video",
+            category="partner/video/PixVerse",
+            description="Generates videos based on prompt and output_size.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                IO.Combo.Input(
+                    "quality",
+                    options=PixverseQuality,
+                    default=PixverseQuality.res_540p,
+                ),
+                IO.Combo.Input(
+                    "duration_seconds",
+                    options=PixverseDuration,
+                ),
+                IO.Combo.Input(
+                    "motion_mode",
+                    options=PixverseMotionMode,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+                IO.Custom(PixverseIO.TEMPLATE).Input(
+                    "pixverse_template",
+                    tooltip="An optional template to influence style of generation, created by the PixVerse Template node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        prompt: str,
+        quality: str,
+        duration_seconds: int,
+        motion_mode: str,
+        seed,
+        negative_prompt: str = None,
+        pixverse_template: int = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+        img_id = await upload_image_to_pixverse(cls, image)
+
+        # 1080p is limited to 5 seconds duration
+        # only normal motion_mode supported for 1080p or for non-5 second duration
+        if quality == PixverseQuality.res_1080p:
+            motion_mode = PixverseMotionMode.normal
+            duration_seconds = PixverseDuration.dur_5
+        elif duration_seconds != PixverseDuration.dur_5:
+            motion_mode = PixverseMotionMode.normal
+
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/pixverse/video/img/generate", method="POST"),
+            response_model=PixverseVideoResponse,
+            data=PixverseImageVideoRequest(
+                img_id=img_id,
+                prompt=prompt,
+                quality=quality,
+                duration=duration_seconds,
+                motion_mode=motion_mode,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                template_id=pixverse_template,
+                seed=seed,
+            ),
+        )
+
+        if response_api.Resp is None:
+            raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'")
+
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}"),
+            response_model=PixverseGenerationStatusResponse,
+            completed_statuses=PIXVERSE_COMPLETED_STATUSES,
+            failed_statuses=PIXVERSE_FAILED_STATUSES,
+            status_extractor=_pixverse_status,
+            estimated_duration=AVERAGE_DURATION_I2V,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response_poll.Resp.url))
+
+
+class PixverseTransitionVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="PixverseTransitionVideoNode",
+            display_name="PixVerse Transition Video",
+            category="partner/video/PixVerse",
+            description="Generates videos based on prompt and output_size.",
+            inputs=[
+                IO.Image.Input("first_frame"),
+                IO.Image.Input("last_frame"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                IO.Combo.Input(
+                    "quality",
+                    options=PixverseQuality,
+                    default=PixverseQuality.res_540p,
+                ),
+                IO.Combo.Input(
+                    "duration_seconds",
+                    options=PixverseDuration,
+                ),
+                IO.Combo.Input(
+                    "motion_mode",
+                    options=PixverseMotionMode,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_VIDEO,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        first_frame: torch.Tensor,
+        last_frame: torch.Tensor,
+        prompt: str,
+        quality: str,
+        duration_seconds: int,
+        motion_mode: str,
+        seed,
+        negative_prompt: str = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False)
+        first_frame_id = await upload_image_to_pixverse(cls, first_frame)
+        last_frame_id = await upload_image_to_pixverse(cls, last_frame)
+
+        # 1080p is limited to 5 seconds duration
+        # only normal motion_mode supported for 1080p or for non-5 second duration
+        if quality == PixverseQuality.res_1080p:
+            motion_mode = PixverseMotionMode.normal
+            duration_seconds = PixverseDuration.dur_5
+        elif duration_seconds != PixverseDuration.dur_5:
+            motion_mode = PixverseMotionMode.normal
+
+        response_api = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/pixverse/video/transition/generate", method="POST"),
+            response_model=PixverseVideoResponse,
+            data=PixverseTransitionVideoRequest(
+                first_frame_img=first_frame_id,
+                last_frame_img=last_frame_id,
+                prompt=prompt,
+                quality=quality,
+                duration=duration_seconds,
+                motion_mode=motion_mode,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                seed=seed,
+            ),
+        )
+
+        if response_api.Resp is None:
+            raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'")
+
+        response_poll = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}"),
+            response_model=PixverseGenerationStatusResponse,
+            completed_statuses=PIXVERSE_COMPLETED_STATUSES,
+            failed_statuses=PIXVERSE_FAILED_STATUSES,
+            status_extractor=_pixverse_status,
+            estimated_duration=AVERAGE_DURATION_T2V,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response_poll.Resp.url))
+
+
+PRICE_BADGE_VIDEO = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["duration_seconds", "quality", "motion_mode"]),
+    expr="""
+    (
+      $prices := {
+        "5": {
+          "1080p": {"normal": 1.716},
+          "720p": {"normal": 0.858, "fast": 1.716},
+          "540p": {"normal": 0.6435, "fast": 1.287},
+          "360p": {"normal": 0.6435, "fast": 1.287}
+        },
+        "8": {
+          "720p": {"normal": 1.716},
+          "540p": {"normal": 1.287},
+          "360p": {"normal": 1.287}
+        }
+      };
+      $quality := $lowercase($string($lookup(widgets, "quality")));
+      $duration := $quality = "1080p" ? "5" : $string($lookup(widgets, "duration_seconds"));
+      $motion := $quality = "1080p" or $duration != "5"
+        ? "normal" : $lowercase($string($lookup(widgets, "motion_mode")));
+      $price := $lookup($lookup($lookup($prices, $duration), $quality), $motion);
+      $type($price) = "number" ? {"type":"usd","usd": $price} : undefined
+    )
+    """,
+)
+
+
+PIXVERSE_MODELS = {
+    "PixVerse V6": "v6",
+}
+
+PRICE_BADGE_PIXVERSE = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(
+        widgets=["model", "model.quality", "model.duration_seconds", "model.generate_audio"]
+    ),
+    expr="""
+    (
+      $prices := {
+        "pixverse v6": {
+          "360p": {"false": 0.0715, "true": 0.1001},
+          "540p": {"false": 0.1001, "true": 0.1287},
+          "720p": {"false": 0.1287, "true": 0.1716},
+          "1080p": {"false": 0.2574, "true": 0.3289}
+        }
+      };
+      $model := $lookup(widgets, "model");
+      $table := $type($model) = "string" ? $lookup($prices, $lowercase($model)) : undefined;
+      $quality := $lookup(widgets, "model.quality");
+      $row := $type($table) = "object" and $type($quality) = "string"
+        ? $lookup($table, $lowercase($quality)) : undefined;
+      $audio := $string($lookup(widgets, "model.generate_audio")) = "true" ? "true" : "false";
+      $pps := $type($row) = "object" ? $lookup($row, $audio) : undefined;
+      $durationRaw := $lookup(widgets, "model.duration_seconds");
+      $duration := $type($durationRaw) in ["string", "number"] ? $number($durationRaw) : undefined;
+      $type($pps) = "number" and $type($duration) = "number"
+        ? {"type":"usd","usd": $pps * $duration}
+        : undefined
+    )
+    """,
+)
+
+
+PRICE_BADGE_PIXVERSE_FUSION = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(
+        widgets=["model", "model.quality", "model.duration_seconds", "model.generate_audio"],
+        input_groups=["videos"],
+    ),
+    expr="""
+    (
+      $rates := {
+        "pixverse v6": {
+          "360p": {"false": 0.0715, "true": 0.1001},
+          "540p": {"false": 0.1001, "true": 0.1287},
+          "720p": {"false": 0.1287, "true": 0.1716},
+          "1080p": {"false": 0.2574, "true": 0.3289}
+        }
+      };
+      $model := $lookup(widgets, "model");
+      $table := $type($model) = "string" ? $lookup($rates, $lowercase($model)) : undefined;
+      $quality := $lookup(widgets, "model.quality");
+      $row := $type($table) = "object" and $type($quality) = "string"
+        ? $lookup($table, $lowercase($quality)) : undefined;
+      $audio := $string($lookup(widgets, "model.generate_audio")) = "true" ? "true" : "false";
+      $pps := $type($row) = "object" ? $lookup($row, $audio) : undefined;
+      $hasVideo := $exists(inputGroups) and $lookup(inputGroups, "videos") > 0;
+      $durationRaw := $lookup(widgets, "model.duration_seconds");
+      $duration := $type($durationRaw) in ["string", "number"] ? $number($durationRaw) : undefined;
+      $type($pps) = "number"
+        ? ($hasVideo
+            ? {"type":"usd","usd": $pps * 2, "format":{"suffix":"/second of reference video"}}
+            : ($type($duration) = "number" ? {"type":"usd","usd": $pps * $duration} : undefined))
+        : undefined
+    )
+    """,
+)
+
+def _pixverse6_inputs(
+    *,
+    aspect_ratio_options: list | None = None,
+    with_multi_clip: bool = False,
+    prompt_tooltip: str = "Prompt for the video generation.",
+    quality_tooltip: str = "Output resolution. Sets the long edge: 360p is 640px, 540p 1024px, "
+    "720p 1280px, 1080p 1920px.",
+) -> list:
+    inputs = [
+        IO.String.Input("prompt", multiline=True, default="", tooltip=prompt_tooltip),
+    ]
+    if aspect_ratio_options is not None:
+        inputs.append(
+            IO.Combo.Input(
+                "aspect_ratio",
+                options=aspect_ratio_options,
+                tooltip="Output aspect ratio.",
+            )
+        )
+    inputs += [
+        IO.Combo.Input(
+            "quality",
+            options=PixverseQuality,
+            default=PixverseQuality.res_720p,
+            tooltip=quality_tooltip,
+        ),
+        IO.Int.Input(
+            "duration_seconds",
+            default=5,
+            min=V6_MIN_DURATION,
+            max=V6_MAX_DURATION,
+            tooltip="Length of the generated video in seconds.",
+        ),
+        IO.Boolean.Input(
+            "generate_audio",
+            default=True,
+            tooltip="Generate a native audio track together with the video.",
+        ),
+    ]
+    if with_multi_clip:
+        inputs.append(
+            IO.Boolean.Input(
+                "multi_clip",
+                default=False,
+                tooltip="Let the model cut the video into several shots instead of one continuous take.",
+            )
+        )
+    inputs += [
+        IO.Int.Input(
+            "seed",
+            default=42,
+            min=0,
+            max=2147483647,
+            control_after_generate=True,
+            tooltip="Seed for video generation. PixVerse records it but does not reproduce a run from it.",
+        ),
+        IO.String.Input(
+            "negative_prompt",
+            default="",
+            multiline=True,
+            optional=True,
+            tooltip="An optional text description of undesired elements in the video.",
+        ),
+        IO.Combo.Input(
+            "style",
+            options=PixverseV6Style,
+            default=PixverseV6Style.none,
+            optional=True,
+            tooltip="An optional visual style applied to the whole video.",
+        ),
+    ]
+    return inputs
+
+
+def _pixverse_model_input(**kwargs) -> IO.DynamicCombo.Input:
+    return IO.DynamicCombo.Input(
+        "model",
+        options=[IO.DynamicCombo.Option("PixVerse V6", _pixverse6_inputs(**kwargs))],
+        tooltip="Model and generation settings.",
+    )
+
+
+def _validate_prompts(prompt: str, negative_prompt: str | None) -> None:
+    validate_string(prompt, strip_whitespace=True, min_length=1, max_length=V6_MAX_PROMPT_LENGTH)
+    if negative_prompt and len(negative_prompt) > V6_MAX_NEGATIVE_PROMPT_LENGTH:
+        raise ValueError(
+            f"Negative prompt must be at most {V6_MAX_NEGATIVE_PROMPT_LENGTH} characters, "
+            f"got {len(negative_prompt)}."
+        )
+
+
+def _model_style(model: dict) -> str | None:
+    style = model.get("style")
+    if not style or style == PixverseV6Style.none:
+        return None
+    return style
+
+
+def _model_common(model: dict) -> dict:
+    _validate_prompts(model["prompt"], model.get("negative_prompt"))
+    return {
+        "model": PIXVERSE_MODELS[model["model"]],
+        "prompt": model["prompt"],
+        "quality": model["quality"],
+        "duration": model["duration_seconds"],
+        "generate_audio_switch": model["generate_audio"],
+        "negative_prompt": model.get("negative_prompt") or None,
+        "style": _model_style(model),
+        "seed": model["seed"],
+    }
+
+
+def _pixverse_error(response) -> Exception:
+    code = response.ErrCode
+    message = response.ErrMsg or "unknown error"
+    if code == 500044:
+        return Exception(
+            "PixVerse is already running the maximum number of simultaneous generations. "
+            "Try again in a moment."
+        )
+    if code == 500090:
+        return Exception("PixVerse rejected the request: the provider account is out of credits.")
+    if code == 500063:
+        return Exception(f"PixVerse content moderation rejected the request: {message}")
+    return Exception(f"PixVerse request failed ({code}): '{message}'")
+
+
+async def _pixverse_generate(cls: type[IO.ComfyNode], path: str, request) -> IO.NodeOutput:
+    response_api = await sync_op(
+        cls,
+        ApiEndpoint(path=path, method="POST"),
+        response_model=PixverseVideoResponse,
+        data=request,
+    )
+    if response_api.Resp is None:
+        raise _pixverse_error(response_api)
+    response_poll = await poll_op(
+        cls,
+        ApiEndpoint(path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}"),
+        response_model=PixverseGenerationStatusResponse,
+        completed_statuses=PIXVERSE_COMPLETED_STATUSES,
+        failed_statuses=PIXVERSE_FAILED_STATUSES,
+        status_extractor=_pixverse_status,
+    )
+    return IO.NodeOutput(await download_url_to_video_output(response_poll.Resp.url))
+
+
+class PixverseV6TextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="PixverseV6TextToVideoNode",
+            display_name="PixVerse V6 Text to Video",
+            category="partner/video/PixVerse",
+            description="Generates a video from a text prompt with PixVerse, optionally with native audio.",
+            inputs=[
+                _pixverse_model_input(
+                    aspect_ratio_options=PixverseV6AspectRatio,
+                    with_multi_clip=True,
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_PIXVERSE,
+        )
+
+    @classmethod
+    async def execute(cls, model: dict) -> IO.NodeOutput:
+        return await _pixverse_generate(
+            cls,
+            "/proxy/pixverse/video/text/generate",
+            PixverseV6TextVideoRequest(
+                **_model_common(model),
+                aspect_ratio=model["aspect_ratio"],
+                generate_multi_clip_switch=model["multi_clip"],
+            ),
+        )
+
+
+class PixverseV6ImageToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="PixverseV6ImageToVideoNode",
+            display_name="PixVerse V6 Image to Video",
+            category="partner/video/PixVerse",
+            description="Animates an image with PixVerse, optionally with native audio. "
+            "The output keeps the aspect ratio of the input image.",
+            inputs=[
+                IO.Image.Input("image"),
+                _pixverse_model_input(with_multi_clip=True),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_PIXVERSE,
+        )
+
+    @classmethod
+    async def execute(cls, image: torch.Tensor, model: dict) -> IO.NodeOutput:
+        common = _model_common(model)
+        return await _pixverse_generate(
+            cls,
+            "/proxy/pixverse/video/img/generate",
+            PixverseV6ImageVideoRequest(
+                **common,
+                img_id=await upload_image_to_pixverse(cls, image),
+                generate_multi_clip_switch=model["multi_clip"],
+            ),
+        )
+
+
+class PixverseV6FirstLastFrameNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="PixverseV6FirstLastFrameNode",
+            display_name="PixVerse V6 First-Last-Frame to Video",
+            category="partner/video/PixVerse",
+            description="Generates a video that transitions from a first frame to a last frame with PixVerse, "
+            "optionally with native audio. The output keeps the aspect ratio of the first frame.",
+            inputs=[
+                IO.Image.Input("first_frame"),
+                IO.Image.Input("last_frame"),
+                _pixverse_model_input(prompt_tooltip="Prompt describing the transition."),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_PIXVERSE,
+        )
+
+    @classmethod
+    async def execute(cls, first_frame: torch.Tensor, last_frame: torch.Tensor, model: dict) -> IO.NodeOutput:
+        common = _model_common(model)
+        return await _pixverse_generate(
+            cls,
+            "/proxy/pixverse/video/transition/generate",
+            PixverseV6TransitionVideoRequest(
+                **common,
+                first_frame_img=await upload_image_to_pixverse(cls, first_frame),
+                last_frame_img=await upload_image_to_pixverse(cls, last_frame),
+            ),
+        )
+
+
+class PixverseV6ExtendVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="PixverseV6ExtendVideoNode",
+            display_name="PixVerse V6 Extend Video",
+            category="partner/video/PixVerse",
+            description="Continues an existing video with PixVerse, optionally with native audio. The source must "
+            "be under 40 seconds and at most 1920px on either side. The output keeps the source's resolution, so "
+            "quality sets how well the continuation is rendered rather than the frame size.",
+            inputs=[
+                IO.Video.Input("video", tooltip="Video to continue."),
+                _pixverse_model_input(
+                    prompt_tooltip="Prompt describing how the video should continue.",
+                    quality_tooltip="Render quality of the generated continuation: 1080p looks markedly better than "
+                    "540p or 360p. It never resizes - the output keeps the source video's resolution.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_PIXVERSE,
+        )
+
+    @classmethod
+    async def execute(cls, video: Input.Video, model: dict) -> IO.NodeOutput:
+        common = _model_common(model)
+        source_seconds = video.get_duration()
+        if source_seconds >= V6_EXTEND_SOURCE_SECONDS_LIMIT:
+            raise ValueError(
+                f"PixVerse extends videos shorter than {V6_EXTEND_SOURCE_SECONDS_LIMIT} seconds; "
+                f"this one is {source_seconds:.2f}s."
+            )
+        validate_video_dimensions(video, max_width=V6_MAX_SOURCE_VIDEO_SIDE, max_height=V6_MAX_SOURCE_VIDEO_SIDE)
+        return await _pixverse_generate(
+            cls,
+            "/proxy/pixverse/video/extend/generate",
+            PixverseV6ExtendVideoRequest(
+                **common,
+                video_media_id=await upload_video_to_pixverse(cls, video),
+            ),
+        )
+
+
+def _rewrite_reference_tags(prompt: str, ref_names: set[str]) -> str:
+    def repl(match: re.Match) -> str:
+        name = match.group(1).lower() + match.group(2)
+        if name not in ref_names:
+            available = ", ".join(f"@{n}" for n in sorted(ref_names)) or "none"
+            raise ValueError(f"@{match.group(1)}{match.group(2)} is not connected. Available references: {available}.")
+        return f"@{name} "
+
+    return re.sub(
+        r"(?<!\S)@(subject|background|video)([0-9]+)\b\s*",
+        repl,
+        prompt,
+        flags=re.IGNORECASE,
+    )
+
+
+class PixverseV6FusionVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="PixverseV6FusionVideoNode",
+            display_name="PixVerse V6 Fusion (Reference to Video)",
+            category="partner/video/PixVerse",
+            description="Composes a video from reference subjects, backgrounds and videos with PixVerse. "
+            "Place a reference in the scene by naming it in the prompt, for example "
+            "'@Subject1 walks through @Background1'. Connecting a reference video switches the model to Omni mode, "
+            "where the output length matches the longest reference video.",
+            inputs=[
+                IO.Autogrow.Input(
+                    "subjects",
+                    template=IO.Autogrow.TemplateNames(
+                        IO.Image.Input("subject"),
+                        names=[f"subject{i}" for i in range(1, V6_MAX_SUBJECTS + 1)],
+                        min=0,
+                    ),
+                    tooltip="Reference images of the subjects to place in the scene.",
+                ),
+                IO.Autogrow.Input(
+                    "backgrounds",
+                    template=IO.Autogrow.TemplateNames(
+                        IO.Image.Input("background"),
+                        names=[f"background{i}" for i in range(1, V6_MAX_BACKGROUNDS + 1)],
+                        min=0,
+                    ),
+                    tooltip="Reference images of the scene the subjects are placed into.",
+                ),
+                IO.Autogrow.Input(
+                    "videos",
+                    template=IO.Autogrow.TemplateNames(
+                        IO.Video.Input("video"),
+                        names=[f"video{i}" for i in range(1, V6_MAX_REFERENCE_VIDEOS + 1)],
+                        min=0,
+                    ),
+                    tooltip="Reference videos to borrow subjects, motion, framing or style from. "
+                    "At most two, at most 15 seconds in total.",
+                ),
+                _pixverse_model_input(
+                    aspect_ratio_options=[*[ratio.value for ratio in PixverseV6AspectRatio], "auto"],
+                    prompt_tooltip="Prompt for the video generation. Refer to connected references as "
+                    "@Subject1, @Background1, @Video1.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=PRICE_BADGE_PIXVERSE_FUSION,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        subjects: IO.Autogrow.Type,
+        backgrounds: IO.Autogrow.Type,
+        videos: IO.Autogrow.Type,
+        model: dict,
+    ) -> IO.NodeOutput:
+        common = _model_common(model)
+        aspect_ratio = model["aspect_ratio"]
+        subjects = subjects or {}
+        backgrounds = backgrounds or {}
+        videos = videos or {}
+        if not subjects and not backgrounds and not videos:
+            raise ValueError("Connect at least one subject, background or reference video.")
+
+        is_omni = bool(videos)
+        image_count = len(subjects) + len(backgrounds)
+        max_images = V6_MAX_REFERENCE_IMAGES_OMNI if is_omni else V6_MAX_REFERENCE_IMAGES_PLAIN
+        if image_count > max_images:
+            raise ValueError(
+                f"PixVerse accepts at most {max_images} reference images "
+                f"{'in Omni mode' if is_omni else 'without a reference video'}, got {image_count}."
+            )
+        if aspect_ratio == "auto" and not is_omni:
+            raise ValueError("aspect_ratio 'auto' requires at least one connected reference video.")
+
+        total_video_seconds = 0.0
+        for video in videos.values():
+            validate_video_duration(video, max_duration=V6_MAX_REFERENCE_VIDEO_SECONDS)
+            total_video_seconds += video.get_duration()
+        if total_video_seconds > V6_MAX_REFERENCE_VIDEO_SECONDS:
+            raise ValueError(
+                f"Reference videos must total at most {V6_MAX_REFERENCE_VIDEO_SECONDS} seconds, "
+                f"got {total_video_seconds:.2f}."
+            )
+
+        common["prompt"] = _rewrite_reference_tags(
+            common["prompt"], set(subjects) | set(backgrounds) | set(videos)
+        )
+        common["duration"] = 0 if is_omni else common["duration"]
+
+        image_references = []
+        for ref_name, image in subjects.items():
+            image_references.append(
+                PixverseImageReference(
+                    img_id=await upload_image_to_pixverse(cls, image),
+                    ref_name=ref_name,
+                    type=PixverseReferenceType.subject,
+                )
+            )
+        for ref_name, image in backgrounds.items():
+            image_references.append(
+                PixverseImageReference(
+                    img_id=await upload_image_to_pixverse(cls, image),
+                    ref_name=ref_name,
+                    type=PixverseReferenceType.background,
+                )
+            )
+        video_references = []
+        for ref_name, video in videos.items():
+            video_references.append(
+                PixverseVideoReference(
+                    ref_name=ref_name,
+                    video_media_id=await upload_video_to_pixverse(cls, video),
+                )
+            )
+
+        return await _pixverse_generate(
+            cls,
+            "/proxy/pixverse/video/fusion/generate",
+            PixverseV6FusionVideoRequest(
+                **common,
+                aspect_ratio=aspect_ratio,
+                image_references=image_references or None,
+                video_references=video_references or None,
+                reference_mode="omni" if is_omni else None,
+            ),
+        )
+
+
+class PixVerseExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            PixverseTextToVideoNode,
+            PixverseImageToVideoNode,
+            PixverseTransitionVideoNode,
+            PixverseTemplateNode,
+            PixverseV6TextToVideoNode,
+            PixverseV6ImageToVideoNode,
+            PixverseV6FirstLastFrameNode,
+            PixverseV6ExtendVideoNode,
+            PixverseV6FusionVideoNode,
+        ]
+
+
+async def comfy_entrypoint() -> PixVerseExtension:
+    return PixVerseExtension()
diff --git a/comfy_api_nodes/nodes_quiver.py b/comfy_api_nodes/nodes_quiver.py
new file mode 100644
index 0000000000000000000000000000000000000000..197537fe5c9ecad77bd2c566f604d8ae35ff43b3
--- /dev/null
+++ b/comfy_api_nodes/nodes_quiver.py
@@ -0,0 +1,286 @@
+from io import BytesIO
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension
+from comfy_api_nodes.apis.quiver import (
+    QuiverImageObject,
+    QuiverImageToSVGRequest,
+    QuiverSVGResponse,
+    QuiverTextToSVGRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    sync_op,
+    upload_image_to_comfyapi,
+    validate_string,
+)
+from comfy_extras.nodes_images import SVG
+
+_ARROW_MODELS = ["arrow-1.1", "arrow-1.1-max", "arrow-preview"]
+
+
+def _arrow_sampling_inputs():
+    """Shared sampling inputs for all Arrow model variants."""
+    return [
+        IO.Float.Input(
+            "temperature",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            step=0.1,
+            display_mode=IO.NumberDisplay.slider,
+            tooltip="Randomness control. Higher values increase randomness.",
+            advanced=True,
+        ),
+        IO.Float.Input(
+            "top_p",
+            default=1.0,
+            min=0.05,
+            max=1.0,
+            step=0.05,
+            display_mode=IO.NumberDisplay.slider,
+            tooltip="Nucleus sampling parameter.",
+            advanced=True,
+        ),
+        IO.Float.Input(
+            "presence_penalty",
+            default=0.0,
+            min=-2.0,
+            max=2.0,
+            step=0.1,
+            display_mode=IO.NumberDisplay.slider,
+            tooltip="Token presence penalty.",
+            advanced=True,
+        ),
+    ]
+
+
+class QuiverTextToSVGNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="QuiverTextToSVGNode",
+            display_name="Quiver Text to SVG",
+            category="partner/image/Quiver",
+            description="Generate an SVG from a text prompt using Quiver AI.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the desired SVG output.",
+                ),
+                IO.String.Input(
+                    "instructions",
+                    multiline=True,
+                    default="",
+                    tooltip="Additional style or formatting guidance.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Autogrow.Input(
+                    "reference_images",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Image.Input("image"),
+                        prefix="ref_",
+                        min=0,
+                        max=4,
+                    ),
+                    tooltip="Up to 4 reference images to guide the generation.",
+                    optional=True,
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[IO.DynamicCombo.Option(m, _arrow_sampling_inputs()) for m in _ARROW_MODELS],
+                    tooltip="Model to use for SVG generation.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.SVG.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $contains(widgets.model, "max")
+                    ? {"type":"usd","usd":0.3575}
+                    : $contains(widgets.model, "preview")
+                      ? {"type":"usd","usd":0.429}
+                      : {"type":"usd","usd":0.286}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        instructions: str = None,
+        reference_images: IO.Autogrow.Type = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1)
+
+        references = None
+        if reference_images:
+            references = []
+            for key in reference_images:
+                url = await upload_image_to_comfyapi(cls, reference_images[key], mime_type="image/png")
+                references.append(QuiverImageObject(url=url))
+            if len(references) > 4:
+                raise ValueError("Maximum 4 reference images are allowed.")
+
+        instructions_val = instructions.strip() if instructions else None
+        if instructions_val == "":
+            instructions_val = None
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/quiver/v1/svgs/generations", method="POST"),
+            response_model=QuiverSVGResponse,
+            data=QuiverTextToSVGRequest(
+                model=model["model"],
+                prompt=prompt,
+                instructions=instructions_val,
+                references=references,
+                temperature=model.get("temperature"),
+                top_p=model.get("top_p"),
+                presence_penalty=model.get("presence_penalty"),
+            ),
+        )
+
+        svg_data = [BytesIO(item.svg.encode("utf-8")) for item in response.data]
+        return IO.NodeOutput(SVG(svg_data))
+
+
+class QuiverImageToSVGNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="QuiverImageToSVGNode",
+            display_name="Quiver Image to SVG",
+            category="partner/image/Quiver",
+            description="Vectorize a raster image into SVG using Quiver AI.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="Input image to vectorize.",
+                ),
+                IO.Boolean.Input(
+                    "auto_crop",
+                    default=False,
+                    tooltip="Automatically crop to the dominant subject.",
+                    advanced=True,
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            m,
+                            [
+                                IO.Int.Input(
+                                    "target_size",
+                                    default=1024,
+                                    min=128,
+                                    max=4096,
+                                    tooltip="Square resize target in pixels.",
+                                    advanced=True,
+                                ),
+                                *_arrow_sampling_inputs(),
+                            ],
+                        )
+                        for m in _ARROW_MODELS
+                    ],
+                    tooltip="Model to use for SVG vectorization.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.SVG.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $contains(widgets.model, "max")
+                    ? {"type":"usd","usd":0.3575}
+                    : $contains(widgets.model, "preview")
+                      ? {"type":"usd","usd":0.429}
+                      : {"type":"usd","usd":0.286}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image,
+        auto_crop: bool,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        image_url = await upload_image_to_comfyapi(cls, image, mime_type="image/png")
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/quiver/v1/svgs/vectorizations", method="POST"),
+            response_model=QuiverSVGResponse,
+            data=QuiverImageToSVGRequest(
+                model=model["model"],
+                image=QuiverImageObject(url=image_url),
+                auto_crop=auto_crop if auto_crop else None,
+                target_size=model.get("target_size"),
+                temperature=model.get("temperature"),
+                top_p=model.get("top_p"),
+                presence_penalty=model.get("presence_penalty"),
+            ),
+        )
+
+        svg_data = [BytesIO(item.svg.encode("utf-8")) for item in response.data]
+        return IO.NodeOutput(SVG(svg_data))
+
+
+class QuiverExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            QuiverTextToSVGNode,
+            QuiverImageToSVGNode,
+        ]
+
+
+async def comfy_entrypoint() -> QuiverExtension:
+    return QuiverExtension()
diff --git a/comfy_api_nodes/nodes_qwen.py b/comfy_api_nodes/nodes_qwen.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e4d7668435d8d754731c488d444b4693d179f02
--- /dev/null
+++ b/comfy_api_nodes/nodes_qwen.py
@@ -0,0 +1,442 @@
+import math
+import re
+
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension
+from comfy_api_nodes.apis.qwen import (
+    QwenImageContentItem,
+    QwenImageGenerationRequest,
+    QwenImageGenerationResponse,
+    QwenImageInputField,
+    QwenImageMessage,
+    QwenImageParametersField,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_image_tensor,
+    sync_op,
+    tensor_to_base64_string,
+    validate_string,
+)
+
+GENERATION_PATH = "/proxy/qwen/api/v1/services/aigc/multimodal-generation/generation"
+QWEN_IMAGE_MODELS = ["qwen-image-3.0-pro", "qwen-image-3.0"]
+MIN_AREA = 262144  # 512*512
+MAX_AREA = 6553600  # 2560*2560
+MAX_ASPECT = 8  # the API allows aspect ratios from 1:8 to 8:1
+MAX_INPUT_BYTES = 10 * 1024 * 1024  # the API rejects decoded input images over 10MB
+
+_IMAGE_REF_RE = re.compile(r"@image(?P<idx>\d*)(?!\w)", re.IGNORECASE | re.ASCII)
+
+
+def _resolve_image_refs(prompt: str, total_images: int) -> str:
+    """Rewrite @Image1-style references (shared partner-node syntax, 1-based; an unnumbered
+    @image means the first image) into the plain 'Image N' wording the model resolves
+    natively. A tag counts only at a word boundary or right after a previous tag, so
+    adjacent tags like '@Image1@Image2' all resolve while addresses like user@image1.com
+    pass through untouched."""
+    parts = []
+    pos = 0
+    prev_end = -1
+    for match in _IMAGE_REF_RE.finditer(prompt):
+        start = match.start()
+        if start > 0 and start != prev_end and (prompt[start - 1].isalnum() or prompt[start - 1] == "_"):
+            continue
+        idx = int(match.group("idx") or 1)
+        if not 1 <= idx <= total_images:
+            raise ValueError(
+                f"The prompt references @Image{idx}, but only {total_images} reference images "
+                f"are connected (a batched input counts once per image)."
+            )
+        parts.append(prompt[pos:start])
+        parts.append(f"Image {idx}")
+        pos = match.end()
+        prev_end = match.end()
+    parts.append(prompt[pos:])
+    return "".join(parts)
+
+
+def _validate_size(width: int, height: int) -> None:
+    if not MIN_AREA <= width * height <= MAX_AREA:
+        raise ValueError(
+            f"Image area must be between {MIN_AREA} (512x512) and {MAX_AREA} (2560x2560) pixels; "
+            f"got {width}x{height} = {width * height}."
+        )
+    if width > MAX_ASPECT * height or height > MAX_ASPECT * width:
+        raise ValueError(f"Aspect ratio must be between 1:8 and 8:1; got {width}x{height}.")
+
+
+def _fit_to_size(width: int, height: int) -> tuple[int, int]:
+    """Scale dimensions into the supported pixel area and 1:8..8:1 aspect range, preserving
+    the aspect ratio where possible."""
+    if width > MAX_ASPECT * height:
+        height = math.ceil(width / MAX_ASPECT)
+    elif height > MAX_ASPECT * width:
+        width = math.ceil(height / MAX_ASPECT)
+    area = width * height
+    if area < MIN_AREA:
+        scale = math.sqrt(MIN_AREA / area)
+        width, height = math.ceil(width * scale), math.ceil(height * scale)
+    elif area > MAX_AREA:
+        scale = math.sqrt(MAX_AREA / area)
+        width, height = math.floor(width * scale), math.floor(height * scale)
+    # rounding can push the ratio a hair past the limit; trimming only ever shrinks the area
+    return min(width, MAX_ASPECT * height), min(height, MAX_ASPECT * width)
+
+
+def _image_data_uri(image: torch.Tensor) -> str:
+    """PNG data URI of an RGB view of the image, downscaled to <=2048x2048; falls back to
+    JPEG when the PNG exceeds the API's decoded-size cap (e.g. noisy, incompressible images)."""
+    image = image[..., :3]
+    b64 = tensor_to_base64_string(image, total_pixels=2048 * 2048)
+    if len(b64) * 3 > MAX_INPUT_BYTES * 4:
+        return "data:image/jpeg;base64," + tensor_to_base64_string(
+            image, total_pixels=2048 * 2048, mime_type="image/jpeg"
+        )
+    return "data:image/png;base64," + b64
+
+
+async def _download_result_images(response: QwenImageGenerationResponse) -> torch.Tensor:
+    if not response.output:
+        raise Exception(f"An unknown error occurred: {response.code} - {response.message}")
+    urls = [
+        item.image
+        for choice in response.output.choices
+        if choice.message
+        for item in choice.message.content
+        if item.image
+    ]
+    if not urls:
+        raise Exception(f"The response contains no images: {response.code} - {response.message}")
+    return torch.cat([await download_url_to_image_tensor(url) for url in urls])
+
+
+def _size_inputs() -> list[IO.Int.Input]:
+    return [
+        IO.Int.Input(
+            "width",
+            default=1024,
+            min=256,
+            max=2560,
+            step=16,
+            tooltip="The total pixel area must be between 512x512 and 2560x2560; "
+            "any aspect ratio within that area works.",
+        ),
+        IO.Int.Input(
+            "height",
+            default=1024,
+            min=256,
+            max=2560,
+            step=16,
+            tooltip="The total pixel area must be between 512x512 and 2560x2560; "
+            "any aspect ratio within that area works.",
+        ),
+    ]
+
+
+def _t2i_model_option(model_id: str) -> IO.DynamicCombo.Option:
+    return IO.DynamicCombo.Option(
+        model_id,
+        [
+            IO.String.Input(
+                "prompt",
+                multiline=True,
+                default="",
+                tooltip="Prompt describing the image. Supports English and Chinese.",
+            ),
+            IO.String.Input(
+                "negative_prompt",
+                multiline=True,
+                default="",
+                tooltip="Negative prompt describing what to avoid.",
+            ),
+            *_size_inputs(),
+        ],
+    )
+
+
+def _edit_model_option(model_id: str) -> IO.DynamicCombo.Option:
+    return IO.DynamicCombo.Option(
+        model_id,
+        [
+            IO.Autogrow.Input(
+                "images",
+                template=IO.Autogrow.TemplateNames(
+                    IO.Image.Input("image"),
+                    names=["image_1", "image_2", "image_3"],
+                    min=1,
+                ),
+                tooltip="1-3 reference images. Refer to them in the prompt as @Image1, @Image2, "
+                "@Image3, numbered in input order; a batched input counts once per image.",
+            ),
+            IO.String.Input(
+                "prompt",
+                multiline=True,
+                default="",
+                tooltip="Editing instructions. Supports English and Chinese, "
+                "and @Image1-style references to the input images.",
+            ),
+            IO.String.Input(
+                "negative_prompt",
+                multiline=True,
+                default="",
+                tooltip="Negative prompt describing what to avoid.",
+            ),
+        ],
+    )
+
+
+class QwenImageTextToImageApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="QwenImageTextToImageApi",
+            display_name="Qwen Image 3 Text to Image",
+            category="partner/image/Qwen",
+            description="Generates images from a text prompt using the Qwen-Image 3.0 models.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[_t2i_model_option(model_id) for model_id in QWEN_IMAGE_MODELS],
+                    tooltip="Model to use.",
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Number of images to generate, returned as a batch.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.width", "model.height", "n"]),
+                expr="""
+                (
+                  $isPro := widgets.model = "qwen-image-3.0-pro";
+                  $area := $lookup(widgets, "model.width") * $lookup(widgets, "model.height");
+                  $rate := $isPro ? ($area > 2250000 ? 0.10725 : 0.0572) : 0.0429;
+                  {"type":"usd","usd": $rate * widgets.n}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        n: int = 1,
+        seed: int = 42,
+        prompt_extend: bool = True,
+        watermark: bool = False,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        width, height = model["width"], model["height"]
+        _validate_size(width, height)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=GENERATION_PATH, method="POST"),
+            response_model=QwenImageGenerationResponse,
+            data=QwenImageGenerationRequest(
+                model=model["model"],
+                input=QwenImageInputField(
+                    messages=[QwenImageMessage(content=[QwenImageContentItem(text=model["prompt"])])],
+                ),
+                parameters=QwenImageParametersField(
+                    size=f"{width}*{height}",
+                    n=n,
+                    seed=seed,
+                    prompt_extend=prompt_extend,
+                    watermark=watermark,
+                    negative_prompt=model["negative_prompt"] or None,
+                ),
+            ),
+        )
+        return IO.NodeOutput(await _download_result_images(response))
+
+
+class QwenImageEditApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="QwenImageEditApi",
+            display_name="Qwen Image 3 Edit",
+            category="partner/image/Qwen",
+            description="Edits or combines up to 3 reference images guided by a text prompt "
+            "using the Qwen-Image 3.0 models.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[_edit_model_option(model_id) for model_id in QWEN_IMAGE_MODELS],
+                    tooltip="Model to use.",
+                ),
+                IO.DynamicCombo.Input(
+                    "size",
+                    options=[
+                        IO.DynamicCombo.Option("match input", []),
+                        IO.DynamicCombo.Option("auto", []),
+                        IO.DynamicCombo.Option("custom", _size_inputs()),
+                    ],
+                    tooltip="Output resolution. 'match input' reuses the first reference image's size, "
+                    "'auto' lets the model pick a size with the same aspect ratio, "
+                    "'custom' sets an explicit width and height.",
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Number of images to generate, returned as a batch.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "size", "size.width", "size.height", "n"],
+                    input_groups=["model.images"],
+                ),
+                expr="""
+                (
+                  $isPro := widgets.model = "qwen-image-3.0-pro";
+                  $mode := widgets.size;
+                  $count := $max([$lookup(inputGroups, "model.images"), 1]);
+                  $inputCost := 0.00429 * $count;
+                  $area := $mode = "custom"
+                    ? $lookup(widgets, "size.width") * $lookup(widgets, "size.height") : 0;
+                  $customRate := $area > 2250000 ? 0.10725 : 0.0572;
+                  $isPro and $mode != "custom"
+                    ? {"type":"range_usd",
+                       "min_usd": 0.0572 * widgets.n + $inputCost,
+                       "max_usd": 0.10725 * widgets.n + $inputCost}
+                    : {"type":"usd",
+                       "usd": ($isPro ? $customRate : 0.0429) * widgets.n + $inputCost}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        size: dict,
+        n: int = 1,
+        seed: int = 42,
+        prompt_extend: bool = True,
+        watermark: bool = False,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        reference_images = [image for key in model["images"] for image in model["images"][key]]
+        if len(reference_images) > 3:
+            raise ValueError(
+                f"A maximum of 3 reference images is supported; got {len(reference_images)} "
+                f"(a batched input counts once per image)."
+            )
+        prompt = _resolve_image_refs(model["prompt"], len(reference_images))
+        if size["size"] == "custom":
+            _validate_size(size["width"], size["height"])
+            size_str = f"{size['width']}*{size['height']}"
+        elif size["size"] == "match input":
+            height, width = reference_images[0].shape[0], reference_images[0].shape[1]
+            width, height = _fit_to_size(width, height)
+            size_str = f"{width}*{height}"
+        else:  # auto: the API picks a size preserving the input aspect ratio (1.9-4.2 MP)
+            size_str = None
+        content = [QwenImageContentItem(image=_image_data_uri(image)) for image in reference_images]
+        content.append(QwenImageContentItem(text=prompt))
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=GENERATION_PATH, method="POST"),
+            response_model=QwenImageGenerationResponse,
+            data=QwenImageGenerationRequest(
+                model=model["model"],
+                input=QwenImageInputField(messages=[QwenImageMessage(content=content)]),
+                parameters=QwenImageParametersField(
+                    size=size_str,
+                    n=n,
+                    seed=seed,
+                    prompt_extend=prompt_extend,
+                    watermark=watermark,
+                    negative_prompt=model["negative_prompt"] or None,
+                ),
+            ),
+        )
+        return IO.NodeOutput(await _download_result_images(response))
+
+
+class QwenApiExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            QwenImageTextToImageApi,
+            QwenImageEditApi,
+        ]
+
+
+async def comfy_entrypoint() -> QwenApiExtension:
+    return QwenApiExtension()
diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4a66039a5f6649ce2b8207977bcc06ade2f1b84
--- /dev/null
+++ b/comfy_api_nodes/nodes_recraft.py
@@ -0,0 +1,1714 @@
+import base64
+import uuid
+from io import BytesIO
+
+import aiohttp
+import torch
+from PIL import UnidentifiedImageError
+from typing_extensions import override
+
+from comfy.utils import ProgressBar
+from comfy_api.latest import IO, ComfyExtension
+from comfy_api_nodes.apis.recraft import (
+    RECRAFT_STYLE_MATCH_OPTIONS,
+    RECRAFT_STYLE_REFERENCES_MAX,
+    RECRAFT_STYLE_REFERENCES_MAX_BYTES,
+    RECRAFT_V4_PRO_SIZES,
+    RECRAFT_V4_SIZES,
+    RECRAFT_V4_STYLES_MODELS,
+    RECRAFT_V4_VECTOR_MODEL_FOR_STYLE,
+    RecraftColor,
+    RecraftColorChain,
+    RecraftControls,
+    RecraftCreateStyleRequest,
+    RecraftCreateStyleResponse,
+    RecraftImageGenerationRequest,
+    RecraftImageGenerationResponse,
+    RecraftImageSize,
+    RecraftIO,
+    RecraftStyle,
+    RecraftStyleV3,
+    get_v3_substyles,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    bytesio_to_image_tensor,
+    download_url_as_bytesio,
+    pad_images_to_common_channels,
+    resize_mask_to_image,
+    sync_op,
+    tensor_to_bytesio,
+    validate_string,
+)
+from comfy_extras.nodes_images import SVG
+
+
+async def handle_recraft_file_request(
+    cls: type[IO.ComfyNode],
+    image: torch.Tensor,
+    path: str,
+    mask: torch.Tensor | None = None,
+    total_pixels: int = 4096 * 4096,
+    timeout: int = 1024,
+    request=None,
+) -> list[BytesIO]:
+    """Handle sending common Recraft file-only request to get back file bytes."""
+
+    files = {"image": tensor_to_bytesio(image, total_pixels=total_pixels).read()}
+    if mask is not None:
+        files["mask"] = tensor_to_bytesio(mask, total_pixels=total_pixels).read()
+
+    response = await sync_op(
+        cls,
+        endpoint=ApiEndpoint(path=path, method="POST"),
+        response_model=RecraftImageGenerationResponse,
+        data=request if request else None,
+        files=files,
+        content_type="multipart/form-data",
+        multipart_parser=recraft_multipart_parser,
+        max_retries=1,
+    )
+    all_bytesio = []
+    if response.image is not None:
+        all_bytesio.append(await download_url_as_bytesio(response.image.url, timeout=timeout))
+    else:
+        for data in response.data:
+            all_bytesio.append(await download_url_as_bytesio(data.url, timeout=timeout))
+
+    return all_bytesio
+
+
+def recraft_multipart_parser(
+    data,
+    parent_key=None,
+    formatter: type[callable] | None = None,
+    converted_to_check: list[list] | None = None,
+    is_list: bool = False,
+    return_mode: str = "formdata",  # "dict" | "formdata"
+) -> dict | aiohttp.FormData:
+    """
+    Formats data such that multipart/form-data will work with aiohttp library when both files and data are present.
+
+    The OpenAI client that Recraft uses has a bizarre way of serializing lists:
+
+    It does NOT keep track of indeces of each list, so for background_color, that must be serialized as:
+        'background_color[rgb][]' = [0, 0, 255]
+    where the array is assigned to a key that has '[]' at the end, to signal it's an array.
+
+    This has the consequence of nested lists having the exact same key, forcing arrays to merge; all colors inputs fall under the same key:
+        if 1 color  -> 'controls[colors][][rgb][]' = [0, 0, 255]
+        if 2 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0]
+        if 3 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0, 0, 255, 0]
+        etc.
+    Whoever made this serialization up at OpenAI added the constraint that lists must be of uniform length on objects of same 'type'.
+    """
+    # Modification of a function that handled a different type of multipart parsing, big ups:
+    # https://gist.github.com/kazqvaizer/4cebebe5db654a414132809f9f88067b
+
+    def handle_converted_lists(item, parent_key, lists_to_check=list[list]):
+        # if list already exists, just extend list with data
+        for check_list in lists_to_check:
+            for conv_tuple in check_list:
+                if conv_tuple[0] == parent_key and isinstance(conv_tuple[1], list):
+                    conv_tuple[1].append(formatter(item))
+                    return True
+        return False
+
+    if converted_to_check is None:
+        converted_to_check = []
+
+    effective_mode = return_mode if parent_key is None else "dict"
+    if formatter is None:
+        formatter = lambda v: v  # Multipart representation of value
+
+    if not isinstance(data, dict):
+        # if list already exists, just extend list with data
+        added = handle_converted_lists(data, parent_key, converted_to_check)
+        if added:
+            return {}
+        # otherwise if is_list, create new list with data
+        if is_list:
+            return {parent_key: [formatter(data)]}
+        # return new key with data
+        return {parent_key: formatter(data)}
+
+    converted = []
+    next_check = [converted]
+    next_check.extend(converted_to_check)
+
+    for key, value in data.items():
+        current_key = key if parent_key is None else f"{parent_key}[{key}]"
+        if isinstance(value, dict):
+            converted.extend(recraft_multipart_parser(value, current_key, formatter, next_check).items())
+        elif isinstance(value, list):
+            for ind, list_value in enumerate(value):
+                iter_key = f"{current_key}[]"
+                converted.extend(
+                    recraft_multipart_parser(list_value, iter_key, formatter, next_check, is_list=True).items()
+                )
+        else:
+            converted.append((current_key, formatter(value)))
+
+    if effective_mode == "formdata":
+        fd = aiohttp.FormData()
+        for k, v in dict(converted).items():
+            if isinstance(v, list):
+                for item in v:
+                    fd.add_field(k, str(item))
+            else:
+                fd.add_field(k, str(v))
+        return fd
+    return dict(converted)
+
+
+class handle_recraft_image_output:
+    """
+    Catch an exception related to receiving SVG data instead of image, when Infinite Style Library style_id is in use.
+    """
+
+    def __init__(self):
+        pass
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if exc_type is not None and exc_type is UnidentifiedImageError:
+            raise Exception(
+                "Received output data was not an image; likely an SVG. "
+                "If you used style_id, make sure it is not a Vector art style."
+            )
+
+
+def style_reference_images(images: IO.Autogrow.Type | None) -> list[torch.Tensor]:
+    references = []
+    for batch in (images or {}).values():
+        if batch is None:
+            continue
+        if batch.ndim == 3:
+            batch = batch.unsqueeze(0)
+        references.extend(batch[i] for i in range(batch.shape[0]))
+    return references
+
+
+def encode_style_references(references: list[torch.Tensor]) -> list[bytes]:
+    if not references:
+        raise ValueError("At least one style reference image is required.")
+    if len(references) > RECRAFT_STYLE_REFERENCES_MAX:
+        raise ValueError(
+            f"At most {RECRAFT_STYLE_REFERENCES_MAX} style reference images are allowed; got {len(references)}."
+        )
+    encoded = []
+    total_size = 0
+    for reference in references:
+        data = tensor_to_bytesio(reference, total_pixels=2048 * 2048, mime_type="image/webp").read()
+        total_size += len(data)
+        if total_size > RECRAFT_STYLE_REFERENCES_MAX_BYTES:
+            raise ValueError("Total size of style reference images exceeds the 10 MB limit.")
+        encoded.append(data)
+    return encoded
+
+
+def resolve_v4_style(
+    model: str, style_id: str, style_references: IO.Autogrow.Type | None
+) -> tuple[str | None, list[str] | None]:
+    style_id = style_id.strip()
+    references = style_reference_images(style_references)
+    if style_id and references:
+        raise ValueError("Provide either a style_id or style reference images, not both.")
+    if style_id:
+        try:
+            uuid.UUID(style_id)
+        except ValueError:
+            raise ValueError(f"style_id must be a UUID; got '{style_id}'.") from None
+        return style_id, None
+    if references:
+        return None, [
+            f"data:image/webp;base64,{base64.b64encode(data).decode()}"
+            for data in encode_style_references(references)
+        ]
+    if model in RECRAFT_V4_STYLES_MODELS:
+        raise ValueError(
+            f"Model '{model}' always requires a style: connect style reference images or provide a style_id."
+        )
+    return None, None
+
+
+class RecraftColorRGBNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftColorRGB",
+            display_name="Recraft Color RGB",
+            category="partner/image/Recraft",
+            description="Create Recraft Color by choosing specific RGB values.",
+            inputs=[
+                IO.Int.Input("r", default=0, min=0, max=255, tooltip="Red value of color."),
+                IO.Int.Input("g", default=0, min=0, max=255, tooltip="Green value of color."),
+                IO.Int.Input("b", default=0, min=0, max=255, tooltip="Blue value of color."),
+                IO.Custom(RecraftIO.COLOR).Input("recraft_color", optional=True),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.COLOR).Output(display_name="recraft_color"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, r: int, g: int, b: int, recraft_color: RecraftColorChain = None) -> IO.NodeOutput:
+        recraft_color = recraft_color.clone() if recraft_color else RecraftColorChain()
+        recraft_color.add(RecraftColor(r, g, b))
+        return IO.NodeOutput(recraft_color)
+
+
+class RecraftControlsNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftControls",
+            display_name="Recraft Controls",
+            category="partner/image/Recraft",
+            description="Create Recraft Controls for customizing Recraft generation.",
+            inputs=[
+                IO.Custom(RecraftIO.COLOR).Input("colors", optional=True),
+                IO.Custom(RecraftIO.COLOR).Input("background_color", optional=True),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.CONTROLS).Output(display_name="recraft_controls"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, colors: RecraftColorChain = None, background_color: RecraftColorChain = None) -> IO.NodeOutput:
+        return IO.NodeOutput(RecraftControls(colors=colors, background_color=background_color))
+
+
+class RecraftStyleV3RealisticImageNode(IO.ComfyNode):
+    RECRAFT_STYLE = RecraftStyleV3.realistic_image
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftStyleV3RealisticImage",
+            display_name="Recraft Style - Realistic Image",
+            category="partner/image/Recraft",
+            description="Select realistic_image style and optional substyle.",
+            inputs=[
+                IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.STYLEV3).Output(display_name="recraft_style"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, substyle: str) -> IO.NodeOutput:
+        if substyle == "None":
+            substyle = None
+        return IO.NodeOutput(RecraftStyle(cls.RECRAFT_STYLE, substyle))
+
+
+class RecraftStyleV3DigitalIllustrationNode(RecraftStyleV3RealisticImageNode):
+    RECRAFT_STYLE = RecraftStyleV3.digital_illustration
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftStyleV3DigitalIllustration",
+            display_name="Recraft Style - Digital Illustration",
+            category="partner/image/Recraft",
+            description="Select realistic_image style and optional substyle.",
+            inputs=[
+                IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.STYLEV3).Output(display_name="recraft_style"),
+            ],
+        )
+
+
+class RecraftStyleV3VectorIllustrationNode(RecraftStyleV3RealisticImageNode):
+    RECRAFT_STYLE = RecraftStyleV3.vector_illustration
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftStyleV3VectorIllustrationNode",
+            display_name="Recraft Style - Vector Illustration",
+            category="partner/image/Recraft",
+            description="Select vector_illustration style and optional substyle.",
+            inputs=[
+                IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.STYLEV3).Output(display_name="recraft_style"),
+            ],
+        )
+
+
+class RecraftStyleV3LogoRasterNode(RecraftStyleV3RealisticImageNode):
+    RECRAFT_STYLE = RecraftStyleV3.logo_raster
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftStyleV3LogoRaster",
+            display_name="Recraft Style - Logo Raster",
+            category="partner/image/Recraft",
+            description="Select logo_raster style and substyle.",
+            inputs=[
+                IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE, include_none=False)),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.STYLEV3).Output(display_name="recraft_style"),
+            ],
+        )
+
+
+class RecraftStyleInfiniteStyleLibrary(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftStyleV3InfiniteStyleLibrary",
+            display_name="Recraft Style - Infinite Style Library",
+            category="partner/image/Recraft",
+            description="Choose style based on preexisting UUID from Recraft's Infinite Style Library.",
+            inputs=[
+                IO.String.Input("style_id", default="", tooltip="UUID of style from Infinite Style Library."),
+            ],
+            outputs=[
+                IO.Custom(RecraftIO.STYLEV3).Output(display_name="recraft_style"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, style_id: str) -> IO.NodeOutput:
+        if not style_id:
+            raise Exception("The style_id input cannot be empty.")
+        return IO.NodeOutput(RecraftStyle(style_id=style_id))
+
+
+class RecraftCreateStyleNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftCreateStyleNode",
+            display_name="Recraft Create Style",
+            category="partner/image/Recraft",
+            description="Create a custom style from reference images. "
+            "Upload 1-5 images to use as style references. "
+            "Total size of all images is limited to 5 MB.",
+            inputs=[
+                IO.Combo.Input(
+                    "style",
+                    options=["realistic_image", "digital_illustration"],
+                    tooltip="The base style of the generated images.",
+                ),
+                IO.Autogrow.Input(
+                    "images",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Image.Input("image"),
+                        prefix="image",
+                        min=1,
+                        max=5,
+                    ),
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="style_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.04}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        style: str,
+        images: IO.Autogrow.Type,
+    ) -> IO.NodeOutput:
+        files = []
+        total_size = 0
+        max_total_size = 5 * 1024 * 1024  # 5 MB limit
+        for i, img in enumerate(list(images.values())):
+            file_bytes = tensor_to_bytesio(img, total_pixels=2048 * 2048, mime_type="image/webp").read()
+            total_size += len(file_bytes)
+            if total_size > max_total_size:
+                raise Exception("Total size of all images exceeds 5 MB limit.")
+            files.append((f"file{i + 1}", file_bytes))
+
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/recraft/styles", method="POST"),
+            response_model=RecraftCreateStyleResponse,
+            files=files,
+            data=RecraftCreateStyleRequest(style=style),
+            content_type="multipart/form-data",
+            max_retries=1,
+        )
+
+        return IO.NodeOutput(response.id)
+
+
+class RecraftV4CreateStyleNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftV4CreateStyleNode",
+            display_name="Recraft V4 Create Style",
+            category="partner/image/Recraft",
+            description="Create a reusable Recraft V4 style from 1-10 reference images. "
+            "The returned style_id works with every Recraft V4 and V4.1 model of the same output type. "
+            "Total size of all images is limited to 10 MB.",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["recraftv4_styles", "recraftv4_styles_vector"],
+                    tooltip="Output type the style is created for: recraftv4_styles for raster images, "
+                    "recraftv4_styles_vector for SVG.",
+                ),
+                IO.Autogrow.Input(
+                    "images",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Image.Input("image"),
+                        prefix="image",
+                        min=1,
+                        max=RECRAFT_STYLE_REFERENCES_MAX,
+                    ),
+                    tooltip="Reference images defining the style. Similar references sharpen the match, "
+                    "varied references widen it.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="style_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.005}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        images: IO.Autogrow.Type,
+    ) -> IO.NodeOutput:
+        files = [
+            (f"file{i + 1}", data)
+            for i, data in enumerate(encode_style_references(style_reference_images(images)))
+        ]
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/recraft/styles", method="POST"),
+            response_model=RecraftCreateStyleResponse,
+            files=files,
+            data=RecraftCreateStyleRequest(
+                style="vector_illustration" if model.endswith("_vector") else "any",
+                model=model,
+            ),
+            content_type="multipart/form-data",
+            max_retries=1,
+        )
+        return IO.NodeOutput(response.id)
+
+
+class RecraftTextToImageNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftTextToImageNode",
+            display_name="Recraft V3 Text to Image",
+            category="partner/image/Recraft",
+            description="Generates images synchronously based on prompt and resolution.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Prompt for the image generation."),
+                IO.Combo.Input(
+                    "size",
+                    options=[res.value for res in RecraftImageSize],
+                    default=RecraftImageSize.res_1024x1024,
+                    tooltip="The size of the generated image.",
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    tooltip="The number of images to generate.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.STYLEV3).Input("recraft_style", optional=True),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+                IO.Custom(RecraftIO.CONTROLS).Input(
+                    "recraft_controls",
+                    tooltip="Optional additional controls over the generation via the Recraft Controls node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["n"]),
+                expr="""{"type":"usd","usd": $round(0.04 * widgets.n, 2)}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        size: str,
+        n: int,
+        seed,
+        recraft_style: RecraftStyle = None,
+        negative_prompt: str = None,
+        recraft_controls: RecraftControls = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=1000)
+        default_style = RecraftStyle(RecraftStyleV3.realistic_image)
+        if recraft_style is None:
+            recraft_style = default_style
+
+        controls_api = None
+        if recraft_controls:
+            controls_api = recraft_controls.create_api_model()
+
+        if not negative_prompt:
+            negative_prompt = None
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/recraft/image_generation", method="POST"),
+            response_model=RecraftImageGenerationResponse,
+            data=RecraftImageGenerationRequest(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                model="recraftv3",
+                size=size,
+                n=n,
+                style=recraft_style.style,
+                substyle=recraft_style.substyle,
+                style_id=recraft_style.style_id,
+                controls=controls_api,
+            ),
+            max_retries=1,
+        )
+        images = []
+        for data in response.data:
+            with handle_recraft_image_output():
+                image = bytesio_to_image_tensor(await download_url_as_bytesio(data.url, timeout=1024))
+            if len(image.shape) < 4:
+                image = image.unsqueeze(0)
+            images.append(image)
+
+        return IO.NodeOutput(torch.cat(images, dim=0))
+
+
+class RecraftImageToImageNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftImageToImageNode",
+            display_name="Recraft V3 Image to Image",
+            category="partner/image/Recraft",
+            description="Modify image based on prompt and strength.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Prompt for the image generation."),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    tooltip="The number of images to generate.",
+                ),
+                IO.Float.Input(
+                    "strength",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Defines the difference with the original image, should lie in [0, 1], "
+                    "where 0 means almost identical, and 1 means miserable similarity.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.STYLEV3).Input("recraft_style", optional=True),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+                IO.Custom(RecraftIO.CONTROLS).Input(
+                    "recraft_controls",
+                    tooltip="Optional additional controls over the generation via the Recraft Controls node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["n"]),
+                expr="""{"type":"usd","usd": $round(0.04 * widgets.n, 2)}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        prompt: str,
+        n: int,
+        strength: float,
+        seed,
+        recraft_style: RecraftStyle = None,
+        negative_prompt: str = None,
+        recraft_controls: RecraftControls = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, max_length=1000)
+        default_style = RecraftStyle(RecraftStyleV3.realistic_image)
+        if recraft_style is None:
+            recraft_style = default_style
+
+        controls_api = None
+        if recraft_controls:
+            controls_api = recraft_controls.create_api_model()
+
+        if not negative_prompt:
+            negative_prompt = None
+
+        request = RecraftImageGenerationRequest(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            model="recraftv3",
+            n=n,
+            strength=round(strength, 2),
+            style=recraft_style.style,
+            substyle=recraft_style.substyle,
+            style_id=recraft_style.style_id,
+            controls=controls_api,
+        )
+
+        images = []
+        total = image.shape[0]
+        pbar = ProgressBar(total)
+        for i in range(total):
+            sub_bytes = await handle_recraft_file_request(
+                cls,
+                image=image[i],
+                path="/proxy/recraft/images/imageToImage",
+                request=request,
+            )
+            with handle_recraft_image_output():
+                images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
+            pbar.update(1)
+
+        return IO.NodeOutput(torch.cat(pad_images_to_common_channels(images), dim=0))
+
+
+class RecraftImageInpaintingNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftImageInpaintingNode",
+            display_name="Recraft Image Inpainting",
+            category="partner/image/Recraft",
+            description="Modify image based on prompt and mask.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Mask.Input("mask"),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Prompt for the image generation."),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    tooltip="The number of images to generate.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.STYLEV3).Input("recraft_style", optional=True),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["n"]),
+                expr="""{"type":"usd","usd": $round(0.04 * widgets.n, 2)}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        mask: torch.Tensor,
+        prompt: str,
+        n: int,
+        seed,
+        recraft_style: RecraftStyle = None,
+        negative_prompt: str = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, max_length=1000)
+        default_style = RecraftStyle(RecraftStyleV3.realistic_image)
+        if recraft_style is None:
+            recraft_style = default_style
+
+        if not negative_prompt:
+            negative_prompt = None
+
+        request = RecraftImageGenerationRequest(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            model="recraftv3",
+            n=n,
+            style=recraft_style.style,
+            substyle=recraft_style.substyle,
+            style_id=recraft_style.style_id,
+        )
+
+        # prepare mask tensor
+        mask = resize_mask_to_image(mask, image, allow_gradient=False, add_channel_dim=True)
+
+        images = []
+        total = image.shape[0]
+        pbar = ProgressBar(total)
+        for i in range(total):
+            sub_bytes = await handle_recraft_file_request(
+                cls,
+                image=image[i],
+                mask=mask[i : i + 1],
+                path="/proxy/recraft/images/inpaint",
+                request=request,
+            )
+            with handle_recraft_image_output():
+                images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
+            pbar.update(1)
+
+        return IO.NodeOutput(torch.cat(pad_images_to_common_channels(images), dim=0))
+
+
+class RecraftTextToVectorNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftTextToVectorNode",
+            display_name="Recraft V3 Text to Vector",
+            category="partner/image/Recraft",
+            description="Generates SVG synchronously based on prompt and resolution.",
+            inputs=[
+                IO.String.Input("prompt", default="", tooltip="Prompt for the image generation.", multiline=True),
+                IO.Combo.Input("substyle", options=get_v3_substyles(RecraftStyleV3.vector_illustration)),
+                IO.Combo.Input(
+                    "size",
+                    options=[res.value for res in RecraftImageSize],
+                    default=RecraftImageSize.res_1024x1024,
+                    tooltip="The size of the generated image.",
+                ),
+                IO.Int.Input("n", default=1, min=1, max=6, tooltip="The number of images to generate."),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+                IO.Custom(RecraftIO.CONTROLS).Input(
+                    "recraft_controls",
+                    tooltip="Optional additional controls over the generation via the Recraft Controls node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.SVG.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["n"]),
+                expr="""{"type":"usd","usd": $round(0.08 * widgets.n, 2)}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        substyle: str,
+        size: str,
+        n: int,
+        seed,
+        negative_prompt: str = None,
+        recraft_controls: RecraftControls = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, max_length=1000)
+        # create RecraftStyle so strings will be formatted properly (i.e. "None" will become None)
+        recraft_style = RecraftStyle(RecraftStyleV3.vector_illustration, substyle=substyle)
+
+        controls_api = None
+        if recraft_controls:
+            controls_api = recraft_controls.create_api_model()
+
+        if not negative_prompt:
+            negative_prompt = None
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/recraft/image_generation", method="POST"),
+            response_model=RecraftImageGenerationResponse,
+            data=RecraftImageGenerationRequest(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                model="recraftv3",
+                size=size,
+                n=n,
+                style=recraft_style.style,
+                substyle=recraft_style.substyle,
+                controls=controls_api,
+            ),
+            max_retries=1,
+        )
+        svg_data = []
+        for data in response.data:
+            svg_data.append(await download_url_as_bytesio(data.url, timeout=1024))
+
+        return IO.NodeOutput(SVG(svg_data))
+
+
+class RecraftVectorizeImageNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftVectorizeImageNode",
+            display_name="Recraft Vectorize Image",
+            category="partner/image/Recraft",
+            essentials_category="Image Tools",
+            description="Generates SVG synchronously from an input image.",
+            inputs=[
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.SVG.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(),
+                expr="""{"type":"usd","usd": 0.01}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, image: torch.Tensor) -> IO.NodeOutput:
+        svgs = []
+        total = image.shape[0]
+        pbar = ProgressBar(total)
+        for i in range(total):
+            sub_bytes = await handle_recraft_file_request(
+                cls,
+                image=image[i],
+                path="/proxy/recraft/images/vectorize",
+            )
+            svgs.append(SVG(sub_bytes))
+            pbar.update(1)
+
+        return IO.NodeOutput(SVG.combine_all(svgs))
+
+
+class RecraftReplaceBackgroundNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftReplaceBackgroundNode",
+            display_name="Recraft Replace Background",
+            category="partner/image/Recraft",
+            description="Replace background on image, based on provided prompt.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.String.Input("prompt", tooltip="Prompt for the image generation.", default="", multiline=True),
+                IO.Int.Input("n", default=1, min=1, max=6, tooltip="The number of images to generate."),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.STYLEV3).Input("recraft_style", optional=True),
+                IO.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.04}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        prompt: str,
+        n: int,
+        seed,
+        recraft_style: RecraftStyle = None,
+        negative_prompt: str = None,
+    ) -> IO.NodeOutput:
+        default_style = RecraftStyle(RecraftStyleV3.realistic_image)
+        if recraft_style is None:
+            recraft_style = default_style
+
+        if not negative_prompt:
+            negative_prompt = None
+
+        request = RecraftImageGenerationRequest(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            model="recraftv3",
+            n=n,
+            style=recraft_style.style,
+            substyle=recraft_style.substyle,
+            style_id=recraft_style.style_id,
+        )
+
+        images = []
+        total = image.shape[0]
+        pbar = ProgressBar(total)
+        for i in range(total):
+            sub_bytes = await handle_recraft_file_request(
+                cls,
+                image=image[i],
+                path="/proxy/recraft/images/replaceBackground",
+                request=request,
+            )
+            images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
+            pbar.update(1)
+
+        return IO.NodeOutput(torch.cat(pad_images_to_common_channels(images), dim=0))
+
+
+class RecraftRemoveBackgroundNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftRemoveBackgroundNode",
+            display_name="Recraft Remove Background",
+            category="partner/image/Recraft",
+            essentials_category="Image Tools",
+            description="Remove background from image, and return processed image and mask.",
+            inputs=[
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.Mask.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.01}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, image: torch.Tensor) -> IO.NodeOutput:
+        images = []
+        total = image.shape[0]
+        pbar = ProgressBar(total)
+        for i in range(total):
+            sub_bytes = await handle_recraft_file_request(
+                cls,
+                image=image[i],
+                path="/proxy/recraft/images/removeBackground",
+            )
+            images.append(torch.cat([bytesio_to_image_tensor(x, mode="RGBA") for x in sub_bytes], dim=0))
+            pbar.update(1)
+
+        images_tensor = torch.cat(images, dim=0)
+        # use alpha channel as masks, in B,H,W format
+        masks_tensor = images_tensor[:, :, :, -1:].squeeze(-1)
+        return IO.NodeOutput(images_tensor, masks_tensor)
+
+
+class RecraftCrispUpscaleNode(IO.ComfyNode):
+    RECRAFT_PATH = "/proxy/recraft/images/crispUpscale"
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftCrispUpscaleNode",
+            display_name="Recraft Crisp Upscale Image",
+            category="partner/image/Recraft",
+            description="Upscale image synchronously.\n"
+            "Enhances a given raster image using ‘crisp upscale’ tool, "
+            "increasing image resolution, making the image sharper and cleaner.",
+            inputs=[
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.004}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, image: torch.Tensor) -> IO.NodeOutput:
+        images = []
+        total = image.shape[0]
+        pbar = ProgressBar(total)
+        for i in range(total):
+            sub_bytes = await handle_recraft_file_request(
+                cls,
+                image=image[i],
+                path=cls.RECRAFT_PATH,
+            )
+            images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
+            pbar.update(1)
+
+        return IO.NodeOutput(torch.cat(pad_images_to_common_channels(images), dim=0))
+
+
+class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode):
+    RECRAFT_PATH = "/proxy/recraft/images/creativeUpscale"
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftCreativeUpscaleNode",
+            display_name="Recraft Creative Upscale Image",
+            category="partner/image/Recraft",
+            description="Upscale image synchronously.\n"
+            "Enhances a given raster image using ‘creative upscale’ tool, "
+            "boosting resolution with a focus on refining small details and faces.",
+            inputs=[
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.25}""",
+            ),
+        )
+
+
+class RecraftV4TextToImageNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftV4TextToImageNode",
+            display_name="Recraft V4 Text to Image",
+            category="partner/image/Recraft",
+            description="Generates images using Recraft V4 and V4.1 models.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Prompt for the image generation. Maximum 10,000 characters.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    tooltip="This input is ignored: negative prompt is not supported by "
+                    "Recraft V4 and V4.1 models.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "recraftv4_1",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_SIZES,
+                                    default="1024x1024",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_1_utility",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_SIZES,
+                                    default="1024x1024",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_1_pro",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_PRO_SIZES,
+                                    default="2048x2048",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_1_utility_pro",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_PRO_SIZES,
+                                    default="2048x2048",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_SIZES,
+                                    default="1024x1024",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_pro",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_PRO_SIZES,
+                                    default="2048x2048",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_styles",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_SIZES,
+                                    default="1024x1024",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_styles_pro",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_PRO_SIZES,
+                                    default="2048x2048",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="The model to use for generation. The recraftv4_styles models are built for "
+                    "style-consistent generation and always require a style_id or style_references.",
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    tooltip="The number of images to generate.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.CONTROLS).Input(
+                    "recraft_controls",
+                    tooltip="Optional additional controls over the generation via the Recraft Controls node.",
+                    optional=True,
+                ),
+                IO.String.Input(
+                    "style_id",
+                    default="",
+                    optional=True,
+                    tooltip="UUID of a Recraft V4 style to apply, e.g. from the Recraft V4 Create Style node "
+                    "or the style_id output of a previous run. Cannot be combined with style_references.",
+                ),
+                IO.Combo.Input(
+                    "style_match",
+                    options=RECRAFT_STYLE_MATCH_OPTIONS,
+                    optional=True,
+                    tooltip="How closely to follow the style: precise reproduces it in detail, "
+                    "flexible matches the general look. Only used when a style is provided.",
+                ),
+                IO.Autogrow.Input(
+                    "style_references",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Image.Input("style_reference"),
+                        prefix="style_reference",
+                        min=0,
+                        max=RECRAFT_STYLE_REFERENCES_MAX,
+                    ),
+                    optional=True,
+                    tooltip="Reference images to create a style from on the fly, billed on top of the generation. "
+                    "The created style is returned as style_id for reuse. Cannot be combined with style_id.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(id="style_id", display_name="style_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "n"], input_groups=["style_references"]),
+                expr="""
+                (
+                    $prices := {
+                        "recraftv4_1": 0.035,
+                        "recraftv4_1_utility": 0.035,
+                        "recraftv4_1_pro": 0.21,
+                        "recraftv4_1_utility_pro": 0.21,
+                        "recraftv4": 0.04,
+                        "recraftv4_pro": 0.25,
+                        "recraftv4_styles": 0.035,
+                        "recraftv4_styles_pro": 0.10
+                    };
+                    $references := $lookup(inputGroups, "style_references");
+                    $style := ($references ? $references : 0) > 0 ? 0.005 : 0;
+                    {"type":"usd","usd": $lookup($prices, widgets.model) * widgets.n + $style}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str,
+        model: dict,
+        n: int,
+        seed: int,
+        recraft_controls: RecraftControls | None = None,
+        style_id: str = "",
+        style_match: str = "precise",
+        style_references: IO.Autogrow.Type | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=10000)
+        style_id, style_reference_urls = resolve_v4_style(model["model"], style_id, style_references)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/recraft/image_generation", method="POST"),
+            response_model=RecraftImageGenerationResponse,
+            data=RecraftImageGenerationRequest(
+                prompt=prompt,
+                model=model["model"],
+                size=model["size"],
+                n=n,
+                style_id=style_id,
+                style_match=style_match if style_id or style_reference_urls else None,
+                style_reference_urls=style_reference_urls,
+                controls=recraft_controls.create_api_model() if recraft_controls else None,
+            ),
+            max_retries=1,
+        )
+        images = []
+        for data in response.data:
+            with handle_recraft_image_output():
+                image = bytesio_to_image_tensor(await download_url_as_bytesio(data.url, timeout=1024))
+            if len(image.shape) < 4:
+                image = image.unsqueeze(0)
+            images.append(image)
+        return IO.NodeOutput(torch.cat(images, dim=0), response.style_id or "")
+
+
+class RecraftV4TextToVectorNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecraftV4TextToVectorNode",
+            display_name="Recraft V4 Text to Vector",
+            category="partner/image/Recraft",
+            description="Generates SVG using Recraft V4 and V4.1 models.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Prompt for the image generation. Maximum 10,000 characters.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    tooltip="This input is ignored: negative prompt is not supported by "
+                    "Recraft V4 and V4.1 models.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "recraftv4_1_vector",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_SIZES,
+                                    default="1024x1024",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_1_utility_vector",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_SIZES,
+                                    default="1024x1024",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_1_pro_vector",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_PRO_SIZES,
+                                    default="2048x2048",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_1_utility_pro_vector",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_PRO_SIZES,
+                                    default="2048x2048",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_SIZES,
+                                    default="1024x1024",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_pro",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_PRO_SIZES,
+                                    default="2048x2048",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_styles_vector",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_SIZES,
+                                    default="1024x1024",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "recraftv4_styles_pro_vector",
+                            [
+                                IO.Combo.Input(
+                                    "size",
+                                    options=RECRAFT_V4_PRO_SIZES,
+                                    default="2048x2048",
+                                    tooltip="The size of the generated image.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="The model to use for generation. The recraftv4_styles models are built for "
+                    "style-consistent generation and always require a style_id or style_references.",
+                ),
+                IO.Int.Input(
+                    "n",
+                    default=1,
+                    min=1,
+                    max=6,
+                    tooltip="The number of images to generate.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+                IO.Custom(RecraftIO.CONTROLS).Input(
+                    "recraft_controls",
+                    tooltip="Optional additional controls over the generation via the Recraft Controls node.",
+                    optional=True,
+                ),
+                IO.String.Input(
+                    "style_id",
+                    default="",
+                    optional=True,
+                    tooltip="UUID of a Recraft V4 vector style to apply, e.g. from the Recraft V4 Create Style node "
+                    "or the style_id output of a previous run. Cannot be combined with style_references.",
+                ),
+                IO.Combo.Input(
+                    "style_match",
+                    options=RECRAFT_STYLE_MATCH_OPTIONS,
+                    optional=True,
+                    tooltip="How closely to follow the style: precise reproduces it in detail, "
+                    "flexible matches the general look. Only used when a style is provided.",
+                ),
+                IO.Autogrow.Input(
+                    "style_references",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Image.Input("style_reference"),
+                        prefix="style_reference",
+                        min=0,
+                        max=RECRAFT_STYLE_REFERENCES_MAX,
+                    ),
+                    optional=True,
+                    tooltip="Reference images to create a vector style from on the fly, billed on top of the generation. "
+                    "The created style is returned as style_id for reuse. Cannot be combined with style_id.",
+                ),
+            ],
+            outputs=[
+                IO.SVG.Output(),
+                IO.String.Output(id="style_id", display_name="style_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "n"], input_groups=["style_references"]),
+                expr="""
+                (
+                    $prices := {
+                        "recraftv4_1_vector": 0.08,
+                        "recraftv4_1_utility_vector": 0.08,
+                        "recraftv4_1_pro_vector": 0.30,
+                        "recraftv4_1_utility_pro_vector": 0.30,
+                        "recraftv4": 0.08,
+                        "recraftv4_pro": 0.30,
+                        "recraftv4_styles_vector": 0.05,
+                        "recraftv4_styles_pro_vector": 0.12
+                    };
+                    $references := $lookup(inputGroups, "style_references");
+                    $style := ($references ? $references : 0) > 0 ? 0.005 : 0;
+                    {"type":"usd","usd": $lookup($prices, widgets.model) * widgets.n + $style}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str,
+        model: dict,
+        n: int,
+        seed: int,
+        recraft_controls: RecraftControls | None = None,
+        style_id: str = "",
+        style_match: str = "precise",
+        style_references: IO.Autogrow.Type | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=10000)
+        model_id = model["model"]
+        style_id, style_reference_urls = resolve_v4_style(model_id, style_id, style_references)
+        has_style = bool(style_id or style_reference_urls)
+        if has_style:
+            model_id = RECRAFT_V4_VECTOR_MODEL_FOR_STYLE.get(model_id, model_id)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/recraft/image_generation", method="POST"),
+            response_model=RecraftImageGenerationResponse,
+            data=RecraftImageGenerationRequest(
+                prompt=prompt,
+                model=model_id,
+                size=model["size"],
+                n=n,
+                style=None if has_style or model_id.endswith("_vector") else "vector_illustration",
+                substyle=None,
+                style_id=style_id,
+                style_match=style_match if has_style else None,
+                style_reference_urls=style_reference_urls,
+                controls=recraft_controls.create_api_model() if recraft_controls else None,
+            ),
+            max_retries=1,
+        )
+        svg_data = []
+        for data in response.data:
+            svg_data.append(await download_url_as_bytesio(data.url, timeout=1024))
+        return IO.NodeOutput(SVG(svg_data), response.style_id or "")
+
+
+class RecraftExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            RecraftTextToImageNode,
+            RecraftImageToImageNode,
+            RecraftImageInpaintingNode,
+            RecraftTextToVectorNode,
+            RecraftVectorizeImageNode,
+            RecraftRemoveBackgroundNode,
+            RecraftReplaceBackgroundNode,
+            RecraftCrispUpscaleNode,
+            RecraftCreativeUpscaleNode,
+            RecraftStyleV3RealisticImageNode,
+            RecraftStyleV3DigitalIllustrationNode,
+            RecraftStyleV3LogoRasterNode,
+            RecraftStyleInfiniteStyleLibrary,
+            RecraftCreateStyleNode,
+            RecraftColorRGBNode,
+            RecraftControlsNode,
+            RecraftV4TextToImageNode,
+            RecraftV4TextToVectorNode,
+            RecraftV4CreateStyleNode,
+        ]
+
+
+async def comfy_entrypoint() -> RecraftExtension:
+    return RecraftExtension()
diff --git a/comfy_api_nodes/nodes_reve.py b/comfy_api_nodes/nodes_reve.py
new file mode 100644
index 0000000000000000000000000000000000000000..93192785c633d9ec541a07541a583324d2dfa504
--- /dev/null
+++ b/comfy_api_nodes/nodes_reve.py
@@ -0,0 +1,417 @@
+from io import BytesIO
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.reve import (
+    ReveImageCreateRequest,
+    ReveImageEditRequest,
+    ReveImageRemixRequest,
+    RevePostprocessingOperation,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    bytesio_to_image_tensor,
+    sync_op_raw,
+    tensor_to_base64_string,
+    validate_string,
+)
+
+
+def _build_postprocessing(upscale: dict, remove_background: bool) -> list[RevePostprocessingOperation] | None:
+    ops = []
+    if upscale["upscale"] == "enabled":
+        ops.append(
+            RevePostprocessingOperation(
+                process="upscale",
+                upscale_factor=upscale["upscale_factor"],
+            )
+        )
+    if remove_background:
+        ops.append(RevePostprocessingOperation(process="remove_background"))
+    return ops or None
+
+
+def _postprocessing_inputs():
+    return [
+        IO.DynamicCombo.Input(
+            "upscale",
+            options=[
+                IO.DynamicCombo.Option("disabled", []),
+                IO.DynamicCombo.Option(
+                    "enabled",
+                    [
+                        IO.Int.Input(
+                            "upscale_factor",
+                            default=2,
+                            min=2,
+                            max=4,
+                            step=1,
+                            tooltip="Upscale factor (2x, 3x, or 4x).",
+                        ),
+                    ],
+                ),
+            ],
+            tooltip="Upscale the generated image. May add additional cost.",
+        ),
+        IO.Boolean.Input(
+            "remove_background",
+            default=False,
+            tooltip="Remove the background from the generated image. May add additional cost.",
+        ),
+    ]
+
+
+def _reve_response_header_validator(headers: dict) -> None:
+    error_code = headers.get("x-reve-error-code")
+    if error_code:
+        raise ValueError(f"Reve API error: {error_code}")
+    if headers.get("x-reve-content-violation", "").lower() == "true":
+        raise ValueError("The generated image was flagged for content policy violation.")
+
+
+def _model_inputs(versions: list[str], aspect_ratios: list[str]):
+    return [
+        IO.DynamicCombo.Option(
+            version,
+            [
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=aspect_ratios,
+                    tooltip="Aspect ratio of the output image.",
+                ),
+                IO.Int.Input(
+                    "test_time_scaling",
+                    default=1,
+                    min=1,
+                    max=5,
+                    step=1,
+                    tooltip="Higher values produce better images but cost more credits.",
+                    advanced=True,
+                ),
+            ],
+        )
+        for version in versions
+    ]
+
+
+class ReveImageCreateNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ReveImageCreateNode",
+            display_name="Reve Image Create",
+            category="partner/image/Reve",
+            description="Generate images from text descriptions using Reve.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the desired image. Maximum 2560 characters.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=_model_inputs(
+                        ["reve-create@20250915"],
+                        aspect_ratios=["3:2", "16:9", "9:16", "2:3", "4:3", "3:4", "1:1"],
+                    ),
+                    tooltip="Model version to use for generation.",
+                ),
+                *_postprocessing_inputs(),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_deprecated=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["upscale", "upscale.upscale_factor"],
+                ),
+                expr="""
+                (
+                    $factor := $lookup(widgets, "upscale.upscale_factor");
+                    $fmt := {"approximate": true, "note": "(base)"};
+                    widgets.upscale = "enabled" ? (
+                        $factor = 4 ? {"type": "usd", "usd": 0.0762, "format": $fmt}
+                        : $factor = 3 ? {"type": "usd", "usd": 0.0591, "format": $fmt}
+                        : {"type": "usd", "usd": 0.0457, "format": $fmt}
+                    ) : {"type": "usd", "usd": 0.03432, "format": $fmt}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        upscale: dict,
+        remove_background: bool,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2560)
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/reve/v1/image/create",
+                method="POST",
+                headers={"Accept": "image/webp"},
+            ),
+            as_binary=True,
+            response_header_validator=_reve_response_header_validator,
+            data=ReveImageCreateRequest(
+                prompt=prompt,
+                aspect_ratio=model["aspect_ratio"],
+                version=model["model"],
+                test_time_scaling=model["test_time_scaling"],
+                postprocessing=_build_postprocessing(upscale, remove_background),
+            ),
+        )
+        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
+
+
+class ReveImageEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ReveImageEditNode",
+            display_name="Reve Image Edit",
+            category="partner/image/Reve",
+            description="Edit images using natural language instructions with Reve.",
+            inputs=[
+                IO.Image.Input("image", tooltip="The image to edit."),
+                IO.String.Input(
+                    "edit_instruction",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of how to edit the image. Maximum 2560 characters.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=_model_inputs(
+                        ["reve-edit@20250915", "reve-edit-fast@20251030"],
+                        aspect_ratios=["auto", "16:9", "9:16", "3:2", "2:3", "4:3", "3:4", "1:1"],
+                    ),
+                    tooltip="Model version to use for editing.",
+                ),
+                *_postprocessing_inputs(),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_deprecated=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "upscale", "upscale.upscale_factor"],
+                ),
+                expr="""
+                (
+                    $fmt := {"approximate": true, "note": "(base)"};
+                    $isFast := $contains(widgets.model, "fast");
+                    $enabled := widgets.upscale = "enabled";
+                    $factor := $lookup(widgets, "upscale.upscale_factor");
+                    $isFast
+                        ? {"type": "usd", "usd": 0.01001, "format": $fmt}
+                        : $enabled ? (
+                            $factor = 4 ? {"type": "usd", "usd": 0.0991, "format": $fmt}
+                            : $factor = 3 ? {"type": "usd", "usd": 0.0819, "format": $fmt}
+                            : {"type": "usd", "usd": 0.0686, "format": $fmt}
+                        ) : {"type": "usd", "usd": 0.0572, "format": $fmt}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        edit_instruction: str,
+        model: dict,
+        upscale: dict,
+        remove_background: bool,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(edit_instruction, min_length=1, max_length=2560)
+        tts = model["test_time_scaling"]
+        ar = model["aspect_ratio"]
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/reve/v1/image/edit",
+                method="POST",
+                headers={"Accept": "image/webp"},
+            ),
+            as_binary=True,
+            response_header_validator=_reve_response_header_validator,
+            data=ReveImageEditRequest(
+                edit_instruction=edit_instruction,
+                reference_image=tensor_to_base64_string(image),
+                aspect_ratio=ar if ar != "auto" else None,
+                version=model["model"],
+                test_time_scaling=tts if tts and tts > 1 else None,
+                postprocessing=_build_postprocessing(upscale, remove_background),
+            ),
+        )
+        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
+
+
+class ReveImageRemixNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ReveImageRemixNode",
+            display_name="Reve Image Remix",
+            category="partner/image/Reve",
+            description="Combine reference images with text prompts to create new images using Reve.",
+            inputs=[
+                IO.Autogrow.Input(
+                    "reference_images",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Image.Input("image"),
+                        prefix="image_",
+                        min=1,
+                        max=6,
+                    ),
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the desired image. "
+                    "May include XML img tags to reference specific images by index, "
+                    "e.g. <img>0</img>, <img>1</img>, etc.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=_model_inputs(
+                        ["reve-remix@20250915", "reve-remix-fast@20251030"],
+                        aspect_ratios=["auto", "16:9", "9:16", "3:2", "2:3", "4:3", "3:4", "1:1"],
+                    ),
+                    tooltip="Model version to use for remixing.",
+                ),
+                *_postprocessing_inputs(),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_deprecated=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "upscale", "upscale.upscale_factor"],
+                ),
+                expr="""
+                (
+                    $fmt := {"approximate": true, "note": "(base)"};
+                    $isFast := $contains(widgets.model, "fast");
+                    $enabled := widgets.upscale = "enabled";
+                    $factor := $lookup(widgets, "upscale.upscale_factor");
+                    $isFast
+                        ? {"type": "usd", "usd": 0.01001, "format": $fmt}
+                        : $enabled ? (
+                            $factor = 4 ? {"type": "usd", "usd": 0.0991, "format": $fmt}
+                            : $factor = 3 ? {"type": "usd", "usd": 0.0819, "format": $fmt}
+                            : {"type": "usd", "usd": 0.0686, "format": $fmt}
+                        ) : {"type": "usd", "usd": 0.0572, "format": $fmt}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        reference_images: IO.Autogrow.Type,
+        prompt: str,
+        model: dict,
+        upscale: dict,
+        remove_background: bool,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2560)
+        if not reference_images:
+            raise ValueError("At least one reference image is required.")
+        ref_base64_list = []
+        for key in reference_images:
+            ref_base64_list.append(tensor_to_base64_string(reference_images[key]))
+        if len(ref_base64_list) > 6:
+            raise ValueError("Maximum 6 reference images are allowed.")
+        tts = model["test_time_scaling"]
+        ar = model["aspect_ratio"]
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/reve/v1/image/remix",
+                method="POST",
+                headers={"Accept": "image/webp"},
+            ),
+            as_binary=True,
+            response_header_validator=_reve_response_header_validator,
+            data=ReveImageRemixRequest(
+                prompt=prompt,
+                reference_images=ref_base64_list,
+                aspect_ratio=ar if ar != "auto" else None,
+                version=model["model"],
+                test_time_scaling=tts if tts and tts > 1 else None,
+                postprocessing=_build_postprocessing(upscale, remove_background),
+            ),
+        )
+        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
+
+
+class ReveExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            ReveImageCreateNode,
+            ReveImageEditNode,
+            ReveImageRemixNode,
+        ]
+
+
+async def comfy_entrypoint() -> ReveExtension:
+    return ReveExtension()
diff --git a/comfy_api_nodes/nodes_rodin.py b/comfy_api_nodes/nodes_rodin.py
new file mode 100644
index 0000000000000000000000000000000000000000..be860874cd7757dfa8e0bef87c67573bb2bfcc65
--- /dev/null
+++ b/comfy_api_nodes/nodes_rodin.py
@@ -0,0 +1,1123 @@
+"""
+ComfyUI X Rodin3D(Deemos) API Nodes
+
+Rodin API docs: https://developer.hyper3d.ai/
+
+"""
+
+import logging
+import math
+import os
+from inspect import cleandoc
+from io import BytesIO
+from typing import Any
+
+import aiohttp
+from PIL import Image
+from typing_extensions import override
+
+import folder_paths as comfy_paths
+from comfy_api.latest import IO, ComfyExtension, Types
+from comfy_api_nodes.apis.rodin import (
+    JobStatus,
+    Rodin3DCheckStatusRequest,
+    Rodin3DCheckStatusResponse,
+    Rodin3DDownloadRequest,
+    Rodin3DDownloadResponse,
+    Rodin3DGen25Request,
+    Rodin3DGenerateRequest,
+    Rodin3DGenerateResponse,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_bytesio,
+    download_url_to_file_3d,
+    poll_op,
+    sync_op,
+    validate_string,
+)
+
+COMMON_PARAMETERS = [
+    IO.Int.Input(
+        "Seed",
+        default=0,
+        min=0,
+        max=65535,
+        display_mode=IO.NumberDisplay.number,
+        optional=True,
+    ),
+    IO.Combo.Input("Material_Type", options=["PBR", "Shaded"], default="PBR", optional=True),
+    IO.Combo.Input(
+        "Polygon_count",
+        options=["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"],
+        default="18K-Quad",
+        optional=True,
+    ),
+]
+
+
+_QUALITY_MESH_OPTIONS: dict[str, tuple[str, int]] = {
+    "4K-Quad":       ("Quad", 4000),
+    "8K-Quad":       ("Quad", 8000),
+    "18K-Quad":      ("Quad", 18000),
+    "50K-Quad":      ("Quad", 50000),
+    "200K-Quad":     ("Quad", 200000),
+    "2K-Triangle":   ("Raw", 2000),
+    "20K-Triangle":  ("Raw", 20000),
+    "150K-Triangle": ("Raw", 150000),
+    "200K-Triangle": ("Raw", 200000),
+    "500K-Triangle": ("Raw", 500000),
+    "1M-Triangle":   ("Raw", 1000000),
+}
+
+
+def get_quality_mode(poly_count: str) -> tuple[str, int]:
+    """Map a polygon-count preset like '18K-Quad' to (mesh_mode, quality_override).
+
+    Falls back to ('Quad', 18000) for unknown labels; legacy parity.
+    """
+    return _QUALITY_MESH_OPTIONS.get(poly_count, ("Quad", 18000))
+
+
+def tensor_to_filelike(tensor, max_pixels: int = 2048 * 2048):
+    """
+    Converts a PyTorch tensor to a file-like object.
+
+    Args:
+    - tensor (torch.Tensor): A tensor representing an image of shape (H, W, C)
+      where C is the number of channels (3 for RGB), H is height, and W is width.
+
+    Returns:
+    - io.BytesIO: A file-like object containing the image data.
+    """
+    array = tensor.cpu().numpy()
+    array = (array * 255).astype("uint8")
+    image = Image.fromarray(array, "RGB")
+
+    original_width, original_height = image.size
+    original_pixels = original_width * original_height
+    if original_pixels > max_pixels:
+        scale = math.sqrt(max_pixels / original_pixels)
+        new_width = int(original_width * scale)
+        new_height = int(original_height * scale)
+    else:
+        new_width, new_height = original_width, original_height
+
+    if new_width != original_width or new_height != original_height:
+        image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+
+    img_byte_arr = BytesIO()
+    image.save(img_byte_arr, format="PNG")  # PNG is used for lossless compression
+    img_byte_arr.seek(0)
+    return img_byte_arr
+
+
+async def create_generate_task(
+    cls: type[IO.ComfyNode],
+    images=None,
+    seed=1,
+    material="PBR",
+    quality_override=18000,
+    tier="Regular",
+    mesh_mode="Quad",
+    ta_pose: bool = False,
+):
+    if images is None:
+        raise Exception("Rodin 3D generate requires at least 1 image.")
+    if len(images) > 5:
+        raise Exception("Rodin 3D generate requires up to 5 image.")
+
+    response = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/rodin/api/v2/rodin", method="POST"),
+        response_model=Rodin3DGenerateResponse,
+        data=Rodin3DGenerateRequest(
+            seed=seed,
+            tier=tier,
+            material=material,
+            quality_override=quality_override,
+            mesh_mode=mesh_mode,
+            TAPose=ta_pose,
+        ),
+        files=[
+            ("images", open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image))
+            for image in images
+            if image is not None
+        ],
+        content_type="multipart/form-data",
+    )
+
+    if hasattr(response, "error"):
+        error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}"
+        logging.error(error_message)
+        raise Exception(error_message)
+
+    logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!")
+    subscription_key = response.jobs.subscription_key
+    task_uuid = response.uuid
+    logging.info("[ Rodin3D API - Submit Jobs ] UUID: %s", task_uuid)
+    return task_uuid, subscription_key
+
+
+def check_rodin_status(response: Rodin3DCheckStatusResponse) -> str:
+    all_done = all(job.status == JobStatus.Done for job in response.jobs)
+    status_list = [str(job.status) for job in response.jobs]
+    logging.info("[ Rodin3D API - CheckStatus ] Generate Status: %s", status_list)
+    if any(job.status == JobStatus.Failed for job in response.jobs):
+        logging.error("[ Rodin3D API - CheckStatus ] Generate Failed: %s, Please try again.", status_list)
+        raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.")
+    if all_done:
+        return "DONE"
+    return "Generating"
+
+
+def extract_progress(response: Rodin3DCheckStatusResponse) -> int | None:
+    if not response.jobs:
+        return None
+    completed_count = sum(1 for job in response.jobs if job.status == JobStatus.Done)
+    return int((completed_count / len(response.jobs)) * 100)
+
+
+async def poll_for_task_status(subscription_key: str, cls: type[IO.ComfyNode]) -> Rodin3DCheckStatusResponse:
+    logging.info("[ Rodin3D API - CheckStatus ] Generate Start!")
+    return await poll_op(
+        cls,
+        ApiEndpoint(path="/proxy/rodin/api/v2/status", method="POST"),
+        response_model=Rodin3DCheckStatusResponse,
+        data=Rodin3DCheckStatusRequest(subscription_key=subscription_key),
+        status_extractor=check_rodin_status,
+        progress_extractor=extract_progress,
+    )
+
+
+async def get_rodin_download_list(uuid: str, cls: type[IO.ComfyNode]) -> Rodin3DDownloadResponse:
+    logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
+    return await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/rodin/api/v2/download", method="POST"),
+        response_model=Rodin3DDownloadResponse,
+        data=Rodin3DDownloadRequest(task_uuid=uuid),
+        monitor_progress=False,
+    )
+
+
+async def download_files(url_list, task_uuid: str) -> tuple[str | None, Types.File3D | None]:
+    result_folder_name = f"Rodin3D_{task_uuid}"
+    save_path = os.path.join(comfy_paths.get_output_directory(), result_folder_name)
+    os.makedirs(save_path, exist_ok=True)
+    model_file_path = None
+    file_3d = None
+
+    for i in url_list.items:
+        file_path = os.path.join(save_path, i.name)
+        if i.name.lower().endswith(".glb"):
+            model_file_path = os.path.join(result_folder_name, i.name)
+            file_3d = await download_url_to_file_3d(i.url, "glb")
+            # Save to disk for backward compatibility
+            with open(file_path, "wb") as f:
+                f.write(file_3d.get_bytes())
+        else:
+            await download_url_to_bytesio(i.url, file_path)
+
+    return model_file_path, file_3d
+
+
+class Rodin3D_Regular(IO.ComfyNode):
+    """Generate 3D Assets using Rodin API"""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Rodin3D_Regular",
+            display_name="Rodin 3D Generate - Regular Generate",
+            category="partner/3d/Rodin",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Image.Input("Images"),
+                *COMMON_PARAMETERS,
+            ],
+            outputs=[
+                IO.String.Output(display_name="3D Model Path"),  # for backward compatibility only
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+    ) -> IO.NodeOutput:
+        tier = "Regular"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality_override = get_quality_mode(Polygon_count)
+        task_uuid, subscription_key = await create_generate_task(
+            cls,
+            images=m_images,
+            seed=Seed,
+            material=Material_Type,
+            quality_override=quality_override,
+            tier=tier,
+            mesh_mode=mesh_mode,
+        )
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
+        model_path, file_3d = await download_files(download_list, task_uuid)
+
+        return IO.NodeOutput(model_path, file_3d)
+
+
+class Rodin3D_Detail(IO.ComfyNode):
+    """Generate 3D Assets using Rodin API"""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Rodin3D_Detail",
+            display_name="Rodin 3D Generate - Detail Generate",
+            category="partner/3d/Rodin",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Image.Input("Images"),
+                *COMMON_PARAMETERS,
+            ],
+            outputs=[
+                IO.String.Output(display_name="3D Model Path"),  # for backward compatibility only
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+    ) -> IO.NodeOutput:
+        tier = "Detail"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality_override = get_quality_mode(Polygon_count)
+        task_uuid, subscription_key = await create_generate_task(
+            cls,
+            images=m_images,
+            seed=Seed,
+            material=Material_Type,
+            quality_override=quality_override,
+            tier=tier,
+            mesh_mode=mesh_mode,
+        )
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
+        model_path, file_3d = await download_files(download_list, task_uuid)
+
+        return IO.NodeOutput(model_path, file_3d)
+
+
+class Rodin3D_Smooth(IO.ComfyNode):
+    """Generate 3D Assets using Rodin API"""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Rodin3D_Smooth",
+            display_name="Rodin 3D Generate - Smooth Generate",
+            category="partner/3d/Rodin",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Image.Input("Images"),
+                *COMMON_PARAMETERS,
+            ],
+            outputs=[
+                IO.String.Output(display_name="3D Model Path"),  # for backward compatibility only
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+    ) -> IO.NodeOutput:
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality_override = get_quality_mode(Polygon_count)
+        task_uuid, subscription_key = await create_generate_task(
+            cls,
+            images=m_images,
+            seed=Seed,
+            material=Material_Type,
+            quality_override=quality_override,
+            tier="Smooth",
+            mesh_mode=mesh_mode,
+        )
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
+        model_path, file_3d = await download_files(download_list, task_uuid)
+
+        return IO.NodeOutput(model_path, file_3d)
+
+
+class Rodin3D_Sketch(IO.ComfyNode):
+    """Generate 3D Assets using Rodin API"""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Rodin3D_Sketch",
+            display_name="Rodin 3D Generate - Sketch Generate",
+            category="partner/3d/Rodin",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Image.Input("Images"),
+                IO.Int.Input(
+                    "Seed",
+                    default=0,
+                    min=0,
+                    max=65535,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="3D Model Path"),  # for backward compatibility only
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        Images,
+        Seed,
+    ) -> IO.NodeOutput:
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        task_uuid, subscription_key = await create_generate_task(
+            cls,
+            images=m_images,
+            seed=Seed,
+            material="PBR",
+            quality_override=18000,
+            tier="Sketch",
+            mesh_mode="Quad",
+        )
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
+        model_path, file_3d = await download_files(download_list, task_uuid)
+
+        return IO.NodeOutput(model_path, file_3d)
+
+
+class Rodin3D_Gen2(IO.ComfyNode):
+    """Generate 3D Assets using Rodin API"""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Rodin3D_Gen2",
+            display_name="Rodin 3D Generate - Gen-2 Generate",
+            category="partner/3d/Rodin",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                IO.Image.Input("Images"),
+                IO.Int.Input(
+                    "Seed",
+                    default=0,
+                    min=0,
+                    max=65535,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                ),
+                IO.Combo.Input("Material_Type", options=["PBR", "Shaded"], default="PBR", optional=True),
+                IO.Combo.Input(
+                    "Polygon_count",
+                    options=[
+                        "4K-Quad",
+                        "8K-Quad",
+                        "18K-Quad",
+                        "50K-Quad",
+                        "2K-Triangle",
+                        "20K-Triangle",
+                        "150K-Triangle",
+                        "500K-Triangle",
+                    ],
+                    default="500K-Triangle",
+                    optional=True,
+                ),
+                IO.Boolean.Input("TAPose", default=False, advanced=True),
+            ],
+            outputs=[
+                IO.String.Output(display_name="3D Model Path"),  # for backward compatibility only
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+        TAPose,
+    ) -> IO.NodeOutput:
+        tier = "Gen-2"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality_override = get_quality_mode(Polygon_count)
+        task_uuid, subscription_key = await create_generate_task(
+            cls,
+            images=m_images,
+            seed=Seed,
+            material=Material_Type,
+            quality_override=quality_override,
+            tier=tier,
+            mesh_mode=mesh_mode,
+            ta_pose=TAPose,
+        )
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
+        model_path, file_3d = await download_files(download_list, task_uuid)
+
+        return IO.NodeOutput(model_path, file_3d)
+
+
+def _rodin_multipart_parser(data: dict[str, Any]) -> aiohttp.FormData:
+    """Convert a Rodin request dict to an aiohttp form, fixing bool/list serialization.
+
+    Booleans --> "true"/"false". Lists --> one field per element.
+    """
+    form = aiohttp.FormData(default_to_multipart=True)
+    for key, value in data.items():
+        if value is None:
+            continue
+        if isinstance(value, bool):
+            form.add_field(key, "true" if value else "false")
+        elif isinstance(value, list):
+            for item in value:
+                form.add_field(key, str(item))
+        elif isinstance(value, (bytes, bytearray)):
+            form.add_field(key, value)
+        else:
+            form.add_field(key, str(value))
+    return form
+
+
+async def _create_gen25_task(
+    cls: type[IO.ComfyNode],
+    request: Rodin3DGen25Request,
+    images: list | None,
+) -> tuple[str, str]:
+    """Submit a Gen-2.5 generate job; returns (task_uuid, subscription_key)."""
+
+    if images is not None and len(images) > 5:
+        raise ValueError("Rodin Gen-2.5 supports at most 5 input images.")
+
+    files = None
+    if images:
+        files = [
+            (
+                "images",
+                open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image),
+            )
+            for image in images
+            if image is not None
+        ]
+
+    response = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/rodin/api/v2/rodin", method="POST"),
+        response_model=Rodin3DGenerateResponse,
+        data=request,
+        files=files,
+        content_type="multipart/form-data",
+        multipart_parser=_rodin_multipart_parser,
+    )
+
+    if not response.uuid or not response.jobs or not response.jobs.subscription_key:
+        raise RuntimeError(f"Rodin Gen-2.5 submit failed: message={response.message!r}")
+    return response.uuid, response.jobs.subscription_key
+
+
+_PREVIEWABLE_3D_EXTS = {".glb", ".obj", ".fbx", ".stl", ".gltf"}
+
+
+async def _download_gen25_files(
+    download_list: Rodin3DDownloadResponse,
+    task_uuid: str,
+    geometry_file_format: str,
+) -> Types.File3D | None:
+    """Download every file in the list; return the File3D matching the chosen format."""
+
+    folder_name = f"Rodin3D_Gen25_{task_uuid}"
+    save_dir = os.path.join(comfy_paths.get_output_directory(), folder_name)
+    os.makedirs(save_dir, exist_ok=True)
+
+    target_ext = f".{geometry_file_format.lower().lstrip('.')}"
+    file_3d: Types.File3D | None = None
+
+    for item in download_list.items:
+        file_path = os.path.join(save_dir, item.name)
+        ext = os.path.splitext(item.name.lower())[1]
+        # Prefer the file matching the user's chosen format; fall back below.
+        if file_3d is None and ext == target_ext and ext in _PREVIEWABLE_3D_EXTS:
+            file_3d = await download_url_to_file_3d(item.url, target_ext.lstrip("."))
+            with open(file_path, "wb") as f:
+                f.write(file_3d.get_bytes())
+            continue
+        await download_url_to_bytesio(item.url, file_path)
+
+    # If the chosen format wasn't found, surface any model file we did get.
+    if file_3d is None:
+        for item in download_list.items:
+            ext = os.path.splitext(item.name.lower())[1]
+            if ext in _PREVIEWABLE_3D_EXTS:
+                file_3d = await download_url_to_file_3d(item.url, ext.lstrip("."))
+                break
+    return file_3d
+
+
+_MODE_REGULAR = "Regular"
+_MODE_FAST = "Fast"
+_MODE_EXTREME_HIGH = "Extreme-High"
+
+_REGULAR_POLY_OPTIONS = [
+    "Default",
+    "4K-Quad",
+    "8K-Quad",
+    "18K-Quad",
+    "50K-Quad",
+    "2K-Triangle",
+    "20K-Triangle",
+    "150K-Triangle",
+    "500K-Triangle",
+    "1M-Triangle",
+]
+
+_TEXTURE_MODE_OPTIONS = ["Default", "legacy", "extreme-low", "low", "medium", "high"]
+_GEOMETRY_FORMAT_OPTIONS = ["glb", "fbx", "obj", "stl"]
+_MATERIAL_OPTIONS = ["PBR", "Shaded", "All", "None"]
+
+
+def _build_mode_input(name: str = "mode") -> IO.DynamicCombo.Input:
+    return IO.DynamicCombo.Input(
+        name,
+        options=[
+            IO.DynamicCombo.Option(
+                _MODE_REGULAR,
+                [
+                    IO.Combo.Input(
+                        "tier",
+                        options=["Gen-2.5-Low", "Gen-2.5-Medium", "Gen-2.5-High"],
+                        default="Gen-2.5-High",
+                        tooltip="Quality tier. Higher tiers produce higher-fidelity geometry.",
+                    ),
+                    IO.Combo.Input(
+                        "polygon_count",
+                        options=_REGULAR_POLY_OPTIONS,
+                        default="Default",
+                        tooltip="Preset face count. 'Default' uses the server's default for the selected tier.",
+                    ),
+                    IO.Boolean.Input(
+                        "creative",
+                        default=False,
+                        tooltip="Creative mode (Medium/High only). Enhances generative robustness.",
+                    ),
+                ],
+            ),
+            IO.DynamicCombo.Option(
+                _MODE_FAST,
+                [
+                    IO.Combo.Input(
+                        "tier",
+                        options=[
+                            "Gen-2.5-Extreme-Low",
+                            "Gen-2.5-Low",
+                            "Gen-2.5-Medium",
+                            "Gen-2.5-High",
+                        ],
+                        default="Gen-2.5-Low",
+                    ),
+                    IO.Int.Input(
+                        "mesh_faces",
+                        default=20000,
+                        min=1000,
+                        max=20000,
+                        display_mode=IO.NumberDisplay.number,
+                        tooltip="Mesh face count (1K-20K in Fast mode).",
+                    ),
+                ],
+            ),
+            IO.DynamicCombo.Option(
+                _MODE_EXTREME_HIGH,
+                [
+                    IO.Combo.Input("mesh_mode", options=["Raw", "Quad"], default="Raw"),
+                    IO.Int.Input(
+                        "mesh_faces",
+                        default=1000000,
+                        min=20000,
+                        max=2000000,
+                        display_mode=IO.NumberDisplay.number,
+                        tooltip=(
+                            "Mesh face count. Raw mode: 20K-2M. "
+                            "Quad mode: keep under 200K (upstream may reject higher values)."
+                        ),
+                    ),
+                    IO.Boolean.Input(
+                        "is_micro",
+                        default=False,
+                        tooltip="Enable micro detail (Extreme-High only).",
+                    ),
+                    IO.Boolean.Input(
+                        "creative",
+                        default=False,
+                        tooltip="Creative mode. Enhances generative robustness.",
+                    ),
+                ],
+            ),
+        ],
+        tooltip=(
+            "Generation mode. Regular = balanced. Fast = 1K-20K faces for rapid prototyping. "
+            "Extreme-High = 20K-2M faces with optional micro details."
+        ),
+    )
+
+
+def _build_common_inputs(*, include_image_only: bool) -> list:
+    inputs: list = [
+        IO.Combo.Input("material", options=_MATERIAL_OPTIONS, default="Shaded"),
+        IO.Combo.Input("geometry_file_format", options=_GEOMETRY_FORMAT_OPTIONS, default="glb"),
+        IO.Combo.Input(
+            "texture_mode",
+            options=_TEXTURE_MODE_OPTIONS,
+            default="Default",
+            optional=True,
+            tooltip="Texture quality preset. 'Default' uses the server's default for the selected tier.",
+        ),
+        IO.Int.Input(
+            "seed",
+            default=0,
+            min=0,
+            max=65535,
+            display_mode=IO.NumberDisplay.number,
+            control_after_generate=True,
+            optional=True,
+        ),
+        IO.Boolean.Input(
+            "TAPose", default=False, optional=True, advanced=True, tooltip="T/A pose for human-like models."
+        ),
+        IO.Boolean.Input(
+            "hd_texture", default=False, optional=True, advanced=True, tooltip="High-quality texture enhancement."
+        ),
+        IO.Boolean.Input(
+            "texture_delight",
+            default=False,
+            optional=True,
+            advanced=True,
+            tooltip="Remove baked lighting from textures.",
+        ),
+    ]
+    if include_image_only:
+        inputs.append(
+            IO.Boolean.Input(
+                "use_original_alpha",
+                default=False,
+                optional=True,
+                advanced=True,
+                tooltip="Preserve image transparency.",
+            )
+        )
+    inputs.extend(
+        [
+            IO.Boolean.Input(
+                "addon_highpack",
+                default=False,
+                optional=True,
+                advanced=True,
+                tooltip="HighPack addon: 4K textures and ~16x faces in Quad mode.",
+            ),
+            IO.Int.Input(
+                "bbox_width",
+                default=0,
+                min=0,
+                max=300,
+                display_mode=IO.NumberDisplay.number,
+                optional=True,
+                advanced=True,
+                tooltip="Bounding-box width (Y axis). Set to 0 with the others to skip bbox.",
+            ),
+            IO.Int.Input(
+                "bbox_height",
+                default=0,
+                min=0,
+                max=300,
+                display_mode=IO.NumberDisplay.number,
+                optional=True,
+                advanced=True,
+                tooltip="Bounding-box height (Z axis).",
+            ),
+            IO.Int.Input(
+                "bbox_length",
+                default=0,
+                min=0,
+                max=300,
+                display_mode=IO.NumberDisplay.number,
+                optional=True,
+                advanced=True,
+                tooltip="Bounding-box length (X axis).",
+            ),
+            IO.Int.Input(
+                "height_cm",
+                default=0,
+                min=0,
+                max=10000,
+                display_mode=IO.NumberDisplay.number,
+                optional=True,
+                advanced=True,
+                tooltip="Approximate model height in centimeters (0 to skip).",
+            ),
+        ]
+    )
+    return inputs
+
+
+_PRICE_EXPR = """
+(
+  $baseCredits := widgets.mode = "extreme-high" ? 1.0 : 0.5;
+  $addonCredits := widgets.addon_highpack ? 1.0 : 0.0;
+  $total := ($baseCredits * 1.5) + ($addonCredits * 0.8);
+  {"type":"usd","usd": $total}
+)
+"""
+
+
+def _resolve_mode_params(mode_input: dict) -> dict:
+    """Translate the DynamicCombo `mode` payload into Gen-2.5 request fields.
+
+    Returns a dict with: tier, quality_override, mesh_mode, geometry_instruct_mode, is_micro.
+    Missing keys mean "do not send" (so we don't override server defaults).
+    """
+    selected = mode_input["mode"]
+    out: dict = {}
+
+    if selected == _MODE_REGULAR:
+        out["tier"] = mode_input["tier"]
+        polygon = mode_input.get("polygon_count", "Default")
+        if polygon != "Default":
+            mesh_mode, faces = get_quality_mode(polygon)
+            out["mesh_mode"] = mesh_mode
+            out["quality_override"] = faces
+        if mode_input.get("creative"):
+            out["geometry_instruct_mode"] = "creative"
+
+    elif selected == _MODE_FAST:
+        out["tier"] = mode_input["tier"]
+        out["mesh_mode"] = "Raw"
+        out["quality_override"] = int(mode_input["mesh_faces"])
+
+    elif selected == _MODE_EXTREME_HIGH:
+        out["tier"] = "Gen-2.5-Extreme-High"
+        out["mesh_mode"] = mode_input["mesh_mode"]
+        out["quality_override"] = int(mode_input["mesh_faces"])
+        if mode_input.get("is_micro"):
+            out["is_micro"] = True
+        if mode_input.get("creative"):
+            out["geometry_instruct_mode"] = "creative"
+    return out
+
+
+def _build_request(
+    *,
+    mode_input: dict,
+    material: str,
+    geometry_file_format: str,
+    texture_mode: str,
+    seed: int,
+    TAPose: bool,
+    hd_texture: bool,
+    texture_delight: bool,
+    addon_highpack: bool,
+    bbox_width: int,
+    bbox_height: int,
+    bbox_length: int,
+    height_cm: int,
+    prompt: str | None = None,
+    use_original_alpha: bool = False,
+) -> Rodin3DGen25Request:
+    mode_params = _resolve_mode_params(mode_input)
+
+    bbox = None
+    if bbox_width and bbox_height and bbox_length:
+        bbox = [bbox_width, bbox_height, bbox_length]
+
+    return Rodin3DGen25Request(
+        tier=mode_params["tier"],
+        prompt=prompt or None,
+        seed=seed,
+        material=material,
+        geometry_file_format=geometry_file_format,
+        texture_mode=None if texture_mode == "Default" else texture_mode,
+        mesh_mode=mode_params.get("mesh_mode"),
+        quality_override=mode_params.get("quality_override"),
+        geometry_instruct_mode=mode_params.get("geometry_instruct_mode"),
+        bbox_condition=bbox,
+        height=height_cm or None,
+        TAPose=TAPose or None,
+        hd_texture=hd_texture or None,
+        texture_delight=texture_delight or None,
+        is_micro=mode_params.get("is_micro"),
+        use_original_alpha=use_original_alpha or None,
+        addons=["HighPack"] if addon_highpack else None,
+    )
+
+
+class Rodin3D_Gen25_Image(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Rodin3D_Gen25_Image",
+            display_name="Rodin 3D Gen-2.5 - Image to 3D",
+            category="partner/3d/Rodin",
+            description=(
+                "Generate a 3D model from 1-5 reference images via Rodin Gen-2.5. "
+                "Pick a mode (Fast / Regular / Extreme-High) to tune quality vs. cost."
+            ),
+            inputs=[
+                IO.Autogrow.Input(
+                    "images",
+                    template=IO.Autogrow.TemplatePrefix(IO.Image.Input("image"), prefix="image", min=1, max=5),
+                    tooltip="1-5 images. The first image is used for materials when multi-view.",
+                ),
+                _build_mode_input(),
+                *_build_common_inputs(include_image_only=True),
+            ],
+            outputs=[IO.File3DAny.Output(display_name="model_file")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode", "addon_highpack"]),
+                expr=_PRICE_EXPR,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        images: IO.Autogrow.Type,
+        mode: dict,
+        material: str,
+        geometry_file_format: str,
+        texture_mode: str,
+        seed: int,
+        TAPose: bool,
+        hd_texture: bool,
+        texture_delight: bool,
+        use_original_alpha: bool,
+        addon_highpack: bool,
+        bbox_width: int,
+        bbox_height: int,
+        bbox_length: int,
+        height_cm: int,
+    ) -> IO.NodeOutput:
+        image_tensors = [img for img in images.values() if img is not None]
+        if not image_tensors:
+            raise ValueError("Rodin Gen-2.5 Image-to-3D requires at least one image.")
+
+        # Flatten multi-image tensors into individual frames; the API accepts each as a separate part.
+        flat_images: list = []
+        for tensor in image_tensors:
+            if hasattr(tensor, "shape") and len(tensor.shape) == 4:
+                for i in range(tensor.shape[0]):
+                    flat_images.append(tensor[i])
+            else:
+                flat_images.append(tensor)
+
+        if len(flat_images) > 5:
+            raise ValueError(f"Rodin Gen-2.5 accepts at most 5 images; received {len(flat_images)}.")
+
+        request = _build_request(
+            mode_input=mode,
+            material=material,
+            geometry_file_format=geometry_file_format,
+            texture_mode=texture_mode,
+            seed=seed,
+            TAPose=TAPose,
+            hd_texture=hd_texture,
+            texture_delight=texture_delight,
+            addon_highpack=addon_highpack,
+            bbox_width=bbox_width,
+            bbox_height=bbox_height,
+            bbox_length=bbox_length,
+            height_cm=height_cm,
+            prompt=None,
+            use_original_alpha=use_original_alpha,
+        )
+
+        task_uuid, subscription_key = await _create_gen25_task(cls, request, flat_images)
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
+        file_3d = await _download_gen25_files(download_list, task_uuid, geometry_file_format)
+        return IO.NodeOutput(file_3d)
+
+
+class Rodin3D_Gen25_Text(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Rodin3D_Gen25_Text",
+            display_name="Rodin 3D Gen-2.5 - Text to 3D",
+            category="partner/3d/Rodin",
+            description=(
+                "Generate a 3D model from a text prompt via Rodin Gen-2.5. "
+                "Pick a mode (Fast / Regular / Extreme-High) to tune quality vs. cost."
+            ),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the 3D model.",
+                ),
+                _build_mode_input(),
+                *_build_common_inputs(include_image_only=False),
+            ],
+            outputs=[IO.File3DAny.Output(display_name="model_file")],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode", "addon_highpack"]),
+                expr=_PRICE_EXPR,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        mode: dict,
+        material: str,
+        geometry_file_format: str,
+        texture_mode: str,
+        seed: int,
+        TAPose: bool,
+        hd_texture: bool,
+        texture_delight: bool,
+        addon_highpack: bool,
+        bbox_width: int,
+        bbox_height: int,
+        bbox_length: int,
+        height_cm: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, field_name="prompt", min_length=1, max_length=2500)
+        request = _build_request(
+            mode_input=mode,
+            material=material,
+            geometry_file_format=geometry_file_format,
+            texture_mode=texture_mode,
+            seed=seed,
+            TAPose=TAPose,
+            hd_texture=hd_texture,
+            texture_delight=texture_delight,
+            addon_highpack=addon_highpack,
+            bbox_width=bbox_width,
+            bbox_height=bbox_height,
+            bbox_length=bbox_length,
+            height_cm=height_cm,
+            prompt=prompt,
+        )
+        task_uuid, subscription_key = await _create_gen25_task(cls, request, images=None)
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
+        file_3d = await _download_gen25_files(download_list, task_uuid, geometry_file_format)
+        return IO.NodeOutput(file_3d)
+
+
+class Rodin3DExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            Rodin3D_Regular,
+            Rodin3D_Detail,
+            Rodin3D_Smooth,
+            Rodin3D_Sketch,
+            Rodin3D_Gen2,
+            Rodin3D_Gen25_Image,
+            Rodin3D_Gen25_Text,
+        ]
+
+
+async def comfy_entrypoint() -> Rodin3DExtension:
+    return Rodin3DExtension()
diff --git a/comfy_api_nodes/nodes_runway.py b/comfy_api_nodes/nodes_runway.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad27b224e7309c170b25ce17cdb8fe7acce5b7a3
--- /dev/null
+++ b/comfy_api_nodes/nodes_runway.py
@@ -0,0 +1,855 @@
+"""Runway API Nodes
+
+API Docs:
+  - https://docs.dev.runwayml.com/api/#tag/Task-management/paths/~1v1~1tasks~1%7Bid%7D/delete
+
+User Guides:
+  - https://help.runwayml.com/hc/en-us/sections/30265301423635-Gen-3-Alpha
+  - https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video
+  - https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo
+  - https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3
+
+"""
+
+from enum import Enum
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
+from comfy_api_nodes.apis.runway import (
+    RunwayImageToVideoRequest,
+    RunwayImageToVideoResponse,
+    RunwayTaskStatusResponse as TaskStatusResponse,
+    RunwayModelEnum as Model,
+    RunwayDurationEnum as Duration,
+    RunwayAspectRatioEnum as AspectRatio,
+    RunwayPromptImageObject,
+    RunwayPromptImageDetailedObject,
+    RunwayTextToImageRequest,
+    RunwayTextToImageResponse,
+    Model4,
+    ReferenceImage,
+    RunwayTextToImageAspectRatioEnum,
+    RunwayAleph2IO,
+    RunwayAleph2KeyframeChain,
+    RunwayAleph2KeyframeItem,
+    RunwayAleph2PromptImageChain,
+    RunwayAleph2PromptImageItem,
+    RunwayAleph2Request,
+    RunwayAleph2Response,
+    RunwayAleph2KeyframeSeconds,
+    RunwayAleph2KeyframeAt,
+    RunwayAleph2PromptImage,
+    RunwayAleph2TimestampPosition,
+    RunwayAleph2RelativePosition,
+    RunwayAleph2ContentModeration,
+    KEYFRAME_MODE_SECONDS,
+    KEYFRAME_MODE_AT,
+    PROMPT_IMAGE_MODE_TIMESTAMP,
+    PROMPT_IMAGE_MODE_POSITION,
+)
+from comfy_api_nodes.util import (
+    image_tensor_pair_to_batch,
+    validate_string,
+    validate_image_dimensions,
+    validate_image_aspect_ratio,
+    validate_video_duration,
+    upload_images_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_video_to_comfyapi,
+    download_url_to_video_output,
+    download_url_to_image_tensor,
+    ApiEndpoint,
+    sync_op,
+    poll_op,
+)
+
+PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video"
+PATH_VIDEO_TO_VIDEO = "/proxy/runway/video_to_video"
+PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image"
+PATH_GET_TASK_STATUS = "/proxy/runway/tasks"
+
+AVERAGE_DURATION_I2V_SECONDS = 64
+AVERAGE_DURATION_FLF_SECONDS = 256
+AVERAGE_DURATION_T2I_SECONDS = 41
+
+
+class RunwayGen4TurboAspectRatio(str, Enum):
+    """Aspect ratios supported for Image to Video API when using gen4_turbo model."""
+
+    field_1280_720 = "1280:720"
+    field_720_1280 = "720:1280"
+    field_1104_832 = "1104:832"
+    field_832_1104 = "832:1104"
+    field_960_960 = "960:960"
+    field_1584_672 = "1584:672"
+
+
+class RunwayGen3aAspectRatio(str, Enum):
+    """Aspect ratios supported for Image to Video API when using gen3a_turbo model."""
+
+    field_768_1280 = "768:1280"
+    field_1280_768 = "1280:768"
+
+
+def get_video_url_from_task_status(response: TaskStatusResponse) -> str | None:
+    """Returns the video URL from the task status response if it exists."""
+    if hasattr(response, "output") and len(response.output) > 0:
+        return response.output[0]
+    return None
+
+
+def get_image_url_from_task_status(response: TaskStatusResponse) -> str | None:
+    """Returns the image URL from the task status response if it exists."""
+    if hasattr(response, "output") and len(response.output) > 0:
+        return response.output[0]
+    return None
+
+
+async def get_response(
+    cls: type[IO.ComfyNode], task_id: str, estimated_duration: int | None = None
+) -> TaskStatusResponse:
+    return await poll_op(
+        cls,
+        ApiEndpoint(path=f"{PATH_GET_TASK_STATUS}/{task_id}"),
+        response_model=TaskStatusResponse,
+        status_extractor=lambda r: r.status,
+        estimated_duration=estimated_duration,
+        progress_extractor=lambda r: r.progress * 100 if r.progress is not None else None,
+    )
+
+
+async def generate_video(
+    cls: type[IO.ComfyNode],
+    request: RunwayImageToVideoRequest,
+    estimated_duration: int | None = None,
+) -> InputImpl.VideoFromFile:
+    initial_response = await sync_op(
+        cls,
+        endpoint=ApiEndpoint(path=PATH_IMAGE_TO_VIDEO, method="POST"),
+        response_model=RunwayImageToVideoResponse,
+        data=request,
+    )
+
+    final_response = await get_response(cls, initial_response.id, estimated_duration)
+    if not final_response.output:
+        raise ValueError("Runway task succeeded but no video data found in response.")
+
+    video_url = get_video_url_from_task_status(final_response)
+    return await download_url_to_video_output(video_url)
+
+
+class RunwayImageToVideoNodeGen3a(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RunwayImageToVideoNodeGen3a",
+            display_name="Runway Image to Video (Gen3a Turbo)",
+            category="partner/video/Runway",
+            description="Generate a video from a single starting frame using Gen3a Turbo model. "
+            "Before diving in, review these best practices to ensure that "
+            "your input selections will set your generation up for success: "
+            "https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the generation",
+                ),
+                IO.Image.Input(
+                    "start_frame",
+                    tooltip="Start frame to be used for the video",
+                ),
+                IO.Combo.Input(
+                    "duration",
+                    options=Duration,
+                ),
+                IO.Combo.Input(
+                    "ratio",
+                    options=RunwayGen3aAspectRatio,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    control_after_generate=True,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Random seed for generation",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration"]),
+                expr="""{"type":"usd","usd": 0.0715 * widgets.duration}""",
+            ),
+            is_deprecated=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        start_frame: Input.Image,
+        duration: str,
+        ratio: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1)
+        validate_image_dimensions(start_frame, max_width=7999, max_height=7999)
+        validate_image_aspect_ratio(start_frame, (1, 2), (2, 1))
+
+        download_urls = await upload_images_to_comfyapi(
+            cls,
+            start_frame,
+            max_images=1,
+            mime_type="image/png",
+        )
+
+        return IO.NodeOutput(
+            await generate_video(
+                cls,
+                RunwayImageToVideoRequest(
+                    promptText=prompt,
+                    seed=seed,
+                    model=Model("gen3a_turbo"),
+                    duration=Duration(duration),
+                    ratio=AspectRatio(ratio),
+                    promptImage=RunwayPromptImageObject(
+                        root=[RunwayPromptImageDetailedObject(uri=str(download_urls[0]), position="first")]
+                    ),
+                ),
+            )
+        )
+
+
+class RunwayImageToVideoNodeGen4(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RunwayImageToVideoNodeGen4",
+            display_name="Runway Image to Video (Gen4 Turbo)",
+            category="partner/video/Runway",
+            description="Generate a video from a single starting frame using Gen4 Turbo model. "
+            "Before diving in, review these best practices to ensure that "
+            "your input selections will set your generation up for success: "
+            "https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the generation",
+                ),
+                IO.Image.Input(
+                    "start_frame",
+                    tooltip="Start frame to be used for the video",
+                ),
+                IO.Combo.Input(
+                    "duration",
+                    options=Duration,
+                ),
+                IO.Combo.Input(
+                    "ratio",
+                    options=RunwayGen4TurboAspectRatio,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    control_after_generate=True,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Random seed for generation",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration"]),
+                expr="""{"type":"usd","usd": 0.0715 * widgets.duration}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        start_frame: Input.Image,
+        duration: str,
+        ratio: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1)
+        validate_image_dimensions(start_frame, max_width=7999, max_height=7999)
+        validate_image_aspect_ratio(start_frame, (1, 2), (2, 1))
+
+        download_urls = await upload_images_to_comfyapi(
+            cls,
+            start_frame,
+            max_images=1,
+            mime_type="image/png",
+        )
+
+        return IO.NodeOutput(
+            await generate_video(
+                cls,
+                RunwayImageToVideoRequest(
+                    promptText=prompt,
+                    seed=seed,
+                    model=Model("gen4_turbo"),
+                    duration=Duration(duration),
+                    ratio=AspectRatio(ratio),
+                    promptImage=RunwayPromptImageObject(
+                        root=[RunwayPromptImageDetailedObject(uri=str(download_urls[0]), position="first")]
+                    ),
+                ),
+                estimated_duration=AVERAGE_DURATION_FLF_SECONDS,
+            )
+        )
+
+
+class RunwayFirstLastFrameNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RunwayFirstLastFrameNode",
+            display_name="Runway First-Last-Frame to Video",
+            category="partner/video/Runway",
+            description="Upload first and last keyframes, draft a prompt, and generate a video. "
+            "More complex transitions, such as cases where the Last frame is completely different "
+            "from the First frame, may benefit from the longer 10s duration. "
+            "This would give the generation more time to smoothly transition between the two inputs. "
+            "Before diving in, review these best practices to ensure that your input selections "
+            "will set your generation up for success: "
+            "https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the generation",
+                ),
+                IO.Image.Input(
+                    "start_frame",
+                    tooltip="Start frame to be used for the video",
+                ),
+                IO.Image.Input(
+                    "end_frame",
+                    tooltip="End frame to be used for the video. Supported for gen3a_turbo only.",
+                ),
+                IO.Combo.Input(
+                    "duration",
+                    options=Duration,
+                ),
+                IO.Combo.Input(
+                    "ratio",
+                    options=RunwayGen3aAspectRatio,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    control_after_generate=True,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Random seed for generation",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration"]),
+                expr="""{"type":"usd","usd": 0.0715 * widgets.duration}""",
+            ),
+            is_deprecated=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        start_frame: Input.Image,
+        end_frame: Input.Image,
+        duration: str,
+        ratio: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1)
+        validate_image_dimensions(start_frame, max_width=7999, max_height=7999)
+        validate_image_dimensions(end_frame, max_width=7999, max_height=7999)
+        validate_image_aspect_ratio(start_frame, (1, 2), (2, 1))
+        validate_image_aspect_ratio(end_frame, (1, 2), (2, 1))
+
+        stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame)
+        download_urls = await upload_images_to_comfyapi(
+            cls,
+            stacked_input_images,
+            max_images=2,
+            mime_type="image/png",
+        )
+        if len(download_urls) != 2:
+            raise ValueError("Failed to upload one or more images to comfy api.")
+
+        return IO.NodeOutput(
+            await generate_video(
+                cls,
+                RunwayImageToVideoRequest(
+                    promptText=prompt,
+                    seed=seed,
+                    model=Model("gen3a_turbo"),
+                    duration=Duration(duration),
+                    ratio=AspectRatio(ratio),
+                    promptImage=RunwayPromptImageObject(
+                        root=[
+                            RunwayPromptImageDetailedObject(uri=str(download_urls[0]), position="first"),
+                            RunwayPromptImageDetailedObject(uri=str(download_urls[1]), position="last"),
+                        ]
+                    ),
+                ),
+                estimated_duration=AVERAGE_DURATION_FLF_SECONDS,
+            )
+        )
+
+
+class RunwayTextToImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RunwayTextToImageNode",
+            display_name="Runway Text to Image",
+            category="partner/image/Runway",
+            description="Generate an image from a text prompt using Runway's Gen 4 model. "
+            "You can also include reference image to guide the generation.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the generation",
+                ),
+                IO.Combo.Input(
+                    "ratio",
+                    options=[model.value for model in RunwayTextToImageAspectRatioEnum],
+                ),
+                IO.Image.Input(
+                    "reference_image",
+                    tooltip="Optional reference image to guide the generation",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.11}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        ratio: str,
+        reference_image: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1)
+
+        # Prepare reference images if provided
+        reference_images = None
+        if reference_image is not None:
+            validate_image_dimensions(reference_image, max_width=7999, max_height=7999)
+            validate_image_aspect_ratio(reference_image, (1, 2), (2, 1))
+            download_urls = await upload_images_to_comfyapi(
+                cls,
+                reference_image,
+                max_images=1,
+                mime_type="image/png",
+            )
+            reference_images = [ReferenceImage(uri=str(download_urls[0]))]
+
+        initial_response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path=PATH_TEXT_TO_IMAGE, method="POST"),
+            response_model=RunwayTextToImageResponse,
+            data=RunwayTextToImageRequest(
+                promptText=prompt,
+                model=Model4.gen4_image,
+                ratio=ratio,
+                referenceImages=reference_images,
+            ),
+        )
+
+        final_response = await get_response(
+            cls,
+            initial_response.id,
+            estimated_duration=AVERAGE_DURATION_T2I_SECONDS,
+        )
+        if not final_response.output:
+            raise ValueError("Runway task succeeded but no image data found in response.")
+
+        return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_task_status(final_response)))
+
+
+_TIMING_ABSOLUTE = "Absolute time (seconds)"
+_TIMING_FRACTION = "Fraction of duration (0.0-1.0)"
+
+
+class RunwayAleph2KeyframeNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RunwayAleph2KeyframeNode",
+            display_name="Runway Aleph2 Keyframe",
+            category="partner/video/Runway",
+            description="Anchor a guidance image to a moment of the input (source) video, so Aleph2 "
+            "steers the edit at that point of your footage. Connect this to the 'keyframes' input of "
+            "the Runway Aleph2 Video to Video node; chain several together (up to 5) via the optional "
+            "'keyframes' input below.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="The guidance image to apply at the chosen moment of the input video.",
+                ),
+                IO.DynamicCombo.Input(
+                    "timing",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            _TIMING_ABSOLUTE,
+                            [
+                                IO.Float.Input(
+                                    "seconds",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=30.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Time in seconds from start of the input video where this image applies.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            _TIMING_FRACTION,
+                            [
+                                IO.Float.Input(
+                                    "fraction",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Where in the input video this image applies, "
+                                    "as a fraction of its duration (0.0 = start, 1.0 = end).",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="How to place this image on the input video's timeline.",
+                ),
+                IO.Custom(RunwayAleph2IO.KEYFRAME).Input(
+                    "keyframes",
+                    optional=True,
+                    tooltip="Optional earlier keyframes to chain with this one.",
+                ),
+            ],
+            outputs=[IO.Custom(RunwayAleph2IO.KEYFRAME).Output(display_name="keyframes")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image: Input.Image,
+        timing: dict,
+        keyframes: RunwayAleph2KeyframeChain | None = None,
+    ) -> IO.NodeOutput:
+        chain = keyframes.clone() if keyframes is not None else RunwayAleph2KeyframeChain()
+        if timing["timing"] == _TIMING_ABSOLUTE:
+            mode, value = KEYFRAME_MODE_SECONDS, float(timing["seconds"])
+        else:
+            mode, value = KEYFRAME_MODE_AT, float(timing["fraction"])
+        chain.add(RunwayAleph2KeyframeItem(image=image, mode=mode, value=value))
+        return IO.NodeOutput(chain)
+
+
+class RunwayAleph2PromptImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RunwayAleph2PromptImageNode",
+            display_name="Runway Aleph2 Prompt Image",
+            category="partner/video/Runway",
+            description="Anchor a guidance image to a moment of the output (result) video, to guide what "
+            "the edited video looks like at that point. Connect this to the 'prompt_images' input of the "
+            "Runway Aleph2 Video to Video node; chain several together (up to 5) via the optional "
+            "'prompt_images' input below.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="The guidance image to place at the chosen moment of the output video.",
+                ),
+                IO.DynamicCombo.Input(
+                    "position",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            _TIMING_ABSOLUTE,
+                            [
+                                IO.Float.Input(
+                                    "seconds",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=30.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Time in seconds from start of the output video where this image applies.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            _TIMING_FRACTION,
+                            [
+                                IO.Float.Input(
+                                    "fraction",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Where in the output video this image applies, "
+                                    "as a fraction of its duration (0.0 = start, 1.0 = end).",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="How to place this image on the output video's timeline.",
+                ),
+                IO.Custom(RunwayAleph2IO.PROMPT_IMAGE).Input(
+                    "prompt_images",
+                    optional=True,
+                    tooltip="Optional earlier prompt images to chain with this one.",
+                ),
+            ],
+            outputs=[IO.Custom(RunwayAleph2IO.PROMPT_IMAGE).Output(display_name="prompt_images")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image: Input.Image,
+        position: dict,
+        prompt_images: RunwayAleph2PromptImageChain | None = None,
+    ) -> IO.NodeOutput:
+        chain = prompt_images.clone() if prompt_images is not None else RunwayAleph2PromptImageChain()
+        if position["position"] == _TIMING_ABSOLUTE:
+            mode, value = PROMPT_IMAGE_MODE_TIMESTAMP, float(position["seconds"])
+        else:
+            mode, value = PROMPT_IMAGE_MODE_POSITION, float(position["fraction"])
+        chain.add(RunwayAleph2PromptImageItem(image=image, mode=mode, value=value))
+        return IO.NodeOutput(chain)
+
+
+class RunwayAleph2VideoToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RunwayAleph2VideoToVideoNode",
+            display_name="Runway Aleph2 Video to Video",
+            category="partner/video/Runway",
+            description="Edit a video with a text prompt using Runway's Aleph2 model. Aleph2 transforms "
+            "your footage (restyle, relight, add or remove elements, change the viewpoint) while keeping "
+            "the original motion and timing; the output resolution matches the input video, which must be "
+            "2-30 seconds at 30 fps or lower. Optionally steer the edit with either keyframes (anchored to "
+            "the input video) or prompt images (anchored to the output video) - use one or the other, not both.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Describes what should appear in the output (1-1000 characters).",
+                ),
+                IO.Video.Input(
+                    "video",
+                    tooltip="Input video to edit. Must be 2-30 seconds at 30 fps or lower.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    control_after_generate=True,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Random seed for generation",
+                ),
+                IO.Combo.Input(
+                    "public_figure_threshold",
+                    options=["auto", "low"],
+                    default="low",
+                    tooltip="Content moderation for recognizable public figures.",
+                ),
+                IO.Custom(RunwayAleph2IO.KEYFRAME).Input(
+                    "keyframes",
+                    optional=True,
+                    tooltip="Guidance images anchored to the input video, from Aleph2 Keyframe nodes (up to 5). "
+                    "Use keyframes or prompt images, not both.",
+                ),
+                IO.Custom(RunwayAleph2IO.PROMPT_IMAGE).Input(
+                    "prompt_images",
+                    optional=True,
+                    tooltip="Guidance images anchored to the output video, from Aleph2 Prompt Image nodes (up to 5). "
+                    "Use keyframes or prompt images, not both.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.4004, "format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        video: Input.Video,
+        seed: int,
+        public_figure_threshold: str = "low",
+        keyframes: RunwayAleph2KeyframeChain | None = None,
+        prompt_images: RunwayAleph2PromptImageChain | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=1000)
+        validate_video_duration(
+            video,
+            min_duration=2.0,
+            max_duration=30.0,
+        )
+        try:
+            fps = float(video.get_frame_rate())
+        except Exception:
+            fps = None
+        if fps is not None and fps > 30.0 + 0.01:
+            raise ValueError(f"Input video frame rate ({fps:.2f} fps) exceeds Aleph2's maximum of 30 fps.")
+
+        if (keyframes and keyframes.items) and (prompt_images and prompt_images.items):
+            raise ValueError("Aleph2 accepts either keyframes or prompt images, not both.")
+
+        video_duration: float | None = None
+        try:
+            video_duration = video.get_duration()
+        except Exception:
+            video_duration = None
+
+        def _check_seconds(value: float, label: str) -> None:
+            if video_duration is not None and value > video_duration + 0.0001:
+                raise ValueError(f"{label} {value:.2f}s exceeds the input video duration ({video_duration:.2f}s).")
+
+        video_url = await upload_video_to_comfyapi(cls, video)
+
+        keyframe_models: list[RunwayAleph2KeyframeSeconds | RunwayAleph2KeyframeAt] = []
+        if keyframes is not None:
+            if len(keyframes.items) > 5:
+                raise ValueError("Aleph2 supports at most 5 keyframes.")
+            for item in keyframes.items:
+                image_url = await upload_image_to_comfyapi(cls, item.image, mime_type="image/png")
+                if item.mode == KEYFRAME_MODE_SECONDS:
+                    _check_seconds(item.value, "Keyframe timestamp")
+                    keyframe_models.append(RunwayAleph2KeyframeSeconds(seconds=item.value, uri=image_url))
+                else:
+                    keyframe_models.append(RunwayAleph2KeyframeAt(at=item.value, uri=image_url))
+
+        prompt_image_models: list[RunwayAleph2PromptImage] = []
+        if prompt_images is not None:
+            if len(prompt_images.items) > 5:
+                raise ValueError("Aleph2 supports at most 5 prompt images.")
+            for item in prompt_images.items:
+                image_url = await upload_image_to_comfyapi(cls, item.image, mime_type="image/png")
+                position: RunwayAleph2TimestampPosition | RunwayAleph2RelativePosition
+                if item.mode == PROMPT_IMAGE_MODE_TIMESTAMP:
+                    _check_seconds(item.value, "Prompt image timestamp")
+                    position = RunwayAleph2TimestampPosition(timestampSeconds=item.value)
+                else:
+                    position = RunwayAleph2RelativePosition(positionPercentage=item.value)
+                prompt_image_models.append(RunwayAleph2PromptImage(position=position, uri=image_url))
+
+        initial_response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path=PATH_VIDEO_TO_VIDEO, method="POST"),
+            response_model=RunwayAleph2Response,
+            data=RunwayAleph2Request(
+                promptText=prompt,
+                videoUri=video_url,
+                seed=seed,
+                contentModeration=RunwayAleph2ContentModeration(publicFigureThreshold=public_figure_threshold),
+                keyframes=keyframe_models or None,
+                promptImage=prompt_image_models or None,
+            ),
+        )
+
+        final_response = await get_response(cls, initial_response.id)
+        if not final_response.output:
+            raise ValueError("Runway task succeeded but no video data found in response.")
+
+        return IO.NodeOutput(await download_url_to_video_output(get_video_url_from_task_status(final_response)))
+
+
+class RunwayExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            RunwayFirstLastFrameNode,
+            RunwayImageToVideoNodeGen3a,
+            RunwayImageToVideoNodeGen4,
+            RunwayTextToImageNode,
+            RunwayAleph2VideoToVideoNode,
+            RunwayAleph2KeyframeNode,
+            RunwayAleph2PromptImageNode,
+        ]
+
+
+async def comfy_entrypoint() -> RunwayExtension:
+    return RunwayExtension()
diff --git a/comfy_api_nodes/nodes_sonilo.py b/comfy_api_nodes/nodes_sonilo.py
new file mode 100644
index 0000000000000000000000000000000000000000..8819797c1abf8a7699a7ded723538dd178111ee2
--- /dev/null
+++ b/comfy_api_nodes/nodes_sonilo.py
@@ -0,0 +1,277 @@
+import base64
+import json
+import logging
+import time
+from urllib.parse import urljoin
+
+import aiohttp
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_bytes_to_audio_input,
+    upload_video_to_comfyapi,
+    validate_string,
+)
+from comfy_api_nodes.util._helpers import (
+    default_base_url,
+    get_comfy_api_headers,
+    get_node_id,
+    is_processing_interrupted,
+)
+from comfy_api_nodes.util.common_exceptions import ProcessingInterrupted
+from server import PromptServer
+
+logger = logging.getLogger(__name__)
+
+
+class SoniloVideoToMusic(IO.ComfyNode):
+    """Generate music from video using Sonilo's AI model."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="SoniloVideoToMusic",
+            display_name="Sonilo Video to Music",
+            category="partner/audio/Sonilo",
+            description="Generate music from video content using Sonilo's AI model. "
+            "Analyzes the video and creates matching music.",
+            inputs=[
+                IO.Video.Input(
+                    "video",
+                    tooltip="Input video to generate music from. Maximum duration: 6 minutes.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Optional text prompt to guide music generation. "
+                    "Leave empty for best quality - the model will fully analyze the video content.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed for reproducibility. Currently ignored by the Sonilo "
+                    "service but kept for graph consistency.",
+                ),
+            ],
+            outputs=[IO.Audio.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr='{"type":"usd","usd":0.009,"format":{"suffix":"/second"}}',
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        prompt: str = "",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        video_url = await upload_video_to_comfyapi(cls, video, max_duration=360)
+        form = aiohttp.FormData()
+        form.add_field("video_url", video_url)
+        if prompt.strip():
+            form.add_field("prompt", prompt.strip())
+        audio_bytes = await _stream_sonilo_music(
+            cls,
+            ApiEndpoint(path="/proxy/sonilo/v2m/generate", method="POST"),
+            form,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(audio_bytes))
+
+
+class SoniloTextToMusic(IO.ComfyNode):
+    """Generate music from a text prompt using Sonilo's AI model."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="SoniloTextToMusic",
+            display_name="Sonilo Text to Music",
+            category="partner/audio/Sonilo",
+            description="Generate music from a text prompt using Sonilo's AI model.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Text prompt describing the music to generate.",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=30,
+                    min=1,
+                    max=360,
+                    tooltip="Target duration in seconds. Maximum: 6 minutes.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed for reproducibility. Currently ignored by the Sonilo "
+                    "service but kept for graph consistency.",
+                ),
+            ],
+            outputs=[IO.Audio.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration"]),
+                expr='{"type":"usd","usd": 0.0025 * widgets.duration}',
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        duration: int = 1,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1, max_length=1000)
+        form = aiohttp.FormData()
+        form.add_field("prompt", prompt)
+        form.add_field("duration", str(duration))
+        audio_bytes = await _stream_sonilo_music(
+            cls,
+            ApiEndpoint(path="/proxy/sonilo/t2m/generate", method="POST"),
+            form,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(audio_bytes))
+
+
+async def _stream_sonilo_music(
+    cls: type[IO.ComfyNode],
+    endpoint: ApiEndpoint,
+    form: aiohttp.FormData,
+) -> bytes:
+    """POST ``form`` to Sonilo, read the NDJSON stream, and return the first stream's audio bytes."""
+    url = urljoin(default_base_url().rstrip("/") + "/", endpoint.path.lstrip("/"))
+
+    headers = get_comfy_api_headers(cls)
+    headers.update(endpoint.headers)
+
+    node_id = get_node_id(cls)
+    start_ts = time.monotonic()
+    last_chunk_status_ts = 0.0
+    audio_streams: dict[int, list[bytes]] = {}
+    title: str | None = None
+
+    timeout = aiohttp.ClientTimeout(total=1200.0, sock_read=300.0)
+    async with aiohttp.ClientSession(timeout=timeout) as session:
+        PromptServer.instance.send_progress_text("Status: Queued", node_id)
+        async with session.post(url, data=form, headers=headers) as resp:
+            if resp.status >= 400:
+                msg = await _extract_error_message(resp)
+                raise Exception(f"Sonilo API error ({resp.status}): {msg}")
+
+            while True:
+                if is_processing_interrupted():
+                    raise ProcessingInterrupted("Task cancelled")
+
+                raw_line = await resp.content.readline()
+                if not raw_line:
+                    break
+
+                line = raw_line.decode("utf-8").strip()
+                if not line:
+                    continue
+
+                try:
+                    evt = json.loads(line)
+                except json.JSONDecodeError:
+                    logger.warning("Sonilo: skipping malformed NDJSON line")
+                    continue
+
+                evt_type = evt.get("type")
+                if evt_type == "error":
+                    code = evt.get("code", "UNKNOWN")
+                    message = evt.get("message", "Unknown error")
+                    raise Exception(f"Sonilo generation error ({code}): {message}")
+                if evt_type == "duration":
+                    duration_sec = evt.get("duration_sec")
+                    if duration_sec is not None:
+                        PromptServer.instance.send_progress_text(
+                            f"Status: Generating\nVideo duration: {duration_sec:.1f}s",
+                            node_id,
+                        )
+                elif evt_type in ("titles", "title"):
+                    # v2m sends a "titles" list, t2m sends a scalar "title"
+                    if evt_type == "titles":
+                        titles = evt.get("titles", [])
+                        if titles:
+                            title = titles[0]
+                    else:
+                        title = evt.get("title") or title
+                    if title:
+                        PromptServer.instance.send_progress_text(
+                            f"Status: Generating\nTitle: {title}",
+                            node_id,
+                        )
+                elif evt_type == "audio_chunk":
+                    stream_idx = evt.get("stream_index", 0)
+                    chunk_data = base64.b64decode(evt["data"])
+
+                    if stream_idx not in audio_streams:
+                        audio_streams[stream_idx] = []
+                    audio_streams[stream_idx].append(chunk_data)
+
+                    now = time.monotonic()
+                    if now - last_chunk_status_ts >= 1.0:
+                        total_chunks = sum(len(chunks) for chunks in audio_streams.values())
+                        elapsed = int(now - start_ts)
+                        status_lines = ["Status: Receiving audio"]
+                        if title:
+                            status_lines.append(f"Title: {title}")
+                        status_lines.append(f"Chunks received: {total_chunks}")
+                        status_lines.append(f"Time elapsed: {elapsed}s")
+                        PromptServer.instance.send_progress_text("\n".join(status_lines), node_id)
+                        last_chunk_status_ts = now
+                elif evt_type == "complete":
+                    break
+
+    if not audio_streams:
+        raise Exception("Sonilo API returned no audio data.")
+
+    PromptServer.instance.send_progress_text("Status: Completed", node_id)
+    selected_stream = 0 if 0 in audio_streams else min(audio_streams)
+    return b"".join(audio_streams[selected_stream])
+
+
+async def _extract_error_message(resp: aiohttp.ClientResponse) -> str:
+    """Extract a human-readable error message from an HTTP error response."""
+    try:
+        error_body = await resp.json()
+        detail = error_body.get("detail", {})
+        if isinstance(detail, dict):
+            return detail.get("message", str(detail))
+        return str(detail)
+    except Exception:
+        return await resp.text()
+
+
+class SoniloExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [SoniloVideoToMusic, SoniloTextToMusic]
+
+
+async def comfy_entrypoint() -> SoniloExtension:
+    return SoniloExtension()
diff --git a/comfy_api_nodes/nodes_sora.py b/comfy_api_nodes/nodes_sora.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7967c932f4e272f788e6a6e22367c85fb3f1bed
--- /dev/null
+++ b/comfy_api_nodes/nodes_sora.py
@@ -0,0 +1,172 @@
+from typing import Optional
+
+import torch
+from pydantic import BaseModel, Field
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    tensor_to_bytesio,
+)
+
+
+class Sora2GenerationRequest(BaseModel):
+    prompt: str = Field(...)
+    model: str = Field(...)
+    seconds: str = Field(...)
+    size: str = Field(...)
+
+
+class Sora2GenerationResponse(BaseModel):
+    id: str = Field(...)
+    error: Optional[dict] = Field(None)
+    status: Optional[str] = Field(None)
+
+
+class OpenAIVideoSora2(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenAIVideoSora2",
+            display_name="OpenAI Sora - Video (DEPRECATED)",
+            category="partner/video/Sora",
+            description=(
+                "OpenAI video and audio generation.\n\n"
+                "DEPRECATION NOTICE: OpenAI will stop serving the Sora v2 API in September 2026. "
+                "This node will be removed from ComfyUI at that time."
+            ),
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["sora-2", "sora-2-pro"],
+                    default="sora-2",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Guiding text; may be empty if an input image is present.",
+                ),
+                IO.Combo.Input(
+                    "size",
+                    options=[
+                        "720x1280",
+                        "1280x720",
+                        "1024x1792",
+                        "1792x1024",
+                    ],
+                    default="1280x720",
+                ),
+                IO.Combo.Input(
+                    "duration",
+                    options=[4, 8, 12],
+                    default=8,
+                ),
+                IO.Image.Input(
+                    "image",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    optional=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "size", "duration"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $size := widgets.size;
+                  $dur := widgets.duration;
+                  $isPro := $contains($m, "sora-2-pro");
+                  $isSora2 := $contains($m, "sora-2");
+                  $isProSize := ($size = "1024x1792" or $size = "1792x1024");
+                  $perSec :=
+                    $isPro ? ($isProSize ? 0.5 : 0.3) :
+                    $isSora2 ? 0.1 :
+                    ($isProSize ? 0.5 : 0.1);
+                  {"type":"usd","usd": $round($perSec * $dur, 2)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        size: str = "1280x720",
+        duration: int = 8,
+        seed: int = 0,
+        image: Optional[torch.Tensor] = None,
+    ):
+        if model == "sora-2" and size not in ("720x1280", "1280x720"):
+            raise ValueError("Invalid size for sora-2 model, only 720x1280 and 1280x720 are supported.")
+        files_input = None
+        if image is not None:
+            if get_number_of_images(image) != 1:
+                raise ValueError("Currently only one input image is supported.")
+            files_input = {"input_reference": ("image.png", tensor_to_bytesio(image), "image/png")}
+        initial_response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/openai/v1/videos", method="POST"),
+            data=Sora2GenerationRequest(
+                model=model,
+                prompt=prompt,
+                seconds=str(duration),
+                size=size,
+            ),
+            files=files_input,
+            response_model=Sora2GenerationResponse,
+            content_type="multipart/form-data",
+        )
+        if initial_response.error:
+            raise Exception(initial_response.error["message"])
+
+        model_time_multiplier = 1 if model == "sora-2" else 2
+        await poll_op(
+            cls,
+            poll_endpoint=ApiEndpoint(path=f"/proxy/openai/v1/videos/{initial_response.id}"),
+            response_model=Sora2GenerationResponse,
+            status_extractor=lambda x: x.status,
+            poll_interval=8.0,
+            estimated_duration=int(45 * (duration / 4) * model_time_multiplier),
+        )
+        return IO.NodeOutput(
+            await download_url_to_video_output(f"/proxy/openai/v1/videos/{initial_response.id}/content", cls=cls),
+        )
+
+
+class OpenAISoraExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            OpenAIVideoSora2,
+        ]
+
+
+async def comfy_entrypoint() -> OpenAISoraExtension:
+    return OpenAISoraExtension()
diff --git a/comfy_api_nodes/nodes_sync_so.py b/comfy_api_nodes/nodes_sync_so.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1b9d1a5796c687663560da3627921f524819eeb
--- /dev/null
+++ b/comfy_api_nodes/nodes_sync_so.py
@@ -0,0 +1,391 @@
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.sync_so import (
+    SyncActiveSpeakerDetection,
+    SyncGeneration,
+    SyncGenerationOptions,
+    SyncGenerationRequest,
+    SyncInputItem,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    downscale_image_tensor,
+    downscale_image_tensor_by_max_side,
+    get_image_dimensions,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    upload_audio_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_audio_duration,
+)
+
+
+class SyncLipSyncNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="SyncLipSyncNode",
+            display_name="sync.so Lip Sync",
+            category="partner/video/sync.so",
+            description=(
+                "Re-sync mouth movement in a video to new speech audio using sync.so. "
+                "Handles close-ups, profiles and obstructions automatically while preserving "
+                "the speaker's expression. Cost scales with output duration."
+            ),
+            inputs=[
+                IO.Video.Input(
+                    "video",
+                    tooltip="Footage of the speaker to re-sync. Up to 4K (4096x2160); "
+                    "a constant frame rate of 24/25/30 fps works best.",
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Speech audio to sync the mouth to.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "sync-3",
+                            [
+                                IO.Combo.Input(
+                                    "sync_mode",
+                                    options=["bounce", "cut_off", "loop", "silence", "remap"],
+                                    default="bounce",
+                                    tooltip=(
+                                        "How to handle a duration mismatch between video and audio; "
+                                        "this also sets the output length. "
+                                        "bounce: video plays forward then backward until the audio ends "
+                                        "(output = audio length). "
+                                        "loop: video restarts until the audio ends (output = audio length). "
+                                        "remap: video is time-stretched to match the audio (output = audio length). "
+                                        "cut_off: the longer track is trimmed (output = shorter length). "
+                                        "silence: nothing is trimmed; the shorter track is padded "
+                                        "(output = longer length)."
+                                    ),
+                                ),
+                                IO.Combo.Input(
+                                    "speaker_selection",
+                                    options=["default", "auto-detect", "coordinates"],
+                                    default="default",
+                                    tooltip=(
+                                        "Which face to lipsync when several people are visible. "
+                                        "default: let the model decide. "
+                                        "auto-detect: detect and follow the active speaker. "
+                                        "coordinates: target the face at pixel (speaker_x, speaker_y) "
+                                        "in the frame chosen by speaker_frame."
+                                    ),
+                                ),
+                                IO.Int.Input(
+                                    "speaker_frame",
+                                    default=0,
+                                    min=0,
+                                    max=1_000_000,
+                                    advanced=True,
+                                    tooltip="Video frame used to locate the speaker. "
+                                    "Only used when speaker_selection is 'coordinates'.",
+                                ),
+                                IO.Int.Input(
+                                    "speaker_x",
+                                    default=0,
+                                    min=0,
+                                    max=4096,
+                                    advanced=True,
+                                    tooltip="X pixel coordinate of the speaker's face. "
+                                    "Only used when speaker_selection is 'coordinates'.",
+                                ),
+                                IO.Int.Input(
+                                    "speaker_y",
+                                    default=0,
+                                    min=0,
+                                    max=4096,
+                                    advanced=True,
+                                    tooltip="Y pixel coordinate of the speaker's face. "
+                                    "Only used when speaker_selection is 'coordinates'.",
+                                ),
+                            ],
+                        )
+                    ],
+                    tooltip="sync.so generation model.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.19019,"format":{"approximate":true,"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        audio: Input.Audio,
+        seed: int,
+        model: dict,
+    ) -> IO.NodeOutput:
+        try:
+            width, height = video.get_dimensions()
+        except Exception:
+            width = height = None
+        if width and height and (max(width, height) > 4096 or width * height > 4096 * 2160):
+            raise ValueError(
+                f"sync.so rejects videos above 4K (4096x2160); got {width}x{height}. Downscale the video first."
+            )
+        validate_audio_duration(audio, max_duration=600)
+
+        if model["speaker_selection"] == "auto-detect":
+            speaker_detection = SyncActiveSpeakerDetection(auto_detect=True)
+        elif model["speaker_selection"] == "coordinates":
+            speaker_detection = SyncActiveSpeakerDetection(
+                frame_number=model["speaker_frame"],
+                coordinates=[model["speaker_x"], model["speaker_y"]],
+            )
+        else:
+            speaker_detection = None
+
+        video_url = await upload_video_to_comfyapi(cls, video, max_duration=600)
+        audio_url = await upload_audio_to_comfyapi(cls, audio)
+
+        generation = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/synclabs/v2/generate", method="POST"),
+            response_model=SyncGeneration,
+            data=SyncGenerationRequest(
+                model=model["model"],
+                input=[
+                    SyncInputItem(type="video", url=video_url),
+                    SyncInputItem(type="audio", url=audio_url),
+                ],
+                options=SyncGenerationOptions(
+                    sync_mode=model["sync_mode"],
+                    active_speaker_detection=speaker_detection,
+                ),
+            ),
+        )
+        generation = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/synclabs/v2/generate/{generation.id}"),
+            response_model=SyncGeneration,
+            status_extractor=lambda g: g.status,
+            completed_statuses=["COMPLETED", "FAILED", "REJECTED"],
+            failed_statuses=[],
+            queued_statuses=["PENDING"],
+            poll_interval=10.0,
+        )
+        if generation.status != "COMPLETED":
+            code = f" [{generation.errorCode}]" if generation.errorCode else ""
+            raise ValueError(
+                f"sync.so generation {generation.status.lower()}{code}: "
+                f"{generation.error or 'no error details provided'}"
+            )
+        if not generation.outputUrl:
+            raise ValueError("sync.so generation completed but no output URL was returned.")
+        return IO.NodeOutput(await download_url_to_video_output(generation.outputUrl))
+
+
+class SyncTalkingImageNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="SyncTalkingImageNode",
+            display_name="sync.so Talking Image",
+            category="partner/video/sync.so",
+            description=(
+                "Animate a still portrait into a talking video driven by speech audio, "
+                "using sync.so's sync-3 model. The output duration matches the audio. "
+                "Cost scales with output duration."
+            ),
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="A single image with a clearly visible face, up to 4K (4096x2160).",
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Speech audio driving the talking video; the output duration matches it. "
+                    "Chain any TTS node here to drive the animation from text.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Optional guidance for how the portrait comes to life, e.g. "
+                    "'make the subject smile and look at the camera'. "
+                    "Leave empty for natural talking motion.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "sync-3",
+                            [
+                                IO.Combo.Input(
+                                    "speaker_selection",
+                                    options=["default", "coordinates"],
+                                    default="default",
+                                    tooltip=(
+                                        "Which face to animate when several people are visible. "
+                                        "default: let the model decide. "
+                                        "coordinates: target the face at pixel (speaker_x, speaker_y) "
+                                        "in the image. Auto-detection is not supported for images."
+                                    ),
+                                ),
+                                IO.Int.Input(
+                                    "speaker_x",
+                                    default=0,
+                                    min=0,
+                                    max=4096,
+                                    advanced=True,
+                                    tooltip="X pixel coordinate of the speaker's face. "
+                                    "Only used when speaker_selection is 'coordinates'.",
+                                ),
+                                IO.Int.Input(
+                                    "speaker_y",
+                                    default=0,
+                                    min=0,
+                                    max=4096,
+                                    advanced=True,
+                                    tooltip="Y pixel coordinate of the speaker's face. "
+                                    "Only used when speaker_selection is 'coordinates'.",
+                                ),
+                                IO.Boolean.Input(
+                                    "auto_downscale",
+                                    default=True,
+                                    advanced=True,
+                                    tooltip="Automatically downscale the image if it exceeds the 4K "
+                                    "(4096x2160) input limit; speaker coordinates are scaled to match. "
+                                    "When disabled, an oversized image raises an error instead.",
+                                ),
+                            ],
+                        )
+                    ],
+                    tooltip="sync.so generation model. Image input is exclusive to sync-3.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.19019,"format":{"approximate":true,"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        audio: Input.Audio,
+        prompt: str,
+        seed: int,
+        model: dict,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one image is required; got a batch. Pick one frame first.")
+        validate_audio_duration(audio, max_duration=600)
+
+        height, width = get_image_dimensions(image)
+        speaker_x, speaker_y = model["speaker_x"], model["speaker_y"]
+        if max(width, height) > 4096 or width * height > 4096 * 2160:
+            if not model["auto_downscale"]:
+                raise ValueError(
+                    f"sync.so rejects images above 4K (4096x2160); got {width}x{height}. "
+                    "Downscale the image first or enable auto_downscale."
+                )
+            image = downscale_image_tensor(image, total_pixels=4096 * 2160)
+            image = downscale_image_tensor_by_max_side(image, max_side=4096)
+            new_height, new_width = get_image_dimensions(image)
+            # speaker coordinates are given in the original image's pixel space
+            speaker_x = min(new_width - 1, round(speaker_x * new_width / width))
+            speaker_y = min(new_height - 1, round(speaker_y * new_height / height))
+
+        if model["speaker_selection"] == "coordinates":
+            speaker_detection = SyncActiveSpeakerDetection(
+                frame_number=0,  # images have a single frame; auto_detect is rejected by the API
+                coordinates=[speaker_x, speaker_y],
+            )
+        else:
+            speaker_detection = None
+
+        image_url = await upload_image_to_comfyapi(cls, image, mime_type="image/png", total_pixels=None)
+        audio_url = await upload_audio_to_comfyapi(cls, audio)
+
+        generation = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/synclabs/v2/generate", method="POST"),
+            response_model=SyncGeneration,
+            data=SyncGenerationRequest(
+                model=model["model"],
+                input=[
+                    SyncInputItem(type="image", url=image_url),
+                    SyncInputItem(type="audio", url=audio_url),
+                ],
+                options=SyncGenerationOptions(
+                    i2v_prompt=prompt.strip() or None,
+                    active_speaker_detection=speaker_detection,
+                ),
+            ),
+        )
+        generation = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/synclabs/v2/generate/{generation.id}"),
+            response_model=SyncGeneration,
+            status_extractor=lambda g: g.status,
+            completed_statuses=["COMPLETED", "FAILED", "REJECTED"],
+            failed_statuses=[],
+            queued_statuses=["PENDING"],
+            poll_interval=10.0,
+        )
+        if generation.status != "COMPLETED":
+            code = f" [{generation.errorCode}]" if generation.errorCode else ""
+            raise ValueError(
+                f"sync.so generation {generation.status.lower()}{code}: "
+                f"{generation.error or 'no error details provided'}"
+            )
+        if not generation.outputUrl:
+            raise ValueError("sync.so generation completed but no output URL was returned.")
+        return IO.NodeOutput(await download_url_to_video_output(generation.outputUrl))
+
+
+class SyncExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            SyncLipSyncNode,
+            SyncTalkingImageNode,
+        ]
+
+
+async def comfy_entrypoint() -> SyncExtension:
+    return SyncExtension()
diff --git a/comfy_api_nodes/nodes_topaz.py b/comfy_api_nodes/nodes_topaz.py
new file mode 100644
index 0000000000000000000000000000000000000000..02339aaddd04a4e09ab85144e5dcdbc26c915b39
--- /dev/null
+++ b/comfy_api_nodes/nodes_topaz.py
@@ -0,0 +1,1175 @@
+import builtins
+from io import BytesIO
+
+import aiohttp
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.topaz import (
+    CreateVideoRequest,
+    CreateVideoRequestSource,
+    CreateVideoResponse,
+    ImageAsyncTaskResponse,
+    ImageDownloadResponse,
+    ImageEnhanceRequest,
+    ImageEnhanceRequestV2,
+    ImageStatusResponse,
+    OutputInformationVideo,
+    Resolution,
+    VideoAcceptResponse,
+    VideoCompleteUploadRequest,
+    VideoCompleteUploadRequestPart,
+    VideoCompleteUploadResponse,
+    VideoEnhancementFilter,
+    VideoFrameInterpolationFilter,
+    VideoStatusResponse,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    get_fs_object_size,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    upload_images_to_comfyapi,
+    validate_container_format_is_mp4,
+)
+
+UPSCALER_MODELS_MAP = {
+    "Astra 2": "ast-2",
+    "Starlight (Astra) Fast": "slf-1",
+    "Starlight (Astra) Creative": "slc-1",
+    "Starlight Precise 2.5": "slp-2.5",
+}
+
+AST2_MAX_FRAMES = 9000
+AST2_MAX_FRAMES_WITH_PROMPT = 450
+
+
+class TopazImageEnhance(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TopazImageEnhance",
+            display_name="Topaz Image Enhance (Legacy)",
+            category="partner/image/Topaz",
+            description="Industry-standard upscaling and image enhancement.",
+            inputs=[
+                IO.Combo.Input("model", options=["Reimagine"]),
+                IO.Image.Input("image"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Optional text prompt for creative upscaling guidance.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "subject_detection",
+                    options=["All", "Foreground", "Background"],
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "face_enhancement",
+                    default=True,
+                    optional=True,
+                    tooltip="Enhance faces (if present) during processing.",
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "face_enhancement_creativity",
+                    default=0.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Set the creativity level for face enhancement.",
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "face_enhancement_strength",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Controls how sharp enhanced faces are relative to the background.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "crop_to_fill",
+                    default=False,
+                    optional=True,
+                    tooltip="By default, the image is letterboxed when the output aspect ratio differs. "
+                    "Enable to crop the image to fill the output dimensions.",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "output_width",
+                    default=0,
+                    min=0,
+                    max=32000,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Zero value means to calculate automatically (usually it will be original size or output_height if specified).",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "output_height",
+                    default=0,
+                    min=0,
+                    max=32000,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Zero value means to output in the same height as original or output width.",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "creativity",
+                    default=3,
+                    min=1,
+                    max=9,
+                    step=1,
+                    display_mode=IO.NumberDisplay.slider,
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "face_preservation",
+                    default=True,
+                    optional=True,
+                    tooltip="Preserve subjects' facial identity.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "color_preservation",
+                    default=True,
+                    optional=True,
+                    tooltip="Preserve the original colors.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_deprecated=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        prompt: str = "",
+        subject_detection: str = "All",
+        face_enhancement: bool = True,
+        face_enhancement_creativity: float = 1.0,
+        face_enhancement_strength: float = 0.8,
+        crop_to_fill: bool = False,
+        output_width: int = 0,
+        output_height: int = 0,
+        creativity: int = 3,
+        face_preservation: bool = True,
+        color_preservation: bool = True,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Only one input image is supported.")
+        download_url = await upload_images_to_comfyapi(
+            cls, image, max_images=1, mime_type="image/png", total_pixels=4096 * 4096
+        )
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/topaz/image/v1/enhance-gen/async", method="POST"),
+            response_model=ImageAsyncTaskResponse,
+            data=ImageEnhanceRequest(
+                model=model,
+                prompt=prompt,
+                subject_detection=subject_detection,
+                face_enhancement=face_enhancement,
+                face_enhancement_creativity=face_enhancement_creativity,
+                face_enhancement_strength=face_enhancement_strength,
+                crop_to_fill=crop_to_fill,
+                output_width=output_width if output_width else None,
+                output_height=output_height if output_height else None,
+                creativity=creativity,
+                face_preservation=str(face_preservation).lower(),
+                color_preservation=str(color_preservation).lower(),
+                source_url=download_url[0],
+                output_format="png",
+            ),
+            content_type="multipart/form-data",
+        )
+
+        await poll_op(
+            cls,
+            poll_endpoint=ApiEndpoint(path=f"/proxy/topaz/image/v1/status/{initial_response.process_id}"),
+            response_model=ImageStatusResponse,
+            status_extractor=lambda x: x.status,
+            progress_extractor=lambda x: getattr(x, "progress", 0),
+            poll_interval=8.0,
+            estimated_duration=60,
+        )
+
+        results = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/topaz/image/v1/download/{initial_response.process_id}"),
+            response_model=ImageDownloadResponse,
+            monitor_progress=False,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(results.download_url))
+
+
+class TopazImageEnhanceV2(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TopazImageEnhanceV2",
+            display_name="Topaz Image Enhance",
+            category="partner/image/Topaz",
+            description="Industry-standard upscaling and image enhancement.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Reimagine",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Optional text prompt for creative upscaling guidance.",
+                                ),
+                                IO.Int.Input(
+                                    "creativity",
+                                    default=3,
+                                    min=1,
+                                    max=9,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                                IO.Combo.Input(
+                                    "subject_detection",
+                                    options=["All", "Foreground", "Background"],
+                                    advanced=True,
+                                ),
+                                IO.Boolean.Input(
+                                    "face_enhancement",
+                                    default=True,
+                                    tooltip="Enhance faces (if present) during processing.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "face_enhancement_creativity",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Set the creativity level for face enhancement.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "face_enhancement_strength",
+                                    default=1.0,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Controls how sharp enhanced faces are relative to the background.",
+                                    advanced=True,
+                                ),
+                                IO.Boolean.Input(
+                                    "face_preservation",
+                                    default=True,
+                                    tooltip="Preserve subjects' facial identity.",
+                                    advanced=True,
+                                ),
+                                IO.Boolean.Input(
+                                    "color_preservation",
+                                    default=True,
+                                    tooltip="Preserve the original colors.",
+                                    advanced=True,
+                                ),
+                                IO.Boolean.Input(
+                                    "crop_to_fill",
+                                    default=False,
+                                    tooltip="By default, the image is letterboxed when the output aspect "
+                                    "ratio differs. Enable to crop the image to fill the output dimensions.",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Bloom 2",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Optional text prompt for generation. "
+                                    "Leave empty to auto-generate a prompt from the input image.",
+                                ),
+                                IO.Int.Input(
+                                    "creativity",
+                                    default=3,
+                                    min=1,
+                                    max=9,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="1 is restrained enhancement, 9 is pronounced reinterpretation "
+                                    "with newly generated detail.",
+                                ),
+                                IO.Int.Input(
+                                    "seed",
+                                    default=2,
+                                    min=1,
+                                    max=2000,
+                                    control_after_generate=True,
+                                    tooltip="Seed for reproducible generation.",
+                                ),
+                                IO.Boolean.Input(
+                                    "color_preservation",
+                                    default=True,
+                                    tooltip="Preserve the original colors.",
+                                    advanced=True,
+                                ),
+                                IO.Boolean.Input(
+                                    "grain",
+                                    default=False,
+                                    tooltip="Add grain to the output image.",
+                                    advanced=True,
+                                ),
+                                IO.Combo.Input(
+                                    "grain_model",
+                                    options=["silver", "gaussian", "grey"],
+                                    tooltip="Is ignored if grain is disabled.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "grain_strength",
+                                    default=0.5,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Strength of the grain effect. Is ignored if grain is disabled.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "grain_size",
+                                    default=1.0,
+                                    min=1.0,
+                                    max=5.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Size of the grain particles. Is ignored if grain is disabled.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "grain_density",
+                                    default=0.5,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Intensity of the grain effect. Is ignored if grain is disabled.",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Wonder 3.5",
+                            [
+                                IO.Combo.Input(
+                                    "enhancement_strength",
+                                    options=["low", "medium", "high"],
+                                    default="high",
+                                    tooltip="Enhancement level for varying input conditions.",
+                                ),
+                                IO.Boolean.Input(
+                                    "grain",
+                                    default=False,
+                                    tooltip="Add grain to the output image.",
+                                    advanced=True,
+                                ),
+                                IO.Combo.Input(
+                                    "grain_model",
+                                    options=["silver", "gaussian", "grey"],
+                                    tooltip="Is ignored if grain is disabled.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "grain_strength",
+                                    default=0.5,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Strength of the grain effect. Is ignored if grain is disabled.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "grain_size",
+                                    default=1.0,
+                                    min=1.0,
+                                    max=5.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Size of the grain particles. Is ignored if grain is disabled.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "grain_density",
+                                    default=0.5,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Intensity of the grain effect. Is ignored if grain is disabled.",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "output_width",
+                    default=0,
+                    min=0,
+                    max=32000,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Zero value means to calculate automatically (usually it will be original size "
+                    "or scaled proportionally to output_height if specified). "
+                    "Wonder 3.5 supports upscale factors from 1x to 6x only. "
+                    "Bloom 2 and Wonder 3.5 preserve the input aspect ratio and treat the "
+                    "requested size as a target.",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "output_height",
+                    default=0,
+                    min=0,
+                    max=32000,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Zero value means to output in the same height as original or scaled "
+                    "proportionally to output_width if specified. "
+                    "Wonder 3.5 supports upscale factors from 1x to 6x only. "
+                    "Bloom 2 and Wonder 3.5 preserve the input aspect ratio and treat the "
+                    "requested size as a target.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $usdPer8Mp := $lookup(
+                    {"reimagine": 0.32, "bloom 2": 0.4576, "wonder 3.5": 0.1144},
+                    $lookup(widgets, "model")
+                  );
+                  {"type":"usd","usd": $usdPer8Mp, "format": {"suffix": "/8MP", "approximate": true}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        model: dict,
+        output_width: int = 0,
+        output_height: int = 0,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Only one input image is supported.")
+        model_choice = model["model"]
+        download_url = await upload_images_to_comfyapi(
+            cls, image, max_images=1, mime_type="image/png", total_pixels=4096 * 4096
+        )
+        request = ImageEnhanceRequestV2(
+            model=model_choice,
+            source_url=download_url[0],
+            output_width=output_width if output_width else None,
+            output_height=output_height if output_height else None,
+        )
+        if model_choice == "Reimagine":
+            request.prompt = model["prompt"]
+            request.creativity = model["creativity"]
+            request.subject_detection = model["subject_detection"]
+            request.face_enhancement = model["face_enhancement"]
+            request.face_enhancement_creativity = model["face_enhancement_creativity"]
+            request.face_enhancement_strength = model["face_enhancement_strength"]
+            request.face_preservation = str(model["face_preservation"]).lower()
+            request.color_preservation = str(model["color_preservation"]).lower()
+            request.crop_to_fill = model["crop_to_fill"]
+        elif model_choice == "Bloom 2":
+            prompt = model["prompt"].strip()
+            if prompt:
+                request.prompt = prompt
+                request.autoprompt = "false"
+            else:
+                request.autoprompt = "true"
+            request.creativity = model["creativity"]
+            request.seed = model["seed"]
+            request.color_preservation = str(model["color_preservation"]).lower()
+            if model["grain"]:
+                request.grain = "true"
+                request.grain_model = model["grain_model"]
+                request.grain_strength = model["grain_strength"]
+                request.grain_size = model["grain_size"]
+                request.grain_density = model["grain_density"]
+        else:
+            request.enhancement_strength = model["enhancement_strength"]
+            if model["grain"]:
+                request.grain = "true"
+                request.grain_model = model["grain_model"]
+                request.grain_strength = model["grain_strength"]
+                request.grain_size = model["grain_size"]
+                request.grain_density = model["grain_density"]
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/topaz/image/v1/enhance-gen/async", method="POST"),
+            response_model=ImageAsyncTaskResponse,
+            data=request,
+            content_type="multipart/form-data",
+        )
+        await poll_op(
+            cls,
+            poll_endpoint=ApiEndpoint(path=f"/proxy/topaz/image/v1/status/{initial_response.process_id}"),
+            response_model=ImageStatusResponse,
+            status_extractor=lambda x: x.status,
+            progress_extractor=lambda x: getattr(x, "progress", 0),
+            poll_interval=8.0,
+            estimated_duration=60,
+        )
+        results = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/topaz/image/v1/download/{initial_response.process_id}"),
+            response_model=ImageDownloadResponse,
+            monitor_progress=False,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(results.download_url))
+
+
+class TopazVideoEnhance(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TopazVideoEnhance",
+            display_name="Topaz Video Enhance (Legacy)",
+            category="partner/video/Topaz",
+            description="Breathe new life into video with powerful upscaling and recovery technology.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Boolean.Input("upscaler_enabled", default=True),
+                IO.Combo.Input(
+                    "upscaler_model",
+                    options=[
+                        "Starlight (Astra) Fast",
+                        "Starlight (Astra) Creative",
+                        "Starlight Precise 2.5",
+                    ],
+                ),
+                IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]),
+                IO.Combo.Input(
+                    "upscaler_creativity",
+                    options=["low", "middle", "high"],
+                    default="low",
+                    tooltip="Creativity level (applies only to Starlight (Astra) Creative).",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input("interpolation_enabled", default=False, optional=True),
+                IO.Combo.Input("interpolation_model", options=["apo-8"], default="apo-8", optional=True, advanced=True),
+                IO.Int.Input(
+                    "interpolation_slowmo",
+                    default=1,
+                    min=1,
+                    max=16,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Slow-motion factor applied to the input video. "
+                    "For example, 2 makes the output twice as slow and doubles the duration.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "interpolation_frame_rate",
+                    default=60,
+                    min=15,
+                    max=240,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Output frame rate.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "interpolation_duplicate",
+                    default=False,
+                    tooltip="Analyze the input for duplicate frames and remove them.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "interpolation_duplicate_threshold",
+                    default=0.01,
+                    min=0.001,
+                    max=0.1,
+                    step=0.001,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Detection sensitivity for duplicate frames.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "dynamic_compression_level",
+                    options=["Low", "Mid", "High"],
+                    default="Low",
+                    tooltip="CQP level.",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_deprecated=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        upscaler_enabled: bool,
+        upscaler_model: str,
+        upscaler_resolution: str,
+        upscaler_creativity: str = "low",
+        interpolation_enabled: bool = False,
+        interpolation_model: str = "apo-8",
+        interpolation_slowmo: int = 1,
+        interpolation_frame_rate: int = 60,
+        interpolation_duplicate: bool = False,
+        interpolation_duplicate_threshold: float = 0.01,
+        dynamic_compression_level: str = "Low",
+    ) -> IO.NodeOutput:
+        if upscaler_enabled is False and interpolation_enabled is False:
+            raise ValueError("There is nothing to do: both upscaling and interpolation are disabled.")
+        validate_container_format_is_mp4(video)
+        src_width, src_height = video.get_dimensions()
+        src_frame_rate = int(video.get_frame_rate())
+        duration_sec = video.get_duration()
+        src_video_stream = video.get_stream_source()
+        target_width = src_width
+        target_height = src_height
+        target_frame_rate = src_frame_rate
+        filters = []
+        if upscaler_enabled:
+            if "1080p" in upscaler_resolution:
+                target_pixel_p = 1080
+                max_long_side = 1920
+            else:
+                target_pixel_p = 2160
+                max_long_side = 3840
+            ar = src_width / src_height
+            if src_width >= src_height:
+                # Landscape or Square; Attempt to set height to target (e.g., 2160), calculate width
+                target_height = target_pixel_p
+                target_width = int(target_height * ar)
+                # Check if width exceeds standard bounds (for ultra-wide e.g., 21:9 ARs)
+                if target_width > max_long_side:
+                    target_width = max_long_side
+                    target_height = int(target_width / ar)
+            else:
+                # Portrait; Attempt to set width to target (e.g., 2160), calculate height
+                target_width = target_pixel_p
+                target_height = int(target_width / ar)
+                # Check if height exceeds standard bounds
+                if target_height > max_long_side:
+                    target_height = max_long_side
+                    target_width = int(target_height * ar)
+            if target_width % 2 != 0:
+                target_width += 1
+            if target_height % 2 != 0:
+                target_height += 1
+            filters.append(
+                VideoEnhancementFilter(
+                    model=UPSCALER_MODELS_MAP[upscaler_model],
+                    creativity=(upscaler_creativity if UPSCALER_MODELS_MAP[upscaler_model] == "slc-1" else None),
+                    isOptimizedMode=(True if UPSCALER_MODELS_MAP[upscaler_model] == "slc-1" else None),
+                ),
+            )
+        if interpolation_enabled:
+            target_frame_rate = interpolation_frame_rate
+            filters.append(
+                VideoFrameInterpolationFilter(
+                    model=interpolation_model,
+                    slowmo=interpolation_slowmo,
+                    fps=interpolation_frame_rate,
+                    duplicate=interpolation_duplicate,
+                    duplicate_threshold=interpolation_duplicate_threshold,
+                ),
+            )
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/topaz/video/", method="POST"),
+            response_model=CreateVideoResponse,
+            data=CreateVideoRequest(
+                source=CreateVideoRequestSource(
+                    container="mp4",
+                    size=get_fs_object_size(src_video_stream),
+                    duration=int(duration_sec),
+                    frameCount=video.get_frame_count(),
+                    frameRate=src_frame_rate,
+                    resolution=Resolution(width=src_width, height=src_height),
+                ),
+                filters=filters,
+                output=OutputInformationVideo(
+                    resolution=Resolution(width=target_width, height=target_height),
+                    frameRate=target_frame_rate,
+                    audioCodec="AAC",
+                    audioTransfer="Copy",
+                    dynamicCompressionLevel=dynamic_compression_level,
+                ),
+            ),
+            wait_label="Creating task",
+            final_label_on_success="Task created",
+        )
+        upload_res = await sync_op(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/topaz/video/{initial_res.requestId}/accept",
+                method="PATCH",
+            ),
+            response_model=VideoAcceptResponse,
+            wait_label="Preparing upload",
+            final_label_on_success="Upload started",
+        )
+        if len(upload_res.urls) > 1:
+            raise NotImplementedError(
+                "Large files are not currently supported. Please open an issue in the ComfyUI repository."
+            )
+        async with aiohttp.ClientSession(headers={"Content-Type": "video/mp4"}) as session:
+            if isinstance(src_video_stream, BytesIO):
+                src_video_stream.seek(0)
+                async with session.put(upload_res.urls[0], data=src_video_stream, raise_for_status=True) as res:
+                    upload_etag = res.headers["Etag"]
+            else:
+                with builtins.open(src_video_stream, "rb") as video_file:
+                    async with session.put(upload_res.urls[0], data=video_file, raise_for_status=True) as res:
+                        upload_etag = res.headers["Etag"]
+        await sync_op(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/topaz/video/{initial_res.requestId}/complete-upload",
+                method="PATCH",
+            ),
+            response_model=VideoCompleteUploadResponse,
+            data=VideoCompleteUploadRequest(
+                uploadResults=[
+                    VideoCompleteUploadRequestPart(
+                        partNum=1,
+                        eTag=upload_etag,
+                    ),
+                ],
+            ),
+            wait_label="Finalizing upload",
+            final_label_on_success="Upload completed",
+        )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/topaz/video/{initial_res.requestId}/status"),
+            response_model=VideoStatusResponse,
+            status_extractor=lambda x: x.status,
+            progress_extractor=lambda x: getattr(x, "progress", 0),
+            poll_interval=10.0,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.download.url))
+
+
+class TopazVideoEnhanceV2(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TopazVideoEnhanceV2",
+            display_name="Topaz Video Enhance",
+            category="partner/video/Topaz",
+            description="Breathe new life into video with powerful upscaling and recovery technology.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.DynamicCombo.Input(
+                    "upscaler_model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Astra 2",
+                            [
+                                IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]),
+                                IO.Float.Input(
+                                    "creativity",
+                                    default=0.5,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Creative strength of the upscale.",
+                                ),
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Optional descriptive (not instructive) scene prompt."
+                                    f"Capping input at {AST2_MAX_FRAMES_WITH_PROMPT} frames (~15s @ 30fps) when set.",
+                                ),
+                                IO.Float.Input(
+                                    "sharp",
+                                    default=0.5,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Pre-enhance sharpness: "
+                                    "0.0=Gaussian blur, 0.5=passthrough (default), 1.0=USM sharpening.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "realism",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Pulls output toward photographic realism."
+                                    "Leave at 0 for the model default.",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Starlight (Astra) Fast",
+                            [IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]),],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Starlight (Astra) Creative",
+                            [
+                                IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]),
+                                IO.Combo.Input(
+                                    "creativity",
+                                    options=["low", "middle", "high"],
+                                    default="low",
+                                    tooltip="Creative strength of the upscale.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Starlight Precise 2.5",
+                            [IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"])],
+                        ),
+                        IO.DynamicCombo.Option("Disabled", []),
+                    ],
+                ),
+                IO.DynamicCombo.Input(
+                    "interpolation_model",
+                    options=[
+                        IO.DynamicCombo.Option("Disabled", []),
+                        IO.DynamicCombo.Option(
+                            "apo-8",
+                            [
+                                IO.Int.Input(
+                                    "interpolation_frame_rate",
+                                    default=60,
+                                    min=15,
+                                    max=240,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Output frame rate.",
+                                ),
+                                IO.Int.Input(
+                                    "interpolation_slowmo",
+                                    default=1,
+                                    min=1,
+                                    max=16,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Slow-motion factor applied to the input video. "
+                                    "For example, 2 makes the output twice as slow and doubles the duration.",
+                                    advanced=True,
+                                ),
+                                IO.Boolean.Input(
+                                    "interpolation_duplicate",
+                                    default=False,
+                                    tooltip="Analyze the input for duplicate frames and remove them.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "interpolation_duplicate_threshold",
+                                    default=0.01,
+                                    min=0.001,
+                                    max=0.1,
+                                    step=0.001,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Detection sensitivity for duplicate frames.",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Combo.Input(
+                    "dynamic_compression_level",
+                    options=["Low", "Mid", "High"],
+                    default="Low",
+                    tooltip="CQP level.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=[
+                    "upscaler_model",
+                    "upscaler_model.upscaler_resolution",
+                    "interpolation_model",
+                ]),
+                expr="""
+                (
+                  $model := $lookup(widgets, "upscaler_model");
+                  $res := $lookup(widgets, "upscaler_model.upscaler_resolution");
+                  $interp := $lookup(widgets, "interpolation_model");
+                  $is4k := $contains($res, "4k");
+                  $hasInterp := $interp != "disabled";
+                  $rates := {
+                    "starlight (astra) fast":     {"hd": 0.43, "uhd": 0.85},
+                    "starlight precise 2.5":      {"hd": 0.70, "uhd": 1.54},
+                    "astra 2":                    {"hd": 1.72, "uhd": 2.85},
+                    "starlight (astra) creative": {"hd": 2.25, "uhd": 3.99}
+                  };
+                  $surcharge := $is4k ? 0.28 : 0.14;
+                  $entry := $lookup($rates, $model);
+                  $base := $is4k ? $entry.uhd : $entry.hd;
+                  $hi := $base + ($hasInterp ? $surcharge : 0);
+                  $model = "disabled"
+                    ? {"type":"text","text":"Interpolation only"}
+                    : ($hasInterp
+                        ? {"type":"text","text":"~" & $string($base) & "–" & $string($hi) & " credits/src frame"}
+                        : {"type":"text","text":"~" & $string($base) & " credits/src frame"})
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        upscaler_model: dict,
+        interpolation_model: dict,
+        dynamic_compression_level: str = "Low",
+    ) -> IO.NodeOutput:
+        upscaler_choice = upscaler_model["upscaler_model"]
+        interpolation_choice = interpolation_model["interpolation_model"]
+        if upscaler_choice == "Disabled" and interpolation_choice == "Disabled":
+            raise ValueError("There is nothing to do: both upscaling and interpolation are disabled.")
+        validate_container_format_is_mp4(video)
+        src_width, src_height = video.get_dimensions()
+        src_frame_rate = int(video.get_frame_rate())
+        duration_sec = video.get_duration()
+        src_video_stream = video.get_stream_source()
+        target_width = src_width
+        target_height = src_height
+        target_frame_rate = src_frame_rate
+        filters = []
+        if upscaler_choice != "Disabled":
+            if "1080p" in upscaler_model["upscaler_resolution"]:
+                target_pixel_p = 1080
+                max_long_side = 1920
+            else:
+                target_pixel_p = 2160
+                max_long_side = 3840
+            ar = src_width / src_height
+            if src_width >= src_height:
+                # Landscape or Square; Attempt to set height to target (e.g., 2160), calculate width
+                target_height = target_pixel_p
+                target_width = int(target_height * ar)
+                # Check if width exceeds standard bounds (for ultra-wide e.g., 21:9 ARs)
+                if target_width > max_long_side:
+                    target_width = max_long_side
+                    target_height = int(target_width / ar)
+            else:
+                # Portrait; Attempt to set width to target (e.g., 2160), calculate height
+                target_width = target_pixel_p
+                target_height = int(target_width / ar)
+                # Check if height exceeds standard bounds
+                if target_height > max_long_side:
+                    target_height = max_long_side
+                    target_width = int(target_height * ar)
+            if target_width % 2 != 0:
+                target_width += 1
+            if target_height % 2 != 0:
+                target_height += 1
+            model_id = UPSCALER_MODELS_MAP[upscaler_choice]
+            if model_id == "slc-1":
+                filters.append(
+                    VideoEnhancementFilter(
+                        model=model_id,
+                        creativity=upscaler_model["creativity"],
+                        isOptimizedMode=True,
+                    )
+                )
+            elif model_id == "ast-2":
+                n_frames = video.get_frame_count()
+                ast2_prompt = (upscaler_model["prompt"] or "").strip()
+                if ast2_prompt and n_frames > AST2_MAX_FRAMES_WITH_PROMPT:
+                    raise ValueError(
+                        f"Astra 2 with a prompt is limited to {AST2_MAX_FRAMES_WITH_PROMPT} input frames "
+                        f"(~15s @ 30fps); video has {n_frames}. Clear the prompt or shorten the clip."
+                    )
+                if n_frames > AST2_MAX_FRAMES:
+                    raise ValueError(f"Astra 2 is limited to {AST2_MAX_FRAMES} input frames; video has {n_frames}.")
+                realism = upscaler_model["realism"]
+                filters.append(
+                    VideoEnhancementFilter(
+                        model=model_id,
+                        creativity=upscaler_model["creativity"],
+                        prompt=(ast2_prompt or None),
+                        sharp=upscaler_model["sharp"],
+                        realism=(realism if realism > 0 else None),
+                    )
+                )
+            else:
+                filters.append(VideoEnhancementFilter(model=model_id))
+        if interpolation_choice != "Disabled":
+            target_frame_rate = interpolation_model["interpolation_frame_rate"]
+            filters.append(
+                VideoFrameInterpolationFilter(
+                    model=interpolation_choice,
+                    slowmo=interpolation_model["interpolation_slowmo"],
+                    fps=interpolation_model["interpolation_frame_rate"],
+                    duplicate=interpolation_model["interpolation_duplicate"],
+                    duplicate_threshold=interpolation_model["interpolation_duplicate_threshold"],
+                ),
+            )
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/topaz/video/", method="POST"),
+            response_model=CreateVideoResponse,
+            data=CreateVideoRequest(
+                source=CreateVideoRequestSource(
+                    container="mp4",
+                    size=get_fs_object_size(src_video_stream),
+                    duration=int(duration_sec),
+                    frameCount=video.get_frame_count(),
+                    frameRate=src_frame_rate,
+                    resolution=Resolution(width=src_width, height=src_height),
+                ),
+                filters=filters,
+                output=OutputInformationVideo(
+                    resolution=Resolution(width=target_width, height=target_height),
+                    frameRate=target_frame_rate,
+                    audioCodec="AAC",
+                    audioTransfer="Copy",
+                    dynamicCompressionLevel=dynamic_compression_level,
+                ),
+            ),
+            wait_label="Creating task",
+            final_label_on_success="Task created",
+        )
+        upload_res = await sync_op(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/topaz/video/{initial_res.requestId}/accept",
+                method="PATCH",
+            ),
+            response_model=VideoAcceptResponse,
+            wait_label="Preparing upload",
+            final_label_on_success="Upload started",
+        )
+        if len(upload_res.urls) > 1:
+            raise NotImplementedError(
+                "Large files are not currently supported. Please open an issue in the ComfyUI repository."
+            )
+        async with aiohttp.ClientSession(headers={"Content-Type": "video/mp4"}) as session:
+            if isinstance(src_video_stream, BytesIO):
+                src_video_stream.seek(0)
+                async with session.put(upload_res.urls[0], data=src_video_stream, raise_for_status=True) as res:
+                    upload_etag = res.headers["Etag"]
+            else:
+                with builtins.open(src_video_stream, "rb") as video_file:
+                    async with session.put(upload_res.urls[0], data=video_file, raise_for_status=True) as res:
+                        upload_etag = res.headers["Etag"]
+        await sync_op(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/topaz/video/{initial_res.requestId}/complete-upload",
+                method="PATCH",
+            ),
+            response_model=VideoCompleteUploadResponse,
+            data=VideoCompleteUploadRequest(
+                uploadResults=[
+                    VideoCompleteUploadRequestPart(
+                        partNum=1,
+                        eTag=upload_etag,
+                    ),
+                ],
+            ),
+            wait_label="Finalizing upload",
+            final_label_on_success="Upload completed",
+        )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/topaz/video/{initial_res.requestId}/status"),
+            response_model=VideoStatusResponse,
+            status_extractor=lambda x: x.status,
+            progress_extractor=lambda x: getattr(x, "progress", 0),
+            poll_interval=10.0,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.download.url))
+
+
+class TopazExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TopazImageEnhance,
+            TopazImageEnhanceV2,
+            TopazVideoEnhance,
+            TopazVideoEnhanceV2,
+        ]
+
+
+async def comfy_entrypoint() -> TopazExtension:
+    return TopazExtension()
diff --git a/comfy_api_nodes/nodes_tripo.py b/comfy_api_nodes/nodes_tripo.py
new file mode 100644
index 0000000000000000000000000000000000000000..2acd491c338969ffa4e3f9cd0466db24c16252c1
--- /dev/null
+++ b/comfy_api_nodes/nodes_tripo.py
@@ -0,0 +1,1359 @@
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input, Types
+from comfy_api_nodes.apis.tripo import (
+    TripoAnimateRetargetRequest,
+    TripoAnimateRigRequest,
+    TripoConvertModelRequest,
+    TripoFileEmptyReference,
+    TripoFileReference,
+    TripoImageToModelRequest,
+    TripoImportModelRequest,
+    TripoModelVersion,
+    TripoMultiviewToModelRequest,
+    TripoOrientation,
+    TripoP1ImageToModelRequest,
+    TripoP1MultiviewToModelRequest,
+    TripoP1TextToModelRequest,
+    TripoStyle,
+    TripoTaskResponse,
+    TripoTaskStatus,
+    TripoTaskType,
+    TripoTextToModelRequest,
+    TripoTextureModelRequest,
+    TripoTexturePrompt,
+    TripoUrlReference,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_file_3d,
+    poll_op,
+    sync_op,
+    upload_3d_model_to_comfyapi,
+    upload_images_to_comfyapi,
+)
+
+
+def get_model_url_from_response(response: TripoTaskResponse) -> str:
+    if response.data is not None:
+        for key in ["pbr_model", "model", "base_model"]:
+            if getattr(response.data.output, key, None) is not None:
+                return getattr(response.data.output, key)
+    raise RuntimeError(f"Failed to get model url from response: {response}")
+
+
+async def poll_until_finished(
+    node_cls: type[IO.ComfyNode],
+    response: TripoTaskResponse,
+    average_duration: int | None = None,
+) -> IO.NodeOutput:
+    """Polls the Tripo API endpoint until the task reaches a terminal state, then returns the response."""
+    if response.code != 0:
+        raise RuntimeError(f"Failed to generate mesh: {response.error}")
+    task_id = response.data.task_id
+    response_poll = await poll_op(
+        node_cls,
+        poll_endpoint=ApiEndpoint(path=f"/proxy/tripo/v2/openapi/task/{task_id}"),
+        response_model=TripoTaskResponse,
+        completed_statuses=[TripoTaskStatus.SUCCESS],
+        failed_statuses=[
+            TripoTaskStatus.FAILED,
+            TripoTaskStatus.CANCELLED,
+            TripoTaskStatus.UNKNOWN,
+            TripoTaskStatus.BANNED,
+            TripoTaskStatus.EXPIRED,
+        ],
+        status_extractor=lambda x: x.data.status,
+        progress_extractor=lambda x: x.data.progress,
+        estimated_duration=average_duration,
+    )
+    if response_poll.data.status == TripoTaskStatus.SUCCESS:
+        url = get_model_url_from_response(response_poll)
+        file_glb = await download_url_to_file_3d(url, "glb", task_id=task_id)
+        return IO.NodeOutput(f"{task_id}.glb", task_id, file_glb)
+    raise RuntimeError(f"Failed to generate mesh: {response_poll}")
+
+
+class TripoTextToModelNode(IO.ComfyNode):
+    """
+    Generates 3D models synchronously based on a text prompt using Tripo's API.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoTextToModelNode",
+            display_name="Tripo: Text to Model",
+            category="partner/3d/Tripo",
+            inputs=[
+                IO.String.Input("prompt", multiline=True),
+                IO.String.Input("negative_prompt", multiline=True, optional=True),
+                IO.Combo.Input(
+                    "model_version", options=TripoModelVersion, default=TripoModelVersion.v2_5_20250123, optional=True
+                ),
+                IO.Combo.Input("style", options=TripoStyle, default="None", optional=True),
+                IO.Boolean.Input("texture", default=True, optional=True),
+                IO.Boolean.Input("pbr", default=True, optional=True),
+                IO.Int.Input("image_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input(
+                    "texture_quality",
+                    default="standard",
+                    options=["standard", "detailed"],
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=2000000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input(
+                    "geometry_quality",
+                    default="standard",
+                    options=["standard", "detailed"],
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=[
+                        "model_version",
+                        "texture",
+                        "pbr",
+                        "quad",
+                        "texture_quality",
+                        "geometry_quality",
+                    ],
+                ),
+                expr="""
+                (
+                  $isV14 := $contains(widgets.model_version,"v1.4");
+                  $isV3OrLater := $contains(widgets.model_version,"v3.");
+                  $withTexture := widgets.texture or widgets.pbr;
+                  $isHdTexture := (widgets.texture_quality = "detailed");
+                  $isDetailedGeometry := (widgets.geometry_quality = "detailed");
+                  $credits := $isV14 ? 20 : (
+                    ($withTexture ? 20 : 10)
+                    + (widgets.quad ? 5 : 0)
+                    + ($isHdTexture ? 10 : 0)
+                    + (($isDetailedGeometry and $isV3OrLater) ? 20 : 0)
+                  );
+                  {"type":"usd","usd": $round($credits * 0.01, 2), "format": {"approximate": true}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str | None = None,
+        model_version=None,
+        style: str | None = None,
+        texture: bool | None = None,
+        pbr: bool | None = None,
+        image_seed: int | None = None,
+        model_seed: int | None = None,
+        texture_seed: int | None = None,
+        texture_quality: str | None = None,
+        geometry_quality: str | None = None,
+        face_limit: int | None = None,
+        quad: bool | None = None,
+    ) -> IO.NodeOutput:
+        style_enum = None if style == "None" else style
+        if not prompt:
+            raise RuntimeError("Prompt is required")
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"),
+            response_model=TripoTaskResponse,
+            data=TripoTextToModelRequest(
+                type=TripoTaskType.TEXT_TO_MODEL,
+                prompt=prompt,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                model_version=model_version,
+                style=style_enum,
+                texture=texture,
+                pbr=pbr,
+                image_seed=image_seed,
+                model_seed=model_seed,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                face_limit=face_limit if face_limit != -1 else None,
+                geometry_quality=geometry_quality,
+                auto_size=True,
+                quad=quad,
+            ),
+        )
+        return await poll_until_finished(cls, response, average_duration=80)
+
+
+class TripoImageToModelNode(IO.ComfyNode):
+    """
+    Generates 3D models synchronously based on a single image using Tripo's API.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoImageToModelNode",
+            display_name="Tripo: Image to Model",
+            category="partner/3d/Tripo",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Combo.Input(
+                    "model_version",
+                    options=TripoModelVersion,
+                    tooltip="The model version to use for generation",
+                    optional=True,
+                ),
+                IO.Combo.Input("style", options=TripoStyle, default="None", optional=True),
+                IO.Boolean.Input("texture", default=True, optional=True),
+                IO.Boolean.Input("pbr", default=True, optional=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input(
+                    "orientation",
+                    options=TripoOrientation,
+                    default=TripoOrientation.DEFAULT,
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input(
+                    "texture_quality",
+                    default="standard",
+                    options=["standard", "detailed"],
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "texture_alignment",
+                    default="original_image",
+                    options=["original_image", "geometry"],
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input(
+                    "geometry_quality",
+                    default="standard",
+                    options=["standard", "detailed"],
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=[
+                        "model_version",
+                        "texture",
+                        "pbr",
+                        "quad",
+                        "texture_quality",
+                        "geometry_quality",
+                    ],
+                ),
+                expr="""
+                (
+                  $isV14 := $contains(widgets.model_version,"v1.4");
+                  $isV3OrLater := $contains(widgets.model_version,"v3.");
+                  $withTexture := widgets.texture or widgets.pbr;
+                  $isHdTexture := (widgets.texture_quality = "detailed");
+                  $isDetailedGeometry := (widgets.geometry_quality = "detailed");
+                  $credits := $isV14 ? 30 : (
+                    ($withTexture ? 30 : 20)
+                    + (widgets.quad ? 5 : 0)
+                    + ($isHdTexture ? 10 : 0)
+                    + (($isDetailedGeometry and $isV3OrLater) ? 20 : 0)
+                  );
+                  {"type":"usd","usd": $round($credits * 0.01, 2), "format": {"approximate": true}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        model_version: str | None = None,
+        style: str | None = None,
+        texture: bool | None = None,
+        pbr: bool | None = None,
+        model_seed: int | None = None,
+        orientation=None,
+        texture_seed: int | None = None,
+        texture_quality: str | None = None,
+        geometry_quality: str | None = None,
+        texture_alignment: str | None = None,
+        face_limit: int | None = None,
+        quad: bool | None = None,
+    ) -> IO.NodeOutput:
+        style_enum = None if style == "None" else style
+        if image is None:
+            raise RuntimeError("Image is required")
+        tripo_file = TripoFileReference(
+            root=TripoUrlReference(
+                url=(await upload_images_to_comfyapi(cls, image, max_images=1))[0],
+                type="jpeg",
+            )
+        )
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"),
+            response_model=TripoTaskResponse,
+            data=TripoImageToModelRequest(
+                type=TripoTaskType.IMAGE_TO_MODEL,
+                file=tripo_file,
+                model_version=model_version,
+                style=style_enum,
+                texture=texture,
+                pbr=pbr,
+                model_seed=model_seed,
+                orientation=orientation,
+                geometry_quality=geometry_quality,
+                texture_alignment=texture_alignment,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                face_limit=face_limit if face_limit != -1 else None,
+                auto_size=True,
+                quad=quad,
+            ),
+        )
+        return await poll_until_finished(cls, response, average_duration=80)
+
+
+class TripoMultiviewToModelNode(IO.ComfyNode):
+    """
+    Generates 3D models synchronously based on up to four images (front, left, back, right) using Tripo's API.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoMultiviewToModelNode",
+            display_name="Tripo: Multiview to Model",
+            category="partner/3d/Tripo",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Image.Input("image_left", optional=True),
+                IO.Image.Input("image_back", optional=True),
+                IO.Image.Input("image_right", optional=True),
+                IO.Combo.Input(
+                    "model_version",
+                    options=TripoModelVersion,
+                    optional=True,
+                    tooltip="The model version to use for generation",
+                ),
+                IO.Combo.Input(
+                    "orientation",
+                    options=TripoOrientation,
+                    default=TripoOrientation.DEFAULT,
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input("texture", default=True, optional=True),
+                IO.Boolean.Input("pbr", default=True, optional=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input(
+                    "texture_quality",
+                    default="standard",
+                    options=["standard", "detailed"],
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "texture_alignment",
+                    default="original_image",
+                    options=["original_image", "geometry"],
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True, advanced=True),
+                IO.Boolean.Input(
+                    "quad",
+                    default=False,
+                    optional=True,
+                    advanced=True,
+                    tooltip="This parameter is deprecated and does nothing.",
+                ),
+                IO.Combo.Input(
+                    "geometry_quality",
+                    default="standard",
+                    options=["standard", "detailed"],
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=[
+                        "model_version",
+                        "texture",
+                        "pbr",
+                        "texture_quality",
+                        "geometry_quality",
+                    ],
+                ),
+                expr="""
+                (
+                  $isV14 := $contains(widgets.model_version,"v1.4");
+                  $isV3OrLater := $contains(widgets.model_version,"v3.");
+                  $withTexture := widgets.texture or widgets.pbr;
+                  $isHdTexture := (widgets.texture_quality = "detailed");
+                  $isDetailedGeometry := (widgets.geometry_quality = "detailed");
+                  $credits := $isV14 ? 30 : (
+                    ($withTexture ? 30 : 20)
+                    + ($isHdTexture ? 10 : 0)
+                    + (($isDetailedGeometry and $isV3OrLater) ? 20 : 0)
+                  );
+                  {"type":"usd","usd": $round($credits * 0.01, 2), "format": {"approximate": true}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        image_left: Input.Image | None = None,
+        image_back: Input.Image | None = None,
+        image_right: Input.Image | None = None,
+        model_version: str | None = None,
+        orientation: str | None = None,
+        texture: bool | None = None,
+        pbr: bool | None = None,
+        model_seed: int | None = None,
+        texture_seed: int | None = None,
+        texture_quality: str | None = None,
+        geometry_quality: str | None = None,
+        texture_alignment: str | None = None,
+        face_limit: int | None = None,
+        quad: bool | None = None,
+    ) -> IO.NodeOutput:
+        if image is None:
+            raise RuntimeError("front image for multiview is required")
+        images = []
+        image_dict = {"image": image, "image_left": image_left, "image_back": image_back, "image_right": image_right}
+        if image_left is None and image_back is None and image_right is None:
+            raise RuntimeError("At least one of left, back, or right image must be provided for multiview")
+        for image_name in ["image", "image_left", "image_back", "image_right"]:
+            image_ = image_dict[image_name]
+            if image_ is not None:
+                images.append(
+                    TripoFileReference(
+                        root=TripoUrlReference(
+                            url=(await upload_images_to_comfyapi(cls, image_, max_images=1))[0], type="jpeg"
+                        )
+                    )
+                )
+            else:
+                images.append(TripoFileEmptyReference())
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"),
+            response_model=TripoTaskResponse,
+            data=TripoMultiviewToModelRequest(
+                type=TripoTaskType.MULTIVIEW_TO_MODEL,
+                files=images,
+                model_version=model_version,
+                orientation=orientation,
+                texture=texture,
+                pbr=pbr,
+                model_seed=model_seed,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                geometry_quality=geometry_quality,
+                texture_alignment=texture_alignment,
+                face_limit=face_limit if face_limit != -1 else None,
+                quad=None,
+            ),
+        )
+        return await poll_until_finished(cls, response, average_duration=80)
+
+
+class TripoTextureNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoTextureNode",
+            display_name="Tripo: Texture model",
+            category="partner/3d/Tripo",
+            inputs=[
+                IO.Custom("MODEL_TASK_ID").Input("model_task_id"),
+                IO.Boolean.Input("texture", default=True, optional=True),
+                IO.Boolean.Input("pbr", default=True, optional=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input(
+                    "texture_quality",
+                    default="standard",
+                    options=["standard", "detailed"],
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "texture_alignment",
+                    default="original_image",
+                    options=["original_image", "geometry"],
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.String.Input(
+                    "texture_prompt",
+                    default="",
+                    multiline=True,
+                    optional=True,
+                    tooltip="Optional text guidance for texturing. Required in practice for imported "
+                    "models (Tripo: Import Model), which carry no source image to infer colors from.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["texture_quality"]),
+                expr="""
+                (
+                  $tq := widgets.texture_quality;
+                  {"type":"usd","usd": ($contains($tq,"detailed") ? 0.2 : 0.1), "format": {"approximate": true}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_task_id,
+        texture: bool | None = None,
+        pbr: bool | None = None,
+        texture_seed: int | None = None,
+        texture_quality: str | None = None,
+        texture_alignment: str | None = None,
+        texture_prompt: str = "",
+    ) -> IO.NodeOutput:
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"),
+            response_model=TripoTaskResponse,
+            data=TripoTextureModelRequest(
+                original_model_task_id=model_task_id,
+                texture=texture,
+                pbr=pbr,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                texture_alignment=texture_alignment,
+                texture_prompt=TripoTexturePrompt(text=texture_prompt.strip()) if texture_prompt.strip() else None,
+            ),
+        )
+        return await poll_until_finished(cls, response, average_duration=80)
+
+
+class TripoRigNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoRigNode",
+            display_name="Tripo: Rig model",
+            category="partner/3d/Tripo",
+            inputs=[IO.Custom("MODEL_TASK_ID").Input("original_model_task_id")],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("RIG_TASK_ID").Output(display_name="rig task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.25, "format": {"approximate": true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, original_model_task_id) -> IO.NodeOutput:
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"),
+            response_model=TripoTaskResponse,
+            data=TripoAnimateRigRequest(original_model_task_id=original_model_task_id, out_format="glb", spec="tripo"),
+        )
+        return await poll_until_finished(cls, response, average_duration=180)
+
+
+class TripoRetargetNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoRetargetNode",
+            display_name="Tripo: Retarget rigged model",
+            category="partner/3d/Tripo",
+            inputs=[
+                IO.Custom("RIG_TASK_ID").Input("original_model_task_id"),
+                IO.Combo.Input(
+                    "animation",
+                    options=[
+                        "preset:idle",
+                        "preset:walk",
+                        "preset:run",
+                        "preset:dive",
+                        "preset:climb",
+                        "preset:jump",
+                        "preset:slash",
+                        "preset:shoot",
+                        "preset:hurt",
+                        "preset:fall",
+                        "preset:turn",
+                        "preset:quadruped:walk",
+                        "preset:hexapod:walk",
+                        "preset:octopod:walk",
+                        "preset:serpentine:march",
+                        "preset:aquatic:march",
+                    ],
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("RETARGET_TASK_ID").Output(display_name="retarget task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.1, "format": {"approximate": true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, original_model_task_id, animation: str) -> IO.NodeOutput:
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"),
+            response_model=TripoTaskResponse,
+            data=TripoAnimateRetargetRequest(
+                original_model_task_id=original_model_task_id,
+                animation=animation,
+                out_format="glb",
+                bake_animation=True,
+            ),
+        )
+        return await poll_until_finished(cls, response, average_duration=30)
+
+
+class TripoConversionNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoConversionNode",
+            display_name="Tripo: Convert model",
+            category="partner/3d/Tripo",
+            inputs=[
+                IO.Custom("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID").Input("original_model_task_id"),
+                IO.Combo.Input("format", options=["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"]),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Int.Input(
+                    "face_limit",
+                    default=-1,
+                    min=-1,
+                    max=2000000,
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "texture_size",
+                    default=4096,
+                    min=128,
+                    max=4096,
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "texture_format",
+                    options=["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"],
+                    default="JPEG",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input("force_symmetry", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("flatten_bottom", default=False, optional=True, advanced=True),
+                IO.Float.Input(
+                    "flatten_bottom_threshold",
+                    default=0.0,
+                    min=0.0,
+                    max=1.0,
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input("pivot_to_center_bottom", default=False, optional=True, advanced=True),
+                IO.Float.Input(
+                    "scale_factor",
+                    default=1.0,
+                    min=0.0,
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input("with_animation", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("pack_uv", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("bake", default=False, optional=True, advanced=True),
+                IO.String.Input("part_names", default="", optional=True, advanced=True),  # comma-separated list
+                IO.Combo.Input(
+                    "fbx_preset",
+                    options=["blender", "mixamo", "3dsmax"],
+                    default="blender",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input("export_vertex_colors", default=False, optional=True, advanced=True),
+                IO.Combo.Input(
+                    "export_orientation",
+                    options=["align_image", "default"],
+                    default="default",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input("animate_in_place", default=False, optional=True, advanced=True),
+            ],
+            outputs=[],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=[
+                        "quad",
+                        "face_limit",
+                        "texture_size",
+                        "texture_format",
+                        "flatten_bottom",
+                        "flatten_bottom_threshold",
+                        "pivot_to_center_bottom",
+                        "scale_factor",
+                    ],
+                ),
+                expr="""
+                (
+                    $face := (widgets.face_limit != null) ? widgets.face_limit : -1;
+                    $texSize := (widgets.texture_size != null) ? widgets.texture_size : 4096;
+                    $flatThresh := (widgets.flatten_bottom_threshold != null) ? widgets.flatten_bottom_threshold : 0;
+                    $scale := (widgets.scale_factor != null) ? widgets.scale_factor : 1;
+                    $texFmt := (widgets.texture_format != "" ? widgets.texture_format : "jpeg");
+                    $advanced :=
+                      widgets.quad or
+                      widgets.flatten_bottom or
+                      widgets.pivot_to_center_bottom or
+                      ($face != -1) or
+                      ($texSize != 4096) or
+                      ($flatThresh != 0) or
+                      ($scale != 1) or
+                      ($texFmt != "jpeg");
+                    {"type":"usd","usd": ($advanced ? 0.1 : 0.05), "format": {"approximate": true}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    def validate_inputs(cls, input_types):
+        # The min and max of input1 and input2 are still validated because
+        # we didn't take `input1` or `input2` as arguments
+        if input_types["original_model_task_id"] not in ("MODEL_TASK_ID", "RIG_TASK_ID", "RETARGET_TASK_ID"):
+            return "original_model_task_id must be MODEL_TASK_ID, RIG_TASK_ID or RETARGET_TASK_ID type"
+        return True
+
+    @classmethod
+    async def execute(
+        cls,
+        original_model_task_id,
+        format: str,
+        quad: bool,
+        force_symmetry: bool,
+        face_limit: int,
+        flatten_bottom: bool,
+        flatten_bottom_threshold: float,
+        texture_size: int,
+        texture_format: str,
+        pivot_to_center_bottom: bool,
+        scale_factor: float,
+        with_animation: bool,
+        pack_uv: bool,
+        bake: bool,
+        part_names: str,
+        fbx_preset: str,
+        export_vertex_colors: bool,
+        export_orientation: str,
+        animate_in_place: bool,
+    ) -> IO.NodeOutput:
+        if not original_model_task_id:
+            raise RuntimeError("original_model_task_id is required")
+
+        # Parse part_names from comma-separated string to list
+        part_names_list = None
+        if part_names and part_names.strip():
+            part_names_list = [name.strip() for name in part_names.split(",") if name.strip()]
+
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"),
+            response_model=TripoTaskResponse,
+            data=TripoConvertModelRequest(
+                original_model_task_id=original_model_task_id,
+                format=format,
+                quad=quad if quad else None,
+                force_symmetry=force_symmetry if force_symmetry else None,
+                face_limit=face_limit if face_limit != -1 else None,
+                flatten_bottom=flatten_bottom if flatten_bottom else None,
+                flatten_bottom_threshold=flatten_bottom_threshold if flatten_bottom_threshold != 0.0 else None,
+                texture_size=texture_size if texture_size != 4096 else None,
+                texture_format=texture_format if texture_format != "JPEG" else None,
+                pivot_to_center_bottom=pivot_to_center_bottom if pivot_to_center_bottom else None,
+                scale_factor=scale_factor if scale_factor != 1.0 else None,
+                with_animation=with_animation if with_animation else None,
+                pack_uv=pack_uv if pack_uv else None,
+                bake=bake if bake else None,
+                part_names=part_names_list,
+                fbx_preset=fbx_preset if fbx_preset != "blender" else None,
+                export_vertex_colors=export_vertex_colors if export_vertex_colors else None,
+                export_orientation=export_orientation if export_orientation != "default" else None,
+                animate_in_place=animate_in_place if animate_in_place else None,
+            ),
+        )
+        return await poll_until_finished(cls, response, average_duration=30)
+
+
+class TripoImportModelNode(IO.ComfyNode):
+    """Imports an external 3D model into Tripo, producing a MODEL_TASK_ID for post-processing nodes."""
+
+    SUPPORTED_FORMATS = ("glb", "fbx", "obj", "stl")
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoImportModelNode",
+            display_name="Tripo: Import Model",
+            category="partner/3d/Tripo",
+            description="Import an external 3D model (e.g. from Rodin, Hunyuan3D or a local file) into Tripo "
+            "to use it with Tripo's post-processing nodes: Texture, Rig, Convert. "
+            "GLB is recommended: textures survive import only when embedded in the file. "
+            "Note that texturing an imported model requires a texture prompt.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DGLB, IO.File3DFBX, IO.File3DOBJ, IO.File3DSTL, IO.File3DAny],
+                    tooltip="3D model to import (GLB / FBX / OBJ / STL, up to 150 MB). "
+                    "OBJ and STL files carry no embedded textures.",
+                ),
+            ],
+            outputs=[
+                IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"text","text":"Free"}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, model_3d: Types.File3D) -> IO.NodeOutput:
+        file_format = (model_3d.format or "").lstrip(".").lower()
+        if file_format == "gltf":
+            raise ValueError(
+                "GLTF (.gltf) references external files and cannot be imported. Export a single-file GLB instead."
+            )
+        if file_format not in cls.SUPPORTED_FORMATS:
+            raise ValueError(
+                f"Unsupported 3D format '{file_format or 'unknown'}'. "
+                f"Tripo import supports: {', '.join(f.upper() for f in cls.SUPPORTED_FORMATS)}."
+            )
+        size = len(model_3d.get_bytes())
+        if size > 150 * 1024 * 1024:
+            raise ValueError(f"Model file is {size / (1024 * 1024):.1f} MB; Tripo import allows up to 150 MB.")
+
+        url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/import", method="POST"),
+            response_model=TripoTaskResponse,
+            data=TripoImportModelRequest(url=url, format=file_format),
+        )
+        if response.code != 0:
+            raise RuntimeError(f"Failed to import model: {response.error}")
+
+        task_id = response.data.task_id
+        response_poll = await poll_op(
+            cls,
+            poll_endpoint=ApiEndpoint(path=f"/proxy/tripo/v2/openapi/task/{task_id}"),
+            response_model=TripoTaskResponse,
+            failed_statuses=[
+                TripoTaskStatus.FAILED,
+                TripoTaskStatus.CANCELLED,
+                TripoTaskStatus.UNKNOWN,
+                TripoTaskStatus.BANNED,
+                TripoTaskStatus.EXPIRED,
+            ],
+            status_extractor=lambda x: x.data.status,
+            progress_extractor=lambda x: x.data.progress,
+            estimated_duration=10,
+        )
+        if response_poll.data.status != TripoTaskStatus.SUCCESS:
+            raise RuntimeError(f"Failed to import model: {response_poll}")
+        return IO.NodeOutput(task_id)
+
+
+def _p1_price_expr(*, geometry_credits: int, textured_credits: int, detailed_credits: int) -> str:
+    return (
+        "("
+        "  $mode := widgets.output_mode;"
+        '  $detailed := $lookup(widgets, "output_mode.texture_quality") = "detailed";'
+        f'  $credits := $mode = "geometry only" ? {geometry_credits} : ($detailed ? {detailed_credits} : {textured_credits});'
+        '  {"type":"usd","usd": $credits * 0.01, "format": {"approximate": true}}'
+        ")"
+    )
+
+
+def _p1_textured_inputs(*, include_image_alignment: bool) -> list:
+    """Inputs shown inside the 'Textured' branch of the P1 output_mode DynamicCombo."""
+    inputs: list = [
+        IO.Boolean.Input("pbr", default=True, tooltip="Include PBR maps. When on, base texture is forced on too."),
+        IO.Combo.Input("texture_quality", options=["standard", "detailed"], default="standard"),
+    ]
+    if include_image_alignment:
+        inputs.extend(
+            [
+                IO.Combo.Input(
+                    "texture_alignment",
+                    options=["original_image", "geometry"],
+                    default="original_image",
+                    tooltip="Prioritize visual fidelity to the source image, or alignment to the mesh geometry.",
+                ),
+                IO.Combo.Input(
+                    "orientation",
+                    options=["default", "align_image"],
+                    default="default",
+                    tooltip="Rotate the output to match the source image. Only applies when textured.",
+                ),
+            ]
+        )
+    inputs.append(IO.Int.Input("texture_seed", default=42, advanced=True))
+    return inputs
+
+
+def _build_p1_output_mode(*, include_image_alignment: bool) -> IO.DynamicCombo.Input:
+    return IO.DynamicCombo.Input(
+        "output_mode",
+        options=[
+            IO.DynamicCombo.Option("Geometry only", []),
+            IO.DynamicCombo.Option("Textured", _p1_textured_inputs(include_image_alignment=include_image_alignment)),
+        ],
+        tooltip='"Geometry only" returns an untextured mesh. "Textured" adds color/PBR maps.',
+    )
+
+
+def _resolve_p1_texture_fields(output_mode: dict) -> dict:
+    """Translate the output_mode DynamicCombo payload into P1 request fields.
+
+    pbr=true forces texture=true server-side, but we send both explicitly so the
+    intent is visible in the request body and logs.
+    """
+    mode = output_mode["output_mode"]
+    if mode == "Geometry only":
+        return {"texture": False, "pbr": False}
+    out = {
+        "texture": True,
+        "pbr": bool(output_mode.get("pbr", True)),
+        "texture_quality": output_mode.get("texture_quality", "standard"),
+        "texture_seed": output_mode.get("texture_seed"),
+    }
+    if "texture_alignment" in output_mode:
+        out["texture_alignment"] = output_mode["texture_alignment"]
+    if "orientation" in output_mode:
+        out["orientation"] = output_mode["orientation"]
+    return out
+
+
+def _p1_common_inputs() -> list:
+    """Inputs shared by all P1 nodes (placed after output_mode)."""
+    return [
+        IO.Int.Input(
+            "face_limit",
+            default=-1,
+            min=-1,
+            max=20000,
+            optional=True,
+            advanced=True,
+            tooltip="Target face count, 48-20000. -1 lets Tripo pick adaptively.",
+        ),
+        IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
+        IO.Boolean.Input(
+            "auto_size",
+            default=False,
+            optional=True,
+            advanced=True,
+            tooltip="Scale the output to approximate real-world meters.",
+        ),
+        IO.Boolean.Input(
+            "export_uv",
+            default=True,
+            optional=True,
+            advanced=True,
+            tooltip="UV unwrap during generation. Turn off for faster geometry-only runs.",
+        ),
+        IO.Boolean.Input(
+            "compress_geometry",
+            default=False,
+            optional=True,
+            advanced=True,
+            tooltip="Apply geometry-based compression. Decompress before editing.",
+        ),
+    ]
+
+
+def _build_p1_request_kwargs(
+    *,
+    output_mode: dict,
+    face_limit: int,
+    model_seed: int,
+    auto_size: bool,
+    export_uv: bool,
+    compress_geometry: bool,
+) -> dict:
+    """Common P1 request fields shared by all three node types."""
+    kwargs: dict = {
+        "model_seed": model_seed,
+        "face_limit": face_limit if face_limit != -1 else None,
+        "auto_size": auto_size,
+        "export_uv": export_uv,
+        "compress": "geometry" if compress_geometry else None,
+    }
+    kwargs.update(_resolve_p1_texture_fields(output_mode))
+    return kwargs
+
+
+class TripoP1TextToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoP1TextToModelNode",
+            display_name="Tripo P1: Text to Model",
+            category="partner/3d/Tripo",
+            description="Tripo P1 text-to-3D. Optimized for low-poly, game-ready meshes with stable topology.",
+            inputs=[
+                IO.String.Input("prompt", multiline=True, tooltip="Up to 1024 characters."),
+                IO.String.Input("negative_prompt", multiline=True, optional=True, tooltip="Up to 255 characters."),
+                _build_p1_output_mode(include_image_alignment=False),
+                IO.Int.Input("image_seed", default=42, optional=True, advanced=True),
+                *_p1_common_inputs(),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["output_mode", "output_mode.texture_quality"]),
+                expr=_p1_price_expr(geometry_credits=30, textured_credits=40, detailed_credits=50),
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        output_mode: dict,
+        negative_prompt: str | None = None,
+        image_seed: int | None = None,
+        face_limit: int = -1,
+        model_seed: int | None = None,
+        auto_size: bool = False,
+        export_uv: bool = True,
+        compress_geometry: bool = False,
+    ) -> IO.NodeOutput:
+        if not prompt:
+            raise RuntimeError("Prompt is required")
+        common = _build_p1_request_kwargs(
+            output_mode=output_mode,
+            face_limit=face_limit,
+            model_seed=model_seed,
+            auto_size=auto_size,
+            export_uv=export_uv,
+            compress_geometry=compress_geometry,
+        )
+        request = TripoP1TextToModelRequest(
+            prompt=prompt,
+            negative_prompt=negative_prompt or None,
+            image_seed=image_seed,
+            **common,
+        )
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"),
+            response_model=TripoTaskResponse,
+            data=request,
+        )
+        return await poll_until_finished(cls, response, average_duration=60)
+
+
+class TripoP1ImageToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoP1ImageToModelNode",
+            display_name="Tripo P1: Image to Model",
+            category="partner/3d/Tripo",
+            description="Tripo P1 image-to-3D. Optimized for low-poly, game-ready meshes.",
+            inputs=[
+                IO.Image.Input("image"),
+                _build_p1_output_mode(include_image_alignment=True),
+                IO.Boolean.Input(
+                    "enable_image_autofix",
+                    default=False,
+                    optional=True,
+                    advanced=True,
+                    tooltip="Pre-process the input image for better generation quality.",
+                ),
+                *_p1_common_inputs(),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["output_mode", "output_mode.texture_quality"]),
+                expr=_p1_price_expr(geometry_credits=40, textured_credits=50, detailed_credits=60),
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        output_mode: dict,
+        enable_image_autofix: bool = False,
+        face_limit: int = -1,
+        model_seed: int | None = None,
+        auto_size: bool = False,
+        export_uv: bool = True,
+        compress_geometry: bool = False,
+    ) -> IO.NodeOutput:
+        if image is None:
+            raise RuntimeError("Image is required")
+        tripo_file = TripoFileReference(
+            root=TripoUrlReference(
+                url=(await upload_images_to_comfyapi(cls, image, max_images=1))[0],
+                type="jpeg",
+            )
+        )
+        common = _build_p1_request_kwargs(
+            output_mode=output_mode,
+            face_limit=face_limit,
+            model_seed=model_seed,
+            auto_size=auto_size,
+            export_uv=export_uv,
+            compress_geometry=compress_geometry,
+        )
+        request = TripoP1ImageToModelRequest(
+            file=tripo_file,
+            enable_image_autofix=enable_image_autofix,
+            **common,
+        )
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"),
+            response_model=TripoTaskResponse,
+            data=request,
+        )
+        return await poll_until_finished(cls, response, average_duration=60)
+
+
+class TripoP1MultiviewToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoP1MultiviewToModelNode",
+            display_name="Tripo P1: Multiview to Model",
+            category="partner/3d/Tripo",
+            description="Tripo P1 multiview-to-3D from 2-4 reference images in [front, left, back, right] order. "
+            "Front is required; any combination of the other three may be omitted.",
+            inputs=[
+                IO.Image.Input("image", tooltip="Front view (0°). Required."),
+                IO.Image.Input(
+                    "image_left",
+                    optional=True,
+                    tooltip="Left view (90°), i.e. the subject's left side.",
+                ),
+                IO.Image.Input("image_back", optional=True, tooltip="Back view (180°)."),
+                IO.Image.Input(
+                    "image_right",
+                    optional=True,
+                    tooltip="Right view (270°), i.e. the subject's right side.",
+                ),
+                _build_p1_output_mode(include_image_alignment=True),
+                *_p1_common_inputs(),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"),
+                IO.File3DGLB.Output(display_name="GLB"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["output_mode", "output_mode.texture_quality"]),
+                expr=_p1_price_expr(geometry_credits=40, textured_credits=50, detailed_credits=60),
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        output_mode: dict,
+        image_left: Input.Image | None = None,
+        image_back: Input.Image | None = None,
+        image_right: Input.Image | None = None,
+        face_limit: int = -1,
+        model_seed: int | None = None,
+        auto_size: bool = False,
+        export_uv: bool = True,
+        compress_geometry: bool = False,
+    ) -> IO.NodeOutput:
+        views = [image, image_left, image_back, image_right]
+        if sum(1 for v in views if v is not None) < 2:
+            raise RuntimeError("Tripo P1 multiview requires at least 2 images (front plus one of left/back/right).")
+
+        files: list[TripoFileReference] = []
+        for view in views:
+            if view is None:
+                files.append(TripoFileReference(root=TripoFileEmptyReference()))
+                continue
+            url = (await upload_images_to_comfyapi(cls, view, max_images=1))[0]
+            files.append(TripoFileReference(root=TripoUrlReference(url=url, type="jpeg")))
+
+        common = _build_p1_request_kwargs(
+            output_mode=output_mode,
+            face_limit=face_limit,
+            model_seed=model_seed,
+            auto_size=auto_size,
+            export_uv=export_uv,
+            compress_geometry=compress_geometry,
+        )
+        request = TripoP1MultiviewToModelRequest(files=files, **common)
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"),
+            response_model=TripoTaskResponse,
+            data=request,
+        )
+        return await poll_until_finished(cls, response, average_duration=80)
+
+
+class TripoExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TripoTextToModelNode,
+            TripoImageToModelNode,
+            TripoMultiviewToModelNode,
+            TripoP1TextToModelNode,
+            TripoP1ImageToModelNode,
+            TripoP1MultiviewToModelNode,
+            TripoImportModelNode,
+            TripoTextureNode,
+            TripoRigNode,
+            TripoRetargetNode,
+            TripoConversionNode,
+        ]
+
+
+async def comfy_entrypoint() -> TripoExtension:
+    return TripoExtension()
diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py
new file mode 100644
index 0000000000000000000000000000000000000000..2651695a14f9f18ab63d8deff4e63371df2f19ae
--- /dev/null
+++ b/comfy_api_nodes/nodes_veo2.py
@@ -0,0 +1,414 @@
+import base64
+from io import BytesIO
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
+from comfy_api_nodes.apis.veo import (
+    VeoGenVidPollRequest,
+    VeoGenVidPollResponse,
+    VeoGenVidRequest,
+    VeoGenVidResponse,
+    VeoRequestInstance,
+    VeoRequestInstanceImage,
+    VeoRequestParameters,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    poll_op,
+    sync_op,
+    tensor_to_base64_string,
+)
+
+AVERAGE_DURATION_VIDEO_GEN = 32
+MODELS_MAP = {
+    "veo-3.1-generate": "veo-3.1-generate-001",
+    "veo-3.1-fast-generate": "veo-3.1-fast-generate-001",
+    "veo-3.1-lite": "veo-3.1-lite-generate-001",
+}
+
+
+class Veo3VideoGenerationNode(IO.ComfyNode):
+    """Generates videos from text prompts using Google's Veo 3 API."""
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Veo3VideoGenerationNode",
+            display_name="Google Veo 3 Video Generation",
+            category="partner/video/Veo",
+            description="Generates videos from text prompts using Google's Veo 3 API",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the video",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["16:9", "9:16"],
+                    default="16:9",
+                    tooltip="Aspect ratio of the output video",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["720p", "1080p", "4k"],
+                    default="720p",
+                    tooltip="Output video resolution. 4K is not available for the veo-3.1-lite model.",
+                    optional=True,
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative text prompt to guide what to avoid in the video",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "duration_seconds",
+                    default=8,
+                    min=4,
+                    max=8,
+                    step=2,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Duration of the output video in seconds",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "enhance_prompt",
+                    default=True,
+                    tooltip="This parameter is deprecated and ignored.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "person_generation",
+                    options=["ALLOW", "BLOCK"],
+                    default="ALLOW",
+                    tooltip="Whether to allow generating people in the video",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFF,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation (0 for random)",
+                    optional=True,
+                ),
+                IO.Image.Input(
+                    "image",
+                    tooltip="Optional reference image to guide video generation",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["veo-3.1-generate", "veo-3.1-fast-generate", "veo-3.1-lite"],
+                    tooltip="Veo 3 model to use for video generation",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    tooltip="Generate audio for the video. Supported by all Veo 3 models.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "resolution", "duration_seconds"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $r := widgets.resolution;
+                  $a := widgets.generate_audio;
+                  $seconds := widgets.duration_seconds;
+                  $pps :=
+                    $contains($m, "lite")
+                      ? ($r = "1080p" ? ($a ? 0.08 : 0.05) : ($a ? 0.05 : 0.03))
+                    : $contains($m, "fast")
+                      ? ($r = "4k" ? ($a ? 0.30 : 0.25) : $r = "1080p" ? ($a ? 0.12 : 0.10) : ($a ? 0.10 : 0.08))
+                    : ($r = "4k" ? ($a ? 0.60 : 0.40) : ($a ? 0.40 : 0.20));
+                  {"type":"usd","usd": $pps * $seconds}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt,
+        aspect_ratio="16:9",
+        resolution="720p",
+        negative_prompt="",
+        duration_seconds=8,
+        enhance_prompt=True,
+        person_generation="ALLOW",
+        seed=0,
+        image=None,
+        model="veo-3.1-generate",
+        generate_audio=False,
+    ):
+        if resolution == "4k" and "lite" in model:
+            raise Exception("4K resolution is not supported by the veo-3.1-lite model.")
+
+        model = MODELS_MAP[model]
+
+        instances = [{"prompt": prompt}]
+        if image is not None:
+            image_base64 = tensor_to_base64_string(image)
+            if image_base64:
+                instances[0]["image"] = {"bytesBase64Encoded": image_base64, "mimeType": "image/png"}
+
+        parameters = {
+            "aspectRatio": aspect_ratio,
+            "personGeneration": person_generation,
+            "durationSeconds": duration_seconds,
+            "enhancePrompt": True,
+            "generateAudio": generate_audio,
+            "resolution": resolution,
+        }
+        if negative_prompt:
+            parameters["negativePrompt"] = negative_prompt
+        if seed > 0:
+            parameters["seed"] = seed
+
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/veo/{model}/generate", method="POST"),
+            response_model=VeoGenVidResponse,
+            data=VeoGenVidRequest(
+                instances=instances,
+                parameters=parameters,
+            ),
+        )
+
+        poll_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/veo/{model}/poll", method="POST"),
+            response_model=VeoGenVidPollResponse,
+            status_extractor=lambda r: "completed" if r.done else "pending",
+            data=VeoGenVidPollRequest(operationName=initial_response.name),
+            poll_interval=9.0,
+            estimated_duration=AVERAGE_DURATION_VIDEO_GEN,
+        )
+
+        if poll_response.error:
+            raise Exception(f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})")
+
+        response = poll_response.response
+        filtered_count = response.raiMediaFilteredCount
+        if filtered_count:
+            reasons = response.raiMediaFilteredReasons or []
+            reason_part = f": {reasons[0]}" if reasons else ""
+            raise Exception(
+                f"Content blocked by Google's Responsible AI filters{reason_part} "
+                f"({filtered_count} video{'s' if filtered_count != 1 else ''} filtered)."
+            )
+
+        if response.videos:
+            video = response.videos[0]
+            if video.bytesBase64Encoded:
+                return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(base64.b64decode(video.bytesBase64Encoded))))
+            if video.gcsUri:
+                return IO.NodeOutput(await download_url_to_video_output(video.gcsUri))
+            raise Exception("Video returned but no data or URL was provided")
+        raise Exception("Video generation completed but no video was returned")
+
+
+class Veo3FirstLastFrameNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Veo3FirstLastFrameNode",
+            display_name="Google Veo 3 First-Last-Frame to Video",
+            category="partner/video/Veo",
+            description="Generate video using prompt and first and last frames.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the video",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative text prompt to guide what to avoid in the video",
+                ),
+                IO.Combo.Input("resolution", options=["720p", "1080p", "4k"]),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["16:9", "9:16"],
+                    default="16:9",
+                    tooltip="Aspect ratio of the output video",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=8,
+                    min=4,
+                    max=8,
+                    step=2,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Duration of the output video in seconds",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFF,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation",
+                ),
+                IO.Image.Input("first_frame", tooltip="Start frame"),
+                IO.Image.Input("last_frame", tooltip="End frame"),
+                IO.Combo.Input(
+                    "model",
+                    options=["veo-3.1-generate", "veo-3.1-fast-generate", "veo-3.1-lite"],
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=True,
+                    tooltip="Generate audio for the video.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "duration", "resolution"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $r := widgets.resolution;
+                  $ga := widgets.generate_audio;
+                  $seconds := widgets.duration;
+                  $pps :=
+                    $contains($m, "lite")
+                      ? ($r = "1080p" ? ($ga ? 0.08 : 0.05) : ($ga ? 0.05 : 0.03))
+                    : $contains($m, "fast")
+                      ? ($r = "4k" ? ($ga ? 0.30 : 0.25) : $r = "1080p" ? ($ga ? 0.12 : 0.10) : ($ga ? 0.10 : 0.08))
+                    : ($r = "4k" ? ($ga ? 0.60 : 0.40) : ($ga ? 0.40 : 0.20));
+                  {"type":"usd","usd": $pps * $seconds}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str,
+        resolution: str,
+        aspect_ratio: str,
+        duration: int,
+        seed: int,
+        first_frame: Input.Image,
+        last_frame: Input.Image,
+        model: str,
+        generate_audio: bool,
+    ):
+        if "lite" in model and resolution == "4k":
+            raise Exception("4K resolution is not supported by the veo-3.1-lite model.")
+
+        model = MODELS_MAP[model]
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/veo/{model}/generate", method="POST"),
+            response_model=VeoGenVidResponse,
+            data=VeoGenVidRequest(
+                instances=[
+                    VeoRequestInstance(
+                        prompt=prompt,
+                        image=VeoRequestInstanceImage(
+                            bytesBase64Encoded=tensor_to_base64_string(first_frame), mimeType="image/png"
+                        ),
+                        lastFrame=VeoRequestInstanceImage(
+                            bytesBase64Encoded=tensor_to_base64_string(last_frame), mimeType="image/png"
+                        ),
+                    ),
+                ],
+                parameters=VeoRequestParameters(
+                    aspectRatio=aspect_ratio,
+                    personGeneration="ALLOW",
+                    durationSeconds=duration,
+                    enhancePrompt=True,  # cannot be False for Veo3
+                    seed=seed,
+                    generateAudio=generate_audio,
+                    negativePrompt=negative_prompt,
+                    resolution=resolution,
+                ),
+            ),
+        )
+        poll_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/veo/{model}/poll", method="POST"),
+            response_model=VeoGenVidPollResponse,
+            status_extractor=lambda r: "completed" if r.done else "pending",
+            data=VeoGenVidPollRequest(
+                operationName=initial_response.name,
+            ),
+            poll_interval=9.0,
+            estimated_duration=AVERAGE_DURATION_VIDEO_GEN,
+        )
+
+        if poll_response.error:
+            raise Exception(f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})")
+
+        response = poll_response.response
+        filtered_count = response.raiMediaFilteredCount
+        if filtered_count:
+            reasons = response.raiMediaFilteredReasons or []
+            reason_part = f": {reasons[0]}" if reasons else ""
+            raise Exception(
+                f"Content blocked by Google's Responsible AI filters{reason_part} "
+                f"({filtered_count} video{'s' if filtered_count != 1 else ''} filtered)."
+            )
+
+        if response.videos:
+            video = response.videos[0]
+            if video.bytesBase64Encoded:
+                return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(base64.b64decode(video.bytesBase64Encoded))))
+            if video.gcsUri:
+                return IO.NodeOutput(await download_url_to_video_output(video.gcsUri))
+            raise Exception("Video returned but no data or URL was provided")
+        raise Exception("Video generation completed but no video was returned")
+
+
+class VeoExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            Veo3VideoGenerationNode,
+            Veo3FirstLastFrameNode,
+        ]
+
+
+async def comfy_entrypoint() -> VeoExtension:
+    return VeoExtension()
diff --git a/comfy_api_nodes/nodes_vidu.py b/comfy_api_nodes/nodes_vidu.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9967f446f26a444570342fe7e8e00e343699dcf
--- /dev/null
+++ b/comfy_api_nodes/nodes_vidu.py
@@ -0,0 +1,1726 @@
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.vidu import (
+    FrameSetting,
+    SubjectReference,
+    TaskCreationRequest,
+    TaskCreationResponse,
+    TaskExtendCreationRequest,
+    TaskMultiFrameCreationRequest,
+    TaskResult,
+    TaskStatusResponse,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    upload_image_to_comfyapi,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_image_aspect_ratio,
+    validate_image_dimensions,
+    validate_images_aspect_ratio_closeness,
+    validate_string,
+    validate_video_duration,
+)
+
+VIDU_TEXT_TO_VIDEO = "/proxy/vidu/text2video"
+VIDU_IMAGE_TO_VIDEO = "/proxy/vidu/img2video"
+VIDU_REFERENCE_VIDEO = "/proxy/vidu/reference2video"
+VIDU_START_END_VIDEO = "/proxy/vidu/start-end2video"
+VIDU_GET_GENERATION_STATUS = "/proxy/vidu/tasks/%s/creations"
+
+
+async def execute_task(
+    cls: type[IO.ComfyNode],
+    vidu_endpoint: str,
+    payload: TaskCreationRequest | TaskExtendCreationRequest | TaskMultiFrameCreationRequest,
+    max_poll_attempts: int = 480,
+) -> list[TaskResult]:
+    task_creation_response = await sync_op(
+        cls,
+        endpoint=ApiEndpoint(path=vidu_endpoint, method="POST"),
+        response_model=TaskCreationResponse,
+        data=payload,
+    )
+    if task_creation_response.state == "failed":
+        raise RuntimeError(f"Vidu request failed. Code: {task_creation_response.code}")
+    response = await poll_op(
+        cls,
+        ApiEndpoint(path=VIDU_GET_GENERATION_STATUS % task_creation_response.task_id),
+        response_model=TaskStatusResponse,
+        status_extractor=lambda r: r.state,
+        progress_extractor=lambda r: r.progress,
+        max_poll_attempts=max_poll_attempts,
+    )
+    if not response.creations:
+        raise RuntimeError(
+            f"Vidu request does not contain results. State: {response.state}, Error Code: {response.err_code}"
+        )
+    return response.creations
+
+
+class ViduTextToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ViduTextToVideoNode",
+            display_name="Vidu Text To Video Generation",
+            category="partner/video/Vidu",
+            description="Generate video from a text prompt",
+            inputs=[
+                IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A textual description for video generation",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=5,
+                    max=5,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Duration of the output video in seconds",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation (0 for random)",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["16:9", "9:16", "1:1"],
+                    tooltip="The aspect ratio of the output video",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["1080p"],
+                    tooltip="Supported values may vary by model & duration",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "movement_amplitude",
+                    options=["auto", "small", "medium", "large"],
+                    tooltip="The movement amplitude of objects in the frame",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        duration: int,
+        seed: int,
+        aspect_ratio: str,
+        resolution: str,
+        movement_amplitude: str,
+    ) -> IO.NodeOutput:
+        if not prompt:
+            raise ValueError("The prompt field is required and cannot be empty.")
+        payload = TaskCreationRequest(
+            model=model,
+            prompt=prompt,
+            duration=duration,
+            seed=seed,
+            aspect_ratio=aspect_ratio,
+            resolution=resolution,
+            movement_amplitude=movement_amplitude,
+        )
+        results = await execute_task(cls, VIDU_TEXT_TO_VIDEO, payload)
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class ViduImageToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ViduImageToVideoNode",
+            display_name="Vidu Image To Video Generation",
+            category="partner/video/Vidu",
+            description="Generate video from image and optional prompt",
+            inputs=[
+                IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
+                IO.Image.Input(
+                    "image",
+                    tooltip="An image to be used as the start frame of the generated video",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="A textual description for video generation",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=5,
+                    max=5,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Duration of the output video in seconds",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation (0 for random)",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["1080p"],
+                    tooltip="Supported values may vary by model & duration",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "movement_amplitude",
+                    options=["auto", "small", "medium", "large"],
+                    tooltip="The movement amplitude of objects in the frame",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        prompt: str,
+        duration: int,
+        seed: int,
+        resolution: str,
+        movement_amplitude: str,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) > 1:
+            raise ValueError("Only one input image is allowed.")
+        validate_image_aspect_ratio(image, (1, 4), (4, 1))
+        payload = TaskCreationRequest(
+            model=model,
+            prompt=prompt,
+            duration=duration,
+            seed=seed,
+            resolution=resolution,
+            movement_amplitude=movement_amplitude,
+        )
+        payload.images = await upload_images_to_comfyapi(
+            cls,
+            image,
+            max_images=1,
+            mime_type="image/png",
+        )
+        results = await execute_task(cls, VIDU_IMAGE_TO_VIDEO, payload)
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class ViduReferenceVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ViduReferenceVideoNode",
+            display_name="Vidu Reference To Video Generation",
+            category="partner/video/Vidu",
+            description="Generate video from multiple images and a prompt",
+            inputs=[
+                IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
+                IO.Image.Input(
+                    "images",
+                    tooltip="Images to use as references to generate a video with consistent subjects (max 7 images).",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A textual description for video generation",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=5,
+                    max=5,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Duration of the output video in seconds",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation (0 for random)",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["16:9", "9:16", "1:1"],
+                    tooltip="The aspect ratio of the output video",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["1080p"],
+                    tooltip="Supported values may vary by model & duration",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "movement_amplitude",
+                    options=["auto", "small", "medium", "large"],
+                    tooltip="The movement amplitude of objects in the frame",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        images: Input.Image,
+        prompt: str,
+        duration: int,
+        seed: int,
+        aspect_ratio: str,
+        resolution: str,
+        movement_amplitude: str,
+    ) -> IO.NodeOutput:
+        if not prompt:
+            raise ValueError("The prompt field is required and cannot be empty.")
+        a = get_number_of_images(images)
+        if a > 7:
+            raise ValueError("Too many images, maximum allowed is 7.")
+        for image in images:
+            validate_image_aspect_ratio(image, (1, 4), (4, 1))
+            validate_image_dimensions(image, min_width=128, min_height=128)
+        payload = TaskCreationRequest(
+            model=model,
+            prompt=prompt,
+            duration=duration,
+            seed=seed,
+            aspect_ratio=aspect_ratio,
+            resolution=resolution,
+            movement_amplitude=movement_amplitude,
+        )
+        payload.images = await upload_images_to_comfyapi(
+            cls,
+            images,
+            max_images=7,
+            mime_type="image/png",
+        )
+        results = await execute_task(cls, VIDU_REFERENCE_VIDEO, payload)
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class ViduStartEndToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ViduStartEndToVideoNode",
+            display_name="Vidu Start End To Video Generation",
+            category="partner/video/Vidu",
+            description="Generate a video from start and end frames and a prompt",
+            inputs=[
+                IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
+                IO.Image.Input(
+                    "first_frame",
+                    tooltip="Start frame",
+                ),
+                IO.Image.Input(
+                    "end_frame",
+                    tooltip="End frame",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A textual description for video generation",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=5,
+                    max=5,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Duration of the output video in seconds",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation (0 for random)",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["1080p"],
+                    tooltip="Supported values may vary by model & duration",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "movement_amplitude",
+                    options=["auto", "small", "medium", "large"],
+                    tooltip="The movement amplitude of objects in the frame",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        first_frame: Input.Image,
+        end_frame: Input.Image,
+        prompt: str,
+        duration: int,
+        seed: int,
+        resolution: str,
+        movement_amplitude: str,
+    ) -> IO.NodeOutput:
+        validate_images_aspect_ratio_closeness(first_frame, end_frame, min_rel=0.8, max_rel=1.25, strict=False)
+        payload = TaskCreationRequest(
+            model=model,
+            prompt=prompt,
+            duration=duration,
+            seed=seed,
+            resolution=resolution,
+            movement_amplitude=movement_amplitude,
+        )
+        payload.images = [
+            (await upload_images_to_comfyapi(cls, frame, max_images=1, mime_type="image/png"))[0]
+            for frame in (first_frame, end_frame)
+        ]
+        results = await execute_task(cls, VIDU_START_END_VIDEO, payload)
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class Vidu2TextToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Vidu2TextToVideoNode",
+            display_name="Vidu2 Text-to-Video Generation",
+            category="partner/video/Vidu",
+            description="Generate video from a text prompt",
+            inputs=[
+                IO.Combo.Input("model", options=["viduq2"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A textual description for video generation, with a maximum length of 2000 characters.",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=1,
+                    max=10,
+                    step=1,
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "3:4", "4:3", "1:1"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
+                IO.Boolean.Input(
+                    "background_music",
+                    default=False,
+                    tooltip="Whether to add background music to the generated video.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution"]),
+                expr="""
+                (
+                  $is1080 := widgets.resolution = "1080p";
+                  $base := $is1080 ? 0.1 : 0.075;
+                  $perSec := $is1080 ? 0.05 : 0.025;
+                  {"type":"usd","usd": $base + $perSec * (widgets.duration - 1)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        duration: int,
+        seed: int,
+        aspect_ratio: str,
+        resolution: str,
+        background_music: bool,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2000)
+        results = await execute_task(
+            cls,
+            VIDU_TEXT_TO_VIDEO,
+            TaskCreationRequest(
+                model=model,
+                prompt=prompt,
+                duration=duration,
+                seed=seed,
+                aspect_ratio=aspect_ratio,
+                resolution=resolution,
+                bgm=background_music,
+            ),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class Vidu2ImageToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Vidu2ImageToVideoNode",
+            display_name="Vidu2 Image-to-Video Generation",
+            category="partner/video/Vidu",
+            description="Generate a video from an image and an optional prompt.",
+            inputs=[
+                IO.Combo.Input("model", options=["viduq2-pro-fast", "viduq2-pro", "viduq2-turbo"]),
+                IO.Image.Input(
+                    "image",
+                    tooltip="An image to be used as the start frame of the generated video.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="An optional text prompt for video generation (max 2000 characters).",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=1,
+                    max=10,
+                    step=1,
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["720p", "1080p"],
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "movement_amplitude",
+                    options=["auto", "small", "medium", "large"],
+                    tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $d := widgets.duration;
+                  $is1080 := widgets.resolution = "1080p";
+                  $contains($m, "pro-fast")
+                    ? (
+                        $base := $is1080 ? 0.08 : 0.04;
+                        $perSec := $is1080 ? 0.02 : 0.01;
+                        {"type":"usd","usd": $base + $perSec * ($d - 1)}
+                      )
+                    : $contains($m, "pro")
+                      ? (
+                          $base := $is1080 ? 0.275 : 0.075;
+                          $perSec := $is1080 ? 0.075 : 0.05;
+                          {"type":"usd","usd": $base + $perSec * ($d - 1)}
+                        )
+                      : $contains($m, "turbo")
+                        ? (
+                            $is1080
+                              ? {"type":"usd","usd": 0.175 + 0.05 * ($d - 1)}
+                              : (
+                                  $d <= 1 ? {"type":"usd","usd": 0.04}
+                                  : $d <= 2 ? {"type":"usd","usd": 0.05}
+                                  : {"type":"usd","usd": 0.05 + 0.05 * ($d - 2)}
+                                )
+                          )
+                        : {"type":"usd","usd": 0.04}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        prompt: str,
+        duration: int,
+        seed: int,
+        resolution: str,
+        movement_amplitude: str,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) > 1:
+            raise ValueError("Only one input image is allowed.")
+        validate_image_aspect_ratio(image, (1, 4), (4, 1))
+        validate_string(prompt, max_length=2000)
+        results = await execute_task(
+            cls,
+            VIDU_IMAGE_TO_VIDEO,
+            TaskCreationRequest(
+                model=model,
+                prompt=prompt,
+                duration=duration,
+                seed=seed,
+                resolution=resolution,
+                movement_amplitude=movement_amplitude,
+                images=await upload_images_to_comfyapi(
+                    cls,
+                    image,
+                    max_images=1,
+                    mime_type="image/png",
+                ),
+            ),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class Vidu2ReferenceVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Vidu2ReferenceVideoNode",
+            display_name="Vidu2 Reference-to-Video Generation",
+            category="partner/video/Vidu",
+            description="Generate a video from multiple reference images and a prompt.",
+            inputs=[
+                IO.Combo.Input("model", options=["viduq2"]),
+                IO.Autogrow.Input(
+                    "subjects",
+                    template=IO.Autogrow.TemplateNames(
+                        IO.Image.Input("reference_images"),
+                        names=["subject1", "subject2", "subject3", "subject4", "subject5", "subject6", "subject7"],
+                        min=1,
+                    ),
+                    tooltip="For each subject, provide up to 3 reference images (7 images total across all subjects). "
+                    "Reference them in prompts via @subject{subject_id}.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="When enabled, the video will include generated speech and background music "
+                    "based on the prompt.",
+                ),
+                IO.Boolean.Input(
+                    "audio",
+                    default=False,
+                    tooltip="When enabled video will contain generated speech and background music based on the prompt.",
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=1,
+                    max=10,
+                    step=1,
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "4:3", "3:4", "1:1"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
+                IO.Combo.Input(
+                    "movement_amplitude",
+                    options=["auto", "small", "medium", "large"],
+                    tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["audio", "duration", "resolution"]),
+                expr="""
+                (
+                  $is1080 := widgets.resolution = "1080p";
+                  $base := $is1080 ? 0.375 : 0.125;
+                  $perSec := $is1080 ? 0.05 : 0.025;
+                  $audioCost := widgets.audio = true ? 0.075 : 0;
+                  {"type":"usd","usd": $base + $perSec * (widgets.duration - 1) + $audioCost}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        subjects: IO.Autogrow.Type,
+        prompt: str,
+        audio: bool,
+        duration: int,
+        seed: int,
+        aspect_ratio: str,
+        resolution: str,
+        movement_amplitude: str,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2000)
+        total_images = 0
+        for i in subjects:
+            if get_number_of_images(subjects[i]) > 3:
+                raise ValueError("Maximum number of images per subject is 3.")
+            for im in subjects[i]:
+                total_images += 1
+                validate_image_aspect_ratio(im, (1, 4), (4, 1))
+                validate_image_dimensions(im, min_width=128, min_height=128)
+        if total_images > 7:
+            raise ValueError("Too many reference images; the maximum allowed is 7.")
+        subjects_param: list[SubjectReference] = []
+        for i in subjects:
+            subjects_param.append(
+                SubjectReference(
+                    id=i,
+                    images=await upload_images_to_comfyapi(
+                        cls,
+                        subjects[i],
+                        max_images=3,
+                        mime_type="image/png",
+                        wait_label=f"Uploading reference images for {i}",
+                    ),
+                ),
+            )
+        payload = TaskCreationRequest(
+            model=model,
+            prompt=prompt,
+            audio=audio,
+            duration=duration,
+            seed=seed,
+            aspect_ratio=aspect_ratio,
+            resolution=resolution,
+            movement_amplitude=movement_amplitude,
+            subjects=subjects_param,
+        )
+        results = await execute_task(cls, VIDU_REFERENCE_VIDEO, payload)
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class Vidu2StartEndToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Vidu2StartEndToVideoNode",
+            display_name="Vidu2 Start/End Frame-to-Video Generation",
+            category="partner/video/Vidu",
+            description="Generate a video from a start frame, an end frame, and a prompt.",
+            inputs=[
+                IO.Combo.Input("model", options=["viduq2-pro-fast", "viduq2-pro", "viduq2-turbo"]),
+                IO.Image.Input("first_frame"),
+                IO.Image.Input("end_frame"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Prompt description (max 2000 characters).",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=2,
+                    max=8,
+                    step=1,
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
+                IO.Combo.Input(
+                    "movement_amplitude",
+                    options=["auto", "small", "medium", "large"],
+                    tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $d := widgets.duration;
+                  $is1080 := widgets.resolution = "1080p";
+                  $contains($m, "pro-fast")
+                    ? (
+                        $base := $is1080 ? 0.08 : 0.04;
+                        $perSec := $is1080 ? 0.02 : 0.01;
+                        {"type":"usd","usd": $base + $perSec * ($d - 1)}
+                      )
+                    : $contains($m, "pro")
+                      ? (
+                          $base := $is1080 ? 0.275 : 0.075;
+                          $perSec := $is1080 ? 0.075 : 0.05;
+                          {"type":"usd","usd": $base + $perSec * ($d - 1)}
+                        )
+                      : $contains($m, "turbo")
+                        ? (
+                            $is1080
+                              ? {"type":"usd","usd": 0.175 + 0.05 * ($d - 1)}
+                              : (
+                                  $d <= 2 ? {"type":"usd","usd": 0.05}
+                                  : {"type":"usd","usd": 0.05 + 0.05 * ($d - 2)}
+                                )
+                          )
+                        : {"type":"usd","usd": 0.04}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        first_frame: Input.Image,
+        end_frame: Input.Image,
+        prompt: str,
+        duration: int,
+        seed: int,
+        resolution: str,
+        movement_amplitude: str,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, max_length=2000)
+        if get_number_of_images(first_frame) > 1:
+            raise ValueError("Only one input image is allowed for `first_frame`.")
+        if get_number_of_images(end_frame) > 1:
+            raise ValueError("Only one input image is allowed for `end_frame`.")
+        validate_images_aspect_ratio_closeness(first_frame, end_frame, min_rel=0.8, max_rel=1.25, strict=False)
+        payload = TaskCreationRequest(
+            model=model,
+            prompt=prompt,
+            duration=duration,
+            seed=seed,
+            resolution=resolution,
+            movement_amplitude=movement_amplitude,
+            images=[
+                (await upload_images_to_comfyapi(cls, frame, max_images=1, mime_type="image/png"))[0]
+                for frame in (first_frame, end_frame)
+            ],
+        )
+        results = await execute_task(cls, VIDU_START_END_VIDEO, payload)
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class ViduExtendVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ViduExtendVideoNode",
+            display_name="Vidu Video Extension",
+            category="partner/video/Vidu",
+            description="Extend an existing video by generating additional frames.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "viduq2-pro",
+                            [
+                                IO.Int.Input(
+                                    "duration",
+                                    default=4,
+                                    min=1,
+                                    max=7,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the extended video in seconds.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "viduq2-turbo",
+                            [
+                                IO.Int.Input(
+                                    "duration",
+                                    default=4,
+                                    min=1,
+                                    max=7,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the extended video in seconds.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for video extension.",
+                ),
+                IO.Video.Input(
+                    "video",
+                    tooltip="The source video to extend.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="An optional text prompt for the extended video (max 2000 characters).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.Image.Input("end_frame", optional=True),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $d := $lookup(widgets, "model.duration");
+                  $res := $lookup(widgets, "model.resolution");
+                  $contains($m, "pro")
+                    ? (
+                        $base := $lookup({"720p": 0.15, "1080p": 0.3}, $res);
+                        $perSec := $lookup({"720p": 0.05, "1080p": 0.075}, $res);
+                        {"type":"usd","usd": $base + $perSec * ($d - 1)}
+                      )
+                    : (
+                        $base := $lookup({"720p": 0.075, "1080p": 0.2}, $res);
+                        $perSec := $lookup({"720p": 0.025, "1080p": 0.05}, $res);
+                        {"type":"usd","usd": $base + $perSec * ($d - 1)}
+                      )
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        video: Input.Video,
+        prompt: str,
+        seed: int,
+        end_frame: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, max_length=2000)
+        validate_video_duration(video, min_duration=4, max_duration=55)
+        image_url = None
+        if end_frame is not None:
+            validate_image_aspect_ratio(end_frame, (1, 4), (4, 1))
+            validate_image_dimensions(end_frame, min_width=128, min_height=128)
+            image_url = await upload_image_to_comfyapi(cls, end_frame, wait_label="Uploading end frame")
+        results = await execute_task(
+            cls,
+            "/proxy/vidu/extend",
+            TaskExtendCreationRequest(
+                model=model["model"],
+                prompt=prompt,
+                duration=model["duration"],
+                seed=seed,
+                resolution=model["resolution"],
+                video_url=await upload_video_to_comfyapi(cls, video, wait_label="Uploading video"),
+                images=[image_url] if image_url else None,
+            ),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+def _generate_frame_inputs(count: int) -> list:
+    """Generate input widgets for a given number of frames."""
+    inputs = []
+    for i in range(1, count + 1):
+        inputs.extend(
+            [
+                IO.String.Input(
+                    f"prompt{i}",
+                    multiline=True,
+                    default="",
+                    tooltip=f"Text prompt for frame {i} transition.",
+                ),
+                IO.Image.Input(
+                    f"end_image{i}",
+                    tooltip=f"End frame image for segment {i}. Aspect ratio must be between 1:4 and 4:1.",
+                ),
+                IO.Int.Input(
+                    f"duration{i}",
+                    default=4,
+                    min=2,
+                    max=7,
+                    step=1,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip=f"Duration for segment {i} in seconds.",
+                ),
+            ]
+        )
+    return inputs
+
+
+class ViduMultiFrameVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ViduMultiFrameVideoNode",
+            display_name="Vidu Multi-Frame Video Generation",
+            category="partner/video/Vidu",
+            description="Generate a video with multiple keyframe transitions.",
+            inputs=[
+                IO.Combo.Input("model", options=["viduq2-pro", "viduq2-turbo"]),
+                IO.Image.Input(
+                    "start_image",
+                    tooltip="The starting frame image. Aspect ratio must be between 1:4 and 4:1.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.DynamicCombo.Input(
+                    "frames",
+                    options=[
+                        IO.DynamicCombo.Option("2", _generate_frame_inputs(2)),
+                        IO.DynamicCombo.Option("3", _generate_frame_inputs(3)),
+                        IO.DynamicCombo.Option("4", _generate_frame_inputs(4)),
+                        IO.DynamicCombo.Option("5", _generate_frame_inputs(5)),
+                        IO.DynamicCombo.Option("6", _generate_frame_inputs(6)),
+                        IO.DynamicCombo.Option("7", _generate_frame_inputs(7)),
+                        IO.DynamicCombo.Option("8", _generate_frame_inputs(8)),
+                        IO.DynamicCombo.Option("9", _generate_frame_inputs(9)),
+                    ],
+                    tooltip="Number of keyframe transitions (2-9).",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=[
+                        "model",
+                        "resolution",
+                        "frames",
+                        "frames.duration1",
+                        "frames.duration2",
+                        "frames.duration3",
+                        "frames.duration4",
+                        "frames.duration5",
+                        "frames.duration6",
+                        "frames.duration7",
+                        "frames.duration8",
+                        "frames.duration9",
+                    ]
+                ),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $n := $number(widgets.frames);
+                  $is1080 := widgets.resolution = "1080p";
+                  $d1 := $lookup(widgets, "frames.duration1");
+                  $d2 := $lookup(widgets, "frames.duration2");
+                  $d3 := $n >= 3 ? $lookup(widgets, "frames.duration3") : 0;
+                  $d4 := $n >= 4 ? $lookup(widgets, "frames.duration4") : 0;
+                  $d5 := $n >= 5 ? $lookup(widgets, "frames.duration5") : 0;
+                  $d6 := $n >= 6 ? $lookup(widgets, "frames.duration6") : 0;
+                  $d7 := $n >= 7 ? $lookup(widgets, "frames.duration7") : 0;
+                  $d8 := $n >= 8 ? $lookup(widgets, "frames.duration8") : 0;
+                  $d9 := $n >= 9 ? $lookup(widgets, "frames.duration9") : 0;
+                  $totalDuration := $d1 + $d2 + $d3 + $d4 + $d5 + $d6 + $d7 + $d8 + $d9;
+                  $contains($m, "pro")
+                    ? (
+                        $base := $is1080 ? 0.3 : 0.15;
+                        $perSec := $is1080 ? 0.075 : 0.05;
+                        {"type":"usd","usd": $n * $base + $perSec * $totalDuration}
+                      )
+                    : (
+                        $base := $is1080 ? 0.2 : 0.075;
+                        $perSec := $is1080 ? 0.05 : 0.025;
+                        {"type":"usd","usd": $n * $base + $perSec * $totalDuration}
+                      )
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        start_image: Input.Image,
+        seed: int,
+        resolution: str,
+        frames: dict,
+    ) -> IO.NodeOutput:
+        validate_image_aspect_ratio(start_image, (1, 4), (4, 1))
+        frame_count = int(frames["frames"])
+        image_settings: list[FrameSetting] = []
+        for i in range(1, frame_count + 1):
+            validate_image_aspect_ratio(frames[f"end_image{i}"], (1, 4), (4, 1))
+            validate_string(frames[f"prompt{i}"], max_length=2000)
+        start_image_url = await upload_image_to_comfyapi(
+            cls,
+            start_image,
+            mime_type="image/png",
+            wait_label="Uploading start image",
+        )
+        for i in range(1, frame_count + 1):
+            image_settings.append(
+                FrameSetting(
+                    prompt=frames[f"prompt{i}"],
+                    key_image=await upload_image_to_comfyapi(
+                        cls,
+                        frames[f"end_image{i}"],
+                        mime_type="image/png",
+                        wait_label=f"Uploading end image({i})",
+                    ),
+                    duration=frames[f"duration{i}"],
+                )
+            )
+        results = await execute_task(
+            cls,
+            "/proxy/vidu/multiframe",
+            TaskMultiFrameCreationRequest(
+                model=model,
+                seed=seed,
+                resolution=resolution,
+                start_image=start_image_url,
+                image_settings=image_settings,
+            ),
+            max_poll_attempts=480 * frame_count,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class Vidu3TextToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Vidu3TextToVideoNode",
+            display_name="Vidu Q3 Text-to-Video Generation",
+            category="partner/video/Vidu",
+            description="Generate video from a text prompt.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "viduq3-pro",
+                            [
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=["16:9", "9:16", "3:4", "4:3", "1:1"],
+                                    tooltip="The aspect ratio of the output video.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=1,
+                                    max=16,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the output video in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=False,
+                                    tooltip="When enabled, outputs video with sound "
+                                    "(including dialogue and sound effects).",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "viduq3-turbo",
+                            [
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=["16:9", "9:16", "3:4", "4:3", "1:1"],
+                                    tooltip="The aspect ratio of the output video.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=1,
+                                    max=16,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the output video in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=False,
+                                    tooltip="When enabled, outputs video with sound "
+                                    "(including dialogue and sound effects).",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for video generation.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A textual description for video generation, with a maximum length of 2000 characters.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $d := $lookup(widgets, "model.duration");
+                  $contains(widgets.model, "turbo")
+                    ? (
+                        $rate := $lookup({"720p": 0.06, "1080p": 0.08}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
+                    : (
+                        $rate := $lookup({"720p": 0.15, "1080p": 0.16}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        prompt: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2000)
+        results = await execute_task(
+            cls,
+            VIDU_TEXT_TO_VIDEO,
+            TaskCreationRequest(
+                model=model["model"],
+                prompt=prompt,
+                duration=model["duration"],
+                seed=seed,
+                aspect_ratio=model["aspect_ratio"],
+                resolution=model["resolution"],
+                audio=model["audio"],
+            ),
+            max_poll_attempts=640,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class Vidu3ImageToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Vidu3ImageToVideoNode",
+            display_name="Vidu Q3 Image-to-Video Generation",
+            category="partner/video/Vidu",
+            description="Generate a video from an image and an optional prompt.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "viduq3-pro",
+                            [
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p", "2K"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=1,
+                                    max=16,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the output video in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=False,
+                                    tooltip="When enabled, outputs video with sound "
+                                    "(including dialogue and sound effects).",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "viduq3-turbo",
+                            [
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=1,
+                                    max=16,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the output video in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=False,
+                                    tooltip="When enabled, outputs video with sound "
+                                    "(including dialogue and sound effects).",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for video generation.",
+                ),
+                IO.Image.Input(
+                    "image",
+                    tooltip="An image to be used as the start frame of the generated video.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="An optional text prompt for video generation (max 2000 characters).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $d := $lookup(widgets, "model.duration");
+                  $contains(widgets.model, "turbo")
+                    ? (
+                        $rate := $lookup({"720p": 0.06, "1080p": 0.08}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
+                    : (
+                        $rate := $lookup({"720p": 0.15, "1080p": 0.16, "2k": 0.2}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        image: Input.Image,
+        prompt: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_image_aspect_ratio(image, (1, 4), (4, 1))
+        validate_string(prompt, max_length=2000)
+        results = await execute_task(
+            cls,
+            VIDU_IMAGE_TO_VIDEO,
+            TaskCreationRequest(
+                model=model["model"],
+                prompt=prompt,
+                duration=model["duration"],
+                seed=seed,
+                resolution=model["resolution"],
+                audio=model["audio"],
+                images=[await upload_image_to_comfyapi(cls, image)],
+            ),
+            max_poll_attempts=720,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class Vidu3StartEndToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Vidu3StartEndToVideoNode",
+            display_name="Vidu Q3 Start/End Frame-to-Video Generation",
+            category="partner/video/Vidu",
+            description="Generate a video from a start frame, an end frame, and a prompt.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "viduq3-pro",
+                            [
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=1,
+                                    max=16,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the output video in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=False,
+                                    tooltip="When enabled, outputs video with sound "
+                                    "(including dialogue and sound effects).",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "viduq3-turbo",
+                            [
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720p", "1080p"],
+                                    tooltip="Resolution of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=1,
+                                    max=16,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of the output video in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=False,
+                                    tooltip="When enabled, outputs video with sound "
+                                    "(including dialogue and sound effects).",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for video generation.",
+                ),
+                IO.Image.Input("first_frame"),
+                IO.Image.Input("end_frame"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Prompt description (max 2000 characters).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $d := $lookup(widgets, "model.duration");
+                  $contains(widgets.model, "turbo")
+                    ? (
+                        $rate := $lookup({"720p": 0.06, "1080p": 0.08}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
+                    : (
+                        $rate := $lookup({"720p": 0.15, "1080p": 0.16}, $res);
+                        {"type":"usd","usd": $rate * $d}
+                      )
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        first_frame: Input.Image,
+        end_frame: Input.Image,
+        prompt: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, max_length=2000)
+        validate_images_aspect_ratio_closeness(first_frame, end_frame, min_rel=0.8, max_rel=1.25, strict=False)
+        payload = TaskCreationRequest(
+            model=model["model"],
+            prompt=prompt,
+            duration=model["duration"],
+            seed=seed,
+            resolution=model["resolution"],
+            audio=model["audio"],
+            images=[
+                (await upload_images_to_comfyapi(cls, frame, max_images=1, mime_type="image/png"))[0]
+                for frame in (first_frame, end_frame)
+            ],
+        )
+        results = await execute_task(cls, VIDU_START_END_VIDEO, payload)
+        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
+
+
+class ViduExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            ViduTextToVideoNode,
+            ViduImageToVideoNode,
+            ViduReferenceVideoNode,
+            ViduStartEndToVideoNode,
+            Vidu2TextToVideoNode,
+            Vidu2ImageToVideoNode,
+            Vidu2ReferenceVideoNode,
+            Vidu2StartEndToVideoNode,
+            ViduExtendVideoNode,
+            ViduMultiFrameVideoNode,
+            Vidu3TextToVideoNode,
+            Vidu3ImageToVideoNode,
+            Vidu3StartEndToVideoNode,
+        ]
+
+
+async def comfy_entrypoint() -> ViduExtension:
+    return ViduExtension()
diff --git a/comfy_api_nodes/nodes_wan.py b/comfy_api_nodes/nodes_wan.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7fc1c1eb2fef59b39ba3b6e07ab6017b0441865
--- /dev/null
+++ b/comfy_api_nodes/nodes_wan.py
@@ -0,0 +1,2880 @@
+import re
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.wan import (
+    Image2ImageInputField,
+    Image2ImageParametersField,
+    Image2ImageTaskCreationRequest,
+    Image2VideoInputField,
+    Image2VideoParametersField,
+    Image2VideoTaskCreationRequest,
+    ImageTaskStatusResponse,
+    Reference2VideoInputField,
+    Reference2VideoParametersField,
+    Reference2VideoTaskCreationRequest,
+    TaskCreationResponse,
+    Text2ImageInputField,
+    Text2ImageTaskCreationRequest,
+    Text2VideoInputField,
+    Text2VideoParametersField,
+    Text2VideoTaskCreationRequest,
+    Txt2ImageParametersField,
+    VideoTaskStatusResponse,
+    Wan27ImageToVideoInputField,
+    Wan27ImageToVideoParametersField,
+    Wan27ImageToVideoTaskCreationRequest,
+    Wan27MediaItem,
+    Wan27ReferenceVideoInputField,
+    Wan27ReferenceVideoParametersField,
+    Wan27ReferenceVideoTaskCreationRequest,
+    Wan27Text2VideoParametersField,
+    Wan27Text2VideoTaskCreationRequest,
+    Wan27VideoEditInputField,
+    Wan27VideoEditParametersField,
+    Wan27VideoEditTaskCreationRequest,
+    Wan3InputField,
+    Wan3MediaItem,
+    Wan3ParametersField,
+    Wan3TaskCreationRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_to_base64_string,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    tensor_to_base64_string,
+    upload_audio_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_audio_duration,
+    validate_image_aspect_ratio,
+    validate_image_dimensions,
+    validate_string,
+    validate_video_duration,
+)
+from comfy_api_nodes.util.client import FAILED_STATUSES, QUEUED_STATUSES
+
+
+RES_IN_PARENS = re.compile(r"\((\d+)\s*[x×]\s*(\d+)\)")
+
+WAN3_QUEUED_STATUSES = [*QUEUED_STATUSES, "pending"]
+WAN3_FAILED_STATUSES = [*FAILED_STATUSES, "unknown"]
+
+_WAN3_REF_TAG_RE = re.compile(r"@(image|video|audio)(?P<idx>\d*)(?!\w)", re.IGNORECASE | re.ASCII)
+
+
+def _wan3_rewrite_reference_prompt(prompt: str, counts: dict[str, int]) -> str:
+    parts = []
+    pos = 0
+    prev_end = -1
+    for match in _WAN3_REF_TAG_RE.finditer(prompt):
+        start = match.start()
+        before = prompt[start - 1] if start > 0 else ""
+        if (before.isascii() and (before.isalnum() or before == "_")) and start != prev_end:
+            continue
+        kind = match.group(1).lower()
+        idx = int(match.group("idx") or 1)
+        total = counts[kind]
+        if not 1 <= idx <= total:
+            raise ValueError(
+                f"The prompt references @{kind.capitalize()}{idx}, "
+                f"but only {total} reference {kind} inputs are connected."
+            )
+        parts.append(prompt[pos:start])
+        parts.append(f"{kind.capitalize()} {idx}")
+        pos = match.end()
+        prev_end = match.end()
+    parts.append(prompt[pos:])
+    return "".join(parts)
+
+
+class WanTextToImageApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WanTextToImageApi",
+            display_name="Wan Text to Image",
+            category="partner/image/Wan",
+            description="Generates an image based on a text prompt.",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["wan2.5-t2i-preview"],
+                    tooltip="Model to use.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt describing the elements and visual features. Supports English and Chinese.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative prompt describing what to avoid.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "width",
+                    default=1024,
+                    min=768,
+                    max=1440,
+                    step=32,
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "height",
+                    default=1024,
+                    min=768,
+                    max=1440,
+                    step=32,
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.03}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        negative_prompt: str = "",
+        width: int = 1024,
+        height: int = 1024,
+        seed: int = 0,
+        prompt_extend: bool = True,
+        watermark: bool = False,
+    ):
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/text2image/image-synthesis", method="POST"),
+            response_model=TaskCreationResponse,
+            data=Text2ImageTaskCreationRequest(
+                model=model,
+                input=Text2ImageInputField(prompt=prompt, negative_prompt=negative_prompt),
+                parameters=Txt2ImageParametersField(
+                    size=f"{width}*{height}",
+                    seed=seed,
+                    prompt_extend=prompt_extend,
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=ImageTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            estimated_duration=9,
+            poll_interval=3,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(str(response.output.results[0].url)))
+
+
+class WanImageToImageApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WanImageToImageApi",
+            display_name="Wan Image to Image",
+            category="partner/image/Wan",
+            description="Generates an image from one or two input images and a text prompt. "
+            "The output image is currently fixed at 1.6 MP, and its aspect ratio matches the input image(s).",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["wan2.5-i2i-preview"],
+                    default="wan2.5-i2i-preview",
+                    tooltip="Model to use.",
+                ),
+                IO.Image.Input(
+                    "image",
+                    tooltip="Single-image editing or multi-image fusion. Maximum 2 images.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt describing the elements and visual features. Supports English and Chinese.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative prompt describing what to avoid.",
+                    optional=True,
+                ),
+                # redo this later as an optional combo of recommended resolutions
+                # IO.Int.Input(
+                #     "width",
+                #     default=1280,
+                #     min=384,
+                #     max=1440,
+                #     step=16,
+                #     optional=True,
+                # ),
+                # IO.Int.Input(
+                #     "height",
+                #     default=1280,
+                #     min=384,
+                #     max=1440,
+                #     step=16,
+                #     optional=True,
+                # ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.03}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        prompt: str,
+        negative_prompt: str = "",
+        # width: int = 1024,
+        # height: int = 1024,
+        seed: int = 0,
+        watermark: bool = False,
+    ):
+        n_images = get_number_of_images(image)
+        if n_images not in (1, 2):
+            raise ValueError(f"Expected 1 or 2 input images, but got {n_images}.")
+        images = []
+        for i in image:
+            images.append("data:image/png;base64," + tensor_to_base64_string(i, total_pixels=4096 * 4096))
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/image2image/image-synthesis", method="POST"),
+            response_model=TaskCreationResponse,
+            data=Image2ImageTaskCreationRequest(
+                model=model,
+                input=Image2ImageInputField(prompt=prompt, negative_prompt=negative_prompt, images=images),
+                parameters=Image2ImageParametersField(
+                    # size=f"{width}*{height}",
+                    seed=seed,
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=ImageTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            estimated_duration=42,
+            poll_interval=4,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(str(response.output.results[0].url)))
+
+
+class WanTextToVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WanTextToVideoApi",
+            display_name="Wan Text to Video",
+            category="partner/video/Wan",
+            description="Generates a video based on a text prompt.",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["wan2.5-t2v-preview", "wan2.6-t2v"],
+                    default="wan2.6-t2v",
+                    tooltip="Model to use.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt describing the elements and visual features. Supports English and Chinese.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative prompt describing what to avoid.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "size",
+                    options=[
+                        "480p: 1:1 (624x624)",
+                        "480p: 16:9 (832x480)",
+                        "480p: 9:16 (480x832)",
+                        "720p: 1:1 (960x960)",
+                        "720p: 16:9 (1280x720)",
+                        "720p: 9:16 (720x1280)",
+                        "720p: 4:3 (1088x832)",
+                        "720p: 3:4 (832x1088)",
+                        "1080p: 1:1 (1440x1440)",
+                        "1080p: 16:9 (1920x1080)",
+                        "1080p: 9:16 (1080x1920)",
+                        "1080p: 4:3 (1632x1248)",
+                        "1080p: 3:4 (1248x1632)",
+                    ],
+                    default="720p: 1:1 (960x960)",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=5,
+                    max=15,
+                    step=5,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="A 15-second duration is available only for the Wan 2.6 model.",
+                    optional=True,
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    optional=True,
+                    tooltip="Audio must contain a clear, loud voice, without extraneous noise or background music.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="If no audio input is provided, generate audio automatically.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "shot_type",
+                    options=["single", "multi"],
+                    tooltip="Specifies the shot type for the generated video, that is, whether the video is a "
+                    "single continuous shot or multiple shots with cuts. "
+                    "This parameter takes effect only when prompt_extend is True.",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration", "size"]),
+                expr="""
+                (
+                  $ppsTable := { "480p": 0.05, "720p": 0.1, "1080p": 0.15 };
+                  $resKey := $substringBefore(widgets.size, ":");
+                  $pps := $lookup($ppsTable, $resKey);
+                  { "type": "usd", "usd": $round($pps * widgets.duration, 2) }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        negative_prompt: str = "",
+        size: str = "720p: 1:1 (960x960)",
+        duration: int = 5,
+        audio: Input.Audio | None = None,
+        seed: int = 0,
+        generate_audio: bool = False,
+        prompt_extend: bool = True,
+        watermark: bool = False,
+        shot_type: str = "single",
+    ):
+        if "480p" in size and model == "wan2.6-t2v":
+            raise ValueError("The Wan 2.6 model does not support 480p.")
+        if duration == 15 and model == "wan2.5-t2v-preview":
+            raise ValueError("A 15-second duration is supported only by the Wan 2.6 model.")
+        width, height = RES_IN_PARENS.search(size).groups()
+        audio_url = None
+        if audio is not None:
+            validate_audio_duration(audio, 3.0, 29.0)
+            audio_url = "data:audio/mp3;base64," + audio_to_base64_string(audio, "mp3", "libmp3lame")
+
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", method="POST"),
+            response_model=TaskCreationResponse,
+            data=Text2VideoTaskCreationRequest(
+                model=model,
+                input=Text2VideoInputField(prompt=prompt, negative_prompt=negative_prompt, audio_url=audio_url),
+                parameters=Text2VideoParametersField(
+                    size=f"{width}*{height}",
+                    duration=duration,
+                    seed=seed,
+                    audio=generate_audio,
+                    prompt_extend=prompt_extend,
+                    watermark=watermark,
+                    shot_type=shot_type,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            estimated_duration=120 * int(duration / 5),
+            poll_interval=6,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class WanImageToVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WanImageToVideoApi",
+            display_name="Wan Image to Video",
+            category="partner/video/Wan",
+            description="Generates a video from the first frame and a text prompt.",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["wan2.5-i2v-preview", "wan2.6-i2v"],
+                    default="wan2.6-i2v",
+                    tooltip="Model to use.",
+                ),
+                IO.Image.Input(
+                    "image",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt describing the elements and visual features. Supports English and Chinese.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative prompt describing what to avoid.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        "480P",
+                        "720P",
+                        "1080P",
+                    ],
+                    default="720P",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=5,
+                    max=15,
+                    step=5,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Duration 15 available only for WAN2.6 model.",
+                    optional=True,
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    optional=True,
+                    tooltip="Audio must contain a clear, loud voice, without extraneous noise or background music.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="If no audio input is provided, generate audio automatically.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    optional=True,
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "shot_type",
+                    options=["single", "multi"],
+                    tooltip="Specifies the shot type for the generated video, that is, whether the video is a "
+                    "single continuous shot or multiple shots with cuts. "
+                    "This parameter takes effect only when prompt_extend is True.",
+                    optional=True,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution"]),
+                expr="""
+                (
+                  $ppsTable := { "480p": 0.05, "720p": 0.1, "1080p": 0.15 };
+                  $pps := $lookup($ppsTable, widgets.resolution);
+                  { "type": "usd", "usd": $round($pps * widgets.duration, 2) }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        prompt: str,
+        negative_prompt: str = "",
+        resolution: str = "720P",
+        duration: int = 5,
+        audio: Input.Audio | None = None,
+        seed: int = 0,
+        generate_audio: bool = False,
+        prompt_extend: bool = True,
+        watermark: bool = False,
+        shot_type: str = "single",
+    ):
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        if "480P" in resolution and model == "wan2.6-i2v":
+            raise ValueError("The Wan 2.6 model does not support 480P.")
+        if duration == 15 and model == "wan2.5-i2v-preview":
+            raise ValueError("A 15-second duration is supported only by the Wan 2.6 model.")
+        image_url = "data:image/png;base64," + tensor_to_base64_string(image, total_pixels=2000 * 2000)
+        audio_url = None
+        if audio is not None:
+            validate_audio_duration(audio, 3.0, 29.0)
+            audio_url = "data:audio/mp3;base64," + audio_to_base64_string(audio, "mp3", "libmp3lame")
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", method="POST"),
+            response_model=TaskCreationResponse,
+            data=Image2VideoTaskCreationRequest(
+                model=model,
+                input=Image2VideoInputField(
+                    prompt=prompt, negative_prompt=negative_prompt, img_url=image_url, audio_url=audio_url
+                ),
+                parameters=Image2VideoParametersField(
+                    resolution=resolution,
+                    duration=duration,
+                    seed=seed,
+                    audio=generate_audio,
+                    prompt_extend=prompt_extend,
+                    watermark=watermark,
+                    shot_type=shot_type,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            estimated_duration=120 * int(duration / 5),
+            poll_interval=6,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class WanReferenceVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WanReferenceVideoApi",
+            display_name="Wan Reference to Video",
+            category="partner/video/Wan",
+            description="Use the character and voice from input videos, combined with a prompt, "
+            "to generate a new video that maintains character consistency.",
+            inputs=[
+                IO.Combo.Input("model", options=["wan2.6-r2v"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt describing the elements and visual features. Supports English and Chinese. "
+                    "Use identifiers such as `character1` and `character2` to refer to the reference characters.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative prompt describing what to avoid.",
+                ),
+                IO.Autogrow.Input(
+                    "reference_videos",
+                    template=IO.Autogrow.TemplateNames(
+                        IO.Video.Input("reference_video"),
+                        names=["character1", "character2", "character3"],
+                        min=1,
+                    ),
+                ),
+                IO.Combo.Input(
+                    "size",
+                    options=[
+                        "720p: 1:1 (960x960)",
+                        "720p: 16:9 (1280x720)",
+                        "720p: 9:16 (720x1280)",
+                        "720p: 4:3 (1088x832)",
+                        "720p: 3:4 (832x1088)",
+                        "1080p: 1:1 (1440x1440)",
+                        "1080p: 16:9 (1920x1080)",
+                        "1080p: 9:16 (1080x1920)",
+                        "1080p: 4:3 (1632x1248)",
+                        "1080p: 3:4 (1248x1632)",
+                    ],
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=5,
+                    max=10,
+                    step=5,
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.Combo.Input(
+                    "shot_type",
+                    options=["single", "multi"],
+                    tooltip="Specifies the shot type for the generated video, that is, whether the video is a "
+                    "single continuous shot or multiple shots with cuts.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["size", "duration"]),
+                expr="""
+                (
+                  $rate := $contains(widgets.size, "1080p") ? 0.15 : 0.10;
+                  $inputMin := 2 * $rate;
+                  $inputMax := 5 * $rate;
+                  $outputPrice := widgets.duration * $rate;
+                  {
+                    "type": "range_usd",
+                    "min_usd": $inputMin + $outputPrice,
+                    "max_usd": $inputMax + $outputPrice
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        negative_prompt: str,
+        reference_videos: IO.Autogrow.Type,
+        size: str,
+        duration: int,
+        seed: int,
+        shot_type: str,
+        watermark: bool,
+    ):
+        reference_video_urls = []
+        for i in reference_videos:
+            validate_video_duration(reference_videos[i], min_duration=2, max_duration=30)
+        for i in reference_videos:
+            reference_video_urls.append(await upload_video_to_comfyapi(cls, reference_videos[i]))
+        width, height = RES_IN_PARENS.search(size).groups()
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", method="POST"),
+            response_model=TaskCreationResponse,
+            data=Reference2VideoTaskCreationRequest(
+                model=model,
+                input=Reference2VideoInputField(
+                    prompt=prompt, negative_prompt=negative_prompt, reference_video_urls=reference_video_urls
+                ),
+                parameters=Reference2VideoParametersField(
+                    size=f"{width}*{height}",
+                    duration=duration,
+                    shot_type=shot_type,
+                    watermark=watermark,
+                    seed=seed,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=6,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class Wan2TextToVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Wan2TextToVideoApi",
+            display_name="Wan 2.7 Text to Video",
+            category="partner/video/Wan",
+            description="Generates a video based on a text prompt using the Wan 2.7 model.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "wan2.7-t2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese.",
+                                ),
+                                IO.String.Input(
+                                    "negative_prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Negative prompt describing what to avoid.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["16:9", "9:16", "1:1", "4:3", "3:4"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=2,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    optional=True,
+                    tooltip="Audio for driving video generation (e.g., lip sync, beat-matched motion). "
+                    "Duration: 3s-30s. If not provided, the model automatically generates matching "
+                    "background music or sound effects.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := { "720p": 0.1, "1080p": 0.15 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps * $dur }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        prompt_extend: bool,
+        watermark: bool,
+        audio: Input.Audio | None = None,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        audio_url = None
+        if audio is not None:
+            validate_audio_duration(audio, 1.5, 60.0)
+            audio_url = await upload_audio_to_comfyapi(
+                cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg"
+            )
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27Text2VideoTaskCreationRequest(
+                model=model["model"],
+                input=Text2VideoInputField(
+                    prompt=model["prompt"],
+                    negative_prompt=model["negative_prompt"] or None,
+                    audio_url=audio_url,
+                ),
+                parameters=Wan27Text2VideoParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=model["duration"],
+                    seed=seed,
+                    prompt_extend=prompt_extend,
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class Wan2ImageToVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Wan2ImageToVideoApi",
+            display_name="Wan 2.7 Image to Video",
+            category="partner/video/Wan",
+            description="Generate a video from a first-frame image, with optional last-frame image and audio.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "wan2.7-i2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese.",
+                                ),
+                                IO.String.Input(
+                                    "negative_prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Negative prompt describing what to avoid.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=2,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Image.Input(
+                    "first_frame",
+                    tooltip="First frame image. The output aspect ratio is derived from this image.",
+                ),
+                IO.Image.Input(
+                    "last_frame",
+                    optional=True,
+                    tooltip="Last frame image. The model generates a video transitioning from first to last frame.",
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    optional=True,
+                    tooltip="Audio for driving video generation (e.g., lip sync, beat-matched motion). "
+                    "Duration: 2s-30s. If not provided, the model automatically generates matching "
+                    "background music or sound effects.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := { "720p": 0.1, "1080p": 0.15 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps * $dur }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        first_frame: Input.Image,
+        seed: int,
+        prompt_extend: bool,
+        watermark: bool,
+        last_frame: Input.Image | None = None,
+        audio: Input.Audio | None = None,
+    ):
+        media = [
+            Wan27MediaItem(
+                type="first_frame",
+                url=await upload_image_to_comfyapi(cls, image=first_frame),
+            )
+        ]
+        if last_frame is not None:
+            media.append(
+                Wan27MediaItem(
+                    type="last_frame",
+                    url=await upload_image_to_comfyapi(cls, image=last_frame),
+                )
+            )
+        if audio is not None:
+            validate_audio_duration(audio, 2.0, 30.0)
+            audio_url = await upload_audio_to_comfyapi(
+                cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg"
+            )
+            media.append(Wan27MediaItem(type="driving_audio", url=audio_url))
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27ImageToVideoTaskCreationRequest(
+                model=model["model"],
+                input=Wan27ImageToVideoInputField(
+                    prompt=model["prompt"] or None,
+                    negative_prompt=model["negative_prompt"] or None,
+                    media=media,
+                ),
+                parameters=Wan27ImageToVideoParametersField(
+                    resolution=model["resolution"],
+                    duration=model["duration"],
+                    seed=seed,
+                    prompt_extend=prompt_extend,
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class Wan2VideoContinuationApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Wan2VideoContinuationApi",
+            display_name="Wan 2.7 Video Continuation",
+            category="partner/video/Wan",
+            description="Continue a video from where it left off, with optional last-frame control.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "wan2.7-i2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. Supports English and Chinese.",
+                                ),
+                                IO.String.Input(
+                                    "negative_prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Negative prompt describing what to avoid.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=2,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                    tooltip="Total output duration in seconds. The model generates continuation "
+                                    "to fill the remaining time after the input clip.",
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Video.Input(
+                    "first_clip",
+                    tooltip="Input video to continue from. Duration: 2s-10s. "
+                    "The output aspect ratio is derived from this video.",
+                ),
+                IO.Image.Input(
+                    "last_frame",
+                    optional=True,
+                    tooltip="Last frame image. The continuation will transition towards this frame.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := { "720p": 0.1, "1080p": 0.15 };
+                  $pps := $lookup($ppsTable, $res);
+                  $outputPrice := $pps * $dur;
+                  {
+                    "type": "range_usd",
+                    "min_usd": 2 * $pps + $outputPrice,
+                    "max_usd": 5 * $pps + $outputPrice
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        first_clip: Input.Video,
+        prompt: str = "",
+        negative_prompt: str = "",
+        last_frame: Input.Image | None = None,
+        seed: int = 0,
+        prompt_extend: bool = True,
+        watermark: bool = False,
+    ):
+        validate_video_duration(first_clip, min_duration=2, max_duration=10)
+        media = [
+            Wan27MediaItem(
+                type="first_clip",
+                url=await upload_video_to_comfyapi(cls, first_clip),
+            )
+        ]
+        if last_frame is not None:
+            media.append(
+                Wan27MediaItem(
+                    type="last_frame",
+                    url=await upload_image_to_comfyapi(cls, image=last_frame),
+                )
+            )
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27ImageToVideoTaskCreationRequest(
+                model=model["model"],
+                input=Wan27ImageToVideoInputField(
+                    prompt=model["prompt"] or None,
+                    negative_prompt=model["negative_prompt"] or None,
+                    media=media,
+                ),
+                parameters=Wan27ImageToVideoParametersField(
+                    resolution=model["resolution"],
+                    duration=model["duration"],
+                    seed=seed,
+                    prompt_extend=prompt_extend,
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class Wan2VideoEditApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Wan2VideoEditApi",
+            display_name="Wan 2.7 Video Edit",
+            category="partner/video/Wan",
+            description="Edit a video using text instructions, reference images, or style transfer.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "wan2.7-videoedit",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Editing instructions or style transfer requirements.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["16:9", "9:16", "1:1", "4:3", "3:4"],
+                                    tooltip="Aspect ratio. If not changed, approximates the input video ratio.",
+                                ),
+                                IO.Combo.Input(
+                                    "duration",
+                                    options=["auto", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
+                                    default="auto",
+                                    tooltip="Output duration in seconds. 'auto' matches the input video duration. "
+                                    "A specific value truncates from the start of the video.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[
+                                            "image1",
+                                            "image2",
+                                            "image3",
+                                            "image4",
+                                        ],
+                                        min=0,
+                                    ),
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Video.Input(
+                    "video",
+                    tooltip="The video to edit.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Combo.Input(
+                    "audio_setting",
+                    options=["auto", "origin"],
+                    default="auto",
+                    tooltip="'auto': model decides whether to regenerate audio based on the prompt. "
+                    "'origin': preserve the original audio from the input video.",
+                    advanced=True,
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := { "720p": 0.1, "1080p": 0.15 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps, "format": { "suffix": "/second", "note": "(input + output)" } }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        video: Input.Video,
+        seed: int,
+        audio_setting: str,
+        watermark: bool,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        validate_video_duration(video, min_duration=2, max_duration=10)
+        duration = 0 if model["duration"] == "auto" else int(model["duration"])
+        media = [Wan27MediaItem(type="video", url=await upload_video_to_comfyapi(cls, video))]
+        reference_images = model.get("reference_images", {})
+        for key in reference_images:
+            media.append(
+                Wan27MediaItem(
+                    type="reference_image", url=await upload_image_to_comfyapi(cls, image=reference_images[key])
+                )
+            )
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27VideoEditTaskCreationRequest(
+                model=model["model"],
+                input=Wan27VideoEditInputField(prompt=model["prompt"], media=media),
+                parameters=Wan27VideoEditParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=duration,
+                    audio_setting=audio_setting,
+                    watermark=watermark,
+                    seed=seed,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class Wan2ReferenceVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Wan2ReferenceVideoApi",
+            display_name="Wan 2.7 Reference to Video",
+            category="partner/video/Wan",
+            description="Generate a video featuring a person or object from reference materials. "
+            "Supports single-character performances and multi-character interactions.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "wan2.7-r2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the video. Use identifiers such as 'character1' and "
+                                    "'character2' to refer to the reference characters.",
+                                ),
+                                IO.String.Input(
+                                    "negative_prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Negative prompt describing what to avoid.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["16:9", "9:16", "1:1", "4:3", "3:4"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=2,
+                                    max=10,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_videos",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Video.Input("reference_video"),
+                                        names=["video1", "video2", "video3"],
+                                        min=0,
+                                    ),
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=["image1", "image2", "image3", "image4", "image5"],
+                                        min=0,
+                                    ),
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := { "720p": 0.1, "1080p": 0.15 };
+                  $pps := $lookup($ppsTable, $res);
+                  $outputPrice := $pps * $dur;
+                  {
+                    "type": "range_usd",
+                    "min_usd": $outputPrice,
+                    "max_usd": 5 * $pps + $outputPrice
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        media = []
+        reference_videos = model.get("reference_videos", {})
+        for key in reference_videos:
+            media.append(
+                Wan27MediaItem(type="reference_video", url=await upload_video_to_comfyapi(cls, reference_videos[key]))
+            )
+        reference_images = model.get("reference_images", {})
+        for key in reference_images:
+            media.append(
+                Wan27MediaItem(
+                    type="reference_image",
+                    url=await upload_image_to_comfyapi(cls, image=reference_images[key]),
+                )
+            )
+        if not media:
+            raise ValueError("At least one reference video or reference image must be provided.")
+        if len(media) > 5:
+            raise ValueError(
+                f"Too many references ({len(media)}). The maximum total of reference videos and images is 5."
+            )
+
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27ReferenceVideoTaskCreationRequest(
+                model=model["model"],
+                input=Wan27ReferenceVideoInputField(
+                    prompt=model["prompt"],
+                    negative_prompt=model["negative_prompt"] or None,
+                    media=media,
+                ),
+                parameters=Wan27ReferenceVideoParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=model["duration"],
+                    watermark=watermark,
+                    seed=seed,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class Wan3ReferenceToVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Wan3ReferenceToVideoApi",
+            display_name="Wan 3.0 Reference to Video",
+            category="partner/video/Wan",
+            description="Generates a video from a text prompt and optional reference images, videos, and audio "
+            "using the Wan 3.0 model. Reference media can be combined freely and mentioned in the prompt "
+            "as @Image1, @Video1, @Audio1.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "wan3.0-video",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese. Refer to connected reference media "
+                                    "as @Image1, @Video1, @Audio1, numbered per type in input order.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["1080P", "720P", "480P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["adaptive", "16:9", "9:16", "1:1", "4:3", "3:4"],
+                                    tooltip="Aspect ratio of the output video. With 'adaptive', the output "
+                                    "dimensions are derived from the input media.",
+                                ),
+                                IO.Combo.Input(
+                                    "duration",
+                                    options=["auto", *(str(i) for i in range(2, 31))],
+                                    tooltip="Output duration in seconds. With 'auto', the model chooses "
+                                    "a duration that fits the prompt and reference media. The combined "
+                                    "duration of reference videos and output must not exceed 30 seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=True,
+                                    tooltip="Whether the output video contains an audio track.",
+                                ),
+                                IO.Boolean.Input(
+                                    "prompt_extend",
+                                    default=True,
+                                    tooltip="Whether to enhance the prompt with AI assistance.",
+                                    advanced=True,
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[f"image{i}" for i in range(1, 11)],
+                                        min=0,
+                                    ),
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_videos",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Video.Input("reference_video"),
+                                        names=[f"video{i}" for i in range(1, 6)],
+                                        min=0,
+                                    ),
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_audios",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Audio.Input("reference_audio"),
+                                        names=[f"audio{i}" for i in range(1, 6)],
+                                        min=0,
+                                    ),
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "wan3.0-video-prime",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese. Refer to connected reference media "
+                                    "as @Image1, @Video1, @Audio1, numbered per type in input order.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["1080P", "720P", "480P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["adaptive", "16:9", "9:16", "1:1", "4:3", "3:4"],
+                                    tooltip="Aspect ratio of the output video. With 'adaptive', the output "
+                                    "dimensions are derived from the input media.",
+                                ),
+                                IO.Combo.Input(
+                                    "duration",
+                                    options=["auto", *(str(i) for i in range(2, 31))],
+                                    tooltip="Output duration in seconds. With 'auto', the model chooses "
+                                    "a duration that fits the prompt and reference media. The combined "
+                                    "duration of reference videos and output must not exceed 30 seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=True,
+                                    tooltip="Whether the output video contains an audio track.",
+                                ),
+                                IO.Boolean.Input(
+                                    "prompt_extend",
+                                    default=True,
+                                    tooltip="Whether to enhance the prompt with AI assistance.",
+                                    advanced=True,
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[f"image{i}" for i in range(1, 11)],
+                                        min=0,
+                                    ),
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_videos",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Video.Input("reference_video"),
+                                        names=[f"video{i}" for i in range(1, 6)],
+                                        min=0,
+                                    ),
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_audios",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Audio.Input("reference_audio"),
+                                        names=[f"audio{i}" for i in range(1, 6)],
+                                        min=0,
+                                    ),
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $ppsTable := $contains(widgets.model, "prime")
+                    ? { "480p": 0.09724, "720p": 0.2002, "1080p": 0.4004 }
+                    : { "480p": 0.0715, "720p": 0.143, "1080p": 0.286 };
+                  $pps := $lookup($ppsTable, $lookup(widgets, "model.resolution"));
+                  $dur := $lookup(widgets, "model.duration");
+                  $dur = "auto"
+                    ? { "type": "usd", "usd": $pps, "format": {"suffix": "/second"} }
+                    : (
+                        $minUsd := $number($dur) * $pps;
+                        $maxUsd := $min([$number($dur) + 15, 30]) * $pps;
+                        $minUsd = $maxUsd
+                          ? { "type": "usd", "usd": $minUsd }
+                          : { "type": "range_usd", "min_usd": $minUsd, "max_usd": $maxUsd }
+                      )
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+    ):
+        reference_images = model.get("reference_images", {})
+        reference_videos = model.get("reference_videos", {})
+        reference_audios = model.get("reference_audios", {})
+        for key in reference_images:
+            if get_number_of_images(reference_images[key]) != 1:
+                raise ValueError(f"Reference image input '{key}' must contain exactly one image, not a batch.")
+        total_video_seconds = 0.0
+        for key in reference_videos:
+            validate_video_duration(reference_videos[key], max_duration=15)
+            try:
+                total_video_seconds += reference_videos[key].get_duration()
+            except Exception:
+                pass
+        if total_video_seconds > 15.0001:
+            raise ValueError(
+                f"The total duration of reference videos ({total_video_seconds:.2f}s) exceeds the 15s limit."
+            )
+        total_audio_seconds = 0.0
+        for key in reference_audios:
+            validate_audio_duration(reference_audios[key], max_duration=15)
+            total_audio_seconds += reference_audios[key]["waveform"].shape[-1] / int(
+                reference_audios[key]["sample_rate"]
+            )
+        if total_audio_seconds > 15.0001:
+            raise ValueError(
+                f"The total duration of reference audios ({total_audio_seconds:.2f}s) exceeds the 15s limit."
+            )
+        duration = -1 if model["duration"] == "auto" else int(model["duration"])
+        if duration != -1 and total_video_seconds + duration > 30.0001:
+            raise ValueError(
+                f"Reference video duration ({total_video_seconds:.2f}s) plus output duration ({duration}s) "
+                "exceeds the 30s combined limit."
+            )
+        prompt = _wan3_rewrite_reference_prompt(
+            model["prompt"],
+            {"image": len(reference_images), "video": len(reference_videos), "audio": len(reference_audios)},
+        )
+        validate_string(prompt, strip_whitespace=False, max_length=20000)
+        if not prompt.strip() and not (reference_images or reference_videos or reference_audios):
+            raise ValueError("Provide a prompt or at least one reference input.")
+        media = []
+        for key in reference_images:
+            media.append(
+                Wan3MediaItem(type="reference_image", url=await upload_image_to_comfyapi(cls, reference_images[key]))
+            )
+        for key in reference_videos:
+            media.append(
+                Wan3MediaItem(type="reference_video", url=await upload_video_to_comfyapi(cls, reference_videos[key]))
+            )
+        for key in reference_audios:
+            media.append(
+                Wan3MediaItem(
+                    type="reference_audio",
+                    url=await upload_audio_to_comfyapi(
+                        cls,
+                        reference_audios[key],
+                        container_format="mp3",
+                        codec_name="libmp3lame",
+                        mime_type="audio/mpeg",
+                    ),
+                )
+            )
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", method="POST"),
+            response_model=TaskCreationResponse,
+            data=Wan3TaskCreationRequest(
+                model=model["model"],
+                input=Wan3InputField(prompt=prompt or None, media=media or None),
+                parameters=Wan3ParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=duration,
+                    seed=seed,
+                    audio=model["audio"],
+                    prompt_extend=model["prompt_extend"],
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            queued_statuses=WAN3_QUEUED_STATUSES,
+            failed_statuses=WAN3_FAILED_STATUSES,
+            poll_interval=10,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class Wan3ImageToVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Wan3ImageToVideoApi",
+            display_name="Wan 3.0 Image to Video",
+            category="partner/video/Wan",
+            description="Generates a video from a first-frame image, with optional last-frame control, "
+            "using the Wan 3.0 model.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "wan3.0-video",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["1080P", "720P", "480P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["adaptive", "16:9", "9:16", "1:1", "4:3", "3:4"],
+                                    tooltip="Aspect ratio of the output video. With 'adaptive', the output "
+                                    "dimensions are derived from the first frame.",
+                                ),
+                                IO.Combo.Input(
+                                    "duration",
+                                    options=["auto", *(str(i) for i in range(2, 31))],
+                                    tooltip="Output duration in seconds. With 'auto', the model chooses "
+                                    "a duration that fits the prompt.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=True,
+                                    tooltip="Whether the output video contains an audio track.",
+                                ),
+                                IO.Boolean.Input(
+                                    "prompt_extend",
+                                    default=True,
+                                    tooltip="Whether to enhance the prompt with AI assistance.",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "wan3.0-video-prime",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["1080P", "720P", "480P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["adaptive", "16:9", "9:16", "1:1", "4:3", "3:4"],
+                                    tooltip="Aspect ratio of the output video. With 'adaptive', the output "
+                                    "dimensions are derived from the first frame.",
+                                ),
+                                IO.Combo.Input(
+                                    "duration",
+                                    options=["auto", *(str(i) for i in range(2, 31))],
+                                    tooltip="Output duration in seconds. With 'auto', the model chooses "
+                                    "a duration that fits the prompt.",
+                                ),
+                                IO.Boolean.Input(
+                                    "audio",
+                                    default=True,
+                                    tooltip="Whether the output video contains an audio track.",
+                                ),
+                                IO.Boolean.Input(
+                                    "prompt_extend",
+                                    default=True,
+                                    tooltip="Whether to enhance the prompt with AI assistance.",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Image.Input(
+                    "first_frame",
+                    tooltip="First frame image.",
+                ),
+                IO.Image.Input(
+                    "last_frame",
+                    optional=True,
+                    tooltip="Last frame image. The model generates a video transitioning from first to last frame.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $ppsTable := $contains(widgets.model, "prime")
+                    ? { "480p": 0.09724, "720p": 0.2002, "1080p": 0.4004 }
+                    : { "480p": 0.0715, "720p": 0.143, "1080p": 0.286 };
+                  $pps := $lookup($ppsTable, $lookup(widgets, "model.resolution"));
+                  $dur := $lookup(widgets, "model.duration");
+                  $dur = "auto"
+                    ? { "type": "usd", "usd": $pps, "format": {"suffix": "/second"} }
+                    : { "type": "usd", "usd": $number($dur) * $pps }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        first_frame: Input.Image,
+        seed: int,
+        watermark: bool,
+        last_frame: Input.Image | None = None,
+    ):
+        if get_number_of_images(first_frame) != 1:
+            raise ValueError("Exactly one first_frame image is required.")
+        if last_frame is not None and get_number_of_images(last_frame) != 1:
+            raise ValueError("Exactly one last_frame image is required.")
+        validate_string(model["prompt"], strip_whitespace=False, max_length=20000)
+        media = [Wan3MediaItem(type="first_frame", url=await upload_image_to_comfyapi(cls, first_frame))]
+        if last_frame is not None:
+            media.append(Wan3MediaItem(type="last_frame", url=await upload_image_to_comfyapi(cls, last_frame)))
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", method="POST"),
+            response_model=TaskCreationResponse,
+            data=Wan3TaskCreationRequest(
+                model=model["model"],
+                input=Wan3InputField(prompt=model["prompt"] or None, media=media),
+                parameters=Wan3ParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=-1 if model["duration"] == "auto" else int(model["duration"]),
+                    seed=seed,
+                    audio=model["audio"],
+                    prompt_extend=model["prompt_extend"],
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            queued_statuses=WAN3_QUEUED_STATUSES,
+            failed_statuses=WAN3_FAILED_STATUSES,
+            poll_interval=10,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class HappyHorseTextToVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HappyHorseTextToVideoApi",
+            display_name="HappyHorse Text to Video",
+            category="partner/video/Wan",
+            description="Generates a video based on a text prompt using the HappyHorse model.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.1-t2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=[
+                                        "16:9",
+                                        "9:16",
+                                        "1:1",
+                                        "4:3",
+                                        "3:4",
+                                        "21:9",
+                                        "9:21",
+                                        "5:4",
+                                        "4:5",
+                                    ],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=3,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.0-t2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["16:9", "9:16", "1:1", "4:3", "3:4"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=3,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := $contains(widgets.model, "1.1")
+                    ? { "720p": 0.2002, "1080p": 0.2574 }
+                    : { "720p": 0.14, "1080p": 0.24 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps * $dur }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27Text2VideoTaskCreationRequest(
+                model=model["model"],
+                input=Text2VideoInputField(
+                    prompt=model["prompt"],
+                    negative_prompt=None,
+                ),
+                parameters=Wan27Text2VideoParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=model["duration"],
+                    seed=seed,
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class HappyHorseImageToVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HappyHorseImageToVideoApi",
+            display_name="HappyHorse Image to Video",
+            category="partner/video/Wan",
+            description="Generate a video from a first-frame image using the HappyHorse model.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.1-i2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=3,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.0-i2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=3,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Image.Input(
+                    "first_frame",
+                    tooltip="First frame image. The output aspect ratio is derived from this image.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := $contains(widgets.model, "1.1")
+                    ? { "720p": 0.2002, "1080p": 0.2574 }
+                    : { "720p": 0.14, "1080p": 0.24 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps * $dur }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        first_frame: Input.Image,
+        seed: int,
+        watermark: bool,
+    ):
+        validate_image_dimensions(first_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(first_frame, (1, 2.5), (2.5, 1), strict=False)
+        media = [
+            Wan27MediaItem(
+                type="first_frame",
+                url=await upload_image_to_comfyapi(cls, image=first_frame),
+            )
+        ]
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27ImageToVideoTaskCreationRequest(
+                model=model["model"],
+                input=Wan27ImageToVideoInputField(
+                    prompt=model["prompt"] or None,
+                    negative_prompt=None,
+                    media=media,
+                ),
+                parameters=Wan27ImageToVideoParametersField(
+                    resolution=model["resolution"],
+                    duration=model["duration"],
+                    seed=seed,
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class HappyHorseVideoEditApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HappyHorseVideoEditApi",
+            display_name="HappyHorse Video Edit",
+            category="partner/video/Wan",
+            description="Edit a video using text instructions or reference images with the HappyHorse model. "
+            "Output duration is 3-15s and matches the input video; inputs longer than 15s are truncated.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.0-video-edit",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Editing instructions or style transfer requirements.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["16:9", "9:16", "1:1", "4:3", "3:4"],
+                                    tooltip="Aspect ratio. If not changed, approximates the input video ratio.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[
+                                            "image1",
+                                            "image2",
+                                            "image3",
+                                            "image4",
+                                            "image5",
+                                        ],
+                                        min=0,
+                                    ),
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Video.Input(
+                    "video",
+                    tooltip="The video to edit.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $ppsTable := { "720p": 0.14, "1080p": 0.24 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps, "format": { "suffix": "/second" } }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        video: Input.Video,
+        seed: int,
+        watermark: bool,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        validate_video_duration(video, min_duration=3, max_duration=60)
+        media = [Wan27MediaItem(type="video", url=await upload_video_to_comfyapi(cls, video))]
+        reference_images = model.get("reference_images", {})
+        for key in reference_images:
+            media.append(
+                Wan27MediaItem(
+                    type="reference_image", url=await upload_image_to_comfyapi(cls, image=reference_images[key])
+                )
+            )
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27VideoEditTaskCreationRequest(
+                model=model["model"],
+                input=Wan27VideoEditInputField(prompt=model["prompt"], media=media),
+                parameters=Wan27VideoEditParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=None,
+                    watermark=watermark,
+                    seed=seed,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class HappyHorseReferenceVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HappyHorseReferenceVideoApi",
+            display_name="HappyHorse Reference to Video",
+            category="partner/video/Wan",
+            description="Generate a video featuring a person or object from reference materials with the HappyHorse "
+            "model. Supports single-character performances and multi-character interactions.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.1-r2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the video. Use identifiers such as 'character1' and "
+                                    "'character2' to refer to the reference characters.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=[
+                                        "16:9",
+                                        "9:16",
+                                        "1:1",
+                                        "4:3",
+                                        "3:4",
+                                        "21:9",
+                                        "9:21",
+                                        "5:4",
+                                        "4:5",
+                                    ],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=3,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[
+                                            "image1",
+                                            "image2",
+                                            "image3",
+                                            "image4",
+                                            "image5",
+                                            "image6",
+                                            "image7",
+                                            "image8",
+                                            "image9",
+                                        ],
+                                        min=1,
+                                    ),
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.0-r2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the video. Use identifiers such as 'character1' and "
+                                    "'character2' to refer to the reference characters.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["16:9", "9:16", "1:1", "4:3", "3:4"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=3,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[
+                                            "image1",
+                                            "image2",
+                                            "image3",
+                                            "image4",
+                                            "image5",
+                                            "image6",
+                                            "image7",
+                                            "image8",
+                                            "image9",
+                                        ],
+                                        min=1,
+                                    ),
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := $contains(widgets.model, "1.1")
+                    ? { "720p": 0.2002, "1080p": 0.2574 }
+                    : { "720p": 0.14, "1080p": 0.24 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps * $dur }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        reference_images = model.get("reference_images", {})
+        for key in reference_images:
+            validate_image_dimensions(reference_images[key], min_width=400, min_height=400)
+            validate_image_aspect_ratio(reference_images[key], (1, 2.5), (2.5, 1), strict=False)
+        media = []
+        for key in reference_images:
+            media.append(
+                Wan27MediaItem(
+                    type="reference_image",
+                    url=await upload_image_to_comfyapi(cls, image=reference_images[key]),
+                )
+            )
+        if not media:
+            raise ValueError("At least one reference image must be provided.")
+
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27ReferenceVideoTaskCreationRequest(
+                model=model["model"],
+                input=Wan27ReferenceVideoInputField(
+                    prompt=model["prompt"],
+                    negative_prompt=None,
+                    media=media,
+                ),
+                parameters=Wan27ReferenceVideoParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=model["duration"],
+                    watermark=watermark,
+                    seed=seed,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class WanApiExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            WanTextToImageApi,
+            WanImageToImageApi,
+            WanTextToVideoApi,
+            WanImageToVideoApi,
+            WanReferenceVideoApi,
+            Wan2TextToVideoApi,
+            Wan2ImageToVideoApi,
+            Wan2VideoContinuationApi,
+            Wan2VideoEditApi,
+            Wan2ReferenceVideoApi,
+            Wan3ReferenceToVideoApi,
+            Wan3ImageToVideoApi,
+            HappyHorseTextToVideoApi,
+            HappyHorseImageToVideoApi,
+            HappyHorseVideoEditApi,
+            HappyHorseReferenceVideoApi,
+        ]
+
+
+async def comfy_entrypoint() -> WanApiExtension:
+    return WanApiExtension()
diff --git a/comfy_api_nodes/nodes_wavespeed.py b/comfy_api_nodes/nodes_wavespeed.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b49f14029c3064c10d9ac865f08f7b9d41f0f73
--- /dev/null
+++ b/comfy_api_nodes/nodes_wavespeed.py
@@ -0,0 +1,176 @@
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.wavespeed import (
+    FlashVSRRequest,
+    TaskCreatedResponse,
+    TaskResultResponse,
+    SeedVR2ImageRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    poll_op,
+    sync_op,
+    upload_video_to_comfyapi,
+    validate_container_format_is_mp4,
+    validate_video_duration,
+    upload_images_to_comfyapi,
+    get_number_of_images,
+    download_url_to_image_tensor,
+)
+
+
+class WavespeedFlashVSRNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WavespeedFlashVSRNode",
+            display_name="FlashVSR Video Upscale",
+            category="partner/video/WaveSpeed",
+            description="Fast, high-quality video upscaler that "
+            "boosts resolution and restores clarity for low-resolution or blurry footage.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Combo.Input("target_resolution", options=["720p", "1080p", "2K", "4K"]),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["target_resolution"]),
+                expr="""
+                (
+                  $price_for_1sec := {"720p": 0.012, "1080p": 0.018, "2k": 0.024, "4k": 0.032};
+                  {
+                    "type":"usd",
+                    "usd": $lookup($price_for_1sec, widgets.target_resolution),
+                    "format":{"suffix": "/second", "approximate": true}
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        target_resolution: str,
+    ) -> IO.NodeOutput:
+        validate_container_format_is_mp4(video)
+        validate_video_duration(video, min_duration=5, max_duration=60 * 10)
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wavespeed/api/v3/wavespeed-ai/flashvsr", method="POST"),
+            response_model=TaskCreatedResponse,
+            data=FlashVSRRequest(
+                target_resolution=target_resolution.lower(),
+                video=await upload_video_to_comfyapi(cls, video),
+                duration=video.get_duration(),
+            ),
+        )
+        if initial_res.code != 200:
+            raise ValueError(f"Task creation fails with code={initial_res.code} and message={initial_res.message}")
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wavespeed/api/v3/predictions/{initial_res.data.id}/result"),
+            response_model=TaskResultResponse,
+            status_extractor=lambda x: "failed" if x.data is None else x.data.status,
+            poll_interval=10.0,
+        )
+        if final_response.code != 200:
+            raise ValueError(
+                f"Task processing failed with code={final_response.code} and message={final_response.message}"
+            )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.outputs[0]))
+
+
+class WavespeedImageUpscaleNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WavespeedImageUpscaleNode",
+            display_name="WaveSpeed Image Upscale",
+            category="partner/image/WaveSpeed",
+            description="Boost image resolution and quality, upscaling photos to 4K or 8K for sharp, detailed results.",
+            inputs=[
+                IO.Combo.Input("model", options=["SeedVR2", "Ultimate"]),
+                IO.Image.Input("image"),
+                IO.Combo.Input("target_resolution", options=["2K", "4K", "8K"]),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $prices := {"seedvr2": 0.01, "ultimate": 0.06};
+                  {"type":"usd", "usd": $lookup($prices, widgets.model)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        target_resolution: str,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        if model == "SeedVR2":
+            model_path = "seedvr2/image"
+        else:
+            model_path = "ultimate-image-upscaler"
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wavespeed/api/v3/wavespeed-ai/{model_path}", method="POST"),
+            response_model=TaskCreatedResponse,
+            data=SeedVR2ImageRequest(
+                target_resolution=target_resolution.lower(),
+                image=(await upload_images_to_comfyapi(cls, image, max_images=1))[0],
+            ),
+        )
+        if initial_res.code != 200:
+            raise ValueError(f"Task creation fails with code={initial_res.code} and message={initial_res.message}")
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wavespeed/api/v3/predictions/{initial_res.data.id}/result"),
+            response_model=TaskResultResponse,
+            status_extractor=lambda x: "failed" if x.data is None else x.data.status,
+            poll_interval=10.0,
+        )
+        if final_response.code != 200:
+            raise ValueError(
+                f"Task processing failed with code={final_response.code} and message={final_response.message}"
+            )
+        return IO.NodeOutput(await download_url_to_image_tensor(final_response.data.outputs[0]))
+
+
+class WavespeedExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            WavespeedFlashVSRNode,
+            WavespeedImageUpscaleNode,
+        ]
+
+
+async def comfy_entrypoint() -> WavespeedExtension:
+    return WavespeedExtension()
diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0de8b7d3775962b0eeb42a9407310e5de2dd4862
--- /dev/null
+++ b/comfy_api_nodes/util/__init__.py
@@ -0,0 +1,123 @@
+from ._helpers import get_fs_object_size
+from .client import (
+    ApiEndpoint,
+    poll_op,
+    poll_op_raw,
+    sync_op,
+    sync_op_raw,
+)
+from .conversions import (
+    audio_bytes_to_audio_input,
+    audio_input_to_mp3,
+    audio_ndarray_to_bytesio,
+    audio_tensor_to_contiguous_ndarray,
+    audio_to_base64_string,
+    bytesio_to_image_tensor,
+    convert_mask_to_image,
+    downscale_image_tensor,
+    downscale_image_tensor_by_max_side,
+    downscale_video_to_max_pixels,
+    image_tensor_pair_to_batch,
+    pad_images_to_common_channels,
+    pil_to_bytesio,
+    resize_mask_to_image,
+    tensor_to_base64_string,
+    tensor_to_bytesio,
+    tensor_to_pil,
+    text_filepath_to_base64_string,
+    text_filepath_to_data_uri,
+    trim_video,
+    upscale_image_tensor_to_min_pixels,
+    upscale_video_to_min_pixels,
+    video_to_base64_string,
+)
+from .download_helpers import (
+    download_url_as_bytesio,
+    download_url_to_bytesio,
+    download_url_to_file_3d,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+)
+from .upload_helpers import (
+    upload_3d_model_to_comfyapi,
+    upload_audio_to_comfyapi,
+    upload_file_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+)
+from .validation_utils import (
+    get_image_dimensions,
+    get_number_of_images,
+    validate_aspect_ratio_string,
+    validate_audio_duration,
+    validate_container_format_is_mp4,
+    validate_image_aspect_ratio,
+    validate_image_dimensions,
+    validate_images_aspect_ratio_closeness,
+    validate_string,
+    validate_video_dimensions,
+    validate_video_duration,
+    validate_video_frame_count,
+)
+
+__all__ = [
+    # API client
+    "ApiEndpoint",
+    "poll_op",
+    "poll_op_raw",
+    "sync_op",
+    "sync_op_raw",
+    # Upload helpers
+    "upload_3d_model_to_comfyapi",
+    "upload_audio_to_comfyapi",
+    "upload_file_to_comfyapi",
+    "upload_image_to_comfyapi",
+    "upload_images_to_comfyapi",
+    "upload_video_to_comfyapi",
+    # Download helpers
+    "download_url_as_bytesio",
+    "download_url_to_bytesio",
+    "download_url_to_file_3d",
+    "download_url_to_image_tensor",
+    "download_url_to_video_output",
+    # Conversions
+    "audio_bytes_to_audio_input",
+    "audio_input_to_mp3",
+    "audio_ndarray_to_bytesio",
+    "audio_tensor_to_contiguous_ndarray",
+    "audio_to_base64_string",
+    "bytesio_to_image_tensor",
+    "convert_mask_to_image",
+    "downscale_image_tensor",
+    "downscale_image_tensor_by_max_side",
+    "downscale_video_to_max_pixels",
+    "image_tensor_pair_to_batch",
+    "pad_images_to_common_channels",
+    "pil_to_bytesio",
+    "resize_mask_to_image",
+    "tensor_to_base64_string",
+    "tensor_to_bytesio",
+    "tensor_to_pil",
+    "text_filepath_to_base64_string",
+    "text_filepath_to_data_uri",
+    "trim_video",
+    "upscale_image_tensor_to_min_pixels",
+    "upscale_video_to_min_pixels",
+    "video_to_base64_string",
+    # Validation utilities
+    "get_image_dimensions",
+    "get_number_of_images",
+    "validate_aspect_ratio_string",
+    "validate_audio_duration",
+    "validate_container_format_is_mp4",
+    "validate_image_aspect_ratio",
+    "validate_image_dimensions",
+    "validate_images_aspect_ratio_closeness",
+    "validate_string",
+    "validate_video_dimensions",
+    "validate_video_duration",
+    "validate_video_frame_count",
+    # Misc functions
+    "get_fs_object_size",
+]
diff --git a/comfy_api_nodes/util/_helpers.py b/comfy_api_nodes/util/_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..e488ce561b5b81777ec4f30b1a7baaf7536f9210
--- /dev/null
+++ b/comfy_api_nodes/util/_helpers.py
@@ -0,0 +1,152 @@
+import asyncio
+import contextlib
+import os
+import re
+import time
+from collections.abc import Callable
+from datetime import datetime, timezone
+from email.utils import parsedate_to_datetime
+from io import BytesIO
+
+from yarl import URL
+
+from comfy.cli_args import args
+from comfy.comfy_api_env import normalize_comfy_api_base
+from comfy.deploy_environment import get_deploy_environment
+from comfy.model_management import processing_interrupted
+from comfy_api.latest import IO
+from comfy_execution.utils import get_executing_context
+from comfyui_version import __version__ as comfyui_version
+
+from .common_exceptions import ProcessingInterrupted
+
+_HAS_PCT_ESC = re.compile(r"%[0-9A-Fa-f]{2}")  # any % followed by 2 hex digits
+_HAS_BAD_PCT = re.compile(r"%(?![0-9A-Fa-f]{2})")  # any % not followed by 2 hex digits
+
+
+def is_processing_interrupted() -> bool:
+    """Return True if user/runtime requested interruption."""
+    return processing_interrupted()
+
+
+def get_node_id(node_cls: type[IO.ComfyNode]) -> str:
+    return node_cls.hidden.unique_id
+
+
+def get_auth_header(node_cls: type[IO.ComfyNode]) -> dict[str, str]:
+    if node_cls.hidden.auth_token_comfy_org:
+        return {"Authorization": f"Bearer {node_cls.hidden.auth_token_comfy_org}"}
+    if node_cls.hidden.api_key_comfy_org:
+        return {"X-API-KEY": node_cls.hidden.api_key_comfy_org}
+    return {}
+
+
+def get_usage_source(node_cls: type[IO.ComfyNode]) -> str:
+    """Source of the prompt that triggered this API node.
+
+    Defaults to "comfyui-api" when the submitting client didn't identify itself,
+    i.e. a direct API call to this server.
+    """
+    return node_cls.hidden.comfy_usage_source or "comfyui-api"
+
+
+def get_comfy_api_headers(node_cls: type[IO.ComfyNode]) -> dict[str, str]:
+    """Common headers (auth, deploy environment, usage source) for Comfy API requests.
+
+    Centralizes the shared header set so every Comfy API request sends a consistent
+    set and new shared headers only need to be added in one place. Intended for
+    relative/cloud URLs resolved against ``default_base_url()``; because the result
+    includes auth, callers must not attach it to arbitrary absolute/presigned URLs.
+    """
+    headers = {
+        **get_auth_header(node_cls),
+        "Comfy-Env": get_deploy_environment(),
+        "Comfy-Usage-Source": get_usage_source(node_cls),
+        "Comfy-Core-Version": comfyui_version,
+    }
+    ctx = get_executing_context()
+    if ctx is not None:
+        headers["Comfy-Job-Id"] = ctx.prompt_id
+    return headers
+
+
+def default_base_url() -> str:
+    return normalize_comfy_api_base(getattr(args, "comfy_api_base", "https://api.comfy.org"))
+
+
+async def sleep_with_interrupt(
+    seconds: float,
+    node_cls: type[IO.ComfyNode] | None,
+    label: str | None = None,
+    start_ts: float | None = None,
+    estimated_total: int | None = None,
+    *,
+    display_callback: Callable[[type[IO.ComfyNode], str, int, int | None], None] | None = None,
+):
+    """
+    Sleep in 1s slices while:
+      - Checking for interruption (raises ProcessingInterrupted).
+      - Optionally emitting time progress via display_callback (if provided).
+    """
+    end = time.monotonic() + seconds
+    while True:
+        if is_processing_interrupted():
+            raise ProcessingInterrupted("Task cancelled")
+        now = time.monotonic()
+        if start_ts is not None and label and display_callback:
+            with contextlib.suppress(Exception):
+                display_callback(node_cls, label, int(now - start_ts), estimated_total)
+        if now >= end:
+            break
+        await asyncio.sleep(min(1.0, end - now))
+
+
+def _retry_after_wait(value: str | None, fallback: float, max_wait: float) -> float:
+    """Delay before the next retry, honoring a server ``Retry-After`` header."""
+
+    seconds: float | None = None
+    if value is not None:
+        value = value.strip()
+        if value.isascii() and value.isdigit():
+            # delay-seconds form. The ASCII-digit guard keeps exotic Unicode "digit" characters away from float()
+            # an all-digit string always converts (huge values become inf, never raising).
+            seconds = float(value)
+        elif value:
+            # HTTP-date form. parsedate_to_datetime raises OverflowError (not a ValueError) on absurd years/offsets
+            try:
+                parsed = parsedate_to_datetime(value)
+            except (TypeError, ValueError, OverflowError):
+                parsed = None
+            if parsed is not None:
+                if parsed.tzinfo is None:  # naive datetime: HTTP-date is UTC
+                    parsed = parsed.replace(tzinfo=timezone.utc)
+                delta = (parsed - datetime.now(timezone.utc)).total_seconds()
+                seconds = delta if delta > 0 else 0.0
+    if seconds is None:
+        return fallback
+    return min(seconds, max_wait)
+
+
+def mimetype_to_extension(mime_type: str) -> str:
+    """Converts a MIME type to a file extension."""
+    return mime_type.split("/")[-1].lower()
+
+
+def get_fs_object_size(path_or_object: str | BytesIO) -> int:
+    if isinstance(path_or_object, str):
+        return os.path.getsize(path_or_object)
+    return len(path_or_object.getvalue())
+
+
+def to_aiohttp_url(url: str) -> URL:
+    """If `url` appears to be already percent-encoded (contains at least one valid %HH
+    escape and no malformed '%' sequences) and contains no raw whitespace/control
+    characters preserve the original encoding byte-for-byte (important for signed/presigned URLs).
+    Otherwise, return `URL(url)` and allow yarl to normalize/quote as needed."""
+    if any(c.isspace() for c in url) or any(ord(c) < 0x20 for c in url):
+        # Avoid encoded=True if URL contains raw whitespace/control chars
+        return URL(url)
+    if _HAS_PCT_ESC.search(url) and not _HAS_BAD_PCT.search(url):
+        # Preserve encoding only if it appears pre-encoded AND has no invalid % sequences
+        return URL(url, encoded=True)
+    return URL(url)
diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py
new file mode 100644
index 0000000000000000000000000000000000000000..49381a73ccb5437ead3732e06b8b7f9aff74cda5
--- /dev/null
+++ b/comfy_api_nodes/util/client.py
@@ -0,0 +1,1028 @@
+import asyncio
+import contextlib
+import json
+import logging
+import math
+import time
+import uuid
+import weakref
+from collections.abc import Callable, Iterable
+from dataclasses import dataclass
+from enum import Enum
+from io import BytesIO
+from typing import Any, Literal, TypeVar
+from urllib.parse import urljoin, urlparse
+
+import aiohttp
+from aiohttp.client_exceptions import ClientError, ContentTypeError
+from pydantic import BaseModel
+
+from comfy import utils
+from comfy_api.latest import IO
+from server import PromptServer
+
+from . import request_logger
+from ._helpers import (
+    _retry_after_wait,
+    default_base_url,
+    get_comfy_api_headers,
+    get_node_id,
+    is_processing_interrupted,
+    sleep_with_interrupt,
+)
+from .common_exceptions import ApiServerError, LocalNetworkError, ProcessingInterrupted
+
+M = TypeVar("M", bound=BaseModel)
+
+
+class ApiEndpoint:
+    def __init__(
+        self,
+        path: str,
+        method: Literal["GET", "POST", "PUT", "DELETE", "PATCH"] = "GET",
+        *,
+        query_params: dict[str, Any] | None = None,
+        headers: dict[str, str] | None = None,
+    ):
+        self.path = path
+        self.method = method
+        self.query_params = query_params or {}
+        self.headers = headers or {}
+
+
+@dataclass
+class _RequestConfig:
+    node_cls: type[IO.ComfyNode]
+    endpoint: ApiEndpoint
+    timeout: float
+    content_type: str
+    data: dict[str, Any] | None
+    files: dict[str, Any] | list[tuple[str, Any]] | None
+    multipart_parser: Callable | None
+    max_retries: int
+    max_retries_on_rate_limit: int
+    retry_delay: float
+    retry_backoff: float
+    wait_label: str = "Waiting"
+    monitor_progress: bool = True
+    estimated_total: int | None = None
+    final_label_on_success: str | None = "Completed"
+    progress_origin_ts: float | None = None
+    price_extractor: Callable[[dict[str, Any]], float | None] | None = None
+    is_rate_limited: Callable[[int, Any], bool] | None = None
+    response_header_validator: Callable[[dict[str, str]], None] | None = None
+
+
+@dataclass
+class _PollUIState:
+    started: float
+    status_label: str = "Queued"
+    is_queued: bool = True
+    price: float | None = None
+    estimated_duration: int | None = None
+    base_processing_elapsed: float = 0.0  # sum of completed active intervals
+    active_since: float | None = None  # start time of current active interval (None if queued)
+
+
+_RETRY_STATUS = {408, 500, 502, 503, 504}  # status 429 is handled separately
+_MAX_RETRY_AFTER_WAIT = 150.0  # Cap a server Retry-After at this many seconds so a large hint can't block execution
+
+PRICE_CREDITS_HEADER = "X-Comfy-Credits-Used"
+"""Proxy response header with the actual cost in Comfy credits. When present on any successful proxied response,
+it takes precedence over ``price_extractor``."""
+
+_credits_used_by_execution: "weakref.WeakKeyDictionary[type, float]" = weakref.WeakKeyDictionary()
+"""Last PRICE_CREDITS_HEADER value per node execution, keyed by the node's per-execution class clone."""
+COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done", "complete"]
+FAILED_STATUSES = ["cancelled", "canceled", "canceling", "fail", "failed", "error"]
+QUEUED_STATUSES = ["created", "queued", "queueing", "submitted", "initializing", "wait", "in_queue"]
+
+
+def _maybe_remember_credits_used(node_cls: type[IO.ComfyNode], header_value: str | None) -> None:
+    """Remember a PRICE_CREDITS_HEADER value from a successful proxied response."""
+    if not header_value:
+        return
+    try:
+        credits_used = float(header_value)
+    except (TypeError, ValueError):
+        logging.debug("Ignoring malformed %s header: %r", PRICE_CREDITS_HEADER, header_value)
+        return
+    if not math.isfinite(credits_used) or credits_used < 0:
+        logging.debug("Ignoring out-of-range %s header: %r", PRICE_CREDITS_HEADER, header_value)
+        return
+    _credits_used_by_execution[node_cls] = credits_used + 0.0  # normalize -0.0
+
+
+def _get_remembered_credits_used(node_cls: type[IO.ComfyNode]) -> float | None:
+    return _credits_used_by_execution.get(node_cls)
+
+
+async def sync_op(
+    cls: type[IO.ComfyNode],
+    endpoint: ApiEndpoint,
+    *,
+    response_model: type[M],
+    price_extractor: Callable[[M | Any], float | None] | None = None,
+    data: BaseModel | None = None,
+    files: dict[str, Any] | list[tuple[str, Any]] | None = None,
+    content_type: str = "application/json",
+    timeout: float = 3600.0,
+    multipart_parser: Callable | None = None,
+    max_retries: int = 3,
+    retry_delay: float = 1.0,
+    retry_backoff: float = 2.0,
+    wait_label: str = "Waiting for server",
+    estimated_duration: int | None = None,
+    final_label_on_success: str | None = "Completed",
+    progress_origin_ts: float | None = None,
+    monitor_progress: bool = True,
+    max_retries_on_rate_limit: int = 16,
+    is_rate_limited: Callable[[int, Any], bool] | None = None,
+) -> M:
+    raw = await sync_op_raw(
+        cls,
+        endpoint,
+        price_extractor=_wrap_model_extractor(response_model, price_extractor),
+        data=data,
+        files=files,
+        content_type=content_type,
+        timeout=timeout,
+        multipart_parser=multipart_parser,
+        max_retries=max_retries,
+        retry_delay=retry_delay,
+        retry_backoff=retry_backoff,
+        wait_label=wait_label,
+        estimated_duration=estimated_duration,
+        as_binary=False,
+        final_label_on_success=final_label_on_success,
+        progress_origin_ts=progress_origin_ts,
+        monitor_progress=monitor_progress,
+        max_retries_on_rate_limit=max_retries_on_rate_limit,
+        is_rate_limited=is_rate_limited,
+    )
+    if not isinstance(raw, dict):
+        raise Exception("Expected JSON response to validate into a Pydantic model, got non-JSON (binary or text).")
+    return _validate_or_raise(response_model, raw)
+
+
+async def poll_op(
+    cls: type[IO.ComfyNode],
+    poll_endpoint: ApiEndpoint,
+    *,
+    response_model: type[M],
+    status_extractor: Callable[[M | Any], str | int | None],
+    progress_extractor: Callable[[M | Any], int | None] | None = None,
+    price_extractor: Callable[[M | Any], float | None] | None = None,
+    completed_statuses: list[str | int] | None = None,
+    failed_statuses: list[str | int] | None = None,
+    queued_statuses: list[str | int] | None = None,
+    data: BaseModel | None = None,
+    poll_interval: float = 5.0,
+    max_poll_attempts: int = 480,
+    timeout_per_poll: float = 120.0,
+    max_retries_per_poll: int = 10,
+    retry_delay_per_poll: float = 1.0,
+    retry_backoff_per_poll: float = 1.4,
+    estimated_duration: int | None = None,
+    cancel_endpoint: ApiEndpoint | None = None,
+    cancel_timeout: float = 10.0,
+    extra_text: str | None = None,
+) -> M:
+    raw = await poll_op_raw(
+        cls,
+        poll_endpoint=poll_endpoint,
+        status_extractor=_wrap_model_extractor(response_model, status_extractor),
+        progress_extractor=_wrap_model_extractor(response_model, progress_extractor),
+        price_extractor=_wrap_model_extractor(response_model, price_extractor),
+        completed_statuses=completed_statuses,
+        failed_statuses=failed_statuses,
+        queued_statuses=queued_statuses,
+        data=data,
+        poll_interval=poll_interval,
+        max_poll_attempts=max_poll_attempts,
+        timeout_per_poll=timeout_per_poll,
+        max_retries_per_poll=max_retries_per_poll,
+        retry_delay_per_poll=retry_delay_per_poll,
+        retry_backoff_per_poll=retry_backoff_per_poll,
+        estimated_duration=estimated_duration,
+        cancel_endpoint=cancel_endpoint,
+        cancel_timeout=cancel_timeout,
+        extra_text=extra_text,
+    )
+    if not isinstance(raw, dict):
+        raise Exception("Expected JSON response to validate into a Pydantic model, got non-JSON (binary or text).")
+    return _validate_or_raise(response_model, raw)
+
+
+async def sync_op_raw(
+    cls: type[IO.ComfyNode],
+    endpoint: ApiEndpoint,
+    *,
+    price_extractor: Callable[[dict[str, Any]], float | None] | None = None,
+    data: dict[str, Any] | BaseModel | None = None,
+    files: dict[str, Any] | list[tuple[str, Any]] | None = None,
+    content_type: str = "application/json",
+    timeout: float = 3600.0,
+    multipart_parser: Callable | None = None,
+    max_retries: int = 3,
+    retry_delay: float = 1.0,
+    retry_backoff: float = 2.0,
+    wait_label: str = "Waiting for server",
+    estimated_duration: int | None = None,
+    as_binary: bool = False,
+    final_label_on_success: str | None = "Completed",
+    progress_origin_ts: float | None = None,
+    monitor_progress: bool = True,
+    max_retries_on_rate_limit: int = 16,
+    is_rate_limited: Callable[[int, Any], bool] | None = None,
+    response_header_validator: Callable[[dict[str, str]], None] | None = None,
+) -> dict[str, Any] | bytes:
+    """
+    Make a single network request.
+      - If as_binary=False (default): returns JSON dict (or {'_raw': '<text>'} if non-JSON).
+      - If as_binary=True: returns bytes.
+      - response_header_validator: optional callback receiving response headers dict
+    """
+    if isinstance(data, BaseModel):
+        data = data.model_dump(exclude_none=True)
+        for k, v in list(data.items()):
+            if isinstance(v, Enum):
+                data[k] = v.value
+    cfg = _RequestConfig(
+        node_cls=cls,
+        endpoint=endpoint,
+        timeout=timeout,
+        content_type=content_type,
+        data=data,
+        files=files,
+        multipart_parser=multipart_parser,
+        max_retries=max_retries,
+        retry_delay=retry_delay,
+        retry_backoff=retry_backoff,
+        wait_label=wait_label,
+        monitor_progress=monitor_progress,
+        estimated_total=estimated_duration,
+        final_label_on_success=final_label_on_success,
+        progress_origin_ts=progress_origin_ts,
+        price_extractor=price_extractor,
+        max_retries_on_rate_limit=max_retries_on_rate_limit,
+        is_rate_limited=is_rate_limited,
+        response_header_validator=response_header_validator,
+    )
+    return await _request_base(cfg, expect_binary=as_binary)
+
+
+async def poll_op_raw(
+    cls: type[IO.ComfyNode],
+    poll_endpoint: ApiEndpoint,
+    *,
+    status_extractor: Callable[[dict[str, Any]], str | int | None],
+    progress_extractor: Callable[[dict[str, Any]], int | None] | None = None,
+    price_extractor: Callable[[dict[str, Any]], float | None] | None = None,
+    completed_statuses: list[str | int] | None = None,
+    failed_statuses: list[str | int] | None = None,
+    queued_statuses: list[str | int] | None = None,
+    data: dict[str, Any] | BaseModel | None = None,
+    poll_interval: float = 5.0,
+    max_poll_attempts: int = 480,
+    timeout_per_poll: float = 120.0,
+    max_retries_per_poll: int = 10,
+    retry_delay_per_poll: float = 1.0,
+    retry_backoff_per_poll: float = 1.4,
+    estimated_duration: int | None = None,
+    cancel_endpoint: ApiEndpoint | None = None,
+    cancel_timeout: float = 10.0,
+    extra_text: str | None = None,
+) -> dict[str, Any]:
+    """
+    Polls an endpoint until the task reaches a terminal state. Displays time while queued/processing,
+    checks interruption every second, and calls Cancel endpoint (if provided) on interruption.
+
+    Uses default complete, failed and queued states assumption.
+
+    Returns the final JSON response from the poll endpoint.
+    """
+    completed_states = _normalize_statuses(COMPLETED_STATUSES if completed_statuses is None else completed_statuses)
+    failed_states = _normalize_statuses(FAILED_STATUSES if failed_statuses is None else failed_statuses)
+    queued_states = _normalize_statuses(QUEUED_STATUSES if queued_statuses is None else queued_statuses)
+    started = time.monotonic()
+    consumed_attempts = 0  # counts only non-queued polls
+
+    progress_bar = utils.ProgressBar(100) if progress_extractor else None
+    last_progress: int | None = None
+
+    state = _PollUIState(started=started, estimated_duration=estimated_duration)
+    stop_ticker = asyncio.Event()
+
+    async def _ticker():
+        """Emit a UI update every second while polling is in progress."""
+        try:
+            while not stop_ticker.is_set():
+                if is_processing_interrupted():
+                    break
+                now = time.monotonic()
+                proc_elapsed = state.base_processing_elapsed + (
+                    (now - state.active_since) if state.active_since is not None else 0.0
+                )
+                _display_time_progress(
+                    cls,
+                    status=state.status_label,
+                    elapsed_seconds=int(now - state.started),
+                    estimated_total=state.estimated_duration,
+                    price=state.price,
+                    is_queued=state.is_queued,
+                    processing_elapsed_seconds=int(proc_elapsed),
+                    extra_text=extra_text,
+                )
+                await asyncio.sleep(1.0)
+        except Exception as exc:
+            logging.debug("Polling ticker exited: %s", exc)
+
+    ticker_task = asyncio.create_task(_ticker())
+    try:
+        while consumed_attempts < max_poll_attempts:
+            try:
+                resp_json = await sync_op_raw(
+                    cls,
+                    poll_endpoint,
+                    data=data,
+                    timeout=timeout_per_poll,
+                    max_retries=max_retries_per_poll,
+                    retry_delay=retry_delay_per_poll,
+                    retry_backoff=retry_backoff_per_poll,
+                    wait_label="Checking",
+                    estimated_duration=None,
+                    as_binary=False,
+                    final_label_on_success=None,
+                    monitor_progress=False,
+                )
+                if not isinstance(resp_json, dict):
+                    raise Exception("Polling endpoint returned non-JSON response.")
+            except ProcessingInterrupted:
+                if cancel_endpoint:
+                    with contextlib.suppress(Exception):
+                        await sync_op_raw(
+                            cls,
+                            cancel_endpoint,
+                            timeout=cancel_timeout,
+                            max_retries=0,
+                            wait_label="Cancelling task",
+                            estimated_duration=None,
+                            as_binary=False,
+                            final_label_on_success=None,
+                            monitor_progress=False,
+                        )
+                raise
+
+            try:
+                status = _normalize_status_value(status_extractor(resp_json))
+            except Exception as e:
+                logging.error("Status extraction failed: %s", e)
+                status = None
+
+            if price_extractor:
+                new_price = price_extractor(resp_json)
+                if new_price is not None:
+                    state.price = new_price
+
+            if progress_extractor:
+                new_progress = progress_extractor(resp_json)
+                if new_progress is not None and last_progress != new_progress:
+                    progress_bar.update_absolute(new_progress, total=100)
+                    last_progress = new_progress
+
+            now_ts = time.monotonic()
+            is_queued = status in queued_states
+
+            if is_queued:
+                if state.active_since is not None:  # If we just moved from active -> queued, close the active interval
+                    state.base_processing_elapsed += now_ts - state.active_since
+                    state.active_since = None
+            else:
+                if state.active_since is None:  # If we just moved from queued -> active, open a new active interval
+                    state.active_since = now_ts
+
+            state.is_queued = is_queued
+            state.status_label = status or ("Queued" if is_queued else "Processing")
+            if status in completed_states:
+                if state.active_since is not None:
+                    state.base_processing_elapsed += now_ts - state.active_since
+                    state.active_since = None
+                stop_ticker.set()
+                with contextlib.suppress(Exception):
+                    await ticker_task
+
+                if progress_bar and last_progress != 100:
+                    progress_bar.update_absolute(100, total=100)
+
+                _display_time_progress(
+                    cls,
+                    status=status if status else "Completed",
+                    elapsed_seconds=int(now_ts - started),
+                    estimated_total=estimated_duration,
+                    price=state.price,
+                    is_queued=False,
+                    processing_elapsed_seconds=int(state.base_processing_elapsed),
+                    extra_text=extra_text,
+                )
+                return resp_json
+
+            if status in failed_states:
+                msg = f"Task failed: {json.dumps(resp_json)}"
+                logging.error(msg)
+                raise Exception(msg)
+
+            try:
+                await sleep_with_interrupt(poll_interval, cls, None, None, None)
+            except ProcessingInterrupted:
+                if cancel_endpoint:
+                    with contextlib.suppress(Exception):
+                        await sync_op_raw(
+                            cls,
+                            cancel_endpoint,
+                            timeout=cancel_timeout,
+                            max_retries=0,
+                            wait_label="Cancelling task",
+                            estimated_duration=None,
+                            as_binary=False,
+                            final_label_on_success=None,
+                            monitor_progress=False,
+                        )
+                raise
+            if not is_queued:
+                consumed_attempts += 1
+
+        raise Exception(
+            f"Polling timed out after {max_poll_attempts} non-queued attempts "
+            f"(~{int(max_poll_attempts * poll_interval)}s of active polling)."
+        )
+    except ProcessingInterrupted:
+        raise
+    except (LocalNetworkError, ApiServerError):
+        raise
+    except Exception as e:
+        raise Exception(f"Polling aborted due to error: {e}") from e
+    finally:
+        stop_ticker.set()
+        with contextlib.suppress(Exception):
+            await ticker_task
+
+
+def _display_text(
+    node_cls: type[IO.ComfyNode],
+    text: str | None,
+    *,
+    status: str | int | None = None,
+    price: float | None = None,
+) -> None:
+    display_lines: list[str] = []
+    if status:
+        display_lines.append(f"Status: {status.capitalize() if isinstance(status, str) else status}")
+    server_credits = _get_remembered_credits_used(node_cls)
+    if server_credits is not None:
+        p = f"{server_credits:,.2f}".rstrip("0").rstrip(".")
+    elif price is not None:
+        p = f"{float(price) * 211:,.1f}".rstrip("0").rstrip(".")
+    else:
+        p = None
+    if p is not None and p != "0":
+        display_lines.append(f"Price: {p} credits")
+    if text is not None:
+        display_lines.append(text)
+    if display_lines:
+        PromptServer.instance.send_progress_text("\n".join(display_lines), get_node_id(node_cls))
+
+
+def _display_time_progress(
+    node_cls: type[IO.ComfyNode],
+    status: str | int | None,
+    elapsed_seconds: int,
+    estimated_total: int | None = None,
+    *,
+    price: float | None = None,
+    is_queued: bool | None = None,
+    processing_elapsed_seconds: int | None = None,
+    extra_text: str | None = None,
+) -> None:
+    if estimated_total is not None and estimated_total > 0 and is_queued is False:
+        pe = processing_elapsed_seconds if processing_elapsed_seconds is not None else elapsed_seconds
+        remaining = max(0, int(estimated_total) - int(pe))
+        time_line = f"Time elapsed: {int(elapsed_seconds)}s (~{remaining}s remaining)"
+    else:
+        time_line = f"Time elapsed: {int(elapsed_seconds)}s"
+    text = f"{time_line}\n\n{extra_text}" if extra_text else time_line
+    _display_text(node_cls, text, status=status, price=price)
+
+
+async def _diagnose_connectivity() -> dict[str, bool]:
+    """Best-effort connectivity diagnostics to distinguish local vs. server issues."""
+    results = {
+        "internet_accessible": False,
+        "api_accessible": False,
+    }
+    timeout = aiohttp.ClientTimeout(total=5.0)
+
+    # Probe Google and Baidu in parallel: Google is blocked by the GFW in mainland China, so a Baidu probe is required
+    # to correctly detect that Chinese users with working internet do have working internet.
+    internet_probe_urls = ("https://www.google.com", "https://www.baidu.com")
+
+    async with aiohttp.ClientSession(timeout=timeout) as session:
+        async def _probe(url: str) -> bool:
+            try:
+                async with session.get(url) as resp:
+                    return resp.status < 500
+            except (ClientError, OSError, asyncio.TimeoutError):
+                return False
+
+        probe_tasks = [asyncio.create_task(_probe(u)) for u in internet_probe_urls]
+        try:
+            for fut in asyncio.as_completed(probe_tasks):
+                if await fut:
+                    results["internet_accessible"] = True
+                    break
+        finally:
+            for t in probe_tasks:
+                if not t.done():
+                    t.cancel()
+            await asyncio.gather(*probe_tasks, return_exceptions=True)
+        if not results["internet_accessible"]:
+            return results
+
+        parsed = urlparse(default_base_url())
+        health_url = f"{parsed.scheme}://{parsed.netloc}/health"
+        with contextlib.suppress(ClientError, OSError):
+            async with session.get(health_url) as resp:
+                results["api_accessible"] = resp.status < 500
+    return results
+
+
+def _unpack_tuple(t: tuple) -> tuple[str, Any, str]:
+    """Normalize (filename, value, content_type)."""
+    if len(t) == 2:
+        return t[0], t[1], "application/octet-stream"
+    if len(t) == 3:
+        return t[0], t[1], t[2]
+    raise ValueError("files tuple must be (filename, file[, content_type])")
+
+
+def _merge_params(endpoint_params: dict[str, Any], method: str, data: dict[str, Any] | None) -> dict[str, Any]:
+    params = dict(endpoint_params or {})
+    if method.upper() == "GET" and data:
+        for k, v in data.items():
+            if v is not None:
+                params[k] = v
+    return params
+
+
+def _friendly_http_message(status: int, body: Any) -> str:
+    if status == 401:
+        return "Unauthorized: Please login first to use this node."
+    if status == 402:
+        return "Payment Required: Please add credits to your account to use this node."
+    if status == 409:
+        return "There is a problem with your account. Please contact support@comfy.org."
+    if status == 429:
+        return "Rate Limit Exceeded: The server returned 429 after all retry attempts. Please wait and try again."
+    try:
+        if isinstance(body, dict):
+            err = body.get("error")
+            if isinstance(err, dict):
+                msg = err.get("message")
+                typ = err.get("type")
+                if msg and typ:
+                    return f"API Error: {msg} (Type: {typ})"
+                if msg:
+                    return f"API Error: {msg}"
+            return f"API Error: {json.dumps(body)}"
+        else:
+            txt = str(body)
+            if len(txt) <= 200:
+                return f"API Error (raw): {txt}"
+            return f"API Error (status {status})"
+    except Exception:
+        return f"HTTP {status}: Unknown error"
+
+
+def _generate_operation_id(method: str, path: str, attempt: int) -> str:
+    slug = path.strip("/").replace("/", "_") or "op"
+    return f"{method}_{slug}_try{attempt}_{uuid.uuid4().hex[:8]}"
+
+
+def _snapshot_request_body_for_logging(
+    content_type: str,
+    method: str,
+    data: dict[str, Any] | None,
+    files: dict[str, Any] | list[tuple[str, Any]] | None,
+) -> dict[str, Any] | str | None:
+    if method.upper() == "GET":
+        return None
+    if content_type == "multipart/form-data":
+        form_fields = sorted([k for k, v in (data or {}).items() if v is not None])
+        file_fields: list[dict[str, str]] = []
+        if files:
+            file_iter = files if isinstance(files, list) else list(files.items())
+            for field_name, file_obj in file_iter:
+                if file_obj is None:
+                    continue
+                if isinstance(file_obj, tuple):
+                    filename = file_obj[0]
+                else:
+                    filename = getattr(file_obj, "name", field_name)
+                file_fields.append({"field": field_name, "filename": str(filename or "")})
+        return {"_multipart": True, "form_fields": form_fields, "file_fields": file_fields}
+    if content_type == "application/x-www-form-urlencoded":
+        return data or {}
+    return data or {}
+
+
+async def _request_base(cfg: _RequestConfig, expect_binary: bool):
+    """Core request with retries, per-second interruption monitoring, true cancellation, and friendly errors."""
+    url = cfg.endpoint.path
+    parsed_url = urlparse(url)
+    is_comfy_api_request = not parsed_url.scheme and not parsed_url.netloc  # is URL relative?
+    if is_comfy_api_request:
+        url = urljoin(default_base_url().rstrip("/") + "/", url.lstrip("/"))
+
+    method = cfg.endpoint.method
+    params = _merge_params(cfg.endpoint.query_params, method, cfg.data if method == "GET" else None)
+
+    async def _monitor(stop_evt: asyncio.Event, start_ts: float):
+        """Every second: update elapsed time and signal interruption."""
+        try:
+            while not stop_evt.is_set():
+                if is_processing_interrupted():
+                    return
+                if cfg.monitor_progress:
+                    _display_time_progress(
+                        cfg.node_cls, cfg.wait_label, int(time.monotonic() - start_ts), cfg.estimated_total
+                    )
+                await asyncio.sleep(1.0)
+        except asyncio.CancelledError:
+            return  # normal shutdown
+
+    start_time = cfg.progress_origin_ts if cfg.progress_origin_ts is not None else time.monotonic()
+    attempt = 0
+    delay = cfg.retry_delay
+    rate_limit_attempts = 0
+    rate_limit_delay = cfg.retry_delay
+    operation_succeeded: bool = False
+    final_elapsed_seconds: int | None = None
+    extracted_price: float | None = None
+    while True:
+        attempt += 1
+        stop_event = asyncio.Event()
+        monitor_task: asyncio.Task | None = None
+        sess: aiohttp.ClientSession | None = None
+
+        operation_id = _generate_operation_id(method, cfg.endpoint.path, attempt)
+        logging.debug("[DEBUG] HTTP %s %s (attempt %d)", method, url, attempt)
+
+        payload_headers = {"Accept": "*/*"} if expect_binary else {"Accept": "application/json"}
+        if is_comfy_api_request:
+            payload_headers.update(get_comfy_api_headers(cfg.node_cls))
+        if cfg.endpoint.headers:
+            payload_headers.update(cfg.endpoint.headers)
+
+        payload_kw: dict[str, Any] = {"headers": payload_headers}
+        if method == "GET":
+            payload_headers.pop("Content-Type", None)
+        request_body_log = _snapshot_request_body_for_logging(cfg.content_type, method, cfg.data, cfg.files)
+        try:
+            if cfg.monitor_progress:
+                monitor_task = asyncio.create_task(_monitor(stop_event, start_time))
+
+            timeout = aiohttp.ClientTimeout(total=cfg.timeout)
+            sess = aiohttp.ClientSession(timeout=timeout)
+
+            if cfg.content_type == "multipart/form-data" and method != "GET":
+                # aiohttp will set Content-Type boundary; remove any fixed Content-Type
+                payload_headers.pop("Content-Type", None)
+                if cfg.multipart_parser and cfg.data:
+                    form = cfg.multipart_parser(cfg.data)
+                    if not isinstance(form, aiohttp.FormData):
+                        raise ValueError("multipart_parser must return aiohttp.FormData")
+                else:
+                    form = aiohttp.FormData(default_to_multipart=True)
+                    if cfg.data:
+                        for k, v in cfg.data.items():
+                            if v is None:
+                                continue
+                            form.add_field(k, str(v) if not isinstance(v, (bytes, bytearray)) else v)
+                if cfg.files:
+                    file_iter = cfg.files if isinstance(cfg.files, list) else cfg.files.items()
+                    for field_name, file_obj in file_iter:
+                        if file_obj is None:
+                            continue
+                        if isinstance(file_obj, tuple):
+                            filename, file_value, content_type = _unpack_tuple(file_obj)
+                        else:
+                            filename = getattr(file_obj, "name", field_name)
+                            file_value = file_obj
+                            content_type = "application/octet-stream"
+                        # Attempt to rewind BytesIO for retries
+                        if isinstance(file_value, BytesIO):
+                            with contextlib.suppress(Exception):
+                                file_value.seek(0)
+                        form.add_field(field_name, file_value, filename=filename, content_type=content_type)
+                payload_kw["data"] = form
+            elif cfg.content_type == "application/x-www-form-urlencoded" and method != "GET":
+                payload_headers["Content-Type"] = "application/x-www-form-urlencoded"
+                payload_kw["data"] = cfg.data or {}
+            elif method != "GET":
+                payload_headers["Content-Type"] = "application/json"
+                payload_kw["json"] = cfg.data or {}
+
+            request_logger.log_request_response(
+                operation_id=operation_id,
+                request_method=method,
+                request_url=url,
+                request_headers=dict(payload_headers) if payload_headers else None,
+                request_params=dict(params) if params else None,
+                request_data=request_body_log,
+            )
+
+            req_coro = sess.request(method, url, params=params, **payload_kw)
+            req_task = asyncio.create_task(req_coro)
+
+            # Race: request vs. monitor (interruption)
+            tasks = {req_task}
+            if monitor_task:
+                tasks.add(monitor_task)
+            done, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)
+
+            if monitor_task and monitor_task in done:
+                # Interrupted – cancel the request and abort
+                if req_task in pending:
+                    req_task.cancel()
+                raise ProcessingInterrupted("Task cancelled")
+
+            # Otherwise, request finished
+            resp = await req_task
+            async with resp:
+                if resp.status >= 400:
+                    try:
+                        body = await resp.json()
+                    except (ContentTypeError, json.JSONDecodeError):
+                        body = await resp.text()
+                    should_retry = False
+                    wait_time = 0.0
+                    retry_label = ""
+                    is_rl = resp.status == 429 or (
+                        cfg.is_rate_limited is not None and cfg.is_rate_limited(resp.status, body)
+                    )
+                    if is_rl and rate_limit_attempts < cfg.max_retries_on_rate_limit:
+                        rate_limit_attempts += 1
+                        wait_time = min(rate_limit_delay, 30.0)
+                        rate_limit_delay *= cfg.retry_backoff
+                        retry_label = f"rate-limit retry {rate_limit_attempts} of {cfg.max_retries_on_rate_limit}"
+                        should_retry = True
+                    elif resp.status in _RETRY_STATUS and (attempt - rate_limit_attempts) <= cfg.max_retries:
+                        wait_time = delay
+                        delay *= cfg.retry_backoff
+                        retry_label = f"retry {attempt - rate_limit_attempts} of {cfg.max_retries}"
+                        should_retry = True
+
+                    if should_retry:
+                        wait_time = _retry_after_wait(resp.headers.get("Retry-After"), wait_time, _MAX_RETRY_AFTER_WAIT)
+                        logging.warning(
+                            "HTTP %s %s -> %s. Waiting %.2fs (%s).",
+                            method,
+                            url,
+                            resp.status,
+                            wait_time,
+                            retry_label,
+                        )
+                        request_logger.log_request_response(
+                            operation_id=operation_id,
+                            request_method=method,
+                            request_url=url,
+                            response_status_code=resp.status,
+                            response_headers=dict(resp.headers),
+                            response_content=body,
+                            error_message=f"HTTP {resp.status} ({retry_label}, will retry in {wait_time:.1f}s)",
+                        )
+                        await sleep_with_interrupt(
+                            wait_time,
+                            cfg.node_cls,
+                            cfg.wait_label if cfg.monitor_progress else None,
+                            start_time if cfg.monitor_progress else None,
+                            cfg.estimated_total,
+                            display_callback=_display_time_progress if cfg.monitor_progress else None,
+                        )
+                        continue
+                    msg = _friendly_http_message(resp.status, body)
+                    request_logger.log_request_response(
+                        operation_id=operation_id,
+                        request_method=method,
+                        request_url=url,
+                        response_status_code=resp.status,
+                        response_headers=dict(resp.headers),
+                        response_content=body,
+                        error_message=msg,
+                    )
+                    raise Exception(msg)
+
+                if expect_binary:
+                    buff = bytearray()
+                    last_tick = time.monotonic()
+                    async for chunk in resp.content.iter_chunked(64 * 1024):
+                        buff.extend(chunk)
+                        now = time.monotonic()
+                        if now - last_tick >= 1.0:
+                            last_tick = now
+                            if is_processing_interrupted():
+                                raise ProcessingInterrupted("Task cancelled")
+                            if cfg.monitor_progress:
+                                _display_time_progress(
+                                    cfg.node_cls, cfg.wait_label, int(now - start_time), cfg.estimated_total
+                                )
+                    bytes_payload = bytes(buff)
+                    resp_headers = {k.lower(): v for k, v in resp.headers.items()}
+                    if is_comfy_api_request:
+                        _maybe_remember_credits_used(cfg.node_cls, resp.headers.get(PRICE_CREDITS_HEADER))
+                    if cfg.price_extractor:
+                        with contextlib.suppress(Exception):
+                            extracted_price = cfg.price_extractor(resp_headers)
+                    if cfg.response_header_validator:
+                        cfg.response_header_validator(resp_headers)
+                    operation_succeeded = True
+                    final_elapsed_seconds = int(time.monotonic() - start_time)
+                    request_logger.log_request_response(
+                        operation_id=operation_id,
+                        request_method=method,
+                        request_url=url,
+                        response_status_code=resp.status,
+                        response_headers=resp_headers,
+                        response_content=bytes_payload,
+                    )
+                    return bytes_payload
+                else:
+                    try:
+                        payload = await resp.json()
+                        response_content_to_log: Any = payload
+                    except (ContentTypeError, json.JSONDecodeError):
+                        text = await resp.text()
+                        try:
+                            payload = json.loads(text) if text else {}
+                        except json.JSONDecodeError:
+                            payload = {"_raw": text}
+                        response_content_to_log = payload if isinstance(payload, dict) else text
+                    if is_comfy_api_request:
+                        _maybe_remember_credits_used(cfg.node_cls, resp.headers.get(PRICE_CREDITS_HEADER))
+                    with contextlib.suppress(Exception):
+                        extracted_price = cfg.price_extractor(payload) if cfg.price_extractor else None
+                    operation_succeeded = True
+                    final_elapsed_seconds = int(time.monotonic() - start_time)
+                    request_logger.log_request_response(
+                        operation_id=operation_id,
+                        request_method=method,
+                        request_url=url,
+                        response_status_code=resp.status,
+                        response_headers=dict(resp.headers),
+                        response_content=response_content_to_log,
+                    )
+                    return payload
+
+        except ProcessingInterrupted:
+            logging.debug("Polling was interrupted by user")
+            raise
+        except (ClientError, OSError) as e:
+            if (attempt - rate_limit_attempts) <= cfg.max_retries:
+                logging.warning(
+                    "Connection error calling %s %s. Retrying in %.2fs (%d/%d): %s",
+                    method,
+                    url,
+                    delay,
+                    attempt - rate_limit_attempts,
+                    cfg.max_retries,
+                    str(e),
+                )
+                request_logger.log_request_response(
+                    operation_id=operation_id,
+                    request_method=method,
+                    request_url=url,
+                    request_headers=dict(payload_headers) if payload_headers else None,
+                    request_params=dict(params) if params else None,
+                    request_data=request_body_log,
+                    error_message=f"{type(e).__name__}: {str(e)} (will retry)",
+                )
+                await sleep_with_interrupt(
+                    delay,
+                    cfg.node_cls,
+                    cfg.wait_label if cfg.monitor_progress else None,
+                    start_time if cfg.monitor_progress else None,
+                    cfg.estimated_total,
+                    display_callback=_display_time_progress if cfg.monitor_progress else None,
+                )
+                delay *= cfg.retry_backoff
+                continue
+            diag = await _diagnose_connectivity()
+            if not diag["internet_accessible"]:
+                request_logger.log_request_response(
+                    operation_id=operation_id,
+                    request_method=method,
+                    request_url=url,
+                    request_headers=dict(payload_headers) if payload_headers else None,
+                    request_params=dict(params) if params else None,
+                    request_data=request_body_log,
+                    error_message=f"LocalNetworkError: {str(e)}",
+                )
+                raise LocalNetworkError(
+                    "Unable to connect to the API server due to local network issues. "
+                    "Please check your internet connection and try again."
+                ) from e
+            request_logger.log_request_response(
+                operation_id=operation_id,
+                request_method=method,
+                request_url=url,
+                request_headers=dict(payload_headers) if payload_headers else None,
+                request_params=dict(params) if params else None,
+                request_data=request_body_log,
+                error_message=f"ApiServerError: {str(e)}",
+            )
+            raise ApiServerError(
+                f"The API server at {default_base_url()} is currently unreachable. "
+                f"The service may be experiencing issues."
+            ) from e
+        finally:
+            stop_event.set()
+            if monitor_task:
+                monitor_task.cancel()
+                with contextlib.suppress(Exception):
+                    await monitor_task
+            if sess:
+                with contextlib.suppress(Exception):
+                    await sess.close()
+            if operation_succeeded and cfg.monitor_progress and cfg.final_label_on_success:
+                _display_time_progress(
+                    cfg.node_cls,
+                    status=cfg.final_label_on_success,
+                    elapsed_seconds=(
+                        final_elapsed_seconds
+                        if final_elapsed_seconds is not None
+                        else int(time.monotonic() - start_time)
+                    ),
+                    estimated_total=cfg.estimated_total,
+                    price=extracted_price,
+                    is_queued=False,
+                    processing_elapsed_seconds=final_elapsed_seconds,
+                )
+
+
+def _validate_or_raise(response_model: type[M], payload: Any) -> M:
+    try:
+        return response_model.model_validate(payload)
+    except Exception as e:
+        logging.error(
+            "Response validation failed for %s: %s",
+            getattr(response_model, "__name__", response_model),
+            e,
+        )
+        raise Exception(
+            f"Response validation failed for {getattr(response_model, '__name__', response_model)}: {e}"
+        ) from e
+
+
+def _wrap_model_extractor(
+    response_model: type[M],
+    extractor: Callable[[M], Any] | None,
+) -> Callable[[dict[str, Any]], Any] | None:
+    """Wrap a typed extractor so it can be used by the dict-based poller.
+    Validates the dict into `response_model` before invoking `extractor`.
+    Uses a small per-wrapper cache keyed by `id(dict)` to avoid re-validating
+    the same response for multiple extractors in a single poll attempt.
+    """
+    if extractor is None:
+        return None
+    _cache: dict[int, M] = {}
+
+    def _wrapped(d: dict[str, Any]) -> Any:
+        try:
+            key = id(d)
+            model = _cache.get(key)
+            if model is None:
+                model = response_model.model_validate(d)
+                _cache[key] = model
+            return extractor(model)
+        except Exception as e:
+            logging.error("Extractor failed (typed -> dict wrapper): %s", e)
+            raise
+
+    return _wrapped
+
+
+def _normalize_statuses(values: Iterable[str | int] | None) -> set[str | int]:
+    if not values:
+        return set()
+    out: set[str | int] = set()
+    for v in values:
+        nv = _normalize_status_value(v)
+        if nv is not None:
+            out.add(nv)
+    return out
+
+
+def _normalize_status_value(val: str | int | None) -> str | int | None:
+    if isinstance(val, str):
+        return val.strip().lower()
+    return val
diff --git a/comfy_api_nodes/util/common_exceptions.py b/comfy_api_nodes/util/common_exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca964bc42430f3a037cd541313e360241eb21ffe
--- /dev/null
+++ b/comfy_api_nodes/util/common_exceptions.py
@@ -0,0 +1,14 @@
+class NetworkError(Exception):
+    """Base exception for network-related errors with diagnostic information."""
+
+
+class LocalNetworkError(NetworkError):
+    """Exception raised when local network connectivity issues are detected."""
+
+
+class ApiServerError(NetworkError):
+    """Exception raised when the API server is unreachable but internet is working."""
+
+
+class ProcessingInterrupted(Exception):
+    """Operation was interrupted by user/runtime via processing_interrupted()."""
diff --git a/comfy_api_nodes/util/conversions.py b/comfy_api_nodes/util/conversions.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2285a1b08f4ebc8921e5d5ace7d2830812d77ad
--- /dev/null
+++ b/comfy_api_nodes/util/conversions.py
@@ -0,0 +1,661 @@
+import base64
+import logging
+import math
+import mimetypes
+import uuid
+from io import BytesIO
+
+import av
+import numpy as np
+import torch
+from PIL import Image
+
+from comfy.utils import common_upscale
+from comfy_api.latest import Input, InputImpl, Types
+
+from ._helpers import mimetype_to_extension
+
+
+def bytesio_to_image_tensor(image_bytesio: BytesIO, mode: str | None = None) -> torch.Tensor:
+    """Converts image data from BytesIO to a torch.Tensor.
+
+    Args:
+        image_bytesio: BytesIO object containing the image data.
+        mode: The PIL mode to convert the image to (e.g., "RGB", "RGBA"). Defaults
+            to RGBA when the decoded image carries transparency and RGB when it
+            does not, so an API that returns no alpha does not get an opaque one.
+
+    Returns:
+        A torch.Tensor representing the image (1, H, W, C).
+
+    Raises:
+        PIL.UnidentifiedImageError: If the image data cannot be identified.
+        ValueError: If the specified mode is invalid.
+    """
+    image = Image.open(image_bytesio)
+    if mode is None:
+        mode = "RGBA" if "A" in image.getbands() or "transparency" in image.info else "RGB"
+    image = image.convert(mode)
+    image_array = np.array(image).astype(np.float32) / 255.0
+    return torch.from_numpy(image_array).unsqueeze(0)
+
+
+def image_tensor_pair_to_batch(image1: torch.Tensor, image2: torch.Tensor) -> torch.Tensor:
+    """
+    Converts a pair of image tensors to a batch tensor.
+    If the images are not the same size, the smaller image is resized to
+    match the larger image.
+    """
+    if image1.shape[1:] != image2.shape[1:]:
+        image2 = common_upscale(
+            image2.movedim(-1, 1),
+            image1.shape[2],
+            image1.shape[1],
+            "bilinear",
+            "center",
+        ).movedim(1, -1)
+    return torch.cat((image1, image2), dim=0)
+
+
+def pad_images_to_common_channels(images: list[torch.Tensor]) -> list[torch.Tensor]:
+    """Pads [B, H, W, C] image tensors with opaque alpha so they all share the largest channel count."""
+    channels = max(image.shape[-1] for image in images)
+    return [
+        torch.nn.functional.pad(image, (0, channels - image.shape[-1]), value=1.0)
+        if image.shape[-1] < channels
+        else image
+        for image in images
+    ]
+
+
+def tensor_to_bytesio(
+    image: torch.Tensor,
+    *,
+    total_pixels: int | None = 2048 * 2048,
+    mime_type: str | None = "image/png",
+) -> BytesIO:
+    """Converts a torch.Tensor image to a named BytesIO object.
+
+    Args:
+        image: Input torch.Tensor image.
+        total_pixels: Maximum total pixels for downscaling. If None, no downscaling is performed.
+        mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4').
+
+    Returns:
+        Named BytesIO object containing the image data, with pointer set to the start of buffer.
+    """
+    if not mime_type:
+        mime_type = "image/png"
+
+    pil_image = tensor_to_pil(image, total_pixels=total_pixels)
+    img_binary = pil_to_bytesio(pil_image, mime_type=mime_type)
+    img_binary.name = f"{uuid.uuid4()}.{mimetype_to_extension(mime_type)}"
+    return img_binary
+
+
+def tensor_to_pil(image: torch.Tensor, total_pixels: int | None = 2048 * 2048) -> Image.Image:
+    """Converts a single torch.Tensor image [H, W, C] to a PIL Image, optionally downscaling."""
+    if len(image.shape) > 3:
+        image = image[0]
+    # TODO: remove alpha if not allowed and present
+    input_tensor = image.cpu()
+    if total_pixels is not None:
+        input_tensor = downscale_image_tensor(input_tensor.unsqueeze(0), total_pixels=total_pixels).squeeze()
+    image_np = (input_tensor.numpy() * 255).astype(np.uint8)
+    img = Image.fromarray(image_np)
+    return img
+
+
+def tensor_to_base64_string(
+    image_tensor: torch.Tensor,
+    total_pixels: int | None = 2048 * 2048,
+    mime_type: str = "image/png",
+) -> str:
+    """Convert [B, H, W, C] or [H, W, C] tensor to a base64 string.
+
+    Args:
+        image_tensor: Input torch.Tensor image.
+        total_pixels: Maximum total pixels for downscaling. If None, no downscaling is performed.
+        mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4').
+
+    Returns:
+        Base64 encoded string of the image.
+    """
+    pil_image = tensor_to_pil(image_tensor, total_pixels=total_pixels)
+    img_byte_arr = pil_to_bytesio(pil_image, mime_type=mime_type)
+    img_bytes = img_byte_arr.getvalue()
+    # Encode bytes to base64 string
+    base64_encoded_string = base64.b64encode(img_bytes).decode("utf-8")
+    return base64_encoded_string
+
+
+def pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO:
+    """Converts a PIL Image to a BytesIO object."""
+    if not mime_type:
+        mime_type = "image/png"
+
+    img_byte_arr = BytesIO()
+    # Derive PIL format from MIME type (e.g., 'image/png' -> 'PNG')
+    pil_format = mime_type.split("/")[-1].upper()
+    if pil_format == "JPG":
+        pil_format = "JPEG"
+    img.save(img_byte_arr, format=pil_format)
+    img_byte_arr.seek(0)
+    return img_byte_arr
+
+
+def _compute_downscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[int, int] | None:
+    """Return downscaled (w, h) with even dims fitting ``total_pixels``, or None if already fits.
+
+    Source aspect ratio is preserved; output may drift by a fraction of a percent because both dimensions
+    are rounded down to even values (many  codecs require divisible-by-2).
+    """
+    pixels = src_w * src_h
+    if pixels <= total_pixels:
+        return None
+    scale = math.sqrt(total_pixels / pixels)
+    new_w = max(2, int(src_w * scale))
+    new_h = max(2, int(src_h * scale))
+    new_w -= new_w % 2
+    new_h -= new_h % 2
+    return new_w, new_h
+
+
+def downscale_image_tensor(image: torch.Tensor, total_pixels: int = 1536 * 1024) -> torch.Tensor:
+    """Downscale input image tensor to roughly the specified total pixels.
+
+    Output dimensions are rounded down to even values so that the result is guaranteed to fit within ``total_pixels``
+    and is compatible with codecs that require even dimensions (e.g. yuv420p).
+    """
+    samples = image.movedim(-1, 1)
+    dims = _compute_downscale_dims(samples.shape[3], samples.shape[2], int(total_pixels))
+    if dims is None:
+        return image
+    new_w, new_h = dims
+    return common_upscale(samples, new_w, new_h, "lanczos", "disabled").movedim(1, -1)
+
+
+def downscale_image_tensor_by_max_side(image: torch.Tensor, *, max_side: int) -> torch.Tensor:
+    """Downscale input image tensor so the largest dimension is at most max_side pixels."""
+    samples = image.movedim(-1, 1)
+    height, width = samples.shape[2], samples.shape[3]
+    max_dim = max(width, height)
+    if max_dim <= max_side:
+        return image
+    scale_by = max_side / max_dim
+    new_width = round(width * scale_by)
+    new_height = round(height * scale_by)
+    s = common_upscale(samples, new_width, new_height, "lanczos", "disabled")
+    s = s.movedim(1, -1)
+    return s
+
+
+def tensor_to_data_uri(
+    image_tensor: torch.Tensor,
+    total_pixels: int | None = 2048 * 2048,
+    mime_type: str = "image/png",
+) -> str:
+    """Converts a tensor image to a Data URI string.
+
+    Args:
+        image_tensor: Input torch.Tensor image.
+        total_pixels: Maximum total pixels for downscaling. If None, no downscaling is performed.
+        mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp').
+
+    Returns:
+        Data URI string (e.g., 'data:image/png;base64,...').
+    """
+    base64_string = tensor_to_base64_string(image_tensor, total_pixels, mime_type)
+    return f"data:{mime_type};base64,{base64_string}"
+
+
+def audio_to_base64_string(audio: Input.Audio, container_format: str = "mp4", codec_name: str = "aac") -> str:
+    """Converts an audio input to a base64 string."""
+    sample_rate: int = audio["sample_rate"]
+    waveform: torch.Tensor = audio["waveform"]
+    audio_data_np = audio_tensor_to_contiguous_ndarray(waveform)
+    audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, sample_rate, container_format, codec_name)
+    audio_bytes = audio_bytes_io.getvalue()
+    return base64.b64encode(audio_bytes).decode("utf-8")
+
+
+def video_to_base64_string(
+    video: Input.Video,
+    container_format: Types.VideoContainer | None = None,
+    codec: Types.VideoCodec | None = None,
+) -> str:
+    """
+    Converts a video input to a base64 string.
+
+    Args:
+        video: The video input to convert
+        container_format: Optional container format to use (defaults to video.container if available)
+        codec: Optional codec to use (defaults to video.codec if available)
+    """
+    video_bytes_io = BytesIO()
+    video.save_to(
+        video_bytes_io,
+        format=container_format or getattr(video, "container", Types.VideoContainer.MP4),
+        codec=codec or getattr(video, "codec", Types.VideoCodec.H264),
+    )
+    video_bytes_io.seek(0)
+    return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8")
+
+
+def audio_ndarray_to_bytesio(
+    audio_data_np: np.ndarray,
+    sample_rate: int,
+    container_format: str = "mp4",
+    codec_name: str = "aac",
+) -> BytesIO:
+    """
+    Encodes a numpy array of audio data into a BytesIO object.
+    """
+    audio_bytes_io = BytesIO()
+    with av.open(audio_bytes_io, mode="w", format=container_format) as output_container:
+        audio_stream = output_container.add_stream(codec_name, rate=sample_rate)
+        frame = av.AudioFrame.from_ndarray(
+            audio_data_np,
+            format="fltp",
+            layout="stereo" if audio_data_np.shape[0] > 1 else "mono",
+        )
+        frame.sample_rate = sample_rate
+        frame.pts = 0
+
+        for packet in audio_stream.encode(frame):
+            output_container.mux(packet)
+
+        # Flush stream
+        for packet in audio_stream.encode(None):
+            output_container.mux(packet)
+
+    audio_bytes_io.seek(0)
+    return audio_bytes_io
+
+
+def audio_tensor_to_contiguous_ndarray(waveform: torch.Tensor) -> np.ndarray:
+    """
+    Prepares audio waveform for av library by converting to a contiguous numpy array.
+
+    Args:
+        waveform: a tensor of shape (1, channels, samples) derived from a Comfy `AUDIO` type.
+
+    Returns:
+        Contiguous numpy array of the audio waveform.
+
+    Raises:
+        ValueError: If the waveform is not shaped (1, channels, samples).
+    """
+    if waveform.ndim != 3 or waveform.shape[0] != 1:
+        raise ValueError("Expected waveform tensor shape (1, channels, samples)")
+
+    # Prepare for av: remove batch dim, move to CPU, make contiguous, convert to numpy array
+    audio_data_np = waveform.squeeze(0).cpu().contiguous().numpy()
+    if audio_data_np.dtype != np.float32:
+        audio_data_np = audio_data_np.astype(np.float32)
+
+    return audio_data_np
+
+
+def audio_input_to_mp3(audio: Input.Audio) -> BytesIO:
+    audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+    sample_rate = int(audio["sample_rate"])
+
+    output_buffer = BytesIO()
+    output_container = av.open(output_buffer, mode="w", format="mp3")
+
+    out_stream = output_container.add_stream("libmp3lame", rate=sample_rate)
+    out_stream.bit_rate = 320000
+
+    frame = av.AudioFrame.from_ndarray(
+        audio_data_np,
+        format="fltp",
+        layout="stereo" if audio_data_np.shape[0] > 1 else "mono",
+    )
+    frame.sample_rate = sample_rate
+    frame.pts = 0
+    output_container.mux(out_stream.encode(frame))
+    output_container.mux(out_stream.encode(None))
+    output_container.close()
+    output_buffer.seek(0)
+    return output_buffer
+
+
+def trim_video(video: Input.Video, duration_sec: float) -> Input.Video:
+    """
+    Returns a new VideoInput object trimmed from the beginning to the specified duration,
+    using av to avoid loading entire video into memory.
+
+    Args:
+        video: Input video to trim
+        duration_sec: Duration in seconds to keep from the beginning
+
+    Returns:
+        VideoFromFile object that owns the output buffer
+    """
+    output_buffer = BytesIO()
+    input_container = None
+    output_container = None
+
+    try:
+        # Get the stream source - this avoids loading entire video into memory
+        # when the source is already a file path
+        input_source = video.get_stream_source()
+
+        # Open containers
+        input_container = av.open(input_source, mode="r")
+        output_container = av.open(output_buffer, mode="w", format="mp4")
+
+        # Set up output streams for re-encoding
+        video_stream = None
+        audio_stream = None
+
+        for stream in input_container.streams:
+            logging.info("Found stream: type=%s, class=%s", stream.type, type(stream))
+            if isinstance(stream, av.VideoStream):
+                # Create output video stream with same parameters
+                video_stream = output_container.add_stream("h264", rate=stream.average_rate)
+                video_stream.width = stream.width
+                video_stream.height = stream.height
+                video_stream.pix_fmt = "yuv420p"
+                logging.info("Added video stream: %sx%s @ %sfps", stream.width, stream.height, stream.average_rate)
+            elif isinstance(stream, av.AudioStream):
+                # Create output audio stream with same parameters
+                audio_stream = output_container.add_stream("aac", rate=stream.sample_rate)
+                audio_stream.sample_rate = stream.sample_rate
+                audio_stream.layout = stream.layout
+                logging.info("Added audio stream: %sHz, %s channels", stream.sample_rate, stream.channels)
+
+        # Calculate target frame count that's divisible by 16
+        fps = input_container.streams.video[0].average_rate
+        estimated_frames = int(duration_sec * fps)
+        target_frames = (estimated_frames // 16) * 16  # Round down to nearest multiple of 16
+
+        if target_frames == 0:
+            raise ValueError("Video too short: need at least 16 frames for Moonvalley")
+
+        frame_count = 0
+        audio_frame_count = 0
+
+        # Decode and re-encode video frames
+        if video_stream:
+            for frame in input_container.decode(video=0):
+                if frame_count >= target_frames:
+                    break
+
+                # Re-encode frame
+                for packet in video_stream.encode(frame):
+                    output_container.mux(packet)
+                frame_count += 1
+
+            # Flush encoder
+            for packet in video_stream.encode():
+                output_container.mux(packet)
+
+            logging.info("Encoded %s video frames (target: %s)", frame_count, target_frames)
+
+        # Decode and re-encode audio frames
+        if audio_stream:
+            input_container.seek(0)  # Reset to beginning for audio
+            for frame in input_container.decode(audio=0):
+                if frame.time >= duration_sec:
+                    break
+
+                # Re-encode frame
+                for packet in audio_stream.encode(frame):
+                    output_container.mux(packet)
+                audio_frame_count += 1
+
+            # Flush encoder
+            for packet in audio_stream.encode():
+                output_container.mux(packet)
+
+            logging.info("Encoded %s audio frames", audio_frame_count)
+
+        # Close containers
+        output_container.close()
+        input_container.close()
+
+        # Return as VideoFromFile using the buffer
+        output_buffer.seek(0)
+        return InputImpl.VideoFromFile(output_buffer)
+
+    except Exception as e:
+        # Clean up on error
+        if input_container is not None:
+            input_container.close()
+        if output_container is not None:
+            output_container.close()
+        raise RuntimeError(f"Failed to trim video: {str(e)}") from e
+
+
+def downscale_video_to_max_pixels(video: Input.Video, max_pixels: int) -> Input.Video:
+    """Downscale a video to fit within ``max_pixels`` (w * h), preserving aspect ratio.
+
+    Returns the original video object untouched when it already fits. Preserves frame rate, duration, and audio.
+    Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
+    """
+    src_w, src_h = video.get_dimensions()
+    scale_dims = _compute_downscale_dims(src_w, src_h, max_pixels)
+    if scale_dims is None:
+        return video
+    return _apply_video_scale(video, scale_dims)
+
+
+def _compute_upscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[int, int] | None:
+    """Return upscaled (w, h) with even dims meeting at least ``total_pixels``, or None if already large enough.
+
+    Source aspect ratio is preserved; output may drift by a fraction of a percent because both dimensions
+    are rounded up to even values (many codecs require divisible-by-2). The result is guaranteed to be at
+    least ``total_pixels``.
+    """
+    pixels = src_w * src_h
+    if pixels >= total_pixels:
+        return None
+    scale = math.sqrt(total_pixels / pixels)
+    new_w = math.ceil(src_w * scale)
+    new_h = math.ceil(src_h * scale)
+    if new_w % 2:
+        new_w += 1
+    if new_h % 2:
+        new_h += 1
+    return new_w, new_h
+
+
+def upscale_image_tensor_to_min_pixels(image: torch.Tensor, total_pixels: int) -> torch.Tensor:
+    samples = image.movedim(-1, 1)
+    dims = _compute_upscale_dims(samples.shape[3], samples.shape[2], int(total_pixels))
+    if dims is None:
+        return image
+    new_w, new_h = dims
+    return common_upscale(samples, new_w, new_h, "lanczos", "disabled").movedim(1, -1)
+
+
+def upscale_video_to_min_pixels(video: Input.Video, min_pixels: int) -> Input.Video:
+    """Upscale a video to meet at least ``min_pixels`` (w * h), preserving aspect ratio.
+
+    Returns the original video object untouched when it already meets the minimum. Preserves frame rate,
+    duration, and audio. Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
+    Note: upscaling a low-resolution source does not add real detail; downstream model quality may suffer.
+    """
+    src_w, src_h = video.get_dimensions()
+    scale_dims = _compute_upscale_dims(src_w, src_h, min_pixels)
+    if scale_dims is None:
+        return video
+    return _apply_video_scale(video, scale_dims)
+
+
+def _apply_video_scale(video: Input.Video, scale_dims: tuple[int, int]) -> Input.Video:
+    """Re-encode ``video`` scaled to ``scale_dims`` with a single decode/encode pass."""
+    out_w, out_h = scale_dims
+    output_buffer = BytesIO()
+    input_container = None
+    output_container = None
+
+    # get_stream_source() is untrimmed, so apply the trim window in this same pass.
+    # start_time is normalized (>= 0); duration == 0 means "until the end".
+    start_time, duration = video.get_active_trim_window()
+    trimming = bool(start_time or duration)
+
+    try:
+        input_source = video.get_stream_source()
+        input_container = av.open(input_source, mode="r")
+        output_container = av.open(output_buffer, mode="w", format="mp4")
+
+        video_stream = output_container.add_stream("h264", rate=video.get_frame_rate())
+        video_stream.width = out_w
+        video_stream.height = out_h
+        video_stream.pix_fmt = "yuv420p"
+
+        audio_stream = None
+        for stream in input_container.streams:
+            if isinstance(stream, av.AudioStream):
+                audio_stream = output_container.add_stream("aac", rate=stream.sample_rate)
+                audio_stream.sample_rate = stream.sample_rate
+                audio_stream.layout = stream.layout
+                break
+
+        in_video = input_container.streams.video[0]
+        start_pts = int(start_time / in_video.time_base) if trimming else 0
+        end_pts = int((start_time + duration) / in_video.time_base) if duration else None
+        if start_pts:
+            input_container.seek(start_pts, stream=in_video)
+
+        encoded = 0
+        for frame in input_container.decode(video=0):
+            if trimming:
+                if frame.pts is None or frame.pts < start_pts:
+                    continue
+                if end_pts is not None and frame.pts >= end_pts:
+                    break
+            frame = frame.reformat(width=out_w, height=out_h, format="yuv420p")
+            # Re-wrap as a fresh frame: dropping irregular source timestamps (VFR/AVI/GIF/...)
+            # lets the encoder assign clean ones and avoids mp4 muxer errors.
+            frame = av.VideoFrame.from_ndarray(frame.to_ndarray(format="yuv420p"), format="yuv420p")
+            for packet in video_stream.encode(frame):
+                output_container.mux(packet)
+            encoded += 1
+        for packet in video_stream.encode():
+            output_container.mux(packet)
+
+        if encoded == 0:
+            raise ValueError(
+                f"resize produced no frames (start_time={start_time}, duration={duration} "
+                "selected nothing from the source)"
+            )
+
+        if audio_stream is not None:
+            input_container.seek(0)
+            for audio_frame in input_container.decode(audio=0):
+                if trimming:
+                    if audio_frame.time is None or audio_frame.time < start_time:
+                        continue
+                    if duration and audio_frame.time > start_time + duration:
+                        break
+                # Carry odd audio time bases the mp4 muxer rejects; reset pts, encoder assigns clean ones (MP3-in-AVI)
+                audio_frame.pts = None
+                for packet in audio_stream.encode(audio_frame):
+                    output_container.mux(packet)
+            for packet in audio_stream.encode():
+                output_container.mux(packet)
+
+        output_container.close()
+        input_container.close()
+        output_buffer.seek(0)
+        return InputImpl.VideoFromFile(output_buffer)
+
+    except Exception as e:
+        if input_container is not None:
+            input_container.close()
+        if output_container is not None:
+            output_container.close()
+        raise RuntimeError(f"Failed to resize video: {str(e)}") from e
+
+
+def _f32_pcm(wav: torch.Tensor) -> torch.Tensor:
+    """Convert audio to float 32 bits PCM format. Copy-paste from nodes_audio.py file."""
+    if wav.dtype.is_floating_point:
+        return wav
+    elif wav.dtype == torch.int16:
+        return wav.float() / (2**15)
+    elif wav.dtype == torch.int32:
+        return wav.float() / (2**31)
+    raise ValueError(f"Unsupported wav dtype: {wav.dtype}")
+
+
+def audio_bytes_to_audio_input(audio_bytes: bytes) -> dict:
+    """
+    Decode any common audio container from bytes using PyAV and return
+    a Comfy AUDIO dict: {"waveform": [1, C, T] float32, "sample_rate": int}.
+    """
+    with av.open(BytesIO(audio_bytes)) as af:
+        if not af.streams.audio:
+            raise ValueError("No audio stream found in response.")
+        stream = af.streams.audio[0]
+
+        in_sr = int(stream.codec_context.sample_rate)
+        out_sr = in_sr
+
+        frames: list[torch.Tensor] = []
+        n_channels = stream.channels or 1
+
+        for frame in af.decode(streams=stream.index):
+            arr = frame.to_ndarray()  # shape can be [C, T] or [T, C] or [T]
+            buf = torch.from_numpy(arr)
+            if buf.ndim == 1:
+                buf = buf.unsqueeze(0)  # [T] -> [1, T]
+            elif buf.shape[0] != n_channels and buf.shape[-1] == n_channels:
+                buf = buf.transpose(0, 1).contiguous()  # [T, C] -> [C, T]
+            elif buf.shape[0] != n_channels:
+                buf = buf.reshape(-1, n_channels).t().contiguous()  # fallback to [C, T]
+            frames.append(buf)
+
+    if not frames:
+        raise ValueError("Decoded zero audio frames.")
+
+    wav = torch.cat(frames, dim=1)  # [C, T]
+    wav = _f32_pcm(wav)
+    return {"waveform": wav.unsqueeze(0).contiguous(), "sample_rate": out_sr}
+
+
+def resize_mask_to_image(
+    mask: torch.Tensor,
+    image: torch.Tensor,
+    upscale_method="nearest-exact",
+    crop="disabled",
+    allow_gradient=True,
+    add_channel_dim=False,
+):
+    """Resize mask to be the same dimensions as an image, while maintaining proper format for API calls."""
+    _, height, width, _ = image.shape
+    mask = mask.unsqueeze(-1)
+    mask = mask.movedim(-1, 1)
+    mask = common_upscale(mask, width=width, height=height, upscale_method=upscale_method, crop=crop)
+    mask = mask.movedim(1, -1)
+    if not add_channel_dim:
+        mask = mask.squeeze(-1)
+    if not allow_gradient:
+        mask = (mask > 0.5).float()
+    return mask
+
+
+def convert_mask_to_image(mask: Input.Image) -> torch.Tensor:
+    """Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image."""
+    mask = mask.unsqueeze(-1)
+    return torch.cat([mask] * 3, dim=-1)
+
+
+def text_filepath_to_base64_string(filepath: str) -> str:
+    """Converts a text file to a base64 string."""
+    with open(filepath, "rb") as f:
+        file_content = f.read()
+    return base64.b64encode(file_content).decode("utf-8")
+
+
+def text_filepath_to_data_uri(filepath: str) -> str:
+    """Converts a text file to a data URI."""
+    base64_string = text_filepath_to_base64_string(filepath)
+    mime_type, _ = mimetypes.guess_type(filepath)
+    if mime_type is None:
+        mime_type = "application/octet-stream"
+    return f"data:{mime_type};base64,{base64_string}"
diff --git a/comfy_api_nodes/util/download_helpers.py b/comfy_api_nodes/util/download_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a6b1bdf6209f13622d25e9448f030614f2f4899
--- /dev/null
+++ b/comfy_api_nodes/util/download_helpers.py
@@ -0,0 +1,297 @@
+import asyncio
+import contextlib
+import uuid
+from io import BytesIO
+from pathlib import Path
+from typing import IO
+from urllib.parse import urljoin, urlparse
+
+import aiohttp
+import torch
+from aiohttp.client_exceptions import ClientError, ContentTypeError
+
+from comfy_api.latest import IO as COMFY_IO
+from comfy_api.latest import InputImpl, Types
+from folder_paths import get_output_directory
+
+from . import request_logger
+from ._helpers import (
+    default_base_url,
+    get_comfy_api_headers,
+    is_processing_interrupted,
+    sleep_with_interrupt,
+    to_aiohttp_url,
+)
+from .client import _diagnose_connectivity
+from .common_exceptions import ApiServerError, LocalNetworkError, ProcessingInterrupted
+from .conversions import bytesio_to_image_tensor
+
+_RETRY_STATUS = {408, 429, 500, 502, 503, 504}
+
+
+async def download_url_to_bytesio(
+    url: str,
+    dest: BytesIO | IO[bytes] | str | Path | None,
+    *,
+    timeout: float | None = None,
+    max_retries: int = 5,
+    retry_delay: float = 1.0,
+    retry_backoff: float = 2.0,
+    cls: type[COMFY_IO.ComfyNode] = None,
+) -> None:
+    """Stream-download a URL to `dest`.
+
+    `dest` must be one of:
+      - a BytesIO (rewound to 0 after write),
+      - a file-like object opened in binary write mode (must implement .write()),
+      - a filesystem path (str | pathlib.Path), which will be opened with 'wb'.
+
+    If `url` starts with `/proxy/`, `cls` must be provided so the URL can be expanded
+    to an absolute URL and authentication headers can be applied.
+
+    Raises:
+        ProcessingInterrupted, LocalNetworkError, ApiServerError, Exception (HTTP and other errors)
+    """
+    if not isinstance(dest, (str, Path)) and not hasattr(dest, "write"):
+        raise ValueError("dest must be a path (str|Path) or a binary-writable object providing .write().")
+
+    attempt = 0
+    delay = retry_delay
+    headers: dict[str, str] = {}
+
+    parsed_url = urlparse(url)
+    if not parsed_url.scheme and not parsed_url.netloc:  # is URL relative?
+        if cls is None:
+            raise ValueError("For relative 'cloud' paths, the `cls` parameter is required.")
+        url = urljoin(default_base_url().rstrip("/") + "/", url.lstrip("/"))
+        headers = get_comfy_api_headers(cls)
+
+    while True:
+        attempt += 1
+        op_id = _generate_operation_id("GET", url, attempt)
+        timeout_cfg = aiohttp.ClientTimeout(total=timeout)
+
+        is_path_sink = isinstance(dest, (str, Path))
+        fhandle = None
+        session: aiohttp.ClientSession | None = None
+        stop_evt: asyncio.Event | None = None
+        monitor_task: asyncio.Task | None = None
+        req_task: asyncio.Task | None = None
+
+        try:
+            with contextlib.suppress(Exception):
+                request_logger.log_request_response(operation_id=op_id, request_method="GET", request_url=url)
+
+            session = aiohttp.ClientSession(timeout=timeout_cfg)
+            stop_evt = asyncio.Event()
+
+            async def _monitor():
+                try:
+                    while not stop_evt.is_set():
+                        if is_processing_interrupted():
+                            return
+                        await asyncio.sleep(1.0)
+                except asyncio.CancelledError:
+                    return
+
+            monitor_task = asyncio.create_task(_monitor())
+
+            req_task = asyncio.create_task(session.get(to_aiohttp_url(url), headers=headers))
+            done, pending = await asyncio.wait({req_task, monitor_task}, return_when=asyncio.FIRST_COMPLETED)
+
+            if monitor_task in done and req_task in pending:
+                req_task.cancel()
+                with contextlib.suppress(Exception):
+                    await req_task
+                raise ProcessingInterrupted("Task cancelled")
+
+            try:
+                resp = await req_task
+            except asyncio.CancelledError:
+                raise ProcessingInterrupted("Task cancelled") from None
+
+            async with resp:
+                if resp.status >= 400:
+                    with contextlib.suppress(Exception):
+                        try:
+                            body = await resp.json()
+                        except (ContentTypeError, ValueError):
+                            text = await resp.text()
+                            body = text if len(text) <= 4096 else f"[text {len(text)} bytes]"
+                        request_logger.log_request_response(
+                            operation_id=op_id,
+                            request_method="GET",
+                            request_url=url,
+                            response_status_code=resp.status,
+                            response_headers=dict(resp.headers),
+                            response_content=body,
+                            error_message=f"HTTP {resp.status}",
+                        )
+
+                    if resp.status in _RETRY_STATUS and attempt <= max_retries:
+                        await sleep_with_interrupt(delay, cls, None, None, None)
+                        delay *= retry_backoff
+                        continue
+                    raise Exception(f"Failed to download (HTTP {resp.status}).")
+
+                if is_path_sink:
+                    p = Path(str(dest))
+                    with contextlib.suppress(Exception):
+                        p.parent.mkdir(parents=True, exist_ok=True)
+                    fhandle = open(p, "wb")
+                    sink = fhandle
+                else:
+                    sink = dest  # BytesIO or file-like
+
+                written = 0
+                while True:
+                    try:
+                        chunk = await asyncio.wait_for(resp.content.read(1024 * 1024), timeout=1.0)
+                    except asyncio.TimeoutError:
+                        chunk = b""
+                    except asyncio.CancelledError:
+                        raise ProcessingInterrupted("Task cancelled") from None
+
+                    if is_processing_interrupted():
+                        raise ProcessingInterrupted("Task cancelled")
+
+                    if not chunk:
+                        if resp.content.at_eof():
+                            break
+                        continue
+
+                    sink.write(chunk)
+                    written += len(chunk)
+
+                if isinstance(dest, BytesIO):
+                    with contextlib.suppress(Exception):
+                        dest.seek(0)
+
+                request_logger.log_request_response(
+                    operation_id=op_id,
+                    request_method="GET",
+                    request_url=url,
+                    response_status_code=resp.status,
+                    response_headers=dict(resp.headers),
+                    response_content=f"[streamed {written} bytes to dest]",
+                )
+                return
+        except asyncio.CancelledError:
+            raise ProcessingInterrupted("Task cancelled") from None
+        except (ClientError, OSError) as e:
+            if attempt <= max_retries:
+                request_logger.log_request_response(
+                    operation_id=op_id,
+                    request_method="GET",
+                    request_url=url,
+                    error_message=f"{type(e).__name__}: {str(e)} (will retry)",
+                )
+                await sleep_with_interrupt(delay, cls, None, None, None)
+                delay *= retry_backoff
+                continue
+
+            diag = await _diagnose_connectivity()
+            if not diag["internet_accessible"]:
+                raise LocalNetworkError(
+                    "Unable to connect to the network. Please check your internet connection and try again."
+                ) from e
+            raise ApiServerError("The remote service appears unreachable at this time.") from e
+        finally:
+            if stop_evt is not None:
+                stop_evt.set()
+            if monitor_task:
+                monitor_task.cancel()
+                with contextlib.suppress(Exception):
+                    await monitor_task
+            if req_task and not req_task.done():
+                req_task.cancel()
+                with contextlib.suppress(Exception):
+                    await req_task
+            if session:
+                with contextlib.suppress(Exception):
+                    await session.close()
+            if fhandle:
+                with contextlib.suppress(Exception):
+                    fhandle.flush()
+                    fhandle.close()
+
+
+async def download_url_to_image_tensor(
+    url: str,
+    *,
+    timeout: float = None,
+    cls: type[COMFY_IO.ComfyNode] = None,
+) -> torch.Tensor:
+    """Downloads an image from a URL and returns a [B, H, W, C] tensor."""
+    result = BytesIO()
+    await download_url_to_bytesio(url, result, timeout=timeout, cls=cls)
+    return bytesio_to_image_tensor(result)
+
+
+async def download_url_to_video_output(
+    video_url: str,
+    *,
+    timeout: float = None,
+    max_retries: int = 5,
+    cls: type[COMFY_IO.ComfyNode] = None,
+) -> InputImpl.VideoFromFile:
+    """Downloads a video from a URL and returns a `VIDEO` output."""
+    result = BytesIO()
+    await download_url_to_bytesio(video_url, result, timeout=timeout, max_retries=max_retries, cls=cls)
+    return InputImpl.VideoFromFile(result)
+
+
+async def download_url_as_bytesio(
+    url: str,
+    *,
+    timeout: float = None,
+    cls: type[COMFY_IO.ComfyNode] = None,
+) -> BytesIO:
+    """Downloads content from a URL and returns a new BytesIO (rewound to 0)."""
+    result = BytesIO()
+    await download_url_to_bytesio(url, result, timeout=timeout, cls=cls)
+    return result
+
+
+def _generate_operation_id(method: str, url: str, attempt: int) -> str:
+    try:
+        parsed = urlparse(url)
+        slug = (parsed.path.rsplit("/", 1)[-1] or parsed.netloc or "download").strip("/").replace("/", "_")
+    except Exception:
+        slug = "download"
+    return f"{method}_{slug}_try{attempt}_{uuid.uuid4().hex[:8]}"
+
+
+async def download_url_to_file_3d(
+    url: str,
+    file_format: str,
+    *,
+    task_id: str | None = None,
+    timeout: float | None = None,
+    max_retries: int = 5,
+    cls: type[COMFY_IO.ComfyNode] = None,
+) -> Types.File3D:
+    """Downloads a 3D model file from a URL into memory as BytesIO.
+
+    If task_id is provided, also writes the file to disk in the output directory
+    for backward compatibility with the old save-to-disk behavior.
+    """
+    file_format = file_format.lstrip(".").lower()
+    data = BytesIO()
+    await download_url_to_bytesio(
+        url,
+        data,
+        timeout=timeout,
+        max_retries=max_retries,
+        cls=cls,
+    )
+
+    if task_id is not None:
+        # This is only for backward compatability with current behavior when every 3D node is output node
+        # All new API nodes should not use "task_id" and instead users should use "SaveGLB" node to save results
+        output_dir = Path(get_output_directory())
+        output_path = output_dir / f"{task_id}.{file_format}"
+        output_path.write_bytes(data.getvalue())
+        data.seek(0)
+
+    return Types.File3D(source=data, file_format=file_format)
diff --git a/comfy_api_nodes/util/request_logger.py b/comfy_api_nodes/util/request_logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..28e9bee29849f508b60190222dab6a044f825700
--- /dev/null
+++ b/comfy_api_nodes/util/request_logger.py
@@ -0,0 +1,170 @@
+import datetime
+import hashlib
+import json
+import logging
+import os
+import re
+from typing import Any
+
+import folder_paths
+
+logger = logging.getLogger(__name__)
+_SENSITIVE_HEADERS = {"authorization", "x-api-key"}
+
+
+def get_log_directory():
+    """Ensures the API log directory exists within ComfyUI's temp directory and returns its path."""
+    base_temp_dir = folder_paths.get_temp_directory()
+    log_dir = os.path.join(base_temp_dir, "api_logs")
+    try:
+        os.makedirs(log_dir, exist_ok=True)
+    except Exception as e:
+        logger.error("Error creating API log directory %s: %s", log_dir, str(e))
+        # Fallback to base temp directory if sub-directory creation fails
+        return base_temp_dir
+    return log_dir
+
+
+def _sanitize_filename_component(name: str) -> str:
+    if not name:
+        return "log"
+    sanitized = re.sub(r"[^A-Za-z0-9._-]+", "_", name)  # Replace disallowed characters with underscore
+    sanitized = sanitized.strip(" ._")  # Windows: trailing dots or spaces are not allowed
+    if not sanitized:
+        sanitized = "log"
+    return sanitized
+
+
+def _short_hash(*parts: str, length: int = 10) -> str:
+    return hashlib.sha1(("|".join(parts)).encode("utf-8")).hexdigest()[:length]
+
+
+def _build_log_filepath(log_dir: str, operation_id: str, request_url: str) -> str:
+    """Build log filepath. We keep it well under common path length limits aiming for <= 240 characters total."""
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+    slug = _sanitize_filename_component(operation_id)  # Best-effort human-readable slug from operation_id
+    h = _short_hash(operation_id or "", request_url or "")  # Short hash ties log to the full operation and URL
+
+    # Compute how much room we have for the slug given the directory length
+    # Keep total path length reasonably below ~260 on Windows.
+    max_total_path = 240
+    prefix = f"{timestamp}_"
+    suffix = f"_{h}.log"
+    if not slug:
+        slug = "op"
+    max_filename_len = max(60, max_total_path - len(log_dir) - 1)
+    max_slug_len = max(8, max_filename_len - len(prefix) - len(suffix))
+    if len(slug) > max_slug_len:
+        slug = slug[:max_slug_len].rstrip(" ._-")
+    return os.path.join(log_dir, f"{prefix}{slug}{suffix}")
+
+
+def _format_data_for_logging(data: Any) -> str:
+    """Helper to format data (dict, str, bytes) for logging."""
+    if isinstance(data, bytes):
+        try:
+            return data.decode("utf-8")  # Try to decode as text
+        except UnicodeDecodeError:
+            return f"[Binary data of length {len(data)} bytes]"
+    elif isinstance(data, (dict, list)):
+        try:
+            return json.dumps(data, indent=2, ensure_ascii=False)
+        except TypeError:
+            return str(data)  # Fallback for non-serializable objects
+    return str(data)
+
+
+def _redact_headers(headers: dict) -> dict:
+    return {k: ("***" if k.lower() in _SENSITIVE_HEADERS else v) for k, v in headers.items()}
+
+
+def log_request_response(
+    operation_id: str,
+    request_method: str,
+    request_url: str,
+    request_headers: dict | None = None,
+    request_params: dict | None = None,
+    request_data: Any = None,
+    response_status_code: int | None = None,
+    response_headers: dict | None = None,
+    response_content: Any = None,
+    error_message: str | None = None,
+):
+    """
+    Logs API request and response details to a file in the temp/api_logs directory.
+    Filenames are sanitized and length-limited for cross-platform safety.
+    If we still fail to write, we fall back to appending into api.log.
+    """
+    try:
+        log_dir = get_log_directory()
+        filepath = _build_log_filepath(log_dir, operation_id, request_url)
+
+        log_content: list[str] = []
+        log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}")
+        log_content.append(f"Operation ID: {operation_id}")
+        log_content.append("-" * 30 + " REQUEST " + "-" * 30)
+        log_content.append(f"Method: {request_method}")
+        log_content.append(f"URL: {request_url}")
+        if request_headers:
+            log_content.append(f"Headers:\n{_format_data_for_logging(_redact_headers(request_headers))}")
+        if request_params:
+            log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
+        if request_data is not None:
+            log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}")
+
+        log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30)
+        if response_status_code is not None:
+            log_content.append(f"Status Code: {response_status_code}")
+        if response_headers:
+            log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}")
+        if response_content is not None:
+            log_content.append(f"Content:\n{_format_data_for_logging(response_content)}")
+        if error_message:
+            log_content.append(f"Error:\n{error_message}")
+
+        try:
+            with open(filepath, "w", encoding="utf-8") as f:
+                f.write("\n".join(log_content))
+            logger.debug("API log saved to: %s", filepath)
+        except Exception as e:
+            logger.error("Error writing API log to %s: %s", filepath, str(e))
+    except Exception as _log_e:
+        logging.debug("[DEBUG] log_request_response failed: %s", _log_e)
+
+
+if __name__ == '__main__':
+    # Example usage (for testing the logger directly)
+    logger.setLevel(logging.DEBUG)
+    # Mock folder_paths for direct execution if not running within ComfyUI full context
+    if not hasattr(folder_paths, 'get_temp_directory'):
+        class MockFolderPaths:
+            def get_temp_directory(self):
+                # Create a local temp dir for testing if needed
+                p = os.path.join(os.path.dirname(__file__), 'temp_test_logs')
+                os.makedirs(p, exist_ok=True)
+                return p
+        folder_paths = MockFolderPaths()
+
+    log_request_response(
+        operation_id="test_operation_get",
+        request_method="GET",
+        request_url="https://api.example.com/test",
+        request_headers={"Authorization": "Bearer testtoken"},
+        request_params={"param1": "value1"},
+        response_status_code=200,
+        response_content={"message": "Success!"}
+    )
+    log_request_response(
+        operation_id="test_operation_post_error",
+        request_method="POST",
+        request_url="https://api.example.com/submit",
+        request_data={"key": "value", "nested": {"num": 123}},
+        error_message="Connection timed out"
+    )
+    log_request_response(
+        operation_id="test_binary_response",
+        request_method="GET",
+        request_url="https://api.example.com/image.png",
+        response_status_code=200,
+        response_content=b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR...' # Sample binary data
+    )
diff --git a/comfy_api_nodes/util/upload_helpers.py b/comfy_api_nodes/util/upload_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d83e907b76f9bb66c8957c94534cc0ad48c4426
--- /dev/null
+++ b/comfy_api_nodes/util/upload_helpers.py
@@ -0,0 +1,394 @@
+import asyncio
+import contextlib
+import logging
+import time
+import uuid
+from io import BytesIO
+from urllib.parse import urlparse
+
+import aiohttp
+import torch
+from pydantic import BaseModel, Field
+
+from comfy_api.latest import IO, Input, Types
+
+from . import request_logger
+from ._helpers import is_processing_interrupted, sleep_with_interrupt
+from .client import (
+    ApiEndpoint,
+    _diagnose_connectivity,
+    _display_time_progress,
+    sync_op,
+)
+from .common_exceptions import ApiServerError, LocalNetworkError, ProcessingInterrupted
+from .conversions import (
+    audio_ndarray_to_bytesio,
+    audio_tensor_to_contiguous_ndarray,
+    tensor_to_bytesio,
+)
+
+
+class UploadRequest(BaseModel):
+    file_name: str = Field(..., description="Filename to upload")
+    content_type: str | None = Field(
+        None,
+        description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.",
+    )
+
+
+class UploadResponse(BaseModel):
+    download_url: str = Field(..., description="URL to GET uploaded file")
+    upload_url: str = Field(..., description="URL to PUT file to upload")
+
+
+async def upload_images_to_comfyapi(
+    cls: type[IO.ComfyNode],
+    image: torch.Tensor | list[torch.Tensor],
+    *,
+    max_images: int = 8,
+    mime_type: str | None = None,
+    wait_label: str | None = "Uploading",
+    show_batch_index: bool = True,
+    total_pixels: int | None = 2048 * 2048,
+) -> list[str]:
+    """
+    Uploads images to ComfyUI API and returns download URLs.
+    To upload multiple images, stack them in the batch dimension first.
+    """
+    tensors: list[torch.Tensor] = []
+    if isinstance(image, list):
+        for img in image:
+            is_batch = len(img.shape) > 3
+            if is_batch:
+                tensors.extend(img[i] for i in range(img.shape[0]))
+            else:
+                tensors.append(img)
+    else:
+        is_batch = len(image.shape) > 3
+        if is_batch:
+            tensors.extend(image[i] for i in range(image.shape[0]))
+        else:
+            tensors.append(image)
+
+    # if batched, try to upload each file if max_images is greater than 0
+    download_urls: list[str] = []
+    num_to_upload = min(len(tensors), max_images)
+    batch_start_ts = time.monotonic()
+
+    for idx in range(num_to_upload):
+        tensor = tensors[idx]
+        img_io = tensor_to_bytesio(tensor, total_pixels=total_pixels, mime_type=mime_type)
+
+        effective_label = wait_label
+        if wait_label and show_batch_index and num_to_upload > 1:
+            effective_label = f"{wait_label} ({idx + 1}/{num_to_upload})"
+
+        url = await upload_file_to_comfyapi(cls, img_io, img_io.name, mime_type, effective_label, batch_start_ts)
+        download_urls.append(url)
+    return download_urls
+
+
+async def upload_image_to_comfyapi(
+    cls: type[IO.ComfyNode],
+    image: torch.Tensor,
+    *,
+    mime_type: str | None = None,
+    wait_label: str | None = "Uploading",
+    total_pixels: int | None = 2048 * 2048,
+) -> str:
+    """Uploads a single image to ComfyUI API and returns its download URL."""
+    return (
+        await upload_images_to_comfyapi(
+            cls,
+            image,
+            max_images=1,
+            mime_type=mime_type,
+            wait_label=wait_label,
+            show_batch_index=False,
+            total_pixels=total_pixels,
+        )
+    )[0]
+
+
+async def upload_audio_to_comfyapi(
+    cls: type[IO.ComfyNode],
+    audio: Input.Audio,
+    *,
+    container_format: str = "mp4",
+    codec_name: str = "aac",
+    mime_type: str = "audio/mp4",
+) -> str:
+    """
+    Uploads a single audio input to ComfyUI API and returns its download URL.
+    Encodes the raw waveform into the specified format before uploading.
+    """
+    sample_rate: int = audio["sample_rate"]
+    waveform: torch.Tensor = audio["waveform"]
+    audio_data_np = audio_tensor_to_contiguous_ndarray(waveform)
+    audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, sample_rate, container_format, codec_name)
+    return await upload_file_to_comfyapi(cls, audio_bytes_io, f"{uuid.uuid4()}.{container_format}", mime_type)
+
+
+async def upload_video_to_comfyapi(
+    cls: type[IO.ComfyNode],
+    video: Input.Video,
+    *,
+    container: Types.VideoContainer = Types.VideoContainer.MP4,
+    codec: Types.VideoCodec = Types.VideoCodec.H264,
+    max_duration: int | None = None,
+    wait_label: str | None = "Uploading",
+) -> str:
+    """
+    Uploads a single video to ComfyUI API and returns its download URL.
+    Uses the specified container and codec for saving the video before upload.
+    """
+    if max_duration is not None:
+        try:
+            actual_duration = video.get_duration()
+            if actual_duration > max_duration:
+                raise ValueError(
+                    f"Video duration ({actual_duration:.2f}s) exceeds the maximum allowed ({max_duration}s)."
+                )
+        except Exception as e:
+            logging.error("Error getting video duration: %s", str(e))
+            raise ValueError(f"Could not verify video duration from source: {e}") from e
+
+    upload_mime_type = f"video/{container.value.lower()}"
+    filename = f"{uuid.uuid4()}.{container.value.lower()}"
+
+    # Convert VideoInput to BytesIO using specified container/codec
+    video_bytes_io = BytesIO()
+    try:
+        video.save_to(video_bytes_io, format=container, codec=codec)
+    except Exception as e:
+        raise ValueError(
+            f"Could not convert the input video to {container.value.upper()} for upload; "
+            f"the file may be corrupted or use an unsupported codec. "
+            f"Try re-exporting it as MP4 (H.264). Original error: {e}"
+        ) from e
+    video_bytes_io.seek(0)
+
+    return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type, wait_label)
+
+
+_3D_MIME_TYPES = {
+    "glb": "model/gltf-binary",
+    "obj": "model/obj",
+    "fbx": "application/octet-stream",
+}
+
+
+async def upload_3d_model_to_comfyapi(
+    cls: type[IO.ComfyNode],
+    model_3d: Types.File3D,
+    file_format: str,
+) -> str:
+    """Uploads a 3D model file to ComfyUI API and returns its download URL."""
+    return await upload_file_to_comfyapi(
+        cls,
+        model_3d.get_data(),
+        f"{uuid.uuid4()}.{file_format}",
+        _3D_MIME_TYPES.get(file_format, "application/octet-stream"),
+    )
+
+
+async def upload_file_to_comfyapi(
+    cls: type[IO.ComfyNode],
+    file_bytes_io: BytesIO,
+    filename: str,
+    upload_mime_type: str | None,
+    wait_label: str | None = "Uploading",
+    progress_origin_ts: float | None = None,
+) -> str:
+    """Uploads a single file to ComfyUI API and returns its download URL."""
+    if upload_mime_type is None:
+        request_object = UploadRequest(file_name=filename)
+    else:
+        request_object = UploadRequest(file_name=filename, content_type=upload_mime_type)
+    create_resp = await sync_op(
+        cls,
+        endpoint=ApiEndpoint(path="/customers/storage", method="POST"),
+        data=request_object,
+        response_model=UploadResponse,
+        final_label_on_success=None,
+        monitor_progress=False,
+    )
+    await upload_file(
+        cls,
+        create_resp.upload_url,
+        file_bytes_io,
+        content_type=upload_mime_type,
+        wait_label=wait_label,
+        progress_origin_ts=progress_origin_ts,
+    )
+    return create_resp.download_url
+
+
+async def upload_file(
+    cls: type[IO.ComfyNode],
+    upload_url: str,
+    file: BytesIO | str,
+    *,
+    content_type: str | None = None,
+    max_retries: int = 3,
+    retry_delay: float = 1.0,
+    retry_backoff: float = 2.0,
+    wait_label: str | None = None,
+    progress_origin_ts: float | None = None,
+) -> None:
+    """
+    Upload a file to a signed URL (e.g., S3 pre-signed PUT) with retries, Comfy progress display, and interruption.
+
+    Raises:
+        ProcessingInterrupted, LocalNetworkError, ApiServerError, Exception
+    """
+    if isinstance(file, BytesIO):
+        with contextlib.suppress(Exception):
+            file.seek(0)
+        data = file.read()
+    elif isinstance(file, str):
+        with open(file, "rb") as f:
+            data = f.read()
+    else:
+        raise ValueError("file must be a BytesIO or a filesystem path string")
+
+    headers: dict[str, str] = {}
+    skip_auto_headers: set[str] = set()
+    if content_type:
+        headers["Content-Type"] = content_type
+    else:
+        skip_auto_headers.add("Content-Type")  # Don't let aiohttp add Content-Type, it can break the signed request
+
+    attempt = 0
+    delay = retry_delay
+    start_ts = progress_origin_ts if progress_origin_ts is not None else time.monotonic()
+    op_uuid = uuid.uuid4().hex[:8]
+    while True:
+        attempt += 1
+        operation_id = _generate_operation_id("PUT", upload_url, attempt, op_uuid)
+        timeout = aiohttp.ClientTimeout(total=None)
+        stop_evt = asyncio.Event()
+
+        async def _monitor():
+            try:
+                while not stop_evt.is_set():
+                    if is_processing_interrupted():
+                        return
+                    if wait_label:
+                        _display_time_progress(cls, wait_label, int(time.monotonic() - start_ts), None)
+                    await asyncio.sleep(1.0)
+            except asyncio.CancelledError:
+                return
+
+        monitor_task = asyncio.create_task(_monitor())
+        sess: aiohttp.ClientSession | None = None
+        try:
+            request_logger.log_request_response(
+                operation_id=operation_id,
+                request_method="PUT",
+                request_url=upload_url,
+                request_headers=headers or None,
+                request_params=None,
+                request_data=f"[File data {len(data)} bytes]",
+            )
+
+            sess = aiohttp.ClientSession(timeout=timeout)
+            req = sess.put(upload_url, data=data, headers=headers, skip_auto_headers=skip_auto_headers)
+            req_task = asyncio.create_task(req)
+
+            done, pending = await asyncio.wait({req_task, monitor_task}, return_when=asyncio.FIRST_COMPLETED)
+
+            if monitor_task in done and req_task in pending:
+                req_task.cancel()
+                raise ProcessingInterrupted("Upload cancelled")
+
+            try:
+                resp = await req_task
+            except asyncio.CancelledError:
+                raise ProcessingInterrupted("Upload cancelled") from None
+
+            async with resp:
+                if resp.status >= 400:
+                    with contextlib.suppress(Exception):
+                        try:
+                            body = await resp.json()
+                        except Exception:
+                            body = await resp.text()
+                        msg = f"Upload failed with status {resp.status}"
+                        request_logger.log_request_response(
+                            operation_id=operation_id,
+                            request_method="PUT",
+                            request_url=upload_url,
+                            response_status_code=resp.status,
+                            response_headers=dict(resp.headers),
+                            response_content=body,
+                            error_message=msg,
+                        )
+                    if resp.status in {408, 429, 500, 502, 503, 504} and attempt <= max_retries:
+                        await sleep_with_interrupt(
+                            delay,
+                            cls,
+                            wait_label,
+                            start_ts,
+                            None,
+                            display_callback=_display_time_progress if wait_label else None,
+                        )
+                        delay *= retry_backoff
+                        continue
+                    raise Exception(f"Failed to upload (HTTP {resp.status}).")
+                request_logger.log_request_response(
+                    operation_id=operation_id,
+                    request_method="PUT",
+                    request_url=upload_url,
+                    response_status_code=resp.status,
+                    response_headers=dict(resp.headers),
+                    response_content="File uploaded successfully.",
+                )
+                return
+        except asyncio.CancelledError:
+            raise ProcessingInterrupted("Task cancelled") from None
+        except (aiohttp.ClientError, OSError) as e:
+            if attempt <= max_retries:
+                request_logger.log_request_response(
+                    operation_id=operation_id,
+                    request_method="PUT",
+                    request_url=upload_url,
+                    request_headers=headers or None,
+                    request_data=f"[File data {len(data)} bytes]",
+                    error_message=f"{type(e).__name__}: {str(e)} (will retry)",
+                )
+                await sleep_with_interrupt(
+                    delay,
+                    cls,
+                    wait_label,
+                    start_ts,
+                    None,
+                    display_callback=_display_time_progress if wait_label else None,
+                )
+                delay *= retry_backoff
+                continue
+
+            diag = await _diagnose_connectivity()
+            if not diag["internet_accessible"]:
+                raise LocalNetworkError(
+                    "Unable to connect to the network. Please check your internet connection and try again."
+                ) from e
+            raise ApiServerError("The API service appears unreachable at this time.") from e
+        finally:
+            stop_evt.set()
+            if monitor_task:
+                monitor_task.cancel()
+                with contextlib.suppress(Exception):
+                    await monitor_task
+            if sess:
+                with contextlib.suppress(Exception):
+                    await sess.close()
+
+
+def _generate_operation_id(method: str, url: str, attempt: int, op_uuid: str) -> str:
+    try:
+        parsed = urlparse(url)
+        slug = (parsed.path.rsplit("/", 1)[-1] or parsed.netloc or "upload").strip("/").replace("/", "_")
+    except Exception:
+        slug = "upload"
+    return f"{method}_{slug}_{op_uuid}_try{attempt}"
diff --git a/comfy_api_nodes/util/validation_utils.py b/comfy_api_nodes/util/validation_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea74e0aa8366d37e4f553e69ef77c1e0e558bc13
--- /dev/null
+++ b/comfy_api_nodes/util/validation_utils.py
@@ -0,0 +1,245 @@
+import logging
+
+import torch
+
+from comfy_api.latest import Input
+
+
+def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]:
+    if len(image.shape) == 4:
+        return image.shape[1], image.shape[2]
+    elif len(image.shape) == 3:
+        return image.shape[0], image.shape[1]
+    else:
+        raise ValueError("Invalid image tensor shape.")
+
+
+def validate_image_dimensions(
+    image: torch.Tensor,
+    min_width: int | None = None,
+    max_width: int | None = None,
+    min_height: int | None = None,
+    max_height: int | None = None,
+):
+    height, width = get_image_dimensions(image)
+
+    if min_width is not None and width < min_width:
+        raise ValueError(f"Image width must be at least {min_width}px, got {width}px")
+    if max_width is not None and width > max_width:
+        raise ValueError(f"Image width must be at most {max_width}px, got {width}px")
+    if min_height is not None and height < min_height:
+        raise ValueError(f"Image height must be at least {min_height}px, got {height}px")
+    if max_height is not None and height > max_height:
+        raise ValueError(f"Image height must be at most {max_height}px, got {height}px")
+
+
+def validate_image_aspect_ratio(
+    image: torch.Tensor,
+    min_ratio: tuple[float, float] | None = None,  # e.g. (1, 4)
+    max_ratio: tuple[float, float] | None = None,  # e.g. (4, 1)
+    *,
+    strict: bool = True,  # True -> (min, max); False -> [min, max]
+) -> float:
+    """Validates that image aspect ratio is within min and max. If a bound is None, that side is not checked."""
+    w, h = get_image_dimensions(image)
+    if w <= 0 or h <= 0:
+        raise ValueError(f"Invalid image dimensions: {w}x{h}")
+    ar = w / h
+    _assert_ratio_bounds(ar, min_ratio=min_ratio, max_ratio=max_ratio, strict=strict)
+    return ar
+
+
+def validate_images_aspect_ratio_closeness(
+    first_image: torch.Tensor,
+    second_image: torch.Tensor,
+    min_rel: float,  # e.g. 0.8
+    max_rel: float,  # e.g. 1.25
+    *,
+    strict: bool = False,  # True -> (min, max); False -> [min, max]
+) -> float:
+    """
+    Validates that the two images' aspect ratios are 'close'.
+    The closeness factor is C = max(ar1, ar2) / min(ar1, ar2)  (C >= 1).
+    We require C <= limit, where limit = max(max_rel, 1.0 / min_rel).
+
+    Returns the computed closeness factor C.
+    """
+    w1, h1 = get_image_dimensions(first_image)
+    w2, h2 = get_image_dimensions(second_image)
+    if min(w1, h1, w2, h2) <= 0:
+        raise ValueError("Invalid image dimensions")
+    ar1 = w1 / h1
+    ar2 = w2 / h2
+    closeness = max(ar1, ar2) / min(ar1, ar2)
+    limit = max(max_rel, 1.0 / min_rel)
+    if (closeness >= limit) if strict else (closeness > limit):
+        raise ValueError(
+            f"Aspect ratios must be close: ar1/ar2={ar1/ar2:.2g}, "
+            f"allowed range {min_rel}–{max_rel} (limit {limit:.2g})."
+        )
+    return closeness
+
+
+def validate_aspect_ratio_string(
+    aspect_ratio: str,
+    min_ratio: tuple[float, float] | None = None,  # e.g. (1, 4)
+    max_ratio: tuple[float, float] | None = None,  # e.g. (4, 1)
+    *,
+    strict: bool = False,  # True -> (min, max); False -> [min, max]
+) -> float:
+    """Parses 'X:Y' and validates it against optional bounds. Returns the numeric ratio."""
+    ar = _parse_aspect_ratio_string(aspect_ratio)
+    _assert_ratio_bounds(ar, min_ratio=min_ratio, max_ratio=max_ratio, strict=strict)
+    return ar
+
+
+def validate_video_dimensions(
+    video: Input.Video,
+    min_width: int | None = None,
+    max_width: int | None = None,
+    min_height: int | None = None,
+    max_height: int | None = None,
+):
+    try:
+        width, height = video.get_dimensions()
+    except Exception as e:
+        logging.error("Error getting dimensions of video: %s", e)
+        return
+
+    if min_width is not None and width < min_width:
+        raise ValueError(f"Video width must be at least {min_width}px, got {width}px")
+    if max_width is not None and width > max_width:
+        raise ValueError(f"Video width must be at most {max_width}px, got {width}px")
+    if min_height is not None and height < min_height:
+        raise ValueError(f"Video height must be at least {min_height}px, got {height}px")
+    if max_height is not None and height > max_height:
+        raise ValueError(f"Video height must be at most {max_height}px, got {height}px")
+
+
+def validate_video_duration(
+    video: Input.Video,
+    min_duration: float | None = None,
+    max_duration: float | None = None,
+):
+    try:
+        duration = video.get_duration()
+    except Exception as e:
+        logging.error("Error getting duration of video: %s", e)
+        return
+
+    epsilon = 0.0001
+    if min_duration is not None and min_duration - epsilon > duration:
+        raise ValueError(f"Video duration must be at least {min_duration}s, got {duration}s")
+    if max_duration is not None and duration > max_duration + epsilon:
+        raise ValueError(f"Video duration must be at most {max_duration}s, got {duration}s")
+
+
+def validate_video_frame_count(
+    video: Input.Video,
+    min_frame_count: int | None = None,
+    max_frame_count: int | None = None,
+):
+    try:
+        frame_count = video.get_frame_count()
+    except Exception as e:
+        logging.error("Error getting frame count of video: %s", e)
+        return
+
+    if min_frame_count is not None and min_frame_count > frame_count:
+        raise ValueError(f"Video frame count must be at least {min_frame_count}, got {frame_count}")
+    if max_frame_count is not None and frame_count > max_frame_count:
+        raise ValueError(f"Video frame count must be at most {max_frame_count}, got {frame_count}")
+
+
+def get_number_of_images(images):
+    if isinstance(images, torch.Tensor):
+        return images.shape[0] if images.ndim >= 4 else 1
+    return len(images)
+
+
+def validate_audio_duration(
+    audio: Input.Audio,
+    min_duration: float | None = None,
+    max_duration: float | None = None,
+) -> None:
+    sr = int(audio["sample_rate"])
+    dur = int(audio["waveform"].shape[-1]) / sr
+    eps = 1.0 / sr
+    if min_duration is not None and dur + eps < min_duration:
+        raise ValueError(f"Audio duration must be at least {min_duration}s, got {dur + eps:.2f}s")
+    if max_duration is not None and dur - eps > max_duration:
+        raise ValueError(f"Audio duration must be at most {max_duration}s, got {dur - eps:.2f}s")
+
+
+def validate_string(
+    string: str,
+    strip_whitespace=True,
+    field_name="prompt",
+    min_length=None,
+    max_length=None,
+):
+    if string is None:
+        raise Exception(f"Field '{field_name}' cannot be empty.")
+    if strip_whitespace:
+        string = string.strip()
+    if min_length and len(string) < min_length:
+        raise Exception(
+            f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long."
+        )
+    if max_length and len(string) > max_length:
+        raise Exception(
+            f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long."
+        )
+
+
+def validate_container_format_is_mp4(video: Input.Video) -> None:
+    """Validates video container format is MP4."""
+    container_format = video.get_container_format()
+    if container_format not in ["mp4", "mov,mp4,m4a,3gp,3g2,mj2"]:
+        raise ValueError(f"Only MP4 container format supported. Got: {container_format}")
+
+
+def _ratio_from_tuple(r: tuple[float, float]) -> float:
+    a, b = r
+    if a <= 0 or b <= 0:
+        raise ValueError(f"Ratios must be positive, got {a}:{b}.")
+    return a / b
+
+
+def _assert_ratio_bounds(
+    ar: float,
+    *,
+    min_ratio: tuple[float, float] | None = None,
+    max_ratio: tuple[float, float] | None = None,
+    strict: bool = True,
+) -> None:
+    """Validate a numeric aspect ratio against optional min/max ratio bounds."""
+    lo = _ratio_from_tuple(min_ratio) if min_ratio is not None else None
+    hi = _ratio_from_tuple(max_ratio) if max_ratio is not None else None
+
+    if lo is not None and hi is not None and lo > hi:
+        lo, hi = hi, lo  # normalize order if caller swapped them
+
+    if lo is not None:
+        if (ar <= lo) if strict else (ar < lo):
+            op = "<" if strict else "≤"
+            raise ValueError(f"Aspect ratio `{ar:.2g}` must be {op} {lo:.2g}.")
+    if hi is not None:
+        if (ar >= hi) if strict else (ar > hi):
+            op = "<" if strict else "≤"
+            raise ValueError(f"Aspect ratio `{ar:.2g}` must be {op} {hi:.2g}.")
+
+
+def _parse_aspect_ratio_string(ar_str: str) -> float:
+    """Parse 'X:Y' with integer parts into a positive float ratio X/Y."""
+    parts = ar_str.split(":")
+    if len(parts) != 2:
+        raise ValueError(f"Aspect ratio must be 'X:Y' (e.g., 16:9), got '{ar_str}'.")
+    try:
+        a = int(parts[0].strip())
+        b = int(parts[1].strip())
+    except ValueError as exc:
+        raise ValueError(f"Aspect ratio must contain integers separated by ':', got '{ar_str}'.") from exc
+    if a <= 0 or b <= 0:
+        raise ValueError(f"Aspect ratio parts must be positive integers, got {a}:{b}.")
+    return a / b
diff --git a/comfy_config/config_parser.py b/comfy_config/config_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6751c4bf8a6c03ffaf08c0e58b69b7c50a987b9
--- /dev/null
+++ b/comfy_config/config_parser.py
@@ -0,0 +1,152 @@
+import os
+from pathlib import Path
+from typing import Optional
+
+from pydantic_settings import PydanticBaseSettingsSource, TomlConfigSettingsSource
+
+from comfy_config.types import (
+    ComfyConfig,
+    ProjectConfig,
+    PyProjectConfig,
+    PyProjectSettings
+)
+
+def validate_and_extract_os_classifiers(classifiers: list) -> list:
+    os_classifiers = [c for c in classifiers if c.startswith("Operating System :: ")]
+    if not os_classifiers:
+        return []
+
+    os_values = [c[len("Operating System :: ") :] for c in os_classifiers]
+    valid_os_prefixes = {"Microsoft", "POSIX", "MacOS", "OS Independent"}
+
+    for os_value in os_values:
+        if not any(os_value.startswith(prefix) for prefix in valid_os_prefixes):
+            return []
+
+    return os_values
+
+
+def validate_and_extract_accelerator_classifiers(classifiers: list) -> list:
+    accelerator_classifiers = [c for c in classifiers if c.startswith("Environment ::")]
+    if not accelerator_classifiers:
+        return []
+
+    accelerator_values = [c[len("Environment :: ") :] for c in accelerator_classifiers]
+
+    valid_accelerators = {
+        "GPU :: NVIDIA CUDA",
+        "GPU :: AMD ROCm",
+        "GPU :: Intel Arc",
+        "NPU :: Huawei Ascend",
+        "GPU :: Apple Metal",
+    }
+
+    for accelerator_value in accelerator_values:
+        if accelerator_value not in valid_accelerators:
+            return []
+
+    return accelerator_values
+
+
+"""
+Extract configuration from a custom node directory's pyproject.toml file or a Python file.
+
+This function reads and parses the pyproject.toml file in the specified directory
+to extract project and ComfyUI-specific configuration information. If no
+pyproject.toml file is found, it creates a minimal configuration using the
+folder name as the project name. If a Python file is provided, it uses the
+file name (without extension) as the project name.
+
+Args:
+    path (str): Path to the directory containing the pyproject.toml file, or
+               path to a .py file. If pyproject.toml doesn't exist in a directory,
+               the folder name will be used as the default project name. If a .py
+               file is provided, the filename (without .py extension) will be used
+               as the project name.
+
+Returns:
+    Optional[PyProjectConfig]: A PyProjectConfig object containing:
+        - project: Basic project information (name, version, dependencies, etc.)
+        - tool_comfy: ComfyUI-specific configuration (publisher_id, models, etc.)
+        Returns None if configuration extraction fails or if the provided file
+        is not a Python file.
+
+Notes:
+    - If pyproject.toml is missing in a directory, creates a default config with folder name
+    - If a .py file is provided, creates a default config with filename (without extension)
+    - Returns None for non-Python files
+
+Example:
+    >>> from comfy_config import config_parser
+    >>> # For directory
+    >>> custom_node_dir = os.path.dirname(os.path.realpath(__file__))
+    >>> project_config = config_parser.extract_node_configuration(custom_node_dir)
+    >>> print(project_config.project.name)  # "my_custom_node" or name from pyproject.toml
+    >>>
+    >>> # For single-file Python node file
+    >>> py_file_path = os.path.realpath(__file__) # "/path/to/my_node.py"
+    >>> project_config = config_parser.extract_node_configuration(py_file_path)
+    >>> print(project_config.project.name)  # "my_node"
+"""
+def extract_node_configuration(path) -> Optional[PyProjectConfig]:
+    if os.path.isfile(path):
+        file_path = Path(path)
+
+        if file_path.suffix.lower() != '.py':
+            return None
+
+        project_name = file_path.stem
+        project = ProjectConfig(name=project_name)
+        comfy = ComfyConfig()
+        return PyProjectConfig(project=project, tool_comfy=comfy)
+
+    folder_name = os.path.basename(path)
+    toml_path = Path(path) / "pyproject.toml"
+
+    if not toml_path.exists():
+        project = ProjectConfig(name=folder_name)
+        comfy = ComfyConfig()
+        return PyProjectConfig(project=project, tool_comfy=comfy)
+
+    raw_settings = load_pyproject_settings(toml_path)
+
+    project_data = raw_settings.project
+
+    tool_data = raw_settings.tool
+    comfy_data = tool_data.get("comfy", {}) if tool_data else {}
+
+    dependencies = project_data.get("dependencies", [])
+    supported_comfyui_frontend_version = ""
+    for dep in dependencies:
+        if isinstance(dep, str) and dep.startswith("comfyui-frontend-package"):
+            supported_comfyui_frontend_version = dep.removeprefix("comfyui-frontend-package")
+            break
+
+    supported_comfyui_version = comfy_data.get("requires-comfyui", "")
+
+    classifiers = project_data.get('classifiers', [])
+    supported_os = validate_and_extract_os_classifiers(classifiers)
+    supported_accelerators = validate_and_extract_accelerator_classifiers(classifiers)
+
+    project_data['supported_os'] = supported_os
+    project_data['supported_accelerators'] = supported_accelerators
+    project_data['supported_comfyui_frontend_version'] = supported_comfyui_frontend_version
+    project_data['supported_comfyui_version'] = supported_comfyui_version
+
+    return PyProjectConfig(project=project_data, tool_comfy=comfy_data)
+
+
+def load_pyproject_settings(toml_path: Path) -> PyProjectSettings:
+    class PyProjectLoader(PyProjectSettings):
+        @classmethod
+        def settings_customise_sources(
+            cls,
+            settings_cls,
+            init_settings: PydanticBaseSettingsSource,
+            env_settings: PydanticBaseSettingsSource,
+            dotenv_settings: PydanticBaseSettingsSource,
+            file_secret_settings: PydanticBaseSettingsSource,
+        ):
+            return (TomlConfigSettingsSource(settings_cls, toml_path),)
+
+    return PyProjectLoader()
diff --git a/comfy_config/types.py b/comfy_config/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9bf8c95dc6cc3366d4a91d5c505dd28c31fa1d5
--- /dev/null
+++ b/comfy_config/types.py
@@ -0,0 +1,97 @@
+from pydantic import BaseModel, Field, field_validator
+from pydantic_settings import BaseSettings, SettingsConfigDict
+from typing import List, Optional
+
+# IMPORTANT: The type definitions specified in pyproject.toml for custom nodes
+# must remain synchronized with the corresponding files in the https://github.com/Comfy-Org/comfy-cli/blob/main/comfy_cli/registry/types.py.
+# Any changes to one must be reflected in the other to maintain consistency.
+
+class NodeVersion(BaseModel):
+    changelog: str
+    dependencies: List[str]
+    deprecated: bool
+    id: str
+    version: str
+    download_url: str
+
+
+class Node(BaseModel):
+    id: str
+    name: str
+    description: str
+    author: Optional[str] = None
+    license: Optional[str] = None
+    icon: Optional[str] = None
+    repository: Optional[str] = None
+    tags: List[str] = Field(default_factory=list)
+    latest_version: Optional[NodeVersion] = None
+
+
+class PublishNodeVersionResponse(BaseModel):
+    node_version: NodeVersion
+    signedUrl: str
+
+
+class URLs(BaseModel):
+    homepage: str = Field(default="", alias="Homepage")
+    documentation: str = Field(default="", alias="Documentation")
+    repository: str = Field(default="", alias="Repository")
+    issues: str = Field(default="", alias="Issues")
+
+
+class Model(BaseModel):
+    location: str
+    model_url: str
+
+
+class ComfyConfig(BaseModel):
+    publisher_id: str = Field(default="", alias="PublisherId")
+    display_name: str = Field(default="", alias="DisplayName")
+    icon: str = Field(default="", alias="Icon")
+    models: List[Model] = Field(default_factory=list, alias="Models")
+    includes: List[str] = Field(default_factory=list)
+    web: Optional[str] = None
+    banner_url: str = ""
+
+class License(BaseModel):
+    file: str = ""
+    text: str = ""
+
+
+class ProjectConfig(BaseModel):
+    name: str = ""
+    description: str = ""
+    version: str = "1.0.0"
+    requires_python: str = Field(default=">= 3.9", alias="requires-python")
+    dependencies: List[str] = Field(default_factory=list)
+    license: License = Field(default_factory=License)
+    urls: URLs = Field(default_factory=URLs)
+    supported_os: List[str] = Field(default_factory=list)
+    supported_accelerators: List[str] = Field(default_factory=list)
+    supported_comfyui_version: str = ""
+    supported_comfyui_frontend_version: str = ""
+
+    @field_validator('license', mode='before')
+    @classmethod
+    def validate_license(cls, v):
+        if isinstance(v, str):
+            return License(text=v)
+        elif isinstance(v, dict):
+            return License(**v)
+        elif isinstance(v, License):
+            return v
+        else:
+            return License()
+
+
+class PyProjectConfig(BaseModel):
+    project: ProjectConfig = Field(default_factory=ProjectConfig)
+    tool_comfy: ComfyConfig = Field(default_factory=ComfyConfig)
+
+
+class PyProjectSettings(BaseSettings):
+    project: dict = Field(default_factory=dict)
+
+    tool: dict = Field(default_factory=dict)
+
+    model_config = SettingsConfigDict(extra='allow')
diff --git a/comfy_execution/asset_enrichment.py b/comfy_execution/asset_enrichment.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa95267e6d6f0f67166bfed55f2e115727a442e6
--- /dev/null
+++ b/comfy_execution/asset_enrichment.py
@@ -0,0 +1,66 @@
+"""Enrich executed-node output entries with asset id."""
+import logging
+import os
+
+
+def enrich_output_with_assets(output_ui: dict) -> dict:
+    """Register file-type output entries as assets and inject their ``id``.
+
+    Runs at output-processing time, once per produced output, when
+    --enable-assets is set. Returns a new dict; entries without a resolvable
+    on-disk file path are left unchanged. Errors are caught per-entry so a
+    failure never blocks execution or the other entries.
+    """
+    from comfy.cli_args import args
+    if not args.enable_assets:
+        return output_ui
+
+    import folder_paths
+    from app.assets.services.ingest import register_file_in_place, DependencyMissingError
+
+    enriched = {}
+    for key, entries in output_ui.items():
+        if not isinstance(entries, list):
+            enriched[key] = entries
+            continue
+        new_entries = []
+        for entry in entries:
+            if not isinstance(entry, dict) or "filename" not in entry or "type" not in entry:
+                new_entries.append(entry)
+                continue
+            try:
+                base = folder_paths.get_directory_by_type(entry["type"])
+                if base is None:
+                    new_entries.append(entry)
+                    continue
+                base_abs = os.path.abspath(base)
+                abs_path = os.path.abspath(os.path.join(base_abs, entry.get("subfolder") or "", entry["filename"]))
+                try:
+                    if os.path.commonpath([base_abs, abs_path]) != base_abs:
+                        raise ValueError("escapes base")
+                except ValueError:
+                    logging.warning("Asset enrichment skipped (path escapes base): %s", entry.get("filename"))
+                    new_entries.append(entry)
+                    continue
+                if not os.path.isfile(abs_path):
+                    new_entries.append(entry)
+                    continue
+
+                # Register unconditionally: the file was just produced, and
+                # register_file_in_place re-hashes so an overwritten path can
+                # never carry a stale id.
+                result = register_file_in_place(
+                    abs_path=abs_path,
+                    name=entry["filename"],
+                    tags=[entry["type"]],
+                )
+
+                entry = dict(entry)
+                entry["id"] = result.ref.id
+            except DependencyMissingError:
+                logging.warning("Asset enrichment skipped (blake3 not available): %s", entry.get("filename"))
+            except Exception:
+                logging.warning("Failed to enrich output entry with asset id: %s", entry.get("filename"), exc_info=True)
+            new_entries.append(entry)
+        enriched[key] = new_entries
+    return enriched
diff --git a/comfy_execution/cache_provider.py b/comfy_execution/cache_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..40e6a3d156dad0e012a6f95ed3343eb920aec740
--- /dev/null
+++ b/comfy_execution/cache_provider.py
@@ -0,0 +1,138 @@
+from typing import Any, Optional, Tuple, List
+import hashlib
+import json
+import logging
+import threading
+
+# Public types — source of truth is comfy_api.latest._caching
+from comfy_api.latest._caching import CacheProvider, CacheContext, CacheValue  # noqa: F401 (re-exported)
+
+_logger = logging.getLogger(__name__)
+
+
+_providers: List[CacheProvider] = []
+_providers_lock = threading.Lock()
+_providers_snapshot: Tuple[CacheProvider, ...] = ()
+
+
+def register_cache_provider(provider: CacheProvider) -> None:
+    """Register an external cache provider. Providers are called in registration order."""
+    global _providers_snapshot
+    with _providers_lock:
+        if provider in _providers:
+            _logger.warning(f"Provider {provider.__class__.__name__} already registered")
+            return
+        _providers.append(provider)
+        _providers_snapshot = tuple(_providers)
+        _logger.debug(f"Registered cache provider: {provider.__class__.__name__}")
+
+
+def unregister_cache_provider(provider: CacheProvider) -> None:
+    global _providers_snapshot
+    with _providers_lock:
+        try:
+            _providers.remove(provider)
+            _providers_snapshot = tuple(_providers)
+            _logger.debug(f"Unregistered cache provider: {provider.__class__.__name__}")
+        except ValueError:
+            _logger.warning(f"Provider {provider.__class__.__name__} was not registered")
+
+
+def _get_cache_providers() -> Tuple[CacheProvider, ...]:
+    return _providers_snapshot
+
+
+def _has_cache_providers() -> bool:
+    return bool(_providers_snapshot)
+
+
+def _clear_cache_providers() -> None:
+    global _providers_snapshot
+    with _providers_lock:
+        _providers.clear()
+        _providers_snapshot = ()
+
+
+def _canonicalize(obj: Any) -> Any:
+    # Convert to canonical JSON-serializable form with deterministic ordering.
+    # Frozensets have non-deterministic iteration order between Python sessions.
+    # Raises ValueError for non-cacheable types (Unhashable, unknown) so that
+    # _serialize_cache_key returns None and external caching is skipped.
+    if isinstance(obj, frozenset):
+        return ("__frozenset__", sorted(
+            [_canonicalize(item) for item in obj],
+            key=lambda x: json.dumps(x, sort_keys=True)
+        ))
+    elif isinstance(obj, set):
+        return ("__set__", sorted(
+            [_canonicalize(item) for item in obj],
+            key=lambda x: json.dumps(x, sort_keys=True)
+        ))
+    elif isinstance(obj, tuple):
+        return ("__tuple__", [_canonicalize(item) for item in obj])
+    elif isinstance(obj, list):
+        return [_canonicalize(item) for item in obj]
+    elif isinstance(obj, dict):
+        return {"__dict__": sorted(
+            [[_canonicalize(k), _canonicalize(v)] for k, v in obj.items()],
+            key=lambda x: json.dumps(x, sort_keys=True)
+        )}
+    elif isinstance(obj, (int, float, str, bool, type(None))):
+        return (type(obj).__name__, obj)
+    elif isinstance(obj, bytes):
+        return ("__bytes__", obj.hex())
+    else:
+        raise ValueError(f"Cannot canonicalize type: {type(obj).__name__}")
+
+
+def _serialize_cache_key(cache_key: Any) -> Optional[str]:
+    # Returns deterministic SHA256 hex digest, or None on failure.
+    # Uses JSON (not pickle) because pickle is non-deterministic across sessions.
+    try:
+        canonical = _canonicalize(cache_key)
+        json_str = json.dumps(canonical, sort_keys=True, separators=(',', ':'))
+        return hashlib.sha256(json_str.encode('utf-8')).hexdigest()
+    except Exception as e:
+        _logger.warning(f"Failed to serialize cache key: {e}")
+        return None
+
+
+def _contains_self_unequal(obj: Any) -> bool:
+    # Local cache matches by ==. Values where not (x == x) (NaN, etc.) will
+    # never hit locally, but serialized form would match externally. Skip these.
+    try:
+        if not (obj == obj):
+            return True
+    except Exception:
+        return True
+    if isinstance(obj, (frozenset, tuple, list, set)):
+        return any(_contains_self_unequal(item) for item in obj)
+    if isinstance(obj, dict):
+        return any(_contains_self_unequal(k) or _contains_self_unequal(v) for k, v in obj.items())
+    if hasattr(obj, 'value'):
+        return _contains_self_unequal(obj.value)
+    return False
+
+
+def _estimate_value_size(value: CacheValue) -> int:
+    try:
+        import torch
+    except ImportError:
+        return 0
+
+    total = 0
+
+    def estimate(obj):
+        nonlocal total
+        if isinstance(obj, torch.Tensor):
+            total += obj.numel() * obj.element_size()
+        elif isinstance(obj, dict):
+            for v in obj.values():
+                estimate(v)
+        elif isinstance(obj, (list, tuple)):
+            for item in obj:
+                estimate(item)
+
+    for output in value.outputs:
+        estimate(output)
+    return total
diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py
new file mode 100644
index 0000000000000000000000000000000000000000..dce9d2d944507b9bc2ba65c4828605ee829c8a2f
--- /dev/null
+++ b/comfy_execution/caching.py
@@ -0,0 +1,602 @@
+import asyncio
+import bisect
+import itertools
+import time
+import torch
+from typing import Sequence, Mapping, Dict
+from comfy.model_patcher import is_model_patcher_output
+from comfy.system_memory import virtual_memory_available
+from comfy_execution.graph import DynamicPrompt
+from abc import ABC, abstractmethod
+
+import nodes
+
+from comfy_execution.graph_utils import is_link
+
+NODE_CLASS_CONTAINS_UNIQUE_ID: Dict[str, bool] = {}
+
+
+def include_unique_id_in_input(class_type: str) -> bool:
+    if class_type in NODE_CLASS_CONTAINS_UNIQUE_ID:
+        return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
+    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+    NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] = "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values()
+    return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
+
+class CacheKeySet(ABC):
+    def __init__(self, dynprompt, node_ids, is_changed_cache):
+        self.keys = {}
+        self.subcache_keys = {}
+
+    @abstractmethod
+    async def add_keys(self, node_ids):
+        raise NotImplementedError()
+
+    def all_node_ids(self):
+        return set(self.keys.keys())
+
+    def get_used_keys(self):
+        return self.keys.values()
+
+    def get_used_subcache_keys(self):
+        return self.subcache_keys.values()
+
+    def get_data_key(self, node_id):
+        return self.keys.get(node_id, None)
+
+    def get_subcache_key(self, node_id):
+        return self.subcache_keys.get(node_id, None)
+
+class Unhashable:
+    def __init__(self):
+        self.value = float("NaN")
+
+def to_hashable(obj):
+    # So that we don't infinitely recurse since frozenset and tuples
+    # are Sequences.
+    if isinstance(obj, (int, float, str, bool, bytes, type(None))):
+        return obj
+    elif isinstance(obj, Mapping):
+        return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())])
+    elif isinstance(obj, Sequence):
+        return frozenset(zip(itertools.count(), [to_hashable(i) for i in obj]))
+    else:
+        # TODO - Support other objects like tensors?
+        return Unhashable()
+
+class CacheKeySetID(CacheKeySet):
+    def __init__(self, dynprompt, node_ids, is_changed_cache):
+        super().__init__(dynprompt, node_ids, is_changed_cache)
+        self.dynprompt = dynprompt
+
+    async def add_keys(self, node_ids):
+        for node_id in node_ids:
+            if node_id in self.keys:
+                continue
+            if not self.dynprompt.has_node(node_id):
+                continue
+            node = self.dynprompt.get_node(node_id)
+            self.keys[node_id] = (node_id, node["class_type"])
+            self.subcache_keys[node_id] = (node_id, node["class_type"])
+
+class CacheKeySetInputSignature(CacheKeySet):
+    def __init__(self, dynprompt, node_ids, is_changed_cache):
+        super().__init__(dynprompt, node_ids, is_changed_cache)
+        self.dynprompt = dynprompt
+        self.is_changed_cache = is_changed_cache
+
+    def include_node_id_in_input(self) -> bool:
+        return False
+
+    async def add_keys(self, node_ids):
+        for node_id in node_ids:
+            if node_id in self.keys:
+                continue
+            if not self.dynprompt.has_node(node_id):
+                continue
+            node = self.dynprompt.get_node(node_id)
+            self.keys[node_id] = await self.get_node_signature(self.dynprompt, node_id)
+            self.subcache_keys[node_id] = (node_id, node["class_type"])
+
+    async def get_node_signature(self, dynprompt, node_id):
+        signature = []
+        ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id)
+        signature.append(await self.get_immediate_node_signature(dynprompt, node_id, order_mapping))
+        for ancestor_id in ancestors:
+            signature.append(await self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping))
+        return to_hashable(signature)
+
+    async def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
+        if not dynprompt.has_node(node_id):
+            # This node doesn't exist -- we can't cache it.
+            return [float("NaN")]
+        node = dynprompt.get_node(node_id)
+        class_type = node["class_type"]
+        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+        signature = [class_type, await self.is_changed_cache.get(node_id)]
+        if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type):
+            signature.append(node_id)
+        inputs = node["inputs"]
+        for key in sorted(inputs.keys()):
+            if is_link(inputs[key]):
+                (ancestor_id, ancestor_socket) = inputs[key]
+                ancestor_index = ancestor_order_mapping[ancestor_id]
+                signature.append((key,("ANCESTOR", ancestor_index, ancestor_socket)))
+            else:
+                signature.append((key, inputs[key]))
+        return signature
+
+    # This function returns a list of all ancestors of the given node. The order of the list is
+    # deterministic based on which specific inputs the ancestor is connected by.
+    def get_ordered_ancestry(self, dynprompt, node_id):
+        ancestors = []
+        order_mapping = {}
+        self.get_ordered_ancestry_internal(dynprompt, node_id, ancestors, order_mapping)
+        return ancestors, order_mapping
+
+    def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping):
+        if not dynprompt.has_node(node_id):
+            return
+        inputs = dynprompt.get_node(node_id)["inputs"]
+        input_keys = sorted(inputs.keys())
+        for key in input_keys:
+            if is_link(inputs[key]):
+                ancestor_id = inputs[key][0]
+                if ancestor_id not in order_mapping:
+                    ancestors.append(ancestor_id)
+                    order_mapping[ancestor_id] = len(ancestors) - 1
+                    self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping)
+
+class BasicCache:
+    def __init__(self, key_class, enable_providers=False):
+        self.key_class = key_class
+        self.initialized = False
+        self.enable_providers = enable_providers
+        self.dynprompt: DynamicPrompt
+        self.cache_key_set: CacheKeySet
+        self.cache = {}
+        self.subcaches = {}
+        self._pending_store_tasks: set = set()
+
+    async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        self.dynprompt = dynprompt
+        self.cache_key_set = self.key_class(dynprompt, node_ids, is_changed_cache)
+        await self.cache_key_set.add_keys(node_ids)
+        self.is_changed_cache = is_changed_cache
+        self.initialized = True
+
+    def all_node_ids(self):
+        assert self.initialized
+        node_ids = self.cache_key_set.all_node_ids()
+        for subcache in self.subcaches.values():
+            node_ids = node_ids.union(subcache.all_node_ids())
+        return node_ids
+
+    def _clean_cache(self):
+        preserve_keys = set(self.cache_key_set.get_used_keys())
+        to_remove = []
+        for key in self.cache:
+            if key not in preserve_keys:
+                to_remove.append(key)
+        for key in to_remove:
+            del self.cache[key]
+
+    def _clean_subcaches(self):
+        preserve_subcaches = set(self.cache_key_set.get_used_subcache_keys())
+
+        to_remove = []
+        for key in self.subcaches:
+            if key not in preserve_subcaches:
+                to_remove.append(key)
+        for key in to_remove:
+            del self.subcaches[key]
+
+    def clean_unused(self):
+        assert self.initialized
+        self._clean_cache()
+        self._clean_subcaches()
+
+    def poll(self, **kwargs):
+        pass
+
+    def get_local(self, node_id):
+        if not self.initialized:
+            return None
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+        return None
+
+    def set_local(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+    async def _set_immediate(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+        await self._notify_providers_store(node_id, cache_key, value)
+
+    async def _get_immediate(self, node_id):
+        if not self.initialized:
+            return None
+        cache_key = self.cache_key_set.get_data_key(node_id)
+
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+
+        external_result = await self._check_providers_lookup(node_id, cache_key)
+        if external_result is not None:
+            self.cache[cache_key] = external_result
+            return external_result
+
+        return None
+
+    async def _notify_providers_store(self, node_id, cache_key, value):
+        from comfy_execution.cache_provider import (
+            _has_cache_providers, _get_cache_providers,
+            CacheValue, _contains_self_unequal, _logger
+        )
+
+        if not self.enable_providers:
+            return
+        if not _has_cache_providers():
+            return
+        if not self._is_external_cacheable_value(value):
+            return
+        if _contains_self_unequal(cache_key):
+            return
+
+        context = self._build_context(node_id, cache_key)
+        if context is None:
+            return
+        cache_value = CacheValue(outputs=value.outputs, ui=value.ui)
+
+        for provider in _get_cache_providers():
+            try:
+                if provider.should_cache(context, cache_value):
+                    task = asyncio.create_task(self._safe_provider_store(provider, context, cache_value))
+                    self._pending_store_tasks.add(task)
+                    task.add_done_callback(self._pending_store_tasks.discard)
+            except Exception as e:
+                _logger.warning(f"Cache provider {provider.__class__.__name__} error on store: {e}")
+
+    @staticmethod
+    async def _safe_provider_store(provider, context, cache_value):
+        from comfy_execution.cache_provider import _logger
+        try:
+            await provider.on_store(context, cache_value)
+        except Exception as e:
+            _logger.warning(f"Cache provider {provider.__class__.__name__} async store error: {e}")
+
+    async def _check_providers_lookup(self, node_id, cache_key):
+        from comfy_execution.cache_provider import (
+            _has_cache_providers, _get_cache_providers,
+            CacheValue, _contains_self_unequal, _logger
+        )
+
+        if not self.enable_providers:
+            return None
+        if not _has_cache_providers():
+            return None
+        if _contains_self_unequal(cache_key):
+            return None
+
+        context = self._build_context(node_id, cache_key)
+        if context is None:
+            return None
+
+        for provider in _get_cache_providers():
+            try:
+                if not provider.should_cache(context):
+                    continue
+                result = await provider.on_lookup(context)
+                if result is not None:
+                    if not isinstance(result, CacheValue):
+                        _logger.warning(f"Provider {provider.__class__.__name__} returned invalid type")
+                        continue
+                    if not isinstance(result.outputs, (list, tuple)):
+                        _logger.warning(f"Provider {provider.__class__.__name__} returned invalid outputs")
+                        continue
+                    from execution import CacheEntry
+                    return CacheEntry(ui=result.ui, outputs=list(result.outputs))
+            except Exception as e:
+                _logger.warning(f"Cache provider {provider.__class__.__name__} error on lookup: {e}")
+
+        return None
+
+    def _is_external_cacheable_value(self, value):
+        return hasattr(value, 'outputs') and hasattr(value, 'ui')
+
+    def _get_class_type(self, node_id):
+        if not self.initialized or not self.dynprompt:
+            return ''
+        try:
+            return self.dynprompt.get_node(node_id).get('class_type', '')
+        except Exception:
+            return ''
+
+    def _build_context(self, node_id, cache_key):
+        from comfy_execution.cache_provider import CacheContext, _serialize_cache_key, _logger
+        try:
+            cache_key_hash = _serialize_cache_key(cache_key)
+            if cache_key_hash is None:
+                return None
+            return CacheContext(
+                node_id=node_id,
+                class_type=self._get_class_type(node_id),
+                cache_key_hash=cache_key_hash,
+            )
+        except Exception as e:
+            _logger.warning(f"Failed to build cache context for node {node_id}: {e}")
+            return None
+
+    async def _ensure_subcache(self, node_id, children_ids):
+        subcache_key = self.cache_key_set.get_subcache_key(node_id)
+        subcache = self.subcaches.get(subcache_key, None)
+        if subcache is None:
+            subcache = BasicCache(self.key_class)
+            self.subcaches[subcache_key] = subcache
+        await subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache)
+        return subcache
+
+    def _get_subcache(self, node_id):
+        assert self.initialized
+        subcache_key = self.cache_key_set.get_subcache_key(node_id)
+        if subcache_key in self.subcaches:
+            return self.subcaches[subcache_key]
+        else:
+            return None
+
+    def recursive_debug_dump(self):
+        result = []
+        for key in self.cache:
+            result.append({"key": key, "value": self.cache[key]})
+        for key in self.subcaches:
+            result.append({"subcache_key": key, "subcache": self.subcaches[key].recursive_debug_dump()})
+        return result
+
+class HierarchicalCache(BasicCache):
+    def __init__(self, key_class, enable_providers=False):
+        super().__init__(key_class, enable_providers=enable_providers)
+
+    def _get_cache_for(self, node_id):
+        assert self.dynprompt is not None
+        parent_id = self.dynprompt.get_parent_node_id(node_id)
+        if parent_id is None:
+            return self
+
+        hierarchy = []
+        while parent_id is not None:
+            hierarchy.append(parent_id)
+            parent_id = self.dynprompt.get_parent_node_id(parent_id)
+
+        cache = self
+        for parent_id in reversed(hierarchy):
+            cache = cache._get_subcache(parent_id)
+            if cache is None:
+                return None
+        return cache
+
+    async def get(self, node_id):
+        cache = self._get_cache_for(node_id)
+        if cache is None:
+            return None
+        return await cache._get_immediate(node_id)
+
+    def get_local(self, node_id):
+        cache = self._get_cache_for(node_id)
+        if cache is None:
+            return None
+        return BasicCache.get_local(cache, node_id)
+
+    async def set(self, node_id, value):
+        cache = self._get_cache_for(node_id)
+        assert cache is not None
+        await cache._set_immediate(node_id, value)
+
+    def set_local(self, node_id, value):
+        cache = self._get_cache_for(node_id)
+        assert cache is not None
+        BasicCache.set_local(cache, node_id, value)
+
+    async def ensure_subcache_for(self, node_id, children_ids):
+        cache = self._get_cache_for(node_id)
+        assert cache is not None
+        return await cache._ensure_subcache(node_id, children_ids)
+
+class NullCache:
+
+    async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        pass
+
+    def all_node_ids(self):
+        return []
+
+    def clean_unused(self):
+        pass
+
+    def poll(self, **kwargs):
+        pass
+
+    async def get(self, node_id):
+        return None
+
+    def get_local(self, node_id):
+        return None
+
+    async def set(self, node_id, value):
+        pass
+
+    def set_local(self, node_id, value):
+        pass
+
+    async def ensure_subcache_for(self, node_id, children_ids):
+        return self
+
+class LRUCache(BasicCache):
+    def __init__(self, key_class, max_size=100, enable_providers=False):
+        super().__init__(key_class, enable_providers=enable_providers)
+        self.max_size = max_size
+        self.min_generation = 0
+        self.generation = 0
+        self.used_generation = {}
+        self.children = {}
+
+    async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        await super().set_prompt(dynprompt, node_ids, is_changed_cache)
+        self.generation += 1
+        for node_id in node_ids:
+            self._mark_used(node_id)
+
+    def clean_unused(self):
+        while len(self.cache) > self.max_size and self.min_generation < self.generation:
+            self.min_generation += 1
+            to_remove = [key for key in self.cache if self.used_generation[key] < self.min_generation]
+            for key in to_remove:
+                del self.cache[key]
+                del self.used_generation[key]
+                if key in self.children:
+                    del self.children[key]
+        self._clean_subcaches()
+
+    async def get(self, node_id):
+        self._mark_used(node_id)
+        return await self._get_immediate(node_id)
+
+    def _mark_used(self, node_id):
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        if cache_key is not None:
+            self.used_generation[cache_key] = self.generation
+
+    async def set(self, node_id, value):
+        self._mark_used(node_id)
+        return await self._set_immediate(node_id, value)
+
+    def set_local(self, node_id, value):
+        self._mark_used(node_id)
+        BasicCache.set_local(self, node_id, value)
+
+    async def ensure_subcache_for(self, node_id, children_ids):
+        # Just uses subcaches for tracking 'live' nodes
+        await super()._ensure_subcache(node_id, children_ids)
+
+        await self.cache_key_set.add_keys(children_ids)
+        self._mark_used(node_id)
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.children[cache_key] = []
+        for child_id in children_ids:
+            self._mark_used(child_id)
+            self.children[cache_key].append(self.cache_key_set.get_data_key(child_id))
+        return self
+
+
+#Small baseline weight used when a cache entry has no measurable CPU tensors.
+#Keeps unknown-sized entries in eviction scoring without dominating tensor-backed entries.
+
+RAM_CACHE_DEFAULT_RAM_USAGE = 0.05
+
+#Exponential bias towards evicting older workflows so garbage will be taken out
+#in constantly changing setups.
+
+RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER = 1.3
+
+RAM_CACHE_LARGE_INTERMEDIATE = 512 * 1024 ** 2
+
+
+def all_outputs_dynamic(outputs):
+    if outputs is None:
+        return False
+
+    for output in outputs:
+        if isinstance(output, (list, tuple)):
+            if not all_outputs_dynamic(output):
+                return False
+        elif not hasattr(output, "is_dynamic") or not output.is_dynamic():
+            return False
+
+    return True
+
+class RAMPressureCache(LRUCache):
+
+    def __init__(self, key_class, enable_providers=False):
+        super().__init__(key_class, 0, enable_providers=enable_providers)
+        self.timestamps = {}
+        self.active_evictions = False
+        self.full_evictions = False
+
+    async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        self.active_evictions = False
+        self.full_evictions = False
+        await super().set_prompt(dynprompt, node_ids, is_changed_cache)
+
+    def clean_unused(self):
+        self._clean_subcaches()
+
+    async def set(self, node_id, value):
+        self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
+        await super().set(node_id, value)
+
+    async def get(self, node_id):
+        self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
+        return await super().get(node_id)
+
+    def set_local(self, node_id, value):
+        self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
+        super().set_local(node_id, value)
+
+    def ram_release(self, target, free_active=False, min_entry_size=0):
+        if virtual_memory_available() >= target:
+            return 0
+
+        clean_list = []
+
+        for key, cache_entry in self.cache.items():
+            if not free_active and self.used_generation[key] == self.generation:
+                continue
+
+            if all_outputs_dynamic(cache_entry.outputs) and self.used_generation[key] == self.generation:
+                continue
+
+            oom_score = RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
+
+            ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE
+            oom_ram_usage = ram_usage
+            def scan_list_for_ram_usage(outputs):
+                nonlocal ram_usage, oom_ram_usage
+                if outputs is None:
+                    return
+                for output in outputs:
+                    if isinstance(output, (list, tuple)):
+                        scan_list_for_ram_usage(output)
+                    elif isinstance(output, torch.Tensor) and output.device.type == 'cpu':
+                        ram_usage += output.numel() * output.element_size()
+                        oom_ram_usage += output.numel() * output.element_size()
+                    elif is_model_patcher_output(output) and self.used_generation[key] != self.generation:
+                        #old ModelPatchers are the first to go
+                        oom_ram_usage = 1e30
+            scan_list_for_ram_usage(cache_entry.outputs)
+
+            if ram_usage < min_entry_size:
+                continue
+
+            oom_score *= oom_ram_usage
+            #In the case where we have no information on the node ram usage at all,
+            #break OOM score ties on the last touch timestamp (pure LRU)
+            bisect.insort(clean_list, (oom_score, self.timestamps[key], key, ram_usage))
+
+        freed = 0
+        while virtual_memory_available() < target and clean_list:
+            _, _, key, ram_usage = clean_list.pop()
+            del self.cache[key]
+            self.used_generation.pop(key, None)
+            self.timestamps.pop(key, None)
+            self.children.pop(key, None)
+            freed += ram_usage
+        if freed and free_active:
+            self.active_evictions = True
+            if min_entry_size == 0:
+                self.full_evictions = True
+        return freed
diff --git a/comfy_execution/graph.py b/comfy_execution/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fdb511af8e4024fe463f1c2da9bcaa3a7f752d6
--- /dev/null
+++ b/comfy_execution/graph.py
@@ -0,0 +1,342 @@
+from typing import Type, Literal
+
+import nodes
+import asyncio
+import inspect
+from comfy_execution.graph_utils import is_link, ExecutionBlocker
+from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions
+
+# NOTE: ExecutionBlocker code got moved to graph_utils.py to prevent torch being imported too soon during unit tests
+ExecutionBlocker = ExecutionBlocker
+
+class DependencyCycleError(Exception):
+    pass
+
+class NodeInputError(Exception):
+    pass
+
+class NodeNotFoundError(Exception):
+    pass
+
+class DynamicPrompt:
+    def __init__(self, original_prompt):
+        # The original prompt provided by the user
+        self.original_prompt = original_prompt
+        # Any extra pieces of the graph created during execution
+        self.ephemeral_prompt = {}
+        self.ephemeral_parents = {}
+        self.ephemeral_display = {}
+
+    def get_node(self, node_id):
+        if node_id in self.ephemeral_prompt:
+            return self.ephemeral_prompt[node_id]
+        if node_id in self.original_prompt:
+            return self.original_prompt[node_id]
+        raise NodeNotFoundError(f"Node {node_id} not found")
+
+    def has_node(self, node_id):
+        return node_id in self.original_prompt or node_id in self.ephemeral_prompt
+
+    def add_ephemeral_node(self, node_id, node_info, parent_id, display_id):
+        self.ephemeral_prompt[node_id] = node_info
+        self.ephemeral_parents[node_id] = parent_id
+        self.ephemeral_display[node_id] = display_id
+
+    def get_real_node_id(self, node_id):
+        while node_id in self.ephemeral_parents:
+            node_id = self.ephemeral_parents[node_id]
+        return node_id
+
+    def get_parent_node_id(self, node_id):
+        return self.ephemeral_parents.get(node_id, None)
+
+    def get_display_node_id(self, node_id):
+        while node_id in self.ephemeral_display:
+            node_id = self.ephemeral_display[node_id]
+        return node_id
+
+    def all_node_ids(self):
+        return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys()))
+
+    def get_original_prompt(self):
+        return self.original_prompt
+
+def get_input_info(
+    class_def: Type[ComfyNodeABC],
+    input_name: str,
+    valid_inputs: InputTypeDict | None = None
+) -> tuple[str, Literal["required", "optional", "hidden"], InputTypeOptions] | tuple[None, None, None]:
+    """Get the input type, category, and extra info for a given input name.
+
+    Arguments:
+        class_def: The class definition of the node.
+        input_name: The name of the input to get info for.
+        valid_inputs: The valid inputs for the node, or None to use the class_def.INPUT_TYPES().
+
+    Returns:
+        tuple[str, str, dict] | tuple[None, None, None]: The input type, category, and extra info for the input name.
+    """
+
+    valid_inputs = valid_inputs or class_def.INPUT_TYPES()
+    input_info = None
+    input_category = None
+    if "required" in valid_inputs and input_name in valid_inputs["required"]:
+        input_category = "required"
+        input_info = valid_inputs["required"][input_name]
+    elif "optional" in valid_inputs and input_name in valid_inputs["optional"]:
+        input_category = "optional"
+        input_info = valid_inputs["optional"][input_name]
+    elif "hidden" in valid_inputs and input_name in valid_inputs["hidden"]:
+        input_category = "hidden"
+        input_info = valid_inputs["hidden"][input_name]
+    if input_info is None:
+        return None, None, None
+    input_type = input_info[0]
+    if len(input_info) > 1:
+        extra_info = input_info[1]
+    else:
+        extra_info = {}
+    # if input_type is a list, it is a Combo defined in outdated format; convert it.
+    # NOTE: uncomment this when we are confident old format going away won't cause too much trouble.
+    # if isinstance(input_type, list):
+    #     extra_info["options"] = input_type
+    #     input_type = IO.Combo.io_type
+    return input_type, input_category, extra_info
+
+class TopologicalSort:
+    def __init__(self, dynprompt):
+        self.dynprompt = dynprompt
+        self.pendingNodes = {}
+        self.blockCount = {} # Number of nodes this node is directly blocked by
+        self.blocking = {} # Which nodes are blocked by this node
+        self.externalBlocks = 0
+        self.unblockedEvent = asyncio.Event()
+
+    def get_input_info(self, unique_id, input_name):
+        class_type = self.dynprompt.get_node(unique_id)["class_type"]
+        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+        return get_input_info(class_def, input_name)
+
+    def make_input_strong_link(self, to_node_id, to_input):
+        inputs = self.dynprompt.get_node(to_node_id)["inputs"]
+        if to_input not in inputs:
+            raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but there is no input to that node at all")
+        value = inputs[to_input]
+        if not is_link(value):
+            raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but that value is a constant")
+        from_node_id, from_socket = value
+        self.add_strong_link(from_node_id, from_socket, to_node_id)
+
+    def add_strong_link(self, from_node_id, from_socket, to_node_id):
+        if not self.is_cached(from_node_id):
+            self.add_node(from_node_id)
+            if to_node_id not in self.blocking[from_node_id]:
+                self.blocking[from_node_id][to_node_id] = {}
+                self.blockCount[to_node_id] += 1
+            self.blocking[from_node_id][to_node_id][from_socket] = True
+
+    def add_node(self, node_unique_id, include_lazy=False, subgraph_nodes=None):
+        node_ids = [node_unique_id]
+        links = []
+
+        while len(node_ids) > 0:
+            unique_id = node_ids.pop()
+            if unique_id in self.pendingNodes:
+                continue
+
+            self.pendingNodes[unique_id] = True
+            self.blockCount[unique_id] = 0
+            self.blocking[unique_id] = {}
+
+            inputs = self.dynprompt.get_node(unique_id)["inputs"]
+            for input_name in inputs:
+                value = inputs[input_name]
+                if is_link(value):
+                    from_node_id, from_socket = value
+                    if subgraph_nodes is not None and from_node_id not in subgraph_nodes:
+                        continue
+                    _, _, input_info = self.get_input_info(unique_id, input_name)
+                    is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
+                    if (include_lazy or not is_lazy):
+                        if not self.is_cached(from_node_id):
+                            node_ids.append(from_node_id)
+                        links.append((from_node_id, from_socket, unique_id))
+
+        for link in links:
+            self.add_strong_link(*link)
+
+    def add_external_block(self, node_id):
+        assert node_id in self.blockCount, "Can't add external block to a node that isn't pending"
+        self.externalBlocks += 1
+        self.blockCount[node_id] += 1
+        def unblock():
+            self.externalBlocks -= 1
+            self.blockCount[node_id] -= 1
+            self.unblockedEvent.set()
+        return unblock
+
+    def is_cached(self, node_id):
+        return False
+
+    def get_ready_nodes(self):
+        return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0]
+
+    def pop_node(self, unique_id):
+        del self.pendingNodes[unique_id]
+        for blocked_node_id in self.blocking[unique_id]:
+            self.blockCount[blocked_node_id] -= 1
+        del self.blocking[unique_id]
+
+    def is_empty(self):
+        return len(self.pendingNodes) == 0
+
+class ExecutionList(TopologicalSort):
+    """
+    ExecutionList implements a topological dissolve of the graph. After a node is staged for execution,
+    it can still be returned to the graph after having further dependencies added.
+    """
+    def __init__(self, dynprompt, output_cache, output_link_callback=None):
+        super().__init__(dynprompt)
+        self.output_cache = output_cache
+        self.output_link_callback = output_link_callback
+        self.staged_node_id = None
+        self.execution_cache = {}
+        self.execution_cache_listeners = {}
+
+    def is_cached(self, node_id):
+        return self.output_cache.get_local(node_id) is not None
+
+    def cache_link(self, from_node_id, to_node_id, from_socket=None):
+        if to_node_id not in self.execution_cache:
+            self.execution_cache[to_node_id] = {}
+        value = self.output_cache.get_local(from_node_id)
+        self.execution_cache[to_node_id][from_node_id] = value
+        if from_node_id not in self.execution_cache_listeners:
+            self.execution_cache_listeners[from_node_id] = set()
+        self.execution_cache_listeners[from_node_id].add((to_node_id, from_socket))
+        if value is not None and from_socket is not None and self.output_link_callback is not None:
+            self.output_link_callback(value.outputs[from_socket])
+
+    def get_cache(self, from_node_id, to_node_id):
+        if to_node_id not in self.execution_cache:
+            return None
+        value = self.execution_cache[to_node_id].get(from_node_id)
+        if value is None:
+            return None
+        #Write back to the main cache on touch.
+        self.output_cache.set_local(from_node_id, value)
+        return value
+
+    def cache_update(self, node_id, value):
+        if node_id in self.execution_cache_listeners:
+            for to_node_id, from_socket in self.execution_cache_listeners[node_id]:
+                if to_node_id in self.execution_cache:
+                    self.execution_cache[to_node_id][node_id] = value
+                if from_socket is not None and self.output_link_callback is not None:
+                    self.output_link_callback(value.outputs[from_socket])
+
+    def add_strong_link(self, from_node_id, from_socket, to_node_id):
+        super().add_strong_link(from_node_id, from_socket, to_node_id)
+        self.cache_link(from_node_id, to_node_id, from_socket)
+
+    async def stage_node_execution(self):
+        assert self.staged_node_id is None
+        if self.is_empty():
+            return None, None, None
+        available = self.get_ready_nodes()
+        while len(available) == 0 and self.externalBlocks > 0:
+            # Wait for an external block to be released
+            await self.unblockedEvent.wait()
+            self.unblockedEvent.clear()
+            available = self.get_ready_nodes()
+        if len(available) == 0:
+            cycled_nodes = self.get_nodes_in_cycle()
+            # Because cycles composed entirely of static nodes are caught during initial validation,
+            # we will 'blame' the first node in the cycle that is not a static node.
+            blamed_node = cycled_nodes[0]
+            for node_id in cycled_nodes:
+                display_node_id = self.dynprompt.get_display_node_id(node_id)
+                if display_node_id != node_id:
+                    blamed_node = display_node_id
+                    break
+            ex = DependencyCycleError("Dependency cycle detected")
+            error_details = {
+                "node_id": blamed_node,
+                "exception_message": str(ex),
+                "exception_type": "graph.DependencyCycleError",
+                "traceback": [],
+                "current_inputs": []
+            }
+            return None, error_details, ex
+
+        self.staged_node_id = self.ux_friendly_pick_node(available)
+        return self.staged_node_id, None, None
+
+    def ux_friendly_pick_node(self, node_list):
+        # If an output node is available, do that first.
+        # Technically this has no effect on the overall length of execution, but it feels better as a user
+        # for a PreviewImage to display a result as soon as it can
+        # Some other heuristics could probably be used here to improve the UX further.
+        def is_output(node_id):
+            class_type = self.dynprompt.get_node(node_id)["class_type"]
+            class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+            if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
+                return True
+            return False
+
+        # If an available node is async, do that first.
+        # This will execute the asynchronous function earlier, reducing the overall time.
+        def is_async(node_id):
+            class_type = self.dynprompt.get_node(node_id)["class_type"]
+            class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+            return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION))
+
+        for node_id in node_list:
+            if is_output(node_id) or is_async(node_id):
+                return node_id
+
+        #This should handle the VAEDecode -> preview case
+        for node_id in node_list:
+            for blocked_node_id in self.blocking[node_id]:
+                if is_output(blocked_node_id):
+                    return node_id
+
+        #This should handle the VAELoader -> VAEDecode -> preview case
+        for node_id in node_list:
+            for blocked_node_id in self.blocking[node_id]:
+                for blocked_node_id1 in self.blocking[blocked_node_id]:
+                    if is_output(blocked_node_id1):
+                        return node_id
+
+        #TODO: this function should be improved
+        return node_list[0]
+
+    def unstage_node_execution(self):
+        assert self.staged_node_id is not None
+        self.staged_node_id = None
+
+    def complete_node_execution(self):
+        node_id = self.staged_node_id
+        self.pop_node(node_id)
+        self.execution_cache.pop(node_id, None)
+        self.execution_cache_listeners.pop(node_id, None)
+        self.staged_node_id = None
+
+    def get_nodes_in_cycle(self):
+        # We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle.
+        # We're skipping some of the performance optimizations from the original TopologicalSort to keep
+        # the code simple (and because having a cycle in the first place is a catastrophic error)
+        blocked_by = { node_id: {} for node_id in self.pendingNodes }
+        for from_node_id in self.blocking:
+            for to_node_id in self.blocking[from_node_id]:
+                if True in self.blocking[from_node_id][to_node_id].values():
+                    blocked_by[to_node_id][from_node_id] = True
+        to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
+        while len(to_remove) > 0:
+            for node_id in to_remove:
+                for to_node_id in blocked_by:
+                    if node_id in blocked_by[to_node_id]:
+                        del blocked_by[to_node_id][node_id]
+                del blocked_by[node_id]
+            to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
+        return list(blocked_by.keys())
diff --git a/comfy_execution/graph_utils.py b/comfy_execution/graph_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5406400bb4a91a113951d9d0324aec1ed0fe779f
--- /dev/null
+++ b/comfy_execution/graph_utils.py
@@ -0,0 +1,155 @@
+def is_link(obj):
+    if not isinstance(obj, list):
+        return False
+    if len(obj) != 2:
+        return False
+    if not isinstance(obj[0], str):
+        return False
+    if not isinstance(obj[1], int) and not isinstance(obj[1], float):
+        return False
+    return True
+
+# The GraphBuilder is just a utility class that outputs graphs in the form expected by the ComfyUI back-end
+class GraphBuilder:
+    _default_prefix_root = ""
+    _default_prefix_call_index = 0
+    _default_prefix_graph_index = 0
+
+    def __init__(self, prefix = None):
+        if prefix is None:
+            self.prefix = GraphBuilder.alloc_prefix()
+        else:
+            self.prefix = prefix
+        self.nodes = {}
+        self.id_gen = 1
+
+    @classmethod
+    def set_default_prefix(cls, prefix_root, call_index, graph_index = 0):
+        cls._default_prefix_root = prefix_root
+        cls._default_prefix_call_index = call_index
+        cls._default_prefix_graph_index = graph_index
+
+    @classmethod
+    def alloc_prefix(cls, root=None, call_index=None, graph_index=None):
+        if root is None:
+            root = GraphBuilder._default_prefix_root
+        if call_index is None:
+            call_index = GraphBuilder._default_prefix_call_index
+        if graph_index is None:
+            graph_index = GraphBuilder._default_prefix_graph_index
+        result = f"{root}.{call_index}.{graph_index}."
+        GraphBuilder._default_prefix_graph_index += 1
+        return result
+
+    def node(self, class_type, id=None, **kwargs):
+        if id is None:
+            id = str(self.id_gen)
+            self.id_gen += 1
+        id = self.prefix + id
+        if id in self.nodes:
+            return self.nodes[id]
+
+        node = Node(id, class_type, kwargs)
+        self.nodes[id] = node
+        return node
+
+    def lookup_node(self, id):
+        id = self.prefix + id
+        return self.nodes.get(id)
+
+    def finalize(self):
+        output = {}
+        for node_id, node in self.nodes.items():
+            output[node_id] = node.serialize()
+        return output
+
+    def replace_node_output(self, node_id, index, new_value):
+        node_id = self.prefix + node_id
+        to_remove = []
+        for node in self.nodes.values():
+            for key, value in node.inputs.items():
+                if is_link(value) and value[0] == node_id and value[1] == index:
+                    if new_value is None:
+                        to_remove.append((node, key))
+                    else:
+                        node.inputs[key] = new_value
+        for node, key in to_remove:
+            del node.inputs[key]
+
+    def remove_node(self, id):
+        id = self.prefix + id
+        del self.nodes[id]
+
+class Node:
+    def __init__(self, id, class_type, inputs):
+        self.id = id
+        self.class_type = class_type
+        self.inputs = inputs
+        self.override_display_id = None
+
+    def out(self, index):
+        return [self.id, index]
+
+    def set_input(self, key, value):
+        if value is None:
+            if key in self.inputs:
+                del self.inputs[key]
+        else:
+            self.inputs[key] = value
+
+    def get_input(self, key):
+        return self.inputs.get(key)
+
+    def set_override_display_id(self, override_display_id):
+        self.override_display_id = override_display_id
+
+    def serialize(self):
+        serialized = {
+            "class_type": self.class_type,
+            "inputs": self.inputs
+        }
+        if self.override_display_id is not None:
+            serialized["override_display_id"] = self.override_display_id
+        return serialized
+
+def add_graph_prefix(graph, outputs, prefix):
+    # Change the node IDs and any internal links
+    new_graph = {}
+    for node_id, node_info in graph.items():
+        # Make sure the added nodes have unique IDs
+        new_node_id = prefix + node_id
+        new_node = { "class_type": node_info["class_type"], "inputs": {} }
+        for input_name, input_value in node_info.get("inputs", {}).items():
+            if is_link(input_value):
+                new_node["inputs"][input_name] = [prefix + input_value[0], input_value[1]]
+            else:
+                new_node["inputs"][input_name] = input_value
+        new_graph[new_node_id] = new_node
+
+    # Change the node IDs in the outputs
+    new_outputs = []
+    for n in range(len(outputs)):
+        output = outputs[n]
+        if is_link(output):
+            new_outputs.append([prefix + output[0], output[1]])
+        else:
+            new_outputs.append(output)
+
+    return new_graph, tuple(new_outputs)
+
+class ExecutionBlocker:
+    """
+    Return this from a node and any users will be blocked with the given error message.
+    If the message is None, execution will be blocked silently instead.
+    Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's
+    possible, a lazy input will be more efficient and have a better user experience.
+    This functionality is useful in two cases:
+    1. You want to conditionally prevent an output node from executing. (Particularly a built-in node
+       like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using
+       lazy evaluation to let it conditionally disable itself.)
+    2. You have a node with multiple possible outputs, some of which are invalid and should not be used.
+       (I would recommend not making nodes like this in the future -- instead, make multiple nodes with
+       different outputs. Unfortunately, there are several popular existing nodes using this pattern.)
+    """
+    def __init__(self, message):
+        self.message = message
diff --git a/comfy_execution/jobs.py b/comfy_execution/jobs.py
new file mode 100644
index 0000000000000000000000000000000000000000..4021339aad9cb14d2cdf8a0b47d5d2dc48bc4659
--- /dev/null
+++ b/comfy_execution/jobs.py
@@ -0,0 +1,550 @@
+"""
+Job utilities for the /api/jobs endpoint.
+Provides normalization and helper functions for job status tracking.
+"""
+
+import uuid
+from typing import Callable, Optional
+
+from comfy_api.internal import prune_dict
+
+
+# Result of classifying a job for cancellation.
+# 'running'  -> job is currently executing (interrupt it)
+# 'pending'  -> job is queued but not started (dequeue it)
+# 'terminal' -> job already finished (present in history); cancel is a no-op
+# 'unknown'  -> job id is not present anywhere
+CANCEL_RUNNING = 'running'
+CANCEL_PENDING = 'pending'
+CANCEL_TERMINAL = 'terminal'
+CANCEL_UNKNOWN = 'unknown'
+
+
+class JobStatus:
+    """Job status constants."""
+    PENDING = 'pending'
+    IN_PROGRESS = 'in_progress'
+    COMPLETED = 'completed'
+    FAILED = 'failed'
+    CANCELLED = 'cancelled'
+
+    ALL = [PENDING, IN_PROGRESS, COMPLETED, FAILED, CANCELLED]
+
+
+def validate_job_id(value) -> str:
+    """Validate a client-supplied job (prompt) id.
+
+    Job ids must be UUIDs in the canonical lowercase hyphenated form. The id
+    is stored and compared verbatim everywhere downstream — history keys,
+    websocket events, and /interrupt matching — so accepting another spelling
+    would silently rewrite the client's id and then miss every exact-match
+    lookup. Rejecting loudly beats that.
+
+    Returns the id unchanged. Raises ValueError when the value is not a
+    string in canonical UUID form.
+    """
+    if not isinstance(value, str):
+        raise ValueError(f"job id must be a string, got {type(value).__name__}")
+    if str(uuid.UUID(value)) != value:
+        raise ValueError("job id must be a UUID in canonical lowercase hyphenated form")
+    return value
+
+
+# Media types that can be previewed in the frontend
+PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d', 'text'})
+
+# 3D file extensions for preview fallback (no dedicated media_type exists)
+THREE_D_EXTENSIONS = frozenset({'.obj', '.fbx', '.gltf', '.glb', '.usdz'})
+
+# Text file extensions for preview fallback (the formats SaveText can produce)
+TEXT_EXTENSIONS = frozenset({'.txt', '.md', '.json'})
+
+
+def has_3d_extension(filename: str) -> bool:
+    lower = filename.lower()
+    return any(lower.endswith(ext) for ext in THREE_D_EXTENSIONS)
+
+
+def normalize_output_item(item):
+    """Normalize a single output list item for the jobs API.
+
+    Returns the normalized item, or None to exclude it.
+    String items with 3D extensions become {filename, type, subfolder} dicts.
+    """
+    if item is None:
+        return None
+    if isinstance(item, str):
+        if has_3d_extension(item):
+            return {'filename': item, 'type': 'output', 'subfolder': '', 'mediaType': '3d'}
+        return None
+    if isinstance(item, dict):
+        return item
+    return None
+
+
+def normalize_outputs(outputs: dict) -> dict:
+    """Normalize raw node outputs for the jobs API.
+
+    Transforms string 3D filenames into file output dicts and removes
+    None items. All other items (non-3D strings, dicts, etc.) are
+    preserved as-is.
+    """
+    normalized = {}
+    for node_id, node_outputs in outputs.items():
+        if not isinstance(node_outputs, dict):
+            normalized[node_id] = node_outputs
+            continue
+        normalized_node = {}
+        for media_type, items in node_outputs.items():
+            if media_type == 'animated' or not isinstance(items, list):
+                normalized_node[media_type] = items
+                continue
+            normalized_items = []
+            for item in items:
+                if item is None:
+                    continue
+                norm = normalize_output_item(item)
+                normalized_items.append(norm if norm is not None else item)
+            normalized_node[media_type] = normalized_items
+        normalized[node_id] = normalized_node
+    return normalized
+
+# Text preview truncation limit (1024 characters) to prevent preview_output bloat
+TEXT_PREVIEW_MAX_LENGTH = 1024
+
+
+def _create_text_preview(value: str) -> dict:
+    """Create a text preview dict with optional truncation.
+
+    Returns:
+        dict with 'content' and optionally 'truncated' flag
+    """
+    if len(value) <= TEXT_PREVIEW_MAX_LENGTH:
+        return {'content': value}
+    return {
+        'content': value[:TEXT_PREVIEW_MAX_LENGTH],
+        'truncated': True
+    }
+
+
+def _extract_job_metadata(extra_data: dict) -> tuple[Optional[int], Optional[str]]:
+    """Extract create_time and workflow_id from extra_data.
+
+    Returns:
+        tuple: (create_time, workflow_id)
+    """
+    create_time = extra_data.get('create_time')
+    extra_pnginfo = extra_data.get('extra_pnginfo', {})
+    workflow_id = extra_pnginfo.get('workflow', {}).get('id')
+    return create_time, workflow_id
+
+
+def is_previewable(media_type: str, item: dict) -> bool:
+    """
+    Check if an output item is previewable.
+    Matches frontend logic in ComfyUI_frontend/src/stores/queueStore.ts
+    Maintains backwards compatibility with existing logic.
+
+    Priority:
+    1. media_type is 'images', 'video', 'audio', '3d', or 'text'
+    2. format field starts with 'video/' or 'audio/'
+    3. filename has a 3D extension (.obj, .fbx, .gltf, .glb, .usdz)
+    4. filename has a text extension (.txt, .md, .json, ...)
+    """
+    if media_type in PREVIEWABLE_MEDIA_TYPES:
+        return True
+
+    # Check format field (MIME type).
+    # Maintains backwards compatibility with how custom node outputs are handled in the frontend.
+    fmt = item.get('format', '')
+    if fmt and (fmt.startswith('video/') or fmt.startswith('audio/')):
+        return True
+
+    # Check for 3D and text files by extension
+    filename = item.get('filename', '').lower()
+    if any(filename.endswith(ext) for ext in THREE_D_EXTENSIONS):
+        return True
+    if any(filename.endswith(ext) for ext in TEXT_EXTENSIONS):
+        return True
+
+    return False
+
+
+def is_text_preview(media_type: str, item: dict) -> bool:
+    """
+    Check if a previewable output item is textual rather than visual media.
+
+    Saved text files (SaveText's .txt/.md/.json) are real outputs but must not
+    outrank visual media when picking the job preview.
+    """
+    if media_type == 'text':
+        return True
+    filename = item.get('filename', '').lower()
+    return any(filename.endswith(ext) for ext in TEXT_EXTENSIONS)
+
+
+def normalize_queue_item(item: tuple, status: str) -> dict:
+    """Convert queue item tuple to unified job dict.
+
+    Expects item with sensitive data already removed (5 elements).
+    """
+    priority, prompt_id, _, extra_data, _ = item
+    create_time, workflow_id = _extract_job_metadata(extra_data)
+
+    return prune_dict({
+        'id': prompt_id,
+        'status': status,
+        'priority': priority,
+        'create_time': create_time,
+        'outputs_count': 0,
+        'previewable_outputs_count': 0,
+        'workflow_id': workflow_id,
+    })
+
+
+def normalize_history_item(prompt_id: str, history_item: dict, include_outputs: bool = False) -> dict:
+    """Convert history item dict to unified job dict.
+
+    History items have sensitive data already removed (prompt tuple has 5 elements).
+    """
+    prompt_tuple = history_item['prompt']
+    priority, _, prompt, extra_data, _ = prompt_tuple
+    create_time, workflow_id = _extract_job_metadata(extra_data)
+
+    status_info = history_item.get('status', {})
+    status_str = status_info.get('status_str') if status_info else None
+
+    outputs = history_item.get('outputs', {})
+    outputs_count, preview_output = get_outputs_summary(outputs)
+    previewable_outputs_count = count_previewable_outputs(outputs)
+
+    execution_error = None
+    execution_start_time = None
+    execution_end_time = None
+    was_interrupted = False
+    if status_info:
+        messages = status_info.get('messages', [])
+        for entry in messages:
+            if isinstance(entry, (list, tuple)) and len(entry) >= 2:
+                event_name, event_data = entry[0], entry[1]
+                if isinstance(event_data, dict):
+                    if event_name == 'execution_start':
+                        execution_start_time = event_data.get('timestamp')
+                    elif event_name in ('execution_success', 'execution_error', 'execution_interrupted'):
+                        execution_end_time = event_data.get('timestamp')
+                        if event_name == 'execution_error':
+                            execution_error = event_data
+                        elif event_name == 'execution_interrupted':
+                            was_interrupted = True
+
+    if status_str == 'success':
+        status = JobStatus.COMPLETED
+    elif status_str == 'error':
+        status = JobStatus.CANCELLED if was_interrupted else JobStatus.FAILED
+    else:
+        status = JobStatus.COMPLETED
+
+    job = prune_dict({
+        'id': prompt_id,
+        'status': status,
+        'priority': priority,
+        'create_time': create_time,
+        'execution_start_time': execution_start_time,
+        'execution_end_time': execution_end_time,
+        'execution_error': execution_error,
+        'outputs_count': outputs_count,
+        'previewable_outputs_count': previewable_outputs_count,
+        'preview_output': preview_output,
+        'workflow_id': workflow_id,
+    })
+
+    if include_outputs:
+        job['outputs'] = normalize_outputs(outputs)
+        job['execution_status'] = status_info
+        job['workflow'] = {
+            'prompt': prompt,
+            'extra_data': extra_data,
+        }
+
+    return job
+
+
+def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
+    """
+    Count outputs and find preview in a single pass.
+    Returns (outputs_count, preview_output).
+
+    Preview priority (matching frontend):
+    1. type="output" visual media (saved images/video/audio/3d)
+    2. any other previewable visual media (e.g. temp/preview images)
+    3. saved text file (e.g. SaveText's .txt/.md/.json)
+    4. raw text (only when the job produced nothing else previewable)
+
+    Text is kept in its own slots so node/execution order can't let a text
+    output mask a visual one (e.g. a text node that runs before an image).
+
+    Text content entries (strings under 'text') are preview-only metadata,
+    matching the frontend's METADATA_KEYS: they can serve as the fallback
+    preview but are not counted as outputs.
+    """
+    count = 0
+    preview_output = None
+    fallback_preview = None
+    text_file_fallback = None
+    text_fallback = None
+
+    for node_id, node_outputs in outputs.items():
+        if not isinstance(node_outputs, dict):
+            continue
+        for media_type, items in node_outputs.items():
+            # 'animated' is a boolean flag, not actual output items
+            if media_type == 'animated' or not isinstance(items, list):
+                continue
+
+            for item in items:
+                if not isinstance(item, dict):
+                    # Handle text outputs (non-dict items like strings or tuples)
+                    normalized = normalize_output_item(item)
+                    if normalized is None:
+                        # Not a 3D file string — check for text preview
+                        if media_type == 'text':
+                            if preview_output is None:
+                                if isinstance(item, tuple):
+                                    text_value = item[0] if item else ''
+                                else:
+                                    text_value = str(item)
+                                text_preview = _create_text_preview(text_value)
+                                enriched = {
+                                    **text_preview,
+                                    'nodeId': node_id,
+                                    'mediaType': media_type
+                                }
+                                if text_fallback is None:
+                                    text_fallback = enriched
+                        continue
+                    # normalize_output_item returned a dict (e.g. 3D file)
+                    item = normalized
+
+                count += 1
+
+                if preview_output is not None:
+                    continue
+
+                if is_previewable(media_type, item):
+                    enriched = {
+                        **item,
+                        'nodeId': node_id,
+                    }
+                    if 'mediaType' not in item:
+                        enriched['mediaType'] = media_type
+                    if is_text_preview(media_type, item):
+                        if text_file_fallback is None:
+                            text_file_fallback = enriched
+                    elif item.get('type') == 'output':
+                        preview_output = enriched
+                    elif fallback_preview is None:
+                        fallback_preview = enriched
+
+    return count, preview_output or fallback_preview or text_file_fallback or text_fallback
+
+
+def count_previewable_outputs(outputs: dict) -> int:
+    """
+    Count only outputs that would actually render in the expanded asset view,
+    i.e. items is_previewable() accepts (image/video/audio/3D/text). Kept
+    separate from get_outputs_summary()'s outputs_count, which counts every
+    output item regardless of media type, so a job with a non-previewable
+    saved file alongside real media (e.g. SaveLatent's .latent output next to
+    a SaveImage output) doesn't inflate the Media Assets badge beyond what
+    the expanded view shows.
+    """
+    count = 0
+    for node_outputs in outputs.values():
+        if not isinstance(node_outputs, dict):
+            continue
+        for media_type, items in node_outputs.items():
+            if media_type == 'animated' or not isinstance(items, list):
+                continue
+            for item in items:
+                if not isinstance(item, dict):
+                    item = normalize_output_item(item)
+                    if item is None:
+                        continue
+                if is_previewable(media_type, item):
+                    count += 1
+    return count
+
+
+def apply_sorting(jobs: list[dict], sort_by: str, sort_order: str) -> list[dict]:
+    """Sort jobs list by specified field and order."""
+    reverse = (sort_order == 'desc')
+
+    if sort_by == 'execution_duration':
+        def get_sort_key(job):
+            start = job.get('execution_start_time', 0)
+            end = job.get('execution_end_time', 0)
+            return end - start if end and start else 0
+    else:
+        def get_sort_key(job):
+            return job.get('create_time', 0)
+
+    return sorted(jobs, key=get_sort_key, reverse=reverse)
+
+
+def get_job(prompt_id: str, running: list, queued: list, history: dict) -> Optional[dict]:
+    """
+    Get a single job by prompt_id from history or queue.
+
+    Args:
+        prompt_id: The prompt ID to look up
+        running: List of currently running queue items
+        queued: List of pending queue items
+        history: Dict of history items keyed by prompt_id
+
+    Returns:
+        Job dict with full details, or None if not found
+    """
+    if prompt_id in history:
+        return normalize_history_item(prompt_id, history[prompt_id], include_outputs=True)
+
+    for item in running:
+        if item[1] == prompt_id:
+            return normalize_queue_item(item, JobStatus.IN_PROGRESS)
+
+    for item in queued:
+        if item[1] == prompt_id:
+            return normalize_queue_item(item, JobStatus.PENDING)
+
+    return None
+
+
+def get_all_jobs(
+    running: list,
+    queued: list,
+    history: dict,
+    status_filter: Optional[list[str]] = None,
+    workflow_id: Optional[str] = None,
+    sort_by: str = "created_at",
+    sort_order: str = "desc",
+    limit: Optional[int] = None,
+    offset: int = 0
+) -> tuple[list[dict], int]:
+    """
+    Get all jobs (running, pending, completed) with filtering and sorting.
+
+    Args:
+        running: List of currently running queue items
+        queued: List of pending queue items
+        history: Dict of history items keyed by prompt_id
+        status_filter: List of statuses to include (from JobStatus.ALL)
+        workflow_id: Filter by workflow ID
+        sort_by: Field to sort by ('created_at', 'execution_duration')
+        sort_order: 'asc' or 'desc'
+        limit: Maximum number of items to return
+        offset: Number of items to skip
+
+    Returns:
+        tuple: (jobs_list, total_count)
+    """
+    jobs = []
+
+    if status_filter is None:
+        status_filter = JobStatus.ALL
+
+    if JobStatus.IN_PROGRESS in status_filter:
+        for item in running:
+            jobs.append(normalize_queue_item(item, JobStatus.IN_PROGRESS))
+
+    if JobStatus.PENDING in status_filter:
+        for item in queued:
+            jobs.append(normalize_queue_item(item, JobStatus.PENDING))
+
+    history_statuses = {JobStatus.COMPLETED, JobStatus.FAILED, JobStatus.CANCELLED}
+    requested_history_statuses = history_statuses & set(status_filter)
+    if requested_history_statuses:
+        for prompt_id, history_item in history.items():
+            job = normalize_history_item(prompt_id, history_item)
+            if job.get('status') in requested_history_statuses:
+                jobs.append(job)
+
+    if workflow_id:
+        jobs = [j for j in jobs if j.get('workflow_id') == workflow_id]
+
+    jobs = apply_sorting(jobs, sort_by, sort_order)
+
+    total_count = len(jobs)
+
+    if offset > 0:
+        jobs = jobs[offset:]
+    if limit is not None:
+        jobs = jobs[:limit]
+
+    return (jobs, total_count)
+
+
+def classify_job_for_cancel(prompt_id: str, running: list, queued: list, history: dict) -> str:
+    """Classify a job id for cancellation.
+
+    Returns one of CANCEL_RUNNING, CANCEL_PENDING, CANCEL_TERMINAL, CANCEL_UNKNOWN.
+
+    Queue items are tuples whose second element (index 1) is the prompt_id.
+    History is a dict keyed by prompt_id, so a job present there has already
+    finished and cancelling it is a no-op.
+    """
+    for item in running:
+        if item[1] == prompt_id:
+            return CANCEL_RUNNING
+    for item in queued:
+        if item[1] == prompt_id:
+            return CANCEL_PENDING
+    if prompt_id in history:
+        return CANCEL_TERMINAL
+    return CANCEL_UNKNOWN
+
+
+def cancel_job(
+    prompt_id: str,
+    running: list,
+    queued: list,
+    history: dict,
+    interrupt: Callable[[str], bool],
+    dequeue: Callable[[str], bool],
+) -> str:
+    """Cancel a single job by id, regardless of state.
+
+    Maps the cancel onto the runtime's existing mechanics:
+      - a running job is interrupted via ``interrupt``
+      - a pending job is removed from the queue via ``dequeue``
+      - a job that already finished (terminal) is a no-op
+      - an unknown id is a no-op (callers that need fail-fast behaviour should
+        validate ids up front with ``classify_job_for_cancel``)
+
+    Both ``interrupt`` and ``dequeue`` take the prompt id and return whether
+    they acted on a job that was *actually* in that state, so the value returned
+    here reflects what truly happened rather than the (possibly stale)
+    classification. This matters around the narrow TOCTOU windows where a job
+    changes state between the caller's snapshot and the action:
+
+      - a job classified RUNNING may have finished before ``interrupt`` fires:
+        ``interrupt`` returns False and this returns CANCEL_UNKNOWN (no-op).
+      - a job classified PENDING may have started executing before ``dequeue``
+        fires: ``dequeue`` returns False, ``interrupt`` then catches the now-
+        running job and this returns CANCEL_RUNNING. If it had simply finished
+        instead, both return False and this returns CANCEL_UNKNOWN.
+
+    ``interrupt`` must be atomic — interrupt the job only if it is still the one
+    running — so a cancel can never land on an unrelated prompt that started in
+    the meantime (see ``execution.PromptQueue.interrupt_if_running``).
+    """
+    classification = classify_job_for_cancel(prompt_id, running, queued, history)
+    if classification == CANCEL_RUNNING:
+        return CANCEL_RUNNING if interrupt(prompt_id) else CANCEL_UNKNOWN
+    if classification == CANCEL_PENDING:
+        if dequeue(prompt_id):
+            return CANCEL_PENDING
+        # Left the pending queue between classification and dequeue: if it
+        # started executing, interrupt the now-running job; otherwise it has
+        # already finished and the cancel is a genuine no-op.
+        return CANCEL_RUNNING if interrupt(prompt_id) else CANCEL_UNKNOWN
+    # CANCEL_TERMINAL and CANCEL_UNKNOWN are intentional no-ops.
+    return classification
diff --git a/comfy_execution/progress.py b/comfy_execution/progress.py
new file mode 100644
index 0000000000000000000000000000000000000000..74df07bd483f6ea74785f4c8bd4185e52404818d
--- /dev/null
+++ b/comfy_execution/progress.py
@@ -0,0 +1,348 @@
+from typing import TypedDict, Dict, Optional, Tuple
+from typing_extensions import override
+from PIL import Image
+from enum import Enum
+from abc import ABC
+from tqdm import tqdm
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from comfy_execution.graph import DynamicPrompt
+from protocol import BinaryEventTypes
+from comfy_api import feature_flags
+
+PreviewImageTuple = Tuple[str, Image.Image, Optional[int]]
+
+class NodeState(Enum):
+    Pending = "pending"
+    Running = "running"
+    Finished = "finished"
+    Error = "error"
+
+
+class NodeProgressState(TypedDict):
+    """
+    A class to represent the state of a node's progress.
+    """
+
+    state: NodeState
+    value: float
+    max: float
+
+
+class ProgressHandler(ABC):
+    """
+    Abstract base class for progress handlers.
+    Progress handlers receive progress updates and display them in various ways.
+    """
+
+    def __init__(self, name: str):
+        self.name = name
+        self.enabled = True
+
+    def set_registry(self, registry: "ProgressRegistry"):
+        pass
+
+    def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        """Called when a node starts processing"""
+        pass
+
+    def update_handler(
+        self,
+        node_id: str,
+        value: float,
+        max_value: float,
+        state: NodeProgressState,
+        prompt_id: str,
+        image: PreviewImageTuple | None = None,
+    ):
+        """Called when a node's progress is updated"""
+        pass
+
+    def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        """Called when a node finishes processing"""
+        pass
+
+    def reset(self):
+        """Called when the progress registry is reset"""
+        pass
+
+    def enable(self):
+        """Enable this handler"""
+        self.enabled = True
+
+    def disable(self):
+        """Disable this handler"""
+        self.enabled = False
+
+
+class CLIProgressHandler(ProgressHandler):
+    """
+    Handler that displays progress using tqdm progress bars in the CLI.
+    """
+
+    def __init__(self):
+        super().__init__("cli")
+        self.progress_bars: Dict[str, tqdm] = {}
+
+    @override
+    def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        # Create a new tqdm progress bar
+        if node_id not in self.progress_bars:
+            self.progress_bars[node_id] = tqdm(
+                total=state["max"],
+                desc=f"Node {node_id}",
+                unit="steps",
+                leave=True,
+                position=len(self.progress_bars),
+            )
+
+    @override
+    def update_handler(
+        self,
+        node_id: str,
+        value: float,
+        max_value: float,
+        state: NodeProgressState,
+        prompt_id: str,
+        image: PreviewImageTuple | None = None,
+    ):
+        # Handle case where start_handler wasn't called
+        if node_id not in self.progress_bars:
+            self.progress_bars[node_id] = tqdm(
+                total=max_value,
+                desc=f"Node {node_id}",
+                unit="steps",
+                leave=True,
+                position=len(self.progress_bars),
+            )
+            self.progress_bars[node_id].update(value)
+        else:
+            # Update existing progress bar
+            if max_value != self.progress_bars[node_id].total:
+                self.progress_bars[node_id].total = max_value
+            # Calculate the update amount (difference from current position)
+            current_position = self.progress_bars[node_id].n
+            update_amount = value - current_position
+            if update_amount > 0:
+                self.progress_bars[node_id].update(update_amount)
+
+    @override
+    def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        # Complete and close the progress bar if it exists
+        if node_id in self.progress_bars:
+            # Ensure the bar shows 100% completion
+            remaining = state["max"] - self.progress_bars[node_id].n
+            if remaining > 0:
+                self.progress_bars[node_id].update(remaining)
+            self.progress_bars[node_id].close()
+            del self.progress_bars[node_id]
+
+    @override
+    def reset(self):
+        # Close all progress bars
+        for bar in self.progress_bars.values():
+            bar.close()
+        self.progress_bars.clear()
+
+
+class WebUIProgressHandler(ProgressHandler):
+    """
+    Handler that sends progress updates to the WebUI via WebSockets.
+    """
+
+    def __init__(self, server_instance):
+        super().__init__("webui")
+        self.server_instance = server_instance
+
+    def set_registry(self, registry: "ProgressRegistry"):
+        self.registry = registry
+
+    def _send_progress_state(self, prompt_id: str, nodes: Dict[str, NodeProgressState]):
+        """Send the current progress state to the client"""
+        if self.server_instance is None:
+            return
+
+        # Only send info for non-pending nodes
+        active_nodes = {
+            node_id: {
+                "value": state["value"],
+                "max": state["max"],
+                "state": state["state"].value,
+                "node_id": node_id,
+                "prompt_id": prompt_id,
+                "display_node_id": self.registry.dynprompt.get_display_node_id(node_id),
+                "parent_node_id": self.registry.dynprompt.get_parent_node_id(node_id),
+                "real_node_id": self.registry.dynprompt.get_real_node_id(node_id),
+            }
+            for node_id, state in nodes.items()
+            if state["state"] != NodeState.Pending
+        }
+
+        # Send a combined progress_state message with all node states
+        # Include client_id to ensure message is only sent to the initiating client
+        self.server_instance.send_sync(
+            "progress_state", {"prompt_id": prompt_id, "nodes": active_nodes}, self.server_instance.client_id
+        )
+
+    @override
+    def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        # Send progress state of all nodes
+        if self.registry:
+            self._send_progress_state(prompt_id, self.registry.nodes)
+
+    @override
+    def update_handler(
+        self,
+        node_id: str,
+        value: float,
+        max_value: float,
+        state: NodeProgressState,
+        prompt_id: str,
+        image: PreviewImageTuple | None = None,
+    ):
+        # Send progress state of all nodes
+        if self.registry:
+            self._send_progress_state(prompt_id, self.registry.nodes)
+        if image:
+            # Only send new format if client supports it
+            if feature_flags.supports_feature(
+                self.server_instance.sockets_metadata,
+                self.server_instance.client_id,
+                "supports_preview_metadata",
+            ):
+                metadata = {
+                    "node_id": node_id,
+                    "prompt_id": prompt_id,
+                    "display_node_id": self.registry.dynprompt.get_display_node_id(
+                        node_id
+                    ),
+                    "parent_node_id": self.registry.dynprompt.get_parent_node_id(
+                        node_id
+                    ),
+                    "real_node_id": self.registry.dynprompt.get_real_node_id(node_id),
+                }
+                self.server_instance.send_sync(
+                    BinaryEventTypes.PREVIEW_IMAGE_WITH_METADATA,
+                    (image, metadata),
+                    self.server_instance.client_id,
+                )
+
+    @override
+    def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+        # Send progress state of all nodes
+        if self.registry:
+            self._send_progress_state(prompt_id, self.registry.nodes)
+
+class ProgressRegistry:
+    """
+    Registry that maintains node progress state and notifies registered handlers.
+    """
+
+    def __init__(self, prompt_id: str, dynprompt: "DynamicPrompt"):
+        self.prompt_id = prompt_id
+        self.dynprompt = dynprompt
+        self.nodes: Dict[str, NodeProgressState] = {}
+        self.handlers: Dict[str, ProgressHandler] = {}
+
+    def register_handler(self, handler: ProgressHandler) -> None:
+        """Register a progress handler"""
+        self.handlers[handler.name] = handler
+
+    def unregister_handler(self, handler_name: str) -> None:
+        """Unregister a progress handler"""
+        if handler_name in self.handlers:
+            # Allow handler to clean up resources
+            self.handlers[handler_name].reset()
+            del self.handlers[handler_name]
+
+    def enable_handler(self, handler_name: str) -> None:
+        """Enable a progress handler"""
+        if handler_name in self.handlers:
+            self.handlers[handler_name].enable()
+
+    def disable_handler(self, handler_name: str) -> None:
+        """Disable a progress handler"""
+        if handler_name in self.handlers:
+            self.handlers[handler_name].disable()
+
+    def ensure_entry(self, node_id: str) -> NodeProgressState:
+        """Ensure a node entry exists"""
+        if node_id not in self.nodes:
+            self.nodes[node_id] = NodeProgressState(
+                state=NodeState.Pending, value=0, max=1
+            )
+        return self.nodes[node_id]
+
+    def start_progress(self, node_id: str) -> None:
+        """Start progress tracking for a node"""
+        entry = self.ensure_entry(node_id)
+        entry["state"] = NodeState.Running
+        entry["value"] = 0.0
+        entry["max"] = 1.0
+
+        # Notify all enabled handlers
+        for handler in self.handlers.values():
+            if handler.enabled:
+                handler.start_handler(node_id, entry, self.prompt_id)
+
+    def update_progress(
+        self, node_id: str, value: float, max_value: float, image: PreviewImageTuple | None = None
+    ) -> None:
+        """Update progress for a node"""
+        entry = self.ensure_entry(node_id)
+        entry["state"] = NodeState.Running
+        entry["value"] = value
+        entry["max"] = max_value
+
+        # Notify all enabled handlers
+        for handler in self.handlers.values():
+            if handler.enabled:
+                handler.update_handler(
+                    node_id, value, max_value, entry, self.prompt_id, image
+                )
+
+    def finish_progress(self, node_id: str) -> None:
+        """Finish progress tracking for a node"""
+        entry = self.ensure_entry(node_id)
+        entry["state"] = NodeState.Finished
+        entry["value"] = entry["max"]
+
+        # Notify all enabled handlers
+        for handler in self.handlers.values():
+            if handler.enabled:
+                handler.finish_handler(node_id, entry, self.prompt_id)
+
+    def reset_handlers(self) -> None:
+        """Reset all handlers"""
+        for handler in self.handlers.values():
+            handler.reset()
+
+# Global registry instance
+global_progress_registry: ProgressRegistry | None = None
+
+def reset_progress_state(prompt_id: str, dynprompt: "DynamicPrompt") -> None:
+    global global_progress_registry
+
+    # Reset existing handlers if registry exists
+    if global_progress_registry is not None:
+        global_progress_registry.reset_handlers()
+
+    # Create new registry
+    global_progress_registry = ProgressRegistry(prompt_id, dynprompt)
+
+
+def add_progress_handler(handler: ProgressHandler) -> None:
+    registry = get_progress_state()
+    handler.set_registry(registry)
+    registry.register_handler(handler)
+
+
+def get_progress_state() -> ProgressRegistry:
+    global global_progress_registry
+    if global_progress_registry is None:
+        from comfy_execution.graph import DynamicPrompt
+
+        global_progress_registry = ProgressRegistry(
+            prompt_id="", dynprompt=DynamicPrompt({})
+        )
+    return global_progress_registry
diff --git a/comfy_execution/utils.py b/comfy_execution/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..27e84c8a6432a0386cda58418398b573d805e8ab
--- /dev/null
+++ b/comfy_execution/utils.py
@@ -0,0 +1,46 @@
+import contextvars
+from typing import Optional, NamedTuple
+
+class ExecutionContext(NamedTuple):
+    """
+    Context information about the currently executing node.
+
+    Attributes:
+        node_id: The ID of the currently executing node
+        list_index: The index in a list being processed (for operations on batches/lists)
+    """
+    prompt_id: str
+    node_id: str
+    list_index: Optional[int]
+
+current_executing_context: contextvars.ContextVar[Optional[ExecutionContext]] = contextvars.ContextVar("current_executing_context", default=None)
+
+def get_executing_context() -> Optional[ExecutionContext]:
+    return current_executing_context.get(None)
+
+class CurrentNodeContext:
+    """
+    Context manager for setting the current executing node context.
+
+    Sets the current_executing_context on enter and resets it on exit.
+
+    Example:
+        with CurrentNodeContext(node_id="123", list_index=0):
+            # Code that should run with the current node context set
+            process_image()
+    """
+    def __init__(self, prompt_id: str, node_id: str, list_index: Optional[int] = None):
+        self.context = ExecutionContext(
+            prompt_id= prompt_id,
+            node_id= node_id,
+            list_index= list_index
+        )
+        self.token = None
+
+    def __enter__(self):
+        self.token = current_executing_context.set(self.context)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self.token is not None:
+            current_executing_context.reset(self.token)
diff --git a/comfy_execution/validation.py b/comfy_execution/validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..880bfb60358759e473b5b848da160265af45ffd8
--- /dev/null
+++ b/comfy_execution/validation.py
@@ -0,0 +1,58 @@
+from comfy_api.latest import IO
+
+
+def validate_node_input(
+    received_type: str, input_type: str, strict: bool = False
+) -> bool:
+    """
+    received_type and input_type are both strings of the form "T1,T2,...".
+
+    If strict is True, the input_type must contain the received_type.
+      For example, if received_type is "STRING" and input_type is "STRING,INT",
+      this will return True. But if received_type is "STRING,INT" and input_type is
+      "INT", this will return False.
+
+    If strict is False, the input_type must have overlap with the received_type.
+      For example, if received_type is "STRING,BOOLEAN" and input_type is "STRING,INT",
+      this will return True.
+
+    Supports pre-union type extension behaviour of ``__ne__`` overrides.
+    """
+    # If the types are exactly the same, we can return immediately
+    # Use pre-union behaviour: inverse of `__ne__`
+    # NOTE: this lets legacy '*' Any types work that override the __ne__ method of the str class.
+    if not received_type != input_type:
+        return True
+
+    # If one of the types is '*', we can return True immediately; this is the 'Any' type.
+    if received_type == IO.AnyType.io_type or input_type == IO.AnyType.io_type:
+        return True
+
+    # If the received type or input_type is a MatchType, we can return True immediately;
+    # validation for this is handled by the frontend
+    if received_type == IO.MatchType.io_type or input_type == IO.MatchType.io_type:
+        return True
+
+    # This accounts for some custom nodes that output lists of options as the type;
+    # if we ever want to break them on purpose, this can be removed
+    if isinstance(received_type, list) and input_type == IO.Combo.io_type:
+        return True
+
+    # Not equal, and not strings
+    if not isinstance(received_type, str) or not isinstance(input_type, str):
+        return False
+
+    # Split the type strings into sets for comparison
+    received_types = set(t.strip() for t in received_type.split(","))
+    input_types = set(t.strip() for t in input_type.split(","))
+
+    # If any of the types is '*', we can return True immediately; this is the 'Any' type.
+    if IO.AnyType.io_type in received_types or IO.AnyType.io_type in input_types:
+        return True
+
+    if strict:
+        # In strict mode, all received types must be in the input types
+        return received_types.issubset(input_types)
+    else:
+        # In non-strict mode, there must be at least one type in common
+        return len(received_types.intersection(input_types)) > 0
diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py
new file mode 100644
index 0000000000000000000000000000000000000000..b97f9db365d78ea242712798bed61b0a1765a4af
--- /dev/null
+++ b/comfy_extras/chainner_models/model_loading.py
@@ -0,0 +1,6 @@
+import logging
+from spandrel import ModelLoader
+
+def load_state_dict(state_dict):
+    logging.warning("comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
+    return ModelLoader().load_from_state_dict(state_dict).eval()
diff --git a/comfy_extras/color_util.py b/comfy_extras/color_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..337d239d6a8930318bea0300cce381f94c9e882a
--- /dev/null
+++ b/comfy_extras/color_util.py
@@ -0,0 +1,23 @@
+def hex_to_rgb(value: str) -> tuple[int, int, int]:
+    h = value.lstrip("#")
+    if len(h) != 6:
+        return (255, 255, 255)
+    try:
+        return (int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16))
+    except ValueError:
+        return (255, 255, 255)
+
+
+def readable_color(rgb: tuple[int, int, int]) -> tuple[int, int, int]:
+    r, g, b = rgb
+    lum = 0.299 * r + 0.587 * g + 0.114 * b
+    if lum >= 130:
+        return (r, g, b)
+    t = (130 - lum) / (255 - lum)
+    return (round(r + (255 - r) * t), round(g + (255 - g) * t), round(b + (255 - b) * t))
+
+
+def normalize_palette(colors) -> list[str]:
+    if isinstance(colors, dict):
+        colors = colors.values()
+    return [c.upper() for c in colors if isinstance(c, str) and c]
diff --git a/comfy_extras/compositor_blend.py b/comfy_extras/compositor_blend.py
new file mode 100644
index 0000000000000000000000000000000000000000..79c08aeacf0c056003dcc221c3fdcb094e1b8398
--- /dev/null
+++ b/comfy_extras/compositor_blend.py
@@ -0,0 +1,331 @@
+import math
+from typing import NamedTuple, Optional, Union
+
+import numpy as np
+
+EPSILON = 1e-6
+
+LUM_R = 0.2224884
+LUM_G = 0.71690369
+LUM_B = 0.06060791
+
+ArrayLike = Union[np.ndarray, float]
+
+
+def srgb_to_linear(c: ArrayLike) -> np.ndarray:
+    c = np.asarray(c, dtype=np.float32)
+    high = ((np.maximum(c, 0.0) + 0.055) / 1.055) ** 2.4
+    return np.where(c <= 0.04045, c / 12.92, high).astype(np.float32)
+
+
+def linear_to_srgb(c: ArrayLike) -> np.ndarray:
+    c = np.asarray(c, dtype=np.float32)
+    high = 1.055 * np.maximum(c, 0.0) ** (1.0 / 2.4) - 0.055
+    return np.where(c <= 0.0031308, 12.92 * c, high).astype(np.float32)
+
+
+def luminance(rgb: np.ndarray) -> np.ndarray:
+    return rgb[..., 0] * LUM_R + rgb[..., 1] * LUM_G + rgb[..., 2] * LUM_B
+
+
+def safe_div(a: ArrayLike, b: ArrayLike) -> np.ndarray:
+    a, b = np.broadcast_arrays(
+        np.asarray(a, dtype=np.float32), np.asarray(b, dtype=np.float32)
+    )
+    out = np.zeros(b.shape, dtype=np.float32)
+    np.divide(a, b, out=out, where=np.abs(b) >= EPSILON)
+    return out
+
+
+CHANNEL_BLEND = {
+    "normal": lambda i, l: l,
+    "multiply": lambda i, l: i * l,
+    "screen": lambda i, l: 1 - (1 - i) * (1 - l),
+    "overlay": lambda i, l: np.where(i < 0.5, 2 * i * l, 1 - 2 * (1 - l) * (1 - i)),
+    "darken": lambda i, l: np.minimum(i, l),
+    "lighten": lambda i, l: np.maximum(i, l),
+    "color-dodge": lambda i, l: np.where(
+        i <= 0,
+        0.0,
+        np.where(1 - l <= EPSILON, 1.0, np.minimum(safe_div(i, 1 - l), 1.0)),
+    ),
+    "color-burn": lambda i, l: np.where(
+        i >= 1,
+        1.0,
+        np.where(l <= EPSILON, 0.0, 1 - np.minimum(safe_div(1 - i, l), 1.0)),
+    ),
+    "hard-light": lambda i, l: np.where(
+        l > 0.5,
+        np.minimum(1 - (1 - i) * (1 - (l - 0.5) * 2), 1),
+        np.minimum(i * (l * 2), 1),
+    ),
+    "soft-light": lambda i, l: (1 - i) * (i * l) + i * (1 - (1 - i) * (1 - l)),
+    "difference": lambda i, l: np.abs(i - l),
+    "exclusion": lambda i, l: 0.5 - 2 * (i - 0.5) * (l - 0.5),
+    "linear-dodge": lambda i, l: i + l,
+    "linear-burn": lambda i, l: i + l - 1,
+    "vivid-light": lambda i, l: np.where(
+        l <= 0.5,
+        np.where(
+            i >= 1,
+            1.0,
+            np.where(
+                2 * l <= EPSILON,
+                0.0,
+                np.maximum(1 - safe_div(1 - i, 2 * l), 0.0),
+            ),
+        ),
+        np.where(
+            i <= 0,
+            0.0,
+            np.where(
+                2 * (1 - l) <= EPSILON,
+                1.0,
+                np.minimum(safe_div(i, 2 * (1 - l)), 1.0),
+            ),
+        ),
+    ),
+    "pin-light": lambda i, l: np.where(
+        l > 0.5, np.maximum(i, 2 * (l - 0.5)), np.minimum(i, 2 * l)
+    ),
+    "linear-light": lambda i, l: i + 2 * l - 1,
+    "hard-mix": lambda i, l: np.where(i + l < 1, 0.0, 1.0),
+    "subtract": lambda i, l: np.maximum(i - l, 0),
+    "divide": lambda i, l: np.clip(i / np.maximum(l, EPSILON), 0, 1),
+    "grain-extract": lambda i, l: i - l + 0.5,
+    "grain-merge": lambda i, l: i + l - 0.5,
+}
+
+
+def _blend_hue(i: np.ndarray, l: np.ndarray) -> np.ndarray:
+    src_min = l.min(axis=-1)
+    src_max = l.max(axis=-1)
+    src_delta = src_max - src_min
+    achromatic = src_delta <= EPSILON
+    dest_max = i.max(axis=-1)
+    dest_delta = dest_max - i.min(axis=-1)
+    dest_s = np.where(dest_max != 0, dest_delta / np.where(dest_max != 0, dest_max, 1), 0)
+    ratio = np.where(
+        achromatic, 0, dest_s * dest_max / np.where(achromatic, 1, src_delta)
+    )
+    offset = dest_max - src_max * ratio
+    return np.where(achromatic[..., None], i, l * ratio[..., None] + offset[..., None])
+
+
+def _blend_saturation(i: np.ndarray, l: np.ndarray) -> np.ndarray:
+    dest_max = i.max(axis=-1)
+    dest_delta = dest_max - i.min(axis=-1)
+    flat = dest_delta <= EPSILON
+    src_max = l.max(axis=-1)
+    src_delta = src_max - l.min(axis=-1)
+    src_s = np.where(src_max != 0, src_delta / np.where(src_max != 0, src_max, 1), 0)
+    ratio = np.where(flat, 0, src_s * dest_max / np.where(flat, 1, dest_delta))
+    offset = (1 - ratio) * dest_max
+    return np.where(
+        flat[..., None],
+        np.broadcast_to(dest_max[..., None], i.shape),
+        i * ratio[..., None] + offset[..., None],
+    )
+
+
+def _blend_color(i: np.ndarray, l: np.ndarray) -> np.ndarray:
+    dest_l = (i.min(axis=-1) + i.max(axis=-1)) / 2
+    src_l = (l.min(axis=-1) + l.max(axis=-1)) / 2
+    gray = (np.abs(src_l) <= EPSILON) | (np.abs(1 - src_l) <= EPSILON)
+    dest_high = dest_l > 0.5
+    src_high = src_l > 0.5
+    dl = np.minimum(dest_l, 1 - dest_l)
+    sl = np.minimum(src_l, 1 - src_l)
+    ratio = dl / np.where(gray, 1, sl)
+    offset = np.where(dest_high, 1 - 2 * dl, 0) + np.where(src_high, 2 * dl - ratio, 0)
+    return np.where(
+        gray[..., None],
+        np.broadcast_to(dest_l[..., None], i.shape),
+        l * ratio[..., None] + offset[..., None],
+    )
+
+
+def _blend_luminosity(i: np.ndarray, l: np.ndarray) -> np.ndarray:
+    # Scale the backdrop so it carries the layer's luminance. Where the backdrop
+    # has no luminance to scale there is no hue or saturation to preserve either,
+    # so the result is a neutral grey at the layer's luminance - which is also the
+    # analytic limit of i * lum(l)/lum(i) as a grey backdrop approaches black.
+    # Guarding the numerator here instead (returning black) makes a luminosity
+    # layer disappear over dark backdrops; see tests-unit/comfy_extras_test/
+    # compositor_blend_golden.json.
+    lum_i = luminance(i)
+    lum_l = luminance(l)
+    degenerate = lum_i <= EPSILON
+    ratio = np.where(degenerate, 0.0, lum_l / np.where(degenerate, 1.0, lum_i))
+    return np.where(
+        degenerate[..., None],
+        np.broadcast_to(lum_l[..., None], i.shape),
+        i * ratio[..., None],
+    )
+
+
+HSL_BLEND = {
+    "hue": _blend_hue,
+    "saturation": _blend_saturation,
+    "color": _blend_color,
+    "luminosity": _blend_luminosity,
+}
+
+
+def blend_pixel(blend: str, in_rgb: np.ndarray, layer_rgb: np.ndarray) -> np.ndarray:
+    in_rgb = np.asarray(in_rgb, dtype=np.float32)
+    layer_rgb = np.asarray(layer_rgb, dtype=np.float32)
+    hsl = HSL_BLEND.get(blend)
+    if hsl is not None:
+        return np.asarray(hsl(in_rgb, layer_rgb), dtype=np.float32)
+    fn = CHANNEL_BLEND.get(blend, CHANNEL_BLEND["normal"])
+    return np.asarray(fn(in_rgb, layer_rgb), dtype=np.float32)
+
+
+def _composite_union(in_c, layer, comp, cov):
+    in_a = in_c[..., 3]
+    layer_a = layer[..., 3] * cov
+    new_a = layer_a + (1 - layer_a) * in_a
+    ratio = np.where(new_a != 0, layer_a / np.where(new_a != 0, new_a, 1), 0)
+    blended = (
+        ratio[..., None]
+        * (in_a[..., None] * (comp - layer[..., :3]) + layer[..., :3] - in_c[..., :3])
+        + in_c[..., :3]
+    )
+    keep = (layer_a == 0) | (new_a == 0)
+    rgb = np.where(
+        keep[..., None],
+        in_c[..., :3],
+        np.where((in_a == 0)[..., None], layer[..., :3], blended),
+    )
+    return np.concatenate([rgb, new_a[..., None]], axis=-1)
+
+
+def _composite_clip_to_backdrop(in_c, layer, comp, cov):
+    in_a = in_c[..., 3]
+    layer_a = layer[..., 3] * cov
+    mixed = comp * layer_a[..., None] + in_c[..., :3] * (1 - layer_a[..., None])
+    keep = (in_a == 0) | (layer_a == 0)
+    rgb = np.where(keep[..., None], in_c[..., :3], mixed)
+    return np.concatenate([rgb, in_a[..., None]], axis=-1)
+
+
+def _composite_clip_to_layer(in_c, layer, comp, cov):
+    in_a = in_c[..., 3]
+    layer_a = layer[..., 3] * cov
+    mixed = comp * in_a[..., None] + layer[..., :3] * (1 - in_a[..., None])
+    rgb = np.where(
+        (layer_a == 0)[..., None],
+        in_c[..., :3],
+        np.where((in_a == 0)[..., None], layer[..., :3], mixed),
+    )
+    return np.concatenate([rgb, layer_a[..., None]], axis=-1)
+
+
+def _composite_intersection(in_c, layer, comp, cov):
+    new_a = in_c[..., 3] * layer[..., 3] * cov
+    rgb = np.where((new_a == 0)[..., None], in_c[..., :3], comp)
+    return np.concatenate([rgb, new_a[..., None]], axis=-1)
+
+
+_COMPOSITE = {
+    "union": _composite_union,
+    "clip-to-backdrop": _composite_clip_to_backdrop,
+    "clip-to-layer": _composite_clip_to_layer,
+    "intersection": _composite_intersection,
+}
+
+
+def run_composite(mode: str, in_c, layer, comp, cov) -> np.ndarray:
+    fn = _COMPOSITE.get(mode, _composite_union)
+    return fn(in_c, layer, comp, cov)
+
+
+def _to_space(rgb: np.ndarray, space: str) -> np.ndarray:
+    return rgb if space == "linear" else linear_to_srgb(rgb)
+
+
+def _from_space(rgb: np.ndarray, space: str) -> np.ndarray:
+    return rgb if space == "linear" else srgb_to_linear(rgb)
+
+
+class EffectiveMode(NamedTuple):
+    blend: str
+    blend_space: str
+    composite: str
+
+
+_LAYER_MODES = {
+    "normal": ("linear", "union"),
+    "multiply": ("linear", "clip-to-backdrop"),
+    "screen": ("perceptual", "clip-to-backdrop"),
+    "overlay": ("perceptual", "clip-to-backdrop"),
+    "darken": ("linear", "clip-to-backdrop"),
+    "lighten": ("linear", "clip-to-backdrop"),
+    "color-dodge": ("perceptual", "clip-to-backdrop"),
+    "color-burn": ("perceptual", "clip-to-backdrop"),
+    "hard-light": ("perceptual", "clip-to-backdrop"),
+    "soft-light": ("perceptual", "clip-to-backdrop"),
+    "difference": ("perceptual", "clip-to-backdrop"),
+    "exclusion": ("perceptual", "clip-to-backdrop"),
+    "linear-dodge": ("linear", "clip-to-backdrop"),
+    "linear-burn": ("perceptual", "clip-to-backdrop"),
+    "vivid-light": ("perceptual", "clip-to-backdrop"),
+    "pin-light": ("perceptual", "clip-to-backdrop"),
+    "linear-light": ("perceptual", "clip-to-backdrop"),
+    "hard-mix": ("perceptual", "clip-to-backdrop"),
+    "subtract": ("linear", "clip-to-backdrop"),
+    "divide": ("linear", "clip-to-backdrop"),
+    "grain-extract": ("perceptual", "clip-to-backdrop"),
+    "grain-merge": ("perceptual", "clip-to-backdrop"),
+    "hue": ("perceptual", "clip-to-backdrop"),
+    "saturation": ("perceptual", "clip-to-backdrop"),
+    "color": ("perceptual", "clip-to-backdrop"),
+    "luminosity": ("linear", "clip-to-backdrop"),
+}
+
+
+def resolve_mode(blend: str = "normal") -> EffectiveMode:
+    blend_space, composite = _LAYER_MODES.get(blend, _LAYER_MODES["normal"])
+    return EffectiveMode(
+        blend=blend,
+        blend_space=blend_space,
+        composite=composite,
+    )
+
+
+def blend_composite(
+    mode: EffectiveMode,
+    backdrop: np.ndarray,
+    layer: np.ndarray,
+    opacity: float,
+    mask: Optional[ArrayLike] = None,
+) -> np.ndarray:
+    backdrop = np.asarray(backdrop, dtype=np.float32)
+    layer = np.asarray(layer, dtype=np.float32)
+    cov = opacity * (1.0 if mask is None else mask)
+
+    in_b = _to_space(backdrop[..., :3], mode.blend_space)
+    layer_b = _to_space(layer[..., :3], mode.blend_space)
+    comp = _from_space(blend_pixel(mode.blend, in_b, layer_b), mode.blend_space)
+
+    return run_composite(mode.composite, backdrop, layer, comp, cov)
+
+
+def placed_bounds(
+    x: float, y: float, w: float, h: float, rotation: float
+) -> tuple[int, int, int, int]:
+    cx = x + w / 2
+    cy = y + h / 2
+    cos = math.cos(rotation)
+    sin = math.sin(rotation)
+    hw = w / 2
+    hh = h / 2
+    corners = ((-hw, -hh), (hw, -hh), (hw, hh), (-hw, hh))
+    xs = [cx + dx * cos - dy * sin for dx, dy in corners]
+    ys = [cy + dx * sin + dy * cos for dx, dy in corners]
+    bx = math.floor(min(xs))
+    by = math.floor(min(ys))
+    bw = max(1, math.ceil(max(xs)) - bx)
+    bh = max(1, math.ceil(max(ys)) - by)
+    return bx, by, bw, bh
diff --git a/comfy_extras/frame_interpolation_models/film_net.py b/comfy_extras/frame_interpolation_models/film_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf28273dc8ed8ff8a4437506ce67b1b9fbd4971a
--- /dev/null
+++ b/comfy_extras/frame_interpolation_models/film_net.py
@@ -0,0 +1,261 @@
+"""FILM: Frame Interpolation for Large Motion (ECCV 2022)."""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ops
+
+ops = comfy.ops.disable_weight_init
+
+
+class FilmConv2d(nn.Module):
+    """Conv2d with optional LeakyReLU and FILM-style padding."""
+
+    def __init__(self, in_channels, out_channels, size, activation=True, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.even_pad = not size % 2
+        self.conv = operations.Conv2d(in_channels, out_channels, kernel_size=size, padding=size // 2 if size % 2 else 0, device=device, dtype=dtype)
+        self.activation = nn.LeakyReLU(0.2) if activation else None
+
+    def forward(self, x):
+        if self.even_pad:
+            x = F.pad(x, (0, 1, 0, 1))
+        x = self.conv(x)
+        if self.activation is not None:
+            x = self.activation(x)
+        return x
+
+
+def _warp_core(image, flow, grid_x, grid_y):
+    dtype = image.dtype
+    H, W = flow.shape[2], flow.shape[3]
+    dx = flow[:, 0].float() / (W * 0.5)
+    dy = flow[:, 1].float() / (H * 0.5)
+    grid = torch.stack([grid_x[None, None, :] + dx, grid_y[None, :, None] + dy], dim=3)
+    return F.grid_sample(image.float(), grid, mode="bilinear", padding_mode="border", align_corners=False).to(dtype)
+
+
+def build_image_pyramid(image, pyramid_levels):
+    pyramid = [image]
+    for _ in range(1, pyramid_levels):
+        image = F.avg_pool2d(image, 2, 2)
+        pyramid.append(image)
+    return pyramid
+
+
+def flow_pyramid_synthesis(residual_pyramid):
+    flow = residual_pyramid[-1]
+    flow_pyramid = [flow]
+    for residual_flow in residual_pyramid[:-1][::-1]:
+        flow = F.interpolate(flow, size=residual_flow.shape[2:4], mode="bilinear", scale_factor=None).mul_(2).add_(residual_flow)
+        flow_pyramid.append(flow)
+    flow_pyramid.reverse()
+    return flow_pyramid
+
+
+def multiply_pyramid(pyramid, scalar):
+    return [image * scalar[:, None, None, None] for image in pyramid]
+
+
+def pyramid_warp(feature_pyramid, flow_pyramid, warp_fn):
+    return [warp_fn(features, flow) for features, flow in zip(feature_pyramid, flow_pyramid)]
+
+
+def concatenate_pyramids(pyramid1, pyramid2):
+    return [torch.cat([f1, f2], dim=1) for f1, f2 in zip(pyramid1, pyramid2)]
+
+
+class SubTreeExtractor(nn.Module):
+    def __init__(self, in_channels=3, channels=64, n_layers=4, device=None, dtype=None, operations=ops):
+        super().__init__()
+        convs = []
+        for i in range(n_layers):
+            out_ch = channels << i
+            convs.append(nn.Sequential(
+                FilmConv2d(in_channels, out_ch, 3, device=device, dtype=dtype, operations=operations),
+                FilmConv2d(out_ch, out_ch, 3, device=device, dtype=dtype, operations=operations)))
+            in_channels = out_ch
+        self.convs = nn.ModuleList(convs)
+
+    def forward(self, image, n):
+        head = image
+        pyramid = []
+        for i, layer in enumerate(self.convs):
+            head = layer(head)
+            pyramid.append(head)
+            if i < n - 1:
+                head = F.avg_pool2d(head, 2, 2)
+        return pyramid
+
+
+class FeatureExtractor(nn.Module):
+    def __init__(self, in_channels=3, channels=64, sub_levels=4, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.extract_sublevels = SubTreeExtractor(in_channels, channels, sub_levels, device=device, dtype=dtype, operations=operations)
+        self.sub_levels = sub_levels
+
+    def forward(self, image_pyramid):
+        sub_pyramids = [self.extract_sublevels(image_pyramid[i], min(len(image_pyramid) - i, self.sub_levels))
+                        for i in range(len(image_pyramid))]
+        feature_pyramid = []
+        for i in range(len(image_pyramid)):
+            features = sub_pyramids[i][0]
+            for j in range(1, self.sub_levels):
+                if j <= i:
+                    features = torch.cat([features, sub_pyramids[i - j][j]], dim=1)
+            feature_pyramid.append(features)
+            # Free sub-pyramids no longer needed by future levels
+            if i >= self.sub_levels - 1:
+                sub_pyramids[i - self.sub_levels + 1] = None
+        return feature_pyramid
+
+
+class FlowEstimator(nn.Module):
+    def __init__(self, in_channels, num_convs, num_filters, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self._convs = nn.ModuleList()
+        for _ in range(num_convs):
+            self._convs.append(FilmConv2d(in_channels, num_filters, 3, device=device, dtype=dtype, operations=operations))
+            in_channels = num_filters
+        self._convs.append(FilmConv2d(in_channels, num_filters // 2, 1, device=device, dtype=dtype, operations=operations))
+        self._convs.append(FilmConv2d(num_filters // 2, 2, 1, activation=False, device=device, dtype=dtype, operations=operations))
+
+    def forward(self, features_a, features_b):
+        net = torch.cat([features_a, features_b], dim=1)
+        for conv in self._convs:
+            net = conv(net)
+        return net
+
+
+class PyramidFlowEstimator(nn.Module):
+    def __init__(self, filters=64, flow_convs=(3, 3, 3, 3), flow_filters=(32, 64, 128, 256), device=None, dtype=None, operations=ops):
+        super().__init__()
+        in_channels = filters << 1
+        predictors = []
+        for i in range(len(flow_convs)):
+            predictors.append(FlowEstimator(in_channels, flow_convs[i], flow_filters[i], device=device, dtype=dtype, operations=operations))
+            in_channels += filters << (i + 2)
+        self._predictor = predictors[-1]
+        self._predictors = nn.ModuleList(predictors[:-1][::-1])
+
+    def forward(self, feature_pyramid_a, feature_pyramid_b, warp_fn):
+        levels = len(feature_pyramid_a)
+        v = self._predictor(feature_pyramid_a[-1], feature_pyramid_b[-1])
+        residuals = [v]
+        # Coarse-to-fine: shared predictor for deep levels, then specialized predictors for fine levels
+        steps = [(i, self._predictor) for i in range(levels - 2, len(self._predictors) - 1, -1)]
+        steps += [(len(self._predictors) - 1 - k, p) for k, p in enumerate(self._predictors)]
+        for i, predictor in steps:
+            v = F.interpolate(v, size=feature_pyramid_a[i].shape[2:4], mode="bilinear").mul_(2)
+            v_residual = predictor(feature_pyramid_a[i], warp_fn(feature_pyramid_b[i], v))
+            residuals.append(v_residual)
+            v = v.add_(v_residual)
+        residuals.reverse()
+        return residuals
+
+
+def _get_fusion_channels(level, filters):
+    # Per direction: multi-scale features + RGB image (3ch) + flow (2ch), doubled for both directions
+    return (sum(filters << i for i in range(level)) + 3 + 2) * 2
+
+
+class Fusion(nn.Module):
+    def __init__(self, n_layers=4, specialized_layers=3, filters=64, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.output_conv = operations.Conv2d(filters, 3, kernel_size=1, device=device, dtype=dtype)
+        self.convs = nn.ModuleList()
+        in_channels = _get_fusion_channels(n_layers, filters)
+        increase = 0
+        for i in range(n_layers)[::-1]:
+            num_filters = (filters << i) if i < specialized_layers else (filters << specialized_layers)
+            self.convs.append(nn.ModuleList([
+                FilmConv2d(in_channels, num_filters, 2, activation=False, device=device, dtype=dtype, operations=operations),
+                FilmConv2d(in_channels + (increase or num_filters), num_filters, 3, device=device, dtype=dtype, operations=operations),
+                FilmConv2d(num_filters, num_filters, 3, device=device, dtype=dtype, operations=operations)]))
+            in_channels = num_filters
+            increase = _get_fusion_channels(i, filters) - num_filters // 2
+
+    def forward(self, pyramid):
+        net = pyramid[-1]
+        for k, layers in enumerate(self.convs):
+            i = len(self.convs) - 1 - k
+            net = layers[0](F.interpolate(net, size=pyramid[i].shape[2:4], mode="nearest"))
+            net = layers[2](layers[1](torch.cat([pyramid[i], net], dim=1)))
+        return self.output_conv(net)
+
+
+class FILMNet(nn.Module):
+    def __init__(self, pyramid_levels=7, fusion_pyramid_levels=5, specialized_levels=3, sub_levels=4,
+                 filters=64, flow_convs=(3, 3, 3, 3), flow_filters=(32, 64, 128, 256), device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.pyramid_levels = pyramid_levels
+        self.fusion_pyramid_levels = fusion_pyramid_levels
+        self.extract = FeatureExtractor(3, filters, sub_levels, device=device, dtype=dtype, operations=operations)
+        self.predict_flow = PyramidFlowEstimator(filters, flow_convs, flow_filters, device=device, dtype=dtype, operations=operations)
+        self.fuse = Fusion(sub_levels, specialized_levels, filters, device=device, dtype=dtype, operations=operations)
+        self._warp_grids = {}
+
+    def get_dtype(self):
+        return self.extract.extract_sublevels.convs[0][0].conv.weight.dtype
+
+    def memory_used_forward(self, shape, dtype):
+        return 1700 * shape[1] * shape[2] * dtype.itemsize
+
+    def _build_warp_grids(self, H, W, device):
+        """Pre-compute warp grids for all pyramid levels."""
+        if (H, W) in self._warp_grids:
+            return
+        self._warp_grids = {}  # clear old resolution grids to prevent memory leaks
+        for _ in range(self.pyramid_levels):
+            self._warp_grids[(H, W)] = (
+                torch.linspace(-(1 - 1 / W), 1 - 1 / W, W, dtype=torch.float32, device=device),
+                torch.linspace(-(1 - 1 / H), 1 - 1 / H, H, dtype=torch.float32, device=device),
+            )
+            H, W = H // 2, W // 2
+
+    def warp(self, image, flow):
+        grid_x, grid_y = self._warp_grids[(flow.shape[2], flow.shape[3])]
+        return _warp_core(image, flow, grid_x, grid_y)
+
+    def extract_features(self, img):
+        """Extract image and feature pyramids for a single frame. Can be cached across pairs."""
+        image_pyramid = build_image_pyramid(img, self.pyramid_levels)
+        feature_pyramid = self.extract(image_pyramid)
+        return image_pyramid, feature_pyramid
+
+    def forward(self, img0, img1, timestep=0.5, cache=None):
+        # FILM uses a scalar timestep per batch element (spatially-varying timesteps not supported)
+        t = timestep.mean(dim=(1, 2, 3)).item() if isinstance(timestep, torch.Tensor) else timestep
+        return self.forward_multi_timestep(img0, img1, [t], cache=cache)
+
+    def forward_multi_timestep(self, img0, img1, timesteps, cache=None):
+        """Compute flow once, synthesize at multiple timesteps. Expects batch=1 inputs."""
+        self._build_warp_grids(img0.shape[2], img0.shape[3], img0.device)
+
+        image_pyr0, feat_pyr0 = cache["img0"] if cache and "img0" in cache else self.extract_features(img0)
+        image_pyr1, feat_pyr1 = cache["img1"] if cache and "img1" in cache else self.extract_features(img1)
+
+        fwd_flow = flow_pyramid_synthesis(self.predict_flow(feat_pyr0, feat_pyr1, self.warp))[:self.fusion_pyramid_levels]
+        bwd_flow = flow_pyramid_synthesis(self.predict_flow(feat_pyr1, feat_pyr0, self.warp))[:self.fusion_pyramid_levels]
+
+        # Build warp targets and free full pyramids (only first fpl levels needed from here)
+        fpl = self.fusion_pyramid_levels
+        p2w = [concatenate_pyramids(image_pyr0[:fpl], feat_pyr0[:fpl]),
+               concatenate_pyramids(image_pyr1[:fpl], feat_pyr1[:fpl])]
+        del image_pyr0, image_pyr1, feat_pyr0, feat_pyr1
+
+        results = []
+        dt_tensors = torch.tensor(timesteps, device=img0.device, dtype=img0.dtype)
+        for idx in range(len(timesteps)):
+            batch_dt = dt_tensors[idx:idx + 1]
+            bwd_scaled = multiply_pyramid(bwd_flow, batch_dt)
+            fwd_scaled = multiply_pyramid(fwd_flow, 1 - batch_dt)
+            fwd_warped = pyramid_warp(p2w[0], bwd_scaled, self.warp)
+            bwd_warped = pyramid_warp(p2w[1], fwd_scaled, self.warp)
+            aligned = [torch.cat([fw, bw, bf, ff], dim=1)
+                       for fw, bw, bf, ff in zip(fwd_warped, bwd_warped, bwd_scaled, fwd_scaled)]
+            del fwd_warped, bwd_warped, bwd_scaled, fwd_scaled
+            results.append(self.fuse(aligned))
+            del aligned
+        return torch.cat(results, dim=0)
diff --git a/comfy_extras/frame_interpolation_models/ifnet.py b/comfy_extras/frame_interpolation_models/ifnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cc62d7fe6afe4a3755a06de98a79ba076a68895
--- /dev/null
+++ b/comfy_extras/frame_interpolation_models/ifnet.py
@@ -0,0 +1,131 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ops
+
+ops = comfy.ops.disable_weight_init
+
+
+def _warp(img, flow, warp_grids):
+    B, _, H, W = img.shape
+    base_grid, flow_div = warp_grids[(H, W)]
+    flow_norm = torch.cat([flow[:, 0:1] / flow_div[0], flow[:, 1:2] / flow_div[1]], 1).float()
+    grid = (base_grid.expand(B, -1, -1, -1) + flow_norm).permute(0, 2, 3, 1)
+    return F.grid_sample(img.float(), grid, mode="bilinear", padding_mode="border", align_corners=True).to(img.dtype)
+
+
+class Head(nn.Module):
+    def __init__(self, out_ch=4, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.cnn0 = operations.Conv2d(3, 16, 3, 2, 1, device=device, dtype=dtype)
+        self.cnn1 = operations.Conv2d(16, 16, 3, 1, 1, device=device, dtype=dtype)
+        self.cnn2 = operations.Conv2d(16, 16, 3, 1, 1, device=device, dtype=dtype)
+        self.cnn3 = operations.ConvTranspose2d(16, out_ch, 4, 2, 1, device=device, dtype=dtype)
+        self.relu = nn.LeakyReLU(0.2, True)
+
+    def forward(self, x):
+        x = self.relu(self.cnn0(x))
+        x = self.relu(self.cnn1(x))
+        x = self.relu(self.cnn2(x))
+        return self.cnn3(x)
+
+
+class ResConv(nn.Module):
+    def __init__(self, c, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.conv = operations.Conv2d(c, c, 3, 1, 1, device=device, dtype=dtype)
+        self.beta = nn.Parameter(torch.ones((1, c, 1, 1), device=device, dtype=dtype))
+        self.relu = nn.LeakyReLU(0.2, True)
+
+    def forward(self, x):
+        return self.relu(torch.addcmul(x, self.conv(x), self.beta))
+
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, c=64, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.conv0 = nn.Sequential(
+            nn.Sequential(operations.Conv2d(in_planes, c // 2, 3, 2, 1, device=device, dtype=dtype), nn.LeakyReLU(0.2, True)),
+            nn.Sequential(operations.Conv2d(c // 2, c, 3, 2, 1, device=device, dtype=dtype), nn.LeakyReLU(0.2, True)))
+        self.convblock = nn.Sequential(*(ResConv(c, device=device, dtype=dtype, operations=operations) for _ in range(8)))
+        self.lastconv = nn.Sequential(operations.ConvTranspose2d(c, 4 * 13, 4, 2, 1, device=device, dtype=dtype), nn.PixelShuffle(2))
+
+    def forward(self, x, flow=None, scale=1):
+        x = F.interpolate(x, scale_factor=1.0 / scale, mode="bilinear")
+        if flow is not None:
+            flow = F.interpolate(flow, scale_factor=1.0 / scale, mode="bilinear").div_(scale)
+            x = torch.cat((x, flow), 1)
+        feat = self.convblock(self.conv0(x))
+        tmp = F.interpolate(self.lastconv(feat), scale_factor=scale, mode="bilinear")
+        return tmp[:, :4] * scale, tmp[:, 4:5], tmp[:, 5:]
+
+
+class IFNet(nn.Module):
+    def __init__(self, head_ch=4, channels=(192, 128, 96, 64, 32), device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.encode = Head(out_ch=head_ch, device=device, dtype=dtype, operations=operations)
+        block_in = [7 + 2 * head_ch] + [8 + 4 + 8 + 2 * head_ch] * 4
+        self.blocks = nn.ModuleList([IFBlock(block_in[i], channels[i], device=device, dtype=dtype, operations=operations) for i in range(5)])
+        self.scale_list = [16, 8, 4, 2, 1]
+        self.pad_align = 64
+        self._warp_grids = {}
+
+    def get_dtype(self):
+        return self.encode.cnn0.weight.dtype
+
+    def memory_used_forward(self, shape, dtype):
+        return 300 * shape[1] * shape[2] * dtype.itemsize
+
+    def _build_warp_grids(self, H, W, device):
+        if (H, W) in self._warp_grids:
+            return
+        self._warp_grids = {}  # clear old resolution grids to prevent memory leaks
+        grid_y, grid_x = torch.meshgrid(
+            torch.linspace(-1.0, 1.0, H, device=device, dtype=torch.float32),
+            torch.linspace(-1.0, 1.0, W, device=device, dtype=torch.float32), indexing="ij")
+        self._warp_grids[(H, W)] = (
+            torch.stack((grid_x, grid_y), dim=0).unsqueeze(0),
+            torch.tensor([(W - 1.0) / 2.0, (H - 1.0) / 2.0], dtype=torch.float32, device=device))
+
+    def warp(self, img, flow):
+        return _warp(img, flow, self._warp_grids)
+
+    def extract_features(self, img):
+        """Extract head features for a single frame. Can be cached across pairs."""
+        return self.encode(img)
+
+    def forward(self, img0, img1, timestep=0.5, cache=None):
+        if not isinstance(timestep, torch.Tensor):
+            timestep = torch.full((img0.shape[0], 1, img0.shape[2], img0.shape[3]), timestep, device=img0.device, dtype=img0.dtype)
+
+        self._build_warp_grids(img0.shape[2], img0.shape[3], img0.device)
+
+        B = img0.shape[0]
+        f0 = cache["img0"].expand(B, -1, -1, -1) if cache and "img0" in cache else self.encode(img0)
+        f1 = cache["img1"].expand(B, -1, -1, -1) if cache and "img1" in cache else self.encode(img1)
+        flow = mask = feat = None
+        warped_img0, warped_img1 = img0, img1
+        for i, block in enumerate(self.blocks):
+            if flow is None:
+                flow, mask, feat = block(torch.cat((img0, img1, f0, f1, timestep), 1), None, scale=self.scale_list[i])
+            else:
+                fd, mask, feat = block(
+                    torch.cat((warped_img0, warped_img1, self.warp(f0, flow[:, :2]), self.warp(f1, flow[:, 2:4]), timestep, mask, feat), 1),
+                    flow, scale=self.scale_list[i])
+                flow = flow.add_(fd)
+            warped_img0 = self.warp(img0, flow[:, :2])
+            warped_img1 = self.warp(img1, flow[:, 2:4])
+        return torch.lerp(warped_img1, warped_img0, torch.sigmoid(mask))
+
+
+def detect_rife_config(state_dict):
+    head_ch = state_dict["encode.cnn3.weight"].shape[1]  # ConvTranspose2d: (in_ch, out_ch, kH, kW)
+    channels = []
+    for i in range(5):
+        key = f"blocks.{i}.conv0.1.0.weight"
+        if key in state_dict:
+            channels.append(state_dict[key].shape[0])
+    if len(channels) != 5:
+        raise ValueError(f"Unsupported RIFE model: expected 5 blocks, found {len(channels)}")
+    return head_ch, channels
diff --git a/comfy_extras/mediapipe/face_geometry.py b/comfy_extras/mediapipe/face_geometry.py
new file mode 100644
index 0000000000000000000000000000000000000000..39009cda5a86311b373477c5bbe431d35e459252
--- /dev/null
+++ b/comfy_extras/mediapipe/face_geometry.py
@@ -0,0 +1,110 @@
+"""Pure-numpy port of MediaPipe's face_geometry (FACE_LANDMARK_PIPELINE mode)
++ weighted Procrustes solver. Computes the 4x4 facial transformation matrix.
+"""
+
+
+import math
+import numpy as np
+
+
+def _solve_weighted_orthogonal_problem(src: np.ndarray, tgt: np.ndarray, weights: np.ndarray) -> np.ndarray:
+    """Weighted orthogonal Procrustes (similarity). Returns 4x4 M with
+    `target ≈ M @ homogeneous(source)` in the weighted LS sense. fp64 for
+    SVD stability. Port of procrustes_solver.cc."""
+    sqrt_w = np.sqrt(weights.astype(np.float64))
+    w_total = float((sqrt_w ** 2).sum())
+    ws = src.astype(np.float64) * sqrt_w
+    wt = tgt.astype(np.float64) * sqrt_w
+
+    c_w = (ws @ sqrt_w) / w_total
+    centered = ws - np.outer(c_w, sqrt_w)
+    U, _S, Vt = np.linalg.svd(wt @ centered.T, full_matrices=True)
+    # Disallow reflection: flip the least-significant axis when det(U)·det(V)<0.
+    post, pre = U.copy(), Vt.T.copy()
+    if np.linalg.det(post) * np.linalg.det(pre) < 0:
+        post[:, 2] *= -1.0
+    R = post @ pre.T
+
+    denom = float((centered * ws).sum())
+    if denom < 1e-12:
+        raise ValueError("Procrustes denominator collapsed (degenerate source).")
+    scale = float((R @ centered * wt).sum()) / denom
+    translation = ((wt - scale * (R @ ws)) @ sqrt_w) / w_total
+
+    M = np.eye(4, dtype=np.float64)
+    M[:3, :3] = scale * R
+    M[:3, 3] = translation
+    return M
+
+
+def _estimate_scale(canonical: np.ndarray, runtime: np.ndarray, weights: np.ndarray) -> float:
+    """scale = ‖first column of M[:3]‖ per geometry_pipeline.cc::EstimateScale."""
+    return float(np.linalg.norm(_solve_weighted_orthogonal_problem(canonical, runtime, weights)[:3, 0]))
+
+
+def solve_facial_transformation_matrix(
+    landmarks_normalized: np.ndarray,
+    canonical_vertices: np.ndarray,
+    procrustes_indices: np.ndarray,
+    procrustes_weights: np.ndarray,
+    image_width: int,
+    image_height: int,
+    # face_geometry_calculator_options.pbtxt defaults
+    vertical_fov_degrees: float = 63.0,
+    near: float = 1.0,
+) -> np.ndarray:
+    """4x4 facial transformation matrix via two-pass scale recovery
+    `landmarks_normalized` is (N, 3) in MediaPipe normalized convention: x, y
+    in [0,1] with TOP-LEFT origin, z in width-scaled units.
+    """
+
+    h_near = 2.0 * near * math.tan(0.5 * math.radians(vertical_fov_degrees))
+    w_near = image_width * h_near / image_height
+
+    sub = procrustes_indices.astype(np.int64)
+    screen = landmarks_normalized[sub].T.astype(np.float64).copy()
+    canon = canonical_vertices[sub].T.astype(np.float64).copy()
+    weights = procrustes_weights.astype(np.float64)
+
+    # ProjectXY (TOP_LEFT y-flip, then scale all 3 axes; z uses x-scale).
+    screen[1] = 1.0 - screen[1]
+    screen[0] = screen[0] * w_near - 0.5 * w_near
+    screen[1] = screen[1] * h_near - 0.5 * h_near
+    screen[2] = screen[2] * w_near
+    depth_offset = float(screen[2].mean())
+
+    def _unproject(s: np.ndarray, scale: float) -> np.ndarray:
+        s = s.copy()
+        s[2] = (s[2] - depth_offset + near) / scale
+        s[0] *= s[2] / near
+        s[1] *= s[2] / near
+        s[2] *= -1.0
+        return s
+
+    first = screen.copy()
+    first[2] *= -1.0
+    s1 = _estimate_scale(canon, first, weights) # 1st pass: Procrustes on projected XY
+    s2 = _estimate_scale(canon, _unproject(screen, s1), weights) # 2nd pass: rescale z by s1, un-project XY
+    return _solve_weighted_orthogonal_problem(canon, _unproject(screen, s1 * s2), weights).astype(np.float32)
+
+
+def transformation_matrix_from_detection(face_dict: dict, image_width: int, image_height: int, canonical_data: dict) -> np.ndarray:
+    """Adapt a FaceLandmarker face dict to MP's normalized convention and solve.
+    FaceMesh emits (x, y, z) in 192-canonical units; MP's geometry expects
+    z_norm = z_canonical * scale_x / image_width"""
+
+    lmks_xy, lmks_3d = face_dict["landmarks_xy"], face_dict["landmarks_3d"]
+    aug = np.concatenate([lmks_3d[:, :2].astype(np.float64), np.ones((lmks_xy.shape[0], 1))], axis=1)
+    M, *_ = np.linalg.lstsq(aug, lmks_xy.astype(np.float64), rcond=None)
+    scale_x = float(np.linalg.norm(M[0]))
+    z_scale = scale_x / image_width if scale_x > 1e-6 else 1.0 / image_width
+
+    normalized = np.empty((lmks_xy.shape[0], 3), dtype=np.float32)
+    normalized[:, 0] = lmks_xy[:, 0] / image_width
+    normalized[:, 1] = lmks_xy[:, 1] / image_height
+    normalized[:, 2] = lmks_3d[:, 2] * z_scale
+    return solve_facial_transformation_matrix(
+        normalized, canonical_data["canonical_vertices"],
+        canonical_data["procrustes_indices"], canonical_data["procrustes_weights"],
+        image_width=image_width, image_height=image_height,
+    )
diff --git a/comfy_extras/mediapipe/face_landmarker.py b/comfy_extras/mediapipe/face_landmarker.py
new file mode 100644
index 0000000000000000000000000000000000000000..0337c03bdcfc667196f370685bbe3d948cc5a07c
--- /dev/null
+++ b/comfy_extras/mediapipe/face_landmarker.py
@@ -0,0 +1,716 @@
+"""Pure-PyTorch port of MediaPipe's face_landmarker_v2_with_blendshapes.task:
+BlazeFace detector → FaceMesh v2 → ARKit-52 blendshapes."""
+
+
+import math
+from functools import lru_cache
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from scipy.special import expit
+from torch import Tensor, nn
+
+
+# Values below must stay verbatim with the published face_landmarker_v2 graph
+
+# face_blendshapes_graph.cc::kLandmarksSubsetIdxs
+_BS_INPUT_INDICES: Tuple[int, ...] = (
+    0, 1, 4, 5, 6, 7, 8, 10, 13, 14, 17, 21, 33, 37, 39, 40, 46, 52, 53, 54,
+    55, 58, 61, 63, 65, 66, 67, 70, 78, 80, 81, 82, 84, 87, 88, 91, 93, 95,
+    103, 105, 107, 109, 127, 132, 133, 136, 144, 145, 146, 148, 149, 150, 152,
+    153, 154, 155, 157, 158, 159, 160, 161, 162, 163, 168, 172, 173, 176, 178,
+    181, 185, 191, 195, 197, 234, 246, 249, 251, 263, 267, 269, 270, 276, 282,
+    283, 284, 285, 288, 291, 293, 295, 296, 297, 300, 308, 310, 311, 312, 314,
+    317, 318, 321, 323, 324, 332, 334, 336, 338, 356, 361, 362, 365, 373, 374,
+    375, 377, 378, 379, 380, 381, 382, 384, 385, 386, 387, 388, 389, 390, 397,
+    398, 400, 402, 405, 409, 415, 454, 466, 468, 469, 470, 471, 472, 473, 474,
+    475, 476, 477,
+)
+
+# face_blendshapes_graph.cc::kCategoryNames
+BLENDSHAPE_NAMES: Tuple[str, ...] = (
+    "_neutral", "browDownLeft", "browDownRight", "browInnerUp", "browOuterUpLeft",
+    "browOuterUpRight", "cheekPuff", "cheekSquintLeft", "cheekSquintRight",
+    "eyeBlinkLeft", "eyeBlinkRight", "eyeLookDownLeft", "eyeLookDownRight",
+    "eyeLookInLeft", "eyeLookInRight", "eyeLookOutLeft", "eyeLookOutRight",
+    "eyeLookUpLeft", "eyeLookUpRight", "eyeSquintLeft", "eyeSquintRight",
+    "eyeWideLeft", "eyeWideRight", "jawForward", "jawLeft", "jawOpen",
+    "jawRight", "mouthClose", "mouthDimpleLeft", "mouthDimpleRight",
+    "mouthFrownLeft", "mouthFrownRight", "mouthFunnel", "mouthLeft",
+    "mouthLowerDownLeft", "mouthLowerDownRight", "mouthPressLeft",
+    "mouthPressRight", "mouthPucker", "mouthRight", "mouthRollLower",
+    "mouthRollUpper", "mouthShrugLower", "mouthShrugUpper", "mouthSmileLeft",
+    "mouthSmileRight", "mouthStretchLeft", "mouthStretchRight",
+    "mouthUpperUpLeft", "mouthUpperUpRight", "noseSneerLeft", "noseSneerRight",
+)
+
+# face_detection.pbtxt — short-range BlazeFace.
+_BF_NUM_LAYERS = 4
+_BF_INPUT_SIZE = 128
+_BF_STRIDES = (8, 16, 16, 16)
+_BF_ANCHOR_OFFSET_X = 0.5
+_BF_ANCHOR_OFFSET_Y = 0.5
+_BF_ASPECT_RATIOS = (1.0,)
+_BF_INTERP_SCALE_AR = 1.0
+_BF_BOX_SCALE = 128.0
+_BF_KP_OFFSET = 4
+_BF_SCORE_CLIP = 100.0
+_BF_MIN_SCORE = 0.5
+
+# face_detection_full_range.pbtxt — 48x48 grid at stride 4, 1 anchor/cell.
+_BF_FR_INPUT_SIZE = 192
+_BF_FR_GRID = 48
+_BF_FR_NUM_ANCHORS = _BF_FR_GRID * _BF_FR_GRID
+_BF_FR_BOX_SCALE = 192.0
+_BF_FR_SCORE_CLIP = 100.0
+
+_FM_INPUT_SIZE = 192
+
+# Face ROI: 1.5xbbox rect warped anisotropically into 192x192.
+_FACE_LEFT_EYE_KP = 0
+_FACE_RIGHT_EYE_KP = 1
+_FACE_ROI_SCALE_X = 1.5
+_FACE_ROI_SCALE_Y = 1.5
+_FACE_ROI_TARGET_ANGLE = 0.0
+
+
+def _tf_same_pad(x: Tensor, kernel: int, stride: int) -> Tensor:
+    """TF SAME pad (asymmetric on stride-2; PyTorch's symmetric pad undershoots by 1 px)."""
+    H, W = x.shape[-2], x.shape[-1]
+    pad_h = max(((H + stride - 1) // stride - 1) * stride + kernel - H, 0)
+    pad_w = max(((W + stride - 1) // stride - 1) * stride + kernel - W, 0)
+    if pad_h == 0 and pad_w == 0:
+        return x
+    return F.pad(x, (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2))
+
+
+# BlazeFace short-range: stem 5x5/s2 → 16 BlazeBlocks → parallel heads at
+# 16²x88 (2 anchors/cell) and 8²x96 (6/cell) = 896 anchors. (in, out, stride):
+_BLAZEFACE_BLOCKS = [
+    (24, 24, 1), (24, 28, 1), (28, 32, 2), (32, 36, 1),
+    (36, 42, 1), (42, 48, 2), (48, 56, 1), (56, 64, 1),
+    (64, 72, 1), (72, 80, 1), (80, 88, 1), (88, 96, 2),
+    (96, 96, 1), (96, 96, 1), (96, 96, 1), (96, 96, 1),
+]
+
+
+class BlazeFaceBlock(nn.Module):
+    """DW 3x3 + PW + residual. Residual max-pools on stride>1, channel-pads on out_ch>in_ch."""
+
+    def __init__(self, in_ch: int, out_ch: int, stride: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        self.in_ch, self.out_ch, self.stride = in_ch, out_ch, stride
+        self.depthwise = ops.Conv2d(in_ch, in_ch, 3, stride=stride, padding=0, groups=in_ch, bias=True, device=device, dtype=dtype)
+        self.pointwise = ops.Conv2d(in_ch, out_ch, 1, padding=0, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x: Tensor) -> Tensor:
+        residual = F.max_pool2d(x, 2, 2) if self.stride > 1 else x
+        if self.out_ch > self.in_ch:
+            residual = F.pad(residual, (0, 0, 0, 0, 0, self.out_ch - self.in_ch))
+        x = _tf_same_pad(x, 3, self.stride) if self.stride > 1 else F.pad(x, (1, 1, 1, 1))
+        return F.relu(self.pointwise(self.depthwise(x)) + residual)
+
+
+class BlazeFace(nn.Module):
+    """Short-range BlazeFace: (B, 3, 128, 128) in [-1, 1] → 896 anchors x 17."""
+
+    def __init__(self, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        self.stem = ops.Conv2d(3, 24, 5, stride=2, padding=0, bias=True, **kw)
+        self.blocks = nn.ModuleList(BlazeFaceBlock(i, o, s, device=device, dtype=dtype, operations=operations)
+                                    for (i, o, s) in _BLAZEFACE_BLOCKS)
+        # 16²x2 + 8²x6 = 512 + 384 = 896 anchors.
+        self.cls_16 = ops.Conv2d(88, 2, 1, padding=0, bias=True, **kw)
+        self.cls_8 = ops.Conv2d(96, 6, 1, padding=0, bias=True, **kw)
+        self.reg_16 = ops.Conv2d(88, 32, 1, padding=0, bias=True, **kw)
+        self.reg_8 = ops.Conv2d(96, 96, 1, padding=0, bias=True, **kw)
+
+    def forward(self, image_chw_normalized: Tensor) -> tuple[Tensor, Tensor]:
+        x = F.relu(self.stem(_tf_same_pad(image_chw_normalized, 5, 2)))
+        # 16x16 tap is block-10 output (before the 88→96 stride-2 in block 11).
+        for i in range(11):
+            x = self.blocks[i](x)
+        feat_16 = x
+        for i in range(11, 16):
+            x = self.blocks[i](x)
+        feat_8 = x
+
+        def flat(t, a, k):  # NHWC flatten → (B, H*W*A, K)
+            B, _, H, W = t.shape
+            return t.permute(0, 2, 3, 1).reshape(B, H * W * a, k)
+
+        cls = torch.cat([flat(self.cls_16(feat_16), 2, 1), flat(self.cls_8(feat_8), 6, 1)], dim=1)
+        reg = torch.cat([flat(self.reg_16(feat_16), 2, 16), flat(self.reg_8(feat_8), 6, 16)], dim=1)
+        return reg, cls
+
+
+# BlazeFace full-range (face_detection_full_range_sparse.tflite): MobileNetV2-ish
+# backbone + top-down FPN, 192² input → 2304 anchors at the 48x48 grid.
+class FRBlock(nn.Module):
+    """Double inverted residual: DW → PW(mid) → DW → PW(out) [+ residual].
+
+    Per source tflite: dw* have no fused activation, pw1 is always ReLU, pw2
+    is ReLU only when no residual (else ReLU fuses into the ADD).
+    """
+
+    def __init__(self, in_ch: int, mid_ch: int, out_ch: int, stride: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        self.has_residual = (in_ch == out_ch and stride == 1)
+        self.dw1 = ops.Conv2d(in_ch, in_ch, 3, stride=stride, padding=0, groups=in_ch, bias=True, **kw)
+        self.pw1 = ops.Conv2d(in_ch, mid_ch, 1, padding=0, bias=True, **kw)
+        self.dw2 = ops.Conv2d(mid_ch, mid_ch, 3, stride=1, padding=0, groups=mid_ch, bias=True, **kw)
+        self.pw2 = ops.Conv2d(mid_ch, out_ch, 1, padding=0, bias=True, **kw)
+
+    def forward(self, x: Tensor) -> Tensor:
+        residual = x if self.has_residual else None
+        x = F.relu(self.pw1(self.dw1(F.pad(x, (1, 1, 1, 1)))))
+        x = self.pw2(self.dw2(F.pad(x, (1, 1, 1, 1))))
+        return F.relu(x + residual) if residual is not None else F.relu(x)
+
+
+# (in_ch, mid_ch, out_ch, stride). Stages downsample 96²x32 → 48²x64 → 24²x128
+# → 12²x192 → 6²x384. Lateral taps at indices 4, 7, 10 (see _FR_LATERAL_*).
+_FR_BACKBONE_BLOCKS = [
+    (32, 8, 32, 1),    (32, 8, 32, 1),                                            # 96²x32
+    (32, 16, 64, 2),   (64, 16, 64, 1),   (64, 16, 64, 1),                        # 48²x64 — tap[0]
+    (64, 32, 128, 2),  (128, 32, 128, 1), (128, 32, 128, 1),                      # 24²x128 — tap[1]
+    (128, 48, 192, 2), (192, 48, 192, 1), (192, 48, 192, 1),                      # 12²x192 — tap[2]
+    (192, 96, 384, 2), (384, 96, 384, 1), (384, 96, 384, 1), (384, 96, 384, 1),   # 6²x384
+]
+_FR_LATERAL_TAP_INDICES = (4, 7, 10)
+_FR_LATERAL_CHANNELS = ((64, 48), (128, 64), (192, 96))  # (in, out) per side-conv
+
+# Decoder blocks per FPN level (after upsample-and-merge with the lateral).
+_FR_DECODER_BLOCKS = [
+    [(96, 48, 96, 1), (96, 48, 96, 1)],  # 12²x96
+    [(64, 32, 64, 1), (64, 32, 64, 1)],  # 24²x64
+    [(48, 24, 48, 1)],                   # 48²x48 — feeds the heads
+]
+
+
+def _dcr_depth_to_space(t: Tensor, r: int, c_out: int) -> Tensor:
+    """TF DEPTH_TO_SPACE in DCR layout (input channels = (i, j, c_out)).
+    pixel_shuffle uses CRD which permutes output channels for c_out > 1."""
+    B_, _, H_, W_ = t.shape
+    t = t.reshape(B_, r, r, c_out, H_, W_)
+    t = t.permute(0, 3, 4, 1, 5, 2).contiguous()
+    return t.reshape(B_, c_out, H_ * r, W_ * r)
+
+
+class BlazeFaceFullRange(nn.Module):
+    """Full-range face detector: (B, 3, 192, 192) in [-1, 1] → 2304 anchors x 17 values."""
+
+    def __init__(self, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        mk_block = lambda i, m, o, s: FRBlock(i, m, o, s, device=device, dtype=dtype, operations=operations)
+        self.stem = ops.Conv2d(3, 32, 3, stride=2, padding=0, bias=True, **kw)
+        self.backbone = nn.ModuleList(mk_block(i, m, o, s) for (i, m, o, s) in _FR_BACKBONE_BLOCKS)
+        self.lateral_convs = nn.ModuleList(ops.Conv2d(i, o, 1, padding=0, bias=True, **kw) for (i, o) in _FR_LATERAL_CHANNELS)
+        self.top_conv = ops.Conv2d(384, 96, 1, padding=0, bias=True, **kw)
+        self.decoder_levels = nn.ModuleList(
+            nn.ModuleList(mk_block(i, m, o, s) for (i, m, o, s) in lvl) for lvl in _FR_DECODER_BLOCKS
+        )
+        # 96→64 before 12→24, 64→48 before 24→48.
+        self.decoder_reduce_convs = nn.ModuleList([
+            ops.Conv2d(96, 64, 1, padding=0, bias=True, **kw),
+            ops.Conv2d(64, 48, 1, padding=0, bias=True, **kw),
+        ])
+        # Heads mix 2x2-cell info via DW-stride-2 + depth_to_space block_size=2.
+        self.cls_conv = ops.Conv2d(48, 4, 1, padding=0, bias=True, **kw)
+        self.cls_dw = ops.Conv2d(4, 4, 3, stride=2, padding=0, groups=4, bias=True, **kw)
+        self.reg_conv = ops.Conv2d(48, 64, 1, padding=0, bias=True, **kw)
+        self.reg_dw = ops.Conv2d(64, 64, 3, stride=2, padding=0, groups=64, bias=True, **kw)
+
+    def forward(self, image_chw_normalized: Tensor) -> tuple[Tensor, Tensor]:
+        # Symmetric pad-1 throughout (full-range tflite uses explicit TF PAD, not SAME).
+        x = F.relu(self.stem(F.pad(image_chw_normalized, (1, 1, 1, 1))))
+        tap_set = set(_FR_LATERAL_TAP_INDICES)
+        laterals: list[Tensor] = []
+        for i, blk in enumerate(self.backbone):
+            x = blk(x)
+            if i in tap_set:
+                laterals.append(x)
+
+        # top_conv / lateral_convs / decoder_reduce_convs all have fused ReLU in the tflite.
+        p = F.relu(self.top_conv(x))
+        laterals_rev = list(reversed(laterals))
+        lateral_convs_rev = list(reversed(self.lateral_convs))
+        for level in range(len(self.decoder_levels)):
+            lateral = laterals_rev[level]
+            p = F.interpolate(p, size=lateral.shape[-2:], mode="bilinear", align_corners=False)
+            p = p + F.relu(lateral_convs_rev[level](lateral))
+            for blk in self.decoder_levels[level]:
+                p = blk(p)
+            if level < len(self.decoder_reduce_convs):
+                p = F.relu(self.decoder_reduce_convs[level](p))
+
+        c = self.cls_dw(F.pad(self.cls_conv(p), (1, 1, 1, 1)))
+        c = _dcr_depth_to_space(c, r=2, c_out=1)
+        r = self.reg_dw(F.pad(self.reg_conv(p), (1, 1, 1, 1)))
+        r = _dcr_depth_to_space(r, r=2, c_out=16)
+        B = c.shape[0]
+        cls_out = c.permute(0, 2, 3, 1).reshape(B, _BF_FR_NUM_ANCHORS, 1)
+        reg_out = r.permute(0, 2, 3, 1).reshape(B, _BF_FR_NUM_ANCHORS, 16)
+        return reg_out, cls_out
+
+
+@lru_cache(maxsize=1)
+def _blazeface_full_range_anchors() -> np.ndarray:
+    """2304 anchors over 48x48; anchor_w=anchor_h=1 (fixed_anchor_size)."""
+    feat = _BF_FR_GRID
+    yy, xx = np.meshgrid(np.arange(feat, dtype=np.float32), np.arange(feat, dtype=np.float32), indexing="ij")
+    cx, cy, ones = (xx + 0.5) / feat, (yy + 0.5) / feat, np.ones_like(xx)
+    return np.stack([cx, cy, ones, ones], axis=-1).reshape(_BF_FR_NUM_ANCHORS, 4)
+
+
+def _decode_blazeface_full_range(regressors: np.ndarray, classificators: np.ndarray,
+                                 score_thresh: float = _BF_MIN_SCORE) -> np.ndarray:
+    """Same decode as short-range with 2304-anchor grid and box_scale=192."""
+    scores = expit(np.clip(classificators[:, 0], -_BF_FR_SCORE_CLIP, _BF_FR_SCORE_CLIP))
+    keep = scores >= score_thresh
+    if not keep.any():
+        return np.empty((0, 17), dtype=np.float32)
+    r = regressors[keep] / _BF_FR_BOX_SCALE
+    a = _blazeface_full_range_anchors()[keep]
+    cxs, cys, aws, ahs = a[:, 0:1], a[:, 1:2], a[:, 2:3], a[:, 3:4]
+    xc, yc = r[:, 0:1] * aws + cxs, r[:, 1:2] * ahs + cys
+    w, h = r[:, 2:3] * aws, r[:, 3:4] * ahs
+    out = np.empty((r.shape[0], 17), dtype=np.float32)
+    out[:, 0:1], out[:, 1:2], out[:, 2:3], out[:, 3:4] = xc - w / 2, yc - h / 2, xc + w / 2, yc + h / 2
+    out[:, 4:16:2] = r[:, _BF_KP_OFFSET::2] * aws + cxs
+    out[:, 5:16:2] = r[:, _BF_KP_OFFSET + 1::2] * ahs + cys
+    out[:, 16] = scores[keep]
+    return out
+
+
+# FaceMesh (face_landmarks_detector.tflite): PReLU variant of BlazeBlock,
+# 17 blocks, heads for 478x3 landmarks + presence.
+_FACEMESH_BLOCKS = [  # (in_ch, out_ch, stride)
+    (16, 16, 1),  (16, 16, 1),  (16, 32, 2),  (32, 32, 1), (32, 32, 1), (32, 64, 2),
+    (64, 64, 1),  (64, 64, 1),  (64, 128, 2), (128, 128, 1), (128, 128, 1), (128, 128, 2),
+    (128, 128, 1), (128, 128, 1), (128, 128, 2), (128, 128, 1), (128, 128, 1),
+]
+
+
+class FaceMeshBlock(nn.Module):
+    """PReLU BlazeBlock: PReLU between DW and PW, and after the residual add."""
+
+    def __init__(self, in_ch: int, out_ch: int, stride: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        self.in_ch, self.out_ch, self.stride = in_ch, out_ch, stride
+        self.depthwise = ops.Conv2d(in_ch, in_ch, 3, stride=stride, padding=0, groups=in_ch, bias=True, **kw)
+        self.prelu_dwise = nn.PReLU(num_parameters=in_ch, **kw)
+        self.pointwise = ops.Conv2d(in_ch, out_ch, 1, padding=0, bias=True, **kw)
+        self.prelu_out = nn.PReLU(num_parameters=out_ch, **kw)
+
+    def forward(self, x: Tensor) -> Tensor:
+        residual = F.max_pool2d(x, 2, 2) if self.stride > 1 else x
+        if self.out_ch > self.in_ch:
+            residual = F.pad(residual, (0, 0, 0, 0, 0, self.out_ch - self.in_ch))
+        x = _tf_same_pad(x, 3, self.stride) if self.stride > 1 else F.pad(x, (1, 1, 1, 1))
+        return self.prelu_out(self.pointwise(self.prelu_dwise(self.depthwise(x))) + residual)
+
+
+class FaceMesh(nn.Module):
+    NUM_LANDMARKS = 478
+
+    def __init__(self, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        self.stem = ops.Conv2d(3, 16, 3, stride=2, padding=0, bias=True, **kw)
+        self.prelu_stem = nn.PReLU(num_parameters=16, **kw)
+        self.blocks = nn.ModuleList(FaceMeshBlock(i, o, s, device=device, dtype=dtype, operations=operations)
+                                    for (i, o, s) in _FACEMESH_BLOCKS)
+        self.head_reduce = ops.Conv2d(128, 8, 1, padding=0, bias=True, **kw)
+        self.prelu_head_reduce = nn.PReLU(num_parameters=8, **kw)
+        self.head_block = FaceMeshBlock(8, 8, 1, device=device, dtype=dtype, operations=operations)
+        self.head_presence = ops.Conv2d(8, 1, 3, padding=0, bias=True, **kw)
+        self.head_landmarks = ops.Conv2d(8, self.NUM_LANDMARKS * 3, 3, padding=0, bias=True, **kw)
+
+    def forward(self, face_chw_normalized: Tensor) -> tuple[Tensor, Tensor]:
+        """(B, 3, 192, 192) in [0, 1] → ((B, 478, 3) landmarks in 192-canonical, (B,) presence)."""
+        x = self.prelu_stem(self.stem(_tf_same_pad(face_chw_normalized, 3, 2)))
+        for blk in self.blocks:
+            x = blk(x)
+        x = self.prelu_head_reduce(self.head_reduce(x))
+        x = self.head_block(x)
+        B = x.shape[0]
+        presence = self.head_presence(x).reshape(B)
+        lmks = self.head_landmarks(x).reshape(B, self.NUM_LANDMARKS, 3)
+        return lmks, presence
+
+
+# FaceBlendshapes (MLP-Mixer "GhumMarkerPoserMlpMixerGeneral"):
+# 146x2 → token-reduce 146→96 → embed 2→64 → +cls token → 4x mixer → cls→52.
+_BS_NUM_INPUT_LANDMARKS = 146
+_BS_NUM_TOKENS_REDUCED = 96
+_BS_NUM_TOKENS = 97  # +1 cls
+_BS_TOKEN_DIM = 64
+_BS_TOKEN_MIX_HIDDEN = 384
+_BS_CHANNEL_MIX_HIDDEN = 256
+_BS_NUM_BLENDSHAPES = 52
+_BS_LN_EPS = 1e-6
+
+
+class MlpMixerBlock(nn.Module):
+    """MLP-Mixer block: token-mixing MLP (over tokens) → channel-mixing MLP (over dim).
+    Both pre-LN, both residual. LN has no beta (bias=False) to match MP."""
+
+    def __init__(self, num_tokens: int, token_dim: int, token_hidden: int, channel_hidden: int,
+                 device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        # bias=False → no LN beta (matches MP).
+        self.ln1 = ops.LayerNorm(token_dim, eps=_BS_LN_EPS, bias=False, **kw)
+        self.ln2 = ops.LayerNorm(token_dim, eps=_BS_LN_EPS, bias=False, **kw)
+        self.token_mlp1 = ops.Linear(num_tokens, token_hidden, bias=True, **kw)
+        self.token_mlp2 = ops.Linear(token_hidden, num_tokens, bias=True, **kw)
+        self.channel_mlp1 = ops.Linear(token_dim, channel_hidden, bias=True, **kw)
+        self.channel_mlp2 = ops.Linear(channel_hidden, token_dim, bias=True, **kw)
+
+    def forward(self, x: Tensor) -> Tensor:
+        y = self.ln1(x).transpose(1, 2)
+        x = x + self.token_mlp2(F.relu(self.token_mlp1(y))).transpose(1, 2)
+        return x + self.channel_mlp2(F.relu(self.channel_mlp1(self.ln2(x))))
+
+
+class FaceBlendshapes(nn.Module):
+    def __init__(self, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        self.token_reduce = ops.Linear(_BS_NUM_INPUT_LANDMARKS, _BS_NUM_TOKENS_REDUCED, bias=True, **kw)
+        self.token_embed = ops.Linear(2, _BS_TOKEN_DIM, bias=True, **kw)
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, _BS_TOKEN_DIM, **kw))
+        self.blocks = nn.ModuleList(
+            MlpMixerBlock(_BS_NUM_TOKENS, _BS_TOKEN_DIM, _BS_TOKEN_MIX_HIDDEN, _BS_CHANNEL_MIX_HIDDEN,
+                          device=device, dtype=dtype, operations=operations) for _ in range(4)
+        )
+        self.head = ops.Linear(_BS_TOKEN_DIM, _BS_NUM_BLENDSHAPES, bias=True, **kw)
+
+    @staticmethod
+    def _input_normalize(landmarks_2d: Tensor) -> Tensor:
+        # Centroid-subtract → L2 scale → x0.5. The 0.5 is baked into training.
+        centroid = landmarks_2d.mean(dim=1, keepdim=True)
+        x = landmarks_2d - centroid
+        mag = torch.sqrt((x * x).sum(dim=-1, keepdim=True))
+        scale = mag.mean(dim=1, keepdim=True)
+        return (x / scale.clamp(min=1e-12)) * 0.5
+
+    def forward(self, landmarks_2d: Tensor) -> Tensor:
+        """(B, 146, 2) → (B, 52) in [0, 1]. Input units don't matter (centroid + L2 normalize)."""
+        x = self._input_normalize(landmarks_2d)
+        x = self.token_reduce(x.transpose(1, 2)).transpose(1, 2)
+        x = self.token_embed(x)
+        cls = self.cls_token.expand(x.shape[0], -1, -1)
+        x = torch.cat([cls, x], dim=1)
+        for blk in self.blocks:
+            x = blk(x)
+        return torch.sigmoid(self.head(x[:, 0]))
+
+
+@lru_cache(maxsize=1)
+def _blazeface_anchors() -> np.ndarray:
+    """896 anchors per SsdAnchorsCalculator (fixed_anchor_size → anchor_w=anchor_h=1)."""
+    per_ar = len(_BF_ASPECT_RATIOS) + (1 if _BF_INTERP_SCALE_AR > 0 else 0)
+    layer_anchors: List[np.ndarray] = []
+    layer = 0
+    while layer < _BF_NUM_LAYERS:
+        stride = _BF_STRIDES[layer]
+        last = layer
+        while last < _BF_NUM_LAYERS and _BF_STRIDES[last] == stride:
+            last += 1
+        per_cell = per_ar * (last - layer)
+        feat = (_BF_INPUT_SIZE + stride - 1) // stride
+        yy, xx = np.meshgrid(np.arange(feat, dtype=np.float32), np.arange(feat, dtype=np.float32), indexing="ij")
+        cx, cy, ones = (xx + _BF_ANCHOR_OFFSET_X) / feat, (yy + _BF_ANCHOR_OFFSET_Y) / feat, np.ones_like(xx)
+        cell = np.stack([cx, cy, ones, ones], axis=-1).reshape(-1, 4)
+        layer_anchors.append(np.repeat(cell, per_cell, axis=0))
+        layer = last
+    out = np.concatenate(layer_anchors, axis=0)
+    assert out.shape == (896, 4), out.shape
+    return out
+
+
+def _decode_blazeface(regressors: np.ndarray, classificators: np.ndarray,
+                      score_thresh: float = _BF_MIN_SCORE) -> np.ndarray:
+    """Decode (regs (896,16), cls (896,1)) → (N, 17) = [xyxy, kp0x..kp5y, score] in [0, 1]."""
+    scores = expit(np.clip(classificators[:, 0], -_BF_SCORE_CLIP, _BF_SCORE_CLIP))
+    keep = scores >= score_thresh
+    if not keep.any():
+        return np.empty((0, 17), dtype=np.float32)
+    r = regressors[keep] / _BF_BOX_SCALE
+    a = _blazeface_anchors()[keep]  # (N, 4) cx, cy, 1, 1
+    cxs, cys, aws, ahs = a[:, 0:1], a[:, 1:2], a[:, 2:3], a[:, 3:4]
+    xc, yc = r[:, 0:1] * aws + cxs, r[:, 1:2] * ahs + cys
+    w, h = r[:, 2:3] * aws, r[:, 3:4] * ahs
+    out = np.empty((r.shape[0], 17), dtype=np.float32)
+    out[:, 0:1], out[:, 1:2], out[:, 2:3], out[:, 3:4] = xc - w / 2, yc - h / 2, xc + w / 2, yc + h / 2
+    out[:, 4:16:2] = r[:, _BF_KP_OFFSET::2] * aws + cxs
+    out[:, 5:16:2] = r[:, _BF_KP_OFFSET + 1::2] * ahs + cys
+    out[:, 16] = scores[keep]
+    return out
+
+
+def _weighted_nms(detections: np.ndarray, iou_thresh: float = 0.5) -> np.ndarray:
+    """MP weighted NMS — kept boxes are score-weighted averages of overlapping detections."""
+    if detections.shape[0] == 0:
+        return detections
+    dets = detections[np.argsort(-detections[:, 16])]
+    N = dets.shape[0]
+    areas = np.clip(dets[:, 2] - dets[:, 0], 0, None) * np.clip(dets[:, 3] - dets[:, 1], 0, None)
+    kept: List[np.ndarray] = []
+    used = np.zeros(N, dtype=bool)
+    for i in range(N):
+        if used[i]:
+            continue
+        ax1, ay1, ax2, ay2 = dets[i, 0:4]
+        merge_idx = [i]
+        for j in range(i + 1, N):
+            if used[j]:
+                continue
+            bx1, by1, bx2, by2 = dets[j, 0:4]
+            iw = max(0.0, min(ax2, bx2) - max(ax1, bx1))
+            ih = max(0.0, min(ay2, by2) - max(ay1, by1))
+            inter = iw * ih
+            union = areas[i] + areas[j] - inter
+            if union > 0 and inter / union > iou_thresh:  # strict > matches MP
+                merge_idx.append(j)
+                used[j] = True
+        used[i] = True
+        cluster = dets[merge_idx]
+        ws = cluster[:, 16:17]
+        ws_sum = ws.sum()
+        merged = np.copy(cluster[0])
+        if ws_sum > 0:
+            merged[:16] = (cluster[:, :16] * ws).sum(axis=0) / ws_sum
+        kept.append(merged)
+    return np.stack(kept, axis=0) if kept else np.empty((0, 17), dtype=np.float32)
+
+
+def _detection_to_face_rect(detection: np.ndarray, image_w: int, image_h: int) -> Tuple[float, float, float, float, float]:
+    """Detection (normalized) → rotated 1.5xbbox ROI in image pixels (anisotropic)."""
+    xmin, ymin, xmax, ymax = detection[0:4]
+    lx = detection[4 + _FACE_LEFT_EYE_KP * 2 + 0] * image_w
+    ly = detection[4 + _FACE_LEFT_EYE_KP * 2 + 1] * image_h
+    rx = detection[4 + _FACE_RIGHT_EYE_KP * 2 + 0] * image_w
+    ry = detection[4 + _FACE_RIGHT_EYE_KP * 2 + 1] * image_h
+    # Image-y-down convention: angle = target - atan2(-dy, dx).
+    angle = _FACE_ROI_TARGET_ANGLE - math.atan2(ly - ry, rx - lx)
+    return (float((xmin + xmax) * 0.5 * image_w),
+            float((ymin + ymax) * 0.5 * image_h),
+            float((xmax - xmin) * image_w * _FACE_ROI_SCALE_X),
+            float((ymax - ymin) * image_h * _FACE_ROI_SCALE_Y),
+            float(angle))
+
+
+def _sample_warp(image_chw: Tensor, src_x: Tensor, src_y: Tensor, padding_mode: str) -> Tensor:
+    """Bilinear-sample image_chw at corner-aligned (src_x, src_y)."""
+    H, W = int(image_chw.shape[-2]), int(image_chw.shape[-1])
+    grid = torch.stack([(2.0 * src_x + 1.0) / W - 1.0,
+                        (2.0 * src_y + 1.0) / H - 1.0], dim=-1).unsqueeze(0)
+    return F.grid_sample(image_chw.unsqueeze(0), grid, mode="bilinear",
+                         align_corners=False, padding_mode=padding_mode).squeeze(0)
+
+
+def _warp_face_crop(image_chw: Tensor, cx: float, cy: float, width: float, height: float,
+                    angle: float, output_size: int = _FM_INPUT_SIZE) -> Tensor:
+    """Rotated rect → output_size² with BORDER_REPLICATE. image_chw must be in [0, 1]."""
+    s_x, s_y = width / output_size, height / output_size
+    cos_a, sin_a = math.cos(angle), math.sin(angle)
+    arange = torch.arange(output_size, dtype=image_chw.dtype, device=image_chw.device) - output_size * 0.5
+    v_grid, u_grid = torch.meshgrid(arange, arange, indexing="ij")
+    src_x = cx + u_grid * s_x * cos_a - v_grid * s_y * sin_a
+    src_y = cy + u_grid * s_x * sin_a + v_grid * s_y * cos_a
+    return _sample_warp(image_chw, src_x, src_y, "border")
+
+
+def _blazeface_input_warp(image_chw_raw: Tensor, target: int = _BF_INPUT_SIZE) -> Tuple[Tensor, float, float, float]:
+    """Centered max(W,H) square → target² with BORDER_ZERO + [-1, 1] norm.
+
+    Sub-pixel grid_sample matters; integer-pad-then-resize drifts the bbox ~5%.
+    Returns (warped, sub_rect_cx, sub_rect_cy, sub_rect_size) — the triplet maps
+    tensor-normalized [0,1] detections back to image pixels.
+    """
+    H, W = int(image_chw_raw.shape[1]), int(image_chw_raw.shape[2])
+    sub_rect_size = float(max(W, H))
+    sub_rect_cx, sub_rect_cy = W * 0.5, H * 0.5
+    s = sub_rect_size / target
+    arange = torch.arange(target, dtype=image_chw_raw.dtype, device=image_chw_raw.device) - target * 0.5
+    v_grid, u_grid = torch.meshgrid(arange, arange, indexing="ij")
+    out = _sample_warp(image_chw_raw, sub_rect_cx + u_grid * s, sub_rect_cy + v_grid * s, "zeros")
+    return (out / 127.5) - 1.0, sub_rect_cx, sub_rect_cy, sub_rect_size
+
+
+class FaceLandmarker(nn.Module):
+    """BlazeFace → FaceMesh v2 → blendshapes."""
+
+    def __init__(self, device=None, dtype=None, operations=None, detector_variant: str = "short"):
+        super().__init__()
+        self.detector_variant = detector_variant
+        if detector_variant == "both":
+            self.detector = BlazeFace(device=device, dtype=dtype, operations=operations)
+            self.detector_full = BlazeFaceFullRange(device=device, dtype=dtype, operations=operations)
+        else:
+            det_cls = {"short": BlazeFace, "full": BlazeFaceFullRange}[detector_variant]
+            self.detector = det_cls(device=device, dtype=dtype, operations=operations)
+        self.mesh = FaceMesh(device=device, dtype=dtype, operations=operations)
+        self.blendshapes = FaceBlendshapes(device=device, dtype=dtype, operations=operations)
+        self.register_buffer("_bs_idx", torch.tensor(_BS_INPUT_INDICES, dtype=torch.long), persistent=False)
+
+    def _detector(self, variant: str) -> nn.Module:
+        if variant not in ("short", "full"):
+            raise ValueError(f"Unknown face detector variant: {variant!r}")
+        if self.detector_variant == "both":
+            return self.detector_full if variant == "full" else self.detector
+        if variant != self.detector_variant:
+            raise ValueError(
+                f"FaceLandmarker was initialized with the {self.detector_variant!r} detector, not {variant!r}"
+            )
+        return self.detector
+
+    def run_detector_batch(self, images_rgb_uint8: List[np.ndarray],
+                           score_thresh: float = _BF_MIN_SCORE,
+                           iou_thresh: float = 0.5,
+                           variant: Optional[str] = None):
+        """Batched detector pass. Returns (img_raws, sub_rects, sizes, per_frame_decoded)
+        where per_frame_decoded[b] is (N, 17) in tensor-normalized [0,1] coords.
+        `variant` overrides per-call."""
+        if not images_rgb_uint8:
+            return [], [], [], []
+        if variant is None:
+            variant = "short" if self.detector_variant == "both" else self.detector_variant
+        detector = self._detector(variant)
+        device, dtype = detector.stem.weight.device, detector.stem.weight.dtype
+        det_input_size, decode_fn = ((_BF_FR_INPUT_SIZE, _decode_blazeface_full_range)
+                                     if variant == "full"
+                                     else (_BF_INPUT_SIZE, _decode_blazeface))
+
+        # Same-size frames: stack once and transfer once. Variable size falls back
+        # to per-image (only triggers for SAM3DBody's head crops).
+        sizes = [tuple(img.shape[:2]) for img in images_rgb_uint8]
+        if len(set(sizes)) == 1:
+            batch_chw = torch.from_numpy(np.stack(images_rgb_uint8, axis=0)).to(device, dtype).movedim(-1, -3).contiguous()
+            img_raws = [batch_chw[bi] for bi in range(batch_chw.shape[0])]
+        else:
+            img_raws = [torch.from_numpy(img).to(device, dtype).movedim(-1, -3).contiguous() for img in images_rgb_uint8]
+
+        warps = [_blazeface_input_warp(img_raw, det_input_size) for img_raw in img_raws]
+        det_crops = [w[0] for w in warps]
+        sub_rects = [(w[1], w[2], w[3]) for w in warps]
+
+        regs_b, cls_b = detector(torch.stack(det_crops, dim=0))
+        regs_np, cls_np = regs_b.float().cpu().numpy(), cls_b.float().cpu().numpy()
+        per_frame = []
+        for b in range(len(images_rgb_uint8)):
+            decoded = decode_fn(regs_np[b], cls_np[b], score_thresh=score_thresh)
+            per_frame.append(_weighted_nms(decoded, iou_thresh=iou_thresh) if decoded.shape[0] > 0 else decoded)
+        return img_raws, sub_rects, sizes, per_frame
+
+    def detect_batch(self, images_rgb_uint8: List[np.ndarray], num_faces: int = 1,
+                     score_thresh: float = _BF_MIN_SCORE,
+                     variant: Optional[str] = None) -> List[List[dict]]:
+        """Full pipeline batched across `images_rgb_uint8`. Returns one face-dict
+        list per image (empty if nothing detected). Face dict:
+            bbox_xyxy (4,) image pixels, blendshapes {52} ∈ [0,1],
+            landmarks_xy (478, 2) image pixels, landmarks_3d (478, 3) in
+            192-canonical (pre-transformation) units, presence float (raw logit).
+        On 'both'-mode instances `variant=None` runs both detectors and keeps
+        whichever found more faces per frame (tie → short).
+        """
+        def normalize_detections(sub_rects, sizes, detections):
+            # tensor-normalized → image-normalized [0,1] for _detection_to_face_rect.
+            for b, decoded in enumerate(detections):
+                if decoded.shape[0] == 0:
+                    continue
+                cx, cy, size = sub_rects[b]
+                H, W = sizes[b]
+                sx0, sy0 = cx - size * 0.5, cy - size * 0.5
+                decoded[:, 0:16:2] = (sx0 + size * decoded[:, 0:16:2]) / W
+                decoded[:, 1:16:2] = (sy0 + size * decoded[:, 1:16:2]) / H
+                if num_faces > 0:
+                    detections[b] = decoded[: int(num_faces)]
+
+        if variant is None and self.detector_variant == "both":
+            img_raws, short_rects, sizes, short_dets = self.run_detector_batch(
+                images_rgb_uint8, score_thresh=score_thresh, variant="short",
+            )
+            _, full_rects, full_sizes, full_dets = self.run_detector_batch(
+                images_rgb_uint8, score_thresh=score_thresh, variant="full",
+            )
+            normalize_detections(short_rects, sizes, short_dets)
+            normalize_detections(full_rects, full_sizes, full_dets)
+            per_frame_dets = [
+                short if len(short) >= len(full) else full
+                for short, full in zip(short_dets, full_dets)
+            ]
+        else:
+            img_raws, sub_rects, sizes, per_frame_dets = self.run_detector_batch(
+                images_rgb_uint8, score_thresh=score_thresh, variant=variant,
+            )
+            normalize_detections(sub_rects, sizes, per_frame_dets)
+
+        # Collect every detected face across all frames into one mesh input.
+        face_params: List[Tuple[int, float, float, float, float, float, float]] = []
+        mesh_crops: List[Tensor] = []
+        for b, dets in enumerate(per_frame_dets):
+            if dets.shape[0] == 0:
+                continue
+            H, W = sizes[b]
+            img_for_mesh = img_raws[b] / 255.0
+            for det in dets:
+                cx, cy, w, h, angle = _detection_to_face_rect(det, W, H)
+                mesh_crops.append(_warp_face_crop(img_for_mesh, cx, cy, w, h, angle, _FM_INPUT_SIZE))
+                face_params.append((b, float(det[16]), cx, cy, w, h, angle))
+
+        results: List[List[dict]] = [[] for _ in range(len(images_rgb_uint8))]
+        if not mesh_crops:
+            return results
+
+        lmks_canon_b, presence_b = self.mesh(torch.stack(mesh_crops, dim=0))
+        bs_out_b = self.blendshapes(lmks_canon_b[:, self._bs_idx, :2])
+
+        # Batched canonical→image affine
+        params_t = torch.tensor(
+            [(cx, cy, w, h, math.cos(a), math.sin(a)) for (_b, _s, cx, cy, w, h, a) in face_params],
+            device=lmks_canon_b.device, dtype=lmks_canon_b.dtype,
+        )
+        cxs, cys, ws, hs, cos_a, sin_a = params_t.unbind(dim=1)
+        inv = 1.0 / _FM_INPUT_SIZE
+        u = lmks_canon_b[..., 0] - _FM_INPUT_SIZE * 0.5
+        v = lmks_canon_b[..., 1] - _FM_INPUT_SIZE * 0.5
+        lmks_xy_t = torch.stack([
+            cxs[:, None] + u * (ws * inv * cos_a)[:, None] - v * (hs * inv * sin_a)[:, None],
+            cys[:, None] + u * (ws * inv * sin_a)[:, None] + v * (hs * inv * cos_a)[:, None],
+        ], dim=-1)
+
+        lmks_xy_np = lmks_xy_t.float().cpu().numpy()
+        lmks_canon_np = lmks_canon_b.float().cpu().numpy()
+        presence_np = presence_b.float().cpu().numpy()
+        bs_np = bs_out_b.float().cpu().numpy()
+
+        for i, (b, score, *_) in enumerate(face_params):
+            lmks_xy = lmks_xy_np[i]
+            mn, mx = lmks_xy.min(0), lmks_xy.max(0)
+            results[b].append({
+                "bbox_xyxy": np.array([mn[0], mn[1], mx[0], mx[1]], dtype=np.float32),
+                "blendshapes": dict(zip(BLENDSHAPE_NAMES, bs_np[i].tolist())),
+                "landmarks_xy": lmks_xy,
+                "landmarks_3d": lmks_canon_np[i],
+                "presence": float(presence_np[i]),
+                "score": score,
+            })
+        return results
diff --git a/comfy_extras/mesh3d/fileio/gltf_read.py b/comfy_extras/mesh3d/fileio/gltf_read.py
new file mode 100644
index 0000000000000000000000000000000000000000..6817edf41547f575429682b72ef34803295126ce
--- /dev/null
+++ b/comfy_extras/mesh3d/fileio/gltf_read.py
@@ -0,0 +1,410 @@
+import base64
+import json
+import os
+import struct
+import urllib.parse
+from io import BytesIO
+
+import numpy as np
+from PIL import Image
+
+_COMPONENT_DTYPES = {
+    5120: np.int8,
+    5121: np.uint8,
+    5122: np.int16,
+    5123: np.uint16,
+    5125: np.uint32,
+    5126: np.float32,
+}
+_TYPE_SIZES = {"SCALAR": 1, "VEC2": 2, "VEC3": 3, "VEC4": 4, "MAT2": 4, "MAT3": 9, "MAT4": 16}
+
+_SUPPORTED_REQUIRED = {
+    "EXT_mesh_gpu_instancing",
+    "EXT_texture_webp",
+    "KHR_materials_emissive_strength",
+    "KHR_materials_unlit",
+}
+
+_JSON_CHUNK = 0x4E4F534A
+_BIN_CHUNK = 0x004E4942
+
+
+def parse_container(data: bytes):
+    if data[:4] == b"glTF":
+        if len(data) < 12:
+            raise ValueError("GLB file truncated (missing 12-byte header)")
+        _, version, _ = struct.unpack_from("<4sII", data, 0)
+        if version != 2:
+            raise ValueError(f"unsupported GLB container version {version}")
+        json_chunk = None
+        bin_chunk = None
+        offset = 12
+        while offset + 8 <= len(data):
+            chunk_len, chunk_type = struct.unpack_from("<II", data, offset)
+            offset += 8
+            chunk = data[offset:offset + chunk_len]
+            offset += chunk_len
+            if chunk_type == _JSON_CHUNK and json_chunk is None:
+                json_chunk = chunk
+            elif chunk_type == _BIN_CHUNK and bin_chunk is None:
+                bin_chunk = chunk
+        if json_chunk is None:
+            raise ValueError("GLB file has no JSON chunk")
+        return json.loads(json_chunk), bin_chunk
+    return json.loads(data), None
+
+
+def _resolve_uri(uri: str, base_dir: str | None) -> bytes:
+    if uri.startswith("data:"):
+        header, _, payload = uri.partition(",")
+        if ";base64" in header:
+            return base64.b64decode(payload)
+        return urllib.parse.unquote_to_bytes(payload)
+    scheme = urllib.parse.urlparse(uri).scheme
+    if scheme:
+        raise ValueError(f"glTF references URI scheme {scheme!r}; only relative file paths are allowed")
+    if base_dir is None:
+        raise ValueError(
+            f"glTF references external file {uri!r} but the source is an in-memory stream; "
+            "use .glb (self-contained) or a disk-backed file"
+        )
+    relative = urllib.parse.unquote(uri)
+    root = os.path.realpath(base_dir)
+    path = os.path.realpath(os.path.join(root, relative))
+    try:
+        contained = not os.path.isabs(relative) and os.path.commonpath([root, path]) == root
+    except ValueError:
+        contained = False
+    if not contained:
+        raise ValueError(f"glTF references file {uri!r} outside the model directory")
+    with open(path, "rb") as f:
+        return f.read()
+
+
+def load_buffers(gltf: dict, bin_chunk: bytes | None, base_dir: str | None) -> list[bytes]:
+    buffers = []
+    for buf in gltf.get("buffers", []):
+        if "uri" in buf:
+            buffers.append(_resolve_uri(buf["uri"], base_dir))
+        else:
+            if bin_chunk is None:
+                raise ValueError("glTF buffer has no uri and there is no GLB BIN chunk")
+            buffers.append(bin_chunk)
+    return buffers
+
+
+def _view_data(gltf: dict, buffers: list[bytes], view_index: int) -> bytes:
+    view = gltf["bufferViews"][view_index]
+    buf = buffers[view.get("buffer", 0)]
+    start = view.get("byteOffset", 0)
+    return buf[start:start + view["byteLength"]]
+
+
+def read_accessor(gltf: dict, buffers: list[bytes], index: int):
+    acc = gltf["accessors"][index]
+    count = acc["count"]
+    ncomp = _TYPE_SIZES[acc["type"]]
+    dtype = np.dtype(_COMPONENT_DTYPES[acc["componentType"]])
+    elem = dtype.itemsize * ncomp
+
+    if "bufferView" in acc:
+        view = gltf["bufferViews"][acc["bufferView"]]
+        buf = buffers[view.get("buffer", 0)]
+        start = view.get("byteOffset", 0) + acc.get("byteOffset", 0)
+        stride = view.get("byteStride") or elem
+        if stride == elem:
+            arr = np.frombuffer(buf, dtype, count * ncomp, start).reshape(count, ncomp).copy()
+        else:
+            raw = np.frombuffer(buf, np.uint8, stride * (count - 1) + elem, start)
+            rows = np.lib.stride_tricks.as_strided(raw, (count, elem), (stride, 1))
+            arr = np.ascontiguousarray(rows).view(dtype).reshape(count, ncomp)
+    else:
+        arr = np.zeros((count, ncomp), dtype)
+
+    sparse = acc.get("sparse")
+    if sparse:
+        n = sparse["count"]
+        idx_def = sparse["indices"]
+        val_def = sparse["values"]
+        idx_dtype = np.dtype(_COMPONENT_DTYPES[idx_def["componentType"]])
+        iview = gltf["bufferViews"][idx_def["bufferView"]]
+        ibuf = buffers[iview.get("buffer", 0)]
+        sidx = np.frombuffer(ibuf, idx_dtype, n,
+                             iview.get("byteOffset", 0) + idx_def.get("byteOffset", 0)).astype(np.int64)
+        vview = gltf["bufferViews"][val_def["bufferView"]]
+        vbuf = buffers[vview.get("buffer", 0)]
+        svals = np.frombuffer(vbuf, dtype, n * ncomp,
+                              vview.get("byteOffset", 0) + val_def.get("byteOffset", 0)).reshape(n, ncomp)
+        arr[sidx] = svals
+    return arr, bool(acc.get("normalized"))
+
+
+def to_float(arr: np.ndarray, normalized: bool) -> np.ndarray:
+    if arr.dtype == np.float32:
+        return arr
+    out = arr.astype(np.float32)
+    if not normalized:
+        return out
+    if arr.dtype == np.uint8:
+        return out / 255.0
+    if arr.dtype == np.uint16:
+        return out / 65535.0
+    if arr.dtype == np.int8:
+        return np.maximum(out / 127.0, -1.0)
+    if arr.dtype == np.int16:
+        return np.maximum(out / 32767.0, -1.0)
+    return out
+
+
+def _quat_to_matrix(x: float, y: float, z: float, w: float) -> np.ndarray:
+    return np.array([
+        [1 - 2 * (y * y + z * z), 2 * (x * y - z * w), 2 * (x * z + y * w)],
+        [2 * (x * y + z * w), 1 - 2 * (x * x + z * z), 2 * (y * z - x * w)],
+        [2 * (x * z - y * w), 2 * (y * z + x * w), 1 - 2 * (x * x + y * y)],
+    ], dtype=np.float32)
+
+
+def _node_local_matrix(node: dict) -> np.ndarray:
+    if "matrix" in node:
+        return np.array(node["matrix"], np.float32).reshape(4, 4).T  # glTF stores column-major
+    m = np.eye(4, dtype=np.float32)
+    rot = _quat_to_matrix(*node.get("rotation", (0.0, 0.0, 0.0, 1.0)))
+    scale = np.array(node.get("scale", (1.0, 1.0, 1.0)), np.float32)
+    m[:3, :3] = rot * scale
+    m[:3, 3] = node.get("translation", (0.0, 0.0, 0.0))
+    return m
+
+
+def _instance_matrices(gltf: dict, buffers: list[bytes], node: dict, warn):
+    attrs = node.get("extensions", {}).get("EXT_mesh_gpu_instancing", {}).get("attributes", {})
+    if not attrs:
+        return None
+    counts = {gltf["accessors"][a]["count"] for a in attrs.values()}
+    if len(counts) > 1:
+        raise ValueError(f"EXT_mesh_gpu_instancing attribute accessors disagree on instance count: {sorted(counts)}")
+    if not any(k in attrs for k in ("TRANSLATION", "ROTATION", "SCALE")):
+        warn("instancing-custom", "EXT_mesh_gpu_instancing has only custom attributes; importing a single copy")
+        return None
+    translation = to_float(*read_accessor(gltf, buffers, attrs["TRANSLATION"])) if "TRANSLATION" in attrs else None
+    rotation = to_float(*read_accessor(gltf, buffers, attrs["ROTATION"])) if "ROTATION" in attrs else None
+    scale = to_float(*read_accessor(gltf, buffers, attrs["SCALE"])) if "SCALE" in attrs else None
+    count = counts.pop()
+    matrices = []
+    for i in range(count):
+        m = np.eye(4, dtype=np.float32)
+        rot = _quat_to_matrix(*rotation[i]) if rotation is not None else np.eye(3, dtype=np.float32)
+        m[:3, :3] = rot * (scale[i] if scale is not None else 1.0)
+        if translation is not None:
+            m[:3, 3] = translation[i]
+        matrices.append(m)
+    return matrices
+
+
+def _iter_mesh_nodes(gltf: dict, buffers: list[bytes], warn):
+    nodes = gltf.get("nodes", [])
+    scenes = gltf.get("scenes")
+    if scenes:
+        roots = scenes[gltf.get("scene", 0)].get("nodes", [])
+    elif nodes:
+        children = {c for n in nodes for c in n.get("children", [])}
+        roots = [i for i in range(len(nodes)) if i not in children]
+    else:
+        for i in range(len(gltf.get("meshes", []))):
+            yield {"mesh": i}, np.eye(4, dtype=np.float32)
+        return
+    seen = set()
+    stack = [(i, np.eye(4, dtype=np.float32)) for i in roots]
+    while stack:
+        index, parent = stack.pop()
+        if index in seen:
+            continue
+        seen.add(index)
+        node = nodes[index]
+        world = parent @ _node_local_matrix(node)
+        if "mesh" in node:
+            instances = _instance_matrices(gltf, buffers, node, warn)
+            if instances is None:
+                yield node, world
+            else:
+                warn("instancing", f"EXT_mesh_gpu_instancing: expanding {len(instances)} instances into merged geometry")
+                for matrix in instances:
+                    yield node, world @ matrix
+        for child in node.get("children", []):
+            stack.append((child, world))
+
+
+def _to_triangles(indices: np.ndarray, mode: int) -> np.ndarray:
+    if mode == 4:
+        if len(indices) % 3:
+            raise ValueError("TRIANGLES primitive index count must be divisible by 3")
+        return indices.reshape(-1, 3)
+    if len(indices) < 3:
+        return np.zeros((0, 3), np.int64)
+    if mode == 6:
+        first = np.full(len(indices) - 2, indices[0], dtype=np.int64)
+        return np.stack([first, indices[1:-1], indices[2:]], axis=1)
+    tris = np.stack([indices[:-2], indices[1:-1], indices[2:]], axis=1)
+    tris[1::2] = tris[1::2, ::-1]
+    return tris
+
+
+def _vertex_attr(gltf, buffers, accessor_index, n_verts, warn, label):
+    arr = to_float(*read_accessor(gltf, buffers, accessor_index))
+    if arr.shape[0] < n_verts:
+        warn(f"count:{label}", f"{label} has {arr.shape[0]} entries for {n_verts} vertices; attribute dropped")
+        return None
+    return arr[:n_verts]
+
+
+def load_scene_geometry(gltf: dict, buffers: list[bytes], warn) -> list[dict]:
+    required = set(gltf.get("extensionsRequired", []))
+    unsupported = required - _SUPPORTED_REQUIRED
+    if unsupported:
+        raise ValueError(f"glTF requires extensions this loader does not support: {sorted(unsupported)}")
+
+    prims = []
+    for node, world in _iter_mesh_nodes(gltf, buffers, warn):
+        if "skin" in node:
+            warn("skin", "skinned mesh: joints/weights ignored, geometry imported in bind pose")
+        linear = world[:3, :3]
+        det = float(np.linalg.det(linear))
+        normal_mat = np.linalg.inv(linear).T if abs(det) > 1e-12 else linear
+        flip_winding = det < 0.0
+
+        for prim in gltf["meshes"][node["mesh"]].get("primitives", []):
+            mode = prim.get("mode", 4)
+            if mode not in (4, 5, 6):
+                warn(f"mode{mode}", f"skipping non-triangle primitive (mode {mode})")
+                continue
+            attrs = prim.get("attributes", {})
+            if "POSITION" not in attrs:
+                continue
+            pos = to_float(*read_accessor(gltf, buffers, attrs["POSITION"]))[:, :3]
+            n_verts = pos.shape[0]
+            if n_verts == 0:
+                continue
+            pos = pos @ linear.T + world[:3, 3]
+
+            if "indices" in prim:
+                indices = read_accessor(gltf, buffers, prim["indices"])[0].reshape(-1).astype(np.int64)
+            else:
+                indices = np.arange(n_verts, dtype=np.int64)
+            faces = _to_triangles(indices, mode)
+            if faces.shape[0] == 0:
+                continue
+            if faces.min() < 0 or faces.max() >= n_verts:
+                raise ValueError("primitive contains a face index outside its POSITION accessor")
+            if flip_winding:
+                faces = np.ascontiguousarray(faces[:, ::-1])
+            if prim.get("targets"):
+                warn("morph", "morph targets ignored; base geometry imported")
+
+            out = {"positions": np.ascontiguousarray(pos, np.float32), "faces": faces,
+                   "uvs": None, "colors": None, "normals": None, "tangents": None,
+                   "material": prim.get("material")}
+            if "TEXCOORD_0" in attrs:
+                uv = _vertex_attr(gltf, buffers, attrs["TEXCOORD_0"], n_verts, warn, "TEXCOORD_0")
+                out["uvs"] = uv[:, :2] if uv is not None else None
+            if "COLOR_0" in attrs:
+                arr, normalized = read_accessor(gltf, buffers, attrs["COLOR_0"])
+                arr = to_float(arr, normalized or arr.dtype != np.float32)
+                if arr.shape[0] >= n_verts:
+                    out["colors"] = np.clip(arr[:n_verts], 0.0, 1.0)
+            if "NORMAL" in attrs:
+                nrm = _vertex_attr(gltf, buffers, attrs["NORMAL"], n_verts, warn, "NORMAL")
+                if nrm is not None:
+                    nrm = nrm[:, :3] @ normal_mat.T
+                    out["normals"] = np.ascontiguousarray(
+                        nrm / np.maximum(np.linalg.norm(nrm, axis=1, keepdims=True), 1e-12), np.float32)
+            if "TANGENT" in attrs:
+                tan = _vertex_attr(gltf, buffers, attrs["TANGENT"], n_verts, warn, "TANGENT")
+                if tan is not None and tan.shape[1] == 4:
+                    txyz = tan[:, :3] @ linear.T
+                    txyz /= np.maximum(np.linalg.norm(txyz, axis=1, keepdims=True), 1e-12)
+                    tw = tan[:, 3:4] * (-1.0 if flip_winding else 1.0)
+                    out["tangents"] = np.ascontiguousarray(np.concatenate([txyz, tw], axis=1), np.float32)
+            prims.append(out)
+    return prims
+
+
+def _decode_texture(gltf, buffers, base_dir, tex_info, warn, label):
+    if tex_info is None:
+        return None
+    if tex_info.get("texCoord", 0) != 0:
+        warn(f"texcoord:{label}", f"{label} uses TEXCOORD_{tex_info['texCoord']}; MESH only carries TEXCOORD_0")
+    if "KHR_texture_transform" in tex_info.get("extensions", {}):
+        warn("textransform", "KHR_texture_transform ignored; UVs used as-is")
+    tex_def = gltf["textures"][tex_info["index"]]
+    source = tex_def.get("source")
+    if source is None:
+        webp = tex_def.get("extensions", {}).get("EXT_texture_webp")
+        source = webp.get("source") if webp is not None else None
+    if source is None:
+        warn(f"compressed:{label}", f"{label}: compressed texture (basisu/ktx2) without fallback; skipped")
+        return None
+    image_def = gltf["images"][source]
+    if "bufferView" in image_def:
+        raw = _view_data(gltf, buffers, image_def["bufferView"])
+    elif "uri" in image_def:
+        raw = _resolve_uri(image_def["uri"], base_dir)
+    else:
+        return None
+    img = Image.open(BytesIO(bytes(raw)))
+    return np.asarray(img.convert("RGB"), dtype=np.float32) / 255.0
+
+
+def extract_material(gltf: dict, buffers: list[bytes], base_dir: str | None, material_index, warn) -> dict:
+    result = {"texture": None, "metallic_roughness": None, "normal_map": None, "emissive": None,
+              "occlusion_in_mr": False, "unlit": False, "material": None}
+    if material_index is None:
+        return result
+    mat = gltf["materials"][material_index]
+    extensions = mat.get("extensions", {})
+    result["unlit"] = "KHR_materials_unlit" in extensions
+
+    pbr = mat.get("pbrMetallicRoughness", {})
+    result["texture"] = _decode_texture(gltf, buffers, base_dir, pbr.get("baseColorTexture"), warn, "baseColorTexture")
+    mr_info = pbr.get("metallicRoughnessTexture")
+    result["metallic_roughness"] = _decode_texture(gltf, buffers, base_dir, mr_info, warn, "metallicRoughnessTexture")
+    normal_info = mat.get("normalTexture")
+    result["normal_map"] = _decode_texture(gltf, buffers, base_dir, normal_info, warn, "normalTexture")
+    result["emissive"] = _decode_texture(gltf, buffers, base_dir, mat.get("emissiveTexture"), warn, "emissiveTexture")
+
+    occlusion = mat.get("occlusionTexture")
+    if occlusion is not None:
+        if mr_info is not None and occlusion["index"] == mr_info["index"]:
+            result["occlusion_in_mr"] = True
+        else:
+            warn("occlusion", "standalone occlusionTexture not representable in MESH (ORM packing only); skipped")
+
+    overrides = {}
+    base_color = pbr.get("baseColorFactor")
+    if base_color is not None and list(base_color) != [1.0, 1.0, 1.0, 1.0]:
+        overrides["base_color_factor"] = [float(c) for c in base_color]
+    overrides["metallic_factor"] = float(pbr.get("metallicFactor", 1.0))
+    overrides["roughness_factor"] = float(pbr.get("roughnessFactor", 1.0))
+    overrides["double_sided"] = bool(mat.get("doubleSided", False))
+    if normal_info is not None and normal_info.get("scale", 1.0) != 1.0:
+        overrides["normal_scale"] = float(normal_info["scale"])
+    if occlusion is not None and occlusion.get("strength", 1.0) != 1.0:
+        overrides["occlusion_strength"] = float(occlusion["strength"])
+    emissive_factor = mat.get("emissiveFactor", (0.0, 0.0, 0.0))
+    if any(c > 0.0 for c in emissive_factor):
+        overrides["emissive_factor"] = [float(c) for c in emissive_factor]
+    strength = extensions.get("KHR_materials_emissive_strength", {}).get("emissiveStrength")
+    if strength is not None:
+        overrides["emissive_strength"] = float(strength)
+    result["material"] = overrides
+    return result
+
+
+def load_gltf(data: bytes, base_dir: str | None, warn):
+    gltf, bin_chunk = parse_container(data)
+    version = str(gltf.get("asset", {}).get("version", ""))
+    if version.partition(".")[0] != "2":
+        raise ValueError(f"unsupported glTF asset version {version or 'unknown'}; only glTF 2.x is supported")
+    buffers = load_buffers(gltf, bin_chunk, base_dir)
+    return gltf, buffers, load_scene_geometry(gltf, buffers, warn)
+
+
+__all__ = ["load_gltf", "extract_material", "parse_container", "read_accessor", "to_float"]
diff --git a/comfy_extras/mesh3d/fileio/mesh_file_read.py b/comfy_extras/mesh3d/fileio/mesh_file_read.py
new file mode 100644
index 0000000000000000000000000000000000000000..9767ffaa897e10c39f2896dd2b8ea380a29e3810
--- /dev/null
+++ b/comfy_extras/mesh3d/fileio/mesh_file_read.py
@@ -0,0 +1,172 @@
+import logging
+import re
+import struct
+
+import numpy as np
+
+
+def _srgb_to_linear(c: np.ndarray) -> np.ndarray:
+    return np.where(c <= 0.04045, c / 12.92, ((c + 0.055) / 1.055) ** 2.4).astype(np.float32)
+
+
+def load_obj(data: bytes) -> dict:
+    text = data.decode("utf-8", errors="replace")
+    text = text.replace("\r\n", "\n").replace("\\\n", "")
+    positions: list[tuple] = []
+    pos_colors: list[tuple] = []
+    uvs: list[tuple] = []
+    normals: list[tuple] = []
+
+    corner_map: dict[tuple, int] = {}
+    out_pos: list[tuple] = []
+    out_col: list[tuple] = []
+    out_uv: list[tuple] = []
+    out_nrm: list[tuple] = []
+    faces: list[tuple] = []
+    has_color = False
+    any_uv = False
+    any_normal = False
+    missing_normal = False
+    warned_mtl = False
+
+    def resolve(index_str: str, count: int) -> int:
+        i = int(index_str)
+        resolved = i - 1 if i > 0 else count + i
+        if i == 0 or not 0 <= resolved < count:
+            raise ValueError(f"OBJ index {i} out of range for {count} entries")
+        return resolved
+
+    def corner(spec: str) -> int:
+        nonlocal any_uv, any_normal, missing_normal
+        parts = spec.split("/")
+        vi = resolve(parts[0], len(positions))
+        ti = resolve(parts[1], len(uvs)) if len(parts) > 1 and parts[1] else None
+        ni = resolve(parts[2], len(normals)) if len(parts) > 2 and parts[2] else None
+        key = (vi, ti, ni)
+        cached = corner_map.get(key)
+        if cached is not None:
+            return cached
+        index = len(out_pos)
+        out_pos.append(positions[vi])
+        out_col.append(pos_colors[vi])
+        if ti is not None:
+            any_uv = True
+            u, v = uvs[ti]
+            out_uv.append((u, 1.0 - v))
+        else:
+            out_uv.append((0.0, 0.0))
+        if ni is not None:
+            any_normal = True
+            out_nrm.append(normals[ni])
+        else:
+            missing_normal = True
+            out_nrm.append((0.0, 0.0, 0.0))
+        corner_map[key] = index
+        return index
+
+    for raw_line in text.splitlines():
+        line = raw_line.strip()
+        if not line or line.startswith("#"):
+            continue
+        parts = line.split()
+        tag = parts[0]
+        if tag == "v":
+            positions.append(tuple(float(x) for x in parts[1:4]))
+            if len(parts) >= 7:
+                pos_colors.append(tuple(float(x) for x in parts[4:7]))
+                has_color = True
+            else:
+                pos_colors.append((1.0, 1.0, 1.0))
+        elif tag == "vt":
+            uvs.append((float(parts[1]), float(parts[2]) if len(parts) > 2 else 0.0))
+        elif tag == "vn":
+            normals.append(tuple(float(x) for x in parts[1:4]))
+        elif tag == "f":
+            specs = parts[1:]
+            if len(specs) < 3:
+                continue
+            indices = [corner(s) for s in specs]
+            for i in range(1, len(indices) - 1):
+                faces.append((indices[0], indices[i], indices[i + 1]))
+        elif tag in ("mtllib", "usemtl") and not warned_mtl:
+            warned_mtl = True
+            logging.warning("Get3DComponents: OBJ materials (.mtl) are not loaded; geometry only")
+
+    if not faces:
+        raise ValueError("OBJ contains no faces")
+
+    prim = {
+        "positions": np.array(out_pos, np.float32),
+        "faces": np.array(faces, np.int64),
+        "uvs": np.array(out_uv, np.float32) if any_uv else None,
+        "colors": _srgb_to_linear(np.clip(np.array(out_col, np.float32), 0.0, 1.0)) if has_color else None,
+        "normals": None,
+        "tangents": None,
+        "material": None,
+    }
+    if any_normal and missing_normal:
+        logging.warning("Get3DComponents: OBJ has faces without vn indices; normals dropped")
+    elif any_normal:
+        nrm = np.array(out_nrm, np.float32)
+        lengths = np.linalg.norm(nrm, axis=1, keepdims=True)
+        if float(lengths.max()) > 1e-6:
+            prim["normals"] = nrm / np.maximum(lengths, 1e-12)
+    return prim
+
+
+_STL_RECORD = np.dtype([("normal", "<f4", (3,)), ("verts", "<f4", (3, 3)), ("attr", "<u2")])
+
+_STL_ASCII_FACET = re.compile(
+    rb"facet\s+normal\s+(\S+)\s+(\S+)\s+(\S+).*?"
+    rb"vertex\s+(\S+)\s+(\S+)\s+(\S+).*?"
+    rb"vertex\s+(\S+)\s+(\S+)\s+(\S+).*?"
+    rb"vertex\s+(\S+)\s+(\S+)\s+(\S+)",
+    re.DOTALL,
+)
+
+
+def load_stl(data: bytes) -> dict:
+    n_faces = struct.unpack_from("<I", data, 80)[0] if len(data) >= 84 else 0
+    if n_faces > 0 and 84 + n_faces * _STL_RECORD.itemsize == len(data):
+        return _load_stl_binary(data, n_faces)
+    if b"solid" in data[:9]:
+        facets = _STL_ASCII_FACET.findall(data)
+        if not facets:
+            raise ValueError("ASCII STL contains no facets")
+        values = np.array(facets).astype(np.float32)
+        positions = values[:, 3:12].reshape(-1, 3)
+        file_normals = np.repeat(values[:, 0:3], 3, axis=0)
+        return _stl_prim(positions, file_normals)
+    available = (len(data) - 84) // _STL_RECORD.itemsize if len(data) >= 84 else 0
+    n_faces = min(n_faces, available)
+    if n_faces <= 0:
+        raise ValueError("not a valid STL file (neither binary layout nor ASCII 'solid')")
+    return _load_stl_binary(data, n_faces)
+
+
+def _load_stl_binary(data: bytes, n_faces: int) -> dict:
+    records = np.frombuffer(data, _STL_RECORD, count=n_faces, offset=84)
+    positions = records["verts"].reshape(-1, 3).astype(np.float32)
+    file_normals = np.repeat(records["normal"], 3, axis=0).astype(np.float32)
+    return _stl_prim(positions, file_normals, _stl_binary_colors(data[:80], records["attr"]))
+
+
+def _stl_binary_colors(header: bytes, attr: np.ndarray):
+    pos = header.find(b"COLOR=")
+    if pos < 0 or pos + 10 > len(header):
+        return None
+    default = np.frombuffer(header, np.uint8, 3, pos + 6).astype(np.float32) / 255.0
+    a = attr.astype(np.uint16)
+    per_face = np.stack([a & 0x1F, (a >> 5) & 0x1F, (a >> 10) & 0x1F], axis=1).astype(np.float32) / 31.0
+    face_colors = np.where(((a & 0x8000) != 0)[:, None], default[None, :], per_face)
+    return _srgb_to_linear(np.repeat(face_colors, 3, axis=0))
+
+
+def _stl_prim(positions: np.ndarray, normals: np.ndarray, colors=None) -> dict:
+    if positions.shape[0] == 0:
+        raise ValueError("STL contains no facets")
+    faces = np.arange(positions.shape[0], dtype=np.int64).reshape(-1, 3)
+    lengths = np.linalg.norm(normals, axis=1, keepdims=True)
+    normals = normals / np.maximum(lengths, 1e-12) if float(lengths.max()) > 1e-6 else None
+    return {"positions": np.ascontiguousarray(positions), "faces": faces, "uvs": None,
+            "colors": colors, "normals": normals, "tangents": None, "material": None}
diff --git a/comfy_extras/mesh3d/postprocess/qem_decimate.py b/comfy_extras/mesh3d/postprocess/qem_decimate.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fe9e4eae9f54fe38b112a65b7933b07a1ccd06b
--- /dev/null
+++ b/comfy_extras/mesh3d/postprocess/qem_decimate.py
@@ -0,0 +1,1705 @@
+"""
+Pure-PyTorch GPU-parallel QEM mesh simplification.
+
+  - Parallel greedy edge-matching collapse loop
+  - Plane/line/feature-edge/boundary quadrics, memoryless accumulation
+  - Normal-flip prevention, link-condition, skinny penalties
+  - Non-manifold/sliver handling without dropping faces
+  - Pre/post-clean pipeline (weld, degenerates, small components)
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import math
+
+import numpy as _np
+import torch
+from scipy.sparse import coo_matrix
+from scipy.sparse.csgraph import connected_components
+from tqdm import tqdm as _tqdm
+import comfy.utils as _comfy_utils
+
+
+@dataclass
+class QEMConfig:
+    # Precision
+    dtype: torch.dtype = torch.float32  # float64 much slower on consumer GPUs
+
+    # Numerical conditioning
+    stabilizer_scale: float = 1e-3  # Tikhonov reg: stabilizer = mesh_scale^2 * this
+    wander_threshold: float = 2.0  # fall back to midpoint if v* lands > N×edge_length from an endpoint
+    clamp_v_to_edge: bool = True  # project v* onto the edge segment (qem mode only)
+
+    # Placement mode (also selects collapse driver):
+    # "midpoint" = threshold-schedule driver, most stable (defaults below match it);
+    # "qem" = sharpest, QEM-optimum placement + ratio driver.
+    placement_mode: str = "midpoint"
+
+    flip_reject_hard: bool = True  # hard-reject (err=+inf) top-K collapses that flip any 1-ring normal
+
+    # Per-iteration batch sizing
+    sampling_cap: int = 10_000_000  # max edges processed per outer iter
+    max_collapses_fraction: float = 0.25  # of remaining faces-to-remove
+    max_collapses_floor: int = 10_000
+    max_collapses_ceiling: int = 1_000_000
+    max_collapses_relative_cap: float = 0.10  # cap per-iter collapses as fraction of current faces; 0 disables
+
+    # Loop control
+    max_iterations: int = 5_000
+    compaction_period: int = 5
+    compaction_threshold: float = 0.85  # compact when alive_frac < this
+
+    # Quality knobs
+    boundary_quadrics: bool = True
+    boundary_weight: float = 1000.0
+    recompute_normals_post: bool = True
+    line_quadric_weight: float = 0.0  # penalise deviation ⟂ to edge dir → more uniform verts; 0 disables
+    line_quadric_skip_opposite_normals_cos: float = 0.0  # skip line quadrics on edges with endpoint cos < this
+
+    # Feature-edge quadrics on sharp interior edges (dihedral > min); 0 disables.
+    feature_edge_quadric_weight: float = 0.0
+    feature_edge_min_dihedral_deg: float = 30.0
+
+    # Flip check (FA-QEM §3.3)
+    quality_topk_multiplier: int = 4  # quality-check band size = this * max_collapses_per_iter
+    flip_cos_threshold: float = 0.0  # 0 = count any sign reversal (dihedral > 90°)
+    flip_check_max_degree: int = 16  # cap on vertex degree for the flip-check table
+
+    # Triangle shape penalty
+    skinny_weight: float = 1e-3  # penalise top-K collapses producing needle/sliver tris; 0 disables
+
+    #  Topology preservation
+    enforce_link_condition: bool = True  # reject collapses that violate the link condition
+
+    # Quadric area weighting
+    area_weighted_quadrics: bool = False  # True: Garland-Heckbert area-weighted; False: un-weighted
+
+    # edge-length cost regularizer
+    lambda_edge_length: float = 1e-2  # add λ*len² to bias toward short edges; 0 disables
+    lambda_edge_length_absolute: bool = True  # apply λ absolutely vs relative-to-QEM-median
+
+    # Threshold-schedule driver (placement_mode == "midpoint"):
+    # each round collapses a disjoint set with cost <= thresh, ×10 when < 1% removed.
+    threshold_start: float = 1e-8
+    memoryless_qem: bool = True  # rebuild quadrics each round vs accumulate
+    repair_nonmanifold: bool = True  # final repair_non_manifold_edges pass
+
+    # Pre-clean (input mesh)
+    preclean: bool = True  # weld coincident verts, drop degenerate/duplicate/unused
+
+    # Post-clean (output mesh)
+    postclean: bool = True  # remove slivers, tiny components, unused verts left by collapse
+    postclean_min_angle_deg: float = 0.5
+    postclean_max_aspect_ratio: float = 100.0
+    postclean_min_component_faces: int = 8  # drop components with fewer faces than this
+
+    # Preclean tuning
+    preclean_weld_epsilon_rel: float = 1e-5  # weld tolerance as fraction of bbox diagonal
+    preclean_min_component_faces: int = 0  # 0 = keep all components
+
+
+    @property
+    def threshold_driver(self) -> bool:
+        """The cost-threshold collapse driver is used by the midpoint placement mode."""
+        return self.placement_mode == "midpoint"
+
+
+def _sorted_edge_halfedges(
+    faces: torch.Tensor, num_verts: int,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """3F half-edges sorted by key min(a,b)*(V+1)+max(a,b); returns (sorted_keys, face_ids, slot_ids)."""
+    device = faces.device
+    F = faces.shape[0]
+    e_all = torch.cat([faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [2, 0]]], dim=0)
+    e_sorted, _ = torch.sort(e_all, dim=1)
+    P = num_verts + 1
+    key = e_sorted[:, 0].long() * P + e_sorted[:, 1].long()
+    face_per_he = torch.arange(F, device=device, dtype=torch.long).repeat(3)
+    slot_per_he = torch.arange(3, device=device, dtype=torch.long).repeat_interleave(F)
+    sort_idx = torch.argsort(key)
+    return key[sort_idx], face_per_he[sort_idx], slot_per_he[sort_idx]
+
+
+def _vert_is_boundary_mask(faces: torch.Tensor, num_verts: int) -> torch.Tensor:
+    """(V,) bool mask: True for verts incident to any boundary edge."""
+    device = faces.device
+    out = torch.zeros(num_verts, dtype=torch.bool, device=device)
+    bedges = _detect_boundary_edges(faces, num_verts)
+    if bedges.numel() == 0:
+        return out
+    out[bedges[:, 0]] = True
+    out[bedges[:, 1]] = True
+    return out
+
+
+def _detect_boundary_edges(faces: torch.Tensor, num_verts: int) -> torch.Tensor:
+    """Boundary edges as [N, 2] of vertex indices (each appearing in exactly one face)."""
+    if faces.numel() == 0:
+        return torch.empty((0, 2), dtype=torch.int64, device=faces.device)
+    sorted_keys, _, _ = _sorted_edge_halfedges(faces, num_verts)
+    unique_key, counts = torch.unique(sorted_keys, return_counts=True)
+    boundary_key = unique_key[counts == 1]
+    if boundary_key.numel() == 0:
+        return torch.empty((0, 2), dtype=torch.int64, device=faces.device)
+    P = num_verts + 1
+    bv0 = boundary_key // P
+    bv1 = boundary_key % P
+    return torch.stack([bv0, bv1], dim=1)
+
+
+def _manifold_edge_pairs(
+    sorted_keys: torch.Tensor, sorted_faces: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Edges shared by exactly 2 faces (filters >2-incident groups); returns (pair_keys, fa, fb)."""
+    if sorted_keys.shape[0] < 2:
+        empty = sorted_keys.new_empty(0)
+        return empty, empty, empty
+    pair_mask = sorted_keys[:-1] == sorted_keys[1:]
+    if not pair_mask.any():
+        empty = sorted_keys.new_empty(0)
+        return empty, empty, empty
+    pair_starts = torch.nonzero(pair_mask, as_tuple=True)[0]
+    # manifold iff neither neighbour half-edge shares the key
+    cur = sorted_keys[pair_starts]
+    prev_ok = (pair_starts == 0) | (sorted_keys[(pair_starts - 1).clamp_min(0)] != cur)
+    nxt_idx = (pair_starts + 2).clamp(max=sorted_keys.shape[0] - 1)
+    nxt_ok = (pair_starts + 2 >= sorted_keys.shape[0]) | (sorted_keys[nxt_idx] != cur)
+    pair_starts = pair_starts[prev_ok & nxt_ok]
+    return (sorted_keys[pair_starts],
+            sorted_faces[pair_starts],
+            sorted_faces[pair_starts + 1])
+
+
+def _line_quadric_planes(
+    pa: torch.Tensor, pb: torch.Tensor
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Two plane equations (E,4) per edge whose squared-dist sum = squared ⟂ distance to the edge line."""
+    e = pb - pa                                                # (E, 3)
+    elen = torch.norm(e, dim=-1, keepdim=True).clamp_min(1e-12)
+    e_unit = e / elen                                          # (E, 3)
+    m = 0.5 * (pa + pb)                                        # (E, 3)
+    # helper axis not parallel to e_unit, then Gram-Schmidt against e_unit
+    helper = torch.zeros_like(e_unit)
+    helper.scatter_(-1, e_unit.abs().argmin(dim=-1, keepdim=True), 1.0)
+    u = helper - (helper * e_unit).sum(-1, keepdim=True) * e_unit
+    u = u / torch.norm(u, dim=-1, keepdim=True).clamp_min(1e-12)
+    w = torch.cross(e_unit, u, dim=-1)
+    d_u = -(u * m).sum(-1, keepdim=True)
+    d_w = -(w * m).sum(-1, keepdim=True)
+    p_u = torch.cat([u, d_u], dim=-1)                          # (E, 4)
+    p_w = torch.cat([w, d_w], dim=-1)
+    return p_u, p_w, elen.squeeze(-1)
+
+
+def _add_line_quadrics(
+    verts: torch.Tensor,
+    faces: torch.Tensor,
+    face_areas: torch.Tensor,
+    Q_flat: torch.Tensor,
+    weight: float,
+    skip_he_mask: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """Add line quadrics for all 3F half-edges, weighted by face_area*weight; skip_he_mask zeroes True positions."""
+    a_all = torch.cat([faces[:, 0], faces[:, 1], faces[:, 2]], dim=0).long()
+    b_all = torch.cat([faces[:, 1], faces[:, 2], faces[:, 0]], dim=0).long()
+    pa = verts[a_all]
+    pb = verts[b_all]
+    p_u, p_w, _ = _line_quadric_planes(pa, pb)
+    area_per_edge = face_areas.repeat(3)
+    w_per_edge = area_per_edge * weight
+    if skip_he_mask is not None:
+        w_per_edge = torch.where(skip_he_mask, torch.zeros_like(w_per_edge), w_per_edge)
+    w_per_edge = w_per_edge.unsqueeze(-1).unsqueeze(-1)
+    K_line = (
+        p_u.unsqueeze(-1) * p_u.unsqueeze(-2)
+        + p_w.unsqueeze(-1) * p_w.unsqueeze(-2)
+    ) * w_per_edge
+    K_flat = K_line.reshape(-1, 16)
+    Q_flat.scatter_add_(0, a_all.unsqueeze(1).expand(-1, 16), K_flat)  # scatter to both endpoints
+    Q_flat.scatter_add_(0, b_all.unsqueeze(1).expand(-1, 16), K_flat)
+    return Q_flat
+
+
+def _build_quadrics(
+    verts: torch.Tensor,
+    faces: torch.Tensor,
+    cfg: QEMConfig,
+) -> torch.Tensor:
+    """Per-vertex area-weighted quadric (V, 4, 4)."""
+    V = verts.shape[0]
+    dtype = verts.dtype
+    device = verts.device
+
+    Q_flat = torch.zeros((V, 16), dtype=dtype, device=device)
+
+    if faces.numel() > 0:
+        v0 = verts[faces[:, 0]]
+        v1 = verts[faces[:, 1]]
+        v2 = verts[faces[:, 2]]
+        e1 = v1 - v0
+        e2 = v2 - v0
+        n = torch.cross(e1, e2, dim=-1)
+        area = torch.norm(n, dim=-1)
+        mask = area > 1e-12
+        # where() avoids boolean-index gather+scatter (fewer index kernels)
+        n_norm = torch.where(mask.unsqueeze(-1),
+                             n / area.unsqueeze(-1).clamp_min(1e-12),
+                             n.new_zeros(()))
+        d = -(n_norm * v0).sum(dim=-1, keepdim=True)
+        p = torch.cat([n_norm, d], dim=-1)           # (F, 4)
+        K = torch.einsum("fi,fj->fij", p, p)         # (F, 4, 4)
+
+        if cfg.area_weighted_quadrics:
+            K.mul_(area[:, None, None])
+        K_flat = K.reshape(-1, 16)
+        for corner in range(3):
+            idx = faces[:, corner].unsqueeze(1).expand(-1, 16)
+            Q_flat.scatter_add_(0, idx, K_flat)
+
+    # Line quadrics: squared ⟂ distance from v to the edge-midpoint line, all 3F half-edges in one pass.
+    if cfg.line_quadric_weight > 0 and faces.numel() > 0:
+        # skip thin-shell rim edges (endpoint normals oppose)
+        skip_he_sharp = None
+        if cfg.line_quadric_skip_opposite_normals_cos < 1.0:
+            v_norm = torch.zeros((V, 3), dtype=dtype, device=device)
+            n_weighted = n_norm * area.unsqueeze(-1)  # normal * 2× area
+            for corner in range(3):
+                v_norm.scatter_add_(0, faces[:, corner].unsqueeze(-1).expand(-1, 3),
+                                     n_weighted)
+            v_norm = torch.nn.functional.normalize(v_norm, p=2, dim=-1, eps=1e-12)
+            a_he = torch.cat([faces[:, 0], faces[:, 1], faces[:, 2]], dim=0).long()
+            b_he = torch.cat([faces[:, 1], faces[:, 2], faces[:, 0]], dim=0).long()
+            cos_endpoints = (v_norm[a_he] * v_norm[b_he]).sum(dim=-1)
+            skip_he_sharp = cos_endpoints < cfg.line_quadric_skip_opposite_normals_cos
+            if not skip_he_sharp.any():
+                skip_he_sharp = None
+        Q_flat = _add_line_quadrics(verts, faces, area, Q_flat,
+                                     cfg.line_quadric_weight,
+                                     skip_he_mask=skip_he_sharp)
+
+    # Boundary line quadrics: pin boundary-edge endpoints to the boundary line.
+    if cfg.boundary_quadrics and faces.numel() > 0:
+        b_edges = _detect_boundary_edges(faces, V)
+        if b_edges.shape[0] > 0:
+            ba = b_edges[:, 0]
+            bb = b_edges[:, 1]
+            pa = verts[ba]
+            pb = verts[bb]
+            p_u, p_w, _ = _line_quadric_planes(pa, pb)
+            K_b = (torch.einsum("ei,ej->eij", p_u, p_u)
+                   + torch.einsum("ei,ej->eij", p_w, p_w)) * cfg.boundary_weight
+            K_b_flat = K_b.reshape(-1, 16)
+            Q_flat.scatter_add_(0, ba.unsqueeze(1).expand(-1, 16), K_b_flat)
+            Q_flat.scatter_add_(0, bb.unsqueeze(1).expand(-1, 16), K_b_flat)
+
+    # Feature-edge quadrics: line quadric on sharp interior edges weighted by (1 - cos(dihedral)).
+    if cfg.feature_edge_quadric_weight > 0 and faces.numel() > 0:
+        v0 = verts[faces[:, 0]]
+        v1 = verts[faces[:, 1]]
+        v2 = verts[faces[:, 2]]
+        fn = torch.cross(v1 - v0, v2 - v0, dim=-1)
+        fn = torch.nn.functional.normalize(fn, p=2, dim=-1, eps=1e-12)
+        sorted_keys_fe, sorted_faces_fe, _ = _sorted_edge_halfedges(faces, V)
+        pair_keys, f1_idx, f2_idx = _manifold_edge_pairs(sorted_keys_fe, sorted_faces_fe)
+        if pair_keys.numel() > 0:
+            P = V + 1
+            edge_a = pair_keys // P
+            edge_b = pair_keys % P
+            cos_dihedral = (fn[f1_idx] * fn[f2_idx]).sum(dim=-1)
+            cos_thresh = math.cos(math.radians(cfg.feature_edge_min_dihedral_deg))
+            sharp = cos_dihedral < cos_thresh
+            if sharp.any():
+                fa = edge_a[sharp]
+                fb = edge_b[sharp]
+                p_u, p_w, _ = _line_quadric_planes(verts[fa], verts[fb])
+                sharpness = (1.0 - cos_dihedral[sharp]).clamp_min(0.0)
+                avg_area = 0.5 * (area[f1_idx[sharp]] + area[f2_idx[sharp]])
+                w = (avg_area * sharpness * cfg.feature_edge_quadric_weight) \
+                    .unsqueeze(-1).unsqueeze(-1)
+                K_feat = (
+                    p_u.unsqueeze(-1) * p_u.unsqueeze(-2)
+                    + p_w.unsqueeze(-1) * p_w.unsqueeze(-2)
+                ) * w
+                K_flat = K_feat.reshape(-1, 16)
+                Q_flat.scatter_add_(0, fa.unsqueeze(1).expand(-1, 16), K_flat)
+                Q_flat.scatter_add_(0, fb.unsqueeze(1).expand(-1, 16), K_flat)
+
+    return Q_flat.reshape(V, 4, 4)
+
+
+def _edge_errors(
+    verts: torch.Tensor,
+    Q: torch.Tensor,
+    edges: torch.Tensor,
+    stabilizer: float,
+    max_edge_length_sq: float,
+    mesh_scale_sq: float,
+    cfg: QEMConfig,
+    vert_is_boundary: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Returns (optimal_pos, error, valid_mask); vert_is_boundary enables boundary-aware midpoint."""
+    n_edges = edges.shape[0]
+    dtype = verts.dtype
+    device = verts.device
+
+    if n_edges == 0:
+        return (
+            torch.empty((0, 3), dtype=dtype, device=device),
+            torch.empty((0,), dtype=dtype, device=device),
+            torch.zeros((0,), dtype=torch.bool, device=device),
+        )
+
+    verts_pair = verts[edges]           # (E, 2, 3)
+    pa = verts_pair[:, 0]
+    pb = verts_pair[:, 1]
+    edge_vec = pb - pa
+    el = torch.norm(edge_vec, dim=-1)
+
+    # boundary-aware midpoint: snap to the boundary endpoint when exactly one is boundary
+    if vert_is_boundary is not None:
+        ba = vert_is_boundary[edges[:, 0]]
+        bb = vert_is_boundary[edges[:, 1]]
+        w_a = torch.where(ba & ~bb, torch.ones_like(el),
+              torch.where(~ba & bb, torch.zeros_like(el),
+              torch.full_like(el, 0.5)))
+        midpoint = pa * w_a.unsqueeze(-1) + pb * (1.0 - w_a).unsqueeze(-1)
+    else:
+        midpoint = torch.lerp(pa, pb, 0.5)
+
+    Qe = Q[edges].sum(dim=1)            # (E, 4, 4) — sum of Q[va] and Q[vb]
+
+    if cfg.placement_mode == "midpoint":
+        opt = midpoint
+    else:
+        A = Qe[:, :3, :3] + torch.eye(3, device=device, dtype=dtype) * stabilizer
+        b = -Qe[:, :3, 3].unsqueeze(-1)
+
+        # stabilizer keeps A invertible; full-batch solve, midpoint fallback via where (no sync)
+        sol = torch.linalg.solve(A, b)
+        dets = torch.det(A)
+        good = (dets.abs() > 1e-12).unsqueeze(-1)
+        opt = torch.where(good, sol.squeeze(-1), midpoint)
+
+        if cfg.clamp_v_to_edge:
+            # project v* onto the edge segment (subsumes the wander check)
+            edge_len_sq = (edge_vec * edge_vec).sum(dim=-1) + 1e-20
+            t = ((opt - pa) * edge_vec).sum(dim=-1) / edge_len_sq
+            t = t.clamp(0.0, 1.0).unsqueeze(-1)
+            opt = torch.lerp(pa, pb, t)
+        else:
+            # fall back to midpoint when v* wanders from both endpoints
+            dist_a = torch.norm(opt - pa, dim=-1)
+            dist_b = torch.norm(opt - pb, dim=-1)
+            wander_bad = ((dist_a > cfg.wander_threshold * el) |
+                          (dist_b > cfg.wander_threshold * el)).unsqueeze(-1)
+            opt = torch.where(wander_bad, midpoint, opt)
+
+    v4 = torch.cat([opt, torch.ones((n_edges, 1), device=device, dtype=dtype)], dim=1)
+    err = torch.abs(torch.einsum("ei,eij,ej->e", v4, Qe, v4))
+
+    # mesh_scale_sq: Python float or 0-d tensor
+    if torch.is_tensor(mesh_scale_sq):
+        length_ok = el * el > mesh_scale_sq * 1e-10
+    else:
+        length_ok = el > math.sqrt(mesh_scale_sq) * 1e-5
+    error_ok = err < max_edge_length_sq
+    nan_ok = ~torch.isnan(opt).any(dim=-1) & ~torch.isnan(err)
+    valid = length_ok & error_ok & nan_ok
+
+    # edge-length regularizer: bias collapse order toward short edges (uniform sizing)
+    if cfg.lambda_edge_length > 0.0 and valid.any():
+        el2 = el * el
+        if cfg.lambda_edge_length_absolute:
+            err = err + cfg.lambda_edge_length * el2
+        else:
+            qem_med = err[valid].median()
+            len_med = el2[valid].median().clamp_min(1e-30)
+            err = err + cfg.lambda_edge_length * el2 * (qem_med / len_med)
+    return opt, err, valid
+
+
+def _greedy_matching(
+    edges: torch.Tensor,
+    err: torch.Tensor,
+    v_alive: torch.Tensor,
+    max_select: int,
+) -> torch.Tensor:
+    """Vectorised independent edge-set selection: an edge wins iff it is the min-key edge at both endpoints."""
+    device = edges.device
+    n_edges = edges.shape[0]
+    if n_edges == 0:
+        return torch.empty(0, dtype=torch.int64, device=device)
+
+    va = edges[:, 0]
+    vb = edges[:, 1]
+    num_verts = v_alive.shape[0]
+
+    err32 = err.to(torch.float32).clamp(min=0).contiguous()
+    err_bits = err32.view(torch.int32).to(torch.int64) & 0xFFFFFFFF
+    edge_idx = torch.arange(n_edges, device=device, dtype=torch.int64)
+    key = (err_bits << 32) | edge_idx
+
+    INT64_MAX = torch.iinfo(torch.int64).max
+    best_key = torch.full((num_verts,), INT64_MAX, dtype=torch.int64, device=device)
+    best_key.scatter_reduce_(0, va, key, reduce="amin", include_self=True)
+    best_key.scatter_reduce_(0, vb, key, reduce="amin", include_self=True)
+
+    is_winner = (key == best_key[va]) & (key == best_key[vb]) & v_alive[va] & v_alive[vb]
+    sel = torch.nonzero(is_winner, as_tuple=True)[0]
+
+    if sel.numel() > max_select:
+        sel_err = err[sel]
+        top = torch.topk(sel_err, max_select, largest=False).indices
+        sel = sel[top]
+    return sel
+
+
+def _build_vert_to_faces_pad(
+    faces: torch.Tensor,
+    num_verts: int,
+    max_deg: int,
+) -> torch.Tensor:
+    """Pad-CSR vertex-to-incident-faces table (V, max_deg) of face indices, -1 padded, degree truncated."""
+    device = faces.device
+    F = faces.shape[0]
+    if F == 0:
+        return torch.full((num_verts, max_deg), -1, dtype=torch.int64, device=device)
+    v_rep = faces.flatten().long()
+    f_rep = torch.arange(F, device=device, dtype=torch.int64).repeat_interleave(3)
+    sort_idx = v_rep.argsort()
+    sorted_v = v_rep[sort_idx]
+    sorted_f = f_rep[sort_idx]
+    offsets = torch.searchsorted(
+        sorted_v, torch.arange(num_verts + 1, device=device, dtype=sorted_v.dtype)
+    )
+    slot = torch.arange(sorted_v.shape[0], device=device, dtype=torch.int64) - offsets[sorted_v]
+    keep = slot < max_deg
+    table = torch.full((num_verts, max_deg), -1, dtype=torch.int64, device=device)
+    table[sorted_v[keep], slot[keep]] = sorted_f[keep]
+    return table
+
+
+def _normal_flip_mask(
+    verts: torch.Tensor,        # (V, 3)
+    faces: torch.Tensor,        # (F, 3) — must be alive faces only
+    edges: torch.Tensor,        # (E, 2) candidate collapse edges
+    opt: torch.Tensor,          # (E, 3) proposed collapse positions
+    vert_to_faces: torch.Tensor,  # (V, max_deg) face indices or -1
+    cos_threshold: float = 0.0,
+    chunk_size: int = 100_000,
+    return_count: bool = False,
+) -> torch.Tensor:
+    """(E,) bool mask (no adjacent-face flip), or int count of would-flip faces per edge if return_count."""
+    E = edges.shape[0]
+    device = verts.device
+    if return_count:
+        out = torch.zeros(E, dtype=torch.int32, device=device)
+    else:
+        out = torch.ones(E, dtype=torch.bool, device=device)
+    if E == 0:
+        return out
+
+    max_deg = vert_to_faces.shape[1]
+    a_all = edges[:, 0]
+    b_all = edges[:, 1]
+
+    for start in range(0, E, chunk_size):
+        stop = min(start + chunk_size, E)
+        Ec = stop - start
+        a = a_all[start:stop]
+        b = b_all[start:stop]
+        oc = opt[start:stop]
+
+        fa = vert_to_faces[a]                                 # (Ec, max_deg)
+        fb = vert_to_faces[b]
+        all_f = torch.cat([fa, fb], dim=1)                    # (Ec, 2*max_deg)
+        valid_f = all_f >= 0
+        all_f_safe = all_f.clamp(min=0)
+        fv = faces[all_f_safe]                                # (Ec, 2*max_deg, 3)
+
+        a_b = a.view(Ec, 1)
+        b_b = b.view(Ec, 1)
+        s0_a = fv[..., 0] == a_b
+        s0_b = fv[..., 0] == b_b
+        s1_a = fv[..., 1] == a_b
+        s1_b = fv[..., 1] == b_b
+        s2_a = fv[..., 2] == a_b
+        s2_b = fv[..., 2] == b_b
+        contains_a = s0_a | s1_a | s2_a
+        contains_b = s0_b | s1_b | s2_b
+        # affected: face contains exactly one of {a, b} and slot is non-pad
+        affected = (contains_a ^ contains_b) & valid_f
+        if not affected.any():
+            continue
+
+        p0 = verts[fv[..., 0]]                                # (Ec, 2*max_deg, 3)
+        p1 = verts[fv[..., 1]]
+        p2 = verts[fv[..., 2]]
+        n_old = torch.cross(p1 - p0, p2 - p0, dim=-1)
+
+        opt_b = oc.view(Ec, 1, 3).expand(-1, 2 * max_deg, -1)
+        rep0 = (s0_a | s0_b).unsqueeze(-1)
+        rep1 = (s1_a | s1_b).unsqueeze(-1)
+        rep2 = (s2_a | s2_b).unsqueeze(-1)
+        p0n = torch.where(rep0, opt_b, p0)
+        p1n = torch.where(rep1, opt_b, p1)
+        p2n = torch.where(rep2, opt_b, p2)
+        n_new = torch.cross(p1n - p0n, p2n - p0n, dim=-1)
+
+        nlen_old = torch.norm(n_old, dim=-1)
+        nlen_new = torch.norm(n_new, dim=-1)
+        # degenerate-before faces can't flip; treat as OK
+        denom = nlen_old * nlen_new
+        safe = denom > 1e-20
+        cos = torch.where(safe, (n_old * n_new).sum(dim=-1) / denom.clamp_min(1e-20),
+                          torch.ones_like(denom))
+        flip = (cos < cos_threshold) & affected & safe
+        if return_count:
+            out[start:stop] = flip.sum(dim=-1).to(torch.int32)
+        else:
+            out[start:stop] = ~flip.any(dim=-1)
+
+    return out
+
+
+def _link_condition_mask(
+    faces: torch.Tensor,         # (F, 3) alive faces only
+    edges: torch.Tensor,         # (E, 2) candidate collapse edges
+    vert_to_faces: torch.Tensor, # (V, max_deg) face idx or -1
+    chunk_size: int = 100_000,
+) -> torch.Tensor:
+    """(E,) bool mask — True where the collapse is topology-safe (link condition: common neighbours <= edge faces)."""
+    E = edges.shape[0]
+    device = faces.device
+    out = torch.ones(E, dtype=torch.bool, device=device)
+    if E == 0:
+        return out
+    D = vert_to_faces.shape[1]
+    a_all = edges[:, 0]
+    b_all = edges[:, 1]
+
+    for s in range(0, E, chunk_size):
+        e = min(s + chunk_size, E)
+        a = a_all[s:e]
+        b = b_all[s:e]
+        Ec = a.shape[0]
+
+        fa = vert_to_faces[a]                       # (Ec, D)
+        fb = vert_to_faces[b]
+        fa_ok = fa >= 0
+        fb_ok = fb >= 0
+        fav = faces[fa.clamp(min=0)]                # (Ec, D, 3)
+        fbv = faces[fb.clamp(min=0)]
+
+        # neighbour verts of a/b: take the 2 non-anchor verts per incident face → (Ec, 2D)
+        a_b = a[:, None]
+        b_b = b[:, None]
+        an1 = torch.where(fav[..., 0] == a_b, fav[..., 1], fav[..., 0])
+        an2 = torch.where(fav[..., 2] == a_b, fav[..., 1], fav[..., 2])
+        bn1 = torch.where(fbv[..., 0] == b_b, fbv[..., 1], fbv[..., 0])
+        bn2 = torch.where(fbv[..., 2] == b_b, fbv[..., 1], fbv[..., 2])
+        na = torch.stack([an1, an2], dim=-1).reshape(Ec, 2 * D)
+        nb = torch.stack([bn1, bn2], dim=-1).reshape(Ec, 2 * D)
+        fa_okx = fa_ok.repeat_interleave(2, dim=1)
+        fb_okx = fb_ok.repeat_interleave(2, dim=1)
+        na[(na == a_b) | (na == b_b) | ~fa_okx] = -1
+        nb[(nb == a_b) | (nb == b_b) | ~fb_okx] = -1
+
+        # common neighbours: na entries also appearing in nb
+        in_b = (na[:, :, None] == nb[:, None, :]) & (na[:, :, None] >= 0)
+        na_common = torch.where(in_b.any(dim=2), na, torch.full_like(na, -1))
+        # distinct count of common neighbours per edge (sort + count transitions)
+        cs, _ = na_common.sort(dim=1)
+        count_common = ((cs[:, 1:] != cs[:, :-1]) & (cs[:, 1:] >= 0)).sum(dim=1) \
+                       + (cs[:, :1] >= 0).sum(dim=1)
+
+        # faces on the edge = a's faces also containing b
+        count_faces = ((fav == b[:, None, None]).any(dim=2) & fa_ok).sum(dim=1)
+
+        out[s:e] = count_common <= count_faces
+
+    return out
+
+
+def _skinny_penalty(
+    verts: torch.Tensor,        # (V, 3)
+    faces: torch.Tensor,        # (F, 3) — alive faces only
+    edges: torch.Tensor,        # (E, 2) candidate collapse edges
+    opt: torch.Tensor,          # (E, 3) proposed collapse positions
+    vert_to_faces: torch.Tensor,  # (V, max_deg)
+    chunk_size: int = 100_000,
+) -> torch.Tensor:
+    """Per-edge post-collapse triangle-shape penalty (lambda_skinny); mean of 1 - clamp(shape,0,1) over the 1-ring."""
+    E = edges.shape[0]
+    device = verts.device
+    out = torch.zeros(E, dtype=verts.dtype, device=device)
+    if E == 0:
+        return out
+
+    max_deg = vert_to_faces.shape[1]
+    a_all = edges[:, 0]
+    b_all = edges[:, 1]
+    sqrt3_4 = 4.0 * math.sqrt(3.0)
+
+    for start in range(0, E, chunk_size):
+        stop = min(start + chunk_size, E)
+        Ec = stop - start
+        a = a_all[start:stop]
+        b = b_all[start:stop]
+        oc = opt[start:stop]
+
+        fa = vert_to_faces[a]
+        fb = vert_to_faces[b]
+        all_f = torch.cat([fa, fb], dim=1)
+        valid_f = all_f >= 0
+        all_f_safe = all_f.clamp(min=0)
+        fv = faces[all_f_safe]
+
+        a_b = a.view(Ec, 1)
+        b_b = b.view(Ec, 1)
+        s0_a = fv[..., 0] == a_b
+        s0_b = fv[..., 0] == b_b
+        s1_a = fv[..., 1] == a_b
+        s1_b = fv[..., 1] == b_b
+        s2_a = fv[..., 2] == a_b
+        s2_b = fv[..., 2] == b_b
+        contains_a = s0_a | s1_a | s2_a
+        contains_b = s0_b | s1_b | s2_b
+        # affected: face contains exactly one of {a, b} and slot is non-pad
+        affected = (contains_a ^ contains_b) & valid_f
+        if not affected.any():
+            continue
+
+        p0 = verts[fv[..., 0]]
+        p1 = verts[fv[..., 1]]
+        p2 = verts[fv[..., 2]]
+        opt_b = oc.view(Ec, 1, 3).expand(-1, 2 * max_deg, -1)
+        rep0 = (s0_a | s0_b).unsqueeze(-1)
+        rep1 = (s1_a | s1_b).unsqueeze(-1)
+        rep2 = (s2_a | s2_b).unsqueeze(-1)
+        p0n = torch.where(rep0, opt_b, p0)
+        p1n = torch.where(rep1, opt_b, p1)
+        p2n = torch.where(rep2, opt_b, p2)
+
+        e01 = p1n - p0n
+        e02 = p2n - p0n
+        e12 = p2n - p1n
+        two_area = torch.cross(e01, e02, dim=-1).norm(dim=-1)
+        edge_sum_sq = ((e01 * e01).sum(-1)
+                       + (e02 * e02).sum(-1)
+                       + (e12 * e12).sum(-1))
+        shape = (sqrt3_4 * 0.5 * two_area) / edge_sum_sq.clamp_min(1e-20)
+        term = 1.0 - shape.clamp(0.0, 1.0)
+        term = torch.where(affected, term, torch.zeros_like(term))
+        n_affected = affected.sum(dim=-1).clamp_min(1).to(term.dtype)
+        out[start:stop] = term.sum(dim=-1) / n_affected
+
+    return out
+
+
+def _quality_checks_fused(
+    verts: torch.Tensor,
+    faces: torch.Tensor,
+    edges: torch.Tensor,
+    opt: torch.Tensor,
+    vert_to_faces: torch.Tensor,
+    cos_threshold: float = 0.0,
+    want_flip: bool = True,
+    want_skinny: bool = True,
+    want_link: bool = False,
+    chunk_size: int = 100_000,
+) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
+    """Fused 1-ring checks (flip count / skinny / link) sharing one faces gather.
+    Returns (flip_count|None, skinny|None, link_safe|None)."""
+    E = edges.shape[0]
+    device = verts.device
+    flip_out = torch.zeros(E, dtype=torch.int32, device=device) if want_flip else None
+    skinny_out = torch.zeros(E, dtype=verts.dtype, device=device) if want_skinny else None
+    link_out = torch.ones(E, dtype=torch.bool, device=device) if want_link else None
+    if E == 0:
+        return flip_out, skinny_out, link_out
+
+    D = vert_to_faces.shape[1]
+    a_all = edges[:, 0]
+    b_all = edges[:, 1]
+    sqrt3_4 = 4.0 * math.sqrt(3.0)
+    need_geom = want_flip or want_skinny
+
+    for start in range(0, E, chunk_size):
+        stop = min(start + chunk_size, E)
+        Ec = stop - start
+        a = a_all[start:stop]
+        b = b_all[start:stop]
+
+        # shared gather of a's and b's incident faces (the expensive part)
+        fa = vert_to_faces[a]
+        fb = vert_to_faces[b]
+        all_f = torch.cat([fa, fb], dim=1)                   # (Ec, 2D)
+        valid_f = all_f >= 0
+        fv = faces[all_f.clamp(min=0)]                       # (Ec, 2D, 3)
+        a_b = a.view(Ec, 1)
+        b_b = b.view(Ec, 1)
+
+        if need_geom:
+            oc = opt[start:stop]
+            s0_a = fv[..., 0] == a_b
+            s0_b = fv[..., 0] == b_b
+            s1_a = fv[..., 1] == a_b
+            s1_b = fv[..., 1] == b_b
+            s2_a = fv[..., 2] == a_b
+            s2_b = fv[..., 2] == b_b
+            contains_a = s0_a | s1_a | s2_a
+            contains_b = s0_b | s1_b | s2_b
+            affected = (contains_a ^ contains_b) & valid_f
+            if affected.any():
+                p0 = verts[fv[..., 0]]
+                p1 = verts[fv[..., 1]]
+                p2 = verts[fv[..., 2]]
+                opt_b = oc.view(Ec, 1, 3).expand(-1, 2 * D, -1)
+                rep0 = (s0_a | s0_b).unsqueeze(-1)
+                rep1 = (s1_a | s1_b).unsqueeze(-1)
+                rep2 = (s2_a | s2_b).unsqueeze(-1)
+                p0n = torch.where(rep0, opt_b, p0)
+                p1n = torch.where(rep1, opt_b, p1)
+                p2n = torch.where(rep2, opt_b, p2)
+
+                # post-collapse normal (skinny's two_area == flip's ‖n_new‖)
+                e01 = p1n - p0n
+                e02 = p2n - p0n
+                n_new = torch.cross(e01, e02, dim=-1)
+                nlen_new = torch.norm(n_new, dim=-1)
+
+                if want_flip:
+                    n_old = torch.cross(p1 - p0, p2 - p0, dim=-1)
+                    nlen_old = torch.norm(n_old, dim=-1)
+                    denom = nlen_old * nlen_new
+                    safe = denom > 1e-20
+                    cos = torch.where(safe, (n_old * n_new).sum(dim=-1) / denom.clamp_min(1e-20),
+                                      torch.ones_like(denom))
+                    flip = (cos < cos_threshold) & affected & safe
+                    flip_out[start:stop] = flip.sum(dim=-1).to(torch.int32)
+
+                if want_skinny:
+                    e12 = p2n - p1n
+                    edge_sum_sq = ((e01 * e01).sum(-1) + (e02 * e02).sum(-1) + (e12 * e12).sum(-1))
+                    shape = (sqrt3_4 * 0.5 * nlen_new) / edge_sum_sq.clamp_min(1e-20)
+                    term = 1.0 - shape.clamp(0.0, 1.0)
+                    term = torch.where(affected, term, torch.zeros_like(term))
+                    n_affected = affected.sum(dim=-1).clamp_min(1).to(term.dtype)
+                    skinny_out[start:stop] = term.sum(dim=-1) / n_affected
+
+        if want_link:
+            # reuses fv / valid_f; matches _link_condition_mask
+            fa_ok = valid_f[:, :D]
+            fb_ok = valid_f[:, D:]
+            fav = fv[:, :D]
+            fbv = fv[:, D:]
+            an1 = torch.where(fav[..., 0] == a_b, fav[..., 1], fav[..., 0])
+            an2 = torch.where(fav[..., 2] == a_b, fav[..., 1], fav[..., 2])
+            bn1 = torch.where(fbv[..., 0] == b_b, fbv[..., 1], fbv[..., 0])
+            bn2 = torch.where(fbv[..., 2] == b_b, fbv[..., 1], fbv[..., 2])
+            na = torch.stack([an1, an2], dim=-1).reshape(Ec, 2 * D)
+            nb = torch.stack([bn1, bn2], dim=-1).reshape(Ec, 2 * D)
+            fa_okx = fa_ok.repeat_interleave(2, dim=1)
+            fb_okx = fb_ok.repeat_interleave(2, dim=1)
+            na[(na == a_b) | (na == b_b) | ~fa_okx] = -1
+            nb[(nb == a_b) | (nb == b_b) | ~fb_okx] = -1
+            in_b = (na[:, :, None] == nb[:, None, :]) & (na[:, :, None] >= 0)
+            na_common = torch.where(in_b.any(dim=2), na, torch.full_like(na, -1))
+            cs, _ = na_common.sort(dim=1)
+            count_common = ((cs[:, 1:] != cs[:, :-1]) & (cs[:, 1:] >= 0)).sum(dim=1) \
+                           + (cs[:, :1] >= 0).sum(dim=1)
+            count_faces = ((fav == b[:, None, None]).any(dim=2) & fa_ok).sum(dim=1)
+            link_out[start:stop] = count_common <= count_faces
+
+    return flip_out, skinny_out, link_out
+
+
+def _compute_vertex_normals(verts: torch.Tensor, faces: torch.Tensor, weld: bool = True) -> torch.Tensor:
+    """Area-weighted smooth vertex normals. `weld` averages face normals across vertices that
+    share a position (UV-seam duplicates from unwrapping) so both sides of a seam get one
+    identical normal — otherwise a visible shading seam appears in the exported GLB."""
+    if faces.numel() == 0:
+        return torch.zeros_like(verts)
+    faces_long = faces.to(torch.int64)
+    i0, i1, i2 = faces_long[:, 0], faces_long[:, 1], faces_long[:, 2]
+    v0, v1, v2 = verts[i0], verts[i1], verts[i2]
+    fn = torch.cross(v1 - v0, v2 - v0, dim=-1)
+    if weld and verts.shape[0]:
+        # Group coincident positions (quantized to ~1e-5 of the bbox) into one shared normal.
+        lo = verts.min(0).values
+        inv_tol = 1.0 / (float((verts.max(0).values - lo).max().clamp_min(1e-9)) * 1e-5)
+        q = ((verts - lo) * inv_tol).round().to(torch.int64)
+        _, group = torch.unique(q, dim=0, return_inverse=True)
+        acc = torch.zeros((int(group.max()) + 1, 3), dtype=verts.dtype, device=verts.device)
+        acc.scatter_add_(0, group[i0].unsqueeze(-1).expand_as(fn), fn)
+        acc.scatter_add_(0, group[i1].unsqueeze(-1).expand_as(fn), fn)
+        acc.scatter_add_(0, group[i2].unsqueeze(-1).expand_as(fn), fn)
+        vn = acc[group]
+    else:
+        vn = torch.zeros_like(verts)
+        vn.scatter_add_(0, i0.unsqueeze(-1).expand_as(fn), fn)
+        vn.scatter_add_(0, i1.unsqueeze(-1).expand_as(fn), fn)
+        vn.scatter_add_(0, i2.unsqueeze(-1).expand_as(fn), fn)
+    return torch.nn.functional.normalize(vn, p=2, dim=-1, eps=1e-6)
+
+
+# Public API
+
+@dataclass
+class CleanStats:
+    in_verts: int = 0
+    in_faces: int = 0
+    out_verts: int = 0
+    out_faces: int = 0
+    welded_verts: int = 0          # how many vertex IDs collapsed during welding
+    degenerate_faces: int = 0      # zero-area or repeated-index faces removed
+    duplicate_faces: int = 0       # same vertex-set removed
+    unused_verts: int = 0          # verts not in any face removed
+    components_dropped: int = 0    # disconnected components below threshold
+
+    def __str__(self):
+        return (f"clean: in={self.in_verts}v/{self.in_faces}f -> "
+                f"out={self.out_verts}v/{self.out_faces}f "
+                f"(welded {self.welded_verts}v, degen {self.degenerate_faces}f, "
+                f"dup {self.duplicate_faces}f, unused {self.unused_verts}v, "
+                f"comps {self.components_dropped})")
+
+
+def _weld_vertices(
+    verts: torch.Tensor, faces: torch.Tensor, epsilon,
+    colors: Optional[torch.Tensor] = None,
+    normals: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor], int]:
+    """Merge vertices closer than epsilon (L_inf grid), cluster-averaging attributes; returns (v, f, colors, normals, n_welded)."""
+    if verts.shape[0] == 0:
+        return verts, faces, colors, normals, 0
+    device = verts.device
+    scale = 1.0 / epsilon
+    bbox_min = verts.min(dim=0)[0]
+    q = ((verts - bbox_min) * scale).round().to(torch.int64)
+    bbox = (verts.max(dim=0)[0] - bbox_min)
+    extent = (bbox * scale).round().to(torch.int64) + 2
+    key = (q[:, 0] * extent[1] + q[:, 1]) * extent[2] + q[:, 2]  # pack 3D quantized pos to 1D key
+    unique_key, inv = torch.unique(key, return_inverse=True)
+    n_unique = unique_key.shape[0]
+    if n_unique == verts.shape[0]:
+        return verts, faces, colors, normals, 0
+    counts = torch.zeros(n_unique, dtype=verts.dtype, device=device)
+    counts.scatter_add_(0, inv, torch.ones(verts.shape[0], dtype=verts.dtype, device=device))
+    counts_div = counts.unsqueeze(-1).clamp_min(1.0)
+
+    new_verts = torch.zeros((n_unique, 3), dtype=verts.dtype, device=device)
+    new_verts.scatter_add_(0, inv.unsqueeze(-1).expand_as(verts), verts)
+    new_verts = new_verts / counts_div
+
+    new_colors = None
+    if colors is not None:
+        new_colors = torch.zeros((n_unique, colors.shape[1]), dtype=colors.dtype, device=device)
+        new_colors.scatter_add_(0, inv.unsqueeze(-1).expand_as(colors), colors)
+        new_colors = new_colors / counts_div.to(colors.dtype)
+
+    new_normals = None
+    if normals is not None:
+        new_normals = torch.zeros((n_unique, normals.shape[1]), dtype=normals.dtype, device=device)
+        new_normals.scatter_add_(0, inv.unsqueeze(-1).expand_as(normals), normals)
+        new_normals = torch.nn.functional.normalize(new_normals, p=2, dim=-1, eps=1e-6)
+
+    new_faces = inv[faces.long()] if faces.numel() > 0 else faces
+    return new_verts, new_faces, new_colors, new_normals, int(verts.shape[0] - n_unique)
+
+
+def _drop_degenerate_faces(
+    verts: torch.Tensor, faces: torch.Tensor,
+    min_area: float = 1e-14,
+) -> Tuple[torch.Tensor, int]:
+    """Drop degenerate-by-construction faces (repeated indices or zero-area); slivers go to _collapse_slivers."""
+    if faces.numel() == 0:
+        return faces, 0
+    idx_bad = (faces[:, 0] == faces[:, 1]) | (faces[:, 1] == faces[:, 2]) | (faces[:, 0] == faces[:, 2])
+    f_good = faces[~idx_bad]
+    v0 = verts[f_good[:, 0]]
+    v1 = verts[f_good[:, 1]]
+    v2 = verts[f_good[:, 2]]
+    e0 = v1 - v0
+    e2 = v0 - v2
+    area = 0.5 * torch.norm(torch.cross(e0, -e2, dim=-1), dim=-1)
+    bad = area < min_area
+    kept = f_good[~bad]
+    n_dropped = idx_bad.sum() + bad.sum()  # tensor-scalar; caller .item()s once
+    return kept, n_dropped
+
+
+def _collapse_slivers(
+    verts: torch.Tensor, faces: torch.Tensor,
+    min_angle_deg: float = 0.0,
+    max_aspect_ratio: float = 0.0,
+) -> Tuple[torch.Tensor, int]:
+    """Resolve sliver triangles by collapsing each sliver's shortest edge (no holes); returns (faces, n_collapsed)."""
+    if faces.numel() == 0 or (min_angle_deg <= 0 and max_aspect_ratio <= 0):
+        return faces, 0
+
+    fl = faces.long()
+    v0 = verts[fl[:, 0]]
+    v1 = verts[fl[:, 1]]
+    v2 = verts[fl[:, 2]]
+    e0 = v1 - v0
+    e1 = v2 - v1
+    e2 = v0 - v2
+    l0 = torch.norm(e0, dim=-1)
+    l1 = torch.norm(e1, dim=-1)
+    l2 = torch.norm(e2, dim=-1)
+    area = 0.5 * torch.norm(torch.cross(e0, -e2, dim=-1), dim=-1)
+
+    bad = torch.zeros(faces.shape[0], dtype=torch.bool, device=verts.device)
+    if max_aspect_ratio > 0:
+        max_edge = torch.maximum(torch.maximum(l0, l1), l2)
+        aspect = max_edge * max_edge / (2.0 * area + 1e-12)
+        bad = bad | (aspect > max_aspect_ratio)
+    if min_angle_deg > 0:
+        cos_a = (l1 * l1 + l2 * l2 - l0 * l0) / (2 * l1 * l2 + 1e-12)
+        cos_b = (l0 * l0 + l2 * l2 - l1 * l1) / (2 * l0 * l2 + 1e-12)
+        cos_c = (l0 * l0 + l1 * l1 - l2 * l2) / (2 * l0 * l1 + 1e-12)
+        cos_all = torch.stack([cos_a, cos_b, cos_c], dim=-1)
+        angles_deg = torch.acos(torch.clamp(cos_all, -1, 1)) * (180.0 / math.pi)
+        bad = bad | (angles_deg.min(dim=-1).values < min_angle_deg)
+
+    if not bad.any():
+        return faces, 0
+
+    # per sliver pick its shortest edge to collapse
+    edge_lens = torch.stack([l0, l1, l2], dim=-1)  # (F, 3)
+    shortest_slot = edge_lens.argmin(dim=-1)        # (F,) ∈ {0,1,2}
+
+    V = verts.shape[0]
+    # collapse higher-index endpoint into lower (min/max ordering avoids cycles)
+    merge_map = torch.arange(V, device=verts.device, dtype=torch.int64)
+    bad_idx = torch.nonzero(bad, as_tuple=True)[0]
+    for slot in range(3):
+        sel = bad_idx[shortest_slot[bad_idx] == slot]
+        if sel.numel() == 0:
+            continue
+        a = fl[sel, slot]
+        b = fl[sel, (slot + 1) % 3]
+        lo = torch.minimum(a, b)
+        hi = torch.maximum(a, b)
+        merge_map[hi] = lo  # last-write-wins on conflict
+
+    # path-compress until stable
+    for _ in range(10):
+        new_map = merge_map[merge_map]
+        if torch.equal(new_map, merge_map):
+            break
+        merge_map = new_map
+
+    new_faces = merge_map[fl]
+    nondeg = ((new_faces[:, 0] != new_faces[:, 1]) &
+              (new_faces[:, 1] != new_faces[:, 2]) &
+              (new_faces[:, 0] != new_faces[:, 2]))
+    new_faces = new_faces[nondeg].to(dtype=faces.dtype)
+    return new_faces, bad.sum()
+
+
+def _drop_duplicate_faces(faces: torch.Tensor, num_verts: int) -> Tuple[torch.Tensor, int]:
+    """Remove duplicate faces (same vertex set), keeping the first occurrence (winding-preserving)."""
+    if faces.shape[0] <= 1:
+        return faces, 0
+    key_sorted = torch.sort(faces, dim=1)[0]
+    P = num_verts + 1
+    packed = (key_sorted[:, 0].long() * P + key_sorted[:, 1].long()) * P + key_sorted[:, 2].long()
+    unique_packed, inv = torch.unique(packed, return_inverse=True)
+    if unique_packed.shape[0] == faces.shape[0]:
+        return faces, 0
+    # first-occurrence index per unique key
+    arange = torch.arange(packed.shape[0], device=packed.device)
+    first = torch.full((unique_packed.shape[0],), packed.shape[0],
+                       dtype=torch.int64, device=packed.device)
+    first.scatter_reduce_(0, inv, arange, reduce="amin", include_self=True)
+    kept = faces[first]
+    return kept, int(faces.shape[0] - kept.shape[0])
+
+
+def _drop_unused_verts(
+    verts: torch.Tensor, faces: torch.Tensor,
+    colors: Optional[torch.Tensor] = None,
+    normals: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor], int]:
+    """Remove vertices not referenced by any face; remap faces and filter attributes."""
+    if verts.shape[0] == 0 or faces.numel() == 0:
+        return verts, faces, colors, normals, 0
+    used = torch.zeros(verts.shape[0], dtype=torch.bool, device=verts.device)
+    used[faces[:, 0]] = True
+    used[faces[:, 1]] = True
+    used[faces[:, 2]] = True
+    # cumsum compact remap: 0..N-1 to used verts in order
+    remap = used.long().cumsum(0) - 1
+    new_verts = verts[used]
+    new_faces = remap[faces.long()]
+    new_colors = colors[used] if colors is not None else None
+    new_normals = normals[used] if normals is not None else None
+    n_dropped = verts.shape[0] - used.sum()
+    return new_verts, new_faces, new_colors, new_normals, n_dropped
+
+
+def _repair_nonmanifold_edges(
+    verts: torch.Tensor, faces: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """repair_non_manifold_edges: explode corners, re-merge only across manifold edges; returns (verts, faces, src)."""
+    if faces.numel() == 0:
+        return verts, faces
+    dev, vdt, fdt = verts.device, verts.dtype, faces.dtype
+    F = faces.detach().cpu().numpy().astype(_np.int64)
+    V = verts.detach().cpu().numpy()
+    nf = F.shape[0]
+    nv = V.shape[0]
+    corner_vert = F.reshape(-1)                      # (3F,) original vertex per corner
+
+    # per-face edges keyed by (vmin,vmax)
+    keys_l, ca_l, cb_l = [], [], []
+    for (i, j) in ((0, 1), (1, 2), (2, 0)):
+        va, vb = F[:, i], F[:, j]
+        ci = 3 * _np.arange(nf) + i
+        cj = 3 * _np.arange(nf) + j
+        amin = _np.where(va <= vb, ci, cj)           # corner of the smaller-id endpoint
+        amax = _np.where(va <= vb, cj, ci)
+        vmin = _np.minimum(va, vb).astype(_np.int64)
+        vmax = _np.maximum(va, vb).astype(_np.int64)
+        keys_l.append(vmin * (nv + 1) + vmax)
+        ca_l.append(amin)
+        cb_l.append(amax)
+    keys = _np.concatenate(keys_l)
+    ca = _np.concatenate(ca_l)
+    cb = _np.concatenate(cb_l)
+    order = _np.argsort(keys, kind="stable")
+    keys = keys[order]
+    ca = ca[order]
+    cb = cb[order]
+    uniq, start, cnt = _np.unique(keys, return_index=True, return_counts=True)
+    man = start[cnt == 2]                            # manifold edges (exactly 2 incident faces)
+    # union both endpoints' corners across each manifold edge
+    rows = _np.concatenate([ca[man], cb[man]])
+    cols = _np.concatenate([ca[man + 1], cb[man + 1]])
+
+    n = 3 * nf
+    g = coo_matrix((_np.ones(rows.shape[0], dtype=_np.int8), (rows, cols)), shape=(n, n))
+    _ncomp, labels = connected_components(g, directed=False)
+
+    new_faces = labels[3 * _np.arange(nf)[:, None] + _np.array([0, 1, 2])[None, :]]
+    nnv = int(labels.max()) + 1
+    # source original-vertex index per new vertex
+    src = _np.zeros(nnv, dtype=_np.int64)
+    src[labels] = corner_vert
+    new_verts = V[src]
+    src_t = torch.from_numpy(src).to(device=dev)
+    return (torch.from_numpy(new_verts).to(device=dev, dtype=vdt),
+            torch.from_numpy(new_faces.astype(_np.int64)).to(device=dev, dtype=fdt),
+            src_t)
+
+
+def _drop_small_components(
+    verts: torch.Tensor, faces: torch.Tensor, min_faces: int,
+    max_propagation_iters: int = 200,
+) -> Tuple[torch.Tensor, torch.Tensor, int]:
+    """Label-propagation connected components; drop components below min_faces."""
+    if faces.numel() == 0 or min_faces <= 1:
+        return verts, faces, 0
+    device = verts.device
+    V = verts.shape[0]
+    labels = torch.arange(V, device=device, dtype=torch.int64)
+    for _ in range(max_propagation_iters):
+        v0, v1, v2 = faces[:, 0], faces[:, 1], faces[:, 2]
+        face_min = torch.minimum(torch.minimum(labels[v0], labels[v1]), labels[v2])
+        new_labels = labels.clone()
+        new_labels.scatter_reduce_(0, v0, face_min, reduce="amin", include_self=True)
+        new_labels.scatter_reduce_(0, v1, face_min, reduce="amin", include_self=True)
+        new_labels.scatter_reduce_(0, v2, face_min, reduce="amin", include_self=True)
+        new_labels = new_labels[new_labels]  # path-compress
+        if torch.equal(new_labels, labels):
+            break
+        labels = new_labels
+    face_label = labels[faces[:, 0]]
+    unique_labels, counts = torch.unique(face_label, return_counts=True)
+    big_labels = unique_labels[counts >= min_faces]
+    if big_labels.shape[0] == unique_labels.shape[0]:
+        return verts, faces, 0
+    # safety: never drop every component (return the small mesh, not an empty one)
+    if big_labels.shape[0] == 0:
+        return verts, faces, 0
+    keep_face = torch.isin(face_label, big_labels)
+    kept_faces = faces[keep_face]
+    n_dropped = int(unique_labels.shape[0] - big_labels.shape[0])
+    return verts, kept_faces, n_dropped
+
+
+def clean_mesh(
+    verts: torch.Tensor, faces: torch.Tensor,
+    colors: Optional[torch.Tensor] = None,
+    normals: Optional[torch.Tensor] = None,
+    weld_epsilon: float = 0.0,
+    weld_epsilon_rel: float = 1e-6,
+    drop_degenerate: bool = True,
+    drop_duplicates: bool = True,
+    drop_unused: bool = True,
+    min_component_faces: int = 0,
+    min_angle_deg: float = 0.0,
+    max_aspect_ratio: float = 0.0,
+) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor], CleanStats]:
+    """Mesh hygiene pipeline; preserves per-vertex attributes through welding. Returns (v, f, colors, normals, stats)."""
+    stats = CleanStats(in_verts=verts.shape[0], in_faces=faces.shape[0])
+    v = verts
+    f = faces.long() if faces.numel() > 0 else faces
+    c = colors
+    n = normals
+
+    if weld_epsilon != 0.0 or weld_epsilon_rel > 0:
+        # eps stays a 0-d tensor (no sync)
+        if weld_epsilon > 0:
+            eps = torch.as_tensor(weld_epsilon, dtype=v.dtype, device=v.device)
+        else:
+            eps = torch.norm(v.max(dim=0)[0] - v.min(dim=0)[0]) * weld_epsilon_rel
+        v, f, c, n, n_welded = _weld_vertices(v, f, eps, c, n)
+        stats.welded_verts = n_welded
+
+    if drop_degenerate:
+        f_new, n_drop = _drop_degenerate_faces(v, f)
+        stats.degenerate_faces = n_drop
+        f = f_new
+        # slivers get collapse-merged instead of dropped (preserves topology)
+        if min_angle_deg > 0 or max_aspect_ratio > 0:
+            f_new, n_sliv = _collapse_slivers(
+                v, f, min_angle_deg=min_angle_deg, max_aspect_ratio=max_aspect_ratio,
+            )
+            stats.degenerate_faces += n_sliv
+            f = f_new
+
+    if drop_duplicates:
+        f_new, n_dup = _drop_duplicate_faces(f, v.shape[0])
+        stats.duplicate_faces = n_dup
+        f = f_new
+
+    if min_component_faces > 1:
+        v, f, n_comp = _drop_small_components(v, f, min_component_faces)
+        stats.components_dropped = n_comp
+
+    if drop_unused:
+        v, f, c, n, n_unused = _drop_unused_verts(v, f, c, n)
+        stats.unused_verts = n_unused
+
+    stats.out_verts = v.shape[0]
+    stats.out_faces = f.shape[0]
+    # materialize tensor-scalar counts to plain ints once at exit
+    for field in ("welded_verts", "degenerate_faces", "duplicate_faces",
+                  "unused_verts", "components_dropped"):
+        val = getattr(stats, field)
+        if torch.is_tensor(val):
+            setattr(stats, field, int(val.item()))
+    return v, f, c, n, stats
+
+
+@dataclass
+class SimplifyStats:
+    input_verts: int = 0
+    input_faces: int = 0
+    output_verts: int = 0
+    output_faces: int = 0
+    iterations: int = 0
+    total_collapses: int = 0
+
+
+def qem_simplify(
+    vertices: torch.Tensor,
+    faces: torch.Tensor,
+    target_faces: int,
+    colors: Optional[torch.Tensor] = None,
+    normals: Optional[torch.Tensor] = None,
+    max_edge_length: Optional[float] = None,
+    config: Optional[QEMConfig] = None,
+) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor], SimplifyStats]:
+    """Single-mesh QEM simplification. Returns (v, f, colors, normals, stats)."""
+    cfg = config or QEMConfig()
+
+    device = vertices.device
+    in_v_dtype = vertices.dtype
+    in_f_dtype = faces.dtype
+    in_c_dtype = colors.dtype if colors is not None else None
+    in_n_dtype = normals.dtype if normals is not None else None
+
+    verts = vertices.to(device=device, dtype=cfg.dtype, copy=True)
+    faces = faces.to(device=device, dtype=torch.int64).clone()
+    colors_w = colors.to(device=device, dtype=cfg.dtype, copy=True) if colors is not None else None
+    normals_w = normals.to(device=device, dtype=cfg.dtype, copy=True) if normals is not None else None
+
+    # preclean: weld + drop degenerate/duplicate, attributes cluster-averaged
+    if cfg.preclean:
+        verts, faces, colors_w, normals_w, _cs = clean_mesh(
+            verts, faces, colors_w, normals_w,
+            weld_epsilon_rel=cfg.preclean_weld_epsilon_rel,
+            min_component_faces=cfg.preclean_min_component_faces,
+        )
+
+    num_verts = verts.shape[0]
+    num_faces = faces.shape[0]
+
+    stats = SimplifyStats(input_verts=num_verts, input_faces=num_faces)
+
+    if num_faces <= target_faces or num_verts < 4:
+        stats.output_verts = num_verts
+        stats.output_faces = num_faces
+        return verts.to(in_v_dtype), faces.to(in_f_dtype), \
+               (colors_w.to(in_c_dtype) if colors_w is not None else None), \
+               (normals_w.to(in_n_dtype) if normals_w is not None else None), \
+               stats
+
+    v_alive = torch.ones(num_verts, dtype=torch.bool, device=device)
+    f_alive = torch.ones(num_faces, dtype=torch.bool, device=device)
+
+    Q = _build_quadrics(verts, faces, cfg)
+
+    bbox = verts.max(dim=0)[0] - verts.min(dim=0)[0]
+    mesh_scale = torch.norm(bbox)                  # 0-d tensor; never .item()'d
+    if max_edge_length is None or max_edge_length <= 0:
+        max_edge_length = mesh_scale * 2.0
+    else:
+        max_edge_length = torch.as_tensor(max_edge_length, dtype=cfg.dtype, device=device)
+    # tiny-bbox guard (tensor-side, no sync)
+    max_edge_length = torch.where(
+        max_edge_length < 1e-6,
+        torch.ones((), dtype=max_edge_length.dtype, device=device),
+        max_edge_length,
+    )
+
+    stabilizer = mesh_scale * mesh_scale * cfg.stabilizer_scale
+    max_edge_length_sq = max_edge_length * max_edge_length
+    mesh_scale_sq = mesh_scale * mesh_scale
+
+    # threshold scaled by mesh_scale² so the 1e-8 start is scale-robust
+    thresh = float(cfg.threshold_start) * float(mesh_scale_sq) if cfg.threshold_driver else 0.0
+
+    # pre-allocated merge_map, reused each iter
+    merge_map = torch.arange(num_verts, device=device)
+
+    # py_n_faces: Python-int face count (no host sync in hot loop), re-synced at compaction
+    py_n_faces = num_faces
+
+    iteration = 0
+    total_collapses = 0
+
+    # progress bars (tqdm + comfy ProgressBar)
+    _start_faces = num_faces
+    _prog_total = max(1, _start_faces - int(target_faces))
+    _qtq = _tqdm(total=100, desc="QEM simplify", leave=False)
+    _qpbar = _comfy_utils.ProgressBar(100)
+
+    def _qreport():
+        pct = min(100, max(0, int(100 * (_start_faces - py_n_faces) / _prog_total)))
+        _qtq.n = pct
+        _qtq.refresh()
+        _qpbar.update_absolute(pct, 100)
+
+    while True:
+        if py_n_faces <= target_faces:
+            break
+        _qreport()
+
+        alive_f = torch.nonzero(f_alive, as_tuple=True)[0]
+        if alive_f.numel() == 0:
+            break
+
+        active_faces = faces[alive_f]
+
+        # memoryless QEM: rebuild Q from current geometry each iter
+        if cfg.threshold_driver and cfg.memoryless_qem and iteration > 0:
+            Q = _build_quadrics(verts, active_faces, cfg)
+
+        Q_for_iter = Q
+        # edge extraction: pack (min*V + max) so unique dedups in one pass
+        af_roll = torch.roll(active_faces, shifts=-1, dims=1)
+        mn = torch.minimum(active_faces, af_roll)
+        mx = torch.maximum(active_faces, af_roll)
+        packed = torch.add(mx, mn, alpha=num_verts).flatten()
+        packed = torch.unique(packed)
+        edges_orig = torch.stack([packed // num_verts, packed % num_verts], dim=1)
+
+        # filter by edge length
+        pab = verts[edges_orig]                              # (E, 2, 3)
+        el = torch.norm(pab[:, 1] - pab[:, 0], dim=-1)
+        edges_orig = edges_orig[el < max_edge_length]
+        if edges_orig.shape[0] == 0:
+            break
+
+        # sampling cap
+        n_edges_total = edges_orig.shape[0]
+        if n_edges_total > cfg.sampling_cap:
+            perm = torch.randperm(n_edges_total, device=device)[: cfg.sampling_cap]
+            edges_orig = edges_orig[perm]
+
+        # boundary mask only needed for non-qem placement
+        if cfg.placement_mode != "qem":
+            vib = _vert_is_boundary_mask(active_faces, num_verts)
+        else:
+            vib = None
+        optimal, err, valid = _edge_errors(
+            verts, Q_for_iter, edges_orig, stabilizer, max_edge_length_sq,
+            mesh_scale_sq, cfg, vert_is_boundary=vib,
+        )
+        valid_idx = torch.nonzero(valid, as_tuple=True)[0]
+        edges_orig = edges_orig[valid_idx]
+        optimal = optimal[valid_idx]
+        err = err[valid_idx]
+
+        faces_to_remove = py_n_faces - target_faces
+        n_faces_round_start = py_n_faces
+        # ~2 faces removed per collapse, so cap the round at faces_to_remove//2
+        cap_to_target = max(1, faces_to_remove // 2)
+
+        if cfg.threshold_driver:
+            # band = cost <= thresh (×10 until non-empty), quality-check, then collapse a disjoint set
+            cand = err <= thresh
+            esc = 0
+            while not bool(cand.any()) and esc < 50:
+                thresh *= 10.0
+                cand = err <= thresh
+                esc += 1
+            cand_idx = torch.nonzero(cand, as_tuple=True)[0]
+            ce = edges_orig[cand_idx]
+            copt = optimal[cand_idx]
+            cerr = err[cand_idx].clone()
+            need_flip = cfg.flip_reject_hard
+            if ((need_flip or cfg.skinny_weight > 0 or cfg.enforce_link_condition)
+                    and ce.shape[0] > 0):
+                afq = faces[alive_f]
+                v_to_f = _build_vert_to_faces_pad(afq, num_verts, cfg.flip_check_max_degree)
+                # link + flip + skinny share one fused 1-ring pass
+                fc, sk, link_safe = _quality_checks_fused(
+                    verts, afq, ce, copt, v_to_f, cos_threshold=cfg.flip_cos_threshold,
+                    want_flip=need_flip, want_skinny=(cfg.skinny_weight > 0),
+                    want_link=cfg.enforce_link_condition)
+                if link_safe is not None:
+                    cerr[~link_safe] = float("inf")
+                if fc is not None:
+                    cerr = torch.where(fc > 0, torch.full_like(cerr, float("inf")), cerr)
+                if sk is not None:
+                    el_sq = (verts[ce[:, 1]] - verts[ce[:, 0]]).pow(2).sum(dim=-1)
+                    cerr = cerr + cfg.skinny_weight * sk * el_sq
+                del v_to_f, afq
+                # penalties may push edges above thresh — re-gate the band
+                keep = cerr <= thresh
+                ce = ce[keep]
+                copt = copt[keep]
+                cerr = cerr[keep]
+            edges_orig = ce
+            optimal = copt
+            sel = _greedy_matching(ce, cerr, v_alive, cap_to_target)
+            if sel.numel() == 0:
+                # band fully rejected → raise thresh and retry
+                thresh *= 10.0
+                iteration += 1
+                if iteration >= cfg.max_iterations:
+                    break
+                continue
+        else:
+            max_collapses = min(
+                cfg.max_collapses_ceiling,
+                max(cfg.max_collapses_floor, int(faces_to_remove * cfg.max_collapses_fraction)),
+            )
+            if cfg.max_collapses_relative_cap > 0:
+                # cap to a fraction of current mesh size (anti cascade-overshoot)
+                rel_cap = max(1, int(py_n_faces * cfg.max_collapses_relative_cap))
+                max_collapses = min(max_collapses, rel_cap)
+            max_collapses = min(max_collapses, cap_to_target)
+
+            # soft quality penalties on top-K: flip + skinny, sharing one v_to_f build
+            need_flip = cfg.flip_reject_hard
+            need_quality = ((need_flip or cfg.skinny_weight > 0 or cfg.enforce_link_condition)
+                            and edges_orig.shape[0] > 0)
+            if need_quality:
+                n_check = min(edges_orig.shape[0],
+                              max(1, cfg.quality_topk_multiplier * max_collapses))
+                if n_check < edges_orig.shape[0]:
+                    topk = torch.topk(err, n_check, largest=False).indices
+                else:
+                    topk = torch.arange(edges_orig.shape[0], device=device)
+                active_for_quality = faces[alive_f]
+                v_to_f = _build_vert_to_faces_pad(active_for_quality, num_verts,
+                                                   cfg.flip_check_max_degree)
+                err = err.clone()
+                if cfg.enforce_link_condition:
+                    # reject link-condition violations on ALL candidate edges, not just top-K
+                    link_safe = _link_condition_mask(active_for_quality, edges_orig, v_to_f)
+                    err[~link_safe] = float("inf")
+                e_tk = edges_orig[topk]
+                o_tk = optimal[topk]
+                _do_flip = need_flip
+                _do_skinny = cfg.skinny_weight > 0
+                if _do_flip and _do_skinny:
+                    flip_count, skinny, _ = _quality_checks_fused(
+                        verts, active_for_quality, e_tk, o_tk, v_to_f,
+                        cos_threshold=cfg.flip_cos_threshold, want_link=False)
+                elif _do_flip:
+                    flip_count = _normal_flip_mask(
+                        verts, active_for_quality, e_tk, o_tk, v_to_f,
+                        cos_threshold=cfg.flip_cos_threshold, return_count=True)
+                    skinny = None
+                else:
+                    skinny = _skinny_penalty(verts, active_for_quality, e_tk, o_tk, v_to_f)
+                    flip_count = None
+                if _do_flip:
+                    # hard reject: any flipping top-K edge → +inf
+                    flips = flip_count > 0
+                    if flips.any():
+                        err[topk] = torch.where(
+                            flips, torch.full_like(err[topk], float("inf")),
+                            err[topk],
+                        )
+                if _do_skinny:
+                    # skinny_cost * len² (match QEM's length² scaling)
+                    elen_sq = (verts[e_tk[:, 1]] - verts[e_tk[:, 0]]).pow(2).sum(dim=-1)
+                    err[topk] = torch.add(err[topk], skinny * elen_sq,
+                                          alpha=cfg.skinny_weight)
+                del v_to_f, active_for_quality
+
+            sel = _greedy_matching(edges_orig, err, v_alive, max_collapses)
+
+            if sel.numel() == 0:
+                break
+
+        ed_sel = edges_orig[sel]
+        v_a = ed_sel[:, 0]
+        v_b = ed_sel[:, 1]
+        new_pos = optimal[sel]
+
+        # interpolate attributes by new_pos's position along [pa, pb]
+        if colors_w is not None or normals_w is not None:
+            pa_sel = verts[v_a]
+            pb_sel = verts[v_b]
+            edge_vec = pb_sel - pa_sel
+            edge_len_sq = (edge_vec * edge_vec).sum(dim=-1) + 1e-20
+            t = ((new_pos - pa_sel) * edge_vec).sum(dim=-1) / edge_len_sq
+            t = t.clamp(0.0, 1.0).unsqueeze(-1)
+            if colors_w is not None:
+                colors_w[v_a] = torch.lerp(colors_w[v_a], colors_w[v_b], t)
+            if normals_w is not None:
+                normals_w[v_a] = torch.lerp(normals_w[v_a], normals_w[v_b], t)
+
+        # apply collapse
+        verts[v_a] = new_pos
+        v_alive[v_b] = False
+        if not (cfg.threshold_driver and cfg.memoryless_qem):
+            Q[v_a] += Q[v_b]
+
+        merge_map[v_b] = v_a
+        faces = merge_map[faces]
+        merge_map[v_b] = v_b  # restore identity for next iter
+
+        bad = (faces[:, 0] == faces[:, 1]) | (faces[:, 1] == faces[:, 2]) | (faces[:, 2] == faces[:, 0])
+        f_alive.masked_fill_(bad, False)
+        py_n_faces -= 2 * v_a.numel()  # ~2 faces/collapse estimate; re-synced at compaction
+
+        # schedule: round removed < 1% → raise thresh ×10
+        if cfg.threshold_driver:
+            removed = n_faces_round_start - py_n_faces
+            if removed < 0.01 * n_faces_round_start:
+                thresh *= 10.0
+
+        total_collapses += int(v_a.numel())
+        iteration += 1
+
+        # periodic compaction (resyncs py_n_faces exactly)
+        if iteration % cfg.compaction_period == 0:
+            alive_frac = py_n_faces / max(1, num_faces)
+            if alive_frac < cfg.compaction_threshold:
+                faces = faces[f_alive]
+                num_faces = faces.shape[0]
+                f_alive = torch.ones(num_faces, dtype=torch.bool, device=device)
+                py_n_faces = num_faces
+
+        if iteration >= cfg.max_iterations:
+            break
+
+    _qreport()
+    _qtq.close()
+
+    # finalize: compact verts and faces
+    final_v = verts[v_alive]
+    final_c = colors_w[v_alive] if colors_w is not None else None
+    final_n = normals_w[v_alive] if normals_w is not None else None
+
+    remap = torch.full((num_verts,), -1, dtype=torch.int64, device=device)
+    remap[v_alive] = v_alive.long().cumsum(0)[v_alive] - 1  # compact remap, no sync
+
+    final_f_raw = faces[f_alive]
+    alive_mask = v_alive[final_f_raw].all(dim=1)
+    final_f_raw = final_f_raw[alive_mask]
+    final_f = remap[final_f_raw]
+    valid_faces = (final_f >= 0).all(dim=1)
+    final_f = final_f[valid_faces]
+
+    # drop degenerate faces (two indices equal)
+    if final_f.numel() > 0:
+        nondeg = (final_f[:, 0] != final_f[:, 1]) & (final_f[:, 1] != final_f[:, 2]) & (final_f[:, 0] != final_f[:, 2])
+        final_f = final_f[nondeg]
+
+    # dedup duplicate faces, winding-preserving
+    if final_f.numel() > 0:
+        key = torch.sort(final_f, dim=1)[0]
+        packed = (key[:, 0].long() * (final_v.shape[0] + 1) + key[:, 1].long()) \
+                 * (final_v.shape[0] + 1) + key[:, 2].long()
+        unique_packed, inv = torch.unique(packed, return_inverse=True)
+        arange = torch.arange(packed.shape[0], device=packed.device)
+        first = torch.full((unique_packed.shape[0],), packed.shape[0],
+                            dtype=torch.int64, device=packed.device)
+        first.scatter_reduce_(0, inv, arange, reduce="amin", include_self=True)
+        final_f = final_f[first]
+
+    # split back fused surface sheets (after dedup, before pruning)
+    if cfg.repair_nonmanifold and final_f.numel() > 0:
+        final_v, final_f, _src = _repair_nonmanifold_edges(final_v, final_f)
+        if final_c is not None:
+            final_c = final_c[_src]
+        if final_n is not None:
+            final_n = final_n[_src]
+
+    # post-clean: drop slivers, tiny components, unused verts
+    if cfg.postclean and final_f.numel() > 0:
+        comp_threshold = cfg.postclean_min_component_faces
+        final_v, final_f, final_c, final_n, _ps = clean_mesh(
+            final_v, final_f, final_c, final_n,
+            weld_epsilon=0.0, weld_epsilon_rel=0.0,  # already welded
+            drop_degenerate=True,
+            drop_duplicates=False,                   # already done above
+            drop_unused=True,
+            min_component_faces=comp_threshold,
+            min_angle_deg=cfg.postclean_min_angle_deg,
+            max_aspect_ratio=cfg.postclean_max_aspect_ratio,
+        )
+
+    # post-simplify normals
+    if cfg.recompute_normals_post and final_f.numel() > 0:
+        final_n = _compute_vertex_normals(final_v, final_f)
+    elif final_n is not None and final_f.numel() > 0:
+        # keep supplied normals; flip face winding where it disagrees
+        v0 = final_v[final_f[:, 0]]
+        v1 = final_v[final_f[:, 1]]
+        v2 = final_v[final_f[:, 2]]
+        fn = torch.cross(v1 - v0, v2 - v0, dim=-1)
+        ref = (final_n[final_f[:, 0]] + final_n[final_f[:, 1]]
+               + final_n[final_f[:, 2]]) / 3.0
+        wrong = (fn * ref).sum(dim=-1) < 0
+        final_f[wrong] = final_f[wrong][:, [0, 2, 1]]
+
+    stats.iterations = iteration
+    stats.total_collapses = total_collapses
+    stats.output_verts = final_v.shape[0]
+    stats.output_faces = final_f.shape[0]
+
+    return (
+        final_v.to(in_v_dtype),
+        final_f.to(in_f_dtype),
+        final_c.to(in_c_dtype) if final_c is not None else None,
+        final_n.to(in_n_dtype) if (final_n is not None and in_n_dtype is not None) else final_n,
+        stats,
+    )
+
+
+def qem_decimate_simplify(
+    vertices: torch.Tensor,
+    faces: torch.Tensor,
+    target: int,
+    colors: Optional[torch.Tensor] = None,
+    normals: Optional[torch.Tensor] = None,
+    max_edge_length: Optional[float] = None,
+    config: Optional[QEMConfig] = None,
+):
+    """Batched wrapper. Accepts (V,3)/(F,3) or (B,V,3)/(B,F,3)."""
+    if vertices.ndim == 3:
+        out_v, out_f, out_c, out_n, out_s = [], [], [], [], []
+        for i in range(vertices.shape[0]):
+            c_in = colors[i] if colors is not None else None
+            n_in = normals[i] if normals is not None else None
+            v, f, c, n, s = qem_simplify(vertices[i], faces[i], target, c_in, n_in, max_edge_length, config)
+            out_v.append(v)
+            out_f.append(f)
+            out_s.append(s)
+            if c is not None:
+                out_c.append(c)
+            if n is not None:
+                out_n.append(n)
+        return (out_v, out_f,
+                out_c if out_c else None,
+                out_n if out_n else None,
+                out_s)
+    return qem_simplify(vertices, faces, target, colors, normals, max_edge_length, config)
+
+
+def qem_cluster_decimate(
+    vertices: torch.Tensor, faces: torch.Tensor,
+    target_verts: int = 1_000_000,
+    colors: Optional[torch.Tensor] = None,
+    face_chunk: int = 4_000_000,
+) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+    """Vertex-cluster decimation (Rossignac-Borrel): grid-bin/average verts, remap faces,
+    drop degenerate/duplicate. Fast O(V+F) prepass for huge meshes. Returns (verts, faces, colors)."""
+    if vertices.shape[0] == 0 or faces.shape[0] == 0:
+        return vertices, faces, colors
+
+    device = vertices.device
+    bbox = vertices.max(dim=0)[0] - vertices.min(dim=0)[0]
+    bbox_min = vertices.min(dim=0)[0]
+    # cell size so the bbox holds ~3× target_verts cells (surface occupancy ~1/3)
+    cell_count_target = max(target_verts * 3, 1000)
+    extent_max = float(bbox.max().item())
+    cells_per_axis = (cell_count_target ** (1 / 3))
+    cell_size = extent_max / max(1.0, cells_per_axis)
+    scale = 1.0 / max(cell_size, 1e-20)
+
+    q = ((vertices - bbox_min) * scale).floor().to(torch.int64)
+    extent = (bbox * scale).floor().to(torch.int64) + 2
+    Wy = extent[1]
+    Wz = extent[2]
+    key = (q[:, 0] * Wy + q[:, 1]) * Wz + q[:, 2]
+
+    unique_key, inv = torch.unique(key, return_inverse=True)
+    n_unique = unique_key.shape[0]
+    counts = torch.zeros(n_unique, dtype=vertices.dtype, device=device)
+    counts.scatter_add_(0, inv, torch.ones(vertices.shape[0], dtype=vertices.dtype, device=device))
+    counts_div = counts.unsqueeze(-1).clamp_min(1.0)
+
+    new_verts = torch.zeros((n_unique, 3), dtype=vertices.dtype, device=device)
+    new_verts.scatter_add_(0, inv.unsqueeze(-1).expand_as(vertices), vertices)
+    new_verts = new_verts / counts_div
+
+    new_colors = None
+    if colors is not None:
+        new_colors = torch.zeros((n_unique, colors.shape[1]), dtype=colors.dtype, device=device)
+        new_colors.scatter_add_(0, inv.unsqueeze(-1).expand_as(colors), colors)
+        new_colors = new_colors / counts_div.to(colors.dtype)
+
+    # remap faces in chunks (face tensor can be huge), drop degenerates per chunk
+    out_chunks = []
+    F = faces.shape[0]
+    for fs in range(0, F, face_chunk):
+        fe = min(fs + face_chunk, F)
+        cf = inv[faces[fs:fe].long()]
+        nondeg = ((cf[:, 0] != cf[:, 1]) & (cf[:, 1] != cf[:, 2]) & (cf[:, 0] != cf[:, 2]))
+        if nondeg.any():
+            out_chunks.append(cf[nondeg])
+    if out_chunks:
+        new_faces = torch.cat(out_chunks, dim=0)
+    else:
+        new_faces = torch.empty((0, 3), dtype=faces.dtype, device=device)
+
+    # drop duplicate faces (same vertex set after clustering)
+    if new_faces.numel() > 0:
+        key_sorted = torch.sort(new_faces, dim=1)[0]
+        P = n_unique + 1
+        packed = (key_sorted[:, 0].long() * P + key_sorted[:, 1].long()) * P + key_sorted[:, 2].long()
+        _, first = torch.unique(packed, return_inverse=True)
+        arange = torch.arange(packed.shape[0], device=device, dtype=torch.int64)
+        first_idx = torch.full((int(first.max().item()) + 1,), packed.shape[0],
+                               dtype=torch.int64, device=device)
+        first_idx.scatter_reduce_(0, first, arange, reduce="amin", include_self=True)
+        new_faces = new_faces[first_idx]
+
+    return new_verts.to(vertices.dtype), new_faces.to(faces.dtype), new_colors
diff --git a/comfy_extras/mesh3d/postprocess/remesh.py b/comfy_extras/mesh3d/postprocess/remesh.py
new file mode 100644
index 0000000000000000000000000000000000000000..05141c36436ea62e664e2980a0136a0223ff20e3
--- /dev/null
+++ b/comfy_extras/mesh3d/postprocess/remesh.py
@@ -0,0 +1,1147 @@
+"""Narrow-band Dual Contouring remeshing.
+
+Re-extracts a mesh from a sparse narrow-band voxel grid around the input
+surface (pure-PyTorch approximation of CuMesh's remesh_narrow_band_dc).
+Coarse-to-fine voxelise the band, sample SDF/UDF at voxel corners, dual
+contour (optionally QEF / Manifold DC), then optionally project back,
+filter components, fix poles, smooth, and interpolate vertex colors.
+"""
+from __future__ import annotations
+
+import functools
+import math
+from typing import Optional, Tuple
+
+import numpy as np
+import torch
+import scipy.spatial
+import comfy.utils
+from tqdm import tqdm as _tqdm
+from comfy.model_management import throw_exception_if_processing_interrupted
+
+from .qem_decimate import _sorted_edge_halfedges
+
+
+# Point-to-triangle distance (exact, vectorised)
+
+def _point_tri_closest(points: torch.Tensor, tris: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Exact closest point + squared distance per (point, triangle) pair; points (N,3), tris (N,3,3)."""
+    a = tris[:, 0]
+    b = tris[:, 1]
+    c = tris[:, 2]
+    ab = b - a
+    ac = c - a
+    ap = points - a
+
+    d1 = (ab * ap).sum(-1)
+    d2 = (ac * ap).sum(-1)
+
+    region_A = (d1 <= 0) & (d2 <= 0)
+
+    bp = points - b
+    d3 = (ab * bp).sum(-1)
+    d4 = (ac * bp).sum(-1)
+    region_B = (d3 >= 0) & (d4 <= d3)
+
+    cp = points - c
+    d5 = (ab * cp).sum(-1)
+    d6 = (ac * cp).sum(-1)
+    region_C = (d6 >= 0) & (d5 <= d6)
+
+    # Edge AB
+    vc = d1 * d4 - d3 * d2
+    region_AB = (vc <= 0) & (d1 >= 0) & (d3 <= 0)
+    v_ab = d1 / (d1 - d3 + 1e-20)
+    closest_AB = a + v_ab.unsqueeze(-1) * ab
+
+    # Edge AC
+    vb = d5 * d2 - d1 * d6
+    region_AC = (vb <= 0) & (d2 >= 0) & (d6 <= 0)
+    v_ac = d2 / (d2 - d6 + 1e-20)
+    closest_AC = a + v_ac.unsqueeze(-1) * ac
+
+    # Edge BC
+    va = d3 * d6 - d5 * d4
+    region_BC = (va <= 0) & ((d4 - d3) >= 0) & ((d5 - d6) >= 0)
+    v_bc = (d4 - d3) / ((d4 - d3) + (d5 - d6) + 1e-20)
+    closest_BC = b + v_bc.unsqueeze(-1) * (c - b)
+
+    # Face interior (barycentric)
+    denom = va + vb + vc + 1e-20
+    v_face = vb / denom
+    w_face = vc / denom
+    closest_face = a + v_face.unsqueeze(-1) * ab + w_face.unsqueeze(-1) * ac
+
+    # Combine by mask via in-place where (out= aliases input, no per-step alloc)
+    closest = closest_face                                         # fresh; safe to mutate
+    torch.where(region_BC.unsqueeze(-1), closest_BC, closest, out=closest)
+    torch.where(region_AC.unsqueeze(-1), closest_AC, closest, out=closest)
+    torch.where(region_AB.unsqueeze(-1), closest_AB, closest, out=closest)
+    torch.where(region_C .unsqueeze(-1), c,          closest, out=closest)
+    torch.where(region_B .unsqueeze(-1), b,          closest, out=closest)
+    torch.where(region_A .unsqueeze(-1), a,          closest, out=closest)
+
+    diff = points - closest
+    return closest, (diff * diff).sum(-1)
+
+
+def _build_centroid_tree(tri_verts: torch.Tensor):
+    """scipy cKDTree over triangle centroids; build once and reuse across _udf_exact calls.
+    balanced_tree/compact_nodes off: ~2.4x faster build (and faster queries on near-uniform
+    centroid clouds) with identical exact-kNN results."""
+    return scipy.spatial.cKDTree(tri_verts.mean(dim=1).detach().cpu().numpy(),
+                                 balanced_tree=False, compact_nodes=False)
+
+
+def _udf_exact(query_points: torch.Tensor, tri_verts: torch.Tensor,
+               k: int = 8, chunk: int = 262144, tree=None):
+    """Exact UDF (no max_dist cap) via centroid kNN; returns (dist [N], closest [N,3], tri_idx [N]). Pass prebuilt `tree` to skip rebuild.
+
+    k=8 nearest centroids before the exact point-triangle test: on dense meshes the true
+    closest triangle is essentially always within the first few neighbours. Measured vs k=16:
+    bit-identical topology, ~0.003-voxel RMS sub-voxel drift, ~15% faster overall."""
+    device = query_points.device
+    F = tri_verts.shape[0]
+    kq = int(min(k, F))
+    if tree is None:
+        tree = _build_centroid_tree(tri_verts)
+    _, cand = tree.query(query_points.detach().cpu().numpy(), k=kq, workers=-1)
+    if cand.ndim == 1:
+        cand = cand[:, None]
+    cand = np.ascontiguousarray(cand)
+
+    N = query_points.shape[0]
+    out_d = torch.empty(N, device=device, dtype=query_points.dtype)
+    out_c = torch.empty(N, 3, device=device, dtype=query_points.dtype)
+    out_t = torch.empty(N, dtype=torch.long, device=device)
+    for s in range(0, N, chunk):
+        e = min(s + chunk, N)
+        n = e - s
+        ci = torch.from_numpy(cand[s:e]).to(device).long()
+        tri = tri_verts[ci].reshape(n * kq, 3, 3)
+        P = query_points[s:e][:, None, :].expand(-1, kq, -1).reshape(n * kq, 3)
+        closest, d2 = _point_tri_closest(P, tri)
+        d2 = d2.reshape(n, kq)
+        closest = closest.reshape(n, kq, 3)
+        best = d2.argmin(dim=1)
+        ar = torch.arange(n, device=device)
+        out_d[s:e] = d2[ar, best].sqrt()
+        out_c[s:e] = closest[ar, best]
+        out_t[s:e] = ci[ar, best]
+    return out_d, out_c, out_t
+
+
+# UDF query via spatial hash on triangle AABBs
+
+def _build_tri_spatial_hash(centroids: torch.Tensor, tri_radii: torch.Tensor,
+                            cell_size: torch.Tensor):
+    """Bucket triangles into `cell_size` cells (each tri into every cell its AABB touches); returns hash tuple."""
+    device = centroids.device
+    aabb_lo = (centroids - tri_radii.unsqueeze(-1))
+    aabb_hi = (centroids + tri_radii.unsqueeze(-1))
+    origin = aabb_lo.min(0)[0]
+    extent = aabb_hi.max(0)[0] - origin
+    dims = (extent / cell_size).long() + 2
+
+    cell_lo = ((aabb_lo - origin) / cell_size).long().clamp(min=0)
+    cell_hi = ((aabb_hi - origin) / cell_size).long()
+    cell_hi = torch.minimum(cell_hi, dims - 1)
+
+    # Cap span at 3 cells/axis to bound memory
+    spans = (cell_hi - cell_lo + 1).clamp(max=3)
+    n_per_tri = spans.prod(dim=-1)
+    total = int(n_per_tri.sum().item())
+
+    # Per-insertion local offset within each tri's cell box
+    rep = torch.repeat_interleave(torch.arange(centroids.shape[0], device=device), n_per_tri)
+    cum = torch.cat([torch.zeros(1, device=device, dtype=n_per_tri.dtype),
+                     n_per_tri.cumsum(0)[:-1]])
+    local = torch.arange(total, device=device) - cum[rep]
+    sx = spans[rep, 0]
+    sy = spans[rep, 1]
+
+    lx = local % sx
+    ly = (local // sx) % sy
+    lz = local // (sx * sy)
+    cx = cell_lo[rep, 0] + lx
+    cy = cell_lo[rep, 1] + ly
+    cz = cell_lo[rep, 2] + lz
+    keys = (cx * dims[1] + cy) * dims[2] + cz
+
+    sort_idx = keys.argsort()
+    sorted_keys = keys[sort_idx]
+    tri_per_cell = rep[sort_idx]
+
+    unique_keys, counts = torch.unique_consecutive(sorted_keys, return_counts=True)
+    cell_starts = torch.cat([torch.zeros(1, dtype=counts.dtype, device=device),
+                             counts.cumsum(0)])
+    return origin, dims, unique_keys, tri_per_cell, cell_starts, centroids, tri_radii
+
+
+def _udf_query(query_points: torch.Tensor,
+               tri_verts: torch.Tensor,
+               hash_data,
+               cell_size: torch.Tensor,
+               max_dist: float,
+               chunk_max: int = 4096,
+               return_closest: bool = False,
+               return_tri_idx: bool = False):
+    """Capped UDF to nearest triangle (<= max_dist), optionally with closest point and/or tri index; chunk size is adaptive to hash density."""
+    origin, dims, unique_keys, tri_per_cell, cell_starts, tri_centroids, tri_radii = hash_data
+    device = query_points.device
+    Q = query_points.shape[0]
+    # Adaptive chunk: bound per-chunk candidate-gather memory by hash density
+    avg_per_cell = tri_per_cell.numel() / max(1, unique_keys.numel())
+    est_cands_per_query = max(1.0, avg_per_cell * 27)
+    chunk = max(256, min(chunk_max, int(50_000_000 / est_cands_per_query)))
+    out_d2 = torch.full((Q,), float(max_dist) ** 2, dtype=query_points.dtype, device=device)
+    # Default closest_pt = query_pt itself, so a missed query's lerp is a no-op
+    out_closest = (query_points.clone() if return_closest else None)
+    out_tri = (torch.full((Q,), -1, dtype=torch.long, device=device)
+               if return_tri_idx else None)
+
+    rng = torch.tensor([-1, 0, 1], device=device, dtype=torch.long)
+    offs = torch.stack(torch.meshgrid(rng, rng, rng, indexing="ij"), dim=-1).reshape(-1, 3)  # (27, 3)
+
+    for cs in range(0, Q, chunk):
+        ce = min(cs + chunk, Q)
+        qp = query_points[cs:ce]
+        q_cell = ((qp - origin) / cell_size).long()
+        # Look up 27 neighbour cells per query
+        n_cell = q_cell.unsqueeze(1) + offs.unsqueeze(0)             # (q, 27, 3)
+        n_valid = ((n_cell >= 0) & (n_cell < dims)).all(-1)
+        n_key = (n_cell[..., 0] * dims[1] + n_cell[..., 1]) * dims[2] + n_cell[..., 2]
+        flat_key = n_key.reshape(-1).contiguous()
+        ins = torch.searchsorted(unique_keys, flat_key)
+        ins_c = ins.clamp(max=unique_keys.numel() - 1)
+        found = (ins < unique_keys.numel()) & (unique_keys[ins_c] == flat_key) & n_valid.reshape(-1)
+        cell_idx = torch.where(found, ins_c, torch.zeros_like(ins_c))
+        c_starts = cell_starts[cell_idx]
+        c_ends = cell_starts[cell_idx + 1]
+        c_counts = (c_ends - c_starts) * found.long()
+        rep_q = torch.repeat_interleave(
+            torch.arange(qp.shape[0] * 27, device=device) // 27, c_counts)
+        if rep_q.numel() == 0:
+            continue
+        total = rep_q.numel()
+        slot_starts_per_pair = torch.cumsum(c_counts, dim=0) - c_counts
+        per_pair_start = torch.repeat_interleave(c_starts, c_counts)
+        slot_within = torch.arange(total, device=device) - torch.repeat_interleave(slot_starts_per_pair, c_counts)
+        tri_indices = tri_per_cell[per_pair_start + slot_within]
+
+        pts = qp[rep_q]
+        # Centroid pre-cull (squared): drop where ||pts-centroid||-radius > max_dist
+        diff = pts - tri_centroids[tri_indices]
+        d2_cand = (diff * diff).sum(-1)
+        thresh = max_dist + tri_radii[tri_indices]
+        cull_keep = d2_cand < thresh * thresh
+        rep_q = rep_q[cull_keep]
+        pts = pts[cull_keep]
+        tri_indices = tri_indices[cull_keep]
+        if rep_q.numel() == 0:
+            continue
+        tri = tri_verts[tri_indices]
+        closest, d2 = _point_tri_closest(pts, tri)
+
+        # Min per query for this chunk.
+        local_min = torch.full((qp.shape[0],), float(max_dist) ** 2,
+                               dtype=query_points.dtype, device=device)
+        local_min.scatter_reduce_(0, rep_q, d2, reduce="amin", include_self=True)
+        # Only update where this chunk improved; ties may overwrite (any is valid)
+        better = local_min < out_d2[cs:ce]
+        out_d2[cs:ce] = torch.where(better, local_min, out_d2[cs:ce])
+        if return_closest or return_tri_idx:
+            ties = (d2 == local_min[rep_q]) & better[rep_q]
+            if return_closest:
+                out_closest[cs + rep_q[ties]] = closest[ties]
+            if return_tri_idx:
+                out_tri[cs + rep_q[ties]] = tri_indices[ties]
+
+    out_d = out_d2.sqrt()
+    extras = []
+    if return_closest:
+        extras.append(out_closest)
+    if return_tri_idx:
+        extras.append(out_tri)
+    if extras:
+        return (out_d, *extras)
+    return out_d
+
+
+# Sparse coarse-to-fine voxel grid in narrow band
+
+def _build_narrow_band_voxels(verts: torch.Tensor, faces: torch.Tensor,
+                              center: torch.Tensor, scale: float,
+                              resolution: int, eps: float,
+                              progress_callback=None) -> torch.Tensor:
+    """Voxel coords (Nv,3) in 0..resolution-1 whose centre is within ~0.87 cell_size of the surface; also returns the kept cKDTree."""
+    device = verts.device
+    tri_verts = verts[faces.long()]
+    # Exact UDF; build the centroid cKDTree once and reuse across refinement levels
+    tree = _build_centroid_tree(tri_verts)
+
+    base_resolution = resolution
+    while base_resolution > 32 and base_resolution % 2 == 0:
+        base_resolution //= 2
+
+    rng = torch.arange(base_resolution, device=device, dtype=torch.long)
+    coords = torch.stack(torch.meshgrid(rng, rng, rng, indexing="ij"), dim=-1).reshape(-1, 3)
+
+    OFFSETS = torch.tensor([
+        [0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0],
+        [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1],
+    ], dtype=torch.long, device=device)
+
+    current_res = base_resolution
+    while True:
+        throw_exception_if_processing_interrupted()
+        cell_size = scale / current_res
+        pts = ((coords.float() + 0.5) / current_res - 0.5) * scale + center
+        dists, _, _ = _udf_exact(pts, tri_verts, tree=tree)
+        keep = dists < 0.87 * cell_size + eps
+        coords = coords[keep]
+        if progress_callback is not None:
+            progress_callback()
+        if current_res >= resolution:
+            break
+        current_res *= 2
+        coords = coords * 2
+        coords = (coords.unsqueeze(1) + OFFSETS.unsqueeze(0)).reshape(-1, 3)
+
+    return coords, tree
+
+
+# Dual Contouring
+
+def _dual_contour(voxel_coords: torch.Tensor, corner_udf: torch.Tensor,
+                  corner_keys: torch.Tensor,
+                  resolution: int, scale: float, center: torch.Tensor,
+                  tri_face_normals: Optional[torch.Tensor] = None,
+                  qef_query=None,
+                  corner_valid: Optional[torch.Tensor] = None,
+                  ) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Dual contour active voxels; returns (Nv,3) dual verts and (M,3) faces into them. QEF placement when tri_face_normals+qef_query given, else centroid of crossings."""
+    device = voxel_coords.device
+    Nv = voxel_coords.shape[0]
+    # 8 corners per voxel, packed into a 1d key
+    CORNER_OFFS = torch.tensor([
+        [0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0],
+        [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1],
+    ], dtype=torch.long, device=device)
+
+    corner_pos_per_voxel = voxel_coords.unsqueeze(1) + CORNER_OFFS.unsqueeze(0)  # (Nv, 8, 3)
+    R1 = resolution + 1
+    keys_per_voxel = (corner_pos_per_voxel[..., 0] * R1
+                      + corner_pos_per_voxel[..., 1]) * R1 + corner_pos_per_voxel[..., 2]
+    # Look up SDF index per corner; missing corners default to +1 (outside)
+    idx_per_voxel = torch.searchsorted(corner_keys, keys_per_voxel.reshape(-1))
+    idx_clamped = idx_per_voxel.clamp(max=corner_keys.numel() - 1)
+    found = (idx_per_voxel < corner_keys.numel()) & (corner_keys[idx_clamped] == keys_per_voxel.reshape(-1))
+    sd = torch.where(found, corner_udf[idx_clamped], torch.full_like(corner_udf[idx_clamped], 1.0))
+    sd = sd.reshape(Nv, 8)         # surface at sign = 0
+
+    # 12 voxel edges as (corner_a, corner_b) pairs (indices into the 8 corners above)
+    EDGES = torch.tensor([
+        [0, 1], [2, 3], [4, 5], [6, 7],   # x-axis edges
+        [0, 2], [1, 3], [4, 6], [5, 7],   # y-axis
+        [0, 4], [1, 5], [2, 6], [3, 7],   # z-axis
+    ], dtype=torch.long, device=device)
+
+    a_sd = sd[:, EDGES[:, 0]]              # (Nv, 12)
+    b_sd = sd[:, EDGES[:, 1]]
+    crosses = (a_sd * b_sd) < 0            # (Nv, 12) bool
+    # Skip crossings touching an invalid corner (avoids fake faces at band edge)
+    if corner_valid is not None:
+        cv_per_voxel = torch.where(found, corner_valid[idx_clamped],
+                                   torch.zeros_like(found)).reshape(Nv, 8)
+        edge_valid = cv_per_voxel[:, EDGES[:, 0]] & cv_per_voxel[:, EDGES[:, 1]]
+        crosses = crosses & edge_valid
+    # Zero-crossing interp factor per edge
+    t = a_sd / (a_sd - b_sd + 1e-20)
+    t = t.clamp(0.0, 1.0).unsqueeze(-1)
+
+    corner_world = (corner_pos_per_voxel.float() / resolution - 0.5) * scale + center.unsqueeze(0).unsqueeze(0)  # (Nv, 8, 3)
+    a_pos = corner_world[:, EDGES[:, 0]]   # (Nv, 12, 3)
+    b_pos = corner_world[:, EDGES[:, 1]]
+    crossing_pts = torch.lerp(a_pos, b_pos, t)  # (Nv, 12, 3)
+
+    # Default dual vert: centroid of crossings (also QEF/no-crossing fallback)
+    crosses_f = crosses.float().unsqueeze(-1)
+    crossing_sum = (crossing_pts * crosses_f).sum(dim=1)
+    n_cross = crosses.float().sum(dim=1, keepdim=True).clamp_min(1.0)
+    centroid_verts = crossing_sum / n_cross
+    centre_world = ((voxel_coords.float() + 0.5) / resolution - 0.5) * scale + center.unsqueeze(0)
+    has_cross = crosses.any(dim=1, keepdim=True)
+    dual_verts = torch.where(has_cross, centroid_verts, centre_world)
+
+    # QEF placement: minimise sum_i (n_i·(x-p_i))² via Tikhonov-regularised
+    # normal equations (A+reg I)x=b; clamp to voxel bbox, else fall back to centroid.
+    if tri_face_normals is not None and qef_query is not None:
+        Nv = voxel_coords.shape[0]
+        flat_pts = crossing_pts.reshape(-1, 3)
+        flat_mask = crosses.reshape(-1)
+        if flat_mask.any():
+            query_pts = flat_pts[flat_mask]
+            _, _, qef_tri_idx = qef_query(query_pts)
+            # Missed queries get a zero normal (null constraint, ignored by solver)
+            valid_q = qef_tri_idx >= 0
+            normals_at_q = torch.zeros_like(query_pts)
+            normals_at_q[valid_q] = tri_face_normals[qef_tri_idx[valid_q]]
+            full_normals = torch.zeros((Nv * 12, 3), dtype=query_pts.dtype, device=device)
+            full_normals[flat_mask] = normals_at_q
+            n_per_edge = full_normals.reshape(Nv, 12, 3)
+
+            # einsum sums into the 3x3 directly, skipping a big intermediate
+            A = torch.einsum('vec,ved->vcd', n_per_edge, n_per_edge)        # (Nv, 3, 3)
+            n_dot_p = (n_per_edge * crossing_pts).sum(dim=-1)               # (Nv, 12)
+            b = torch.einsum('ve,vec->vc', n_dot_p, n_per_edge)             # (Nv, 3)
+
+            # Tikhonov regularisation in-place (A, b are fresh einsum outputs)
+            reg = 1e-2
+            A.diagonal(dim1=-2, dim2=-1).add_(reg)
+            b.add_(centroid_verts, alpha=reg)
+            try:
+                qef_solution = torch.linalg.solve(A, b.unsqueeze(-1)).squeeze(-1)
+            except torch.linalg.LinAlgError:
+                qef_solution = centroid_verts
+
+            # Clamp QEF output to the voxel bbox
+            lo = corner_world[:, 0]                                          # (Nv, 3) min corner
+            hi = corner_world[:, 7]                                          # (Nv, 3) max corner
+            in_box = (qef_solution >= lo).all(dim=-1) & (qef_solution <= hi).all(dim=-1)
+            qef_solution = torch.where(in_box.unsqueeze(-1), qef_solution, centroid_verts)
+
+            dual_verts = torch.where(has_cross, qef_solution, centre_world)
+
+    # Topology: each crossing grid edge is shared by 4 voxels -> quad -> 2 tris.
+    # NEIGHBOUR_OFFS lays out the 4 sharing voxels per axis; y-axis order is
+    # reversed vs x/z to keep manifold winding around each shared edge.
+    NEIGHBOUR_OFFS = torch.tensor([
+        [[0, 0, 0], [0, -1, 0], [0, -1, -1], [0, 0, -1]],
+        [[0, 0, 0], [0, 0, -1], [-1, 0, -1], [-1, 0, 0]],
+        [[0, 0, 0], [-1, 0, 0], [-1, -1, 0], [0, -1, 0]],
+    ], dtype=torch.long, device=device)
+
+    # Min-corner +axis edge index per axis (slots 0/4/8 in EDGES)
+    EDGE_OF_AXIS = torch.tensor([0, 4, 8], dtype=torch.long, device=device)
+
+    # Sorted voxel-coord keys for neighbour lookup
+    vox_dims = voxel_coords.max(dim=0)[0] + 2
+    vox_key = (voxel_coords[:, 0] * vox_dims[1] + voxel_coords[:, 1]) * vox_dims[2] + voxel_coords[:, 2]
+    sort_v = vox_key.argsort()
+    sorted_vox_key = vox_key[sort_v]
+
+    tris = []
+    for axis in range(3):
+        edge_idx = EDGE_OF_AXIS[axis]
+        owner_mask = crosses[:, edge_idx]                 # (Nv,) bool
+        if not owner_mask.any():
+            continue
+        owner_voxels = voxel_coords[owner_mask]           # (No, 3)
+        a_sign = a_sd[owner_mask, edge_idx]               # (No,) sign at corner a
+        nbrs = owner_voxels.unsqueeze(1) + NEIGHBOUR_OFFS[axis].unsqueeze(0)   # (No, 4, 3)
+        nbr_keys = (nbrs[..., 0] * vox_dims[1] + nbrs[..., 1]) * vox_dims[2] + nbrs[..., 2]
+        flat = nbr_keys.reshape(-1).contiguous()
+        ins = torch.searchsorted(sorted_vox_key, flat)
+        ins_c = ins.clamp(max=sorted_vox_key.numel() - 1)
+        valid = (ins < sorted_vox_key.numel()) & (sorted_vox_key[ins_c] == flat)
+        valid = valid.reshape(-1, 4).all(dim=1)
+        if not valid.any():
+            continue
+        dual_indices = sort_v[ins_c].reshape(-1, 4)[valid]    # (Mv, 4)
+        sign_a = a_sign[valid]
+        # Winding: flip when corner a is outside (sign_a > 0) so normal points out
+        d0 = dual_indices[:, 0]
+        d1 = dual_indices[:, 1]
+        d2 = dual_indices[:, 2]
+        d3 = dual_indices[:, 3]
+        flip = sign_a > 0
+        t1a = torch.stack([d0, d1, d2], dim=1)
+        t2a = torch.stack([d0, d2, d3], dim=1)
+        t1b = torch.stack([d0, d2, d1], dim=1)
+        t2b = torch.stack([d0, d3, d2], dim=1)
+        t1 = torch.where(flip.unsqueeze(-1), t1b, t1a)
+        t2 = torch.where(flip.unsqueeze(-1), t2b, t2a)
+        tris.append(t1)
+        tris.append(t2)
+
+    if not tris:
+        return dual_verts, torch.empty((0, 3), dtype=torch.long, device=device)
+    new_faces = torch.cat(tris, dim=0)
+    return dual_verts, new_faces
+
+
+# Manifold Dual Contouring (Schaefer, Ju, Warren 2007)
+
+@functools.lru_cache(maxsize=None)
+def _build_mdc_lut() -> Tuple[torch.Tensor, torch.Tensor]:
+    """Per 8-corner sign pattern: K (256,) patch count and group (256,12) patch id per edge (-1 if non-crossing)."""
+    EDGE_PAIRS = [
+        (0, 1), (2, 3), (4, 5), (6, 7),         # x-axis edges
+        (0, 2), (1, 3), (4, 6), (5, 7),         # y-axis edges
+        (0, 4), (1, 5), (2, 6), (3, 7),         # z-axis edges
+    ]
+    K = torch.zeros(256, dtype=torch.int64)
+    group = torch.full((256, 12), -1, dtype=torch.int64)
+
+    for pat in range(256):
+        signs = [(pat >> i) & 1 for i in range(8)]    # 1=outside, 0=inside
+
+        parent = list(range(8))
+
+        def find(x: int) -> int:
+            r = x
+            while parent[r] != r:
+                r = parent[r]
+            while parent[x] != r:
+                nxt = parent[x]
+                parent[x] = r
+                x = nxt
+            return r
+
+        # Union same-sign corners (not separated by the surface)
+        for a, b in EDGE_PAIRS:
+            if signs[a] == signs[b]:
+                ra, rb = find(a), find(b)
+                if ra != rb:
+                    parent[ra] = rb
+
+        # Distinct (interior_root, exterior_root) pairs are distinct patches
+        group_map: dict[tuple[int, int], int] = {}
+        for ei, (a, b) in enumerate(EDGE_PAIRS):
+            if signs[a] == signs[b]:
+                continue
+            in_c = a if signs[a] == 0 else b
+            ex_c = b if signs[a] == 0 else a
+            key = (find(in_c), find(ex_c))
+            if key not in group_map:
+                group_map[key] = len(group_map)
+            group[pat, ei] = group_map[key]
+        K[pat] = len(group_map)
+
+    return K, group
+
+
+def _mdc_lut(device: torch.device) -> Tuple[torch.Tensor, torch.Tensor]:
+    K, g = _build_mdc_lut()
+    return K.to(device), g.to(device)
+
+
+def _dual_contour_manifold(voxel_coords: torch.Tensor, corner_udf: torch.Tensor,
+                           corner_keys: torch.Tensor,
+                           resolution: int, scale: float, center: torch.Tensor,
+                           corner_valid: Optional[torch.Tensor] = None,
+                           ) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Manifold DC: like _dual_contour but emits 1-4 dual verts per voxel via the patch LUT (centroid placement only)."""
+    device = voxel_coords.device
+    Nv = voxel_coords.shape[0]
+
+    CORNER_OFFS = torch.tensor([
+        [0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0],
+        [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1],
+    ], dtype=torch.long, device=device)
+    corner_pos = voxel_coords.unsqueeze(1) + CORNER_OFFS.unsqueeze(0)        # (Nv, 8, 3)
+    R1 = resolution + 1
+    keys = (corner_pos[..., 0] * R1 + corner_pos[..., 1]) * R1 + corner_pos[..., 2]
+    flat_keys = keys.reshape(-1)
+    idx = torch.searchsorted(corner_keys, flat_keys)
+    idx_c = idx.clamp(max=corner_keys.numel() - 1)
+    found = (idx < corner_keys.numel()) & (corner_keys[idx_c] == flat_keys)
+    sd = torch.where(found, corner_udf[idx_c],
+                     torch.full_like(corner_udf[idx_c], 1.0)).reshape(Nv, 8)
+
+    # Sign pattern: bit i = (sd[i] > 0), matching the LUT convention
+    sign_bits = (sd > 0).to(torch.int64)                                     # (Nv, 8)
+    weights = (1 << torch.arange(8, device=device, dtype=torch.int64))
+    pat_per_voxel = (sign_bits * weights).sum(dim=-1)                        # (Nv,) in 0..255
+
+    K_lut, group_lut = _mdc_lut(device)
+    K_per_voxel = K_lut[pat_per_voxel]                                       # (Nv,)
+    total_verts = int(K_per_voxel.sum().item())
+    if total_verts == 0:
+        return (torch.empty((0, 3), dtype=voxel_coords.dtype, device=device),
+                torch.empty((0, 3), dtype=torch.long, device=device))
+
+    vert_offset = (torch.cumsum(K_per_voxel, dim=0) - K_per_voxel)           # (Nv,)
+    voxel_per_subvol = torch.repeat_interleave(
+        torch.arange(Nv, device=device), K_per_voxel)                        # (total_verts,)
+
+    EDGES = torch.tensor([
+        [0, 1], [2, 3], [4, 5], [6, 7],
+        [0, 2], [1, 3], [4, 6], [5, 7],
+        [0, 4], [1, 5], [2, 6], [3, 7],
+    ], dtype=torch.long, device=device)
+    sb_a = sign_bits[:, EDGES[:, 0]]                                         # (Nv, 12)
+    sb_b = sign_bits[:, EDGES[:, 1]]
+    crosses = sb_a != sb_b                                                   # (Nv, 12)
+
+    if corner_valid is not None:
+        cv = torch.where(found, corner_valid[idx_c],
+                         torch.zeros_like(found)).reshape(Nv, 8)
+        edge_valid = cv[:, EDGES[:, 0]] & cv[:, EDGES[:, 1]]
+        crosses = crosses & edge_valid
+
+    edge_group_per_voxel = group_lut[pat_per_voxel]                          # (Nv, 12), -1 if not crossing
+    # LUT already gives -1 for non-crossing edges; only re-mask for corner_valid
+    if corner_valid is not None:
+        edge_group_per_voxel = torch.where(crosses, edge_group_per_voxel,
+                                           torch.full_like(edge_group_per_voxel, -1))
+
+    a_sd = sd[:, EDGES[:, 0]]                                                # (Nv, 12)
+    b_sd = sd[:, EDGES[:, 1]]
+    denom = a_sd - b_sd
+    t = torch.where(denom.abs() > 1e-20, a_sd / denom, torch.zeros_like(a_sd))
+    t = t.clamp(0.0, 1.0).unsqueeze(-1)
+    corner_world = (corner_pos.float() / resolution - 0.5) * scale + center.unsqueeze(0).unsqueeze(0)
+    a_pos = corner_world[:, EDGES[:, 0]]
+    b_pos = corner_world[:, EDGES[:, 1]]
+    crossing_pts = torch.lerp(a_pos, b_pos, t)                               # (Nv, 12, 3)
+
+    # Aggregate crossing positions per (voxel, subvolume) into global dual verts
+    flat_group = edge_group_per_voxel.reshape(-1)
+    valid_mask = flat_group >= 0
+    flat_voxel = torch.arange(Nv, device=device).unsqueeze(-1).expand(Nv, 12).reshape(-1)
+    flat_pos = crossing_pts.reshape(-1, 3)
+    v_idx = flat_voxel[valid_mask]
+    g_idx = flat_group[valid_mask]
+    pos = flat_pos[valid_mask]
+    global_idx = vert_offset[v_idx] + g_idx                                  # (Nvalid,)
+
+    pos_dtype = crossing_pts.dtype
+    sums = torch.zeros((total_verts, 3), dtype=pos_dtype, device=device)
+    counts = torch.zeros(total_verts, dtype=pos_dtype, device=device)
+    sums.scatter_add_(0, global_idx.unsqueeze(-1).expand(-1, 3), pos)
+    counts.scatter_add_(0, global_idx, torch.ones_like(g_idx, dtype=pos_dtype))
+    # Fully-masked subvolumes default to the voxel centre (unreferenced)
+    voxel_centre = ((voxel_coords.float() + 0.5) / resolution - 0.5) * scale + center.unsqueeze(0)
+    dual_verts = torch.where(
+        counts.unsqueeze(-1) > 0,
+        sums / counts.clamp_min(1.0).unsqueeze(-1),
+        voxel_centre[voxel_per_subvol].to(pos_dtype),
+    )
+
+    # Face emission. SHARED_LOCAL_EDGE[axis,k] = the k-th neighbour's local edge
+    # slot corresponding to the shared grid edge (owner's slot = EDGE_OF_AXIS[axis]).
+    NEIGHBOUR_OFFS = torch.tensor([
+        [[0, 0, 0], [0, -1, 0], [0, -1, -1], [0, 0, -1]],
+        [[0, 0, 0], [0, 0, -1], [-1, 0, -1], [-1, 0, 0]],
+        [[0, 0, 0], [-1, 0, 0], [-1, -1, 0], [0, -1, 0]],
+    ], dtype=torch.long, device=device)
+    SHARED_LOCAL_EDGE = torch.tensor([
+        [0, 1, 3, 2],     # x-axis
+        [4, 6, 7, 5],     # y-axis
+        [8, 9, 11, 10],   # z-axis
+    ], dtype=torch.long, device=device)
+    EDGE_OF_AXIS = torch.tensor([0, 4, 8], dtype=torch.long, device=device)
+
+    vox_dims = voxel_coords.max(dim=0)[0] + 2
+    vox_key = (voxel_coords[:, 0] * vox_dims[1] + voxel_coords[:, 1]) * vox_dims[2] + voxel_coords[:, 2]
+    sort_v = vox_key.argsort()
+    sorted_vox_key = vox_key[sort_v]
+
+    tris_out = []
+    for axis in range(3):
+        edge_idx = EDGE_OF_AXIS[axis]
+        owner_mask = crosses[:, edge_idx]
+        if not owner_mask.any():
+            continue
+        owner_voxels = voxel_coords[owner_mask]
+        sign_a_at_owner = sb_a[owner_mask, edge_idx]                         # (No,) — 0 inside, 1 outside
+
+        nbrs = owner_voxels.unsqueeze(1) + NEIGHBOUR_OFFS[axis].unsqueeze(0)  # (No, 4, 3)
+        nbr_keys = (nbrs[..., 0] * vox_dims[1] + nbrs[..., 1]) * vox_dims[2] + nbrs[..., 2]
+        flat = nbr_keys.reshape(-1).contiguous()
+        ins = torch.searchsorted(sorted_vox_key, flat)
+        ins_c = ins.clamp(max=sorted_vox_key.numel() - 1)
+        valid_nbr = (ins < sorted_vox_key.numel()) & (sorted_vox_key[ins_c] == flat)
+        valid_quad = valid_nbr.reshape(-1, 4).all(dim=1)
+        if not valid_quad.any():
+            continue
+
+        nbr_orig = sort_v[ins_c].reshape(-1, 4)[valid_quad]                  # (Mv, 4) voxel idx
+        nbr_pat = pat_per_voxel[nbr_orig]                                    # (Mv, 4)
+        local_e = SHARED_LOCAL_EDGE[axis].unsqueeze(0).expand_as(nbr_pat)
+        nbr_subvol = group_lut[nbr_pat, local_e]                             # (Mv, 4)
+        # Every neighbour must agree the shared edge is crossing
+        ok = (nbr_subvol >= 0).all(dim=1)
+        if not ok.any():
+            continue
+        nbr_subvol = nbr_subvol[ok]
+        nbr_orig = nbr_orig[ok]
+        dual_indices = vert_offset[nbr_orig] + nbr_subvol                    # (Mv', 4)
+        sign_a = sign_a_at_owner[valid_quad][ok]                             # 0 = inside, 1 = outside
+
+        # Winding: flip when corner a is outside (same as _dual_contour)
+        flip = sign_a > 0
+        d0, d1, d2, d3 = dual_indices.unbind(dim=1)
+        t1a = torch.stack([d0, d1, d2], dim=1)
+        t2a = torch.stack([d0, d2, d3], dim=1)
+        t1b = torch.stack([d0, d2, d1], dim=1)
+        t2b = torch.stack([d0, d3, d2], dim=1)
+        tris_out.append(torch.where(flip.unsqueeze(-1), t1b, t1a))
+        tris_out.append(torch.where(flip.unsqueeze(-1), t2b, t2a))
+
+    if not tris_out:
+        return dual_verts, torch.empty((0, 3), dtype=torch.long, device=device)
+    return dual_verts, torch.cat(tris_out, dim=0)
+
+
+# Main entry
+
+def _filter_components(verts: torch.Tensor, faces: torch.Tensor,
+                       min_fraction: float = 0.01,
+                       drop_inverted: bool = True,
+                       drop_enclosed: bool = True) -> torch.Tensor:
+    """Drop tiny / inverted-volume / bbox-enclosed connected components; returns filtered faces."""
+    device = faces.device
+    V = verts.shape[0]
+
+    # Connected components via min-label propagation across faces (200-iter max)
+    label = torch.arange(V, dtype=torch.long, device=device)
+    for _ in range(200):
+        f_min = torch.minimum(torch.minimum(label[faces[:, 0]], label[faces[:, 1]]),
+                              label[faces[:, 2]])
+        new_label = label.clone()
+        new_label.scatter_reduce_(0, faces[:, 0], f_min, reduce="amin", include_self=True)
+        new_label.scatter_reduce_(0, faces[:, 1], f_min, reduce="amin", include_self=True)
+        new_label.scatter_reduce_(0, faces[:, 2], f_min, reduce="amin", include_self=True)
+        new_label = new_label[new_label]   # path compression
+        if torch.equal(new_label, label):
+            break
+        label = new_label
+
+    face_label = label[faces[:, 0]]                                    # (F,)
+    unique_labels, inv = torch.unique(face_label, return_inverse=True)
+    C = unique_labels.shape[0]
+    counts = torch.bincount(inv, minlength=C)
+    max_count = int(counts.max().item())
+    keep = torch.ones(C, dtype=torch.bool, device=device)
+
+    if min_fraction > 0:
+        threshold = max(1, int(max_count * min_fraction))
+        keep = keep & (counts >= threshold)
+
+    if drop_inverted:
+        # Drop components with negative signed volume, but always keep the largest
+        v0 = verts[faces[:, 0]]
+        v1 = verts[faces[:, 1]]
+        v2 = verts[faces[:, 2]]
+        face_vol = (v0 * torch.cross(v1, v2, dim=-1)).sum(dim=-1)        # (F,)
+        comp_vol = torch.zeros(C, dtype=face_vol.dtype, device=device)
+        comp_vol.scatter_add_(0, inv, face_vol)
+        if C > 1:
+            large = counts.argmax()
+            vol_ok = (comp_vol >= 0)
+            vol_ok[large] = True
+            keep = keep & vol_ok
+
+    if drop_enclosed and C > 1:
+        # Two-pass: (1) bbox-inside-largest test, then (2) +X raycast point-in-mesh
+        large = counts.argmax()
+        face_v = verts[faces]
+        face_min = face_v.min(dim=1).values
+        face_max = face_v.max(dim=1).values
+        comp_min = torch.full((C, 3), float("inf"), dtype=verts.dtype, device=device)
+        comp_max = torch.full((C, 3), float("-inf"), dtype=verts.dtype, device=device)
+        comp_min.scatter_reduce_(0, inv[:, None].expand(-1, 3), face_min,
+                                 reduce="amin", include_self=True)
+        comp_max.scatter_reduce_(0, inv[:, None].expand(-1, 3), face_max,
+                                 reduce="amax", include_self=True)
+        big_min = comp_min[large]
+        big_max = comp_max[large]
+        enclosed = ((comp_min >= big_min).all(dim=-1)
+                    & (comp_max <= big_max).all(dim=-1))
+        enclosed[large] = False
+
+        # Per-component centroid for the raycast test
+        face_centroid = face_v.mean(dim=1)                               # (F, 3)
+        comp_centroid = torch.zeros((C, 3), dtype=verts.dtype, device=device)
+        comp_centroid.scatter_add_(0, inv[:, None].expand(-1, 3), face_centroid)
+        comp_centroid = comp_centroid / counts.to(verts.dtype).unsqueeze(-1).clamp_min(1.0)
+
+        # Raycast surviving non-largest candidates (small loop)
+        big_faces = faces[inv == large]
+        bv0 = verts[big_faces[:, 0]]
+        bv1 = verts[big_faces[:, 1]]
+        bv2 = verts[big_faces[:, 2]]
+        candidates = torch.nonzero((keep & ~enclosed)
+                                   & (torch.arange(C, device=device) != large),
+                                   as_tuple=True)[0]
+        for ci in candidates.tolist():
+            origin = comp_centroid[ci]
+            # 2D point-in-triangle in YZ for the ray origin's (y, z)
+            oy, oz = origin[1], origin[2]
+            s12 = (bv1[:, 1] - oy) * (bv2[:, 2] - oz) - (bv1[:, 2] - oz) * (bv2[:, 1] - oy)
+            s20 = (bv2[:, 1] - oy) * (bv0[:, 2] - oz) - (bv2[:, 2] - oz) * (bv0[:, 1] - oy)
+            s01 = (bv0[:, 1] - oy) * (bv1[:, 2] - oz) - (bv0[:, 2] - oz) * (bv1[:, 1] - oy)
+            total = s12 + s20 + s01
+            inside_yz = (((s12 >= 0) & (s20 >= 0) & (s01 >= 0))
+                         | ((s12 <= 0) & (s20 <= 0) & (s01 <= 0)))
+            inside_yz = inside_yz & (total.abs() > 1e-20)
+            inv_t = 1.0 / total.where(total.abs() > 1e-20, torch.ones_like(total))
+            hit_x = (s12 * bv0[:, 0] + s20 * bv1[:, 0] + s01 * bv2[:, 0]) * inv_t
+            crossings = int((inside_yz & (hit_x > origin[0])).sum().item())
+            if crossings % 2 == 1:
+                enclosed[ci] = True
+        keep = keep & ~enclosed
+
+    if keep.all():
+        return faces
+    face_keep = keep[inv]
+    return faces[face_keep]
+
+
+def _taubin_smooth(verts: torch.Tensor, faces: torch.Tensor,
+                   iters: int, lam: float = 0.5, mu: float = -0.53,
+                   progress_callback=None) -> torch.Tensor:
+    """Taubin lambda|mu low-pass smoothing (volume-preserving); boundary verts are no-ops."""
+    if iters <= 0 or verts.numel() == 0 or faces.numel() == 0:
+        return verts
+    device = verts.device
+    V = verts.shape[0]
+    sorted_keys, _, _ = _sorted_edge_halfedges(faces, V)
+    uniq_keys, _ = torch.unique_consecutive(sorted_keys, return_counts=True)
+    P = V + 1
+    a = uniq_keys // P
+    b = uniq_keys % P
+    ones = torch.ones_like(a, dtype=verts.dtype)
+    counts = torch.zeros(V, dtype=verts.dtype, device=device)
+    counts.scatter_add_(0, a, ones)
+    counts.scatter_add_(0, b, ones)
+    counts_safe = counts.clamp_min(1.0).unsqueeze(-1)
+    has_nb = (counts > 0).unsqueeze(-1)
+    a_exp = a.unsqueeze(-1).expand(-1, 3)
+    b_exp = b.unsqueeze(-1).expand(-1, 3)
+
+    out = verts
+    for _ in range(iters):
+        throw_exception_if_processing_interrupted()
+        for w in (lam, mu):
+            sums = torch.zeros_like(out)
+            sums.scatter_add_(0, a_exp, out[b])
+            sums.scatter_add_(0, b_exp, out[a])
+            delta = (sums / counts_safe - out) * has_nb
+            out = out + w * delta
+        if progress_callback is not None:
+            progress_callback()
+    return out
+
+
+def _fix_poles(verts: torch.Tensor, faces: torch.Tensor,
+               colors: Optional[torch.Tensor] = None
+               ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+    """Midpoint-collapse edge-sharing valence-3 vertex pairs (DC T-junction poles); boundary verts excluded."""
+    device = verts.device
+    V = verts.shape[0]
+    if V == 0 or faces.numel() == 0:
+        return verts, faces, colors
+
+    sorted_keys, _, _ = _sorted_edge_halfedges(faces, V)
+    uniq_keys, key_counts = torch.unique_consecutive(sorted_keys, return_counts=True)
+    P = V + 1
+    a = uniq_keys // P
+    b = uniq_keys % P
+    # Boundary verts (endpoints of single-face edges) are excluded from poles
+    boundary_v = torch.zeros(V, dtype=torch.bool, device=device)
+    bnd_mask = key_counts == 1
+    if bnd_mask.any():
+        boundary_v[a[bnd_mask]] = True
+        boundary_v[b[bnd_mask]] = True
+    ones = torch.ones_like(a)
+    valence = torch.zeros(V, dtype=torch.long, device=device)
+    valence.scatter_add_(0, a, ones)
+    valence.scatter_add_(0, b, ones)
+    is_pole = (valence == 3) & ~boundary_v
+    if int(is_pole.sum().item()) < 2:
+        return verts, faces, colors
+
+    pp_edge = is_pole[a] & is_pole[b]
+    if not pp_edge.any():
+        return verts, faces, colors
+    cand_a = a[pp_edge]
+    cand_b = b[pp_edge]
+
+    # Greedy maximal matching: accept candidates whose endpoints are still free
+    used = torch.zeros(V, dtype=torch.bool, device="cpu")
+    cand_a_cpu = cand_a.cpu().tolist()
+    cand_b_cpu = cand_b.cpu().tolist()
+    pairs: list[tuple[int, int]] = []
+    for ai, bi in zip(cand_a_cpu, cand_b_cpu):
+        if not used[ai] and not used[bi]:
+            pairs.append((ai, bi))
+            used[ai] = True
+            used[bi] = True
+    if not pairs:
+        return verts, faces, colors
+
+    pairs_t = torch.tensor(pairs, dtype=torch.long, device=device)         # (P, 2)
+    keep_i = torch.minimum(pairs_t[:, 0], pairs_t[:, 1])
+    drop_i = torch.maximum(pairs_t[:, 0], pairs_t[:, 1])
+
+    new_verts = verts.clone()
+    new_verts[keep_i] = 0.5 * (verts[pairs_t[:, 0]] + verts[pairs_t[:, 1]])
+    new_colors = None
+    if colors is not None:
+        new_colors = colors.clone()
+        new_colors[keep_i] = 0.5 * (colors[pairs_t[:, 0]] + colors[pairs_t[:, 1]])
+
+    remap = torch.arange(V, dtype=torch.long, device=device)
+    remap[drop_i] = keep_i
+    new_faces = remap[faces.long()]
+    degen = ((new_faces[:, 0] == new_faces[:, 1])
+             | (new_faces[:, 1] == new_faces[:, 2])
+             | (new_faces[:, 0] == new_faces[:, 2]))
+    new_faces = new_faces[~degen]
+
+    used_mask = torch.zeros(V, dtype=torch.bool, device=device)
+    used_mask[new_faces.reshape(-1)] = True
+    if not used_mask.all():
+        compact = used_mask.long().cumsum(0) - 1
+        new_verts = new_verts[used_mask]
+        if new_colors is not None:
+            new_colors = new_colors[used_mask]
+        new_faces = compact[new_faces]
+    return new_verts, new_faces.to(faces.dtype), new_colors
+
+
+def remesh_narrow_band_dc(
+    vertices: torch.Tensor,
+    faces: torch.Tensor,
+    resolution: int = 256,
+    target_faces: int = 0,                  # 0 = use `resolution`; >0 = auto-derive resolution
+    band: float = 1.0,
+    project_back: float = 0.0,
+    qef: bool = True,
+    sign_mode: str = "udf",                 # "sdf" | "udf"
+    drop_small_components: float = 0.01,    # drop components below this fraction of max
+    drop_inverted_components: bool = True,  # drop closed components with negative signed volume
+    drop_enclosed_components: bool = True,  # drop components whose bbox is inside the largest's bbox
+    fix_poles: bool = False,                # collapse 3-3 valence vertex pairs (DC T-junction artifact)
+    smooth_iters: int = 0,                  # Taubin smoothing iterations (low-pass, volume-preserving)
+    smooth_lambda: float = 0.5,
+    smooth_mu: float = -0.53,
+    manifold: bool = False,                 # Manifold DC: emit 1-4 dual verts per voxel for multi-sheet cases
+    colors: Optional[torch.Tensor] = None,
+    scale: Optional[float] = None,
+    center: Optional[torch.Tensor] = None,
+):
+    """Narrow-band Dual Contouring re-extraction; returns (new_vertices, new_faces, new_colors), new_colors None unless `colors` given.
+
+    Key params: target_faces>0 auto-derives resolution; sign_mode sdf/udf
+    (UDF disables qef and may need component filters); project_back lerps verts
+    toward the closest surface point; scale/center default to bbox.
+    """
+    assert vertices.ndim == 2 and vertices.shape[1] == 3
+    assert faces.ndim == 2 and faces.shape[1] == 3
+    device = vertices.device
+
+    if center is None:
+        center = 0.5 * (vertices.max(dim=0)[0] + vertices.min(dim=0)[0])
+    else:
+        center = center.to(device=device, dtype=vertices.dtype)
+    if scale is None:
+        bbox = vertices.max(dim=0)[0] - vertices.min(dim=0)[0]
+        scale = float(bbox.max().item()) * 1.1
+
+    # Auto-derive resolution from target_faces (~3 tris/crossing-voxel; +-30%)
+    if target_faces > 0:
+        tv = vertices[faces.long()]
+        cross_v = torch.cross(tv[:, 1] - tv[:, 0], tv[:, 2] - tv[:, 0], dim=-1)
+        surface_area = 0.5 * cross_v.norm(dim=-1).sum().item()
+        relative_area = max(surface_area / (scale * scale), 1e-6)
+        derived = int(math.sqrt(target_faces / (3.0 * relative_area)))
+        # Round to a multiple of 32 (builder doubles from a <=32 base)
+        derived = ((derived + 31) // 32) * 32
+        derived = max(32, min(1024, derived))
+        resolution = derived
+
+    eps = band * scale / resolution
+
+    # progress: one tick per narrow-band level + 3 stages (SDF/DC/post) + each smoothing iter
+    n_levels, _b = 1, resolution
+    while _b > 32 and _b % 2 == 0:
+        _b //= 2
+    while _b < resolution:
+        _b *= 2
+        n_levels += 1
+    _total_ticks = n_levels + 3 + int(smooth_iters)
+    _pbar = comfy.utils.ProgressBar(_total_ticks)
+    _tq = _tqdm(total=_total_ticks, desc="Remesh DC", leave=False)
+
+    def tick():
+        _pbar.update(1)
+        _tq.update(1)
+
+    # Step 1: sparse narrow-band voxel grid (coarse-to-fine)
+    voxel_coords, _band_tree = _build_narrow_band_voxels(
+        vertices, faces, center, scale, resolution, eps,
+        progress_callback=tick)
+    if voxel_coords.numel() == 0:
+        return (torch.empty((0, 3), dtype=vertices.dtype, device=device),
+                torch.empty((0, 3), dtype=faces.dtype, device=device),
+                None if colors is None else torch.empty((0, colors.shape[1]),
+                                                        dtype=colors.dtype, device=device))
+
+    # Step 2: collect unique corner positions of all active voxels
+    CORNER_OFFS = torch.tensor([
+        [0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0],
+        [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1],
+    ], dtype=torch.long, device=device)
+    corners = (voxel_coords.unsqueeze(1) + CORNER_OFFS.unsqueeze(0)).reshape(-1, 3)
+    R1 = resolution + 1
+    corner_keys = (corners[:, 0] * R1 + corners[:, 1]) * R1 + corners[:, 2]
+    unique_corner_keys, corner_inv = torch.unique(corner_keys, return_inverse=True)
+    unique_corners = torch.zeros((unique_corner_keys.shape[0], 3), dtype=torch.long, device=device)
+    unique_corners[corner_inv] = corners
+
+    if sign_mode == "sdf":
+        use_sdf = True
+    elif sign_mode == "udf":
+        use_sdf = False
+    else:
+        raise ValueError(f"sign_mode must be 'sdf'|'udf', got {sign_mode!r}")
+
+    # Step 3: distance field at every unique corner.
+    tri_verts_g = vertices[faces.long()]
+    centroids = tri_verts_g.mean(dim=1)
+    tri_radii = (tri_verts_g - centroids.unsqueeze(1)).norm(dim=-1).max(dim=-1).values
+    # face normals: needed for the SDF sign AND for QEF placement (QEF is sign-agnostic,
+    # so it works in UDF mode too — (n·(x-p))² is unchanged by normal orientation)
+    if use_sdf or qef:
+        tri_face_normals_all = torch.nn.functional.normalize(
+            torch.cross(tri_verts_g[:, 1] - tri_verts_g[:, 0],
+                        tri_verts_g[:, 2] - tri_verts_g[:, 0], dim=-1),
+            p=2, dim=-1, eps=1e-12)
+    cell_size = scale / resolution
+    corner_world = (unique_corners.float() / resolution - 0.5) * scale + center.unsqueeze(0)
+    # Exact corner UDF (no max_dist cap) so DC crossings keep fine detail
+    udf, corner_closest, corner_tri = _udf_exact(corner_world, tri_verts_g, tree=_band_tree)
+    corner_valid = corner_tri >= 0
+    if use_sdf:
+        sign = torch.ones_like(udf)
+        n_for_corner = tri_face_normals_all[corner_tri.clamp(min=0)]
+        offset = corner_world - corner_closest
+        sign_dot = (offset * n_for_corner).sum(-1)
+        sign = torch.where(corner_valid & (sign_dot < 0), -sign, sign)
+        sdf = sign * udf
+    else:
+        # UDF mode: iso at UDF=eps; double surface on closed meshes, weld after
+        sdf = udf - eps
+    tick()  # SDF done
+
+    # Short-range hash reused by project_back / colors sampling (max_dist up to 4*cell)
+    short_hash_cell_t = torch.tensor(2.0 * cell_size, dtype=vertices.dtype, device=device)
+    short_hash = _build_tri_spatial_hash(centroids, tri_radii, short_hash_cell_t)
+
+    # Step 4 + 5: dual contouring + topology. QEF works in both modes (sign-agnostic);
+    # in UDF it pulls the ±eps crossing back onto the triangle planes → sharper edges.
+    if qef:
+        tri_face_normals = tri_face_normals_all
+        # QEF needs the nearest triangle per crossing point. The centroid cKDTree
+        # (_band_tree) is already built, and its exact k-NN query is markedly faster
+        # here than a spatial-hash gather (which builds ~100-triangle candidate lists
+        # per query on a dense input) — and it's exact. So reuse it directly.
+        def _qef_query(pts):
+            return _udf_exact(pts, tri_verts_g, tree=_band_tree)
+    else:
+        tri_face_normals = None
+        _qef_query = None
+
+    if manifold and use_sdf:
+        # MDC ignores qef / tri_face_normals — centroid placement only.
+        dual_verts, new_faces = _dual_contour_manifold(
+            voxel_coords, sdf, unique_corner_keys,
+            resolution, scale, center,
+            corner_valid=corner_valid)
+    else:
+        dual_verts, new_faces = _dual_contour(
+            voxel_coords, sdf, unique_corner_keys,
+            resolution, scale, center,
+            tri_face_normals=tri_face_normals, qef_query=_qef_query,
+            # corner_valid filter only matters in SDF mode
+            corner_valid=corner_valid if use_sdf else None)
+    tick()  # DC done
+
+    # Step 6: project_back and / or color sampling share one closest-point query
+    need_query = (project_back > 0 or colors is not None) and dual_verts.numel() > 0
+    out_colors = None
+    if need_query:
+        result = _udf_query(
+            dual_verts, tri_verts_g, short_hash, short_hash_cell_t,
+            max_dist=4.0 * cell_size,
+            return_closest=True,
+            return_tri_idx=(colors is not None))
+        if colors is not None:
+            _, closest_pts, closest_tri = result
+        else:
+            _, closest_pts = result
+
+        if project_back > 0:
+            dual_verts = torch.lerp(dual_verts, closest_pts, float(project_back))
+
+        if colors is not None:
+            # Barycentric-interpolate input colors at the closest point
+            safe_tri = closest_tri.clamp(min=0)
+            tri_v_idx = faces[safe_tri].long()                 # (N, 3)
+            tri_v = vertices[tri_v_idx]                        # (N, 3, 3)
+            v0 = tri_v[:, 0]
+            v1 = tri_v[:, 1]
+            v2 = tri_v[:, 2]
+            e0 = v1 - v0
+            e1 = v2 - v0
+            e2 = closest_pts - v0
+            d00 = (e0 * e0).sum(-1)
+            d01 = (e0 * e1).sum(-1)
+            d11 = (e1 * e1).sum(-1)
+            d20 = (e2 * e0).sum(-1)
+            d21 = (e2 * e1).sum(-1)
+            denom = d00 * d11 - d01 * d01 + 1e-20
+            bv = ((d11 * d20 - d01 * d21) / denom).clamp(0.0, 1.0)
+            bw = ((d00 * d21 - d01 * d20) / denom).clamp(0.0, 1.0)
+            bu = (1.0 - bv - bw).clamp(0.0, 1.0)
+            tri_c = colors[tri_v_idx]                          # (N, 3, C)
+            out_colors = (bu.unsqueeze(-1) * tri_c[:, 0]
+                          + bv.unsqueeze(-1) * tri_c[:, 1]
+                          + bw.unsqueeze(-1) * tri_c[:, 2])
+            # Zero out failed-query rows (their barycentric used bogus triangle 0)
+            invalid = closest_tri < 0
+            if invalid.any():
+                out_colors[invalid] = 0
+
+    # Filter spurious components (tiny pieces, inverted inner shells)
+    if (new_faces.numel() > 0
+            and (drop_small_components > 0 or drop_inverted_components
+                 or drop_enclosed_components)):
+        new_faces = _filter_components(
+            dual_verts, new_faces,
+            min_fraction=drop_small_components if drop_small_components > 0 else 0.0,
+            drop_inverted=drop_inverted_components,
+            drop_enclosed=drop_enclosed_components)
+
+    if fix_poles and new_faces.numel() > 0:
+        dual_verts, new_faces, out_colors = _fix_poles(
+            dual_verts, new_faces, out_colors)
+    tick()  # post-process done
+
+    if smooth_iters > 0 and dual_verts.numel() > 0 and new_faces.numel() > 0:
+        dual_verts = _taubin_smooth(dual_verts, new_faces,
+                                    iters=int(smooth_iters),
+                                    lam=float(smooth_lambda),
+                                    mu=float(smooth_mu),
+                                    progress_callback=tick)
+
+    # Drop unused verts (non-crossing voxels' dual verts) and compact faces
+    if dual_verts.numel() > 0 and new_faces.numel() > 0:
+        used = torch.zeros(dual_verts.shape[0], dtype=torch.bool, device=device)
+        used[new_faces[:, 0]] = True
+        used[new_faces[:, 1]] = True
+        used[new_faces[:, 2]] = True
+        remap = used.long().cumsum(0) - 1
+        dual_verts = dual_verts[used]
+        new_faces = remap[new_faces.long()]
+        if out_colors is not None:
+            out_colors = out_colors[used]
+
+    return (dual_verts.to(vertices.dtype),
+            new_faces.to(faces.dtype),
+            out_colors.to(colors.dtype) if (out_colors is not None and colors is not None) else None)
diff --git a/comfy_extras/mesh3d/uv_unwrap/mesh.py b/comfy_extras/mesh3d/uv_unwrap/mesh.py
new file mode 100644
index 0000000000000000000000000000000000000000..25377d59ed4c038a379377deb36a7cc7d303d662
--- /dev/null
+++ b/comfy_extras/mesh3d/uv_unwrap/mesh.py
@@ -0,0 +1,162 @@
+"""Mesh container, edge/face adjacency, manifold cleanup."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Dict, List
+
+import numpy as np
+import torch
+from scipy.sparse import csr_matrix
+from scipy.sparse.csgraph import connected_components
+from torch import Tensor
+
+
+# ---- Per-face / per-vertex geometry ----
+
+def face_normals(vertices: Tensor, faces: Tensor) -> Tensor:
+    """[F,3] unit face normals (degenerate faces -> zero)."""
+    v0 = vertices[faces[:, 0]]
+    v1 = vertices[faces[:, 1]]
+    v2 = vertices[faces[:, 2]]
+    n = torch.linalg.cross(v1 - v0, v2 - v0)
+    return n / n.norm(dim=1, keepdim=True).clamp_min(1e-20)
+
+
+def face_areas(vertices: Tensor, faces: Tensor) -> Tensor:
+    """[F] triangle areas."""
+    v0 = vertices[faces[:, 0]]
+    v1 = vertices[faces[:, 1]]
+    v2 = vertices[faces[:, 2]]
+    return 0.5 * torch.linalg.cross(v1 - v0, v2 - v0).norm(dim=1)
+
+
+def face_centroids(vertices: Tensor, faces: Tensor) -> Tensor:
+    """[F,3] triangle centroids."""
+    return vertices[faces].mean(dim=1)
+
+
+def face_edge_lengths(vertices: Tensor, faces: Tensor) -> Tensor:
+    """[F,3] edge lengths; column e = |v[faces[:,e]] - v[faces[:,(e+1)%3]]|."""
+    va = vertices[faces]
+    vb = vertices[faces.roll(shifts=-1, dims=1)]
+    return (vb - va).norm(dim=-1).to(torch.float32)
+
+
+def chart_3d_areas(face_area: Tensor, face_chart: Tensor, n_charts: int) -> Tensor:
+    """[n_charts] sum of face areas per chart."""
+    out = torch.zeros(n_charts, dtype=face_area.dtype, device=face_area.device)
+    out.scatter_add_(0, face_chart, face_area)
+    return out
+
+
+@dataclass
+class MeshData:
+    """Cleaned mesh with adjacency; face_face[f, i] = face sharing edge (faces[f,i], faces[f,(i+1)%3]) or -1 if boundary."""
+
+    vertices: Tensor                 # [V, 3] float
+    faces: Tensor                    # [F, 3] long
+    face_face: Tensor                # [F, 3] long, neighbor face id or -1
+    face_normal: Tensor              # [F, 3] float
+    face_area: Tensor                # [F] float
+    face_centroid: Tensor            # [F, 3] float
+    component: Tensor                # [F] long, connected-component id
+    n_components: int
+
+
+def build_mesh(vertices: Tensor, faces: Tensor) -> MeshData:
+    """Build adjacency; non-manifold edges (>2 incident faces) get no neighbor and act as boundary."""
+    if vertices.dtype != torch.float32:
+        vertices = vertices.to(torch.float32)
+    if faces.dtype != torch.long:
+        faces = faces.to(torch.long)
+
+    device = faces.device
+    V = vertices.shape[0]
+    F = faces.shape[0]
+
+    # Per directed face-edge; flat layout p = f*3+i.
+    a = faces.flatten()
+    b = faces.roll(shifts=-1, dims=1).flatten()
+    lo = torch.minimum(a, b)
+    hi = torch.maximum(a, b)
+    edge_key = lo * (V + 1) + hi
+
+    # Pair manifold (count==2) face-edges; others get no neighbor.
+    _, inverse, counts = torch.unique(edge_key, return_inverse=True, return_counts=True)
+    edge_count = counts[inverse]
+    manifold_mask = edge_count == 2
+
+    sort_idx = torch.argsort(edge_key, stable=True)
+    sorted_manifold = manifold_mask[sort_idx]
+    pair_positions = sort_idx[sorted_manifold]
+    pair_a = pair_positions[0::2]
+    pair_b = pair_positions[1::2]
+
+    face_id_flat = torch.arange(F, device=device).repeat_interleave(3)
+    face_face_flat = torch.full((3 * F,), -1, dtype=torch.long, device=device)
+    face_face_flat[pair_a] = face_id_flat[pair_b]
+    face_face_flat[pair_b] = face_id_flat[pair_a]
+    face_face = face_face_flat.view(F, 3)
+
+    face_face_np = face_face.cpu().numpy()
+    rows_mask = face_face_np >= 0
+    if rows_mask.any():
+        rows = np.broadcast_to(np.arange(F)[:, None], (F, 3))[rows_mask]
+        cols = face_face_np[rows_mask]
+        adj = csr_matrix(
+            (np.ones(rows.size, dtype=np.int8), (rows, cols)),
+            shape=(F, F),
+        )
+    else:
+        adj = csr_matrix((F, F), dtype=np.int8)
+    n_components, labels = connected_components(adj, directed=False)
+
+    face_normal = face_normals(vertices, faces)
+    face_area = face_areas(vertices, faces)
+    face_centroid = face_centroids(vertices, faces)
+
+    return MeshData(
+        vertices=vertices,
+        faces=faces,
+        face_face=face_face,
+        face_normal=face_normal,
+        face_area=face_area,
+        face_centroid=face_centroid,
+        component=torch.from_numpy(labels.astype(np.int64)).to(device),
+        n_components=int(n_components),
+    )
+
+
+def chart_boundary_loops(
+    faces_subset: Tensor, face_face_subset: Tensor
+) -> List[List[int]]:
+    """Return ordered boundary vertex loops for a chart submesh (face_face_subset[f,i]==-1 marks a boundary edge)."""
+    F = faces_subset.shape[0]
+    faces_np = faces_subset.cpu().numpy()
+    ff = face_face_subset.cpu().numpy()
+
+    next_v: Dict[int, int] = {}
+    for f in range(F):
+        for i in range(3):
+            if ff[f, i] == -1:
+                a = int(faces_np[f, i])
+                b = int(faces_np[f, (i + 1) % 3])
+                next_v[a] = b
+
+    loops: List[List[int]] = []
+    visited = set()
+    for start in list(next_v.keys()):
+        if start in visited:
+            continue
+        loop = [start]
+        visited.add(start)
+        cur = next_v.get(start)
+        while cur is not None and cur != start:
+            if cur in visited:
+                break
+            loop.append(cur)
+            visited.add(cur)
+            cur = next_v.get(cur)
+        if len(loop) >= 3:
+            loops.append(loop)
+    return loops
diff --git a/comfy_extras/mesh3d/uv_unwrap/pack.py b/comfy_extras/mesh3d/uv_unwrap/pack.py
new file mode 100644
index 0000000000000000000000000000000000000000..d40c0644b0c8739375c8dfa3501481ae50207dac
--- /dev/null
+++ b/comfy_extras/mesh3d/uv_unwrap/pack.py
@@ -0,0 +1,867 @@
+"""Atlas packing via bitmap rasterize-and-place."""
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Tuple
+
+import numpy as np
+import torch
+from torch import Tensor
+from torch.nn.functional import max_pool1d
+
+import comfy.model_management
+
+# Numba is optional, but ~5x faster than torch on these operations, potential TODO: comfy-kitchen cuda/triton kernels as even faster alternative
+try:
+    from numba import njit as _njit, prange as _prange, get_num_threads as _nb_threads
+    _HAVE_NUMBA_PACK = True
+except ImportError:
+    _HAVE_NUMBA_PACK = False
+    _prange = range
+    def _nb_threads(): return 1
+    def _njit(*args, **kwargs):
+        def deco(fn): return fn
+        return deco if not args else args[0]
+
+
+# Cap on deterministic sweep density: tiny charts on a large atlas would otherwise enumerate every texel column.
+_SWEEP_CAP = 1024
+
+
+@dataclass
+class ChartPlacement:
+    chart_id: int
+    offset: Tuple[float, float]       # in texels
+    scale: float                      # texels per UV unit
+    rotation: float = 0.0             # radians
+    swap_xy: bool = False             # extra 90° bitmap rotation chosen at place time
+    chart_h: float = 0.0              # unswapped bitmap height in texels (rotation pivot)
+
+
+@_njit(cache=True, boundscheck=False, parallel=True)
+def _prepare_dims_jit(uvs, uv_off, a3, auv, tpu, padding, theta, scale, bw, bh, rot_uv):
+    """Pass 1: per-chart best rotation, texel scale, rotated/scaled UVs, padded bitmap dims."""
+    n = uv_off.shape[0] - 1
+    half_pi = math.pi * 0.5
+    for c in _prange(n):
+        v0, v1 = uv_off[c], uv_off[c + 1]
+        best_area = 1e30
+        best_t = 0.0
+        for k in range(36):
+            th = half_pi * k / 36.0
+            co = math.cos(th)
+            si = math.sin(th)
+            xmin = 1e30
+            xmax = -1e30
+            ymin = 1e30
+            ymax = -1e30
+            for i in range(v0, v1):
+                xr = uvs[i, 0] * co - uvs[i, 1] * si
+                yr = uvs[i, 0] * si + uvs[i, 1] * co
+                if xr < xmin:
+                    xmin = xr
+                if xr > xmax:
+                    xmax = xr
+                if yr < ymin:
+                    ymin = yr
+                if yr > ymax:
+                    ymax = yr
+            area = (xmax - xmin) * (ymax - ymin)
+            if area < best_area:
+                best_area = area
+                best_t = th
+        theta[c] = best_t
+        co = math.cos(best_t)
+        si = math.sin(best_t)
+        xmin = 1e30
+        xmax = -1e30
+        ymin = 1e30
+        ymax = -1e30
+        for i in range(v0, v1):
+            xr = uvs[i, 0] * co - uvs[i, 1] * si
+            yr = uvs[i, 0] * si + uvs[i, 1] * co
+            rot_uv[i, 0] = xr
+            rot_uv[i, 1] = yr
+            if xr < xmin:
+                xmin = xr
+            if xr > xmax:
+                xmax = xr
+            if yr < ymin:
+                ymin = yr
+            if yr > ymax:
+                ymax = yr
+        if v1 == v0:
+            xmin = 0.0
+            xmax = 0.0
+            ymin = 0.0
+            ymax = 0.0
+        s = math.sqrt(max(a3[c], 1e-12) / max(auv[c], 1e-12)) * tpu
+        nominal = math.sqrt(max(a3[c], 1e-12)) * tpu
+        max_bbox = max(8.0, 4.0 * nominal)
+        bbox_max = max(max(xmax - xmin, ymax - ymin), 1e-12)
+        if s * bbox_max > max_bbox:
+            s = max_bbox / bbox_max
+        scale[c] = s
+        wmax = 0.0
+        hmax = 0.0
+        for i in range(v0, v1):
+            rot_uv[i, 0] = (rot_uv[i, 0] - xmin) * s
+            rot_uv[i, 1] = (rot_uv[i, 1] - ymin) * s
+            if rot_uv[i, 0] > wmax:
+                wmax = rot_uv[i, 0]
+            if rot_uv[i, 1] > hmax:
+                hmax = rot_uv[i, 1]
+        bw[c] = int(math.ceil(wmax)) + padding + 1
+        bh[c] = int(math.ceil(hmax)) + padding + 1
+
+
+@_njit(cache=True, boundscheck=False, parallel=True)
+def _raster_all_jit(rot_uv, uv_off, faces, f_off, bw, bh, boff, buf, padding,
+                    tw, th_out, perim):
+    """Pass 2: rasterize + dilate each chart into the flat buffer; records trimmed dims
+    (origin kept) and the perimeter used for placement ordering."""
+    n = uv_off.shape[0] - 1
+    eps = 1e-7
+    for c in _prange(n):
+        f0, f1 = f_off[c], f_off[c + 1]
+        v0 = uv_off[c]
+        V = uv_off[c + 1] - v0
+        w = bw[c]
+        h = bh[c]
+        o = boff[c]
+        for fi in range(f0, f1):
+            i0 = faces[fi, 0] + v0
+            i1 = faces[fi, 1] + v0
+            i2 = faces[fi, 2] + v0
+            x0 = rot_uv[i0, 0]
+            y0 = rot_uv[i0, 1]
+            x1 = rot_uv[i1, 0]
+            y1 = rot_uv[i1, 1]
+            x2 = rot_uv[i2, 0]
+            y2 = rot_uv[i2, 1]
+            xmin_f = min(x0, min(x1, x2))
+            xmax_f = max(x0, max(x1, x2))
+            ymin_f = min(y0, min(y1, y2))
+            ymax_f = max(y0, max(y1, y2))
+            xmin = max(int(math.floor(xmin_f)), 0)
+            xmax = min(int(math.ceil(xmax_f)), w - 1)
+            ymin = max(int(math.floor(ymin_f)), 0)
+            ymax = min(int(math.ceil(ymax_f)), h - 1)
+            if xmax < xmin or ymax < ymin:
+                continue
+            denom = (y1 - y2) * (x0 - x2) + (x2 - x1) * (y0 - y2)
+            if abs(denom) < 1e-20:
+                continue
+            inv_denom = 1.0 / denom
+            for py in range(ymin, ymax + 1):
+                yc = py + 0.5
+                for px in range(xmin, xmax + 1):
+                    xc = px + 0.5
+                    aa = ((y1 - y2) * (xc - x2) + (x2 - x1) * (yc - y2)) * inv_denom
+                    bb = ((y2 - y0) * (xc - x2) + (x0 - x2) * (yc - y2)) * inv_denom
+                    cc = 1.0 - aa - bb
+                    if aa >= -eps and bb >= -eps and cc >= -eps:
+                        buf[o + py * w + px] = True
+        # Manhattan dilation by `padding` steps (ping-pong on a scratch copy)
+        if padding > 0 and f1 > f0:
+            tmp = np.empty(h * w, dtype=np.bool_)
+            for _ in range(padding):
+                for j in range(h * w):
+                    tmp[j] = buf[o + j]
+                for py in range(h):
+                    for px in range(w):
+                        if tmp[py * w + px]:
+                            continue
+                        hit = False
+                        if py > 0 and tmp[(py - 1) * w + px]:
+                            hit = True
+                        elif py < h - 1 and tmp[(py + 1) * w + px]:
+                            hit = True
+                        elif px > 0 and tmp[py * w + px - 1]:
+                            hit = True
+                        elif px < w - 1 and tmp[py * w + px + 1]:
+                            hit = True
+                        if hit:
+                            buf[o + py * w + px] = True
+        # trimmed dims (keep origin; 1x1 empty bitmap when nothing was rasterized)
+        rmax = -1
+        cmax = -1
+        for py in range(h):
+            for px in range(w):
+                if buf[o + py * w + px]:
+                    if py > rmax:
+                        rmax = py
+                    if px > cmax:
+                        cmax = px
+        if rmax < 0:
+            for j in range(h * w):
+                buf[o + j] = False
+            tw[c] = 1
+            th_out[c] = 1
+        else:
+            tw[c] = cmax + 1
+            th_out[c] = rmax + 1
+        # unique-edge perimeter via sorted int64 keys
+        Fc = f1 - f0
+        if Fc > 0 and V > 0:
+            keys = np.empty(Fc * 3, dtype=np.int64)
+            for fi in range(f0, f1):
+                for j in range(3):
+                    a = faces[fi, j]
+                    b = faces[fi, (j + 1) % 3]
+                    if a < b:
+                        keys[(fi - f0) * 3 + j] = a * V + b
+                    else:
+                        keys[(fi - f0) * 3 + j] = b * V + a
+            keys = np.sort(keys)
+            p = 0.0
+            for i in range(keys.shape[0]):
+                if i > 0 and keys[i] == keys[i - 1]:
+                    continue
+                a = keys[i] // V + v0
+                b = keys[i] % V + v0
+                dx = rot_uv[a, 0] - rot_uv[b, 0]
+                dy = rot_uv[a, 1] - rot_uv[b, 1]
+                p += math.sqrt(dx * dx + dy * dy)
+            perim[c] = p
+
+
+@_njit(cache=True, boundscheck=False, parallel=True)
+def _place_all_jit(buf, boff, stride_w, tw, th, order, start, stop,
+                   atlas, skyline, pool, attempts, sweep_cap, margin,
+                   n_threads, cur_wh, out_x, out_y, out_sw):
+    """Place charts order[start:stop]; returns the first index NOT processed (== stop when
+    done, earlier when the atlas must grow — the caller resizes and resumes). The candidate
+    scan is striped with a (score, index) min-reduction: deterministic for any thread count,
+    and no thread intrinsics (dynamic globals would defeat cache=True)."""
+    aw = atlas.shape[1]
+    ah = atlas.shape[0]
+    cur_w = cur_wh[0]
+    cur_h = cur_wh[1]
+    n_pool = pool.shape[0]
+    big = np.int64(1) << 62
+    nt = n_threads
+    t_score = np.empty(nt, dtype=np.int64)
+    t_k = np.empty(nt, dtype=np.int64)
+    t_x = np.empty(nt, dtype=np.int64)
+    t_y = np.empty(nt, dtype=np.int64)
+    t_sw = np.empty(nt, dtype=np.int64)
+    for oi in range(start, stop):
+        ci = order[oi]
+        if cur_h + margin > ah or cur_w + margin > aw:
+            cur_wh[0] = cur_w
+            cur_wh[1] = cur_h
+            return oi
+        w0 = tw[ci]                                   # unswapped trimmed dims
+        h0 = th[ci]
+        W = stride_w[ci]                              # row stride of the untrimmed block
+        o = boff[ci]
+        step = min(w0, h0) // 8
+        if step < 1:
+            step = 1
+        cap_step = max(cur_w, cur_h) // sweep_cap
+        if cap_step > step:
+            step = cap_step
+
+        poff = (oi * attempts) % (n_pool - attempts + 1)
+        x_range = cur_w + 1 if cur_w > 0 else 1
+        y_range = cur_h + 1 if cur_h > 0 else 1
+        # candidate groups per orientation: skyline-flush sweep, y=0 / y=cur_h sweeps,
+        # x=0 / x=cur_w sweeps; then the shared random pool
+        nx = max(cur_w, 1) // step + 2
+        ny = max(cur_h, 1) // step + 2
+        n_det = nx * 3 + ny * 2
+        total = n_det * 2 + attempts
+        for t in range(nt):
+            t_score[t] = big
+            t_k[t] = big
+        for t2 in _prange(nt):
+            for k in range(t2, total, nt):
+                x = 0                                      # int inits and no body-level continue:
+                y = 0                                      # parfor lowering types undef-path
+                swap = 0                                   # variables as f64
+                valid = True
+                if k < 2 * n_det:
+                    if k >= n_det:
+                        swap = 1
+                    kk = k - n_det if swap == 1 else k
+                    cw = w0 if swap == 0 else h0
+                    if kk < nx:                            # skyline-flush sweep
+                        x = kk * step
+                        if x > cur_w:
+                            valid = False
+                        else:
+                            x_end = x + cw
+                            if x_end > skyline.shape[0]:
+                                x_end = skyline.shape[0]
+                            for xs in range(x, x_end):
+                                if skyline[xs] > y:
+                                    y = int(skyline[xs])
+                    elif kk < 3 * nx:                      # y=0 and y=cur_h sweeps
+                        kk2 = kk - nx
+                        x = (kk2 % nx) * step
+                        if x > cur_w:
+                            valid = False
+                        elif kk2 >= nx:
+                            y = cur_h
+                    else:                                  # x=0 and x=cur_w sweeps
+                        kk2 = kk - 3 * nx
+                        if kk2 >= 2 * ny:
+                            valid = False
+                        else:
+                            y = (kk2 % ny) * step
+                            if y > cur_h:
+                                valid = False
+                            elif kk2 >= ny:
+                                x = cur_w
+                else:
+                    r = k - 2 * n_det
+                    x = int(pool[poff + r, 0] % x_range)
+                    y = int(pool[poff + r, 1] % y_range)
+                    swap = int(r & 1)
+
+                if valid:
+                    ch = h0 if swap == 0 else w0
+                    cw = w0 if swap == 0 else h0
+                    nw = cur_w if cur_w > x + cw else x + cw
+                    nh = cur_h if cur_h > y + ch else y + ch
+                    ext = nw if nw > nh else nh
+                    score = ext * ext + nw * nh
+                    if score < t_score[t2] or (score == t_score[t2] and k < t_k[t2]):
+                        ok = True
+                        for j in range(ch):
+                            yy = int(y + j)
+                            if yy >= ah:
+                                continue
+                            for i in range(cw):
+                                if swap == 0:
+                                    bit = buf[o + j * W + i]
+                                else:
+                                    # 90deg rotation: bm_rot[j, i] = bm[h0-1-i, j]
+                                    bit = buf[o + (h0 - 1 - i) * W + j]
+                                if not bit:
+                                    continue
+                                xx = int(x + i)
+                                if xx >= aw:
+                                    continue
+                                if atlas[yy, xx]:
+                                    ok = False
+                                    break
+                            if not ok:
+                                break
+                        if ok:
+                            t_score[t2] = score
+                            t_k[t2] = k
+                            t_x[t2] = x
+                            t_y[t2] = y
+                            t_sw[t2] = swap
+
+        best_x = -1
+        best_y = -1
+        best_swap = 0
+        bs = big
+        bk = big
+        for t in range(nt):
+            if t_score[t] < bs or (t_score[t] == bs and t_k[t] < bk):
+                bs = t_score[t]
+                bk = t_k[t]
+                best_x = t_x[t]
+                best_y = t_y[t]
+                best_swap = t_sw[t]
+
+        if best_x < 0:                                 # fallback: extension corner
+            best_x = cur_w
+            best_y = 0
+            best_swap = 0
+        bh_ = h0 if best_swap == 0 else w0
+        bw_ = w0 if best_swap == 0 else h0
+        # blit + extents + skyline lift
+        for j in range(bh_):
+            for i in range(bw_):
+                if best_swap == 0:
+                    bit = buf[o + j * W + i]
+                else:
+                    bit = buf[o + (h0 - 1 - i) * W + j]
+                if bit:
+                    atlas[best_y + j, best_x + i] = True
+        if best_x + bw_ > cur_w:
+            cur_w = best_x + bw_
+        if best_y + bh_ > cur_h:
+            cur_h = best_y + bh_
+        for i in range(bw_):
+            col_x = best_x + i
+            if col_x >= skyline.shape[0]:
+                continue
+            col_top = -1
+            for j in range(bh_ - 1, -1, -1):
+                if best_swap == 0:
+                    bit = buf[o + j * W + i]
+                else:
+                    bit = buf[o + (h0 - 1 - i) * W + j]
+                if bit:
+                    col_top = j
+                    break
+            if col_top >= 0:
+                nh2 = best_y + col_top + 1
+                if nh2 > skyline[col_x]:
+                    skyline[col_x] = nh2
+        out_x[ci] = best_x
+        out_y[ci] = best_y
+        out_sw[ci] = best_swap
+    cur_wh[0] = cur_w
+    cur_wh[1] = cur_h
+    return stop
+
+
+# Torch fallback (used when numba is unavailable; runs on GPU if present)
+
+def _dilate_local(x: Tensor, p: int) -> Tensor:
+    """4-connectivity dilation by p over a batch of (cnt,g,g) bitmaps. Dilation distributes
+    over union, so dilating per-triangle then OR-scattering equals dilating the chart."""
+    for _ in range(p):
+        y = x.clone()
+        y[:, 1:, :] |= x[:, :-1, :]
+        y[:, :-1, :] |= x[:, 1:, :]
+        y[:, :, 1:] |= x[:, :, :-1]
+        y[:, :, :-1] |= x[:, :, 1:]
+        x = y
+    return x
+
+
+def _raster_all_torch(uvs_tex_pad, faces_pad, fmask, bw_t, bh_t, padding, device):
+    """Rasterize every chart into one flat bool buffer; buf[cbase[i]:cbase[i+1]].view(bh,bw)
+    is chart i's bitmap. Triangles are bucketed by next-pow2 bbox size to bound memory."""
+    n = uvs_tex_pad.shape[0]
+    fmax = faces_pad.shape[1]
+    bwL, bhL = bw_t.long(), bh_t.long()
+    cbase = torch.zeros(n + 1, dtype=torch.long, device=device)
+    torch.cumsum(bwL * bhL, 0, out=cbase[1:])
+    buf = torch.zeros(int(cbase[-1].item()), dtype=torch.bool, device=device)
+
+    # gather all triangle coords, keep only valid faces -> (Ttot,3,2) + chart id per triangle
+    fp = faces_pad.reshape(n, fmax * 3)
+    tri = torch.gather(uvs_tex_pad, 1, fp[..., None].expand(-1, -1, 2)).reshape(n * fmax, 3, 2)
+    fm = fmask.reshape(-1)
+    tri_f = tri[fm]
+    if tri_f.shape[0] == 0:
+        return buf, cbase
+    cid = torch.arange(n, device=device).repeat_interleave(fmax)[fm]
+
+    # per-triangle pixel bbox, inflated by padding (origin >= 0); bucket by next-pow2 max-dim
+    tmin = tri_f.amin(1)
+    tmax = tri_f.amax(1)
+    x0 = (tmin[:, 0].floor().long() - padding).clamp_min(0)
+    y0 = (tmin[:, 1].floor().long() - padding).clamp_min(0)
+    bbw = (tmax[:, 0].ceil().long() + padding) - x0 + 1
+    bbh = (tmax[:, 1].ceil().long() + padding) - y0 + 1
+    mxd = torch.maximum(bbw, bbh).clamp_min(1)
+    bsz = (2 ** torch.ceil(torch.log2(mxd.float())).long()).long()
+
+    a = tri_f[:, 0]
+    b = tri_f[:, 1]
+    c = tri_f[:, 2]
+    v0 = b - a
+    v1 = c - a
+    d00 = (v0 * v0).sum(-1)
+    d01 = (v0 * v1).sum(-1)
+    d11 = (v1 * v1).sum(-1)
+    den = (d00 * d11 - d01 * d01).clamp(min=1e-20)
+
+
+    free = comfy.model_management.get_free_memory(device)
+    budget = int(min(1 << 23, max(1 << 20, (free * 0.25) / 56)))
+    for g in sorted(set(bsz.tolist())):                                  # one batch per pow2 grid
+        sel_g = (bsz == g).nonzero(as_tuple=True)[0]
+        per = max(1, budget // (g * g))
+        for cs in range(0, sel_g.shape[0], per):
+            sel = sel_g[cs:cs + per]
+            m = sel.shape[0]
+            xs0 = x0[sel].view(m, 1, 1)
+            ys0 = y0[sel].view(m, 1, 1)
+            cc = cid[sel]
+            bwp = bwL[cc].view(m, 1, 1)
+            bhp = bhL[cc].view(m, 1, 1)
+            gi = torch.arange(g, device=device)
+            px = xs0 + gi.view(1, 1, g)
+            py = ys0 + gi.view(1, g, 1)                                        # (m,g,g) int
+            pxf = px.float() + 0.5
+            pyf = py.float() + 0.5
+            v2x = pxf - a[sel, 0].view(m, 1, 1)
+            v2y = pyf - a[sel, 1].view(m, 1, 1)
+            d20 = v2x * v0[sel, 0].view(m, 1, 1) + v2y * v0[sel, 1].view(m, 1, 1)
+            d21 = v2x * v1[sel, 0].view(m, 1, 1) + v2y * v1[sel, 1].view(m, 1, 1)
+            idn = den[sel].view(m, 1, 1).reciprocal()
+            vv = torch.addcmul(d11[sel].view(m, 1, 1) * d20, d01[sel].view(m, 1, 1), d21, value=-1) * idn
+            ww = torch.addcmul(d00[sel].view(m, 1, 1) * d21, d01[sel].view(m, 1, 1), d20, value=-1) * idn
+            uu = 1.0 - vv - ww
+            inside = (uu >= -1e-6) & (vv >= -1e-6) & (ww >= -1e-6)
+            if padding > 0:
+                inside = _dilate_local(inside, padding)
+            valid = inside & (px < bwp) & (py < bhp)
+            flat = (cbase[cc].view(m, 1, 1) + py * bwp + px)[valid]
+            buf[flat] = True
+    return buf, cbase
+
+
+def _build_candidates_gpu(sky_t, ar, cur_w, cur_h, bw0, bw1, step, rand01, device):
+    """Candidate (x, y) positions as a (2, M, 2) tensor (dim 0 = orientation). The first
+    n_sky rows per orientation are skyline-flush and collision-free by construction.
+    rand01 is (2, rand_n, 2) pre-drawn uniforms; ar a preallocated arange."""
+    hi_x = max(cur_w, 1) + 1
+    hi_y = max(cur_h, 1) + 1
+    xs = ar[0:hi_x:step]
+    ys = ar[0:hi_y:step]
+    n_sky = (hi_x + step - 1) // step
+    zx = torch.zeros_like(xs)
+    zy = torch.zeros_like(ys)
+    common = torch.cat([
+        torch.stack([xs, zx], 1), torch.stack([xs, zx + cur_h], 1),
+        torch.stack([zy, ys], 1), torch.stack([zy + cur_w, ys], 1)])
+    wm = []
+    for cw in (bw0, bw1):
+        span = (n_sky - 1) * step + cw
+        wm.append(max_pool1d(sky_t[:span].view(1, 1, -1).float(), kernel_size=cw,
+                             stride=step).view(-1))
+    sky = torch.stack([torch.stack([xs, wm[0].long()], 1),
+                       torch.stack([xs, wm[1].long()], 1)])
+    lim = torch.tensor([hi_x, hi_y], dtype=rand01.dtype, device=device)
+    rnd = (rand01 * lim).long()
+    return torch.cat([sky, common.expand(2, -1, -1), rnd], 1), n_sky
+
+
+def _best_placement_torch(atlas, pix0, dim0, dim1, cands, n_sky, cur_w, cur_h, device):
+    """Lowest-score non-colliding placement as a (3,) int tensor [x, y, swap]. The best
+    skyline candidate bounds the score; only strictly better candidates are pixel-tested."""
+    m = cands.shape[1]
+    chw = torch.tensor([[dim0[0], dim0[1]], [dim1[0], dim1[1]]], device=device)
+    nw = torch.clamp(cands[..., 0] + chw[:, 1:], min=cur_w)             # (2,M)
+    nh = torch.clamp(cands[..., 1] + chw[:, :1], min=cur_h)
+    ext = torch.maximum(nw, nh)
+    sc = ext * ext + nw * nh
+    js = sc[:, :n_sky].reshape(-1).argmin()                             # best skyline candidate
+    sky_o = js // n_sky
+    s_star = sc[:, :n_sky].reshape(-1)[js]
+    sky = torch.cat([cands[sky_o, js % n_sky], sky_o.reshape(1)])
+    cflat = cands.reshape(-1, 2)
+    surv = (sc.reshape(-1) < s_star).nonzero(as_tuple=True)[0]          # compact once
+    total = surv.shape[0]
+    if total == 0:
+        return sky
+
+    k = pix0.shape[0]
+    if k == 0:                                                          # empty chart: anywhere free
+        j = surv[sc.reshape(-1)[surv].argmin()]
+        return torch.cat([cflat[j], (j // m).reshape(1)])
+    ordr = surv[torch.argsort(sc.reshape(-1)[surv], stable=True)]
+
+    # flattened-index collision test: one int32 gather index instead of two int64 rows/cols
+    aw = atlas.shape[1]
+    idt = torch.int32 if atlas.numel() < (1 << 31) else torch.long
+    lin0 = (pix0[:, 0] * aw + pix0[:, 1]).to(idt)                       # (y, x)
+    lin1 = (pix0[:, 1] * aw + (dim0[0] - 1 - pix0[:, 0])).to(idt)       # rotated: (x, h-1-y)
+    linp = torch.stack([lin0, lin1])
+    aflat = atlas.view(-1)
+    og = (ordr >= m).long()
+    base = (cflat[ordr, 1] * aw + cflat[ordr, 0]).to(idt)
+
+    # prescreen survivors on ~128 strided pixels: a sampled hit proves collision, so only
+    # subsample-clean candidates need the exact test
+    stride = (k + 127) // 128
+    linp_sub = linp[:, ::stride].contiguous()
+    maybe = ~aflat[base[:, None] + linp_sub[og]].any(1)
+    passers = maybe.nonzero(as_tuple=True)[0]                           # ascending = score-sorted
+    npass = passers.shape[0]
+    if npass == 0:
+        return sky
+    if stride == 1:                                                     # prescreen was already exact
+        j = ordr[passers[0]]
+        return torch.cat([cflat[j], (j // m).reshape(1)])
+
+    budget = 1 << 22                                                    # pixel-tests per chunk
+    start = 0
+    while start < npass:
+        take = max(1, budget // k)
+        pi = passers[start:start + take]
+        free = ~aflat[base[pi][:, None] + linp[og[pi]]].any(1)          # (t,k) True-pixel gather
+        # single host read per chunk: whether a free hit exists and where
+        has, first = torch.stack([free.any().long(), free.long().argmax()]).tolist()
+        if has:
+            j = ordr[pi[first]]                                         # lowest score: sorted order
+            return torch.cat([cflat[j], (j // m).reshape(1)])
+        start += take
+        budget = min(budget * 4, 1 << 25)
+    return sky
+
+
+def _pack_bitmap_torch(chart_uvs, chart_3d_areas, chart_uv_areas, chart_faces,
+                       texels_per_unit, padding_texels, attempts=4096, rng_seed=0,
+                       progress_callback=None):
+    """Torch rasterize-and-place packer (numba-free fallback). Returns (placements, atlas_w, atlas_h)."""
+    n = len(chart_uvs)
+    if n == 0:
+        return [], 1, 1
+    device = comfy.model_management.get_torch_device()
+    ang = torch.linspace(0.0, math.pi / 2.0, 37, device=device)[:-1]
+    cos_a, sin_a = ang.cos(), ang.sin()
+
+    # ---- Prepare pass 1: best-rotation + scale + bbox for ALL charts at once (batched) ----
+    vcount = [int(u.shape[0]) for u in chart_uvs]
+    fcount = [int(f.shape[0]) for f in chart_faces]
+    vmax = max(vcount)
+    fmax = max(fcount)
+    uvs_pad = torch.zeros(n, vmax, 2, device=device)
+    vmask = torch.zeros(n, vmax, dtype=torch.bool, device=device)
+    faces_pad = torch.zeros(n, fmax, 3, dtype=torch.long, device=device)
+    fmask = torch.zeros(n, fmax, dtype=torch.bool, device=device)
+    for i in range(n):
+        uvs_pad[i, :vcount[i]] = chart_uvs[i].to(device=device, dtype=torch.float32)
+        vmask[i, :vcount[i]] = True
+        if fcount[i]:
+            faces_pad[i, :fcount[i]] = chart_faces[i].to(device=device, dtype=torch.long)
+            fmask[i, :fcount[i]] = True
+    u0, u1 = uvs_pad[..., 0], uvs_pad[..., 1]                            # (N,Vmax)
+    BIG = 1e30
+    mlo = torch.where(vmask, torch.zeros_like(u0), u0.new_full((), BIG))
+    mhi = torch.where(vmask, torch.zeros_like(u0), u0.new_full((), -BIG))
+    xr = torch.addcmul(u0[:, :, None] * cos_a, u1[:, :, None], sin_a, value=-1)   # (N,Vmax,A)
+    yr = torch.addcmul(u0[:, :, None] * sin_a, u1[:, :, None], cos_a)
+    xsp = (xr + mhi[:, :, None]).amax(1) - (xr + mlo[:, :, None]).amin(1)         # (N,A) masked span
+    ysp = (yr + mhi[:, :, None]).amax(1) - (yr + mlo[:, :, None]).amin(1)
+    ti = (xsp * ysp).argmin(1)                                          # (N,) best angle per chart
+    cc, ss = cos_a[ti][:, None], sin_a[ti][:, None]                     # (N,1)
+    rx = torch.addcmul(u0 * cc, u1, ss, value=-1)                      # (N,Vmax)
+    ry = torch.addcmul(u0 * ss, u1, cc)
+    rxmin = (rx + mlo).amin(1)                                          # (N,)
+    rxmax = (rx + mhi).amax(1)
+    rymin = (ry + mlo).amin(1)
+    rymax = (ry + mhi).amax(1)
+    a3 = torch.tensor([max(a, 1e-12) for a in chart_3d_areas], device=device)
+    au = torch.tensor([max(a, 1e-12) for a in chart_uv_areas], device=device)
+    base = (a3 / au).sqrt() * texels_per_unit
+    maxb = (4.0 * a3.sqrt() * texels_per_unit).clamp_min(8.0)
+    bbm = torch.maximum(rxmax - rxmin, rymax - rymin).clamp_min(1e-12)
+    scale = torch.minimum(base, maxb / bbm)                            # (N,)
+    uvs_tex_pad = torch.stack([(rx - rxmin[:, None]) * scale[:, None],
+                               (ry - rymin[:, None]) * scale[:, None]], dim=-1)   # (N,Vmax,2)
+    bw_t = ((rxmax - rxmin) * scale).ceil().int() + padding_texels + 1
+    bh_t = ((rymax - rymin) * scale).ceil().int() + padding_texels + 1
+
+    # one sync: pull all per-chart scalars
+    thetas = ang[ti].cpu().tolist()
+    scales = scale.cpu().tolist()
+
+    # ---- Prepare pass 2: rasterize ALL charts at once, then derive per-chart sparse data ----
+    buf, cbase = _raster_all_torch(uvs_tex_pad, faces_pad, fmask, bw_t, bh_t, padding_texels, device)
+
+    # nonzero over the flat buffer is ascending, so pixels come out grouped by chart
+    nz = buf.nonzero(as_tuple=True)[0]
+    del buf
+    cid = torch.searchsorted(cbase, nz, right=True) - 1
+    bwl = bw_t.long()
+    local = nz - cbase[cid]
+    py = local // bwl[cid]
+    px = local - py * bwl[cid]
+    del nz, local
+    counts = torch.bincount(cid, minlength=n)
+    rmax = torch.full((n,), -1, dtype=torch.long, device=device)
+    cmax = torch.full((n,), -1, dtype=torch.long, device=device)
+    rmax.scatter_reduce_(0, cid, py, reduce="amax")
+    cmax.scatter_reduce_(0, cid, px, reduce="amax")
+    ht = (rmax + 1).clamp_min(1)                    # trimmed bitmap dims (1x1 when empty)
+    wt = (cmax + 1).clamp_min(1)
+    pix_all = torch.stack([py, px], 1)              # True-pixel (row, col) offsets, sparse
+    pixr_all = torch.stack([px, rmax[cid] - py], 1)  # 90deg rotation: (y, x) -> (x, h-1-y)
+    meta = torch.stack([ht, wt, counts.cumsum(0)], 1).cpu().tolist()     # one sync for all charts
+    dim_l = [(m[0], m[1]) for m in meta]
+    dimr_l = [(w, h) for (h, w) in dim_l]
+    offs = [0] + [m[2] for m in meta]
+    pix_l = [pix_all[offs[i]:offs[i + 1]] for i in range(n)]
+    pixr_l = [pixr_all[offs[i]:offs[i + 1]] for i in range(n)]
+
+    # column tops (skyline lift), batched via flat scatter-amax over (chart, column) keys
+    wmax = max(max(h, w) for (h, w) in dim_l)
+    ct_pad = torch.full((n * wmax,), -1, dtype=torch.long, device=device)
+    ctr_pad = torch.full((n * wmax,), -1, dtype=torch.long, device=device)
+    ct_pad.scatter_reduce_(0, cid * wmax + px, py, reduce="amax")
+    ctr_pad.scatter_reduce_(0, cid * wmax + (rmax[cid] - py), px, reduce="amax")
+    ct_pad = ct_pad.view(n, wmax)
+    ctr_pad = ctr_pad.view(n, wmax)
+    del cid, py, px, rmax, cmax
+
+    # ---- Placement: skyline bin-pack on GPU ----
+    order = sorted(range(n), key=lambda i: -(dim_l[i][0] * dim_l[i][1]))   # biggest bitmap first
+    max_b = max(max(d) for d in dim_l)
+    margin = max_b + 8
+    side_guess = int(math.sqrt(sum(d[0] * d[1] for d in dim_l)) * 2) + 16
+    cap = side_guess + margin
+    atlas = torch.zeros((cap, cap), dtype=torch.bool, device=device)
+    sky_t = torch.zeros(cap, dtype=torch.long, device=device)
+    ar = torch.arange(cap + 1, device=device)
+    cur_w = cur_h = 0
+    placements = [None] * n
+    gen = torch.Generator(device=device).manual_seed(rng_seed)
+    rand_n = min(512, attempts)                     # random samples per orientation
+    # no _SWEEP_CAP here: the skyline-bound pruning depends on the dense sweep
+    rand01 = torch.rand(n, 2, rand_n, 2, generator=gen, device=device)   # all draws upfront
+
+    for t_i, ci in enumerate(order):
+        if progress_callback is not None and (t_i & 255) == 0:
+            progress_callback(n + t_i, 2 * n)
+        if cur_h + margin > atlas.shape[0] or cur_w + margin > atlas.shape[1]:
+            ns = max(atlas.shape[0], cur_h + margin, cur_w + margin)
+            na = torch.zeros((ns, ns), dtype=torch.bool, device=device)
+            na[:atlas.shape[0], :atlas.shape[1]] = atlas
+            atlas = na
+            nsk = torch.zeros(ns, dtype=torch.long, device=device)
+            nsk[:sky_t.shape[0]] = sky_t
+            sky_t = nsk
+            ar = torch.arange(ns + 1, device=device)
+        dim, dimr = dim_l[ci], dimr_l[ci]
+        step = max(1, min(dim[0], dim[1]) // 8)
+        cands, n_sky = _build_candidates_gpu(
+            sky_t, ar, cur_w, cur_h, dim[1], dimr[1], step, rand01[t_i], device)
+        res = _best_placement_torch(atlas, pix_l[ci], dim, dimr,
+                                    cands, n_sky, cur_w, cur_h, device)
+        bx, by, swap = (int(v) for v in res.tolist())
+        if bx < 0:
+            bx, by, swap = cur_w, 0, 0
+        pix = pixr_l[ci] if swap else pix_l[ci]
+        bh_, bw_ = (dimr if swap else dim)
+        atlas[by + pix[:, 0], bx + pix[:, 1]] = True                    # sparse blit
+        cur_w = max(cur_w, bx + bw_)
+        cur_h = max(cur_h, by + bh_)
+        ct = (ctr_pad if swap else ct_pad)[ci, :bw_]                    # GPU skyline lift
+        ix = ar[bx:bx + bw_]
+        sky_t[ix] = torch.where(ct >= 0, torch.maximum(sky_t[ix], by + ct + 1), sky_t[ix])
+        placements[ci] = ChartPlacement(chart_id=ci, offset=(float(bx), float(by)),
+                                        scale=scales[ci], rotation=thetas[ci], swap_xy=bool(swap),
+                                        chart_h=float(dim_l[ci][0]))
+    return placements, cur_w, cur_h
+
+
+def pack_bitmap_concat(
+    uvs_cat: np.ndarray,          # (sumV, 2) per-chart concatenated UVs
+    uv_offsets: np.ndarray,       # (n+1,)
+    faces_cat: np.ndarray,        # (sumF, 3) local vert ids per chart
+    face_offsets: np.ndarray,     # (n+1,)
+    chart_3d_areas: np.ndarray,
+    chart_uv_areas: np.ndarray,
+    texels_per_unit: float = 256.0,
+    padding_texels: int = 2,
+    attempts: int = 4096,
+    rng_seed: int = 0,
+    progress_callback=None,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, int, int]:
+    """Rasterize-and-place packer over concatenated chart arrays (no per-chart python).
+    Returns (x, y, swap, rotation, scale, chart_h, atlas_w, atlas_h) with one entry per chart.
+    progress_callback(done, total) is invoked periodically; total is 2*n_charts."""
+    n = int(uv_offsets.shape[0]) - 1
+    empty = np.zeros(n, dtype=np.int64)
+    if n == 0:
+        return empty, empty, empty, empty.astype(np.float64), empty.astype(np.float64), empty, 1, 1
+    if not _HAVE_NUMBA_PACK:
+        chart_uvs = [torch.from_numpy(np.ascontiguousarray(uvs_cat[uv_offsets[c]:uv_offsets[c + 1]]))
+                     for c in range(n)]
+        chart_faces = [torch.from_numpy(np.ascontiguousarray(faces_cat[face_offsets[c]:face_offsets[c + 1]]))
+                       for c in range(n)]
+        placements, w, h = _pack_bitmap_torch(
+            chart_uvs, [float(a) for a in chart_3d_areas], [float(a) for a in chart_uv_areas],
+            chart_faces, texels_per_unit, padding_texels, attempts=attempts,
+            rng_seed=rng_seed, progress_callback=progress_callback)
+        px = np.array([p.offset[0] for p in placements], dtype=np.int64)
+        py = np.array([p.offset[1] for p in placements], dtype=np.int64)
+        sw = np.array([1 if p.swap_xy else 0 for p in placements], dtype=np.int64)
+        th = np.array([p.rotation for p in placements], dtype=np.float64)
+        sc = np.array([p.scale for p in placements], dtype=np.float64)
+        chh = np.array([p.chart_h for p in placements], dtype=np.int64)
+        return px, py, sw, th, sc, chh, w, h
+
+    uvs64 = np.ascontiguousarray(uvs_cat, dtype=np.float64)
+    faces64 = np.ascontiguousarray(faces_cat, dtype=np.int64)
+    uv_off = np.ascontiguousarray(uv_offsets, dtype=np.int64)
+    f_off = np.ascontiguousarray(face_offsets, dtype=np.int64)
+    a3 = np.ascontiguousarray(chart_3d_areas, dtype=np.float64)
+    auv = np.ascontiguousarray(chart_uv_areas, dtype=np.float64)
+
+    theta = np.zeros(n, dtype=np.float64)
+    scale = np.zeros(n, dtype=np.float64)
+    bw = np.zeros(n, dtype=np.int64)
+    bh = np.zeros(n, dtype=np.int64)
+    rot_uv = np.empty_like(uvs64)
+    _prepare_dims_jit(uvs64, uv_off, a3, auv, float(texels_per_unit), int(padding_texels),
+                      theta, scale, bw, bh, rot_uv)
+    boff = np.zeros(n + 1, dtype=np.int64)
+    np.cumsum(bw * bh, out=boff[1:])
+    buf = np.zeros(int(boff[-1]), dtype=np.bool_)
+    tw = np.zeros(n, dtype=np.int64)
+    th_arr = np.zeros(n, dtype=np.int64)
+    perim = np.zeros(n, dtype=np.float64)
+    _raster_all_jit(rot_uv, uv_off, faces64, f_off, bw, bh, boff, buf,
+                    int(padding_texels), tw, th_arr, perim)
+    if progress_callback is not None:
+        progress_callback(n, 2 * n)
+
+    order = np.argsort(-perim, kind="stable")
+    max_b = int(max(int(tw.max()), int(th_arr.max())))
+    margin = max_b + 8
+    side_guess = int(math.sqrt(float((tw * th_arr).sum()))) * 2 + 16
+    cap = side_guess + margin
+    atlas = np.zeros((cap, cap), dtype=np.bool_)
+    skyline = np.zeros(cap, dtype=np.int64)
+    rng = np.random.default_rng(rng_seed)
+    # shared random pool, sliced at a rotating offset per chart
+    pool = rng.integers(0, 1 << 31, size=(attempts * 8, 2)).astype(np.int64)
+    out_x = np.full(n, -1, dtype=np.int64)
+    out_y = np.full(n, -1, dtype=np.int64)
+    out_sw = np.zeros(n, dtype=np.int64)
+    cur_wh = np.zeros(2, dtype=np.int64)
+    start = 0
+    while start < n:
+        stop = min(n, start + 1024)
+        nxt = _place_all_jit(buf, boff, bw, tw, th_arr, order, start, stop,
+                             atlas, skyline, pool, int(attempts), int(_SWEEP_CAP),
+                             int(margin), int(_nb_threads()), cur_wh, out_x, out_y, out_sw)
+        if nxt < stop:                               # atlas must grow before this chart fits
+            ns = max(atlas.shape[0], int(cur_wh[1]) + margin, int(cur_wh[0]) + margin)
+            na = np.zeros((ns, ns), dtype=np.bool_)
+            na[:atlas.shape[0], :atlas.shape[1]] = atlas
+            atlas = na
+            nsk = np.zeros(ns, dtype=np.int64)
+            nsk[:skyline.shape[0]] = skyline
+            skyline = nsk
+        start = nxt
+        if progress_callback is not None:
+            progress_callback(n + start, 2 * n)
+    return out_x, out_y, out_sw, theta, scale, th_arr, int(cur_wh[0]), int(cur_wh[1])
+
+
+def apply_placements_concat(
+    uvs_cat: np.ndarray, uv_offsets: np.ndarray,
+    px: np.ndarray, py: np.ndarray, sw: np.ndarray,
+    theta: np.ndarray, scale: np.ndarray, chart_h: np.ndarray,
+    atlas_w: int, atlas_h: int,
+) -> np.ndarray:
+    """apply_placements over concatenated charts, fully vectorized. Returns (sumV, 2) float32."""
+    n = int(uv_offsets.shape[0]) - 1
+    side = float(max(atlas_w, atlas_h, 1))
+    cov = np.repeat(np.arange(n), np.diff(uv_offsets))
+    u_in = uvs_cat[:, 0].astype(np.float64)
+    v_in = uvs_cat[:, 1].astype(np.float64)
+    c = np.cos(theta)[cov]
+    s = np.sin(theta)[cov]
+    u = u_in * c - v_in * s
+    v = u_in * s + v_in * c
+    umin = np.full(n, np.inf)
+    vmin = np.full(n, np.inf)
+    np.minimum.at(umin, cov, u)
+    np.minimum.at(vmin, cov, v)
+    u = (u - umin[cov]) * scale[cov]
+    v = (v - vmin[cov]) * scale[cov]
+    swv = sw[cov].astype(bool)
+    # 90 deg rotation matching the rotated-bitmap access: (u, v) -> (chart_h - v, u)
+    u2 = np.where(swv, chart_h[cov] - v, u) + px[cov]
+    v2 = np.where(swv, u, v) + py[cov]
+    out = np.stack([u2, v2], axis=1) / side
+    np.clip(out, 0.0, 1.0, out=out)                  # slivers can stick sub-texel past extents
+    return out.astype(np.float32)
diff --git a/comfy_extras/mesh3d/uv_unwrap/parameterize.py b/comfy_extras/mesh3d/uv_unwrap/parameterize.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef002dc4d2f9e5cbad2e9e836f42b6c484bbe295
--- /dev/null
+++ b/comfy_extras/mesh3d/uv_unwrap/parameterize.py
@@ -0,0 +1,565 @@
+"""Chart parameterization: ortho PCA projection, falling back to ABF/LSCM."""
+from __future__ import annotations
+
+import warnings
+from typing import List, Tuple
+
+import numpy as np
+import scipy.sparse as sp
+import scipy.sparse.linalg as spla
+import torch
+from torch import Tensor
+
+from . import mesh as _mesh
+
+LSCM_BATCH_MAX_VERTS = 256      # charts above this solve per-chart sparse (lscm_chart)
+
+
+def solve_least_squares(A: sp.csr_matrix, b: np.ndarray) -> np.ndarray:
+    """Solve ||Ax - b||^2 by factorizing AtA."""
+    At = A.T.tocsr()
+    AtA = (At @ A).tocsc()
+    Atb = At @ b
+    return spla.spsolve(AtA, Atb)
+
+
+def _triangle_local_2d(verts_3d: np.ndarray, faces: np.ndarray) -> np.ndarray:
+    """Per-triangle 2D coords [F, 3, 2] with v0 at origin, v1 along +x."""
+    v0 = verts_3d[faces[:, 0]]
+    v1 = verts_3d[faces[:, 1]]
+    v2 = verts_3d[faces[:, 2]]
+    e01 = v1 - v0
+    e02 = v2 - v0
+    L01 = np.linalg.norm(e01, axis=1).clip(min=1e-20)
+    x_axis = e01 / L01[:, None]
+    n = np.cross(e01, e02)
+    n /= np.linalg.norm(n, axis=1, keepdims=True).clip(min=1e-20)
+    y_axis = np.cross(n, x_axis)
+
+    out = np.zeros((faces.shape[0], 3, 2), dtype=np.float64)
+    out[:, 1, 0] = L01
+    out[:, 2, 0] = (e02 * x_axis).sum(axis=1)
+    out[:, 2, 1] = (e02 * y_axis).sum(axis=1)
+    return out
+
+
+def _pick_pins(loops: List[List[int]], verts_3d: np.ndarray) -> Tuple[int, int]:
+    """Pick the longest-diameter axis-extremal boundary vertex pair across all boundary verts."""
+    if not loops:
+        # Closed surface: two far verts via two-pass farthest.
+        d2 = np.sum((verts_3d - verts_3d[0]) ** 2, axis=1)
+        a = int(np.argmax(d2))
+        d2 = np.sum((verts_3d - verts_3d[a]) ** 2, axis=1)
+        b = int(np.argmax(d2))
+        return a, b
+    boundary_verts: List[int] = []
+    for loop in loops:
+        boundary_verts.extend(loop)
+    seen = set()
+    uniq = []
+    for v in boundary_verts:
+        if v not in seen:
+            seen.add(v)
+            uniq.append(v)
+    bv = np.asarray(uniq, dtype=np.int64)
+    pts = verts_3d[bv]
+    pin_pairs = []
+    for axis in range(3):
+        i_min = int(bv[int(np.argmin(pts[:, axis]))])
+        i_max = int(bv[int(np.argmax(pts[:, axis]))])
+        d = float(np.linalg.norm(verts_3d[i_min] - verts_3d[i_max]))
+        pin_pairs.append((d, i_min, i_max))
+    d0, _, _ = pin_pairs[0]
+    d1, _, _ = pin_pairs[1]
+    d2, _, _ = pin_pairs[2]
+    if d0 > d1 and d0 > d2:
+        _, a, b = pin_pairs[0]
+    elif d1 > d2:
+        _, a, b = pin_pairs[1]
+    else:
+        _, a, b = pin_pairs[2]
+    return a, b
+
+
+def _ortho_project(verts_3d: np.ndarray) -> np.ndarray:
+    """PCA-fit plane normal, axis-aligned tangent, project verts to 2D."""
+    centroid = verts_3d.mean(axis=0)
+    pts = verts_3d - centroid
+    cov = pts.T @ pts
+    _w, ev = np.linalg.eigh(cov)
+    normal = ev[:, 0]
+    a = np.abs(normal)
+    if a[0] < a[1] and a[0] < a[2]:
+        t = np.array([1.0, 0.0, 0.0])
+    elif a[1] < a[2]:
+        t = np.array([0.0, 1.0, 0.0])
+    else:
+        t = np.array([0.0, 0.0, 1.0])
+    t = t - normal * float(np.dot(normal, t))
+    t /= max(float(np.linalg.norm(t)), 1e-20)
+    b = np.cross(normal, t)
+    return np.stack([verts_3d @ t, verts_3d @ b], axis=1)
+
+
+def ortho_project_concat(verts: np.ndarray, chart_of_vert: np.ndarray, n_charts: int) -> np.ndarray:
+    """_ortho_project for every chart at once over concatenated per-chart vertices."""
+    cnt = np.bincount(chart_of_vert, minlength=n_charts).clip(min=1).astype(np.float64)
+    cen = np.stack([np.bincount(chart_of_vert, weights=verts[:, i], minlength=n_charts)
+                    for i in range(3)], axis=1) / cnt[:, None]
+    d = verts - cen[chart_of_vert]
+    cov = np.zeros((n_charts, 3, 3), dtype=np.float64)
+    for i in range(3):
+        for j in range(i, 3):
+            s = np.bincount(chart_of_vert, weights=d[:, i] * d[:, j], minlength=n_charts)
+            cov[:, i, j] = s
+            cov[:, j, i] = s
+    _w, ev = np.linalg.eigh(cov)
+    normal = ev[:, :, 0]
+    t = np.eye(3, dtype=np.float64)[np.argmin(np.abs(normal), axis=1)]
+    t = t - normal * (normal * t).sum(axis=1, keepdims=True)
+    t /= np.linalg.norm(t, axis=1, keepdims=True).clip(min=1e-20)
+    b = np.cross(normal, t)
+    tt, bb = t[chart_of_vert], b[chart_of_vert]
+    return np.stack([(verts * tt).sum(1), (verts * bb).sum(1)], axis=1)
+
+
+def stretch_metrics_concat(
+    verts: np.ndarray, uvs: np.ndarray, faces: np.ndarray,
+    chart_of_face: np.ndarray, n_charts: int,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Per-chart Sander stretch metrics (rms, max, n_flipped, n_zero_area); rms/max inf where undefined."""
+    p = verts[faces]
+    t = uvs[faces]
+    pa_signed = 0.5 * (
+        (t[:, 1, 1] - t[:, 0, 1]) * (t[:, 2, 0] - t[:, 0, 0])
+        - (t[:, 2, 1] - t[:, 0, 1]) * (t[:, 1, 0] - t[:, 0, 0]))
+    n_flip = np.bincount(chart_of_face[pa_signed < -1e-12], minlength=n_charts)
+    n_zero = np.bincount(chart_of_face[np.abs(pa_signed) < 1e-12], minlength=n_charts)
+    pa = np.abs(pa_signed).clip(min=1e-20)
+    ga = 0.5 * np.linalg.norm(np.cross(p[:, 1] - p[:, 0], p[:, 2] - p[:, 0]), axis=1)
+    keep = (ga > 1e-12) & (np.abs(pa_signed) > 1e-12)
+    t1, s1 = t[:, 0, 0], t[:, 0, 1]
+    t2, s2 = t[:, 1, 0], t[:, 1, 1]
+    t3, s3 = t[:, 2, 0], t[:, 2, 1]
+    inv_2pa = 1.0 / (2.0 * pa)
+    Ss = (p[:, 0] * (t2 - t3)[:, None] + p[:, 1] * (t3 - t1)[:, None]
+          + p[:, 2] * (t1 - t2)[:, None]) * inv_2pa[:, None]
+    St = (p[:, 0] * (s3 - s2)[:, None] + p[:, 1] * (s1 - s3)[:, None]
+          + p[:, 2] * (s2 - s1)[:, None]) * inv_2pa[:, None]
+    a = (Ss * Ss).sum(axis=1)
+    bb = (Ss * St).sum(axis=1)
+    c = (St * St).sum(axis=1)
+    sigma2_sq = 0.5 * (a + c + np.sqrt(np.maximum(0.0, (a - c) ** 2 + 4 * bb ** 2)))
+    rms_sq = (a + c) * 0.5
+    cf = chart_of_face[keep]
+    tg = np.bincount(cf, weights=ga[keep], minlength=n_charts)
+    tp = np.bincount(cf, weights=pa[keep], minlength=n_charts)
+    rs = np.bincount(cf, weights=(rms_sq * ga)[keep], minlength=n_charts)
+    smax = np.zeros(n_charts, dtype=np.float64)
+    np.maximum.at(smax, cf, sigma2_sq[keep])
+    ok = tg > 0.0
+    tg_safe = np.where(ok, tg, 1.0)
+    norm = np.sqrt(tp / tg_safe)
+    rms = np.where(ok, np.sqrt(rs / tg_safe) * norm, np.inf)
+    mx = np.where(ok, np.sqrt(smax) * norm, np.inf)
+    return rms, mx, n_flip, n_zero
+
+
+def _segment_argmax(vals: np.ndarray, seg: np.ndarray, n: int) -> np.ndarray:
+    """Index of the (first) max element per segment; -1 for empty segments."""
+    amax = np.full(n, -np.inf)
+    np.maximum.at(amax, seg, vals)
+    hit = vals == amax[seg]
+    out = np.full(n, np.iinfo(np.int64).max, dtype=np.int64)
+    np.minimum.at(out, seg[hit], np.nonzero(hit)[0])
+    return np.where(out == np.iinfo(np.int64).max, -1, out)
+
+
+def lscm_charts_batch(
+    verts: np.ndarray,            # (sumV, 3) float64, per-chart concatenated
+    uv_pins: np.ndarray,          # (sumV, 2) float64, ortho UVs (pin values + fallback)
+    faces_gl: np.ndarray,         # (sumF, 3) global-local ids into verts
+    face_pos: np.ndarray,         # (sumF,) row index of each face within its chart
+    chart_of_face: np.ndarray,    # (sumF,)
+    chart_of_vert: np.ndarray,    # (sumV,)
+    vert_offsets: np.ndarray,     # (n_charts+1,)
+    chart_ids: np.ndarray,        # charts to solve (each with >=3 verts, >=1 face)
+    n_charts: int,
+    max_bucket_verts: int = LSCM_BATCH_MAX_VERTS,
+    device: "torch.device | None" = None,
+) -> dict:
+    """Batched dense ABF/LSCM; returns {chart_id: (Vc, 2) float32}. Charts larger than
+    max_bucket_verts are left out (the caller solves those sparse)."""
+    out: dict = {}
+    if chart_ids.size == 0:
+        return out
+    sel = np.zeros(n_charts, dtype=bool)
+    sel[chart_ids] = True
+    vcounts = np.diff(vert_offsets)
+
+    # ABF coefficients for all selected faces in one shot
+    fmask = sel[chart_of_face]
+    f_ids = np.nonzero(fmask)[0]
+    abf_ids, abf_cos, abf_sin, abf_valid = _abf_face_coefficients(verts, faces_gl[f_ids])
+
+    # farthest-point pin pair per chart (two passes)
+    vmask = sel[chart_of_vert]
+    v_ids = np.nonzero(vmask)[0]
+    cv = chart_of_vert[v_ids]
+    first = vert_offsets[:-1]
+    d0 = ((verts[v_ids] - verts[first[cv]]) ** 2).sum(1)
+    pin_a = _segment_argmax(d0, cv, n_charts)          # global vert index (into v_ids space)
+    pin_a = np.where(pin_a >= 0, v_ids[pin_a.clip(min=0)], -1)
+    d1 = ((verts[v_ids] - verts[pin_a.clip(min=0)[cv]]) ** 2).sum(1)
+    pin_b = _segment_argmax(d1, cv, n_charts)
+    pin_b = np.where(pin_b >= 0, v_ids[pin_b.clip(min=0)], -1)
+    # degenerate (all verts coincide): any distinct vert within the chart (Vc >= 3 guaranteed)
+    alt = np.where(pin_a == first, first + 1, first)
+    pin_b = np.where(pin_a == pin_b, alt, pin_b)
+
+    fcounts = np.bincount(chart_of_face[f_ids], minlength=n_charts)
+    # size-sorted chunks padded to their own max, bounded by an element budget so one
+    # face-heavy chart can't inflate a whole chunk
+    small = chart_ids[vcounts[chart_ids] <= max_bucket_verts]
+    sorted_ids = small[np.argsort(vcounts[small], kind="stable")]
+    budget = (96 << 20) // 8                            # float64 elements in a chunk's A
+    chunks = []
+    cs = 0
+    fmax_r = vmax_r = 0
+    for idx in range(sorted_ids.size):
+        c2 = sorted_ids[idx]
+        fm2 = max(fmax_r, int(fcounts[c2]))
+        vm2 = max(vmax_r, int(vcounts[c2]))
+        nb = idx - cs + 1
+        if nb > 1 and (nb > 128 or nb * 4 * fm2 * vm2 > budget):
+            chunks.append((cs, idx))
+            cs = idx
+            fmax_r, vmax_r = int(fcounts[c2]), int(vcounts[c2])
+        else:
+            fmax_r, vmax_r = fm2, vm2
+    if sorted_ids.size:
+        chunks.append((cs, sorted_ids.size))
+    for s, e in chunks:
+        cids = sorted_ids[s:e]
+        B = cids.size
+        Vmax = int(vcounts[cids].max())
+        Fmax = int(fcounts[cids].max())
+        N = 2 * Vmax
+        R = 2 * Fmax
+        compact = np.full(n_charts, -1, dtype=np.int64)
+        compact[cids] = np.arange(B)
+        fm = compact[chart_of_face[f_ids]] >= 0
+        fi = f_ids[fm]                               # face rows for this chunk
+        bi = compact[chart_of_face[fi]]              # chart slot per face
+        frow = face_pos[fi]
+        v0 = vert_offsets[chart_of_face[fi]]         # local id = global-local - v0
+        am = fm.nonzero()[0]                         # index into abf_* arrays
+
+        pieces_i: list = []
+        pieces_v: list = []
+
+        def scatter(rows, cols, vals, bsel):
+            pieces_i.append((bsel * R + rows) * N + cols)
+            pieces_v.append(vals)
+
+        val = abf_valid[am]
+        ii = am[val]
+        ids = abf_ids[ii] - v0[val, None]            # local vert ids, reordered
+        cosf, sinf = abf_cos[ii], abf_sin[ii]
+        rr, bsel = frow[val] * 2, bi[val]
+        ones = np.ones(ii.size)
+        for cc2, vv in ((ids[:, 0], cosf - 1.0), (ids[:, 0] + Vmax, -sinf),
+                        (ids[:, 1], -cosf), (ids[:, 1] + Vmax, sinf), (ids[:, 2], ones)):
+            scatter(rr, cc2, vv, bsel)
+        for cc2, vv in ((ids[:, 0], sinf), (ids[:, 0] + Vmax, cosf - 1.0),
+                        (ids[:, 1], -sinf), (ids[:, 1] + Vmax, -cosf), (ids[:, 2] + Vmax, ones)):
+            scatter(rr + 1, cc2, vv, bsel)
+
+        inv = ~val
+        if inv.any():
+            jj = fi[inv]
+            tri2d = _triangle_local_2d(verts, faces_gl[jj])
+            twice = tri2d[:, 1, 0] * tri2d[:, 2, 1] - tri2d[:, 1, 1] * tri2d[:, 2, 0]
+            w = 1.0 / np.sqrt(2.0 * np.abs(twice).clip(min=1e-20))
+            rr2, bs2 = frow[inv] * 2, bi[inv]
+            lids = faces_gl[jj] - v0[inv, None]
+            for j in range(3):
+                jp1, jp2 = (j + 1) % 3, (j + 2) % 3
+                aj = (tri2d[:, jp1, 0] - tri2d[:, jp2, 0]) * w
+                bj = (tri2d[:, jp1, 1] - tri2d[:, jp2, 1]) * w
+                vc2 = lids[:, j]
+                scatter(rr2, vc2, aj, bs2)
+                scatter(rr2, vc2 + Vmax, -bj, bs2)
+                scatter(rr2 + 1, vc2, bj, bs2)
+                scatter(rr2 + 1, vc2 + Vmax, aj, bs2)
+
+        flat = np.concatenate(pieces_i)
+        A = np.bincount(flat, weights=np.concatenate(pieces_v),
+                        minlength=B * R * N).reshape(B, R, N)
+
+        # pins: move their columns to the RHS, then constrain via identity rows
+        voff = vert_offsets[cids]
+        pa_l = pin_a[cids] - voff
+        pb_l = pin_b[cids] - voff
+        pin_cols = np.stack([pa_l, pb_l, pa_l + Vmax, pb_l + Vmax], 1)       # (B,4)
+        pin_vals = np.stack([uv_pins[pin_a[cids], 0], uv_pins[pin_b[cids], 0],
+                             uv_pins[pin_a[cids], 1], uv_pins[pin_b[cids], 1]], 1)
+        rhs = np.zeros((B, R), dtype=np.float64)
+        barange = np.arange(B)
+        for k in range(4):
+            rhs -= A[barange, :, pin_cols[:, k]] * pin_vals[:, k, None]
+            A[barange, :, pin_cols[:, k]] = 0.0
+
+        # constrained columns: the 4 pins + padding beyond each chart's vert count
+        vcs = vcounts[cids]
+        padm = np.arange(Vmax)[None, :] >= vcs[:, None]
+        con = np.concatenate([padm, padm], axis=1)                            # (B,N)
+        np.put_along_axis(con, pin_cols, True, axis=1)
+        cval = np.zeros((B, N), dtype=np.float64)
+        np.put_along_axis(cval, pin_cols, pin_vals, axis=1)
+
+        # normal equations + batched solve; the fp64 dense algebra goes to the GPU when available
+        use_gpu = device is not None and device.type == "cuda"
+        if use_gpu:
+            A_t = torch.from_numpy(A).to(device)
+            At = A_t.transpose(1, 2)
+            AtA = At @ A_t
+            Atb = (At @ torch.from_numpy(rhs).to(device).unsqueeze(2)).squeeze(2)
+            con_t = torch.from_numpy(con).to(device)
+            free2 = (~con_t[:, :, None]) & (~con_t[:, None, :])
+            AtA = AtA * free2
+            diag = torch.diagonal(AtA, dim1=1, dim2=2)
+            # median (not max) positive diagonal: a degenerate face's ~1e19 squared row
+            # weight would blow a max-scaled eps past the unit ABF rows
+            dpos = torch.where(diag > 0, diag, torch.full_like(diag, float("nan")))
+            dsc = 1e-12 * torch.nan_to_num(dpos.nanmedian(dim=1).values, nan=1e-8).clamp_min(1e-20)
+            diag += torch.where(con_t, torch.ones_like(diag), dsc[:, None].expand_as(diag))
+            Atb = torch.where(con_t, torch.from_numpy(cval).to(device), Atb)
+            x = torch.linalg.solve(AtA, Atb).cpu().numpy()
+        else:
+            At = A.transpose(0, 2, 1)
+            AtA = At @ A                             # batched BLAS dgemm
+            Atb = (At @ rhs[:, :, None])[:, :, 0]
+            AtA *= (~con[:, :, None]) & (~con[:, None, :])
+            dg = AtA.reshape(B, -1)[:, ::N + 1]
+            # median positive diagonal (see GPU branch): robust to degenerate-face weights
+            dpos = np.where(dg > 0, dg, np.nan)
+            with np.errstate(all="ignore"):
+                dsc = 1e-12 * np.nan_to_num(np.nanmedian(dpos, axis=1), nan=1e-8).clip(min=1e-20)
+            dg += np.where(con, 1.0, dsc[:, None])
+            Atb2 = np.where(con, cval, Atb)
+            x = np.linalg.solve(AtA, Atb2[..., None])[..., 0]
+        for i2, c2 in enumerate(cids):
+            vc3 = int(vcs[i2])
+            out[int(c2)] = np.stack([x[i2, :vc3], x[i2, Vmax:Vmax + vc3]], 1).astype(np.float32)
+    return out
+
+
+def _uv_boundary_self_intersects(
+    uvs: np.ndarray, faces: np.ndarray, face_face: np.ndarray, eps: float = 1e-9
+) -> bool:
+    """True if any chart-boundary edge pair crosses in 2D (ortho folded the chart)."""
+    fi, ei = np.nonzero(face_face < 0)
+    n = fi.size
+    if n < 2:
+        return False
+    a = uvs[faces[fi, ei]].astype(np.float64)
+    b = uvs[faces[fi, (ei + 1) % 3]].astype(np.float64)
+    d = b - a
+    # Pairwise segment crossings, row-chunked to bound memory at chunk*n.
+    chunk = max(1, min(n, 1_000_000 // max(n, 1)))
+    for s in range(0, n, chunk):
+        e = min(s + chunk, n)
+        d1 = d[s:e, None, :]
+        denom = d1[:, :, 0] * d[None, :, 1] - d1[:, :, 1] * d[None, :, 0]
+        rx = a[None, :, 0] - a[s:e, None, 0]
+        ry = a[None, :, 1] - a[s:e, None, 1]
+        with np.errstate(divide="ignore", invalid="ignore"):
+            t = (rx * d[None, :, 1] - ry * d[None, :, 0]) / denom
+            u = (rx * d1[:, :, 1] - ry * d1[:, :, 0]) / denom
+        cross = (
+            (np.abs(denom) >= eps)
+            & (t > eps) & (t < 1.0 - eps)
+            & (u > eps) & (u < 1.0 - eps)
+        )
+        if bool(cross.any()):
+            return True
+    return False
+
+
+def _abf_face_coefficients(
+    verts_3d: np.ndarray, faces: np.ndarray
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Per-face ABF constraint (largest-sine vertex at local index 2); returns (faces_reordered, cosine, sine, valid_mask) with valid_mask False for degenerate tris."""
+    p0 = verts_3d[faces[:, 0]]
+    p1 = verts_3d[faces[:, 1]]
+    p2 = verts_3d[faces[:, 2]]
+    e01 = p1 - p0
+    e12 = p2 - p1
+    e20 = p0 - p2
+    L01 = np.linalg.norm(e01, axis=1).clip(min=1e-20)
+    L12 = np.linalg.norm(e12, axis=1).clip(min=1e-20)
+    L20 = np.linalg.norm(e20, axis=1).clip(min=1e-20)
+    cos_a0 = ((-e20) * e01).sum(axis=1) / (L20 * L01)
+    cos_a1 = ((-e01) * e12).sum(axis=1) / (L01 * L12)
+    cos_a2 = ((-e12) * e20).sum(axis=1) / (L12 * L20)
+    cos_a0 = cos_a0.clip(-1.0, 1.0)
+    cos_a1 = cos_a1.clip(-1.0, 1.0)
+    cos_a2 = cos_a2.clip(-1.0, 1.0)
+    a = np.arccos(cos_a0)
+    b_ang = np.arccos(cos_a1)
+    c_ang = np.arccos(cos_a2)
+    angles = np.stack([a, b_ang, c_ang], axis=1)
+    sines = np.stack([np.sin(a), np.sin(b_ang), np.sin(c_ang)], axis=1)
+    valid = (angles > 1e-12).all(axis=1)
+    ids = faces.astype(np.int64).copy()
+
+    s0, s1, s2 = sines[:, 0], sines[:, 1], sines[:, 2]
+    pattA = (s1 > s0) & (s1 > s2)
+    pattB = (~pattA) & (s0 > s1) & (s0 > s2)
+
+    if pattA.any():
+        old_a = angles[pattA].copy()
+        old_s = sines[pattA].copy()
+        old_id = ids[pattA].copy()
+        angles[pattA] = old_a[:, [2, 0, 1]]
+        sines[pattA] = old_s[:, [2, 0, 1]]
+        ids[pattA] = old_id[:, [2, 0, 1]]
+    if pattB.any():
+        old_a = angles[pattB].copy()
+        old_s = sines[pattB].copy()
+        old_id = ids[pattB].copy()
+        angles[pattB] = old_a[:, [1, 2, 0]]
+        sines[pattB] = old_s[:, [1, 2, 0]]
+        ids[pattB] = old_id[:, [1, 2, 0]]
+
+    a0 = angles[:, 0]
+    s0 = sines[:, 0]
+    s1 = sines[:, 1]
+    s2 = sines[:, 2]
+    c0 = np.cos(a0)
+    ratio = np.where(s2 > 0.0, s1 / s2.clip(min=1e-20), 1.0)
+    cosine = c0 * ratio
+    sine = s0 * ratio
+    return ids, cosine, sine, valid
+
+
+def lscm_chart(
+    local_verts: Tensor,
+    local_faces: Tensor,
+    local_face_face: Tensor,
+    pin_positions: "np.ndarray | None" = None,
+) -> Tensor:
+    """ABF parameterization on one chart (degenerate faces use plain LSCM rows; two pins fix gauge at pin_positions)."""
+    verts_np = local_verts.detach().cpu().numpy().astype(np.float64)
+    faces_np = local_faces.detach().cpu().numpy().astype(np.int64)
+    Vc = verts_np.shape[0]
+    Fc = faces_np.shape[0]
+
+    if Vc < 3 or Fc == 0:
+        return torch.zeros((Vc, 2), dtype=torch.float32, device=local_verts.device)
+
+    loops = _mesh.chart_boundary_loops(local_faces, local_face_face)
+    pin_a, pin_b = _pick_pins(loops, verts_np)
+
+    if pin_positions is not None and pin_positions.shape == (Vc, 2):
+        pa = pin_positions[pin_a]
+        pb = pin_positions[pin_b]
+        u_a, v_a = float(pa[0]), float(pa[1])
+        u_b, v_b = float(pb[0]), float(pb[1])
+    else:
+        u_a, v_a = 0.0, 0.0
+        u_b, v_b = 1.0, 0.0
+
+    abf_ids, abf_cos, abf_sin, abf_valid = _abf_face_coefficients(verts_np, faces_np)
+
+    rows_list: List[np.ndarray] = []
+    cols_list: List[np.ndarray] = []
+    vals_list: List[np.ndarray] = []
+
+    # ABF rows for valid faces.
+    valid_idx = np.nonzero(abf_valid)[0]
+    if valid_idx.size:
+        Nv = valid_idx.size
+        id0 = abf_ids[valid_idx, 0]
+        id1 = abf_ids[valid_idx, 1]
+        id2 = abf_ids[valid_idx, 2]
+        cosf = abf_cos[valid_idx]
+        sinf = abf_sin[valid_idx]
+        r_real = valid_idx * 2
+        r_imag = valid_idx * 2 + 1
+        ones = np.ones(Nv, dtype=np.float64)
+        rows_list.extend([r_real] * 5)
+        cols_list.extend([id0, id0 + Vc, id1, id1 + Vc, id2])
+        vals_list.extend([cosf - 1.0, -sinf, -cosf, sinf, ones])
+        rows_list.extend([r_imag] * 5)
+        cols_list.extend([id0, id0 + Vc, id1, id1 + Vc, id2 + Vc])
+        vals_list.extend([sinf, cosf - 1.0, -sinf, -cosf, ones])
+
+    # Plain-LSCM rows for invalid (degenerate) faces.
+    invalid_idx = np.nonzero(~abf_valid)[0]
+    if invalid_idx.size:
+        tri2d_inv = _triangle_local_2d(verts_np, faces_np[invalid_idx])
+        twice_area_inv = (
+            tri2d_inv[:, 1, 0] * tri2d_inv[:, 2, 1]
+            - tri2d_inv[:, 1, 1] * tri2d_inv[:, 2, 0]
+        )
+        weight_inv = 1.0 / np.sqrt(2.0 * np.abs(twice_area_inv).clip(min=1e-20))
+        r_real_inv = invalid_idx * 2
+        r_imag_inv = invalid_idx * 2 + 1
+        for j in range(3):
+            jp1 = (j + 1) % 3
+            jp2 = (j + 2) % 3
+            a_j = (tri2d_inv[:, jp1, 0] - tri2d_inv[:, jp2, 0]) * weight_inv
+            b_j = (tri2d_inv[:, jp1, 1] - tri2d_inv[:, jp2, 1]) * weight_inv
+            v_idx = faces_np[invalid_idx, j]
+            rows_list.extend([r_real_inv, r_real_inv, r_imag_inv, r_imag_inv])
+            cols_list.extend([v_idx, v_idx + Vc, v_idx, v_idx + Vc])
+            vals_list.extend([a_j, -b_j, b_j, a_j])
+
+    rows = np.concatenate(rows_list) if rows_list else np.empty(0, dtype=np.int64)
+    cols = np.concatenate(cols_list) if cols_list else np.empty(0, dtype=np.int64)
+    vals = np.concatenate(vals_list) if vals_list else np.empty(0, dtype=np.float64)
+
+    A_full = sp.csr_matrix((vals, (rows, cols)), shape=(2 * Fc, 2 * Vc))
+
+    pin_cols = np.array([pin_a, pin_b, pin_a + Vc, pin_b + Vc], dtype=np.int64)
+    pin_vals = np.array([u_a, u_b, v_a, v_b], dtype=np.float64)
+
+    free_mask = np.ones(2 * Vc, dtype=bool)
+    free_mask[pin_cols] = False
+    free_cols = np.nonzero(free_mask)[0]
+
+    A_pinned = A_full[:, pin_cols]
+    A_free = A_full[:, free_cols]
+    b = -(A_pinned @ pin_vals)
+
+    # Singular system (under-constrained chart) falls back to ortho.
+    fallback_to_ortho = False
+    try:
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", category=sp.linalg.MatrixRankWarning)
+            x_free = solve_least_squares(A_free, b)
+        if not np.all(np.isfinite(x_free)):
+            fallback_to_ortho = True
+    except (sp.linalg.MatrixRankWarning, RuntimeError):
+        fallback_to_ortho = True  # singular / under-constrained system
+
+    if fallback_to_ortho:
+        if pin_positions is not None and pin_positions.shape == (Vc, 2):
+            uvs = pin_positions.astype(np.float32)
+        else:
+            uvs = _ortho_project(verts_np).astype(np.float32)
+        return torch.from_numpy(uvs).to(local_verts.device)
+
+    full = np.zeros(2 * Vc, dtype=np.float64)
+    full[free_cols] = x_free
+    full[pin_cols] = pin_vals
+    uvs = np.stack([full[:Vc], full[Vc:]], axis=1).astype(np.float32)
+    if not np.all(np.isfinite(uvs)):
+        if pin_positions is not None and pin_positions.shape == (Vc, 2):
+            uvs = pin_positions.astype(np.float32)
+        else:
+            uvs = _ortho_project(verts_np).astype(np.float32)
+
+    return torch.from_numpy(uvs).to(local_verts.device)
diff --git a/comfy_extras/mesh3d/uv_unwrap/segment.py b/comfy_extras/mesh3d/uv_unwrap/segment.py
new file mode 100644
index 0000000000000000000000000000000000000000..448b77e240adad139a362fe5f584488287a13f45
--- /dev/null
+++ b/comfy_extras/mesh3d/uv_unwrap/segment.py
@@ -0,0 +1,414 @@
+"""Adaptive cost-grow chart segmentation (vectorized torch, CPU or GPU)."""
+from __future__ import annotations
+
+from typing import Tuple
+
+import torch
+from torch import Tensor
+from tqdm import tqdm
+
+from .mesh import MeshData, face_edge_lengths
+
+
+DEFAULT_W_NORMAL_DEVIATION = 2.0
+DEFAULT_W_ROUNDNESS = 0.01
+DEFAULT_W_STRAIGHTNESS = 6.0
+DEFAULT_MAX_COST = 2.0
+NORMAL_DEVIATION_HARD_CUTOFF = 0.707  # ~75°
+
+
+def _grow_iter(face_chart, frontier, ff, fn, fa, fel, basis, nsum, area, perim, K,
+               nd_cutoff, tau, w_nd, w_round, w_straight):
+    """One grow pass: each frontier face joins its lowest-cost adjacent chart if cost <= tau;
+    returns the number of faces assigned."""
+    u = frontier.nonzero(as_tuple=True)[0]
+    if u.numel() == 0:
+        return 0
+    nb = ff[u]                                       # (U,3) neighbor face ids
+    nbc = torch.where(nb >= 0, face_chart[nb.clamp_min(0)], nb.new_full((), -1))
+    valid = nbc >= 0
+    d = (fn[u][:, None, :] * basis[nbc.clamp_min(0)]).sum(-1)
+    nd = (1.0 - d).clamp(0.0, 1.0)
+    valid &= nd < nd_cutoff
+    el = fel[u]                                      # (U,3)
+    # l_in per candidate chart j: edge k counts if its (assigned) neighbor is in chart j
+    inm = (nbc[:, :, None] == nbc[:, None, :]) & valid[:, None, :]
+    l_in = (el[:, None, :] * inm).sum(-1)            # (U,3)
+    tot = el.sum(-1, keepdim=True)
+    l_out = tot - l_in
+    ca = area[nbc.clamp_min(0)]
+    cp = perim[nbc.clamp_min(0)]
+    new_perim = cp - l_in + l_out
+    new_r = new_perim * new_perim / (ca + fa[u][:, None]).clamp_min(1e-20)
+    round_cost = torch.where((cp <= 1e-20) | (ca <= 1e-20) | (new_r <= 1e-20),
+                             torch.zeros_like(new_r),
+                             1.0 - (cp * cp / ca.clamp_min(1e-20)) / new_r.clamp_min(1e-20))
+    straight_cost = ((l_out - l_in) / tot.clamp_min(1e-20)).clamp(max=0.0)
+    cost = w_nd * nd + w_round * round_cost + w_straight * straight_cost
+    cost = torch.where(valid, cost, cost.new_full((), float("inf")))
+    best_cost, best_j = cost.min(1)
+    acc = best_cost <= tau
+    n_acc = int(acc.sum())
+    if n_acc == 0:
+        return 0
+
+    f_acc = u[acc]
+    c_acc = nbc.gather(1, best_j[:, None]).squeeze(1)[acc]
+    nbc_old = nbc[acc]                               # neighbor charts before this commit
+    face_chart[f_acc] = c_acc
+    nb_acc = nb[acc]
+    nbs_acc = nb_acc.clamp_min(0)
+    nbc_post = torch.where(nb_acc >= 0, face_chart[nbs_acc], nb_acc.new_full((), -1))
+    # frontier update: committed faces leave; their still-unassigned neighbors enter
+    frontier[f_acc] = False
+    grow_nb = nbs_acc[(nb_acc >= 0) & (nbc_post < 0)]
+    frontier[grow_nb] = True
+    el_acc = el[acc]
+    cx = c_acc[:, None]
+    dper = torch.where(nbc_old == cx, -el_acc,       # was member: edge turns interior
+                       torch.where(nbc_post == cx, torch.zeros_like(el_acc),  # co-committer
+                                   el_acc)).sum(1)   # boundary / other chart
+    perim.scatter_add_(0, c_acc, dper)
+    area.scatter_add_(0, c_acc, fa[f_acc])
+    nsum.index_add_(0, c_acc, fn[f_acc] * fa[f_acc, None])
+    nl = nsum[:K].norm(dim=1, keepdim=True)
+    basis[:K] = torch.where(nl > 1e-20, nsum[:K] / nl.clamp_min(1e-20), basis[:K])
+    return n_acc
+
+
+def segment_charts(
+    mesh: MeshData,
+    max_cost: float = DEFAULT_MAX_COST,
+    w_normal_deviation: float = DEFAULT_W_NORMAL_DEVIATION,
+    w_roundness: float = DEFAULT_W_ROUNDNESS,
+    w_straightness: float = DEFAULT_W_STRAIGHTNESS,
+    progress_callback=None,
+) -> Tensor:
+    """Segment mesh into charts (parallel batch cost-grow). Returns face -> chart_id."""
+    F = mesh.faces.shape[0]
+    device = mesh.faces.device
+    if F == 0:
+        return torch.zeros(0, dtype=torch.long, device=device)
+
+    fn = mesh.face_normal.detach().to(torch.float32)
+    fa = mesh.face_area.detach().to(torch.float32)
+    fc = mesh.face_centroid.detach().to(torch.float32)
+    ff = mesh.face_face.detach().long()
+    fel = face_edge_lengths(mesh.vertices, mesh.faces).detach().to(torch.float32)
+    nd_cutoff = NORMAL_DEVIATION_HARD_CUTOFF
+
+    # one seed per connected component (first face of each)
+    comp = mesh.component.detach().long().to(device)
+    ncomp = int(comp.max()) + 1 if comp.numel() else 0
+    if ncomp:
+        seeds = torch.full((ncomp,), F, dtype=torch.long, device=device)
+        seeds.scatter_reduce_(0, comp, torch.arange(F, device=device), reduce="amin")
+    else:
+        seeds = torch.zeros(1, dtype=torch.long, device=device)
+    K = seeds.shape[0]
+
+    max_total_charts = max(F, 8000)
+    cap = K + F + 1                                  # every re-seed assigns a face, so K < K0 + F
+    face_chart = torch.full((F,), -1, dtype=torch.long, device=device)
+    basis = torch.zeros(cap, 3, dtype=torch.float32, device=device)
+    nsum = torch.zeros(cap, 3, dtype=torch.float32, device=device)
+    area = torch.zeros(cap, dtype=torch.float32, device=device)
+    perim = torch.zeros(cap, dtype=torch.float32, device=device)
+    face_chart[seeds] = torch.arange(K, device=device)
+    basis[:K] = fn[seeds]
+    nsum[:K] = fn[seeds] * fa[seeds, None]
+    area[:K] = fa[seeds]
+    perim[:K] = fel[seeds].sum(1)
+    frontier = torch.zeros(F, dtype=torch.bool, device=device)
+    seed_nb = ff[seeds]
+    seed_nb = seed_nb[seed_nb >= 0]
+    frontier[seed_nb] = True
+    frontier &= face_chart < 0
+
+    min_d2 = torch.full((F,), float("inf"), dtype=torch.float32, device=device)
+    for i in range(0, K, 32):                        # chunked: (F, <=32, 3) stays small
+        d2 = ((fc[:, None, :] - fc[seeds[i:i + 32]][None, :, :]) ** 2).sum(-1)
+        min_d2 = torch.minimum(min_d2, d2.amin(1))
+
+    # Multi-pass threshold schedule (low-cost first); tau cap 0.5 keeps cones ~30deg.
+    tau_final = min(max_cost * 0.25, 0.5)
+    thresholds = [t for t in (0.05, 0.1, 0.25) if t < tau_final] + [tau_final]
+    max_inner = max(64, int(F ** 0.5) * 2)
+    outer_iter = 0
+    assigned = 0
+    tq = tqdm(total=F, desc="unwrap: segment (adaptive)", unit="face", leave=False)
+    while True:
+        outer_iter += 1
+        if outer_iter > F + 16:
+            break
+        for tau in thresholds:
+            for _ in range(max_inner):
+                n_added = _grow_iter(face_chart, frontier, ff, fn, fa, fel, basis, nsum,
+                                     area, perim, K, nd_cutoff, tau, w_normal_deviation,
+                                     w_roundness, w_straightness)
+                if n_added == 0:
+                    break
+                tq.update(n_added)
+                assigned += n_added
+                if progress_callback is not None:
+                    progress_callback(assigned, F)
+        unassigned = face_chart < 0
+        if int(unassigned.sum()) == 0:
+            break
+        if K >= max_total_charts:
+            break
+        # re-seed at the unassigned face farthest from every existing seed
+        new_seed = int(torch.where(unassigned, min_d2,
+                                   min_d2.new_full((), float("-inf"))).argmax())
+        face_chart[new_seed] = K
+        basis[K] = fn[new_seed]
+        nsum[K] = fn[new_seed] * fa[new_seed]
+        area[K] = fa[new_seed]
+        perim[K] = fel[new_seed].sum()
+        K += 1
+        min_d2 = torch.minimum(min_d2, ((fc - fc[new_seed]) ** 2).sum(-1))
+        tq.update(1)
+        frontier[new_seed] = False
+        ns_nb = ff[new_seed]
+        ns_nb = ns_nb[ns_nb >= 0]
+        frontier[ns_nb[face_chart[ns_nb] < 0]] = True
+
+    tq.close()
+
+    # Orphan cleanup: leftover faces join their best-matching neighbor's chart.
+    while True:
+        orphans = (face_chart < 0).nonzero(as_tuple=True)[0]
+        if orphans.numel() == 0:
+            break
+        nb = ff[orphans]
+        nbc = torch.where(nb >= 0, face_chart[nb.clamp_min(0)], nb.new_full((), -1))
+        valid = nbc >= 0
+        assignable = valid.any(1)
+        if not bool(assignable.any()):
+            break
+        d = (fn[orphans][:, None, :] * basis[nbc.clamp_min(0)]).sum(-1)
+        ndv = torch.where(valid, 1.0 - d, d.new_full((), float("inf")))
+        best_c = nbc.gather(1, ndv.argmin(1, keepdim=True)).squeeze(1)
+        face_chart[orphans[assignable]] = best_c[assignable]
+    leftover = (face_chart < 0).nonzero(as_tuple=True)[0]
+    if leftover.numel():                             # isolated faces become singleton charts
+        face_chart[leftover] = K + torch.arange(leftover.numel(), device=device)
+
+    _, inverse = torch.unique(face_chart, sorted=True, return_inverse=True)
+    return inverse
+
+
+# Parallel edge-collapse (PEC) chart clustering (GPU)
+def _combine_normal_cones(
+    axis_a: Tensor, half_a: Tensor,
+    axis_b: Tensor, half_b: Tensor,
+) -> Tuple[Tensor, Tensor, Tensor]:
+    """Merge two normal cones along the great circle from axis_a; returns (combined_axis, combined_half_angle, axis_angle)."""
+    cos_angle = (axis_a * axis_b).sum(dim=-1).clamp(-1.0, 1.0)
+    axis_angle = torch.acos(cos_angle)
+    new_low = torch.minimum(-half_a, axis_angle - half_b)
+    new_high = torch.maximum(half_a, axis_angle + half_b)
+    new_half = (new_high - new_low) * 0.5
+    rot_angle = (new_high + new_low) * 0.5
+    b_perp = axis_b - axis_a * cos_angle.unsqueeze(-1)
+    b_perp_norm = b_perp.norm(dim=-1, keepdim=True).clamp_min(1e-12)
+    b_perp_unit = b_perp / b_perp_norm
+    new_axis = (
+        axis_a * torch.cos(rot_angle).unsqueeze(-1)
+        + b_perp_unit * torch.sin(rot_angle).unsqueeze(-1)
+    )
+    new_axis_norm = new_axis.norm(dim=-1, keepdim=True).clamp_min(1e-12)
+    new_axis = new_axis / new_axis_norm
+    return new_axis, new_half, axis_angle
+
+
+def _build_chart_edges(
+    face_face: Tensor,
+    chart_id: Tensor,
+    face_edge_len: Tensor,
+) -> Tuple[Tensor, Tensor]:
+    """Build chart-edge list (chart_pairs[E,2] with a<b, edge_length[E]); same-chart edges dropped, duplicates summed."""
+    F = face_face.shape[0]
+    device = face_face.device
+    f_idx = torch.arange(F, device=device).repeat_interleave(3)
+    nb = face_face.flatten()
+    valid = nb >= 0
+    f_idx = f_idx[valid]
+    nb = nb[valid]
+    el = face_edge_len.flatten()[valid]
+
+    ca = chart_id[f_idx]
+    cb = chart_id[nb]
+    diff = ca != cb
+    ca = ca[diff]
+    cb = cb[diff]
+    el = el[diff]
+    if ca.numel() == 0:
+        return (
+            torch.empty((0, 2), dtype=torch.long, device=device),
+            torch.empty(0, device=device),
+        )
+
+    lo = torch.minimum(ca, cb)
+    hi = torch.maximum(ca, cb)
+    V = int(chart_id.max().item()) + 1
+    key = lo * V + hi
+    sort_idx = torch.argsort(key)
+    sorted_key = key[sort_idx]
+    sorted_lo = lo[sort_idx]
+    sorted_hi = hi[sort_idx]
+    sorted_el = el[sort_idx]
+    unique_key, inverse, counts = torch.unique(
+        sorted_key, return_inverse=True, return_counts=True
+    )
+    n_unique = unique_key.shape[0]
+    reduced_el = torch.zeros(n_unique, device=device, dtype=el.dtype)
+    reduced_el.scatter_add_(0, inverse, sorted_el)
+    first_idx = torch.cat([
+        torch.zeros(1, dtype=torch.long, device=device),
+        counts.cumsum(0)[:-1],
+    ])
+    pair_lo = sorted_lo[first_idx]
+    pair_hi = sorted_hi[first_idx]
+    chart_pairs = torch.stack([pair_lo, pair_hi], dim=1)
+    return chart_pairs, reduced_el
+
+
+def _merge_small_charts(
+    chart_id: Tensor, face_normal: Tensor, face_area: Tensor,
+    face_face: Tensor, face_edge_len: Tensor,
+    min_faces: int, cost_cap: float,
+) -> Tensor:
+    """Absorb charts under min_faces faces into their lowest-cone-cost neighbor (capped at cost_cap)."""
+    if chart_id.numel() == 0:
+        return chart_id
+    device = chart_id.device
+    for _ in range(16):
+        N = int(chart_id.max().item()) + 1
+        sizes = torch.bincount(chart_id, minlength=N)
+        # recompute cones from scratch: area-weighted mean axis, max deviation as half-angle
+        axis = torch.zeros(N, 3, dtype=torch.float32, device=device)
+        axis.index_add_(0, chart_id, face_normal * face_area[:, None])
+        axis = axis / axis.norm(dim=1, keepdim=True).clamp_min(1e-12)
+        dev = torch.acos((face_normal * axis[chart_id]).sum(1).clamp(-1.0, 1.0))
+        half = torch.zeros(N, dtype=torch.float32, device=device)
+        half.scatter_reduce_(0, chart_id, dev, reduce="amax")
+
+        edges, _ = _build_chart_edges(face_face, chart_id, face_edge_len)
+        if edges.shape[0] == 0:
+            break
+        a, b = edges[:, 0], edges[:, 1]
+        _, new_half, _ = _combine_normal_cones(axis[a], half[a], axis[b], half[b])
+        ok = new_half <= cost_cap
+        E = edges.shape[0]
+        key = (torch.clamp(new_half * 1e6, max=2e9).to(torch.int64) << 32) \
+            | torch.arange(E, dtype=torch.long, device=device)
+        best = torch.full((N,), 1 << 62, dtype=torch.long, device=device)
+        va = (sizes[a] < min_faces) & ok
+        vb = (sizes[b] < min_faces) & ok
+        best.scatter_reduce_(0, a[va], key[va], reduce="amin")
+        best.scatter_reduce_(0, b[vb], key[vb], reduce="amin")
+        src = (best < (1 << 62)).nonzero(as_tuple=True)[0]
+        if src.numel() == 0:
+            break
+        eid = best[src] & 0xFFFFFFFF
+        ea, eb = a[eid], b[eid]
+        tgt = torch.where(ea == src, eb, ea)
+        # cycle break: keep src->tgt only if tgt merges nowhere itself or src > tgt;
+        # the kept graph is then a DAG, so the pointer-doubling below terminates
+        prop = torch.arange(N, dtype=torch.long, device=device)
+        prop[src] = tgt
+        keepm = (prop[tgt] == tgt) | (src > tgt)
+        remap = torch.arange(N, dtype=torch.long, device=device)
+        remap[src[keepm]] = tgt[keepm]
+        for _ in range(32):
+            nr = remap[remap]
+            if torch.equal(nr, remap):
+                break
+            remap = nr
+        chart_id = remap[chart_id]
+        _, chart_id = torch.unique(chart_id, return_inverse=True)
+    return chart_id
+
+
+def cluster_charts_pec(
+    mesh: MeshData,
+    max_cost: float = 0.7,
+    max_iters: int = 1024,
+    min_faces: int = 8,
+    progress_callback=None,
+) -> Tensor:
+    """Parallel edge-collapse clustering; returns face_chart [F]. max_cost is the per-merge
+    cutoff (~0.7 rad ~ 40deg); charts under min_faces are then absorbed at a relaxed 2x cutoff."""
+    device = mesh.faces.device
+    F = mesh.faces.shape[0]
+    faces = mesh.faces.to(torch.long)
+    vertices = mesh.vertices.to(torch.float32)
+    face_normal = mesh.face_normal.to(torch.float32)
+    face_face = mesh.face_face.to(torch.long)
+
+    face_edge_len = face_edge_lengths(vertices, faces)
+
+    chart_id = torch.arange(F, dtype=torch.long, device=device)
+    chart_axis = face_normal.clone()
+    chart_half = torch.zeros(F, dtype=torch.float32, device=device)
+
+    for it in range(max_iters):
+        edges, _ = _build_chart_edges(face_face, chart_id, face_edge_len)
+        if edges.shape[0] == 0:
+            break
+
+        a = edges[:, 0]
+        b = edges[:, 1]
+        axis_a = chart_axis[a]
+        axis_b = chart_axis[b]
+        half_a = chart_half[a]
+        half_b = chart_half[b]
+        _, new_half, _ = _combine_normal_cones(axis_a, half_a, axis_b, half_b)
+        cost = new_half.clone()
+
+        # Pack (cost, edge_id) so scatter_reduce amin picks the right edge.
+        E = edges.shape[0]
+        N = int(chart_id.max().item()) + 1
+        edge_ids = torch.arange(E, dtype=torch.long, device=device)
+        cost_i32 = torch.clamp(cost * 1e6, max=2e9).to(torch.int64)
+        key = (cost_i32 << 32) | edge_ids
+        chart_min = torch.full((N,), (2**62), dtype=torch.long, device=device)
+        chart_min.scatter_reduce_(0, a, key, reduce="amin", include_self=True)
+        chart_min.scatter_reduce_(0, b, key, reduce="amin", include_self=True)
+
+        # Mutual-min collapse: each chart in at most one merge per iter (winners are disjoint pairs).
+        is_a_min = chart_min[a] == key
+        is_b_min = chart_min[b] == key
+        mutual = is_a_min & is_b_min
+        within = cost <= max_cost
+        winners = mutual & within
+
+        n_merge = int(winners.sum().item())
+        if n_merge == 0:
+            break
+        if progress_callback is not None:
+            progress_callback(F - N + n_merge, F)      # saturating: charts remaining vs faces
+
+        win_a = a[winners]
+        win_b = b[winners]
+
+        axis_a_w = chart_axis[win_a]
+        half_a_w = chart_half[win_a]
+        axis_b_w = chart_axis[win_b]
+        half_b_w = chart_half[win_b]
+        new_axis, new_half_w, _ = _combine_normal_cones(
+            axis_a_w, half_a_w, axis_b_w, half_b_w,
+        )
+        chart_axis[win_a] = new_axis
+        chart_half[win_a] = new_half_w
+
+        remap = torch.arange(N, dtype=torch.long, device=device)
+        remap[win_b] = win_a
+        chart_id = remap[chart_id]
+
+    if min_faces > 1:
+        chart_id = _merge_small_charts(chart_id, face_normal, mesh.face_area.to(torch.float32),
+                                       face_face, face_edge_len, min_faces, 2.0 * max_cost)
+    _, inverse = torch.unique(chart_id, sorted=True, return_inverse=True)
+    return inverse
diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py
new file mode 100644
index 0000000000000000000000000000000000000000..79780e30768055660640ba3063550e35fbbb3e7d
--- /dev/null
+++ b/comfy_extras/nodes_ace.py
@@ -0,0 +1,145 @@
+import torch
+from typing_extensions import override
+
+import comfy.model_management
+import node_helpers
+from comfy_api.latest import ComfyExtension, IO
+
+
+class TextEncodeAceStepAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TextEncodeAceStepAudio",
+            category="model/conditioning/ace",
+            inputs=[
+                IO.Clip.Input("clip"),
+                IO.String.Input("tags", multiline=True, dynamic_prompts=True),
+                IO.String.Input("lyrics", multiline=True, dynamic_prompts=True),
+                IO.Float.Input("lyrics_strength", default=1.0, min=0.0, max=10.0, step=0.01),
+            ],
+            outputs=[IO.Conditioning.Output()],
+        )
+
+    @classmethod
+    def execute(cls, clip, tags, lyrics, lyrics_strength) -> IO.NodeOutput:
+        tokens = clip.tokenize(tags, lyrics=lyrics)
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+        conditioning = node_helpers.conditioning_set_values(conditioning, {"lyrics_strength": lyrics_strength})
+        return IO.NodeOutput(conditioning)
+
+class TextEncodeAceStepAudio15(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TextEncodeAceStepAudio1.5",
+            category="model/conditioning/ace",
+            inputs=[
+                IO.Clip.Input("clip"),
+                IO.String.Input("tags", multiline=True, dynamic_prompts=True),
+                IO.String.Input("lyrics", multiline=True, dynamic_prompts=True),
+                IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
+                IO.Int.Input("bpm", default=120, min=10, max=300),
+                IO.Float.Input("duration", default=120.0, min=0.0, max=2000.0, step=0.1),
+                IO.Combo.Input("timesignature", options=['2', '3', '4', '6']),
+                IO.Combo.Input("language", options=['ar', 'az', 'bg', 'bn', 'ca', 'cs', 'da', 'de', 'el', 'en', 'es', 'fa', 'fi', 'fr', 'he', 'hi', 'hr', 'ht', 'hu', 'id', 'is', 'it', 'ja', 'ko', 'la', 'lt', 'ms', 'ne', 'nl', 'no', 'pa', 'pl', 'pt', 'ro', 'ru', 'sa', 'sk', 'sr', 'sv', 'sw', 'ta', 'te', 'th', 'tl', 'tr', 'uk', 'ur', 'vi', 'yue', 'zh', 'unknown'], default='en'),
+                IO.Combo.Input("keyscale", options=[f"{root} {quality}" for quality in ["major", "minor"] for root in ["C", "C#", "Db", "D", "D#", "Eb", "E", "F", "F#", "Gb", "G", "G#", "Ab", "A", "A#", "Bb", "B"]]),
+                IO.Boolean.Input("generate_audio_codes", default=True, tooltip="Enable the LLM that generates audio codes. This can be slow but will increase the quality of the generated audio. Turn this off if you are giving the model an audio reference.", advanced=True),
+                IO.Float.Input("cfg_scale", default=2.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                IO.Float.Input("temperature", default=0.85, min=0.0, max=2.0, step=0.01, advanced=True),
+                IO.Float.Input("top_p", default=0.9, min=0.0, max=2000.0, step=0.01, advanced=True),
+                IO.Int.Input("top_k", default=0, min=0, max=100, advanced=True),
+                IO.Float.Input("min_p", default=0.000, min=0.0, max=1.0, step=0.001, advanced=True),
+            ],
+            outputs=[IO.Conditioning.Output()],
+        )
+
+    @classmethod
+    def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale, generate_audio_codes, cfg_scale, temperature, top_p, top_k, min_p) -> IO.NodeOutput:
+        tokens = clip.tokenize(tags, lyrics=lyrics, bpm=bpm, duration=duration, timesignature=int(timesignature), language=language, keyscale=keyscale, seed=seed, generate_audio_codes=generate_audio_codes, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p)
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+        return IO.NodeOutput(conditioning)
+
+
+class EmptyAceStepLatentAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="EmptyAceStepLatentAudio",
+            display_name="Empty Ace Step 1.0 Latent Audio",
+            category="model/latent/ace",
+            inputs=[
+                IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1),
+                IO.Int.Input(
+                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
+                ),
+            ],
+            outputs=[IO.Latent.Output()],
+        )
+
+    @classmethod
+    def execute(cls, seconds, batch_size) -> IO.NodeOutput:
+        length = int(seconds * 44100 / 512 / 8)
+        latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+        return IO.NodeOutput({"samples": latent, "type": "audio"})
+
+
+class EmptyAceStep15LatentAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="EmptyAceStep1.5LatentAudio",
+            display_name="Empty Ace Step 1.5 Latent Audio",
+            category="model/latent/ace",
+            inputs=[
+                IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01),
+                IO.Int.Input(
+                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
+                ),
+            ],
+            outputs=[IO.Latent.Output()],
+        )
+
+    @classmethod
+    def execute(cls, seconds, batch_size) -> IO.NodeOutput:
+        length = round((seconds * 48000 / 1920))
+        latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+        return IO.NodeOutput({"samples": latent, "type": "audio", "downscale_ratio_temporal": 1764})
+
+class ReferenceAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ReferenceTimbreAudio",
+            display_name="Set Reference Audio",
+            category="model/conditioning",
+            is_experimental=True,
+            description="This node sets the reference audio for ace step 1.5",
+            inputs=[
+                IO.Conditioning.Input("conditioning"),
+                IO.Latent.Input("latent", optional=True),
+            ],
+            outputs=[
+                IO.Conditioning.Output(),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, conditioning, latent=None) -> IO.NodeOutput:
+        if latent is not None:
+            conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_audio_timbre_latents": [latent["samples"]]}, append=True)
+        return IO.NodeOutput(conditioning)
+
+class AceExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TextEncodeAceStepAudio,
+            EmptyAceStepLatentAudio,
+            TextEncodeAceStepAudio15,
+            EmptyAceStep15LatentAudio,
+            ReferenceAudio,
+        ]
+
+async def comfy_entrypoint() -> AceExtension:
+    return AceExtension()
diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..baf21e924e0a2d69a2144166e933dcbbbdefdd90
--- /dev/null
+++ b/comfy_extras/nodes_advanced_samplers.py
@@ -0,0 +1,153 @@
+import numpy as np
+import torch
+from tqdm.auto import trange
+from typing_extensions import override
+
+import comfy.model_patcher
+import comfy.samplers
+import comfy.utils
+from comfy.k_diffusion.sampling import to_d
+from comfy_api.latest import ComfyExtension, io
+
+
+@torch.no_grad()
+def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    if upscale_steps is None:
+        upscale_steps = max(len(sigmas) // 2 + 1, 2)
+    else:
+        upscale_steps += 1
+        upscale_steps = min(upscale_steps, len(sigmas) + 1)
+
+    upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:]
+
+    orig_shape = x.size()
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        x = denoised
+        if i < len(upscales):
+            x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled")
+
+        if sigmas[i + 1] > 0:
+            x += sigmas[i + 1] * torch.randn_like(x)
+    return x
+
+
+class SamplerLCMUpscale(io.ComfyNode):
+    UPSCALE_METHODS = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"]
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="SamplerLCMUpscale",
+            category="model/sampling/samplers",
+            inputs=[
+                io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01, advanced=True),
+                io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1, advanced=True),
+                io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
+            ],
+            outputs=[io.Sampler.Output()],
+        )
+
+    @classmethod
+    def execute(cls, scale_ratio, scale_steps, upscale_method) -> io.NodeOutput:
+        if scale_steps < 0:
+            scale_steps = None
+        sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
+        return io.NodeOutput(sampler)
+
+
+@torch.no_grad()
+def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        sigma_hat = sigmas[i]
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x - denoised + temp[0], sigmas[i], denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        x = x + d * dt
+    return x
+
+
+class SamplerLCM(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="SamplerLCM",
+            category="model/sampling/samplers",
+            description=("LCM sampler with tunable per-step noise. s_noise is a multiplier on the model's training noise scale"),
+            inputs=[
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=64.0, step=0.01,
+                               tooltip="Per-step noise multiplier at the first step (1.0 = match training)."),
+                io.Float.Input("s_noise_end", default=1.0, min=0.0, max=64.0, step=0.01,
+                               tooltip="Per-step noise multiplier at the last step. Set equal to s_noise for a constant schedule."),
+                io.Float.Input("noise_clip_std", default=0.0, min=0.0, max=10.0, step=0.01,
+                               tooltip="Clamp per-step noise to +/- N*std. 0 disables."),
+            ],
+            outputs=[io.Sampler.Output()],
+        )
+
+    @classmethod
+    def execute(cls, s_noise, s_noise_end, noise_clip_std) -> io.NodeOutput:
+        sampler = comfy.samplers.ksampler(
+            "lcm",
+            {
+                "s_noise": float(s_noise),
+                "s_noise_end": float(s_noise_end),
+                "noise_clip_std": float(noise_clip_std),
+            },
+        )
+        return io.NodeOutput(sampler)
+
+
+class SamplerEulerCFGpp(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="SamplerEulerCFGpp",
+            display_name="SamplerEulerCFG++",
+            category="experimental",  # "sampling/samplers"
+            inputs=[
+                io.Combo.Input("version", options=["regular", "alternative"], advanced=True),
+            ],
+            outputs=[io.Sampler.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, version) -> io.NodeOutput:
+        if version == "alternative":
+            sampler = comfy.samplers.KSAMPLER(sample_euler_pp)
+        else:
+            sampler = comfy.samplers.ksampler("euler_cfg_pp")
+        return io.NodeOutput(sampler)
+
+
+class AdvancedSamplersExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SamplerLCMUpscale,
+            SamplerLCM,
+            SamplerEulerCFGpp,
+        ]
+
+async def comfy_entrypoint() -> AdvancedSamplersExtension:
+    return AdvancedSamplersExtension()
diff --git a/comfy_extras/nodes_align_your_steps.py b/comfy_extras/nodes_align_your_steps.py
new file mode 100644
index 0000000000000000000000000000000000000000..e99c1dad3a183b36777404c778ae737d0761b06f
--- /dev/null
+++ b/comfy_extras/nodes_align_your_steps.py
@@ -0,0 +1,70 @@
+#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
+import numpy as np
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
+
+def loglinear_interp(t_steps, num_steps):
+    """
+    Performs log-linear interpolation of a given array of decreasing numbers.
+    """
+    xs = np.linspace(0, 1, len(t_steps))
+    ys = np.log(t_steps[::-1])
+
+    new_xs = np.linspace(0, 1, num_steps)
+    new_ys = np.interp(new_xs, xs, ys)
+
+    interped_ys = np.exp(new_ys)[::-1].copy()
+    return interped_ys
+
+NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956,  3.8636745985,  2.6946151520, 1.8841921177,  1.3943805092,  0.9642583904,  0.6523686016, 0.3977456272,  0.1515232662,  0.0291671582],
+                "SDXL":[14.6146412293, 6.3184485287,  3.7681790315,  2.1811480769, 1.3405244945,  0.8620721141,  0.5550693289,  0.3798540708, 0.2332364134,  0.1114188177,  0.0291671582],
+                "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]}
+
+class AlignYourStepsScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="AlignYourStepsScheduler",
+            search_aliases=["AYS scheduler"],
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Combo.Input("model_type", options=["SD1", "SDXL", "SVD"]),
+                io.Int.Input("steps", default=10, min=1, max=10000),
+                io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[io.Sigmas.Output()],
+        )
+
+    def get_sigmas(self, model_type, steps, denoise):
+        # Deprecated: use the V3 schema's `execute` method instead of this.
+        return AlignYourStepsScheduler().execute(model_type, steps, denoise).result
+
+    @classmethod
+    def execute(cls, model_type, steps, denoise) -> io.NodeOutput:
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return io.NodeOutput(torch.FloatTensor([]))
+            total_steps = round(steps * denoise)
+
+        sigmas = NOISE_LEVELS[model_type][:]
+        if (steps + 1) != len(sigmas):
+            sigmas = loglinear_interp(sigmas, steps + 1)
+
+        sigmas = sigmas[-(total_steps + 1):]
+        sigmas[-1] = 0
+        return io.NodeOutput(torch.FloatTensor(sigmas))
+
+
+class AlignYourStepsExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            AlignYourStepsScheduler,
+        ]
+
+async def comfy_entrypoint() -> AlignYourStepsExtension:
+    return AlignYourStepsExtension()
diff --git a/comfy_extras/nodes_apg.py b/comfy_extras/nodes_apg.py
new file mode 100644
index 0000000000000000000000000000000000000000..0234a9ec53a25ed2a946d99faf6665256877839d
--- /dev/null
+++ b/comfy_extras/nodes_apg.py
@@ -0,0 +1,110 @@
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
+
+def project(v0, v1):
+    v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
+    v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
+    v0_orthogonal = v0 - v0_parallel
+    return v0_parallel, v0_orthogonal
+
+class APG(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="APG",
+            display_name="Adaptive Projected Guidance",
+            category="model/sampling/custom",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input(
+                    "eta",
+                    default=1.0,
+                    min=-10.0,
+                    max=10.0,
+                    step=0.01,
+                    tooltip="Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1.",
+                    advanced=True,
+                ),
+                io.Float.Input(
+                    "norm_threshold",
+                    default=5.0,
+                    min=0.0,
+                    max=50.0,
+                    step=0.1,
+                    tooltip="Normalize guidance vector to this value, normalization disable at a setting of 0.",
+                    advanced=True,
+                ),
+                io.Float.Input(
+                    "momentum",
+                    default=0.0,
+                    min=-5.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Controls a running average of guidance during diffusion, disabled at a setting of 0.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, eta, norm_threshold, momentum) -> io.NodeOutput:
+        running_avg = 0
+        prev_sigma = None
+
+        def pre_cfg_function(args):
+            nonlocal running_avg, prev_sigma
+
+            if len(args["conds_out"]) == 1:
+                return args["conds_out"]
+
+            cond = args["conds_out"][0]
+            uncond = args["conds_out"][1]
+            sigma = args["sigma"][0]
+            cond_scale = args["cond_scale"]
+
+            if prev_sigma is not None and sigma > prev_sigma:
+                running_avg = 0
+            prev_sigma = sigma
+
+            guidance = cond - uncond
+
+            if momentum != 0:
+                if not torch.is_tensor(running_avg):
+                    running_avg = guidance
+                else:
+                    running_avg = momentum * running_avg + guidance
+                guidance = running_avg
+
+            if norm_threshold > 0:
+                guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True)
+                scale = torch.minimum(
+                    torch.ones_like(guidance_norm),
+                    norm_threshold / guidance_norm
+                )
+                guidance = guidance * scale
+
+            guidance_parallel, guidance_orthogonal = project(guidance, cond)
+            modified_guidance = guidance_orthogonal + eta * guidance_parallel
+
+            modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale
+
+            return [modified_cond, uncond] + args["conds_out"][2:]
+
+        m = model.clone()
+        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
+        return io.NodeOutput(m)
+
+
+class ApgExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            APG,
+        ]
+
+async def comfy_entrypoint() -> ApgExtension:
+    return ApgExtension()
diff --git a/comfy_extras/nodes_ar_video.py b/comfy_extras/nodes_ar_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4cbccb52b84b26c02b1391d316c7f878946bf3d
--- /dev/null
+++ b/comfy_extras/nodes_ar_video.py
@@ -0,0 +1,136 @@
+"""
+ComfyUI nodes for autoregressive video generation (Causal Forcing, Self-Forcing, etc.).
+  - EmptyARVideoLatent: create 5D [B, C, T, H, W] video latent tensors
+  - SamplerARVideo: SAMPLER for the block-by-block autoregressive denoising loop
+  - ARVideoI2V: image-to-video conditioning for AR models (seeds KV cache with start image)
+"""
+
+import torch
+from typing_extensions import override
+
+import comfy.model_management
+import comfy.samplers
+import comfy.utils
+from comfy_api.latest import ComfyExtension, io
+
+
+class EmptyARVideoLatent(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyARVideoLatent",
+            category="model/latent/autoregressive",
+            inputs=[
+                io.Int.Input("width", default=832, min=16, max=8192, step=16),
+                io.Int.Input("height", default=480, min=16, max=8192, step=16),
+                io.Int.Input("length", default=81, min=1, max=1024, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=64),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="LATENT"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, width, height, length, batch_size) -> io.NodeOutput:
+        lat_t = ((length - 1) // 4) + 1
+        latent = torch.zeros(
+            [batch_size, 16, lat_t, height // 8, width // 8],
+            device=comfy.model_management.intermediate_device(),
+        )
+        return io.NodeOutput({"samples": latent})
+
+
+class SamplerARVideo(io.ComfyNode):
+    """Sampler for autoregressive video models (Causal Forcing, Self-Forcing).
+
+    All AR-loop parameters are owned by this node so they live in the workflow.
+    Add new widgets here as the AR sampler grows new options.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerARVideo",
+            display_name="Sampler AR Video",
+            category="model/sampling/samplers",
+            inputs=[
+                io.Int.Input(
+                    "num_frame_per_block",
+                    default=1, min=1, max=64,
+                    tooltip="Frames per autoregressive block. 1 = framewise, "
+                            "3 = chunkwise. Must match the checkpoint's training mode.",
+                ),
+            ],
+            outputs=[io.Sampler.Output()],
+        )
+
+    @classmethod
+    def execute(cls, num_frame_per_block) -> io.NodeOutput:
+        extra_options = {
+            "num_frame_per_block": num_frame_per_block,
+        }
+        return io.NodeOutput(comfy.samplers.ksampler("ar_video", extra_options))
+
+
+class ARVideoI2V(io.ComfyNode):
+    """Image-to-video setup for AR video models (Causal Forcing, Self-Forcing).
+
+    VAE-encodes the start image and stores it in the model's transformer_options
+    so that sample_ar_video can seed the KV cache before denoising.
+    Uses the same T2V model checkpoint -- no separate I2V architecture needed.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ARVideoI2V",
+            category="model/conditioning/autoregressive",
+            inputs=[
+                io.Model.Input("model"),
+                io.Vae.Input("vae"),
+                io.Image.Input("start_image"),
+                io.Int.Input("width", default=832, min=16, max=8192, step=16),
+                io.Int.Input("height", default=480, min=16, max=8192, step=16),
+                io.Int.Input("length", default=81, min=1, max=1024, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=64),
+            ],
+            outputs=[
+                io.Model.Output(display_name="MODEL"),
+                io.Latent.Output(display_name="LATENT"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, vae, start_image, width, height, length, batch_size) -> io.NodeOutput:
+        start_image = comfy.utils.common_upscale(
+            start_image[:1].movedim(-1, 1), width, height, "bilinear", "center"
+        ).movedim(1, -1)
+
+        initial_latent = vae.encode(start_image[:, :, :, :3])
+
+        m = model.clone()
+        to = m.model_options.setdefault("transformer_options", {})
+        ar_cfg = to.setdefault("ar_config", {})
+        ar_cfg["initial_latent"] = initial_latent
+
+        lat_t = ((length - 1) // 4) + 1
+        latent = torch.zeros(
+            [batch_size, 16, lat_t, height // 8, width // 8],
+            device=comfy.model_management.intermediate_device(),
+        )
+        return io.NodeOutput(m, {"samples": latent})
+
+
+class ARVideoExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EmptyARVideoLatent,
+            SamplerARVideo,
+            ARVideoI2V,
+        ]
+
+
+async def comfy_entrypoint() -> ARVideoExtension:
+    return ARVideoExtension()
diff --git a/comfy_extras/nodes_attention_multiply.py b/comfy_extras/nodes_attention_multiply.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a28db73e4ed474981caa84767b98ca9fb3c17c7
--- /dev/null
+++ b/comfy_extras/nodes_attention_multiply.py
@@ -0,0 +1,151 @@
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
+
+def attention_multiply(attn, model, q, k, v, out):
+    m = model.clone()
+    sd = model.model_state_dict()
+
+    for key in sd:
+        if key.endswith("{}.to_q.bias".format(attn)) or key.endswith("{}.to_q.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, q)
+        if key.endswith("{}.to_k.bias".format(attn)) or key.endswith("{}.to_k.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, k)
+        if key.endswith("{}.to_v.bias".format(attn)) or key.endswith("{}.to_v.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, v)
+        if key.endswith("{}.to_out.0.bias".format(attn)) or key.endswith("{}.to_out.0.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, out)
+
+    return m
+
+
+class UNetSelfAttentionMultiply(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="UNetSelfAttentionMultiply",
+            category="experimental/attention_experiments",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+            ],
+            outputs=[io.Model.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, q, k, v, out) -> io.NodeOutput:
+        m = attention_multiply("attn1", model, q, k, v, out)
+        return io.NodeOutput(m)
+
+
+class UNetCrossAttentionMultiply(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="UNetCrossAttentionMultiply",
+            category="experimental/attention_experiments",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+            ],
+            outputs=[io.Model.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, q, k, v, out) -> io.NodeOutput:
+        m = attention_multiply("attn2", model, q, k, v, out)
+        return io.NodeOutput(m)
+
+
+class CLIPAttentionMultiply(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CLIPAttentionMultiply",
+            search_aliases=["clip attention scale", "text encoder attention"],
+            category="experimental/attention_experiments",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+            ],
+            outputs=[io.Clip.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, clip, q, k, v, out) -> io.NodeOutput:
+        m = clip.clone()
+        sd = m.patcher.model_state_dict()
+
+        for key in sd:
+            if key.endswith("self_attn.q_proj.weight") or key.endswith("self_attn.q_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, q)
+            if key.endswith("self_attn.k_proj.weight") or key.endswith("self_attn.k_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, k)
+            if key.endswith("self_attn.v_proj.weight") or key.endswith("self_attn.v_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, v)
+            if key.endswith("self_attn.out_proj.weight") or key.endswith("self_attn.out_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, out)
+        return io.NodeOutput(m)
+
+
+class UNetTemporalAttentionMultiply(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="UNetTemporalAttentionMultiply",
+            category="experimental/attention_experiments",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+            ],
+            outputs=[io.Model.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, self_structural, self_temporal, cross_structural, cross_temporal) -> io.NodeOutput:
+        m = model.clone()
+        sd = model.model_state_dict()
+
+        for k in sd:
+            if (k.endswith("attn1.to_out.0.bias") or k.endswith("attn1.to_out.0.weight")):
+                if '.time_stack.' in k:
+                    m.add_patches({k: (None,)}, 0.0, self_temporal)
+                else:
+                    m.add_patches({k: (None,)}, 0.0, self_structural)
+            elif (k.endswith("attn2.to_out.0.bias") or k.endswith("attn2.to_out.0.weight")):
+                if '.time_stack.' in k:
+                    m.add_patches({k: (None,)}, 0.0, cross_temporal)
+                else:
+                    m.add_patches({k: (None,)}, 0.0, cross_structural)
+        return io.NodeOutput(m)
+
+
+class AttentionMultiplyExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            UNetSelfAttentionMultiply,
+            UNetCrossAttentionMultiply,
+            CLIPAttentionMultiply,
+            UNetTemporalAttentionMultiply,
+        ]
+
+async def comfy_entrypoint() -> AttentionMultiplyExtension:
+    return AttentionMultiplyExtension()
diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcc913d376583bca92b52ff9fd4d4ee37883e8e8
--- /dev/null
+++ b/comfy_extras/nodes_audio.py
@@ -0,0 +1,896 @@
+import av
+import torchaudio
+import torch
+import comfy.model_management
+import folder_paths
+import os
+import hashlib
+import node_helpers
+import logging
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO, UI
+
+class EmptyLatentAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="EmptyLatentAudio",
+            display_name="Empty Latent Audio",
+            category="model/latent",
+            essentials_category="Audio",
+            inputs=[
+                IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
+                IO.Int.Input(
+                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch.",
+                ),
+            ],
+            outputs=[IO.Latent.Output()],
+        )
+
+    @classmethod
+    def execute(cls, seconds, batch_size) -> IO.NodeOutput:
+        length = round((seconds * 44100 / 2048) / 2) * 2
+        latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
+        return IO.NodeOutput({"samples": latent, "type": "audio", "downscale_ratio_temporal": 2048})
+
+    generate = execute  # TODO: remove
+
+
+class ConditioningStableAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ConditioningStableAudio",
+            category="model/conditioning/stable audio",
+            inputs=[
+                IO.Conditioning.Input("positive"),
+                IO.Conditioning.Input("negative"),
+                IO.Float.Input("seconds_start", default=0.0, min=0.0, max=1000.0, step=0.1),
+                IO.Float.Input("seconds_total", default=47.0, min=0.0, max=1000.0, step=0.1),
+            ],
+            outputs=[
+                IO.Conditioning.Output(display_name="positive"),
+                IO.Conditioning.Output(display_name="negative"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, seconds_start, seconds_total) -> IO.NodeOutput:
+        positive = node_helpers.conditioning_set_values(positive, {"seconds_start": seconds_start, "seconds_total": seconds_total})
+        negative = node_helpers.conditioning_set_values(negative, {"seconds_start": seconds_start, "seconds_total": seconds_total})
+        return IO.NodeOutput(positive, negative)
+
+    append = execute  # TODO: remove
+
+
+class VAEEncodeAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VAEEncodeAudio",
+            search_aliases=["audio to latent"],
+            display_name="VAE Encode Audio",
+            category="model/latent",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.Vae.Input("vae"),
+            ],
+            outputs=[IO.Latent.Output()],
+        )
+
+    @classmethod
+    def execute(cls, vae, audio) -> IO.NodeOutput:
+        if audio is None:
+            raise ValueError("VAEEncodeAudio: input audio is None (source video may have no audio track).")
+        sample_rate = audio["sample_rate"]
+        vae_sample_rate = getattr(vae, "audio_sample_rate", 44100)
+        if vae_sample_rate != sample_rate:
+            waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, vae_sample_rate)
+        else:
+            waveform = audio["waveform"]
+
+        t = vae.encode(waveform.movedim(1, -1))
+        return IO.NodeOutput({"samples": t})
+
+    encode = execute  # TODO: remove
+
+
+def vae_decode_audio(vae, samples, tile=None, overlap=None):
+    latent = samples["samples"]
+    if latent.is_nested:
+        latent = latent.unbind()[-1]
+
+    if tile is not None:
+        audio = vae.decode_tiled(latent, tile_x=tile, tile_y=tile, overlap=overlap).movedim(-1, 1)
+    else:
+        audio = vae.decode(latent).movedim(-1, 1)
+
+    std = torch.std(audio, dim=[1, 2], keepdim=True) * 5.0
+    std[std < 1.0] = 1.0
+    audio /= std
+    vae_sample_rate = getattr(vae, "audio_sample_rate_output", getattr(vae, "audio_sample_rate", 44100))
+    return {"waveform": audio, "sample_rate": vae_sample_rate if "sample_rate" not in samples else samples["sample_rate"]}
+
+
+class VAEDecodeAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VAEDecodeAudio",
+            search_aliases=["latent to audio"],
+            display_name="VAE Decode Audio",
+            category="model/latent",
+            inputs=[
+                IO.Latent.Input("samples"),
+                IO.Vae.Input("vae"),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, vae, samples) -> IO.NodeOutput:
+        return IO.NodeOutput(vae_decode_audio(vae, samples))
+
+    decode = execute  # TODO: remove
+
+
+class VAEDecodeAudioTiled(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VAEDecodeAudioTiled",
+            search_aliases=["latent to audio"],
+            display_name="VAE Decode Audio (Tiled)",
+            category="model/latent",
+            inputs=[
+                IO.Latent.Input("samples"),
+                IO.Vae.Input("vae"),
+                IO.Int.Input("tile_size", default=512, min=32, max=8192, step=8),
+                IO.Int.Input("overlap", default=64, min=0, max=1024, step=8),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, vae, samples, tile_size, overlap) -> IO.NodeOutput:
+        return IO.NodeOutput(vae_decode_audio(vae, samples, tile_size, overlap))
+
+
+class SaveAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAudio",
+            search_aliases=["export flac"],
+            display_name="Save Audio (FLAC) (DEPRECATED)",
+            category="audio",
+            essentials_category="Audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.String.Input("filename_prefix", default="audio/ComfyUI"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_deprecated=True,
+            is_output_node=True,
+            outputs=[IO.Audio.Output("audio")]
+        )
+
+    @classmethod
+    def execute(cls, audio, filename_prefix="ComfyUI", format="flac") -> IO.NodeOutput:
+        if audio is None:
+            raise ValueError("SaveAudio: input audio is None (source video may have no audio track).")
+        return IO.NodeOutput(
+            audio,
+            ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=format)
+        )
+
+
+class SaveAudioMP3(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAudioMP3",
+            search_aliases=["export mp3"],
+            display_name="Save Audio (MP3) (DEPRECATED)",
+            category="audio",
+            essentials_category="Audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.String.Input("filename_prefix", default="audio/ComfyUI"),
+                IO.Combo.Input("quality", options=["V0", "128k", "320k"], default="V0"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_deprecated=True,
+            is_output_node=True,
+            outputs=[IO.Audio.Output("audio")]
+        )
+
+    @classmethod
+    def execute(cls, audio, filename_prefix="ComfyUI", format="mp3", quality="128k") -> IO.NodeOutput:
+        if audio is None:
+            raise ValueError("SaveAudioMP3: input audio is None (source video may have no audio track).")
+        return IO.NodeOutput(
+            audio,
+            ui=UI.AudioSaveHelper.get_save_audio_ui(
+                audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality
+            )
+        )
+
+
+class SaveAudioOpus(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAudioOpus",
+            search_aliases=["export opus"],
+            display_name="Save Audio (Opus) (DEPRECATED)",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.String.Input("filename_prefix", default="audio/ComfyUI"),
+                IO.Combo.Input("quality", options=["64k", "96k", "128k", "192k", "320k"], default="128k"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_deprecated=True,
+            is_output_node=True,
+            outputs=[IO.Audio.Output("audio")]
+        )
+
+    @classmethod
+    def execute(cls, audio, filename_prefix="ComfyUI", format="opus", quality="V3") -> IO.NodeOutput:
+        if audio is None:
+            raise ValueError("SaveAudioOpus: input audio is None (source video may have no audio track).")
+        return IO.NodeOutput(
+            audio,
+            ui=UI.AudioSaveHelper.get_save_audio_ui(
+                audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality
+            )
+        )
+
+
+class SaveAudioAdvanced(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAudioAdvanced",
+            search_aliases=["save audio", "export audio", "output audio", "write audio", "flac", "mp3", "opus"],
+            display_name="Save Audio (Advanced)",
+            description="Saves the input audio to your ComfyUI output directory.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio", tooltip="The audio to save."),
+                IO.String.Input(
+                    "filename_prefix",
+                    default="audio/ComfyUI",
+                    tooltip=("The prefix for the file to save. May include formatting tokens such as %date:yyyy-MM-dd%."),
+                ),
+                IO.DynamicCombo.Input(
+                    "format",
+                    options=[
+                        IO.DynamicCombo.Option("flac", []),
+                        IO.DynamicCombo.Option("mp3", [
+                            IO.Combo.Input("quality", options=["V0", "128k", "320k"], default="V0"),
+                        ]),
+                        IO.DynamicCombo.Option("opus", [
+                            IO.Combo.Input("quality", options=["64k", "96k", "128k", "192k", "320k"], default="128k"),
+                        ]),
+                    ],
+                    tooltip="The file format in which to save the audio.",
+                ),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+            outputs=[IO.Audio.Output("audio")],
+        )
+
+    @classmethod
+    def execute(cls, audio, filename_prefix: str, format: dict) -> IO.NodeOutput:
+        file_format = format.get("format", None)
+        quality = format.get("quality", None)
+        if quality:
+            ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=file_format, quality=quality)
+        else:
+            ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=file_format)
+        return IO.NodeOutput(audio, ui=ui)
+
+
+class PreviewAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="PreviewAudio",
+            search_aliases=["play audio"],
+            display_name="Preview Audio",
+            category="audio",
+            description="Preview the audio without saving it to the ComfyUI output directory.",
+            inputs=[
+                IO.Audio.Input("audio"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+            outputs=[IO.Audio.Output("audio")]
+        )
+
+    @classmethod
+    def execute(cls, audio) -> IO.NodeOutput:
+        if audio is None:
+            raise ValueError("PreviewAudio: input audio is None (source video may have no audio track).")
+        return IO.NodeOutput(audio, ui=UI.PreviewAudio(audio, cls=cls))
+
+    save_flac = execute  # TODO: remove
+
+
+def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
+    """Convert audio to float 32 bits PCM format."""
+    if wav.dtype.is_floating_point:
+        return wav
+    elif wav.dtype == torch.int16:
+        return wav.float() / (2 ** 15)
+    elif wav.dtype == torch.int32:
+        return wav.float() / (2 ** 31)
+    raise ValueError(f"Unsupported wav dtype: {wav.dtype}")
+
+def load(filepath: str) -> tuple[torch.Tensor, int]:
+    with av.open(filepath) as af:
+        if not af.streams.audio:
+            raise ValueError("No audio stream found in the file.")
+
+        stream = af.streams.audio[0]
+        sr = stream.codec_context.sample_rate
+        n_channels = stream.channels
+
+        frames = []
+        length = 0
+        for frame in af.decode(streams=stream.index):
+            buf = torch.from_numpy(frame.to_ndarray())
+            if buf.shape[0] != n_channels:
+                buf = buf.view(-1, n_channels).t()
+
+            frames.append(buf)
+            length += buf.shape[1]
+
+        if not frames:
+            raise ValueError("No audio frames decoded.")
+
+        wav = torch.cat(frames, dim=1)
+        wav = f32_pcm(wav)
+        return wav, sr
+
+class LoadAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        input_dir = folder_paths.get_input_directory()
+        os.makedirs(input_dir, exist_ok=True)
+        files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"])
+        return IO.Schema(
+            node_id="LoadAudio",
+            search_aliases=["import audio", "open audio", "audio file"],
+            display_name="Load Audio",
+            category="audio",
+            essentials_category="Audio",
+            inputs=[
+                IO.Combo.Input("audio", upload=IO.UploadType.audio, options=sorted(files)),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio) -> IO.NodeOutput:
+        audio_path = folder_paths.get_annotated_filepath(audio)
+        waveform, sample_rate = load(audio_path)
+        audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
+        return IO.NodeOutput(audio)
+
+    @classmethod
+    def fingerprint_inputs(cls, audio):
+        image_path = folder_paths.get_annotated_filepath(audio)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def validate_inputs(cls, audio):
+        if not folder_paths.exists_annotated_filepath(audio):
+            return "Invalid audio file: {}".format(audio)
+        return True
+
+    load = execute  # TODO: remove
+
+
+class RecordAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecordAudio",
+            search_aliases=["microphone input", "audio capture", "voice input"],
+            display_name="Record Audio",
+            category="audio",
+            inputs=[
+                IO.Custom("AUDIO_RECORD").Input("audio"),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio) -> IO.NodeOutput:
+        audio_path = folder_paths.get_annotated_filepath(audio)
+
+        waveform, sample_rate = load(audio_path)
+        audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
+        return IO.NodeOutput(audio)
+
+    load = execute  # TODO: remove
+
+
+class TrimAudioDuration(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TrimAudioDuration",
+            search_aliases=["cut audio", "audio clip", "shorten audio"],
+            display_name="Trim Audio Duration",
+            description="Trim audio tensor into chosen time range.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.Float.Input(
+                    "start_index",
+                    default=0.0,
+                    min=-0xffffffffffffffff,
+                    max=0xffffffffffffffff,
+                    step=0.01,
+                    tooltip="Start time in seconds, can be negative to count from the end (supports sub-seconds).",
+                ),
+                IO.Float.Input(
+                    "duration",
+                    default=60.0,
+                    min=0.0,
+                    step=0.01,
+                    tooltip="Duration in seconds",
+                ),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio, start_index, duration) -> IO.NodeOutput:
+        if audio is None:
+            return IO.NodeOutput(None)
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+        audio_length = waveform.shape[-1]
+
+        if audio_length == 0:
+            return IO.NodeOutput(audio)
+
+        if start_index < 0:
+            start_frame = audio_length + int(round(start_index * sample_rate))
+        else:
+            start_frame = int(round(start_index * sample_rate))
+        start_frame = max(0, min(start_frame, audio_length))
+
+        end_frame = start_frame + int(round(duration * sample_rate))
+        end_frame = max(0, min(end_frame, audio_length))
+
+        if start_frame >= end_frame:
+            raise ValueError("TrimAudioDuration: Start time must be less than end time and be within the audio length.")
+
+        return IO.NodeOutput({"waveform": waveform[..., start_frame:end_frame], "sample_rate": sample_rate})
+
+    trim = execute  # TODO: remove
+
+
+class SplitAudioChannels(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SplitAudioChannels",
+            search_aliases=["stereo to mono"],
+            display_name="Split Audio Channels",
+            description="Separates the audio into left and right channels.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+            ],
+            outputs=[
+                IO.Audio.Output(display_name="left"),
+                IO.Audio.Output(display_name="right"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, audio) -> IO.NodeOutput:
+        if audio is None:
+            return IO.NodeOutput(None, None)
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+
+        if waveform.shape[1] != 2:
+            raise ValueError(f"AudioSplit: Input audio must be stereo (2 channels), got {waveform.shape[1]} channel(s).")
+
+        left_channel = waveform[..., 0:1, :]
+        right_channel = waveform[..., 1:2, :]
+
+        return IO.NodeOutput({"waveform": left_channel, "sample_rate": sample_rate}, {"waveform": right_channel, "sample_rate": sample_rate})
+
+    separate = execute  # TODO: remove
+
+class JoinAudioChannels(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="JoinAudioChannels",
+            display_name="Join Audio Channels",
+            description="Joins left and right mono audio channels into a stereo audio.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio_left"),
+                IO.Audio.Input("audio_right"),
+            ],
+            outputs=[
+                IO.Audio.Output(display_name="audio"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, audio_left, audio_right) -> IO.NodeOutput:
+        if audio_left is None and audio_right is None:
+            return IO.NodeOutput(None)
+        if audio_left is None:
+            return IO.NodeOutput(audio_right)
+        if audio_right is None:
+            return IO.NodeOutput(audio_left)
+        waveform_left = audio_left["waveform"]
+        sample_rate_left = audio_left["sample_rate"]
+        waveform_right = audio_right["waveform"]
+        sample_rate_right = audio_right["sample_rate"]
+
+        if waveform_left.shape[1] != 1 or waveform_right.shape[1] != 1:
+            raise ValueError("AudioJoin: Both input audios must be mono.")
+
+        # Handle different sample rates by resampling to the higher rate
+        waveform_left, waveform_right, output_sample_rate = match_audio_sample_rates(
+            waveform_left, sample_rate_left, waveform_right, sample_rate_right
+        )
+
+        # Handle different lengths by trimming to the shorter length
+        length_left = waveform_left.shape[-1]
+        length_right = waveform_right.shape[-1]
+
+        if length_left != length_right:
+            min_length = min(length_left, length_right)
+            if length_left > min_length:
+                logging.info(f"JoinAudioChannels: Trimming left channel from {length_left} to {min_length} samples.")
+                waveform_left = waveform_left[..., :min_length]
+            if length_right > min_length:
+                logging.info(f"JoinAudioChannels: Trimming right channel from {length_right} to {min_length} samples.")
+                waveform_right = waveform_right[..., :min_length]
+
+        # Join the channels into stereo
+        left_channel = waveform_left[..., 0:1, :]
+        right_channel = waveform_right[..., 0:1, :]
+        stereo_waveform = torch.cat([left_channel, right_channel], dim=1)
+
+        return IO.NodeOutput({"waveform": stereo_waveform, "sample_rate": output_sample_rate})
+
+
+def match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2):
+    if sample_rate_1 != sample_rate_2:
+        if sample_rate_1 > sample_rate_2:
+            waveform_2 = torchaudio.functional.resample(waveform_2, sample_rate_2, sample_rate_1)
+            output_sample_rate = sample_rate_1
+            logging.info(f"Resampling audio2 from {sample_rate_2}Hz to {sample_rate_1}Hz for merging.")
+        else:
+            waveform_1 = torchaudio.functional.resample(waveform_1, sample_rate_1, sample_rate_2)
+            output_sample_rate = sample_rate_2
+            logging.info(f"Resampling audio1 from {sample_rate_1}Hz to {sample_rate_2}Hz for merging.")
+    else:
+        output_sample_rate = sample_rate_1
+    return waveform_1, waveform_2, output_sample_rate
+
+
+class AudioConcat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="AudioConcat",
+            search_aliases=["join audio", "combine audio", "append audio"],
+            display_name="Concatenate Audio",
+            description="Concatenates the audio1 to audio2 in the specified direction.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio1"),
+                IO.Audio.Input("audio2"),
+                IO.Combo.Input(
+                    "direction",
+                    options=['after', 'before'],
+                    default="after",
+                    tooltip="Whether to append audio2 after or before audio1.",
+                )
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio1, audio2, direction) -> IO.NodeOutput:
+        if audio1 is None and audio2 is None:
+            return IO.NodeOutput(None)
+        if audio1 is None:
+            return IO.NodeOutput(audio2)
+        if audio2 is None:
+            return IO.NodeOutput(audio1)
+        waveform_1 = audio1["waveform"]
+        waveform_2 = audio2["waveform"]
+        sample_rate_1 = audio1["sample_rate"]
+        sample_rate_2 = audio2["sample_rate"]
+
+        if waveform_1.shape[1] == 1:
+            waveform_1 = waveform_1.repeat(1, 2, 1)
+            logging.info("AudioConcat: Converted mono audio1 to stereo by duplicating the channel.")
+        if waveform_2.shape[1] == 1:
+            waveform_2 = waveform_2.repeat(1, 2, 1)
+            logging.info("AudioConcat: Converted mono audio2 to stereo by duplicating the channel.")
+
+        waveform_1, waveform_2, output_sample_rate = match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2)
+
+        if direction == 'after':
+            concatenated_audio = torch.cat((waveform_1, waveform_2), dim=2)
+        elif direction == 'before':
+            concatenated_audio = torch.cat((waveform_2, waveform_1), dim=2)
+
+        return IO.NodeOutput({"waveform": concatenated_audio, "sample_rate": output_sample_rate})
+
+    concat = execute  # TODO: remove
+
+
+class AudioMerge(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="AudioMerge",
+            search_aliases=["mix audio", "overlay audio", "layer audio"],
+            display_name="Merge Audio",
+            description="Combine two audio tracks by overlaying their waveforms.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio1"),
+                IO.Audio.Input("audio2"),
+                IO.Combo.Input(
+                    "merge_method",
+                    options=["add", "mean", "subtract", "multiply"],
+                    tooltip="The method used to combine the audio waveforms.",
+                )
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio1, audio2, merge_method) -> IO.NodeOutput:
+        if audio1 is None and audio2 is None:
+            return IO.NodeOutput(None)
+        if audio1 is None:
+            return IO.NodeOutput(audio2)
+        if audio2 is None:
+            return IO.NodeOutput(audio1)
+        waveform_1 = audio1["waveform"]
+        waveform_2 = audio2["waveform"]
+        sample_rate_1 = audio1["sample_rate"]
+        sample_rate_2 = audio2["sample_rate"]
+
+        waveform_1, waveform_2, output_sample_rate = match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2)
+
+        length_1 = waveform_1.shape[-1]
+        length_2 = waveform_2.shape[-1]
+
+        if length_1 == 0 or length_2 == 0:
+            return IO.NodeOutput({"waveform": waveform_1, "sample_rate": output_sample_rate})
+
+        if length_2 > length_1:
+            logging.info(f"AudioMerge: Trimming audio2 from {length_2} to {length_1} samples to match audio1 length.")
+            waveform_2 = waveform_2[..., :length_1]
+        elif length_2 < length_1:
+            logging.info(f"AudioMerge: Padding audio2 from {length_2} to {length_1} samples to match audio1 length.")
+            pad_shape = list(waveform_2.shape)
+            pad_shape[-1] = length_1 - length_2
+            pad_tensor = torch.zeros(pad_shape, dtype=waveform_2.dtype, device=waveform_2.device)
+            waveform_2 = torch.cat((waveform_2, pad_tensor), dim=-1)
+
+        if merge_method == "add":
+            waveform = waveform_1 + waveform_2
+        elif merge_method == "subtract":
+            waveform = waveform_1 - waveform_2
+        elif merge_method == "multiply":
+            waveform = waveform_1 * waveform_2
+        elif merge_method == "mean":
+            waveform = (waveform_1 + waveform_2) / 2
+
+        max_val = waveform.abs().max()
+        if max_val > 1.0:
+            waveform = waveform / max_val
+
+        return IO.NodeOutput({"waveform": waveform, "sample_rate": output_sample_rate})
+
+    merge = execute  # TODO: remove
+
+
+class AudioAdjustVolume(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="AudioAdjustVolume",
+            search_aliases=["audio gain", "loudness", "audio level"],
+            display_name="Adjust Audio Volume",
+            category="audio",
+            description="Adjust the volume of the audio by a specified amount in decibels (dB).",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.Int.Input(
+                    "volume",
+                    default=1,
+                    min=-100,
+                    max=100,
+                    tooltip="Volume adjustment in decibels (dB). 0 = no change, +6 = double, -6 = half, etc",
+                )
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio, volume) -> IO.NodeOutput:
+        if audio is None:
+            return IO.NodeOutput(None)
+        if volume == 0:
+            return IO.NodeOutput(audio)
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+
+        gain = 10 ** (volume / 20)
+        waveform = waveform * gain
+
+        return IO.NodeOutput({"waveform": waveform, "sample_rate": sample_rate})
+
+    adjust_volume = execute  # TODO: remove
+
+
+class EmptyAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="EmptyAudio",
+            search_aliases=["blank audio"],
+            display_name="Empty Audio",
+            category="audio",
+            inputs=[
+                IO.Float.Input(
+                    "duration",
+                    default=60.0,
+                    min=0.0,
+                    max=0xffffffffffffffff,
+                    step=0.01,
+                    tooltip="Duration of the empty audio clip in seconds",
+                ),
+                IO.Int.Input(
+                    "sample_rate",
+                    default=44100,
+                    tooltip="Sample rate of the empty audio clip.",
+                    min=1,
+                    max=192000,
+                    advanced=True,
+                ),
+                IO.Int.Input(
+                    "channels",
+                    default=2,
+                    min=1,
+                    max=2,
+                    tooltip="Number of audio channels (1 for mono, 2 for stereo).",
+                    advanced=True,
+                ),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, duration, sample_rate, channels) -> IO.NodeOutput:
+        num_samples = int(round(duration * sample_rate))
+        waveform = torch.zeros((1, channels, num_samples), dtype=torch.float32)
+        return IO.NodeOutput({"waveform": waveform, "sample_rate": sample_rate})
+
+    create_empty_audio = execute  # TODO: remove
+
+
+class AudioEqualizer3Band(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="AudioEqualizer3Band",
+            search_aliases=["eq", "bass boost", "treble boost", "equalizer"],
+            display_name="Audio Equalizer (3-Band)",
+            category="audio",
+            is_experimental=True,
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.Float.Input("low_gain_dB", default=0.0, min=-24.0, max=24.0, step=0.1, tooltip="Gain for Low frequencies (Bass)"),
+                IO.Int.Input("low_freq", default=100, min=20, max=500, tooltip="Cutoff frequency for Low shelf"),
+                IO.Float.Input("mid_gain_dB", default=0.0, min=-24.0, max=24.0, step=0.1, tooltip="Gain for Mid frequencies"),
+                IO.Int.Input("mid_freq", default=1000, min=200, max=4000, tooltip="Center frequency for Mids"),
+                IO.Float.Input("mid_q", default=0.707, min=0.1, max=10.0, step=0.1, tooltip="Q factor (bandwidth) for Mids"),
+                IO.Float.Input("high_gain_dB", default=0.0, min=-24.0, max=24.0, step=0.1, tooltip="Gain for High frequencies (Treble)"),
+                IO.Int.Input("high_freq", default=5000, min=1000, max=15000, tooltip="Cutoff frequency for High shelf"),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio, low_gain_dB, low_freq, mid_gain_dB, mid_freq, mid_q, high_gain_dB, high_freq) -> IO.NodeOutput:
+        if audio is None:
+            return IO.NodeOutput(None)
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+
+        if waveform.shape[-1] == 0:
+            return IO.NodeOutput(audio)
+
+        eq_waveform = waveform.clone()
+
+        # 1. Apply Low Shelf (Bass)
+        if low_gain_dB != 0:
+            eq_waveform = torchaudio.functional.bass_biquad(
+                eq_waveform,
+                sample_rate,
+                gain=low_gain_dB,
+                central_freq=float(low_freq),
+                Q=0.707
+            )
+
+        # 2. Apply Peaking EQ (Mids)
+        if mid_gain_dB != 0:
+            eq_waveform = torchaudio.functional.equalizer_biquad(
+                eq_waveform,
+                sample_rate,
+                center_freq=float(mid_freq),
+                gain=mid_gain_dB,
+                Q=mid_q
+            )
+
+        # 3. Apply High Shelf (Treble)
+        if high_gain_dB != 0:
+            eq_waveform = torchaudio.functional.treble_biquad(
+                eq_waveform,
+                sample_rate,
+                gain=high_gain_dB,
+                central_freq=float(high_freq),
+                Q=0.707
+            )
+
+        return IO.NodeOutput({"waveform": eq_waveform, "sample_rate": sample_rate})
+
+
+class AudioExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            EmptyLatentAudio,
+            VAEEncodeAudio,
+            VAEDecodeAudio,
+            VAEDecodeAudioTiled,
+            SaveAudio,
+            SaveAudioMP3,
+            SaveAudioOpus,
+            SaveAudioAdvanced,
+            LoadAudio,
+            PreviewAudio,
+            ConditioningStableAudio,
+            RecordAudio,
+            TrimAudioDuration,
+            SplitAudioChannels,
+            JoinAudioChannels,
+            AudioConcat,
+            AudioMerge,
+            AudioAdjustVolume,
+            EmptyAudio,
+            AudioEqualizer3Band,
+        ]
+
+async def comfy_entrypoint() -> AudioExtension:
+    return AudioExtension()
diff --git a/comfy_extras/nodes_audio_encoder.py b/comfy_extras/nodes_audio_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0d0ee88951317188cfde25a89da2fe01b19538f
--- /dev/null
+++ b/comfy_extras/nodes_audio_encoder.py
@@ -0,0 +1,63 @@
+import folder_paths
+import comfy.audio_encoders.audio_encoders
+import comfy.utils
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class AudioEncoderLoader(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="AudioEncoderLoader",
+            display_name="Load Audio Encoder",
+            category="model/loaders",
+            inputs=[
+                io.Combo.Input(
+                    "audio_encoder_name",
+                    options=folder_paths.get_filename_list("audio_encoders"),
+                ),
+            ],
+            outputs=[io.AudioEncoder.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio_encoder_name) -> io.NodeOutput:
+        audio_encoder_name = folder_paths.get_full_path_or_raise("audio_encoders", audio_encoder_name)
+        sd = comfy.utils.load_torch_file(audio_encoder_name, safe_load=True)
+        audio_encoder = comfy.audio_encoders.audio_encoders.load_audio_encoder_from_sd(sd)
+        if audio_encoder is None:
+            raise RuntimeError("ERROR: audio encoder file is invalid and does not contain a valid model.")
+        return io.NodeOutput(audio_encoder)
+
+
+class AudioEncoderEncode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="AudioEncoderEncode",
+            category="model/conditioning",
+            inputs=[
+                io.AudioEncoder.Input("audio_encoder"),
+                io.Audio.Input("audio"),
+            ],
+            outputs=[io.AudioEncoderOutput.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio_encoder, audio) -> io.NodeOutput:
+        output = audio_encoder.encode_audio(audio["waveform"], audio["sample_rate"])
+        return io.NodeOutput(output)
+
+
+class AudioEncoder(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            AudioEncoderLoader,
+            AudioEncoderEncode,
+        ]
+
+
+async def comfy_entrypoint() -> AudioEncoder:
+    return AudioEncoder()
diff --git a/comfy_extras/nodes_bernini.py b/comfy_extras/nodes_bernini.py
new file mode 100644
index 0000000000000000000000000000000000000000..a501e4be5989abc82734fa10d860ab304083b1c5
--- /dev/null
+++ b/comfy_extras/nodes_bernini.py
@@ -0,0 +1,108 @@
+import torch
+from typing_extensions import override
+
+import comfy.model_management
+import comfy.utils
+import node_helpers
+from comfy_api.latest import ComfyExtension, io
+
+
+def _resize_long_edge(image, max_size, stride=16):
+    """Resize (preserve aspect) so the long edge <= max_size, then snap each side to `stride`"""
+    h, w = image.shape[1], image.shape[2]
+    scale = min(max_size / max(h, w), 1.0)
+    nh = max(stride, round(h * scale / stride) * stride)
+    nw = max(stride, round(w * scale / stride) * stride)
+    return comfy.utils.common_upscale(image[:, :, :, :3].movedim(-1, 1), nw, nh, "area", "disabled").movedim(1, -1)
+
+
+class BerniniConditioning(io.ComfyNode):
+    """Bernini in-context conditioning for a Wan2.2-A14B model.
+
+    Attaches the VAE-encoded source video / reference images to the conditioning
+    source video first, then each reference image
+
+    The task is inferred from which inputs are connected:
+      (nothing)                  -> t2v (text-to-video)
+      source_video               -> v2v (video-to-video)
+      source_video + ref_images  -> rv2v (reference-guided video editing)
+      ref_images only            -> r2v (reference-to-video)
+      source_video + ref_video   -> ads2v (insert image/video into video)
+
+    source_video is the edit base / canvas (resized to width x height).
+    reference_video is moving content to composite in.
+    Streams are ordered source_video, reference_video, then reference_images -> source_id (1, 2, 3, ...).
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="BerniniConditioning",
+            display_name="Bernini Conditioning",
+            category="model/conditioning/bernini",
+            description="Conditioning node for Bernini in-context video/image conditioning. It can be used for the following tasks: t2v (text-to-video), v2v (video-to-video), rv2v (reference-guided video editing), r2v (reference-to-video), ads2v (insert image/video into video)."
+                "Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=832, min=16, max=8192, step=16),
+                io.Int.Input("height", default=480, min=16, max=8192, step=16),
+                io.Int.Input("length", default=81, min=1, max=8192, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("source_video", optional=True, tooltip=("Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")),
+                io.Image.Input("reference_video", optional=True, tooltip=("Video to insert into the source video (ads2v).")),
+                io.Autogrow.Input("reference_images", optional=True,
+                    template=io.Autogrow.TemplatePrefix(
+                        input=io.Image.Input("reference_image", tooltip=("Reference image injected as an in-context token (r2v, rv2v).")),
+                        prefix="reference_image_", min=0, max=8)),
+                io.Int.Input("ref_max_size", default=848, min=16, max=8192, step=16, optional=True, tooltip=(
+                    "Max size for the long edge of reference_video and reference_images. Resized with preserved aspect ratio and snapped to 16px.")),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+
+        # source_video (1), reference_video (2), reference_images (3, 4, ...).
+        context = []
+        if source_video is not None:
+            vid = comfy.utils.common_upscale(source_video[:length, :, :, :3].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
+            context.append(vae.encode(vid[:, :, :, :3]))
+
+        if reference_video is not None:
+            ref_vid = _resize_long_edge(reference_video[:length], ref_max_size)  # moving content, native aspect
+            context.append(vae.encode(ref_vid[:, :, :, :3]))
+
+        # reference_images is an autogrow dict {reference_image_0: IMAGE, ...}; each slot is a
+        # separate stream at its own native aspect (a multi-image batch in one slot -> one stream per frame).
+        if reference_images:
+            for name in sorted(reference_images):
+                imgs = reference_images[name]
+                if imgs is None:
+                    continue
+                for i in range(imgs.shape[0]):
+                    img = _resize_long_edge(imgs[i:i + 1], ref_max_size)  # native aspect per ref
+                    context.append(vae.encode(img[:, :, :, :3]))
+
+        if context:
+            positive = node_helpers.conditioning_set_values(positive, {"context_latents": context})
+            negative = node_helpers.conditioning_set_values(negative, {"context_latents": context})
+
+        return io.NodeOutput(positive, negative, {"samples": latent})
+
+
+class BerniniExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [BerniniConditioning,]
+
+
+async def comfy_entrypoint() -> BerniniExtension:
+    return BerniniExtension()
diff --git a/comfy_extras/nodes_bg_removal.py b/comfy_extras/nodes_bg_removal.py
new file mode 100644
index 0000000000000000000000000000000000000000..91d4b9a0feda8668bb1eee1a1472c4a641a679e9
--- /dev/null
+++ b/comfy_extras/nodes_bg_removal.py
@@ -0,0 +1,61 @@
+import folder_paths
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO
+from comfy.bg_removal_model import load
+
+
+class LoadBackgroundRemovalModel(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        files = folder_paths.get_filename_list("background_removal")
+        return IO.Schema(
+            node_id="LoadBackgroundRemovalModel",
+            display_name="Load Background Removal Model",
+            category="model/loaders",
+            inputs=[
+                IO.Combo.Input("bg_removal_name", options=sorted(files), tooltip="The model used to remove backgrounds from images"),
+            ],
+            outputs=[
+                IO.BackgroundRemoval.Output("bg_model")
+            ]
+        )
+    @classmethod
+    def execute(cls, bg_removal_name):
+        path = folder_paths.get_full_path_or_raise("background_removal", bg_removal_name)
+        bg = load(path)
+        if bg is None:
+            raise RuntimeError("ERROR: background model file is invalid and does not contain a valid background removal model.")
+        return IO.NodeOutput(bg)
+
+class RemoveBackground(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RemoveBackground",
+            display_name="Remove Background",
+            category="image/background removal",
+            description="Generates a foreground mask to remove the background from an image using a background removal model.",
+            inputs=[
+                IO.BackgroundRemoval.Input("bg_removal_model", tooltip="Background removal model used to generate the mask"),
+                IO.Image.Input("image", tooltip="Input image to remove the background from")
+            ],
+            outputs=[
+                IO.Mask.Output("mask", tooltip="Generated foreground mask")
+            ]
+        )
+    @classmethod
+    def execute(cls, bg_removal_model, image):
+        mask = bg_removal_model.encode_image(image)
+        return IO.NodeOutput(mask)
+
+class BackgroundRemovalExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            LoadBackgroundRemovalModel,
+            RemoveBackground
+        ]
+
+
+async def comfy_entrypoint() -> BackgroundRemovalExtension:
+    return BackgroundRemovalExtension()
diff --git a/comfy_extras/nodes_boogu.py b/comfy_extras/nodes_boogu.py
new file mode 100644
index 0000000000000000000000000000000000000000..21ced04859680c0a31af3f37edc6c31d77227c02
--- /dev/null
+++ b/comfy_extras/nodes_boogu.py
@@ -0,0 +1,97 @@
+import math
+
+import node_helpers
+import comfy.utils
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class TextEncodeBooguEdit(io.ComfyNode):
+    """Boogu-Image Edit conditioning.
+
+    The edit image is used twice, matching the reference pipeline:
+      - Qwen3-VL vision tokens (instruction understanding) -> positive only
+      - VAE reference latent (image identity)              -> positive and negative
+    The ref latent is in both conds so it cancels under CFG (identity preserved);
+    the vision tokens are only in the positive so CFG amplifies the instruction.
+    The tokenizer selects the right system prompt automatically (image -> TI2I,
+    empty negative -> DROP), so no template plumbing is needed here.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeBooguEdit",
+            category="model/conditioning/boogu",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.String.Input("negative_prompt", multiline=True, dynamic_prompts=True, advanced=True),
+                io.Vae.Input("vae"),
+                io.Autogrow.Input(
+                    "images",
+                    template=io.Autogrow.TemplateNames(
+                        io.Image.Input("image"),
+                        names=[f"image_{i}" for i in range(1, 17)],
+                        min=0,
+                    ),
+                    tooltip="Reference image(s) to edit. Boogu focuses on one reference per sample; more are allowed.",
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, negative_prompt, vae=None, images: io.Autogrow.Type = None) -> io.NodeOutput:
+        ref_latents = []
+        images_vl = []
+
+        images = images or {}
+        for name in sorted(images, key=lambda n: int(n.rsplit("_", 1)[-1])):
+            image = images[name]
+            if image is None:
+                continue
+            samples = image.movedim(-1, 1)
+
+            # Vision tower input: the reference caps the VLM image at 384x384
+            # (max_vlm_input_pil_pixels in pipeline_boogu.py).
+            total = int(384 * 384)
+            scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+            width = round(samples.shape[3] * scale_by)
+            height = round(samples.shape[2] * scale_by)
+            s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
+            images_vl.append(s.movedim(1, -1)[:, :, :, :3])
+
+            # Reference latent: align to 16 px (VAE /8 * patch_size 2).
+            if vae is not None:
+                total = int(1024 * 1024)
+                scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+                width = round(samples.shape[3] * scale_by / 16.0) * 16
+                height = round(samples.shape[2] * scale_by / 16.0) * 16
+                s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
+                ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3]))
+
+        # positive: instruction + vision tokens; negative: empty (no vision). Ref latent on both.
+        positive = clip.encode_from_tokens_scheduled(clip.tokenize(prompt, images=images_vl))
+        negative = clip.encode_from_tokens_scheduled(clip.tokenize(negative_prompt))
+
+        if len(ref_latents) > 0:
+            positive = node_helpers.conditioning_set_values(positive, {"reference_latents": ref_latents}, append=True)
+            negative = node_helpers.conditioning_set_values(negative, {"reference_latents": ref_latents}, append=True)
+
+        return io.NodeOutput(positive, negative)
+
+
+class BooguExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TextEncodeBooguEdit,
+        ]
+
+
+async def comfy_entrypoint() -> BooguExtension:
+    return BooguExtension()
diff --git a/comfy_extras/nodes_bounding_boxes.py b/comfy_extras/nodes_bounding_boxes.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebbff3efea198e04d729be1a02882c577f166c48
--- /dev/null
+++ b/comfy_extras/nodes_bounding_boxes.py
@@ -0,0 +1,379 @@
+import json
+
+import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageEnhance, ImageFont
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+from comfy_extras.color_util import hex_to_rgb, normalize_palette, readable_color
+
+_PREVIEW_LONG_EDGE = 1024
+_PREVIEW_DIM = 0.25
+
+
+def pixels_to_fractions(box: dict, width: int, height: int) -> dict:
+    w = width or 1
+    h = height or 1
+    return {
+        "x": box.get("x", 0) / w,
+        "y": box.get("y", 0) / h,
+        "w": box.get("width", 0) / w,
+        "h": box.get("height", 0) / h,
+    }
+
+
+def fractions_to_pixels(box: dict, width: int, height: int) -> dict:
+    x, y = box.get("x", 0.0), box.get("y", 0.0)
+    w, h = box.get("w", 0.0), box.get("h", 0.0)
+    if w < 0:
+        x, w = x + w, -w
+    if h < 0:
+        y, h = y + h, -h
+    return {
+        "x": round(x * width),
+        "y": round(y * height),
+        "width": round(w * width),
+        "height": round(h * height),
+    }
+
+
+def fractions_to_bbox_frame(boxes: list, width: int, height: int) -> list:
+    pixels = [
+        fractions_to_pixels(box, width, height)
+        for box in boxes
+        if isinstance(box, dict)
+    ]
+    return [pixels] if pixels else []
+
+
+def _font(size: int):
+    try:
+        return ImageFont.load_default(size)
+    except Exception:
+        return ImageFont.load_default()
+
+
+def _wrap(draw, text: str, font, max_w: float) -> list[str]:
+    lines = []
+    for para in text.split("\n"):
+        line = ""
+        for word in para.split():
+            test = word if not line else line + " " + word
+            if line and draw.textlength(test, font=font) > max_w:
+                lines.append(line)
+                line = word
+            else:
+                line = test
+        lines.append(line)
+    return lines
+
+
+def _bg_from_image(image) -> Image.Image | None:
+    if image is None:
+        return None
+    try:
+        arr = (image[0].detach().cpu().numpy() * 255).clip(0, 255).astype(np.uint8)
+        return Image.fromarray(arr)
+    except Exception:
+        return None
+
+
+def render_preview(regions, width, height, bg=None):
+    if bg is not None:
+        iw, ih = bg.size
+        long_edge = max(iw, ih) or 1
+        scale = min(1.0, _PREVIEW_LONG_EDGE / long_edge)
+        rw, rh = max(1, round(iw * scale)), max(1, round(ih * scale))
+        base = bg.convert("RGB").resize((rw, rh), Image.LANCZOS)
+        base = ImageEnhance.Brightness(base).enhance(_PREVIEW_DIM)
+        img = base.convert("RGBA")
+    else:
+        long_edge = max(width, height) or 1
+        scale = min(1.0, _PREVIEW_LONG_EDGE / long_edge)
+        rw, rh = max(1, round(width * scale)), max(1, round(height * scale))
+        grey = round(_PREVIEW_DIM * 128)
+        img = Image.new("RGBA", (rw, rh), (grey, grey, grey, 255))
+
+    overlay = Image.new("RGBA", (rw, rh), (0, 0, 0, 0))
+    draw = ImageDraw.Draw(overlay)
+    fs = max(10, round(rh / 64))
+    font = _font(fs)
+    tag_font = _font(max(9, fs - 2))
+    line_h = fs + 2
+
+    for i, region in enumerate(regions):
+        if not isinstance(region, dict):
+            continue
+        palette = [c for c in (region.get("palette") or []) if c]
+        r, g, b = hex_to_rgb(palette[0]) if palette else (140, 140, 140)
+        x1 = max(0, min(rw, round(region.get("x", 0) * rw)))
+        y1 = max(0, min(rh, round(region.get("y", 0) * rh)))
+        x2 = max(0, min(rw, round((region.get("x", 0) + region.get("w", 0)) * rw)))
+        y2 = max(0, min(rh, round((region.get("y", 0) + region.get("h", 0)) * rh)))
+        if x2 < x1:
+            x1, x2 = x2, x1
+        if y2 < y1:
+            y1, y2 = y2, y1
+
+        draw.rectangle([x1, y1, x2, y2], outline=(r, g, b, 255), width=2)
+
+        swatches = palette[:5]
+        if swatches and (x2 - x1) > 2:
+            sh = max(5, fs // 2)
+            seg = (x2 - x1) / len(swatches)
+            for p, hexc in enumerate(swatches):
+                sx = x1 + round(p * seg)
+                draw.rectangle([sx, y1, x1 + round((p + 1) * seg), y1 + sh], fill=hex_to_rgb(hexc))
+
+        etype = "text" if region.get("type") == "text" else "obj"
+        tag = str(i + 1).zfill(2)
+        tw = draw.textlength(tag, font=tag_font)
+        draw.rectangle([x1, y1, x1 + tw + 6, y1 + fs + 2], fill=(r, g, b, 255))
+        tag_fill = (0, 0, 0, 255) if (0.299 * r + 0.587 * g + 0.114 * b) > 140 else (255, 255, 255, 255)
+        draw.text((x1 + 3, y1 + 1), tag, fill=tag_fill, font=tag_font)
+
+        body = region.get("desc", "") or ""
+        if etype == "text" and region.get("text"):
+            body = '"%s"%s' % (region["text"], " — " + body if body else "")
+        if body and (x2 - x1) > 8:
+            ty = y1 + fs + 5
+            for line in _wrap(draw, body, font, x2 - x1 - 8):
+                if ty > y2:
+                    break
+                draw.text((x1 + 4, ty), line, fill=readable_color((r, g, b)) + (255,), font=font)
+                ty += line_h
+
+    composed = Image.alpha_composite(img, overlay).convert("RGB")
+    arr = np.asarray(composed, dtype=np.float32) / 255.0
+    return torch.from_numpy(arr).unsqueeze(0)
+
+
+def boxes_to_regions(boxes, width: int, height: int) -> list:
+    regions: list = []
+    if not isinstance(boxes, list):
+        return regions
+    for box in boxes:
+        if not isinstance(box, dict):
+            continue
+        meta = box.get("metadata")
+        meta = meta if isinstance(meta, dict) else {}
+        regions.append({
+            **pixels_to_fractions(box, width, height),
+            "type": meta.get("type", "obj"),
+            "text": meta.get("text", ""),
+            "desc": meta.get("desc", ""),
+            "palette": meta.get("palette", []),
+        })
+    return regions
+
+
+def normalize_incoming_boxes(bboxes) -> list:
+    if isinstance(bboxes, dict):
+        frame = [bboxes]
+    elif not isinstance(bboxes, list) or not bboxes:
+        frame = []
+    elif isinstance(bboxes[0], dict):
+        frame = bboxes
+    else:
+        frame = bboxes[0] if isinstance(bboxes[0], list) else []
+    boxes = []
+    for box in frame:
+        if not isinstance(box, dict):
+            continue
+        norm = {
+            "x": box.get("x", 0),
+            "y": box.get("y", 0),
+            "width": box.get("width", 0),
+            "height": box.get("height", 0),
+        }
+        meta = box.get("metadata")
+        if isinstance(meta, dict):
+            norm["metadata"] = meta
+        boxes.append(norm)
+    return boxes
+
+
+def _looks_like_element(box: dict) -> bool:
+    bbox = box.get("bbox")
+    return isinstance(bbox, (list, tuple)) and len(bbox) == 4
+
+
+def _looks_like_bbox(box: dict) -> bool:
+    return all(key in box for key in ("x", "y", "width", "height"))
+
+
+def elements_to_boxes(elements: list, width: int, height: int) -> list:
+    boxes = []
+    for element in elements:
+        if not isinstance(element, dict):
+            continue
+        bbox = element.get("bbox")
+        if not (isinstance(bbox, (list, tuple)) and len(bbox) == 4):
+            raise ValueError("bboxes element is missing a valid 'bbox' [ymin, xmin, ymax, xmax]")
+        try:
+            ymin, xmin, ymax, xmax = (float(v) / 1000.0 for v in bbox)
+        except (TypeError, ValueError):
+            raise ValueError("bboxes element 'bbox' must contain four numbers")
+        etype = "text" if element.get("type") == "text" else "obj"
+        boxes.append({
+            "x": round(min(xmin, xmax) * width),
+            "y": round(min(ymin, ymax) * height),
+            "width": round(abs(xmax - xmin) * width),
+            "height": round(abs(ymax - ymin) * height),
+            "metadata": {
+                "type": etype,
+                "text": element.get("text", "") if etype == "text" else "",
+                "desc": element.get("desc", ""),
+                "palette": element.get("color_palette", []) or [],
+            },
+        })
+    return boxes
+
+
+def boxes_from_input(data, width: int, height: int) -> list:
+    if data is None:
+        return []
+    if isinstance(data, str):
+        text = data.strip()
+        if not text:
+            return []
+        try:
+            data = json.loads(text)
+        except (ValueError, TypeError) as exc:
+            raise ValueError(f"bboxes string input is not valid JSON: {exc}") from exc
+    if isinstance(data, dict):
+        if _looks_like_element(data):
+            return elements_to_boxes([data], width, height)
+        if _looks_like_bbox(data):
+            return normalize_incoming_boxes(data)
+        raise ValueError(
+            "bboxes dict must be a bounding box (x, y, width, height) or an element (with a 'bbox')"
+        )
+    if not isinstance(data, list):
+        raise ValueError(
+            "bboxes input must be bounding boxes, elements, or a JSON string, "
+            f"got {type(data).__name__}"
+        )
+    if not data:
+        return []
+    first = data[0]
+    if isinstance(first, list):
+        return normalize_incoming_boxes(data)
+    if isinstance(first, dict):
+        if _looks_like_element(first):
+            return elements_to_boxes(data, width, height)
+        if _looks_like_bbox(first):
+            return normalize_incoming_boxes(data)
+        raise ValueError(
+            "bboxes items must be bounding boxes (x, y, width, height) or elements (with a 'bbox')"
+        )
+    raise ValueError(
+        f"bboxes list must contain bounding boxes or elements, got {type(first).__name__}"
+    )
+
+
+def _norm_bbox(region: dict) -> list[int]:
+    def grid(value: float) -> int:
+        return max(0, min(1000, round(value * 1000)))
+
+    x, y = region.get("x", 0.0), region.get("y", 0.0)
+    w, h = region.get("w", 0.0), region.get("h", 0.0)
+    ymin, xmin, ymax, xmax = grid(y), grid(x), grid(y + h), grid(x + w)
+    if ymin > ymax:
+        ymin, ymax = ymax, ymin
+    if xmin > xmax:
+        xmin, xmax = xmax, xmin
+    return [ymin, xmin, ymax, xmax]
+
+
+def build_elements(regions: list) -> list:
+    elements = []
+    for region in regions:
+        if not isinstance(region, dict):
+            continue
+        etype = "text" if region.get("type") == "text" else "obj"
+        element = {"type": etype}
+        element["bbox"] = _norm_bbox(region)
+        if etype == "text":
+            element["text"] = region.get("text", "")
+        element["desc"] = region.get("desc", "")
+        palette = normalize_palette(region.get("palette", []))
+        if palette:
+            element["color_palette"] = palette[:5]
+        elements.append(element)
+    return elements
+
+
+class CreateBoundingBoxes(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        editor_state = io.BoundingBoxes.Input(
+            "editor_state",
+            socketless=False,
+            tooltip="Draw bounding boxes and set each box type, text, description, color palette. Start with background element first and foreground last.",
+        )
+        return io.Schema(
+            node_id="CreateBoundingBoxes",
+            display_name="Create Bounding Boxes",
+            category="utilities",
+            description="Draw bounding boxes in a canvas. Outputs Ideogram prompt elements, pixel-space bounding boxes, and a preview image.",
+            inputs=[
+                io.Image.Input(
+                    "background",
+                    optional=True,
+                    tooltip="Optional image used as background in the canvas and preview.",
+                ),
+                io.MultiType.Input(
+                    "bboxes",
+                    [io.BoundingBox, io.Array, io.String],
+                    optional=True,
+                    tooltip="Bounding boxes, elements, or a JSON string to initialize the canvas. A new upstream value initializes the canvas; edits made on the canvas take priority and are kept until the upstream value changes again.",
+                ),
+                io.Int.Input("width", default=1024, min=64, max=16384, step=16,
+                             tooltip="Width of the canvas and the pixel grid for the bounding boxes."),
+                io.Int.Input("height", default=1024, min=64, max=16384, step=16,
+                             tooltip="Height of the canvas and the pixel grid for the bounding boxes."),
+                editor_state,
+                io.BoundingBoxes.Input(
+                    "last_incoming",
+                    optional=True,
+                    tooltip="Internal state managed by the canvas: the upstream bboxes value that last initialized it. Leave empty to re-initialize the canvas from the bboxes input on the next run.",
+                ),
+            ],
+            outputs=[
+                io.Image.Output(display_name="preview"),
+                io.BoundingBox.Output(display_name="bboxes"),
+                io.Array.Output(display_name="elements"),
+            ],
+            is_output_node=True,
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, width, height, editor_state=None, last_incoming=None, background=None, bboxes=None) -> io.NodeOutput:
+        incoming = boxes_from_input(bboxes, width, height)
+        applied = last_incoming if isinstance(last_incoming, list) else []
+        upstream_changed = bool(incoming) and incoming != applied
+        source = incoming if upstream_changed else (editor_state or [])
+        regions = boxes_to_regions(source, width, height)
+        preview = render_preview(regions, width, height, _bg_from_image(background))
+        ui = {"dims": [width, height]}
+        if incoming:
+            ui["input_bboxes"] = incoming
+        return io.NodeOutput(
+            preview,
+            fractions_to_bbox_frame(regions, width, height),
+            build_elements(regions),
+            ui=ui,
+        )
+
+
+class BoundingBoxesExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [CreateBoundingBoxes]
+
+
+async def comfy_entrypoint() -> BoundingBoxesExtension:
+    return BoundingBoxesExtension()
diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py
new file mode 100644
index 0000000000000000000000000000000000000000..2154d31297f96789d944e5af3e4e91a532e931ff
--- /dev/null
+++ b/comfy_extras/nodes_camera_trajectory.py
@@ -0,0 +1,239 @@
+import nodes
+import torch
+import numpy as np
+from einops import rearrange
+from typing_extensions import override
+import comfy.model_management
+
+from comfy_api.latest import ComfyExtension, io
+
+
+CAMERA_DICT = {
+    "base_T_norm": 1.5,
+    "base_angle": np.pi/3,
+    "Static": {     "angle":[0., 0., 0.],   "T":[0., 0., 0.]},
+    "Pan Up": {     "angle":[0., 0., 0.],   "T":[0., -1., 0.]},
+    "Pan Down": {   "angle":[0., 0., 0.],   "T":[0.,1.,0.]},
+    "Pan Left": {   "angle":[0., 0., 0.],   "T":[-1.,0.,0.]},
+    "Pan Right": {  "angle":[0., 0., 0.],   "T": [1.,0.,0.]},
+    "Zoom In": {    "angle":[0., 0., 0.],   "T": [0.,0.,2.]},
+    "Zoom Out": {   "angle":[0., 0., 0.],   "T": [0.,0.,-2.]},
+    "Anti Clockwise (ACW)": {        "angle": [0., 0., -1.],  "T":[0., 0., 0.]},
+    "ClockWise (CW)": {         "angle": [0., 0., 1.], "T":[0., 0., 0.]},
+}
+
+
+def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'):
+
+    def get_relative_pose(cam_params):
+        """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+        """
+        abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
+        abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
+        cam_to_origin = 0
+        target_cam_c2w = np.array([
+            [1, 0, 0, 0],
+            [0, 1, 0, -cam_to_origin],
+            [0, 0, 1, 0],
+            [0, 0, 0, 1]
+        ])
+        abs2rel = target_cam_c2w @ abs_w2cs[0]
+        ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
+        ret_poses = np.array(ret_poses, dtype=np.float32)
+        return ret_poses
+
+    """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    cam_params = [Camera(cam_param) for cam_param in cam_params]
+
+    sample_wh_ratio = width / height
+    pose_wh_ratio = original_pose_width / original_pose_height  # Assuming placeholder ratios, change as needed
+
+    if pose_wh_ratio > sample_wh_ratio:
+        resized_ori_w = height * pose_wh_ratio
+        for cam_param in cam_params:
+            cam_param.fx = resized_ori_w * cam_param.fx / width
+    else:
+        resized_ori_h = width / pose_wh_ratio
+        for cam_param in cam_params:
+            cam_param.fy = resized_ori_h * cam_param.fy / height
+
+    intrinsic = np.asarray([[cam_param.fx * width,
+                            cam_param.fy * height,
+                            cam_param.cx * width,
+                            cam_param.cy * height]
+                            for cam_param in cam_params], dtype=np.float32)
+
+    K = torch.as_tensor(intrinsic)[None]  # [1, 1, 4]
+    c2ws = get_relative_pose(cam_params)  # Assuming this function is defined elsewhere
+    c2ws = torch.as_tensor(c2ws)[None]  # [1, n_frame, 4, 4]
+    plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous()  # V, 6, H, W
+    plucker_embedding = plucker_embedding[None]
+    plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
+    return plucker_embedding
+
+class Camera(object):
+    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    def __init__(self, entry):
+        fx, fy, cx, cy = entry[1:5]
+        self.fx = fx
+        self.fy = fy
+        self.cx = cx
+        self.cy = cy
+        c2w_mat = np.array(entry[7:]).reshape(4, 4)
+        self.c2w_mat = c2w_mat
+        self.w2c_mat = np.linalg.inv(c2w_mat)
+
+def ray_condition(K, c2w, H, W, device):
+    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    # c2w: B, V, 4, 4
+    # K: B, V, 4
+
+    B = K.shape[0]
+
+    j, i = torch.meshgrid(
+        torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+        torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
+        indexing='ij'
+    )
+    i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
+    j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
+
+    fx, fy, cx, cy = K.chunk(4, dim=-1)  # B,V, 1
+
+    zs = torch.ones_like(i)  # [B, HxW]
+    xs = (i - cx) / fx * zs
+    ys = (j - cy) / fy * zs
+    zs = zs.expand_as(ys)
+
+    directions = torch.stack((xs, ys, zs), dim=-1)  # B, V, HW, 3
+    directions = directions / directions.norm(dim=-1, keepdim=True)  # B, V, HW, 3
+
+    rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2)  # B, V, 3, HW
+    rays_o = c2w[..., :3, 3]  # B, V, 3
+    rays_o = rays_o[:, :, None].expand_as(rays_d)  # B, V, 3, HW
+    # c2w @ dirctions
+    rays_dxo = torch.cross(rays_o, rays_d)
+    plucker = torch.cat([rays_dxo, rays_d], dim=-1)
+    plucker = plucker.reshape(B, c2w.shape[1], H, W, 6)  # B, V, H, W, 6
+    # plucker = plucker.permute(0, 1, 4, 2, 3)
+    return plucker
+
+def get_camera_motion(angle, T, speed, n=81):
+    def compute_R_form_rad_angle(angles):
+        theta_x, theta_y, theta_z = angles
+        Rx = np.array([[1, 0, 0],
+                    [0, np.cos(theta_x), -np.sin(theta_x)],
+                    [0, np.sin(theta_x), np.cos(theta_x)]])
+
+        Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)],
+                    [0, 1, 0],
+                    [-np.sin(theta_y), 0, np.cos(theta_y)]])
+
+        Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0],
+                    [np.sin(theta_z), np.cos(theta_z), 0],
+                    [0, 0, 1]])
+
+        R = np.dot(Rz, np.dot(Ry, Rx))
+        return R
+    RT = []
+    for i in range(n):
+        _angle = (i/n)*speed*(CAMERA_DICT["base_angle"])*angle
+        R = compute_R_form_rad_angle(_angle)
+        _T=(i/n)*speed*(CAMERA_DICT["base_T_norm"])*(T.reshape(3,1))
+        _RT = np.concatenate([R,_T], axis=1)
+        RT.append(_RT)
+    RT = np.stack(RT)
+    return RT
+
+class WanCameraEmbedding(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanCameraEmbedding",
+            category="model/conditioning/wan/camera",
+            inputs=[
+                io.Combo.Input(
+                    "camera_pose",
+                    options=[
+                        "Static",
+                        "Pan Up",
+                        "Pan Down",
+                        "Pan Left",
+                        "Pan Right",
+                        "Zoom In",
+                        "Zoom Out",
+                        "Anti Clockwise (ACW)",
+                        "ClockWise (CW)",
+                    ],
+                    default="Static",
+                ),
+                io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Float.Input("speed", default=1.0, min=0, max=10.0, step=0.1, optional=True),
+                io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True, advanced=True),
+                io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True, advanced=True),
+                io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True, advanced=True),
+                io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True, advanced=True),
+            ],
+            outputs=[
+                io.WanCameraEmbedding.Output(display_name="camera_embedding"),
+                io.Int.Output(display_name="width"),
+                io.Int.Output(display_name="height"),
+                io.Int.Output(display_name="length"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5) -> io.NodeOutput:
+        """
+        Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021)
+        Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py
+        """
+        motion_list = [camera_pose]
+        speed = speed
+        angle = np.array(CAMERA_DICT[motion_list[0]]["angle"])
+        T = np.array(CAMERA_DICT[motion_list[0]]["T"])
+        RT = get_camera_motion(angle, T, speed, length)
+
+        trajs=[]
+        for cp in RT.tolist():
+            traj=[fx,fy,cx,cy,0,0]
+            traj.extend(cp[0])
+            traj.extend(cp[1])
+            traj.extend(cp[2])
+            traj.extend([0,0,0,1])
+            trajs.append(traj)
+
+        cam_params = np.array([[float(x) for x in pose] for pose in trajs])
+        cam_params = np.concatenate([np.zeros_like(cam_params[:, :1]), cam_params], 1)
+        control_camera_video = process_pose_params(cam_params, width=width, height=height)
+        control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0).to(device=comfy.model_management.intermediate_device())
+
+        control_camera_video = torch.concat(
+            [
+                torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2),
+                control_camera_video[:, :, 1:]
+            ], dim=2
+        ).transpose(1, 2)
+
+        # Reshape, transpose, and view into desired shape
+        b, f, c, h, w = control_camera_video.shape
+        control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
+        control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
+
+        return io.NodeOutput(control_camera_video, width, height, length)
+
+
+class CameraTrajectoryExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            WanCameraEmbedding,
+        ]
+
+async def comfy_entrypoint() -> CameraTrajectoryExtension:
+    return CameraTrajectoryExtension()
diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py
new file mode 100644
index 0000000000000000000000000000000000000000..894376145227b96c8057de72955b1ee61ec2c842
--- /dev/null
+++ b/comfy_extras/nodes_canny.py
@@ -0,0 +1,45 @@
+from kornia.filters import canny
+from typing_extensions import override
+
+import comfy.model_management
+from comfy_api.latest import ComfyExtension, io
+import torch
+
+
+class Canny(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Canny",
+            display_name="Detect Edges (Canny)",
+            search_aliases=["edge detection", "outline", "contour detection", "line art"],
+            category="image/filters",
+            essentials_category="Image Tools",
+            inputs=[
+                io.Image.Input("image"),
+                io.Float.Input("low_threshold", default=0.4, min=0.01, max=0.99, step=0.01),
+                io.Float.Input("high_threshold", default=0.8, min=0.01, max=0.99, step=0.01),
+            ],
+            outputs=[io.Image.Output()],
+        )
+
+    @classmethod
+    def detect_edge(cls, image, low_threshold, high_threshold):
+        # Deprecated: use the V3 schema's `execute` method instead of this.
+        return cls.execute(image, low_threshold, high_threshold)
+
+    @classmethod
+    def execute(cls, image, low_threshold, high_threshold) -> io.NodeOutput:
+        output = canny(image.to(device=comfy.model_management.get_torch_device(), dtype=torch.float32).movedim(-1, 1), low_threshold, high_threshold)
+        img_out = output[1].to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()).repeat(1, 3, 1, 1).movedim(1, -1)
+        return io.NodeOutput(img_out)
+
+
+class CannyExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [Canny]
+
+
+async def comfy_entrypoint() -> CannyExtension:
+    return CannyExtension()
diff --git a/comfy_extras/nodes_cfg.py b/comfy_extras/nodes_cfg.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b0df703db7235629a20cc4b3baefabcda3fb766
--- /dev/null
+++ b/comfy_extras/nodes_cfg.py
@@ -0,0 +1,122 @@
+from typing_extensions import override
+
+import torch
+
+from comfy_api.latest import ComfyExtension, io
+
+
+# https://github.com/WeichenFan/CFG-Zero-star
+def optimized_scale(positive, negative):
+    positive_flat = positive.reshape(positive.shape[0], -1)
+    negative_flat = negative.reshape(negative.shape[0], -1)
+
+    # Calculate dot production
+    dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
+
+    # Squared norm of uncondition
+    squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
+
+    # st_star = v_cond^T * v_uncond / ||v_uncond||^2
+    st_star = dot_product / squared_norm
+
+    return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1))
+
+class CFGZeroStar(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CFGZeroStar",
+            category="advanced/guidance",
+            inputs=[
+                io.Model.Input("model"),
+            ],
+            outputs=[io.Model.Output(display_name="patched_model")],
+        )
+
+    @classmethod
+    def execute(cls, model) -> io.NodeOutput:
+        m = model.clone()
+        def cfg_zero_star(args):
+            guidance_scale = args['cond_scale']
+            x = args['input']
+            cond_p = args['cond_denoised']
+            uncond_p = args['uncond_denoised']
+            out = args["denoised"]
+            alpha = optimized_scale(x - cond_p, x - uncond_p)
+
+            return out + uncond_p * (alpha - 1.0)  + guidance_scale * uncond_p * (1.0 - alpha)
+        m.set_model_sampler_post_cfg_function(cfg_zero_star)
+        return io.NodeOutput(m)
+
+class CFGNorm(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CFGNorm",
+            category="advanced/guidance",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Boolean.Input(
+                    "pre_cfg",
+                    default=False,
+                    optional=True,
+                    tooltip=(
+                        "If true, rescale the combined noise BEFORE the sampler's CFG combine, "
+                        "without clamping (can amplify). Matches the norm-scaled CFG used by "
+                        "models like Lens. Default false keeps the original post-CFG x0-space "
+                        "attenuate-only behavior."
+                    ),
+                ),
+            ],
+            outputs=[io.Model.Output(display_name="patched_model")],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, strength, pre_cfg=False) -> io.NodeOutput:
+        m = model.clone()
+        if pre_cfg:
+            def cfg_norm_pre(args):
+                cond = args["cond"]
+                uncond = args["uncond"]
+                cond_scale = args["cond_scale"]
+                comb = uncond + cond_scale * (cond - uncond)
+                cond_norm = torch.linalg.vector_norm(cond, dim=1, keepdim=True)
+                comb_norm = torch.linalg.vector_norm(comb, dim=1, keepdim=True)
+                rescale = torch.where(
+                    comb_norm > 0,
+                    cond_norm / comb_norm.clamp_min(1e-12),
+                    torch.ones_like(comb_norm),
+                )
+                rescaled = comb * rescale
+                # strength blends back toward standard linear CFG (1.0 = full rescale).
+                if strength != 1.0:
+                    rescaled = strength * rescaled + (1.0 - strength) * comb
+                return rescaled
+            m.set_model_sampler_cfg_function(cfg_norm_pre)
+        else:
+            def cfg_norm(args):
+                cond_p = args['cond_denoised']
+                pred_text_ = args["denoised"]
+
+                norm_full_cond = torch.norm(cond_p, dim=1, keepdim=True)
+                norm_pred_text = torch.norm(pred_text_, dim=1, keepdim=True)
+                scale = (norm_full_cond / (norm_pred_text + 1e-8)).clamp(min=0.0, max=1.0)
+                return pred_text_ * scale * strength
+
+            m.set_model_sampler_post_cfg_function(cfg_norm)
+        return io.NodeOutput(m)
+
+
+class CfgExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CFGZeroStar,
+            CFGNorm,
+        ]
+
+
+async def comfy_entrypoint() -> CfgExtension:
+    return CfgExtension()
diff --git a/comfy_extras/nodes_chroma_radiance.py b/comfy_extras/nodes_chroma_radiance.py
new file mode 100644
index 0000000000000000000000000000000000000000..74721786b19da9bd9ea5a5e5537d86c68bc1bfd3
--- /dev/null
+++ b/comfy_extras/nodes_chroma_radiance.py
@@ -0,0 +1,127 @@
+from typing_extensions import override
+from typing import Callable
+
+import torch
+
+import comfy.model_management
+from comfy_api.latest import ComfyExtension, io
+
+import nodes
+
+class EmptyChromaRadianceLatentImage(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="EmptyChromaRadianceLatentImage",
+            category="model/latent/chroma radiance",
+            inputs=[
+                io.Int.Input(id="width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input(id="height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input(id="batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[io.Latent().Output()],
+        )
+
+    @classmethod
+    def execute(cls, *, width: int, height: int, batch_size: int=1) -> io.NodeOutput:
+        latent = torch.zeros((batch_size, 3, height, width), device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples":latent})
+
+
+class ChromaRadianceOptions(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="ChromaRadianceOptions",
+            category="model/patch/chroma radiance",
+            description="Allows setting advanced options for the Chroma Radiance model.",
+            inputs=[
+                io.Model.Input(id="model"),
+                io.Boolean.Input(
+                    id="preserve_wrapper",
+                    default=True,
+                    tooltip="When enabled, will delegate to an existing model function wrapper if it exists. Generally should be left enabled.",
+                ),
+                io.Float.Input(
+                    id="start_sigma",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    tooltip="First sigma that these options will be in effect.",
+                    advanced=True,
+                ),
+                io.Float.Input(
+                    id="end_sigma",
+                    default=0.0,
+                    min=0.0,
+                    max=1.0,
+                    tooltip="Last sigma that these options will be in effect.",
+                    advanced=True,
+                ),
+                io.Int.Input(
+                    id="nerf_tile_size",
+                    default=-1,
+                    min=-1,
+                    tooltip="Allows overriding the default NeRF tile size. -1 means use the default (32). 0 means use non-tiling mode (may require a lot of VRAM).",
+                    advanced=True,
+                ),
+                io.Boolean.Input(
+                    id="force_sequential_txt_ids",
+                    default=False,
+                    tooltip="Force usage of sequential text token IDs instead of zeroes. Should be used for checkpoints from 2026-05-22 to 2026-06-01 that are trained in this way but do not contain the __sequential__ key in the state dict.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        *,
+        model: io.Model.Type,
+        preserve_wrapper: bool,
+        start_sigma: float,
+        end_sigma: float,
+        nerf_tile_size: int,
+        force_sequential_txt_ids: bool,
+    ) -> io.NodeOutput:
+        radiance_options = {}
+        if nerf_tile_size >= 0:
+            radiance_options["nerf_tile_size"] = nerf_tile_size
+
+        if force_sequential_txt_ids:
+            radiance_options["use_sequential_txt_ids"] = True
+
+        if not radiance_options:
+            return io.NodeOutput(model)
+
+        old_wrapper = model.model_options.get("model_function_wrapper")
+
+        def model_function_wrapper(apply_model: Callable, args: dict) -> torch.Tensor:
+            c = args["c"].copy()
+            sigma = args["timestep"].max().detach().cpu().item()
+            if end_sigma <= sigma <= start_sigma:
+                transformer_options = c.get("transformer_options", {}).copy()
+                transformer_options["chroma_radiance_options"] = radiance_options.copy()
+                c["transformer_options"] = transformer_options
+            if not (preserve_wrapper and old_wrapper):
+                return apply_model(args["input"], args["timestep"], **c)
+            return old_wrapper(apply_model, args | {"c": c})
+
+        model = model.clone()
+        model.set_model_unet_function_wrapper(model_function_wrapper)
+        return io.NodeOutput(model)
+
+
+class ChromaRadianceExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EmptyChromaRadianceLatentImage,
+            ChromaRadianceOptions,
+        ]
+
+
+async def comfy_entrypoint() -> ChromaRadianceExtension:
+    return ChromaRadianceExtension()
diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py
new file mode 100644
index 0000000000000000000000000000000000000000..d43e14e4c647e1582514bcbd5d1194551c17861a
--- /dev/null
+++ b/comfy_extras/nodes_clip_sdxl.py
@@ -0,0 +1,73 @@
+from typing_extensions import override
+
+import nodes
+from comfy_api.latest import ComfyExtension, io
+
+
+class CLIPTextEncodeSDXLRefiner(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeSDXLRefiner",
+            display_name="CLIP Text Encode (SDXL Refiner)",
+            category="model/conditioning/stable diffusion",
+            inputs=[
+                io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
+                io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.String.Input("text", multiline=True, dynamic_prompts=True),
+                io.Clip.Input("clip"),
+            ],
+            outputs=[io.Conditioning.Output()],
+        )
+
+    @classmethod
+    def execute(cls, clip, ascore, width, height, text) -> io.NodeOutput:
+        tokens = clip.tokenize(text)
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}))
+
+class CLIPTextEncodeSDXL(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeSDXL",
+            display_name="CLIP Text Encode (SDXL)",
+            category="model/conditioning/stable diffusion",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION, advanced=True),
+                io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION, advanced=True),
+                io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.String.Input("text_g", multiline=True, dynamic_prompts=True),
+                io.String.Input("text_l", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[io.Conditioning.Output()],
+        )
+
+    @classmethod
+    def execute(cls, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l) -> io.NodeOutput:
+        tokens = clip.tokenize(text_g)
+        tokens["l"] = clip.tokenize(text_l)["l"]
+        if len(tokens["l"]) != len(tokens["g"]):
+            empty = clip.tokenize("")
+            while len(tokens["l"]) < len(tokens["g"]):
+                tokens["l"] += empty["l"]
+            while len(tokens["l"]) > len(tokens["g"]):
+                tokens["g"] += empty["g"]
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}))
+
+
+class ClipSdxlExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeSDXLRefiner,
+            CLIPTextEncodeSDXL,
+        ]
+
+
+async def comfy_entrypoint() -> ClipSdxlExtension:
+    return ClipSdxlExtension()
diff --git a/comfy_extras/nodes_color.py b/comfy_extras/nodes_color.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b38f176d88598deb8a3ed6880dc41251475f32f
--- /dev/null
+++ b/comfy_extras/nodes_color.py
@@ -0,0 +1,52 @@
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from comfy_extras.color_util import hex_to_rgb
+
+
+class ColorToRGBInt(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="ColorToRGBInt",
+            display_name="Color Picker",
+            category="utilities",
+            description="Return a color RGB integer value and hexadecimal representation.",
+            inputs=[
+                io.Color.Input("color"),
+            ],
+            outputs=[
+                io.Int.Output(display_name="rgb_int"),
+                io.Color.Output(display_name="hex"),
+                io.Float.Output(display_name="alpha"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, color: str) -> io.NodeOutput:
+        # expect format #RRGGBB or #RRGGBBAA
+        if len(color) not in (7, 9) or color[0] != "#":
+            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA")
+        try:
+            int(color[1:], 16)
+        except ValueError:
+            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA") from None
+
+        alpha = 1.0
+        if len(color) == 9:
+            alpha = int(color[7:9], 16) / 255.0
+            color = color[:7]
+
+        r, g, b = hex_to_rgb(color)
+
+        rgb_int = r * 256 * 256 + g * 256 + b
+        return io.NodeOutput(rgb_int, color, alpha)
+
+
+class ColorExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [ColorToRGBInt]
+
+
+async def comfy_entrypoint() -> ColorExtension:
+    return ColorExtension()
diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1117e65b4f1999aca11565ac90f8db95c330411
--- /dev/null
+++ b/comfy_extras/nodes_compositing.py
@@ -0,0 +1,223 @@
+import torch
+import comfy.utils
+from enum import Enum
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+def resize_mask(mask, shape):
+    return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1)
+
+class PorterDuffMode(Enum):
+    ADD = 0
+    CLEAR = 1
+    DARKEN = 2
+    DST = 3
+    DST_ATOP = 4
+    DST_IN = 5
+    DST_OUT = 6
+    DST_OVER = 7
+    LIGHTEN = 8
+    MULTIPLY = 9
+    OVERLAY = 10
+    SCREEN = 11
+    SRC = 12
+    SRC_ATOP = 13
+    SRC_IN = 14
+    SRC_OUT = 15
+    SRC_OVER = 16
+    XOR = 17
+
+
+def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode):
+    # convert mask to alpha
+    src_alpha = 1 - src_alpha
+    dst_alpha = 1 - dst_alpha
+    # premultiply alpha
+    src_image = src_image * src_alpha
+    dst_image = dst_image * dst_alpha
+
+    # composite ops below assume alpha-premultiplied images
+    if mode == PorterDuffMode.ADD:
+        out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1)
+        out_image = torch.clamp(src_image + dst_image, 0, 1)
+    elif mode == PorterDuffMode.CLEAR:
+        out_alpha = torch.zeros_like(dst_alpha)
+        out_image = torch.zeros_like(dst_image)
+    elif mode == PorterDuffMode.DARKEN:
+        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+        out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image)
+    elif mode == PorterDuffMode.DST:
+        out_alpha = dst_alpha
+        out_image = dst_image
+    elif mode == PorterDuffMode.DST_ATOP:
+        out_alpha = src_alpha
+        out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image
+    elif mode == PorterDuffMode.DST_IN:
+        out_alpha = src_alpha * dst_alpha
+        out_image = dst_image * src_alpha
+    elif mode == PorterDuffMode.DST_OUT:
+        out_alpha = (1 - src_alpha) * dst_alpha
+        out_image = (1 - src_alpha) * dst_image
+    elif mode == PorterDuffMode.DST_OVER:
+        out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha
+        out_image = dst_image + (1 - dst_alpha) * src_image
+    elif mode == PorterDuffMode.LIGHTEN:
+        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+        out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image)
+    elif mode == PorterDuffMode.MULTIPLY:
+        out_alpha = src_alpha * dst_alpha
+        out_image = src_image * dst_image
+    elif mode == PorterDuffMode.OVERLAY:
+        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+        out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image,
+            src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image))
+    elif mode == PorterDuffMode.SCREEN:
+        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+        out_image = src_image + dst_image - src_image * dst_image
+    elif mode == PorterDuffMode.SRC:
+        out_alpha = src_alpha
+        out_image = src_image
+    elif mode == PorterDuffMode.SRC_ATOP:
+        out_alpha = dst_alpha
+        out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image
+    elif mode == PorterDuffMode.SRC_IN:
+        out_alpha = src_alpha * dst_alpha
+        out_image = src_image * dst_alpha
+    elif mode == PorterDuffMode.SRC_OUT:
+        out_alpha = (1 - dst_alpha) * src_alpha
+        out_image = (1 - dst_alpha) * src_image
+    elif mode == PorterDuffMode.SRC_OVER:
+        out_alpha = src_alpha + (1 - src_alpha) * dst_alpha
+        out_image = src_image + (1 - src_alpha) * dst_image
+    elif mode == PorterDuffMode.XOR:
+        out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha
+        out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image
+    else:
+        return None, None
+
+    # back to non-premultiplied alpha
+    out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image))
+    out_image = torch.clamp(out_image, 0, 1)
+    # convert alpha to mask
+    out_alpha = 1 - out_alpha
+    return out_image, out_alpha
+
+
+class PorterDuffImageComposite(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PorterDuffImageComposite",
+            search_aliases=["alpha composite", "blend modes", "layer blend", "transparency blend"],
+            display_name="Porter-Duff Image Composite",
+            category="image/compositing",
+            inputs=[
+                io.Image.Input("source"),
+                io.Mask.Input("source_alpha"),
+                io.Image.Input("destination"),
+                io.Mask.Input("destination_alpha"),
+                io.Combo.Input("mode", options=[mode.name for mode in PorterDuffMode], default=PorterDuffMode.DST.name),
+            ],
+            outputs=[
+                io.Image.Output(),
+                io.Mask.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode) -> io.NodeOutput:
+        batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha))
+        out_images = []
+        out_alphas = []
+
+        for i in range(batch_size):
+            src_image = source[i]
+            dst_image = destination[i]
+
+            assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels
+
+            src_alpha = source_alpha[i].unsqueeze(2)
+            dst_alpha = destination_alpha[i].unsqueeze(2)
+
+            if dst_alpha.shape[:2] != dst_image.shape[:2]:
+                upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2)
+                upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
+                dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
+            if src_image.shape != dst_image.shape:
+                upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2)
+                upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
+                src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)
+            if src_alpha.shape != dst_alpha.shape:
+                upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2)
+                upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
+                src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
+
+            out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode])
+
+            out_images.append(out_image)
+            out_alphas.append(out_alpha.squeeze(2))
+
+        return io.NodeOutput(torch.stack(out_images), torch.stack(out_alphas))
+
+
+class SplitImageWithAlpha(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SplitImageWithAlpha",
+            search_aliases=["extract alpha", "separate transparency", "remove alpha"],
+            display_name="Split Image with Alpha",
+            category="image/compositing",
+            inputs=[
+                io.Image.Input("image"),
+            ],
+            outputs=[
+                io.Image.Output(),
+                io.Mask.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image: torch.Tensor) -> io.NodeOutput:
+        out_images = [i[:,:,:3] for i in image]
+        out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image]
+        return io.NodeOutput(torch.stack(out_images), 1.0 - torch.stack(out_alphas))
+
+
+class JoinImageWithAlpha(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="JoinImageWithAlpha",
+            search_aliases=["add transparency", "apply alpha", "composite alpha", "RGBA"],
+            display_name="Join Image with Alpha",
+            category="image/compositing",
+            inputs=[
+                io.Image.Input("image"),
+                io.Mask.Input("alpha"),
+            ],
+            outputs=[io.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput:
+        batch_size = max(len(image), len(alpha))
+        alpha = 1.0 - resize_mask(alpha.to(image), image.shape[1:])
+        alpha = comfy.utils.repeat_to_batch_size(alpha, batch_size)
+        image = comfy.utils.repeat_to_batch_size(image, batch_size)
+        return io.NodeOutput(torch.cat((image[..., :3], alpha.unsqueeze(-1)), dim=-1))
+
+
+class CompositingExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            PorterDuffImageComposite,
+            SplitImageWithAlpha,
+            JoinImageWithAlpha,
+        ]
+
+
+async def comfy_entrypoint() -> CompositingExtension:
+    return CompositingExtension()
diff --git a/comfy_extras/nodes_compositor.py b/comfy_extras/nodes_compositor.py
new file mode 100644
index 0000000000000000000000000000000000000000..925013ebaf9110e52e160bc98d0983f91629aba3
--- /dev/null
+++ b/comfy_extras/nodes_compositor.py
@@ -0,0 +1,855 @@
+import hashlib
+import json
+import math
+
+import numpy as np
+import torch
+from PIL import Image
+
+from comfy_api.latest import ComfyExtension, io, UI
+from comfy_extras.compositor_blend import (
+    _LAYER_MODES,
+    blend_composite,
+    linear_to_srgb,
+    placed_bounds,
+    resolve_mode,
+    srgb_to_linear,
+)
+from comfy_extras.color_util import hex_to_rgb
+from comfy_extras.nodes_bounding_boxes import boxes_from_input
+from nodes import MAX_RESOLUTION
+from typing_extensions import override
+
+
+MAX_LAYERS = 50
+
+
+def document_items(doc) -> list[dict]:
+    if not isinstance(doc, dict):
+        return []
+    version = doc.get("version")
+    if version is not None and version != 1:
+        raise ValueError(f"LAYERS document version {version!r} is not supported")
+    items = []
+    for item in doc.get("layers") or []:
+        if not isinstance(item, dict):
+            continue
+        item_type = item.get("type", "raster")
+        if item_type != "raster":
+            raise ValueError(f"LAYERS item type {item_type!r} is not supported yet")
+        if not isinstance(item.get("image"), torch.Tensor):
+            continue
+        blend = item.get("blend_mode")
+        if blend is not None and blend not in _LAYER_MODES:
+            raise ValueError(f"LAYERS item blend_mode {blend!r} is not a known blend mode")
+        items.append(item)
+    return sorted(items, key=lambda item: _int(item.get("z_index"), 0))
+
+
+def document_canvas(doc) -> tuple[int, int] | None:
+    if not isinstance(doc, dict):
+        return None
+    canvas = doc.get("canvas")
+    if not isinstance(canvas, (tuple, list)) or len(canvas) != 2:
+        return None
+    w, h = _int(canvas[0], 0), _int(canvas[1], 0)
+    return (w, h) if w > 0 and h > 0 else None
+
+
+def _int(value, default: int) -> int:
+    return int(value) if isinstance(value, (int, float)) and not isinstance(value, bool) else default
+
+
+def _bbox_list(bboxes, canvas_width: int, canvas_height: int) -> list[dict]:
+    if bboxes is None:
+        return []
+    if isinstance(bboxes, str):
+        text = bboxes.strip()
+        if not text:
+            return []
+        try:
+            bboxes = json.loads(text)
+        except (json.JSONDecodeError, ValueError) as exc:
+            raise ValueError(f"bboxes string input is not valid JSON: {exc}") from exc
+    probe = bboxes if isinstance(bboxes, list) else [bboxes]
+    if probe and isinstance(probe[0], list):
+        probe = probe[0]
+    has_elements = any(
+        isinstance(box, dict) and isinstance(box.get("bbox"), (list, tuple))
+        for box in probe
+    )
+    if has_elements and (canvas_width <= 0 or canvas_height <= 0):
+        raise ValueError(
+            "normalized element boxes need canvas_width and canvas_height to resolve to pixels"
+        )
+    return boxes_from_input(bboxes, canvas_width, canvas_height)
+
+
+def _item_mask_frame(mask, index: int) -> torch.Tensor | None:
+    if not isinstance(mask, torch.Tensor):
+        return None
+    if mask.shape[0] == 1:
+        return mask[:1]
+    if index < mask.shape[0]:
+        return mask[index : index + 1]
+    return None
+
+
+def expand_item_frames(items: list[dict]) -> list[dict]:
+    frames = []
+    for item in items:
+        image = item["image"]
+        for index in range(image.shape[0]):
+            width = _int(item.get("w"), 0)
+            height = _int(item.get("h"), 0)
+            rotation = item.get("rotation")
+            frames.append({
+                "tensor": image[index : index + 1],
+                "mask": _item_mask_frame(item.get("mask"), index),
+                "name": item.get("name") if isinstance(item.get("name"), str) else None,
+                "x": _int(item.get("x"), 0),
+                "y": _int(item.get("y"), 0),
+                "w": width if width > 0 else int(image.shape[2]),
+                "h": height if height > 0 else int(image.shape[1]),
+                "rotation": float(rotation)
+                if isinstance(rotation, (int, float)) and not isinstance(rotation, bool)
+                else 0.0,
+                "opacity": item.get("opacity", 1.0),
+                "blend": item.get("blend_mode", "normal"),
+                "visible": item.get("visible", True),
+                "flip_h": bool(item.get("flip_h", False)),
+                "flip_v": bool(item.get("flip_v", False)),
+            })
+    if len(frames) > MAX_LAYERS:
+        raise ValueError(
+            f"Compositor supports at most {MAX_LAYERS} layers, got {len(frames)}"
+        )
+    return frames
+
+
+def frame_alpha(
+    tensor: torch.Tensor, mask: torch.Tensor | None
+) -> torch.Tensor | None:
+    alpha = tensor[:1, :, :, 3] if tensor.shape[-1] == 4 else None
+    if mask is None:
+        return alpha
+    h, w = tensor.shape[1], tensor.shape[2]
+    m = mask[:1].to(device=tensor.device, dtype=torch.float32)
+    if m.shape[1] != h or m.shape[2] != w:
+        m = torch.nn.functional.interpolate(
+            m.unsqueeze(1), size=(h, w), mode="bilinear"
+        ).squeeze(1)
+    inv = torch.clamp(1.0 - m, 0.0, 1.0)
+    return inv if alpha is None else alpha * inv
+
+
+def layer_preview_tensor(
+    tensor: torch.Tensor, alpha: torch.Tensor | None
+) -> torch.Tensor:
+    rgb = tensor[:1, :, :, :3]
+    if alpha is None:
+        return rgb
+    return torch.cat([rgb, alpha.unsqueeze(-1)], dim=-1)
+
+
+def canvas_extent(frames: list[dict]) -> tuple[int, int]:
+    right = 1
+    bottom = 1
+    for frame in frames:
+        bx, by, bw, bh = placed_bounds(
+            frame["x"], frame["y"], frame["w"], frame["h"], frame["rotation"]
+        )
+        right = max(right, bx + bw)
+        bottom = max(bottom, by + bh)
+    return (right, bottom)
+
+
+def input_fingerprints(
+    frames: list[dict], alphas: list[torch.Tensor | None]
+) -> list[str]:
+    fingerprints = []
+    for frame, alpha in zip(frames, alphas):
+        tensor = frame["tensor"]
+        rgb = tensor[0, :, :, :3].detach().cpu().numpy()
+        rgb8 = np.clip(np.rint(rgb * 255.0), 0, 255).astype(np.uint8)
+        digest = hashlib.sha256()
+        digest.update(repr(tuple(tensor.shape)).encode())
+        digest.update(rgb8.tobytes())
+        if alpha is not None:
+            alpha8 = np.clip(
+                np.rint(alpha[0].detach().cpu().numpy() * 255.0), 0, 255
+            ).astype(np.uint8)
+            digest.update(alpha8.tobytes())
+        digest.update(
+            repr((
+                frame["x"],
+                frame["y"],
+                frame["w"],
+                frame["h"],
+                frame["rotation"],
+                frame["opacity"],
+                frame["blend"],
+                bool(frame["visible"]),
+                frame["flip_h"],
+                frame["flip_v"],
+            )).encode()
+        )
+        fingerprints.append(digest.hexdigest()[:16])
+    return fingerprints
+
+
+def state_from_items(frames: list[dict], canvas: tuple[int, int]) -> dict:
+    layers = []
+    for frame in frames:
+        layers.append({
+            "name": frame["name"],
+            "visible": bool(frame["visible"]),
+            "opacity": frame["opacity"],
+            "blend": frame["blend"],
+            "flipH": frame["flip_h"],
+            "flipV": frame["flip_v"],
+            "transform": {
+                "x": frame["x"],
+                "y": frame["y"],
+                "w": frame["w"],
+                "h": frame["h"],
+                "rotation": frame["rotation"],
+            },
+        })
+    return {
+        "canvas": canvas,
+        "layers": layers,
+        "inputs": None,
+        "background": {"color": "#ffffff", "opacity": 1.0, "visible": False},
+    }
+
+
+def layer_ui_entries(frames: list[dict]) -> list:
+    entries = []
+    for frame in frames:
+        entries.append({
+            "x": frame["x"],
+            "y": frame["y"],
+            "width": int(frame["w"]),
+            "height": int(frame["h"]),
+            "rotation": frame["rotation"],
+            "name": frame["name"],
+            "visible": bool(frame["visible"]),
+            "opacity": frame["opacity"] if isinstance(frame["opacity"], (int, float)) else 1.0,
+            "blend": frame["blend"] if isinstance(frame["blend"], str) else "normal",
+            "flipH": frame["flip_h"],
+            "flipV": frame["flip_v"],
+        })
+    return entries
+
+
+_HEX_DIGITS = set("0123456789abcdef")
+
+
+def _normalize_hex_color(value) -> str:
+    if isinstance(value, str):
+        text = value.strip().lower()
+        if text.startswith("#"):
+            digits = text[1:]
+            if len(digits) == 3 and set(digits) <= _HEX_DIGITS:
+                digits = "".join(ch * 2 for ch in digits)
+            if len(digits) == 6 and set(digits) <= _HEX_DIGITS:
+                return "#" + digits
+    return "#ffffff"
+
+
+def _parse_background(entry) -> dict | None:
+    if not isinstance(entry, dict):
+        return None
+    return {
+        "color": _normalize_hex_color(entry.get("color")),
+        "opacity": min(max(_number(entry, "opacity", 1.0), 0.0), 1.0),
+        "visible": bool(entry.get("visible", True)),
+    }
+
+
+def _parse_order(value, layer_count: int) -> list[int] | None:
+    if not isinstance(value, list) or not value:
+        return None
+    if not all(
+        isinstance(item, int) and not isinstance(item, bool) for item in value
+    ):
+        return None
+    if sorted(value) != list(range(layer_count)):
+        return None
+    return value
+
+
+def layer_state_provided(raw) -> bool:
+    if isinstance(raw, dict):
+        return bool(raw)
+    if isinstance(raw, str):
+        return raw not in ("", "{}")
+    return False
+
+
+def parse_layer_state(raw) -> dict | None:
+    if isinstance(raw, str):
+        if not raw.strip():
+            return None
+        try:
+            raw = json.loads(raw)
+        except (json.JSONDecodeError, ValueError):
+            return None
+    if not isinstance(raw, dict):
+        return None
+    state = raw
+    version = state.get("version")
+    if version is not None and version != 1:
+        return None
+    canvas = state.get("canvas")
+    layers = state.get("layers")
+    if not isinstance(canvas, dict) or not isinstance(layers, list) or not layers:
+        return None
+    try:
+        w = int(round(float(canvas.get("w"))))
+        h = int(round(float(canvas.get("h"))))
+    except (TypeError, ValueError, OverflowError):
+        return None
+    if w <= 0 or h <= 0:
+        return None
+    inputs = state.get("inputs")
+    if (
+        not isinstance(inputs, list)
+        or len(inputs) != len(layers)
+        or not all(isinstance(entry, str) for entry in inputs)
+    ):
+        inputs = None
+    return {
+        "canvas": (w, h),
+        "layers": layers,
+        "inputs": inputs,
+        "background": _parse_background(state.get("background")),
+        "order": _parse_order(state.get("order"), len(layers)),
+    }
+
+
+def _number(source: dict, key: str, default: float) -> float:
+    value = source.get(key, default)
+    if not isinstance(value, (int, float)) or not math.isfinite(value):
+        return float(default)
+    return float(value)
+
+
+def _clamped_size(value: float, natural: int) -> float:
+    return float(natural) if value <= 0 else min(value, float(MAX_RESOLUTION))
+
+
+def _layer_params(entry, natural_w: int, natural_h: int) -> dict:
+    if not isinstance(entry, dict):
+        entry = {}
+    transform = entry.get("transform")
+    if not isinstance(transform, dict):
+        transform = {}
+    blend = entry.get("blend")
+    return {
+        "visible": bool(entry.get("visible", True)),
+        # The layer state is untrusted input: it round-trips through the saved
+        # workflow and can be posted directly to /prompt. An out-of-range opacity
+        # would otherwise reach blend_composite as a raw coverage multiplier and
+        # produce negative or greater-than-white RGB. _parse_background already
+        # clamps the same field.
+        "opacity": min(max(_number(entry, "opacity", 1.0), 0.0), 1.0),
+        "blend": blend if isinstance(blend, str) else "normal",
+        "x": min(max(_number(transform, "x", 0.0), -MAX_RESOLUTION), MAX_RESOLUTION),
+        "y": min(max(_number(transform, "y", 0.0), -MAX_RESOLUTION), MAX_RESOLUTION),
+        "w": _clamped_size(_number(transform, "w", natural_w), natural_w),
+        "h": _clamped_size(_number(transform, "h", natural_h), natural_h),
+        "rotation": _number(transform, "rotation", 0.0),
+        "flip_h": bool(entry.get("flipH", False)),
+        "flip_v": bool(entry.get("flipV", False)),
+    }
+
+
+def _prepare_layer_bitmap(
+    tensor: torch.Tensor, params: dict, alpha: torch.Tensor | None
+) -> Image.Image:
+    frame = tensor[0, :, :, :3].detach().cpu().numpy()
+    rgb8 = np.clip(np.rint(frame * 255.0), 0, 255).astype(np.uint8)
+    if alpha is None:
+        img = Image.fromarray(rgb8, "RGB").convert("RGBA")
+    else:
+        alpha8 = np.clip(
+            np.rint(alpha[0].detach().cpu().numpy() * 255.0), 0, 255
+        ).astype(np.uint8)
+        img = Image.fromarray(np.dstack([rgb8, alpha8]), "RGBA")
+    if params["flip_h"]:
+        img = img.transpose(Image.Transpose.FLIP_LEFT_RIGHT)
+    if params["flip_v"]:
+        img = img.transpose(Image.Transpose.FLIP_TOP_BOTTOM)
+    target = (max(1, round(params["w"])), max(1, round(params["h"])))
+    if img.size != target:
+        img = img.resize(target, Image.Resampling.LANCZOS)
+    if params["rotation"] != 0:
+        img = img.rotate(
+            -math.degrees(params["rotation"]),
+            expand=True,
+            resample=Image.Resampling.BICUBIC,
+            fillcolor=(0, 0, 0, 0),
+        )
+    return img
+
+
+def _place_in_bounds(img: Image.Image, bw: int, bh: int) -> np.ndarray:
+    arr = np.asarray(img, dtype=np.float32) / 255.0
+    rgba = np.concatenate([srgb_to_linear(arr[..., :3]), arr[..., 3:4]], axis=-1)
+    aw, ah = img.size
+    buf = np.zeros((bh, bw, 4), dtype=np.float32)
+    ox = (bw - aw) // 2
+    oy = (bh - ah) // 2
+    dx0, dy0 = max(ox, 0), max(oy, 0)
+    dx1, dy1 = min(ox + aw, bw), min(oy + ah, bh)
+    if dx0 < dx1 and dy0 < dy1:
+        buf[dy0:dy1, dx0:dx1] = rgba[dy0 - oy : dy1 - oy, dx0 - ox : dx1 - ox]
+    return buf
+
+
+def _fill_background(canvas: np.ndarray, background: dict) -> np.ndarray:
+    layer = np.empty(canvas.shape, dtype=np.float32)
+    layer[..., :3] = srgb_to_linear(
+        np.array(hex_to_rgb(background["color"]), dtype=np.float32) / 255.0
+    )
+    layer[..., 3] = 1.0
+    return blend_composite(
+        resolve_mode("normal"), canvas, layer, background["opacity"]
+    )
+
+
+def composite_from_state(
+    tensors: list[torch.Tensor],
+    state: dict,
+    alphas: list[torch.Tensor | None],
+) -> torch.Tensor:
+    cw, ch = state["canvas"]
+    if cw > MAX_RESOLUTION or ch > MAX_RESOLUTION:
+        raise ValueError(
+            f"Compositor canvas {cw}x{ch} exceeds the maximum supported size of "
+            f"{MAX_RESOLUTION}x{MAX_RESOLUTION}"
+        )
+    canvas = np.zeros((ch, cw, 4), dtype=np.float32)
+    background = state.get("background")
+    if background is not None and background["visible"] and background["opacity"] > 0:
+        canvas = _fill_background(canvas, background)
+    layers = state["layers"]
+    order = state.get("order") or range(len(tensors))
+    for index in order:
+        if index < 0 or index >= len(tensors):
+            continue
+        tensor = tensors[index]
+        entry = layers[index] if index < len(layers) else None
+        params = _layer_params(entry, tensor.shape[2], tensor.shape[1])
+        if not params["visible"]:
+            continue
+        img = _prepare_layer_bitmap(
+            tensor, params, alphas[index] if index < len(alphas) else None
+        )
+        bx, by, bw, bh = placed_bounds(
+            params["x"], params["y"], params["w"], params["h"], params["rotation"]
+        )
+        buf = _place_in_bounds(img, bw, bh)
+        x0, y0 = max(bx, 0), max(by, 0)
+        x1, y1 = min(bx + bw, cw), min(by + bh, ch)
+        if x0 >= x1 or y0 >= y1:
+            continue
+        region = buf[y0 - by : y1 - by, x0 - bx : x1 - bx]
+        mode = resolve_mode(params["blend"])
+        canvas[y0:y1, x0:x1] = blend_composite(
+            mode, canvas[y0:y1, x0:x1], region, params["opacity"]
+        )
+    rgb = linear_to_srgb(np.clip(canvas[..., :3], 0.0, 1.0))
+    alpha = np.clip(canvas[..., 3:4], 0.0, 1.0)
+    rgba = np.concatenate([rgb, alpha], axis=-1)
+    return torch.from_numpy(rgba.astype(np.float32)).unsqueeze(0)
+
+
+OPAQUE_EPSILON = 1e-3
+
+
+def composite_outputs(out: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    if out.shape[-1] != 4:
+        return out, torch.zeros(out.shape[:3], dtype=torch.float32)
+    alpha = out[..., 3]
+    if bool((alpha >= 1.0 - OPAQUE_EPSILON).all()):
+        return out[..., :3], torch.zeros_like(alpha)
+    return out, torch.clamp(1.0 - alpha, 0.0, 1.0)
+
+
+class ImageCompositor(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageCompositor",
+            display_name="Create Layered Image",
+            category="image",
+            search_aliases=["compositor", "composite", "layer", "layers", "layer editor", "psd"],
+            is_experimental=True,
+            # both flags on purpose: terminal compositor graphs must execute (the
+            # editor needs a run to open), and cache hits must replay the layer UI
+            is_output_node=True,
+            has_intermediate_output=True,
+            inputs=[
+                io.Layers.Input(
+                    "layers",
+                    tooltip="Layer stack to composite; build it with Add Layer. Items are stacked by z_index, batch frames inside an item expand to consecutive layers, and item placement, opacity, and blend mode define the initial composition. Without an explicit document canvas the size is a best-effort maximum extent of the placed layers. A saved composition that matches the current inputs takes priority.",
+                ),
+                io.Compositor.Input(
+                    "compositor",
+                    tooltip="Layered composition saved by the compositor editor.",
+                ),
+            ],
+            outputs=[
+                io.Image.Output(
+                    tooltip="Composited image. Carries an alpha channel when the composite has transparent areas (e.g. hidden background), otherwise plain RGB."
+                ),
+                io.Mask.Output(
+                    tooltip="Transparency of the composite (1 = fully transparent). All zeros when the composite is opaque."
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, layers: io.Layers.Type, compositor: io.Compositor.Type = None) -> io.NodeOutput:
+        frames = expand_item_frames(document_items(layers))
+        tensors = [frame["tensor"] for frame in frames]
+        alphas = [frame_alpha(frame["tensor"], frame["mask"]) for frame in frames]
+
+        layer_refs = []
+        for tensor, alpha in zip(tensors, alphas):
+            layer_refs.extend(
+                UI.PreviewImage(layer_preview_tensor(tensor, alpha), cls=cls).values
+            )
+
+        fp = input_fingerprints(frames, alphas)
+        raw_state = compositor
+        state = parse_layer_state(raw_state)
+        replay = bool(state is not None and tensors and state["inputs"] == fp)
+        if replay:
+            out = composite_from_state(tensors, state, alphas)
+        elif tensors:
+            canvas = document_canvas(layers) or canvas_extent(frames)
+            out = composite_from_state(
+                tensors, state_from_items(frames, canvas), alphas
+            )
+        else:
+            out = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+        state_stale = layer_state_provided(raw_state) and not replay
+        out, mask = composite_outputs(out)
+
+        ui_dict = UI.PreviewImage(out, cls=cls).as_dict()
+        ui_dict["compositor_layers"] = layer_refs
+        ui_dict["compositor_inputs"] = fp
+        ui_dict["compositor_bboxes"] = layer_ui_entries(frames)
+        if state_stale:
+            ui_dict["compositor_state_stale"] = [True]
+        return io.NodeOutput(out, mask, ui=ui_dict)
+
+
+class AddLayer(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="AddLayer",
+            display_name="Add Layer",
+            category="image",
+            is_experimental=True,
+            inputs=[
+                io.Layers.Input(
+                    "layers",
+                    optional=True,
+                    tooltip="Layer stack to append to. Leave unconnected to start a new stack.",
+                ),
+                io.Image.Input(
+                    "image",
+                    tooltip="Layer content at its native size. A batch expands to consecutive layers.",
+                ),
+                io.Mask.Input(
+                    "mask",
+                    optional=True,
+                    tooltip="Transparency mask for this layer. Masked areas (value 1) become transparent, multiplying with any alpha channel the image already carries.",
+                ),
+                io.String.Input(
+                    "name",
+                    optional=True,
+                    default="",
+                    tooltip="Layer name shown in the compositor editor.",
+                ),
+                io.Int.Input(
+                    "x",
+                    optional=True,
+                    default=0,
+                    min=-MAX_RESOLUTION,
+                    max=MAX_RESOLUTION,
+                    tooltip="Initial horizontal placement on the canvas.",
+                ),
+                io.Int.Input(
+                    "y",
+                    optional=True,
+                    default=0,
+                    min=-MAX_RESOLUTION,
+                    max=MAX_RESOLUTION,
+                    tooltip="Initial vertical placement on the canvas.",
+                ),
+                io.Float.Input(
+                    "opacity",
+                    optional=True,
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Initial layer opacity.",
+                ),
+                io.Combo.Input(
+                    "blend_mode",
+                    options=list(_LAYER_MODES),
+                    default="normal",
+                    optional=True,
+                    tooltip="Initial blend mode, applied against the layers below. On the bottom layer over the default transparent background, non-normal modes produce transparency.",
+                ),
+                io.Float.Input(
+                    "rotation",
+                    optional=True,
+                    default=0.0,
+                    min=-360.0,
+                    max=360.0,
+                    step=1.0,
+                    tooltip="Initial rotation in degrees, clockwise.",
+                ),
+                io.Int.Input(
+                    "width",
+                    optional=True,
+                    default=0,
+                    min=0,
+                    max=MAX_RESOLUTION,
+                    tooltip="Initial display width. 0 keeps the image's native width.",
+                ),
+                io.Int.Input(
+                    "height",
+                    optional=True,
+                    default=0,
+                    min=0,
+                    max=MAX_RESOLUTION,
+                    tooltip="Initial display height. 0 keeps the image's native height.",
+                ),
+                io.Int.Input(
+                    "z_index",
+                    optional=True,
+                    default=0,
+                    min=-1000,
+                    max=1000,
+                    tooltip="Stacking override. Layers are stable-sorted by z_index; equal values keep their list order.",
+                ),
+                io.Boolean.Input(
+                    "flip_h",
+                    optional=True,
+                    default=False,
+                    tooltip="Flip the layer horizontally.",
+                ),
+                io.Boolean.Input(
+                    "flip_v",
+                    optional=True,
+                    default=False,
+                    tooltip="Flip the layer vertically.",
+                ),
+            ],
+            outputs=[
+                io.Layers.Output(tooltip="The layer stack with this layer appended."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image: io.Image.Type, layers: io.Layers.Type = None, mask: io.Mask.Type = None, name: str = "", x: int = 0, y: int = 0, opacity: float = 1.0, blend_mode: str = "normal", rotation: float = 0.0, width: int = 0, height: int = 0, z_index: int = 0, flip_h: bool = False, flip_v: bool = False) -> io.NodeOutput:
+        item: dict = {
+            "image": image,
+            "type": "raster",
+            "x": int(x),
+            "y": int(y),
+            "z_index": int(z_index),
+        }
+        if mask is not None:
+            item["mask"] = mask
+        if name:
+            item["name"] = name
+        if opacity != 1.0:
+            item["opacity"] = float(opacity)
+        if blend_mode != "normal":
+            item["blend_mode"] = blend_mode
+        if rotation != 0.0:
+            item["rotation"] = math.radians(rotation)
+        if width > 0:
+            item["w"] = int(width)
+        if height > 0:
+            item["h"] = int(height)
+        if flip_h:
+            item["flip_h"] = True
+        if flip_v:
+            item["flip_v"] = True
+        previous = layers if isinstance(layers, dict) else None
+        document: dict = {
+            "version": 1,
+            "layers": [*(previous.get("layers") or []), item] if previous else [item],
+        }
+        previous_canvas = document_canvas(previous)
+        if previous_canvas:
+            document["canvas"] = previous_canvas
+        return io.NodeOutput(document)
+
+
+class LayersFromBoundingBoxes(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LayersFromBoundingBoxes",
+            display_name="Layers From Bounding Boxes",
+            category="image",
+            is_experimental=True,
+            description=(
+                "Turn an image batch plus its bounding boxes into a layer stack, one layer per frame, "
+                "each placed by its own box. Use this when a node emits layers as a batch - a batch "
+                "carries a single placement for every frame, so the individual positions are otherwise lost."
+            ),
+            inputs=[
+                io.Image.Input(
+                    "image",
+                    tooltip="Image batch; each frame becomes one layer.",
+                ),
+                io.MultiType.Input(
+                    "bboxes",
+                    [io.BoundingBox, io.Array, io.String],
+                    tooltip=(
+                        "Placement boxes, index-aligned with the image batch. Accepts bounding boxes "
+                        "(x, y, width, height), normalized elements (with a 'bbox' - these need "
+                        "canvas_width/canvas_height to resolve to pixels), or a JSON string of either. "
+                        "Frames without a matching box are placed at the origin. A box's width/height "
+                        "scales the layer to fit it. metadata.name (or desc) and metadata.z_index are "
+                        "used when present, and metadata.content_rect (frame-relative) crops the frame "
+                        "to its real content."
+                    ),
+                ),
+                io.Mask.Input(
+                    "mask",
+                    optional=True,
+                    tooltip=(
+                        "Per-frame transparency, index-aligned with the image batch "
+                        "(1 = transparent, LoadImage convention)."
+                    ),
+                ),
+                io.Layers.Input(
+                    "layers",
+                    optional=True,
+                    tooltip="Layer stack to append to. Leave unconnected to start a new stack.",
+                ),
+                io.Boolean.Input(
+                    "crop_to_content",
+                    default=True,
+                    optional=True,
+                    tooltip=(
+                        "Crop each frame to metadata.content_rect where present and place the content "
+                        "at the box position plus the rect offset. Leave on for batches whose frames "
+                        "are padded - it keeps only the real content at its true spot."
+                    ),
+                ),
+                io.Int.Input(
+                    "canvas_width",
+                    default=0,
+                    min=0,
+                    max=MAX_RESOLUTION,
+                    optional=True,
+                    tooltip="Document canvas width. 0 derives it from the placed layers.",
+                ),
+                io.Int.Input(
+                    "canvas_height",
+                    default=0,
+                    min=0,
+                    max=MAX_RESOLUTION,
+                    optional=True,
+                    tooltip="Document canvas height. 0 derives it from the placed layers.",
+                ),
+            ],
+            outputs=[
+                io.Layers.Output(tooltip="The layer stack, ready for Create Layered Image."),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image: io.Image.Type,
+        bboxes: io.MultiType.Type,
+        mask: io.Mask.Type = None,
+        layers: io.Layers.Type = None,
+        crop_to_content: bool = True,
+        canvas_width: int = 0,
+        canvas_height: int = 0,
+    ) -> io.NodeOutput:
+        boxes = _bbox_list(bboxes, canvas_width, canvas_height)
+        previous = layers if isinstance(layers, dict) else None
+        items: list[dict] = list((previous.get("layers") or []) if previous else [])
+        base_z = max((_int(i.get("z_index"), 0) for i in items), default=-1) + 1
+
+        for index in range(image.shape[0]):
+            box = boxes[index] if index < len(boxes) else {}
+            meta = box.get("metadata") if isinstance(box.get("metadata"), dict) else {}
+            frame = image[index : index + 1]
+            frame_mask = _item_mask_frame(mask, index)
+
+            x, y = _int(box.get("x"), 0), _int(box.get("y"), 0)
+            box_w, box_h = _int(box.get("width"), 0), _int(box.get("height"), 0)
+            cropped = False
+            rect = meta.get("content_rect")
+            if crop_to_content and isinstance(rect, (list, tuple)) and len(rect) == 4:
+                left, top, cw, ch = (_int(v, 0) for v in rect)
+                left = min(max(left, 0), int(frame.shape[2]))
+                top = min(max(top, 0), int(frame.shape[1]))
+                cw = min(max(cw, 0), int(frame.shape[2]) - left)
+                ch = min(max(ch, 0), int(frame.shape[1]) - top)
+                if cw > 0 and ch > 0:
+                    frame = frame[:, top : top + ch, left : left + cw]
+                    if frame_mask is not None:
+                        frame_mask = frame_mask[:, top : top + ch, left : left + cw]
+                    x, y = x + left, y + top
+                    cropped = True
+
+            item: dict = {
+                "image": frame,
+                "type": "raster",
+                "x": x,
+                "y": y,
+                "z_index": _int(meta.get("z_index"), base_z + index),
+            }
+            if not cropped:
+                if box_w > 0:
+                    item["w"] = box_w
+                if box_h > 0:
+                    item["h"] = box_h
+            if frame_mask is not None:
+                item["mask"] = frame_mask
+            name = meta.get("name")
+            if not (isinstance(name, str) and name):
+                name = meta.get("desc")
+            if isinstance(name, str) and name:
+                item["name"] = name
+            items.append(item)
+
+        document: dict = {"version": 1, "layers": items}
+        if canvas_width > 0 and canvas_height > 0:
+            document["canvas"] = (canvas_width, canvas_height)
+        else:
+            inherited = document_canvas(previous)
+            if inherited:
+                document["canvas"] = inherited
+        return io.NodeOutput(document)
+
+
+class CompositorExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [ImageCompositor, AddLayer, LayersFromBoundingBoxes]
+
+
+async def comfy_entrypoint() -> CompositorExtension:
+    return CompositorExtension()
diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py
new file mode 100644
index 0000000000000000000000000000000000000000..61fc2bb61f761e4da710d517cf6ebf7909073512
--- /dev/null
+++ b/comfy_extras/nodes_cond.py
@@ -0,0 +1,70 @@
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
+
+class CLIPTextEncodeControlnet(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CLIPTextEncodeControlnet",
+            display_name="CLIP Text Encode (Controlnet)",
+            category="model/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.Conditioning.Input("conditioning"),
+                io.String.Input("text", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[io.Conditioning.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, clip, conditioning, text) -> io.NodeOutput:
+        tokens = clip.tokenize(text)
+        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
+        c = []
+        for t in conditioning:
+            n = [t[0], t[1].copy()]
+            n[1]['cross_attn_controlnet'] = cond
+            n[1]['pooled_output_controlnet'] = pooled
+            c.append(n)
+        return io.NodeOutput(c)
+
+class T5TokenizerOptions(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="T5TokenizerOptions",
+            display_name="T5 Tokenizer Options",
+            category="model/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),
+                io.Int.Input("min_length", default=0, min=0, max=10000, step=1),
+            ],
+            outputs=[io.Clip.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, clip, min_padding, min_length) -> io.NodeOutput:
+        clip = clip.clone()
+        for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]:
+            clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding)
+            clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length)
+
+        return io.NodeOutput(clip)
+
+
+class CondExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeControlnet,
+            T5TokenizerOptions,
+        ]
+
+
+async def comfy_entrypoint() -> CondExtension:
+    return CondExtension()
diff --git a/comfy_extras/nodes_context_windows.py b/comfy_extras/nodes_context_windows.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5f2c8e3b32882d7559ebeddc5ca0f694f3570b5
--- /dev/null
+++ b/comfy_extras/nodes_context_windows.py
@@ -0,0 +1,146 @@
+from comfy_api.latest import ComfyExtension, io
+import comfy.context_windows
+import nodes
+
+
+class ContextWindowsManualNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="ContextWindowsManual",
+            display_name="Context Windows (Manual)",
+            category="model/patch",
+            description="Manually set context windows.",
+            inputs=[
+                io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
+                io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window."),
+                io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window."),
+                io.Combo.Input("context_schedule", options=[
+                    comfy.context_windows.ContextSchedules.STATIC_STANDARD,
+                    comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
+                    comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
+                    comfy.context_windows.ContextSchedules.BATCHED,
+                    ], default=comfy.context_windows.ContextSchedules.STATIC_STANDARD, tooltip="Step-dependent scheduling algorithm for context windows."),
+                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
+                io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
+                io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
+                io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."),
+                io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),
+                io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window. For concat-style I2V models (e.g. Wan I2V, HunyuanVideo I2V, Cosmos I2V, SVD) the encoded start image lives in the c_concat conditioning channels; setting this to '0' will retain that start image content at sub-pos 0 of every window."),
+                io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."),
+                io.String.Input("latent_retain_index_list", default="", tooltip="List of latent indices to retain in the noise latent itself for each window. Use for workflows where reference content (e.g. a start image) lives directly in the noise latent rather than in separate conditioning channels (e.g. inplace-style I2V like LTXV, AnimateDiff). Independent of cond_retain_index_list."),
+                io.Boolean.Input("causal_window_fix", default=True, tooltip="Whether to add a causal fix frame to non-0-indexed context windows."),
+            ],
+            outputs=[
+                io.Model.Output(tooltip="The model with context windows applied during sampling."),
+            ],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int, freenoise: bool,
+                cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False, latent_retain_index_list: list[int]=[], causal_window_fix: bool=True) -> io.Model:
+        model = model.clone()
+        model.model_options["context_handler"] = comfy.context_windows.IndexListContextHandler(
+            context_schedule=comfy.context_windows.get_matching_context_schedule(context_schedule),
+            fuse_method=comfy.context_windows.get_matching_fuse_method(fuse_method),
+            context_length=context_length,
+            context_overlap=context_overlap,
+            context_stride=context_stride,
+            closed_loop=closed_loop,
+            dim=dim,
+            freenoise=freenoise,
+            cond_retain_index_list=cond_retain_index_list,
+            split_conds_to_windows=split_conds_to_windows,
+            latent_retain_index_list=latent_retain_index_list,
+            causal_window_fix=causal_window_fix,
+        )
+        # make memory usage calculation only take into account the context window latents
+        comfy.context_windows.create_prepare_sampling_wrapper(model)
+        if freenoise: # no other use for this wrapper at this time
+            comfy.context_windows.create_sampler_sample_wrapper(model)
+        return io.NodeOutput(model)
+
+class WanContextWindowsManualNode(ContextWindowsManualNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        schema = super().define_schema()
+        schema.node_id = "WanContextWindowsManual"
+        schema.display_name = "WAN Context Windows (Manual)"
+        schema.display_name = "Wan Context Windows"
+        schema.description = "Set context windows for Wan-like models."
+        schema.category="model/patch/wan"
+        schema.inputs = [
+            io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
+                io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window in real frames. Must be 4*n + 1."),
+                io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window in real frames."),
+                io.Combo.Input("context_schedule", options=[
+                    comfy.context_windows.ContextSchedules.STATIC_STANDARD,
+                    comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
+                    comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
+                    comfy.context_windows.ContextSchedules.BATCHED,
+                    ], default=comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, tooltip="Step-dependent scheduling algorithm for context windows."),
+                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
+                io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules.", advanced=True),
+                io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
+                io.Boolean.Input("freenoise", default=True, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending.", advanced=True),
+                io.Boolean.Input("retain_first_frame", default=False, tooltip="Retain the first I2V frame in every context window (may help retain initial reference)."),
+                io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index.", advanced=True),
+        ]
+        return schema
+
+    @classmethod
+    def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, freenoise: bool,
+                retain_first_frame: bool=False, split_conds_to_windows: bool=False) -> io.Model:
+        context_length = max(((context_length - 1) // 4) + 1, 1) # at least length 1
+        context_overlap = max(context_overlap // 4, 0)  # at least overlap 0
+        retain_index_list = "0" if retain_first_frame else ""
+        return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2, freenoise=freenoise, cond_retain_index_list=retain_index_list, split_conds_to_windows=split_conds_to_windows)
+
+
+class LTXVContextWindowsNode(ContextWindowsManualNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        schema = super().define_schema()
+        schema.node_id = "LTXVContextWindows"
+        schema.display_name = "LTXV Context Windows"
+        schema.description = "Set context windows for LTXV-like models."
+        schema.inputs = [
+            io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
+            io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=8, default=145, tooltip="The length of the context window in real frames. Must be 8*n + 1."),
+            io.Int.Input("context_overlap", min=0, step=8, default=40, tooltip="The overlap of the context window in real frames."),
+            io.Combo.Input("context_schedule", options=[
+                comfy.context_windows.ContextSchedules.STATIC_STANDARD,
+                comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
+                comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
+                comfy.context_windows.ContextSchedules.BATCHED,
+                ], default=comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, tooltip="Step-dependent scheduling algorithm for context windows."),
+            io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
+            io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules.", advanced=True),
+            io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
+            io.Boolean.Input("freenoise", default=True, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending.", advanced=True),
+            io.Boolean.Input("retain_first_frame", default=False, tooltip="Retain the first latent frame in every context window (may help retain initial reference)."),
+            io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index.", advanced=True),
+        ]
+        return schema
+
+    @classmethod
+    def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, fuse_method: str, freenoise: bool,
+                retain_first_frame: bool=False, split_conds_to_windows: bool=False, context_stride: int=1, closed_loop: bool=False) -> io.Model:
+        context_length = max(((context_length - 1) // 8) + 1, 1)  # at least length 1
+        context_overlap = max(context_overlap // 8, 0)  # at least overlap 0
+        retain_index_list = "0" if retain_first_frame else ""
+        return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2, freenoise=freenoise,
+                               cond_retain_index_list=retain_index_list, latent_retain_index_list=retain_index_list, split_conds_to_windows=split_conds_to_windows)
+
+
+class ContextWindowsExtension(ComfyExtension):
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            ContextWindowsManualNode,
+            WanContextWindowsManualNode,
+            LTXVContextWindowsNode,
+        ]
+
+def comfy_entrypoint():
+    return ContextWindowsExtension()
diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..9db7fd7207f3b127edda5e87502dfb9c56379464
--- /dev/null
+++ b/comfy_extras/nodes_controlnet.py
@@ -0,0 +1,88 @@
+from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
+import nodes
+import comfy.utils
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+class SetUnionControlNetType(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SetUnionControlNetType",
+            search_aliases=["set controlnet type", "union controlnet type"],
+            display_name="Set Union ControlNet Type",
+            category="model/conditioning/controlnet",
+            inputs=[
+                io.ControlNet.Input("control_net"),
+                io.Combo.Input("type", options=["auto"] + list(UNION_CONTROLNET_TYPES.keys())),
+            ],
+            outputs=[
+                io.ControlNet.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, control_net, type) -> io.NodeOutput:
+        control_net = control_net.copy()
+        type_number = UNION_CONTROLNET_TYPES.get(type, -1)
+        if type_number >= 0:
+            control_net.set_extra_arg("control_type", [type_number])
+        else:
+            control_net.set_extra_arg("control_type", [])
+
+        return io.NodeOutput(control_net)
+
+    set_controlnet_type = execute  # TODO: remove
+
+
+class ControlNetInpaintingAliMamaApply(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ControlNetInpaintingAliMamaApply",
+            search_aliases=["masked controlnet"],
+            display_name="Apply ControlNet Inpainting (AliMama)",
+            category="model/conditioning/controlnet",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.ControlNet.Input("control_net"),
+                io.Vae.Input("vae"),
+                io.Image.Input("image"),
+                io.Mask.Input("mask"),
+                io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent) -> io.NodeOutput:
+        extra_concat = []
+        if control_net.concat_mask:
+            mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
+            mask_apply = comfy.utils.common_upscale(mask, image.shape[2], image.shape[1], "bilinear", "center").round()
+            image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3])
+            extra_concat = [mask]
+
+        result = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat)
+        return io.NodeOutput(result[0], result[1])
+
+    apply_inpaint_controlnet = execute  # TODO: remove
+
+
+class ControlNetExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SetUnionControlNetType,
+            ControlNetInpaintingAliMamaApply,
+        ]
+
+
+async def comfy_entrypoint() -> ControlNetExtension:
+    return ControlNetExtension()
diff --git a/comfy_extras/nodes_cosmos.py b/comfy_extras/nodes_cosmos.py
new file mode 100644
index 0000000000000000000000000000000000000000..70370150a4261c6697ae6b611af70fcf23e03a12
--- /dev/null
+++ b/comfy_extras/nodes_cosmos.py
@@ -0,0 +1,143 @@
+from typing_extensions import override
+import nodes
+import torch
+import comfy.model_management
+import comfy.utils
+import comfy.latent_formats
+
+from comfy_api.latest import ComfyExtension, io
+
+
+class EmptyCosmosLatentVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="EmptyCosmosLatentVideo",
+            category="model/latent/cosmos",
+            inputs=[
+                io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[io.Latent.Output()],
+        )
+
+    @classmethod
+    def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples": latent})
+
+
+def vae_encode_with_padding(vae, image, width, height, length, padding=0):
+    pixels = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+    pixel_len = min(pixels.shape[0], length)
+    padded_length = min(length, (((pixel_len - 1) // 8) + 1 + padding) * 8 - 7)
+    padded_pixels = torch.ones((padded_length, height, width, 3)) * 0.5
+    padded_pixels[:pixel_len] = pixels[:pixel_len]
+    latent_len = ((pixel_len - 1) // 8) + 1
+    latent_temp = vae.encode(padded_pixels)
+    return latent_temp[:, :, :latent_len]
+
+
+class CosmosImageToVideoLatent(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CosmosImageToVideoLatent",
+            category="model/conditioning/cosmos",
+            inputs=[
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("start_image", optional=True),
+                io.Image.Input("end_image", optional=True),
+            ],
+            outputs=[io.Latent.Output()],
+        )
+
+    @classmethod
+    def execute(cls, vae, width, height, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput:
+        latent = torch.zeros([1, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        if start_image is None and end_image is None:
+            out_latent = {}
+            out_latent["samples"] = latent
+            return io.NodeOutput(out_latent)
+
+        mask = torch.ones([latent.shape[0], 1, ((length - 1) // 8) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
+
+        if start_image is not None:
+            latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1)
+            latent[:, :, :latent_temp.shape[-3]] = latent_temp
+            mask[:, :, :latent_temp.shape[-3]] *= 0.0
+
+        if end_image is not None:
+            latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0)
+            latent[:, :, -latent_temp.shape[-3]:] = latent_temp
+            mask[:, :, -latent_temp.shape[-3]:] *= 0.0
+
+        out_latent = {}
+        out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
+        out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
+        return io.NodeOutput(out_latent)
+
+class CosmosPredict2ImageToVideoLatent(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CosmosPredict2ImageToVideoLatent",
+            category="model/conditioning/cosmos",
+            inputs=[
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=93, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("start_image", optional=True),
+                io.Image.Input("end_image", optional=True),
+            ],
+            outputs=[io.Latent.Output()],
+        )
+
+    @classmethod
+    def execute(cls, vae, width, height, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput:
+        latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        if start_image is None and end_image is None:
+            out_latent = {}
+            out_latent["samples"] = latent
+            return io.NodeOutput(out_latent)
+
+        mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
+
+        if start_image is not None:
+            latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1)
+            latent[:, :, :latent_temp.shape[-3]] = latent_temp
+            mask[:, :, :latent_temp.shape[-3]] *= 0.0
+
+        if end_image is not None:
+            latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0)
+            latent[:, :, -latent_temp.shape[-3]:] = latent_temp
+            mask[:, :, -latent_temp.shape[-3]:] *= 0.0
+
+        out_latent = {}
+        latent_format = comfy.latent_formats.Wan21()
+        latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask)
+        out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
+        out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
+        return io.NodeOutput(out_latent)
+
+
+class CosmosExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EmptyCosmosLatentVideo,
+            CosmosImageToVideoLatent,
+            CosmosPredict2ImageToVideoLatent,
+        ]
+
+
+async def comfy_entrypoint() -> CosmosExtension:
+    return CosmosExtension()
diff --git a/comfy_extras/nodes_curve.py b/comfy_extras/nodes_curve.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a2f740cc9a56d612e116ef2315a38be878fa2f8
--- /dev/null
+++ b/comfy_extras/nodes_curve.py
@@ -0,0 +1,91 @@
+import numpy as np
+
+from comfy_api.latest import ComfyExtension, io
+from comfy_api.input import CurveInput
+from typing_extensions import override
+
+
+class CurveEditor(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CurveEditor",
+            display_name="Curve Editor",
+            category="utilities",
+            has_intermediate_output=True,
+            inputs=[
+                io.Curve.Input("curve"),
+                io.Histogram.Input("histogram", optional=True),
+            ],
+            outputs=[
+                io.Curve.Output("curve"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, curve, histogram=None) -> io.NodeOutput:
+        result = CurveInput.from_raw(curve)
+
+        ui = {}
+        if histogram is not None:
+            ui["histogram"] = histogram if isinstance(histogram, list) else list(histogram)
+
+        return io.NodeOutput(result, ui=ui) if ui else io.NodeOutput(result)
+
+
+class ImageHistogram(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageHistogram",
+            display_name="Image Histogram",
+            category="utilities",
+            inputs=[
+                io.Image.Input("image"),
+            ],
+            outputs=[
+                io.Histogram.Output("rgb"),
+                io.Histogram.Output("luminance"),
+                io.Histogram.Output("red"),
+                io.Histogram.Output("green"),
+                io.Histogram.Output("blue"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image) -> io.NodeOutput:
+        img = image[0].cpu().numpy()
+        img_uint8 = np.clip(img * 255, 0, 255).astype(np.uint8)
+
+        def bincount(data):
+            return np.bincount(data.ravel(), minlength=256)[:256]
+
+        hist_r = bincount(img_uint8[:, :, 0])
+        hist_g = bincount(img_uint8[:, :, 1])
+        hist_b = bincount(img_uint8[:, :, 2])
+
+        # Average of R, G, B histograms (same as Photoshop's RGB composite)
+        rgb = ((hist_r + hist_g + hist_b) // 3).tolist()
+
+        # ITU-R BT.709-6, Item 3.2 (p.6) — Derivation of luminance signal
+        # https://www.itu.int/rec/R-REC-BT.709-6-201506-I/en
+        lum = 0.2126 * img[:, :, 0] + 0.7152 * img[:, :, 1] + 0.0722 * img[:, :, 2]
+        luminance = bincount(np.clip(lum * 255, 0, 255).astype(np.uint8)).tolist()
+
+        return io.NodeOutput(
+            rgb,
+            luminance,
+            hist_r.tolist(),
+            hist_g.tolist(),
+            hist_b.tolist(),
+        )
+
+
+class CurveExtension(ComfyExtension):
+    @override
+    async def get_node_list(self):
+        return [CurveEditor, ImageHistogram]
+
+
+async def comfy_entrypoint():
+    return CurveExtension()
diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..4814c5473d91c8ef3c7c7b08c44eb3b49425feab
--- /dev/null
+++ b/comfy_extras/nodes_custom_sampler.py
@@ -0,0 +1,1232 @@
+import math
+import comfy.samplers
+import comfy.sampler_helpers
+import comfy.patcher_extension
+import comfy.sample
+from comfy.k_diffusion import sampling as k_diffusion_sampling
+from comfy.k_diffusion import sa_solver
+import latent_preview
+import torch
+import comfy.utils
+import node_helpers
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+import re
+
+
+class BasicScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="BasicScheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Model.Input("model"),
+                io.Combo.Input("scheduler", options=comfy.samplers.SCHEDULER_NAMES),
+                io.Int.Input("steps", default=20, min=1, max=10000),
+                io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, model, scheduler, steps, denoise) -> io.NodeOutput:
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return io.NodeOutput(torch.FloatTensor([]))
+            total_steps = int(steps/denoise)
+
+        sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu()
+        sigmas = sigmas[-(steps + 1):]
+        return io.NodeOutput(sigmas)
+
+    get_sigmas = execute
+
+
+class KarrasScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="KarrasScheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Int.Input("steps", default=20, min=1, max=10000),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, steps, sigma_max, sigma_min, rho) -> io.NodeOutput:
+        sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
+        return io.NodeOutput(sigmas)
+
+    get_sigmas = execute
+
+class ExponentialScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ExponentialScheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Int.Input("steps", default=20, min=1, max=10000),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, steps, sigma_max, sigma_min) -> io.NodeOutput:
+        sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max)
+        return io.NodeOutput(sigmas)
+
+    get_sigmas = execute
+
+class PolyexponentialScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PolyexponentialScheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Int.Input("steps", default=20, min=1, max=10000),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, steps, sigma_max, sigma_min, rho) -> io.NodeOutput:
+        sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
+        return io.NodeOutput(sigmas)
+
+    get_sigmas = execute
+
+class LaplaceScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LaplaceScheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Int.Input("steps", default=20, min=1, max=10000),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False, advanced=True),
+                io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False, advanced=True),
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, steps, sigma_max, sigma_min, mu, beta) -> io.NodeOutput:
+        sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, mu=mu, beta=beta)
+        return io.NodeOutput(sigmas)
+
+    get_sigmas = execute
+
+
+class SDTurboScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SDTurboScheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Model.Input("model"),
+                io.Int.Input("steps", default=1, min=1, max=10),
+                io.Float.Input("denoise", default=1.0, min=0, max=1.0, step=0.01),
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, model, steps, denoise) -> io.NodeOutput:
+        start_step = 10 - int(10 * denoise)
+        timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
+        sigmas = model.get_model_object("model_sampling").sigma(timesteps)
+        sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
+        return io.NodeOutput(sigmas)
+
+    get_sigmas = execute
+
+class BetaSamplingScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="BetaSamplingScheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Model.Input("model"),
+                io.Int.Input("steps", default=20, min=1, max=10000),
+                io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, model, steps, alpha, beta) -> io.NodeOutput:
+        sigmas = comfy.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=alpha, beta=beta)
+        return io.NodeOutput(sigmas)
+
+    get_sigmas = execute
+
+class VPScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VPScheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Int.Input("steps", default=20, min=1, max=10000),
+                io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False, advanced=True), #TODO: fix default values
+                io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False, advanced=True),
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, steps, beta_d, beta_min, eps_s) -> io.NodeOutput:
+        sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s)
+        return io.NodeOutput(sigmas)
+
+    get_sigmas = execute
+
+class SplitSigmas(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SplitSigmas",
+            category="model/sampling/sigmas",
+            inputs=[
+                io.Sigmas.Input("sigmas"),
+                io.Int.Input("step", default=0, min=0, max=10000),
+            ],
+            outputs=[
+                io.Sigmas.Output(display_name="high_sigmas"),
+                io.Sigmas.Output(display_name="low_sigmas"),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, sigmas, step) -> io.NodeOutput:
+        sigmas1 = sigmas[:step + 1]
+        sigmas2 = sigmas[step:]
+        return io.NodeOutput(sigmas1, sigmas2)
+
+    get_sigmas = execute
+
+class SplitSigmasDenoise(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SplitSigmasDenoise",
+            category="model/sampling/sigmas",
+            inputs=[
+                io.Sigmas.Input("sigmas"),
+                io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Sigmas.Output(display_name="high_sigmas"),
+                io.Sigmas.Output(display_name="low_sigmas"),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, sigmas, denoise) -> io.NodeOutput:
+        steps = max(sigmas.shape[-1] - 1, 0)
+        total_steps = round(steps * denoise)
+        sigmas1 = sigmas[:-(total_steps)]
+        sigmas2 = sigmas[-(total_steps + 1):]
+        return io.NodeOutput(sigmas1, sigmas2)
+
+    get_sigmas = execute
+
+class FlipSigmas(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FlipSigmas",
+            category="model/sampling/sigmas",
+            inputs=[io.Sigmas.Input("sigmas")],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, sigmas) -> io.NodeOutput:
+        if len(sigmas) == 0:
+            return io.NodeOutput(sigmas)
+
+        sigmas = sigmas.flip(0)
+        if sigmas[0] == 0:
+            sigmas[0] = 0.0001
+        return io.NodeOutput(sigmas)
+
+    get_sigmas = execute
+
+class SetFirstSigma(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SetFirstSigma",
+            category="model/sampling/sigmas",
+            inputs=[
+                io.Sigmas.Input("sigmas"),
+                io.Float.Input("sigma", default=136.0, min=0.0, max=20000.0, step=0.001, round=False),
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, sigmas, sigma) -> io.NodeOutput:
+        sigmas = sigmas.clone()
+        sigmas[0] = sigma
+        return io.NodeOutput(sigmas)
+
+    set_first_sigma = execute
+
+class ExtendIntermediateSigmas(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ExtendIntermediateSigmas",
+            search_aliases=["interpolate sigmas"],
+            category="model/sampling/sigmas",
+            inputs=[
+                io.Sigmas.Input("sigmas"),
+                io.Int.Input("steps", default=2, min=1, max=100),
+                io.Float.Input("start_at_sigma", default=-1.0, min=-1.0, max=20000.0, step=0.01, round=False),
+                io.Float.Input("end_at_sigma", default=12.0, min=0.0, max=20000.0, step=0.01, round=False),
+                io.Combo.Input("spacing", options=['linear', 'cosine', 'sine']),
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, sigmas: torch.Tensor, steps: int, start_at_sigma: float, end_at_sigma: float, spacing: str) -> io.NodeOutput:
+        if start_at_sigma < 0:
+            start_at_sigma = float("inf")
+
+        interpolator = {
+            'linear': lambda x: x,
+            'cosine': lambda x: torch.sin(x*math.pi/2),
+            'sine':   lambda x: 1 - torch.cos(x*math.pi/2)
+        }[spacing]
+
+        # linear space for our interpolation function
+        x = torch.linspace(0, 1, steps + 1, device=sigmas.device)[1:-1]
+        computed_spacing = interpolator(x)
+
+        extended_sigmas = []
+        for i in range(len(sigmas) - 1):
+            sigma_current = sigmas[i]
+            sigma_next = sigmas[i+1]
+
+            extended_sigmas.append(sigma_current)
+
+            if end_at_sigma <= sigma_current <= start_at_sigma:
+                interpolated_steps = computed_spacing * (sigma_next - sigma_current) + sigma_current
+                extended_sigmas.extend(interpolated_steps.tolist())
+
+        # Add the last sigma value
+        if len(sigmas) > 0:
+            extended_sigmas.append(sigmas[-1])
+
+        extended_sigmas = torch.FloatTensor(extended_sigmas)
+
+        return io.NodeOutput(extended_sigmas)
+
+    extend = execute
+
+
+class SamplingPercentToSigma(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplingPercentToSigma",
+            category="model/sampling/sigmas",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("sampling_percent", default=0.0, min=0.0, max=1.0, step=0.0001),
+                io.Boolean.Input("return_actual_sigma", default=False, tooltip="Return the actual sigma value instead of the value used for interval checks.\nThis only affects results at 0.0 and 1.0."),
+            ],
+            outputs=[io.Float.Output(display_name="sigma_value")]
+        )
+
+    @classmethod
+    def execute(cls, model, sampling_percent, return_actual_sigma) -> io.NodeOutput:
+        model_sampling = model.get_model_object("model_sampling")
+        sigma_val = model_sampling.percent_to_sigma(sampling_percent)
+        if return_actual_sigma:
+            if sampling_percent == 0.0:
+                sigma_val = model_sampling.sigma_max.item()
+            elif sampling_percent == 1.0:
+                sigma_val = model_sampling.sigma_min.item()
+        return io.NodeOutput(sigma_val)
+
+    get_sigma = execute
+
+
+class KSamplerSelect(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="KSamplerSelect",
+            category="model/sampling/samplers",
+            inputs=[io.Combo.Input("sampler_name", options=comfy.samplers.SAMPLER_NAMES)],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, sampler_name) -> io.NodeOutput:
+        sampler = comfy.samplers.sampler_object(sampler_name)
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+class SamplerDPMPP_3M_SDE(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerDPMPP_3M_SDE",
+            category="model/sampling/samplers",
+            inputs=[
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, eta, s_noise, noise_device) -> io.NodeOutput:
+        if noise_device == 'cpu':
+            sampler_name = "dpmpp_3m_sde"
+        else:
+            sampler_name = "dpmpp_3m_sde_gpu"
+        sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise})
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+class SamplerDPMPP_2M_SDE(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerDPMPP_2M_SDE",
+            category="model/sampling/samplers",
+            inputs=[
+                io.Combo.Input("solver_type", options=['midpoint', 'heun']),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, solver_type, eta, s_noise, noise_device) -> io.NodeOutput:
+        if noise_device == 'cpu':
+            sampler_name = "dpmpp_2m_sde"
+        else:
+            sampler_name = "dpmpp_2m_sde_gpu"
+        sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+
+class SamplerDPMPP_SDE(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerDPMPP_SDE",
+            category="model/sampling/samplers",
+            inputs=[
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, eta, s_noise, r, noise_device) -> io.NodeOutput:
+        if noise_device == 'cpu':
+            sampler_name = "dpmpp_sde"
+        else:
+            sampler_name = "dpmpp_sde_gpu"
+        sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+class SamplerDPMPP_2S_Ancestral(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerDPMPP_2S_Ancestral",
+            category="model/sampling/samplers",
+            inputs=[
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, eta, s_noise) -> io.NodeOutput:
+        sampler = comfy.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise})
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+class SamplerEulerAncestral(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerEulerAncestral",
+            category="model/sampling/samplers",
+            inputs=[
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, eta, s_noise) -> io.NodeOutput:
+        sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise})
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+class SamplerEulerAncestralCFGPP(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerEulerAncestralCFGPP",
+            display_name="SamplerEulerAncestralCFG++",
+            category="model/sampling/samplers",
+            inputs=[
+                io.Float.Input("eta", default=1.0, min=0.0, max=1.0, step=0.01, round=False),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=10.0, step=0.01, round=False),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, eta, s_noise) -> io.NodeOutput:
+        sampler = comfy.samplers.ksampler(
+            "euler_ancestral_cfg_pp",
+            {"eta": eta, "s_noise": s_noise})
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+class SamplerLMS(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerLMS",
+            category="model/sampling/samplers",
+            inputs=[io.Int.Input("order", default=4, min=1, max=100, advanced=True)],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, order) -> io.NodeOutput:
+        sampler = comfy.samplers.ksampler("lms", {"order": order})
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+class SamplerDPMAdaptative(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerDPMAdaptative",
+            category="model/sampling/samplers",
+            inputs=[
+                io.Int.Input("order", default=3, min=2, max=3, advanced=True),
+                io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("eta", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise) -> io.NodeOutput:
+        sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff,
+                                                              "icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta,
+                                                              "s_noise":s_noise })
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+
+class SamplerER_SDE(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerER_SDE",
+            category="model/sampling/samplers",
+            inputs=[
+                io.Combo.Input("solver_type", options=["ER-SDE", "Reverse-time SDE", "ODE"]),
+                io.Int.Input("max_stage", default=3, min=1, max=3, advanced=True),
+                io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False, tooltip="Stochastic strength of SDEs.\nWhen eta=0, they reduce to deterministic ODE.\nLarge eta may cause invalid outputs. If this occurs, try decreasing this value.", advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, solver_type, max_stage, eta, s_noise) -> io.NodeOutput:
+        # Extend existing noise scalers phi(x) with eta-controlled noise scalers:
+        #   psi(x) = x**(1-eta) * phi(x)**eta
+        # where eta is constant and directly scales the h^2(t) contribution.
+
+        def er_sde_noise_scaler(x: torch.Tensor) -> torch.Tensor:
+            return x * ((x ** 0.3).exp() + 10.0) ** eta
+
+        def reverse_time_sde_noise_scaler(x: torch.Tensor) -> torch.Tensor:
+            return x ** (eta + 1)
+
+        def ode_noise_scaler(x: torch.Tensor) -> torch.Tensor:
+            return x
+
+        solver_scalers = {
+            "ER-SDE": er_sde_noise_scaler,
+            "Reverse-time SDE": reverse_time_sde_noise_scaler,
+            "ODE": ode_noise_scaler,
+        }
+
+        if solver_type == "ODE" or eta == 0:
+            s_noise = 0.0
+            solver_type = "ODE"
+        noise_scaler = solver_scalers[solver_type]
+
+        sampler_name = "er_sde"
+        sampler = comfy.samplers.ksampler(
+            sampler_name,
+            {"s_noise": s_noise, "noise_scaler": noise_scaler, "max_stage": max_stage},
+        )
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+
+class SamplerSASolver(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerSASolver",
+            search_aliases=["sde"],
+            category="model/sampling/samplers",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Int.Input("predictor_order", default=3, min=1, max=6, advanced=True),
+                io.Int.Input("corrector_order", default=4, min=0, max=6, advanced=True),
+                io.Boolean.Input("use_pece", advanced=True),
+                io.Boolean.Input("simple_order_2", advanced=True),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, model, eta, sde_start_percent, sde_end_percent, s_noise, predictor_order, corrector_order, use_pece, simple_order_2) -> io.NodeOutput:
+        model_sampling = model.get_model_object("model_sampling")
+        start_sigma = model_sampling.percent_to_sigma(sde_start_percent)
+        end_sigma = model_sampling.percent_to_sigma(sde_end_percent)
+        tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=eta)
+
+        sampler_name = "sa_solver"
+        sampler = comfy.samplers.ksampler(
+            sampler_name,
+            {
+                "tau_func": tau_func,
+                "s_noise": s_noise,
+                "predictor_order": predictor_order,
+                "corrector_order": corrector_order,
+                "use_pece": use_pece,
+                "simple_order_2": simple_order_2,
+            },
+        )
+        return io.NodeOutput(sampler)
+
+    get_sampler = execute
+
+
+class SamplerSEEDS2(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerSEEDS2",
+            search_aliases=["sde", "exp heun"],
+            category="model/sampling/samplers",
+            inputs=[
+                io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength", advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier", advanced=True),
+                io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)", advanced=True),
+            ],
+            outputs=[io.Sampler.Output()],
+            description=(
+                "This sampler node can represent multiple samplers:\n\n"
+                "seeds_2\n"
+                "- default setting\n\n"
+                "exp_heun_2_x0\n"
+                "- solver_type=phi_2, r=1.0, eta=0.0\n\n"
+                "exp_heun_2_x0_sde\n"
+                "- solver_type=phi_2, r=1.0, eta=1.0, s_noise=1.0"
+            )
+        )
+
+    @classmethod
+    def execute(cls, solver_type, eta, s_noise, r) -> io.NodeOutput:
+        sampler_name = "seeds_2"
+        sampler = comfy.samplers.ksampler(
+            sampler_name,
+            {"eta": eta, "s_noise": s_noise, "r": r, "solver_type": solver_type},
+        )
+        return io.NodeOutput(sampler)
+
+
+class Noise_EmptyNoise:
+    def __init__(self):
+        self.seed = 0
+
+    def generate_noise(self, input_latent):
+        return comfy.sample.prepare_empty_noise(input_latent["samples"])
+
+
+class Noise_RandomNoise:
+    def __init__(self, seed):
+        self.seed = seed
+
+    def generate_noise(self, input_latent):
+        latent_image = input_latent["samples"]
+        batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None
+        return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds)
+
+class SamplerCustom(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerCustom",
+            category="model/sampling/custom",
+            inputs=[
+                io.Model.Input("model"),
+                io.Boolean.Input("add_noise", default=True, advanced=True),
+                io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
+                io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Sampler.Input("sampler"),
+                io.Sigmas.Input("sigmas"),
+                io.Latent.Input("latent_image"),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="output"),
+                io.Latent.Output(display_name="denoised_output"),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image) -> io.NodeOutput:
+        latent = latent_image
+        latent_image = latent["samples"]
+        latent = latent.copy()
+        latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None), latent.get("downscale_ratio_temporal", None))
+        latent["samples"] = latent_image
+
+        if not add_noise:
+            noise = Noise_EmptyNoise().generate_noise(latent)
+        else:
+            noise = Noise_RandomNoise(noise_seed).generate_noise(latent)
+
+        noise_mask = None
+        if "noise_mask" in latent:
+            noise_mask = latent["noise_mask"]
+
+        x0_output = {}
+        callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+
+        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+        samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+        out = latent.copy()
+        out.pop("downscale_ratio_spacial", None)
+        out.pop("downscale_ratio_temporal", None)
+        out["samples"] = samples
+        if "x0" in x0_output:
+            x0 = x0_output["x0"]
+            if samples.is_nested and not x0.is_nested:
+                latent_shapes = [x.shape for x in samples.unbind()]
+                x0 = comfy.nested_tensor.NestedTensor(comfy.utils.unpack_latents(x0, latent_shapes))
+            x0_out = model.model.process_latent_out(x0.cpu())
+            out_denoised = latent.copy()
+            out_denoised["samples"] = x0_out
+        else:
+            out_denoised = out
+        return io.NodeOutput(out, out_denoised)
+
+    sample = execute
+
+class Guider_Basic(comfy.samplers.CFGGuider):
+    def set_conds(self, positive):
+        self.inner_set_conds({"positive": positive})
+
+class BasicGuider(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="BasicGuider",
+            display_name="Basic Guider",
+            category="model/sampling/guiders",
+            inputs=[
+                io.Model.Input("model"),
+                io.Conditioning.Input("conditioning"),
+            ],
+            outputs=[io.Guider.Output()]
+        )
+
+    @classmethod
+    def execute(cls, model, conditioning) -> io.NodeOutput:
+        guider = Guider_Basic(model)
+        guider.set_conds(conditioning)
+        return io.NodeOutput(guider)
+
+    get_guider = execute
+
+class CFGGuider(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CFGGuider",
+            display_name="CFG Guider",
+            category="model/sampling/guiders",
+            inputs=[
+                io.Model.Input("model"),
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
+            ],
+            outputs=[io.Guider.Output()]
+        )
+
+    @classmethod
+    def execute(cls, model, positive, negative, cfg) -> io.NodeOutput:
+        guider = comfy.samplers.CFGGuider(model)
+        guider.set_conds(positive, negative)
+        guider.set_cfg(cfg)
+        return io.NodeOutput(guider)
+
+    get_guider = execute
+
+class Guider_DualCFG(comfy.samplers.CFGGuider):
+    def set_cfg(self, cfg1, cfg2, nested=False):
+        self.cfg1 = cfg1
+        self.cfg2 = cfg2
+        self.nested = nested
+
+    def set_conds(self, positive, middle, negative):
+        middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"})
+        self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative})
+
+    def predict_noise(self, x, timestep, model_options={}, seed=None):
+        negative_cond = self.conds.get("negative", None)
+        middle_cond = self.conds.get("middle", None)
+        positive_cond = self.conds.get("positive", None)
+
+        if self.nested:
+            out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options)
+            pred_text = comfy.samplers.cfg_function(self.inner_model, out[2], out[1], self.cfg1, x, timestep, model_options=model_options, cond=positive_cond, uncond=middle_cond)
+            return out[0] + self.cfg2 * (pred_text - out[0])
+        else:
+            if model_options.get("disable_cfg1_optimization", False) == False:
+                if math.isclose(self.cfg2, 1.0):
+                    negative_cond = None
+                    if math.isclose(self.cfg1, 1.0):
+                        middle_cond = None
+
+            out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options)
+            return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1
+
+class DualCFGGuider(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DualCFGGuider",
+            search_aliases=["dual prompt guidance"],
+            display_name="Dual CFG Guider",
+            category="model/sampling/guiders",
+            inputs=[
+                io.Model.Input("model"),
+                io.Conditioning.Input("cond1"),
+                io.Conditioning.Input("cond2"),
+                io.Conditioning.Input("negative"),
+                io.Float.Input("cfg_conds", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
+                io.Float.Input("cfg_cond2_negative", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
+                io.Combo.Input("style", options=["regular", "nested"]),
+            ],
+            outputs=[io.Guider.Output()]
+        )
+
+    @classmethod
+    def execute(cls, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative, style) -> io.NodeOutput:
+        guider = Guider_DualCFG(model)
+        guider.set_conds(cond1, cond2, negative)
+        guider.set_cfg(cfg_conds, cfg_cond2_negative, nested=(style == "nested"))
+        return io.NodeOutput(guider)
+
+    get_guider = execute
+
+class Guider_DualModel(comfy.samplers.CFGGuider):
+    # Runs the positive (cond) pass on the main model and the negative (uncond) pass on a separate model
+    def __init__(self, model_patcher, uncond_model_patcher):
+        super().__init__(model_patcher)
+        self.uncond_model_patcher = uncond_model_patcher
+        self.uncond_inner = None
+
+    def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, latent_shapes=None):
+        self.uncond_inner = None
+        self.uncond_loaded = []
+        self._uncond_neg = None
+        # skip at cfg 1.0
+        if not math.isclose(self.cfg, 1.0):
+            uc = {"negative": list(map(lambda a: a.copy(), self.conds["negative"]))}
+            self.uncond_inner, uc, self.uncond_loaded = comfy.sampler_helpers.prepare_sampling(
+                self.uncond_model_patcher, noise.shape, uc, self.uncond_model_patcher.model_options)
+            self._uncond_neg = uc["negative"]
+            self.uncond_model_patcher.pre_run()
+        try:
+            return super().outer_sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
+        finally:
+            if self.uncond_inner is not None:
+                self.uncond_model_patcher.cleanup()
+                comfy.sampler_helpers.cleanup_models({"negative": self._uncond_neg}, self.uncond_loaded)
+                self.uncond_inner = None
+
+    def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=None):
+        if self.uncond_inner is not None:
+            li = latent_image
+            if li is not None and torch.count_nonzero(li) > 0:
+                li = self.uncond_inner.process_latent_in(li)
+            self._uncond_conds = comfy.samplers.process_conds(
+                self.uncond_inner, noise, {"negative": self._uncond_neg}, device, li, denoise_mask, seed, latent_shapes=latent_shapes)["negative"]
+        return super().inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
+
+    def predict_noise(self, x, timestep, model_options={}, seed=None):
+        positive = self.conds.get("positive", None)
+        cond = comfy.samplers.calc_cond_batch(self.inner_model, [positive], x, timestep, model_options)[0]
+        # uncond model not loaded (base cfg==1/no negative), or cfg driven to 1.0 this step -> single model, cond only
+        if self.uncond_inner is None or (math.isclose(self.cfg, 1.0) and not model_options.get("disable_cfg1_optimization", False)):
+            return cond
+
+        uncond_model_options = model_options
+        if "multigpu_clones" in model_options: # TODO: support multigpu instead of just running uncond on a single GPU
+            uncond_model_options = {k: v for k, v in model_options.items() if k != "multigpu_clones"}
+        uncond = comfy.samplers.calc_cond_batch(self.uncond_inner, [self._uncond_conds], x, timestep, uncond_model_options)[0]
+        return comfy.samplers.cfg_function(self.inner_model, cond, uncond, self.cfg, x, timestep,
+                                           model_options=model_options, cond=positive, uncond=self._uncond_conds)
+
+class DualModelGuider(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DualModelGuider",
+            display_name="Dual Model CFG Guider",
+            category="model/sampling/guiders",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model", tooltip="Model used for the positive (conditional) pass."),
+                io.Model.Input("model_negative", optional=True, tooltip="Model used for the negative (unconditional) pass. Use the same model for ordinary CFG."),
+                io.Conditioning.Input("positive"),
+                io.Float.Input("cfg", default=4.0, min=0.0, max=100.0, step=0.1, round=0.01),
+                io.Conditioning.Input("negative", optional=True, tooltip="Negative conditioning run on the negative model. Leave unconnected for a text-free (image-only) unconditional pass."),
+            ],
+            outputs=[io.Guider.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, positive, cfg, model_negative=None, negative=None) -> io.NodeOutput:
+        if negative is None:
+            negative = [[None, {}]]  # null cond -> no cross_attn -> model runs image-only
+
+        guider = Guider_DualModel(model, model_negative) if model_negative is not None else comfy.samplers.CFGGuider(model)
+        guider.set_conds(positive, negative)
+        guider.set_cfg(cfg)
+        return io.NodeOutput(guider)
+
+    get_guider = execute
+
+class DisableNoise(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DisableNoise",
+            search_aliases=["zero noise"],
+            category="model/sampling/noise",
+            inputs=[],
+            outputs=[io.Noise.Output()]
+        )
+
+    @classmethod
+    def execute(cls) -> io.NodeOutput:
+        return io.NodeOutput(Noise_EmptyNoise())
+
+    get_noise = execute
+
+
+class RandomNoise(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="RandomNoise",
+            category="model/sampling/noise",
+            inputs=[io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True)],
+            outputs=[io.Noise.Output()]
+        )
+
+    @classmethod
+    def execute(cls, noise_seed) -> io.NodeOutput:
+        return io.NodeOutput(Noise_RandomNoise(noise_seed))
+
+    get_noise = execute
+
+
+class SamplerCustomAdvanced(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerCustomAdvanced",
+            category="model/sampling/custom",
+            inputs=[
+                io.Noise.Input("noise"),
+                io.Guider.Input("guider"),
+                io.Sampler.Input("sampler"),
+                io.Sigmas.Input("sigmas"),
+                io.Latent.Input("latent_image"),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="output"),
+                io.Latent.Output(display_name="denoised_output"),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, noise, guider, sampler, sigmas, latent_image) -> io.NodeOutput:
+        latent = latent_image
+        latent_image = latent["samples"]
+        latent = latent.copy()
+        latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image, latent.get("downscale_ratio_spacial", None), latent.get("downscale_ratio_temporal", None))
+        latent["samples"] = latent_image
+
+        noise_mask = None
+        if "noise_mask" in latent:
+            noise_mask = latent["noise_mask"]
+
+        x0_output = {}
+        callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
+
+        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+        samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed)
+        samples = samples.to(comfy.model_management.intermediate_device())
+
+        out = latent.copy()
+        out.pop("downscale_ratio_spacial", None)
+        out.pop("downscale_ratio_temporal", None)
+        out["samples"] = samples
+        if "x0" in x0_output:
+            x0 = x0_output["x0"]
+            if samples.is_nested and not x0.is_nested:
+                latent_shapes = [x.shape for x in samples.unbind()]
+                x0 = comfy.nested_tensor.NestedTensor(comfy.utils.unpack_latents(x0, latent_shapes))
+            x0_out = guider.model_patcher.model.process_latent_out(x0.cpu())
+            out_denoised = latent.copy()
+            out_denoised["samples"] = x0_out
+        else:
+            out_denoised = out
+        return io.NodeOutput(out, out_denoised)
+
+    sample = execute
+
+class AddNoise(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="AddNoise",
+            category="model/sampling/noise",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.Noise.Input("noise"),
+                io.Sigmas.Input("sigmas"),
+                io.Latent.Input("latent_image"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, model, noise, sigmas, latent_image) -> io.NodeOutput:
+        if len(sigmas) == 0:
+            return io.NodeOutput(latent_image)
+
+        latent = latent_image
+        latent_image = latent["samples"]
+
+        noisy = noise.generate_noise(latent)
+
+        model_sampling = model.get_model_object("model_sampling")
+        process_latent_out = model.get_model_object("process_latent_out")
+        process_latent_in = model.get_model_object("process_latent_in")
+
+        if len(sigmas) > 1:
+            scale = torch.abs(sigmas[0] - sigmas[-1])
+        else:
+            scale = sigmas[0]
+
+        if torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.
+            latent_image = process_latent_in(latent_image)
+        noisy = model_sampling.noise_scaling(scale, noisy, latent_image)
+        noisy = process_latent_out(noisy)
+        noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0)
+
+        out = latent.copy()
+        out["samples"] = noisy
+        return io.NodeOutput(out)
+
+    add_noise = execute
+
+class ManualSigmas(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ManualSigmas",
+            search_aliases=["custom noise schedule", "define sigmas"],
+            category="model/sampling/sigmas",
+            is_experimental=True,
+            inputs=[
+                io.String.Input("sigmas", default="1, 0.5", multiline=False)
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, sigmas) -> io.NodeOutput:
+        sigmas = re.findall(r"[-+]?(?:\d*\.*\d+)", sigmas)
+        sigmas = [float(i) for i in sigmas]
+        sigmas = torch.FloatTensor(sigmas)
+        return io.NodeOutput(sigmas)
+
+class CFGOverride(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CFGOverride",
+            display_name="CFG Override",
+            description="Override cfg to a fixed value over a [start, end] percent (sigma) range. "
+                        "With multiple overrides, the one nearest the sampler wins on overlap.",
+            category="model/sampling/guiders",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("cfg", default=1.0, min=0.0, max=100.0, step=0.1, round=0.01),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, cfg, start_percent, end_percent) -> io.NodeOutput:
+        ms = model.get_model_object("model_sampling")
+        sigma_hi = ms.percent_to_sigma(start_percent)  # percent->sigma decreasing, so hi >= lo
+        sigma_lo = ms.percent_to_sigma(end_percent)
+
+        def predict_noise_wrapper(executor, *args, **kwargs):
+            sigma = float(args[1].flatten()[0])        # args = (x, timestep, model_options, seed)
+            if not (sigma_lo <= sigma <= sigma_hi):
+                return executor(*args, **kwargs)
+            guider = executor.class_obj                # guider.cfg feeds cond_scale
+            saved = guider.cfg
+            guider.cfg = cfg
+            try:
+                return executor(*args, **kwargs)
+            finally:
+                guider.cfg = saved                     # restore for other steps/overrides
+
+        m = model.clone()
+        m.add_wrapper(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, predict_noise_wrapper)
+        return io.NodeOutput(m)
+
+
+class CustomSamplersExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SamplerCustom,
+            CFGOverride,
+            BasicScheduler,
+            KarrasScheduler,
+            ExponentialScheduler,
+            PolyexponentialScheduler,
+            LaplaceScheduler,
+            VPScheduler,
+            BetaSamplingScheduler,
+            SDTurboScheduler,
+            KSamplerSelect,
+            SamplerEulerAncestral,
+            SamplerEulerAncestralCFGPP,
+            SamplerLMS,
+            SamplerDPMPP_3M_SDE,
+            SamplerDPMPP_2M_SDE,
+            SamplerDPMPP_SDE,
+            SamplerDPMPP_2S_Ancestral,
+            SamplerDPMAdaptative,
+            SamplerER_SDE,
+            SamplerSASolver,
+            SamplerSEEDS2,
+            SplitSigmas,
+            SplitSigmasDenoise,
+            FlipSigmas,
+            SetFirstSigma,
+            ExtendIntermediateSigmas,
+            SamplingPercentToSigma,
+            CFGGuider,
+            DualCFGGuider,
+            DualModelGuider,
+            BasicGuider,
+            RandomNoise,
+            DisableNoise,
+            AddNoise,
+            SamplerCustomAdvanced,
+            ManualSigmas,
+        ]
+
+
+async def comfy_entrypoint() -> CustomSamplersExtension:
+    return CustomSamplersExtension()
diff --git a/comfy_extras/nodes_dataset.py b/comfy_extras/nodes_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..210a293c3222069aa6b0aafdc1a51680655cd452
--- /dev/null
+++ b/comfy_extras/nodes_dataset.py
@@ -0,0 +1,2163 @@
+import logging
+import os
+import json
+
+import av
+import numpy as np
+import torch
+from PIL import Image
+from typing_extensions import override
+
+import folder_paths
+import node_helpers
+from comfy_api.latest import ComfyExtension, io, Input, InputImpl, Types
+
+
+def load_and_process_images(image_files, input_dir):
+    """Utility function to load and process a list of images.
+
+    Args:
+        image_files: List of image filenames
+        input_dir: Base directory containing the images
+        resize_method: How to handle images of different sizes ("None", "Stretch", "Crop", "Pad")
+
+    Returns:
+        torch.Tensor: Batch of processed images
+    """
+    if not image_files:
+        raise ValueError("No valid images found in input")
+
+    output_images = []
+
+    for file in image_files:
+        image_path = os.path.join(input_dir, file)
+        img = node_helpers.pillow(Image.open, image_path)
+
+        if img.mode == "I":
+            img = img.point(lambda i: i * (1 / 255))
+        img = img.convert("RGB")
+        img_array = np.array(img).astype(np.float32) / 255.0
+        img_tensor = torch.from_numpy(img_array)[None,]
+        output_images.append(img_tensor)
+
+    return output_images
+
+
+def secure_subfolder_path(base_dir, folder_name):
+    """Resolve folder_name inside base_dir, rejecting anything that escapes it.
+
+    Blocks '..', absolute paths, drive letters and symlink escapes using the
+    same realpath containment check as the core file endpoints.
+    """
+    target = os.path.abspath(os.path.join(base_dir, folder_name))
+    if not folder_paths.is_within_directory(base_dir, target):
+        raise ValueError(f"Invalid folder name {folder_name!r}: resolves outside of {base_dir}")
+    return target
+
+
+def list_dataset_folders():
+    """Relative paths of dataset folders found under all dataset roots.
+
+    Any subfolder containing a metadata.json or *.safetensors shard counts as
+    a dataset; the walk doesn't descend into matched folders.
+
+    Symlinked directories are followed, but symlink loops are avoided.
+    """
+    found = set()
+
+    for root in folder_paths.get_folder_paths("datasets"):
+        if not os.path.isdir(root):
+            continue
+
+        root = os.path.abspath(root)
+        seen_dirs = set()
+
+        for dirpath, subdirs, filenames in os.walk(root, followlinks=True):
+            try:
+                st = os.stat(dirpath)  # follows symlinks
+            except OSError:
+                subdirs[:] = []
+                continue
+
+            dir_key = (st.st_dev, st.st_ino)
+            if dir_key in seen_dirs:
+                subdirs[:] = []
+                continue
+
+            seen_dirs.add(dir_key)
+
+            if dirpath != root and (
+                "metadata.json" in filenames
+                or any(f.endswith(".safetensors") for f in filenames)
+            ):
+                found.add(os.path.relpath(dirpath, root).replace(os.sep, "/"))
+                subdirs[:] = []
+                continue
+
+            kept_subdirs = []
+            for name in subdirs:
+                child = os.path.join(dirpath, name)
+                try:
+                    child_st = os.stat(child)  # follows symlinks
+                except OSError:
+                    continue
+
+                child_key = (child_st.st_dev, child_st.st_ino)
+                if child_key not in seen_dirs:
+                    kept_subdirs.append(name)
+
+            subdirs[:] = kept_subdirs
+
+    return sorted(found)
+
+
+def get_dataset_save_dir(folder_name):
+    """Resolve the folder to save a new dataset into, inside the default root.
+
+    The folder is not created here; callers makedirs after validation.
+    """
+    root = folder_paths.get_folder_paths("datasets")[0]
+    target = secure_subfolder_path(root, folder_name)
+    if os.path.realpath(target) == os.path.realpath(root):
+        raise ValueError("folder_name must name a subfolder of the datasets directory, e.g. 'my_dataset'.")
+    return target
+
+
+def get_dataset_dir(folder_name):
+    """Find an existing dataset folder by relative name across all dataset roots."""
+    roots = folder_paths.get_folder_paths("datasets")
+    for root in roots:
+        target = secure_subfolder_path(root, folder_name)
+        if os.path.realpath(target) == os.path.realpath(root):
+            raise ValueError("folder_name must name a subfolder of the datasets directory, e.g. 'my_dataset'.")
+        if os.path.isdir(target):
+            return target
+    raise ValueError(f"Dataset folder {folder_name!r} not found in: {', '.join(roots)}")
+
+
+VALID_VIDEO_EXTENSIONS = [".mp4", ".avi", ".mov", ".webm", ".mkv", ".flv"]
+
+
+def _decode_selected_frames(video: Input.Video, indices: list[int]) -> Input.Video:
+    """Decode only the requested frame indices from a video.
+
+    Opens the underlying container once, decodes frames in presentation order,
+    keeps only the ones whose index is in ``indices``, and returns the result
+    wrapped in a VideoFromComponents so it still satisfies the VideoInput
+    contract for downstream nodes.
+    """
+    indices_sorted = sorted(set(indices))
+    max_idx = indices_sorted[-1]
+    source = video.get_stream_source()
+
+    frames_by_idx: dict[int, torch.Tensor] = {}
+    with av.open(source, mode="r") as container:
+        stream = container.streams.video[0]
+        wanted = set(indices_sorted)
+        for frame_idx, frame in enumerate(container.decode(stream)):
+            if frame_idx in wanted:
+                img = frame.to_ndarray(format="rgb24")
+                frames_by_idx[frame_idx] = torch.from_numpy(img.copy()).float() / 255.0
+            if frame_idx >= max_idx:
+                break
+
+    stacked = torch.stack([frames_by_idx[i] for i in indices])
+    return InputImpl.VideoFromComponents(
+        Types.VideoComponents(images=stacked, frame_rate=video.get_frame_rate())
+    )
+
+
+class LoadImageDataSetFromFolderNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadImageDataSetFromFolder",
+            search_aliases=["load folder", "load from folder", "load dataset", "load images", "import dataset"],
+            display_name="Load Image (from Folder)",
+            category="image",
+            description="Load a dataset of images from a specified folder and return a list of images. Supported formats: PNG, JPG, JPEG, WEBP.",
+            is_experimental=True,
+            inputs=[
+                io.Combo.Input(
+                    "folder",
+                    options=folder_paths.get_input_subfolders(),
+                    tooltip="The folder to load images from.",
+                )
+            ],
+            outputs=[
+                io.Image.Output(
+                    display_name="images",
+                    is_output_list=True,
+                    tooltip="List of loaded images",
+                )
+            ],
+        )
+
+    @classmethod
+    def execute(cls, folder):
+        sub_input_dir = secure_subfolder_path(folder_paths.get_input_directory(), folder)
+        valid_extensions = [".png", ".jpg", ".jpeg", ".webp"]
+        image_files = [
+            f
+            for f in os.listdir(sub_input_dir)
+            if any(f.lower().endswith(ext) for ext in valid_extensions)
+        ]
+        output_tensor = load_and_process_images(image_files, sub_input_dir)
+        return io.NodeOutput(output_tensor)
+
+
+class LoadImageTextDataSetFromFolderNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadImageTextDataSetFromFolder",
+            search_aliases=["load folder", "load from folder", "load dataset", "load images", "import dataset"],
+            display_name="Load Image-Text (from Folder)",
+            category="image",
+            description="Load a dataset of pairs of images and text captions from a specified folder and return them as a list. Supported formats: PNG, JPG, JPEG, WEBP.",
+            is_experimental=True,
+            inputs=[
+                io.Combo.Input(
+                    "folder",
+                    options=folder_paths.get_input_subfolders(),
+                    tooltip="The folder to load images and text captions from.",
+                )
+            ],
+            outputs=[
+                io.Image.Output(
+                    display_name="images",
+                    is_output_list=True,
+                    tooltip="List of loaded images",
+                ),
+                io.String.Output(
+                    display_name="texts",
+                    is_output_list=True,
+                    tooltip="List of text captions",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, folder):
+        logging.info(f"Loading images from folder: {folder}")
+
+        sub_input_dir = secure_subfolder_path(folder_paths.get_input_directory(), folder)
+        valid_extensions = [".png", ".jpg", ".jpeg", ".webp"]
+
+        image_files = []
+        for item in os.listdir(sub_input_dir):
+            path = os.path.join(sub_input_dir, item)
+            if any(item.lower().endswith(ext) for ext in valid_extensions):
+                image_files.append(path)
+            elif os.path.isdir(path):
+                # Support kohya-ss/sd-scripts folder structure
+                repeat = 1
+                if item.split("_")[0].isdigit():
+                    repeat = int(item.split("_")[0])
+                image_files.extend(
+                    [
+                        os.path.join(path, f)
+                        for f in os.listdir(path)
+                        if any(f.lower().endswith(ext) for ext in valid_extensions)
+                    ]
+                    * repeat
+                )
+
+        caption_file_path = [
+            f.replace(os.path.splitext(f)[1], ".txt") for f in image_files
+        ]
+        captions = []
+        for caption_file in caption_file_path:
+            caption_path = os.path.join(sub_input_dir, caption_file)
+            if os.path.exists(caption_path):
+                with open(caption_path, "r", encoding="utf-8") as f:
+                    caption = f.read().strip()
+                    captions.append(caption)
+            else:
+                captions.append("")
+
+        output_tensor = load_and_process_images(image_files, sub_input_dir)
+
+        logging.info(f"Loaded {len(output_tensor)} images from {sub_input_dir}.")
+        return io.NodeOutput(output_tensor, captions)
+
+
+class LoadVideoDataSetFromFolderNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadVideoDataSetFromFolder",
+            search_aliases=["load folder", "load from folder", "load dataset", "load videos", "import dataset"],
+            display_name="Load Video (from Folder)",
+            category="video",
+            description="Load a dataset of videos from a specified folder and return a list of videos. Supported formats: MP4, AVI, MOV, WEBM, MKV, FLV.",
+            is_experimental=True,
+            inputs=[
+                io.Combo.Input(
+                    "folder",
+                    options=folder_paths.get_input_subfolders(),
+                    tooltip="The folder containing video files.",
+                ),
+            ],
+            outputs=[
+                io.Video.Output(
+                    display_name="videos",
+                    is_output_list=True,
+                    tooltip="Lazy video references; frames are decoded only when needed downstream.",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, folder):
+        sub_input_dir = secure_subfolder_path(folder_paths.get_input_directory(), folder)
+        video_files = sorted([
+            f for f in os.listdir(sub_input_dir)
+            if any(f.lower().endswith(ext) for ext in VALID_VIDEO_EXTENSIONS)
+        ])
+
+        if not video_files:
+            raise ValueError(f"No video files found in {sub_input_dir}")
+
+        videos = [InputImpl.VideoFromFile(os.path.join(sub_input_dir, f)) for f in video_files]
+        logging.info(f"Loaded {len(videos)} lazy video references from {sub_input_dir}")
+        return io.NodeOutput(videos)
+
+
+class LoadVideoTextDataSetFromFolderNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadVideoTextDataSetFromFolder",
+            search_aliases=["load folder", "load from folder", "load dataset", "load videos", "import dataset"],
+            display_name="Load Video-Text (from Folder)",
+            category="video",
+            description="Load a dataset of pairs of videos and text captions from a specified folder and return them as a list. Supported formats: MP4, AVI, MOV, WEBM, MKV, FLV.",
+            is_experimental=True,
+            inputs=[
+                io.Combo.Input(
+                    "folder",
+                    options=folder_paths.get_input_subfolders(),
+                    tooltip="The folder containing video files and .txt captions.",
+                ),
+            ],
+            outputs=[
+                io.Video.Output(
+                    display_name="videos",
+                    is_output_list=True,
+                    tooltip="Lazy video references; frames are decoded only when needed downstream.",
+                ),
+                io.String.Output(
+                    display_name="texts",
+                    is_output_list=True,
+                    tooltip="List of text captions.",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, folder):
+        sub_input_dir = secure_subfolder_path(folder_paths.get_input_directory(), folder)
+
+        video_files = []
+        for item in sorted(os.listdir(sub_input_dir)):
+            path = os.path.join(sub_input_dir, item)
+            if any(item.lower().endswith(ext) for ext in VALID_VIDEO_EXTENSIONS):
+                video_files.append(path)
+            elif os.path.isdir(path):
+                # Support kohya-ss/sd-scripts folder structure: {repeat}_{desc}/
+                repeat = 1
+                if item.split("_")[0].isdigit():
+                    repeat = int(item.split("_")[0])
+                video_files.extend([
+                    os.path.join(path, f)
+                    for f in sorted(os.listdir(path))
+                    if any(f.lower().endswith(ext) for ext in VALID_VIDEO_EXTENSIONS)
+                ] * repeat)
+
+        if not video_files:
+            raise ValueError(f"No video files found in {sub_input_dir}")
+
+        captions = []
+        for vf in video_files:
+            caption_path = os.path.splitext(vf)[0] + ".txt"
+            if os.path.exists(caption_path):
+                with open(caption_path, "r", encoding="utf-8") as f:
+                    captions.append(f.read().strip())
+            else:
+                captions.append("")
+
+        videos = [InputImpl.VideoFromFile(vf) for vf in video_files]
+        logging.info(f"Loaded {len(videos)} lazy video references with captions from {sub_input_dir}")
+        return io.NodeOutput(videos, captions)
+
+
+def save_images_to_folder(image_list, output_dir, prefix="image", overwrite=True):
+    """Utility function to save a list of image tensors to disk.
+
+    Args:
+        image_list: List of image tensors (each [1, H, W, C] or [H, W, C] or [C, H, W])
+        output_dir: Directory to save images to
+        prefix: Filename prefix
+
+    Returns:
+        List of saved filenames
+    """
+    os.makedirs(output_dir, exist_ok=True)
+    saved_files = []
+
+    for idx, img_tensor in enumerate(image_list):
+        # Handle different tensor shapes
+        if isinstance(img_tensor, torch.Tensor):
+            # Remove batch dimension if present [1, H, W, C] -> [H, W, C]
+            if img_tensor.dim() == 4 and img_tensor.shape[0] == 1:
+                img_tensor = img_tensor.squeeze(0)
+
+            # If tensor is [C, H, W], permute to [H, W, C]
+            if img_tensor.dim() == 3 and img_tensor.shape[0] in [1, 3, 4]:
+                if (
+                    img_tensor.shape[0] <= 4
+                    and img_tensor.shape[1] > 4
+                    and img_tensor.shape[2] > 4
+                ):
+                    img_tensor = img_tensor.permute(1, 2, 0)
+
+            # Convert to numpy and scale to 0-255
+            img_array = img_tensor.cpu().numpy()
+            img_array = np.clip(img_array * 255.0, 0, 255).astype(np.uint8)
+
+            # Convert to PIL Image
+            img = Image.fromarray(img_array)
+        else:
+            raise ValueError(f"Expected torch.Tensor, got {type(img_tensor)}")
+
+        # Save image
+        if overwrite:
+            filename = f"{prefix}_{idx:05d}.png"
+        else:
+            _, _, counter, _, resolved_prefix = folder_paths.get_save_image_path(prefix, output_dir)
+            filename = f"{resolved_prefix}_{counter:05}_{idx:05d}.png"
+        filepath = os.path.join(output_dir, filename)
+        img.save(filepath)
+        saved_files.append(filename)
+
+    return saved_files
+
+
+class SaveImageDataSetToFolderNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveImageDataSetToFolder",
+            search_aliases=["save folder", "save to folder", "save dataset", "save images", "export dataset"],
+            display_name="Save Image (to Folder) (DEPRECATED)",
+            category="image",
+            description="Save a dataset of images to a specified folder. Supported formats: PNG.",
+            is_experimental=True,
+            is_output_node=True,
+            is_input_list=True,  # Receive images as list
+            inputs=[
+                io.Image.Input("images", tooltip="List of images to save."),
+                io.String.Input(
+                    "folder_name",
+                    default="dataset",
+                    tooltip="Name of the folder to save images to (inside output directory).",
+                ),
+                io.String.Input(
+                    "filename_prefix",
+                    default="image",
+                    tooltip="Prefix for saved image filenames.",
+                    advanced=True,
+                ),
+                io.Combo.Input(
+                    "mode",
+                    default="overwrite",
+                    options=["overwrite", "increment"],
+                    tooltip="Whether to overwrite existing files or increment filenames to avoid overwriting."
+                ),
+            ],
+            outputs=[],
+            is_deprecated=True,  # This node is redundant and superseded by existing Save Image nodes where the target folder can be specified in the filename_prefix
+        )
+
+    @classmethod
+    def execute(cls, images, folder_name, filename_prefix, mode):
+        # Extract scalar values
+        folder_name = folder_name[0]
+        filename_prefix = filename_prefix[0]
+        mode = mode[0]
+
+        output_dir = secure_subfolder_path(folder_paths.get_output_directory(), folder_name)
+        saved_files = save_images_to_folder(images, output_dir, filename_prefix, mode=='overwrite')
+
+        logging.info(f"Saved {len(saved_files)} images to {output_dir}.")
+        return io.NodeOutput()
+
+
+class SaveImageTextDataSetToFolderNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveImageTextDataSetToFolder",
+            search_aliases=["save folder", "save to folder", "save dataset", "save images", "save text", "export dataset"],
+            display_name="Save Image-Text (to Folder)",
+            category="image",
+            description="Save a dataset of pairs of images and text captions to a specified folder. Images are saved as PNG files and captions are saved as TXT files with the same filename_prefix.",
+            is_experimental=True,
+            is_output_node=True,
+            is_input_list=True,  # Receive both images and texts as lists
+            inputs=[
+                io.Image.Input("images", tooltip="List of images to save."),
+                io.String.Input("texts",
+                    optional=True,
+                    force_input=True,
+                    tooltip="List of text captions to save."
+                ),
+                io.String.Input(
+                    "folder_name",
+                    default="dataset",
+                    tooltip="Name of the folder to save images to (inside output directory).",
+                ),
+                io.String.Input(
+                    "filename_prefix",
+                    default="image",
+                    tooltip="Prefix for saved image filenames.",
+                    advanced=True,
+                ),
+                io.Combo.Input(
+                    "mode",
+                    default="overwrite",
+                    options=["overwrite", "increment"],
+                    tooltip="Whether to overwrite existing files or increment filenames to avoid overwriting."
+                ),
+            ],
+            outputs=[],
+        )
+
+    @classmethod
+    def execute(cls, images, folder_name, filename_prefix, mode, texts=None):
+        # Extract scalar values
+        folder_name = folder_name[0]
+        filename_prefix = filename_prefix[0]
+        mode = mode[0]
+
+        output_dir = secure_subfolder_path(folder_paths.get_output_directory(), folder_name)
+        saved_files = save_images_to_folder(images, output_dir, filename_prefix, mode=='overwrite')
+
+        # Save captions
+        if texts:
+            for idx, (filename, caption) in enumerate(zip(saved_files, texts)):
+                caption_filename = filename.replace(".png", ".txt")
+                caption_path = os.path.join(output_dir, caption_filename)
+                with open(caption_path, "w", encoding="utf-8") as f:
+                    f.write(caption)
+
+        logging.info(f"Saved {len(saved_files)} images and captions to {output_dir}.")
+        return io.NodeOutput()
+
+
+# ========== Helper Functions for Transform Nodes ==========
+
+
+def tensor_to_pil(img_tensor):
+    """Convert tensor to PIL Image."""
+    if img_tensor.dim() == 4 and img_tensor.shape[0] == 1:
+        img_tensor = img_tensor.squeeze(0)
+    img_array = (img_tensor.cpu().numpy() * 255).clip(0, 255).astype(np.uint8)
+    return Image.fromarray(img_array)
+
+
+def pil_to_tensor(img):
+    """Convert PIL Image to tensor."""
+    img_array = np.array(img).astype(np.float32) / 255.0
+    return torch.from_numpy(img_array)[None,]
+
+
+# ========== Base Classes for Transform Nodes ==========
+
+
+class ImageProcessingNode(io.ComfyNode):
+    """Base class for image processing nodes that operate on images.
+
+    Child classes should set:
+        node_id: Unique node identifier (required)
+        search_aliases: List of search aliases (optional)
+        display_name: Display name (optional, defaults to node_id)
+        description: Node description (optional)
+        extra_inputs: List of additional io.Input objects beyond "images" (optional)
+        is_group_process: None (auto-detect), True (group), or False (individual) (optional)
+        is_output_list: True (list output) or False (single output) (optional, default True)
+        is_deprecated: True if the node is deprecated (optional, default False)
+
+    Child classes must implement ONE of:
+        _process(cls, image, **kwargs) -> tensor  (for single-item processing)
+        _group_process(cls, images, **kwargs) -> list[tensor]  (for group processing)
+    """
+
+    node_id = None
+    search_aliases = []
+    display_name = None
+    description = None
+    extra_inputs = []
+    is_group_process = None  # None = auto-detect, True/False = explicit
+    is_output_list = None  # None = auto-detect based on processing mode
+    is_deprecated = False
+    @classmethod
+    def _detect_processing_mode(cls):
+        """Detect whether this node uses group or individual processing.
+
+        Returns:
+            bool: True if group processing, False if individual processing
+        """
+        # Explicit setting takes precedence
+        if cls.is_group_process is not None:
+            return cls.is_group_process
+
+        # Check which method is overridden by looking at the defining class in MRO
+        base_class = ImageProcessingNode
+
+        # Find which class in MRO defines _process
+        process_definer = None
+        for klass in cls.__mro__:
+            if "_process" in klass.__dict__:
+                process_definer = klass
+                break
+
+        # Find which class in MRO defines _group_process
+        group_definer = None
+        for klass in cls.__mro__:
+            if "_group_process" in klass.__dict__:
+                group_definer = klass
+                break
+
+        # Check what was overridden (not defined in base class)
+        has_process = process_definer is not None and process_definer is not base_class
+        has_group = group_definer is not None and group_definer is not base_class
+
+        if has_process and has_group:
+            raise ValueError(
+                f"{cls.__name__}: Cannot override both _process and _group_process. "
+                "Override only one, or set is_group_process explicitly."
+            )
+        if not has_process and not has_group:
+            raise ValueError(
+                f"{cls.__name__}: Must override either _process or _group_process"
+            )
+
+        return has_group
+
+    @classmethod
+    def _ensure_image_list(cls, images):
+        """Normalize to a flat list of [1, H, W, C] tensors."""
+        if isinstance(images, torch.Tensor):
+            if images.ndim != 4:
+                raise ValueError(f"Expected 4D image tensor, got shape {tuple(images.shape)}")
+            return [images[i:i+1] for i in range(images.shape[0])]
+
+        flat = []
+        for item in images:
+            if not isinstance(item, torch.Tensor) or item.ndim != 4:
+                raise ValueError(f"Expected 4D image tensor, got {type(item).__name__} shape {getattr(item, 'shape', None)}")
+            flat.extend([item[i:i+1] for i in range(item.shape[0])])
+        return flat
+
+    @classmethod
+    def define_schema(cls):
+        if cls.node_id is None:
+            raise NotImplementedError(f"{cls.__name__} must set node_id class variable")
+
+        is_group = cls._detect_processing_mode()
+
+        # Auto-detect is_output_list if not explicitly set
+        # Single processing: False (backend collects results into list)
+        # Group processing: True by default (can be False for single-output nodes)
+        output_is_list = (
+            cls.is_output_list if cls.is_output_list is not None else is_group
+        )
+
+        inputs = [
+            io.Image.Input(
+                "images",
+                tooltip=(
+                    "List of images to process." if is_group else "Image to process."
+                ),
+            )
+        ]
+        inputs.extend(cls.extra_inputs)
+
+        return io.Schema(
+            node_id=cls.node_id,
+            search_aliases=cls.search_aliases,
+            display_name=cls.display_name or cls.node_id,
+            category=cls.category,
+            description=cls.description,
+            is_experimental=True,
+            is_deprecated=cls.is_deprecated,
+            is_input_list=is_group,  # True for group, False for individual
+            inputs=inputs,
+            outputs=[
+                io.Image.Output(
+                    display_name="images",
+                    is_output_list=output_is_list,
+                    tooltip="Processed images",
+                )
+            ],
+        )
+
+    @classmethod
+    def execute(cls, images, **kwargs):
+        """Execute the node. Routes to _process or _group_process based on mode.
+
+        For individual processing (_process), automatically handles multi-frame
+        inputs (video tensors [T, H, W, C]) by applying _process per-frame and
+        concatenating the results. This allows all spatial transform nodes to
+        work with video without modification. Nodes that natively handle batched
+        tensors (e.g. pure tensor math) can set per_frame_process = False to
+        skip the per-frame loop.
+        """
+        is_group = cls._detect_processing_mode()
+
+        if is_group:
+            images = cls._ensure_image_list(images)
+
+        # Extract scalar values from lists for parameters
+        params = {}
+        for k, v in kwargs.items():
+            if isinstance(v, list) and len(v) == 1:
+                params[k] = v[0]
+            else:
+                params[k] = v
+
+        if is_group:
+            # Group processing: images is list, call _group_process
+            result = cls._group_process(images, **params)
+        else:
+            # Individual processing: images is single item, call _process
+            # Auto-loop over frames for multi-frame inputs (video [T, H, W, C])
+            # so that PIL-based spatial transforms work per-frame automatically.
+            if images.shape[0] > 1 and getattr(cls, 'per_frame_process', True):
+                results = []
+                for i in range(images.shape[0]):
+                    frame_result = cls._process(images[i:i + 1], **params)
+                    results.append(frame_result)
+                result = torch.cat(results, dim=0)
+            else:
+                result = cls._process(images, **params)
+
+        return io.NodeOutput(result)
+
+    @classmethod
+    def _process(cls, image, **kwargs):
+        """Override this method for single-item processing.
+
+        Args:
+            image: tensor - Single image tensor
+            **kwargs: Additional parameters (already extracted from lists)
+
+        Returns:
+            tensor - Processed image
+        """
+        raise NotImplementedError(f"{cls.__name__} must implement _process method")
+
+    @classmethod
+    def _group_process(cls, images, **kwargs):
+        """Override this method for group processing.
+
+        Args:
+            images: list[tensor] - List of image tensors
+            **kwargs: Additional parameters (already extracted from lists)
+
+        Returns:
+            list[tensor] - Processed images
+        """
+        raise NotImplementedError(
+            f"{cls.__name__} must implement _group_process method"
+        )
+
+
+class TextProcessingNode(io.ComfyNode):
+    """Base class for text processing nodes that operate on texts.
+
+    Child classes should set:
+        node_id: Unique node identifier (required)
+        search_aliases: List of search aliases (optional)
+        display_name: Display name (optional, defaults to node_id)
+        description: Node description (optional)
+        extra_inputs: List of additional io.Input objects beyond "texts" (optional)
+        is_group_process: None (auto-detect), True (group), or False (individual) (optional)
+        is_output_list: True (list output) or False (single output) (optional, default True)
+        is_deprecated: True if the node is deprecated (optional, default False)
+
+    Child classes must implement ONE of:
+        _process(cls, text, **kwargs) -> str  (for single-item processing)
+        _group_process(cls, texts, **kwargs) -> list[str]  (for group processing)
+    """
+
+    node_id = None
+    search_aliases = []
+    display_name = None
+    description = None
+    extra_inputs = []
+    is_group_process = None  # None = auto-detect, True/False = explicit
+    is_output_list = None  # None = auto-detect based on processing mode
+    is_deprecated = False
+    @classmethod
+    def _detect_processing_mode(cls):
+        """Detect whether this node uses group or individual processing.
+
+        Returns:
+            bool: True if group processing, False if individual processing
+        """
+        # Explicit setting takes precedence
+        if cls.is_group_process is not None:
+            return cls.is_group_process
+
+        # Check which method is overridden by looking at the defining class in MRO
+        base_class = TextProcessingNode
+
+        # Find which class in MRO defines _process
+        process_definer = None
+        for klass in cls.__mro__:
+            if "_process" in klass.__dict__:
+                process_definer = klass
+                break
+
+        # Find which class in MRO defines _group_process
+        group_definer = None
+        for klass in cls.__mro__:
+            if "_group_process" in klass.__dict__:
+                group_definer = klass
+                break
+
+        # Check what was overridden (not defined in base class)
+        has_process = process_definer is not None and process_definer is not base_class
+        has_group = group_definer is not None and group_definer is not base_class
+
+        if has_process and has_group:
+            raise ValueError(
+                f"{cls.__name__}: Cannot override both _process and _group_process. "
+                "Override only one, or set is_group_process explicitly."
+            )
+        if not has_process and not has_group:
+            raise ValueError(
+                f"{cls.__name__}: Must override either _process or _group_process"
+            )
+
+        return has_group
+
+    @classmethod
+    def define_schema(cls):
+        if cls.node_id is None:
+            raise NotImplementedError(f"{cls.__name__} must set node_id class variable")
+
+        is_group = cls._detect_processing_mode()
+
+        inputs = [
+            io.String.Input(
+                "texts",
+                tooltip="List of texts to process." if is_group else "Text to process.",
+            )
+        ]
+        inputs.extend(cls.extra_inputs)
+
+        return io.Schema(
+            node_id=cls.node_id,
+            search_aliases=cls.search_aliases,
+            display_name=cls.display_name or cls.node_id,
+            category="text",
+            description=cls.description,
+            is_experimental=True,
+            is_deprecated=cls.is_deprecated,
+            is_input_list=is_group,  # True for group, False for individual
+            inputs=inputs,
+            outputs=[
+                io.String.Output(
+                    display_name="texts",
+                    is_output_list=cls.is_output_list,
+                    tooltip="Processed texts",
+                )
+            ],
+        )
+
+    @classmethod
+    def execute(cls, texts, **kwargs):
+        """Execute the node. Routes to _process or _group_process based on mode."""
+        is_group = cls._detect_processing_mode()
+
+        # Extract scalar values from lists for parameters
+        params = {}
+        for k, v in kwargs.items():
+            if isinstance(v, list) and len(v) == 1:
+                params[k] = v[0]
+            else:
+                params[k] = v
+
+        if is_group:
+            # Group processing: texts is list, call _group_process
+            result = cls._group_process(texts, **params)
+        else:
+            # Individual processing: texts is single item, call _process
+            result = cls._process(texts, **params)
+
+        # Wrap result based on is_output_list
+        if cls.is_output_list:
+            # Result should already be a list (or will be for individual)
+            return io.NodeOutput(result if is_group else [result])
+        else:
+            # Single output - wrap in list for NodeOutput
+            return io.NodeOutput([result])
+
+    @classmethod
+    def _process(cls, text, **kwargs):
+        """Override this method for single-item processing.
+
+        Args:
+            text: str - Single text string
+            **kwargs: Additional parameters (already extracted from lists)
+
+        Returns:
+            str - Processed text
+        """
+        raise NotImplementedError(f"{cls.__name__} must implement _process method")
+
+    @classmethod
+    def _group_process(cls, texts, **kwargs):
+        """Override this method for group processing.
+
+        Args:
+            texts: list[str] - List of text strings
+            **kwargs: Additional parameters (already extracted from lists)
+
+        Returns:
+            list[str] - Processed texts
+        """
+        raise NotImplementedError(
+            f"{cls.__name__} must implement _group_process method"
+        )
+
+
+# ========== Image Transform Nodes ==========
+
+
+class ResizeImagesByShorterEdgeNode(ImageProcessingNode):
+    node_id = "ResizeImagesByShorterEdge"
+    display_name = "Resize Images by Shorter Edge (DEPRECATED)"
+    category = "image/transform"
+    description = "Resize images so that the shorter edge matches the specified dimension while preserving aspect ratio."
+    is_deprecated = True  # This node is superseded by Resize Image/Mask with resize_type = scale shorter dimension
+    extra_inputs = [
+        io.Int.Input(
+            "shorter_edge",
+            default=512,
+            min=1,
+            max=8192,
+            tooltip="Target dimension for the shorter edge.",
+        ),
+    ]
+
+    @classmethod
+    def _process(cls, image, shorter_edge):
+        img = tensor_to_pil(image)
+        w, h = img.size
+        if w < h:
+            new_w = shorter_edge
+            new_h = int(h * (shorter_edge / w))
+        else:
+            new_h = shorter_edge
+            new_w = int(w * (shorter_edge / h))
+        img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
+        return pil_to_tensor(img)
+
+
+class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
+    node_id = "ResizeImagesByLongerEdge"
+    display_name = "Resize Images by Longer Edge (DEPRECATED)"
+    category = "image/transform"
+    description = "Resize images so that the longer edge matches the specified dimension while preserving aspect ratio."
+    is_deprecated = True  # This node is superseded by Resize Image/Mask with resize_type = scale longer dimension
+    extra_inputs = [
+        io.Int.Input(
+            "longer_edge",
+            default=1024,
+            min=1,
+            max=8192,
+            tooltip="Target dimension for the longer edge.",
+        ),
+    ]
+
+    @classmethod
+    def _process(cls, image, longer_edge):
+        resized_images = []
+        for image_i in image:
+            img = tensor_to_pil(image_i)
+            w, h = img.size
+            if w > h:
+                new_w = longer_edge
+                new_h = int(h * (longer_edge / w))
+            else:
+                new_h = longer_edge
+                new_w = int(w * (longer_edge / h))
+            img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
+            resized_images.append(pil_to_tensor(img))
+        return torch.cat(resized_images, dim=0)
+
+
+class CenterCropImagesNode(ImageProcessingNode):
+    node_id = "CenterCropImages"
+    search_aliases=["crop", "cut", "trim"]
+    display_name="Crop Image (Center)"
+    category="image/transform"
+    description = "Center crop an image to the specified dimensions."
+    extra_inputs = [
+        io.Int.Input("width", default=512, min=1, max=8192, tooltip="Crop width."),
+        io.Int.Input("height", default=512, min=1, max=8192, tooltip="Crop height."),
+    ]
+
+    @classmethod
+    def _process(cls, image, width, height):
+        img = tensor_to_pil(image)
+        left = max(0, (img.width - width) // 2)
+        top = max(0, (img.height - height) // 2)
+        right = min(img.width, left + width)
+        bottom = min(img.height, top + height)
+        img = img.crop((left, top, right, bottom))
+        return pil_to_tensor(img)
+
+
+class RandomCropImagesNode(ImageProcessingNode):
+    node_id = "RandomCropImages"
+    search_aliases=["crop", "cut", "trim"]
+    display_name = "Crop Image (Random)"
+    category="image/transform"
+    description = "Randomly crop an image to the specified dimensions."
+
+    extra_inputs = [
+        io.Int.Input("width", default=512, min=1, max=8192, tooltip="Crop width."),
+        io.Int.Input("height", default=512, min=1, max=8192, tooltip="Crop height."),
+        io.Int.Input(
+            "seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF, tooltip="Random seed."
+        ),
+    ]
+
+    @classmethod
+    def _process(cls, image, width, height, seed):
+        np.random.seed(seed % (2**32 - 1))
+        img = tensor_to_pil(image)
+        max_left = max(0, img.width - width)
+        max_top = max(0, img.height - height)
+        left = np.random.randint(0, max_left + 1) if max_left > 0 else 0
+        top = np.random.randint(0, max_top + 1) if max_top > 0 else 0
+        right = min(img.width, left + width)
+        bottom = min(img.height, top + height)
+        img = img.crop((left, top, right, bottom))
+        return pil_to_tensor(img)
+
+
+class NormalizeImagesNode(ImageProcessingNode):
+    node_id = "NormalizeImages"
+    search_aliases=["normalize", "normalize colors"]
+    display_name = "Normalize Image Colors"
+    category = "image/color"
+    description = "Normalize images using mean and standard deviation."
+    per_frame_process = False  # Pure tensor math, handles any batch size
+    extra_inputs = [
+        io.Float.Input(
+            "mean",
+            default=0.5,
+            min=0.0,
+            max=1.0,
+            tooltip="Mean value for normalization.",
+            advanced=True,
+        ),
+        io.Float.Input(
+            "std",
+            default=0.5,
+            min=0.001,
+            max=1.0,
+            tooltip="Standard deviation for normalization.",
+            advanced=True,
+        ),
+    ]
+
+    @classmethod
+    def _process(cls, image, mean, std):
+        return (image - mean) / std
+
+
+class AdjustBrightnessNode(ImageProcessingNode):
+    node_id = "AdjustBrightness"
+    search_aliases=["brightness"]
+    display_name = "Adjust Brightness"
+    category="image/adjustments"
+    description = "Adjust the brightness of an image."
+    per_frame_process = False  # Pure tensor math, handles any batch size
+    extra_inputs = [
+        io.Float.Input(
+            "factor",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            tooltip="Brightness factor. 1.0 = no change, <1.0 = darker, >1.0 = brighter.",
+        ),
+    ]
+
+    @classmethod
+    def _process(cls, image, factor):
+        return (image * factor).clamp(0.0, 1.0)
+
+
+class AdjustContrastNode(ImageProcessingNode):
+    node_id = "AdjustContrast"
+    search_aliases=["contrast"]
+    display_name = "Adjust Contrast"
+    category="image/adjustments"
+    description = "Adjust the contrast of an image."
+    per_frame_process = False  # Pure tensor math, handles any batch size
+    extra_inputs = [
+        io.Float.Input(
+            "factor",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            tooltip="Contrast factor. 1.0 = no change, <1.0 = less contrast, >1.0 = more contrast.",
+        ),
+    ]
+
+    @classmethod
+    def _process(cls, image, factor):
+        return ((image - 0.5) * factor + 0.5).clamp(0.0, 1.0)
+
+
+class ShuffleDatasetNode(ImageProcessingNode):
+    node_id = "ShuffleDataset"
+    search_aliases=["shuffle", "randomize", "mix"]
+    display_name = "Shuffle Images List"
+    category = "image/batch"
+    description = "Randomly shuffle the order of images in a list."
+    is_group_process = True  # Requires full list to shuffle
+    extra_inputs = [
+        io.Int.Input(
+            "seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF, tooltip="Random seed."
+        ),
+    ]
+
+    @classmethod
+    def _group_process(cls, images, seed):
+        np.random.seed(seed % (2**32 - 1))
+        indices = np.random.permutation(len(images))
+        return [images[i] for i in indices]
+
+
+class ShuffleImageTextDatasetNode(io.ComfyNode):
+    """Special node that shuffles both images and texts together."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ShuffleImageTextDataset",
+            search_aliases=["shuffle", "randomize", "mix"],
+            display_name = "Shuffle Pairs of Image-Text",
+            category = "image/batch",
+            description = "Randomly shuffle the order of pairs of image-text in a list.",
+            is_experimental=True,
+            is_input_list=True,
+            inputs=[
+                io.Image.Input("images", tooltip="List of images to shuffle."),
+                io.String.Input("texts", tooltip="List of texts to shuffle.", force_input=True),
+                io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    tooltip="Random seed.",
+                ),
+            ],
+            outputs=[
+                io.Image.Output(
+                    display_name="images",
+                    is_output_list=True,
+                    tooltip="Shuffled images",
+                ),
+                io.String.Output(
+                    display_name="texts", is_output_list=True, tooltip="Shuffled texts"
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, images, texts, seed):
+        seed = seed[0]  # Extract scalar
+        np.random.seed(seed % (2**32 - 1))
+        indices = np.random.permutation(len(images))
+        shuffled_images = [images[i] for i in indices]
+        shuffled_texts = [texts[i] for i in indices]
+        return io.NodeOutput(shuffled_images, shuffled_texts)
+
+
+# ========== Video Processing Nodes ==========
+
+
+class VideoFrameSampleNode(io.ComfyNode):
+    """Sample a fixed number of frames from a video using various strategies.
+
+    For contiguous strategies ("head"/"tail") the result is a fully lazy
+    VideoInput (no frames decoded). For non-contiguous strategies
+    ("uniform"/"random") only the selected indices are decoded.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VideoFrameSample",
+            search_aliases=["sample frames", "extract frames"],
+            display_name="Sample Video Frame",
+            category="video",
+            description="Sample a fixed number of frames from a video using various strategies.",
+            is_experimental=True,
+            inputs=[
+                io.Video.Input("video", tooltip="Input video."),
+                io.Int.Input(
+                    "num_frames",
+                    default=16,
+                    min=1,
+                    max=9999,
+                    tooltip="Number of frames to sample.",
+                ),
+                io.Combo.Input(
+                    "strategy",
+                    options=["uniform", "head", "tail", "random"],
+                    default="uniform",
+                    tooltip="uniform: evenly spaced, head: first N, tail: last N, random: random sorted.",
+                ),
+                io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    tooltip="Random seed (only used with 'random' strategy).",
+                ),
+            ],
+            outputs=[
+                io.Video.Output(display_name="video", tooltip="Sampled video."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, video, num_frames, strategy, seed):
+        total_frames = video.get_frame_count()
+        num_frames = min(num_frames, total_frames)
+        fps = float(video.get_frame_rate())
+
+        if strategy == "head":
+            return io.NodeOutput(
+                video.as_trimmed(0.0, num_frames / fps, strict_duration=False)
+            )
+        if strategy == "tail":
+            start_t = (total_frames - num_frames) / fps
+            return io.NodeOutput(
+                video.as_trimmed(start_t, num_frames / fps, strict_duration=False)
+            )
+
+        if strategy == "uniform":
+            if num_frames == 1:
+                indices = [total_frames // 2]
+            else:
+                indices = [round(i * (total_frames - 1) / (num_frames - 1)) for i in range(num_frames)]
+        elif strategy == "random":
+            rng = np.random.RandomState(seed % (2**32 - 1))
+            indices = sorted(rng.choice(total_frames, size=num_frames, replace=False).tolist())
+        else:
+            raise ValueError(f"Unknown strategy: {strategy}")
+
+        return io.NodeOutput(_decode_selected_frames(video, indices))
+
+
+class VideoTemporalCropNode(io.ComfyNode):
+    """Crop a continuous range of frames from a video (fully lazy)."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VideoTemporalCrop",
+            search_aliases=["crop", "crop video", "temporal crop", "truncate video"],
+            display_name="Crop Video (Temporal)",
+            category="video/transform",
+            description="Crop a continuous range of frames from a video.",
+            is_experimental=True,
+            inputs=[
+                io.Video.Input("video", tooltip="Input video."),
+                io.Int.Input(
+                    "start_frame",
+                    default=0,
+                    min=0,
+                    max=99999,
+                    tooltip="Starting frame index.",
+                ),
+                io.Int.Input(
+                    "length",
+                    default=16,
+                    min=1,
+                    max=99999,
+                    tooltip="Number of frames to keep.",
+                ),
+            ],
+            outputs=[
+                io.Video.Output(display_name="video", tooltip="Cropped video (lazy)."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, video, start_frame, length):
+        total_frames = video.get_frame_count()
+        fps = float(video.get_frame_rate())
+        start_frame = min(start_frame, max(total_frames - 1, 0))
+        length = min(length, total_frames - start_frame)
+        return io.NodeOutput(
+            video.as_trimmed(start_frame / fps, length / fps, strict_duration=False)
+        )
+
+
+class VideoRandomTemporalCropNode(io.ComfyNode):
+    """Randomly crop a continuous range of frames from a video (fully lazy)."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VideoRandomTemporalCrop",
+            search_aliases=["crop", "crop video", "temporal crop", "truncate video", "random crop"],
+            display_name="Crop Video (Temporal Random)",
+            category="video/transform",
+            description="Randomly crop a continuous range of frames from a video.",
+            is_experimental=True,
+            inputs=[
+                io.Video.Input("video", tooltip="Input video."),
+                io.Int.Input(
+                    "length",
+                    default=16,
+                    min=1,
+                    max=99999,
+                    tooltip="Number of frames to keep.",
+                ),
+                io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    tooltip="Random seed.",
+                ),
+            ],
+            outputs=[
+                io.Video.Output(display_name="video", tooltip="Cropped video (lazy)."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, video, length, seed):
+        total_frames = video.get_frame_count()
+        fps = float(video.get_frame_rate())
+        length = min(length, total_frames)
+        max_start = total_frames - length
+        rng = np.random.RandomState(seed % (2**32 - 1))
+        start = rng.randint(0, max_start + 1) if max_start > 0 else 0
+        return io.NodeOutput(
+            video.as_trimmed(start / fps, length / fps, strict_duration=False)
+        )
+
+
+class ShuffleVideoDatasetNode(io.ComfyNode):
+    """Randomly shuffle the order of videos in the dataset."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ShuffleVideoDataset",
+            search_aliases=["shuffle", "randomize", "mix"],
+            display_name="Shuffle Videos List",
+            category="video/batch",
+            description="Randomly shuffle the order of videos in a list.",
+            is_experimental=True,
+            is_input_list=True,
+            inputs=[
+                io.Video.Input("videos", tooltip="List of videos to shuffle."),
+                io.Int.Input(
+                    "seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF, tooltip="Random seed."
+                ),
+            ],
+            outputs=[
+                io.Video.Output(
+                    display_name="videos",
+                    is_output_list=True,
+                    tooltip="Shuffled videos",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, videos, seed):
+        seed = seed[0] if isinstance(seed, list) else seed
+        np.random.seed(seed % (2**32 - 1))
+        indices = np.random.permutation(len(videos))
+        return io.NodeOutput([videos[i] for i in indices])
+
+
+class ShuffleVideoTextDatasetNode(io.ComfyNode):
+    """Shuffle videos and their captions together, preserving pairs."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ShuffleVideoTextDataset",
+            search_aliases=["shuffle", "randomize", "mix"],
+            display_name="Shuffle Pairs of Video-Text",
+            category="dataset/video",
+            description="Randomly shuffle the order of pairs of video-text in a list.",
+            is_experimental=True,
+            is_input_list=True,
+            inputs=[
+                io.Video.Input("videos", tooltip="List of videos to shuffle."),
+                io.String.Input("texts", tooltip="List of texts to shuffle."),
+                io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    tooltip="Random seed.",
+                ),
+            ],
+            outputs=[
+                io.Video.Output(
+                    display_name="videos",
+                    is_output_list=True,
+                    tooltip="Shuffled videos",
+                ),
+                io.String.Output(
+                    display_name="texts",
+                    is_output_list=True,
+                    tooltip="Shuffled texts",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, videos, texts, seed):
+        seed = seed[0] if isinstance(seed, list) else seed
+        np.random.seed(seed % (2**32 - 1))
+        indices = np.random.permutation(len(videos))
+        return io.NodeOutput(
+            [videos[i] for i in indices],
+            [texts[i] for i in indices],
+        )
+
+
+# ========== Text Transform Nodes ==========
+
+
+class TextToLowercaseNode(TextProcessingNode):
+    node_id = "TextToLowercase"
+    search_aliases=["lowercase"]
+    display_name = "Convert Text to Lowercase (DEPRECATED)"
+    category = "text"
+    description = "Convert text to lowercase."
+    is_deprecated = True  # This node is superseded by the Convert Text Case node
+
+    @classmethod
+    def _process(cls, text):
+        return text.lower()
+
+
+class TextToUppercaseNode(TextProcessingNode):
+    node_id = "TextToUppercase"
+    search_aliases=["uppercase"]
+    display_name = "Convert Text to Uppercase (DEPRECATED)"
+    category = "text"
+    description = "Convert text to uppercase."
+    is_deprecated = True  # This node is superseded by the Convert Text Case node
+
+    @classmethod
+    def _process(cls, text):
+        return text.upper()
+
+
+class TruncateTextNode(TextProcessingNode):
+    node_id = "TruncateText"
+    search_aliases=["truncate", "cut", "shorten"]
+    display_name = "Truncate Text"
+    category = "text"
+    description = "Truncate text to a maximum length."
+    extra_inputs = [
+        io.Int.Input(
+            "max_length", default=77, min=1, max=10000, tooltip="Maximum text length."
+        ),
+    ]
+
+    @classmethod
+    def _process(cls, text, max_length):
+        return text[:max_length]
+
+
+class AddTextPrefixNode(TextProcessingNode):
+    node_id = "AddTextPrefix"
+    display_name = "Add Text Prefix (DEPRECATED)"
+    category = "text"
+    description = "Add a prefix to all texts."
+    is_deprecated = True  # This node is superseded by the Concatenate Text node
+    extra_inputs = [
+        io.String.Input("prefix", default="", tooltip="Prefix to add."),
+    ]
+
+    @classmethod
+    def _process(cls, text, prefix):
+        return prefix + text
+
+
+class AddTextSuffixNode(TextProcessingNode):
+    node_id = "AddTextSuffix"
+    display_name = "Add Text Suffix (DEPRECATED)"
+    category = "text"
+    description = "Add a suffix to all texts."
+    is_deprecated = True  # This node is superseded by the Concatenate Text node
+    extra_inputs = [
+        io.String.Input("suffix", default="", tooltip="Suffix to add."),
+    ]
+
+    @classmethod
+    def _process(cls, text, suffix):
+        return text + suffix
+
+
+class ReplaceTextNode(TextProcessingNode):
+    node_id = "ReplaceText"
+    display_name = "Replace Text (DEPRECATED)"
+    category = "text"
+    description = "Replace text in all texts."
+    is_deprecated = True  # This node is superseded by the other Replace Text node
+    extra_inputs = [
+        io.String.Input("find", default="", tooltip="Text to find."),
+        io.String.Input("replace", default="", tooltip="Text to replace with."),
+    ]
+
+    @classmethod
+    def _process(cls, text, find, replace):
+        return text.replace(find, replace)
+
+
+class StripWhitespaceNode(TextProcessingNode):
+    node_id = "StripWhitespace"
+    display_name = "Strip Whitespace (DEPRECATED)"
+    category = "text"
+    description = "Strip leading and trailing whitespace from all texts."
+    is_deprecated = True  # This node is superseded by the Trim Text node
+
+    @classmethod
+    def _process(cls, text):
+        return text.strip()
+
+
+# ========== Group Processing Example Nodes ==========
+
+
+class ImageDeduplicationNode(ImageProcessingNode):
+    """Remove duplicate or very similar images from a list using perceptual hashing."""
+
+    node_id = "ImageDeduplication"
+    search_aliases=["deduplicate", "remove duplicates", "similarity filter"]
+    display_name = "Deduplicate Images"
+    category = "image/batch"
+    description = "Remove duplicate or very similar images from a list."
+    is_group_process = True  # Requires full list to compare images
+    extra_inputs = [
+        io.Float.Input(
+            "similarity_threshold",
+            default=0.95,
+            min=0.0,
+            max=1.0,
+            tooltip="Similarity threshold (0-1). Higher means more similar. Images above this threshold are considered duplicates.",
+            advanced=True,
+        ),
+    ]
+
+    @classmethod
+    def _group_process(cls, images, similarity_threshold):
+        """Remove duplicate images using perceptual hashing."""
+        if len(images) == 0:
+            return []
+
+        # Compute simple perceptual hash for each image
+        def compute_hash(img_tensor):
+            """Compute a simple perceptual hash by resizing to 8x8 and comparing to average."""
+            img = tensor_to_pil(img_tensor)
+            # Resize to 8x8
+            img_small = img.resize((8, 8), Image.Resampling.LANCZOS).convert("L")
+            # Get pixels
+            pixels = list(img_small.getdata())
+            # Compute average
+            avg = sum(pixels) / len(pixels)
+            # Create hash (1 if above average, 0 otherwise)
+            hash_bits = "".join("1" if p > avg else "0" for p in pixels)
+            return hash_bits
+
+        def hamming_distance(hash1, hash2):
+            """Compute Hamming distance between two hash strings."""
+            return sum(c1 != c2 for c1, c2 in zip(hash1, hash2))
+
+        # Compute hashes for all images
+        hashes = [compute_hash(img) for img in images]
+
+        # Find duplicates
+        keep_indices = []
+        for i in range(len(images)):
+            is_duplicate = False
+            for j in keep_indices:
+                # Compare hashes
+                distance = hamming_distance(hashes[i], hashes[j])
+                similarity = 1.0 - (distance / 64.0)  # 64 bits total
+                if similarity >= similarity_threshold:
+                    is_duplicate = True
+                    logging.info(
+                        f"Image {i} is similar to image {j} (similarity: {similarity:.3f}), skipping"
+                    )
+                    break
+
+            if not is_duplicate:
+                keep_indices.append(i)
+
+        # Return only unique images
+        unique_images = [images[i] for i in keep_indices]
+        logging.info(
+            f"Deduplication: kept {len(unique_images)} out of {len(images)} images"
+        )
+        return unique_images
+
+
+class ImageGridNode(ImageProcessingNode):
+    """Combine multiple images into a single grid/collage."""
+
+    node_id = "ImageGrid"
+    search_aliases=["grid", "collage", "combine"]
+    display_name = "Make Image Grid"
+    category="image/batch"
+    description = "Arrange multiple images into a grid layout."
+    is_group_process = True  # Requires full list to create grid
+    is_output_list = False  # Outputs single grid image
+    extra_inputs = [
+        io.Int.Input(
+            "columns",
+            default=4,
+            min=1,
+            max=20,
+            tooltip="Number of columns in the grid.",
+        ),
+        io.Int.Input(
+            "cell_width",
+            default=256,
+            min=32,
+            max=2048,
+            tooltip="Width of each cell in the grid.",
+            advanced=True,
+        ),
+        io.Int.Input(
+            "cell_height",
+            default=256,
+            min=32,
+            max=2048,
+            tooltip="Height of each cell in the grid.",
+            advanced=True,
+        ),
+        io.Int.Input(
+            "padding", default=4, min=0, max=50, tooltip="Padding between images.", advanced=True
+        ),
+    ]
+
+    @classmethod
+    def _group_process(cls, images, columns, cell_width, cell_height, padding):
+        """Arrange images into a grid."""
+        if len(images) == 0:
+            raise ValueError("Cannot create grid from empty image list")
+
+        # Calculate grid dimensions
+        num_images = len(images)
+        rows = (num_images + columns - 1) // columns  # Ceiling division
+
+        # Calculate total grid size
+        grid_width = columns * cell_width + (columns - 1) * padding
+        grid_height = rows * cell_height + (rows - 1) * padding
+
+        # Create blank grid
+        grid = Image.new("RGB", (grid_width, grid_height), (0, 0, 0))
+
+        # Place images
+        for idx, img_tensor in enumerate(images):
+            row = idx // columns
+            col = idx % columns
+
+            # Convert to PIL and resize to cell size
+            img = tensor_to_pil(img_tensor)
+            img = img.resize((cell_width, cell_height), Image.Resampling.LANCZOS)
+
+            # Calculate position
+            x = col * (cell_width + padding)
+            y = row * (cell_height + padding)
+
+            # Paste into grid
+            grid.paste(img, (x, y))
+
+        logging.info(
+            f"Created {columns}x{rows} grid with {num_images} images ({grid_width}x{grid_height})"
+        )
+        return pil_to_tensor(grid)
+
+
+class MergeImageListsNode(ImageProcessingNode):
+    """Merge multiple image lists into a single list."""
+
+    node_id = "MergeImageLists"
+    search_aliases=["list", "merge list", "make list"]
+    display_name = "Merge Image Lists (DEPRECATED)"
+    category = "image/batch"
+    description = "Concatenate multiple image lists into one."
+    is_group_process = True  # Receives images as list
+    is_deprecated = True  # This node is superseded by the Create List node
+
+    @classmethod
+    def _group_process(cls, images):
+        """Simply return the images list (already merged by input handling)."""
+        # When multiple list inputs are connected, they're concatenated
+        # For now, this is a simple pass-through
+        logging.info(f"Merged image list contains {len(images)} images")
+        return images
+
+
+class MergeTextListsNode(TextProcessingNode):
+    """Merge multiple text lists into a single list."""
+
+    node_id = "MergeTextLists"
+    display_name = "Merge Text Lists (DEPRECATED)"
+    category = "text"
+    description = "Concatenate multiple text lists into one."
+    is_group_process = True  # Receives texts as list
+    is_deprecated = True  # This node is superseded by the Create List node
+
+    @classmethod
+    def _group_process(cls, texts):
+        """Simply return the texts list (already merged by input handling)."""
+        # When multiple list inputs are connected, they're concatenated
+        # For now, this is a simple pass-through
+        logging.info(f"Merged text list contains {len(texts)} texts")
+        return texts
+
+
+# ========== Training Dataset Nodes ==========
+
+
+class ResolutionBucket(io.ComfyNode):
+    """Bucket latents and conditions by resolution for efficient batch training."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ResolutionBucket",
+            search_aliases=["bucket by resolution", "group by resolution", "batch by resolution"],
+            display_name="Resolution Bucket",
+            category="model/training",
+            description="Group latents and conditionings into buckets",
+            is_experimental=True,
+            is_input_list=True,
+            inputs=[
+                io.Latent.Input(
+                    "latents",
+                    tooltip="List of latent dicts to bucket by resolution.",
+                ),
+                io.Conditioning.Input(
+                    "conditioning",
+                    tooltip="List of conditioning lists (must match latents length).",
+                ),
+            ],
+            outputs=[
+                io.Latent.Output(
+                    display_name="latents",
+                    is_output_list=True,
+                    tooltip="List of batched latent dicts, one per resolution bucket.",
+                ),
+                io.Conditioning.Output(
+                    display_name="conditioning",
+                    is_output_list=True,
+                    tooltip="List of condition lists, one per resolution bucket.",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, latents, conditioning):
+        # latents: list[{"samples": tensor}] where tensor is (B, C, H, W), typically B=1
+        # conditioning: list[list[cond]]
+
+        # Validate lengths match
+        if len(latents) != len(conditioning):
+            raise ValueError(
+                f"Number of latents ({len(latents)}) does not match number of conditions ({len(conditioning)})."
+            )
+
+        # Flatten latents and conditions to individual samples
+        flat_latents = []  # list of (C, H, W) tensors
+        flat_conditions = []  # list of condition lists
+
+        for latent_dict, cond in zip(latents, conditioning):
+            samples = latent_dict["samples"]  # (B, C, H, W)
+            batch_size = samples.shape[0]
+
+            # cond is a list of conditions with length == batch_size
+            for i in range(batch_size):
+                flat_latents.append(samples[i])  # (C, H, W)
+                flat_conditions.append(cond[i])  # single condition
+
+        # Group by resolution (H, W)
+        buckets = {}  # (H, W) -> {"latents": list, "conditions": list}
+
+        for latent, cond in zip(flat_latents, flat_conditions):
+            # latent shape is (..., H, W) (B, C, H, W) or (B, T, C, H ,W)
+            h, w = latent.shape[-2], latent.shape[-1]
+            key = (h, w)
+
+            if key not in buckets:
+                buckets[key] = {"latents": [], "conditions": []}
+
+            buckets[key]["latents"].append(latent)
+            buckets[key]["conditions"].append(cond)
+
+        # Convert buckets to output format
+        output_latents = []  # list[{"samples": tensor}] where tensor is (Bi, ..., H, W)
+        output_conditions = []  # list[list[cond]] where each inner list has Bi conditions
+
+        for (h, w), bucket_data in buckets.items():
+            # Stack latents into batch: list of (..., H, W) -> (Bi, ..., H, W)
+            stacked_latents = torch.stack(bucket_data["latents"], dim=0)
+            output_latents.append({"samples": stacked_latents})
+
+            # Conditions stay as list of condition lists
+            output_conditions.append(bucket_data["conditions"])
+
+            logging.info(
+                f"Resolution bucket ({h}x{w}): {len(bucket_data['latents'])} samples"
+            )
+
+        logging.info(f"Created {len(buckets)} resolution buckets from {len(flat_latents)} samples")
+        return io.NodeOutput(output_latents, output_conditions)
+
+
+class MakeTrainingDataset(io.ComfyNode):
+    """Encode images with VAE and texts with CLIP to create a training dataset."""
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="MakeTrainingDataset",
+            search_aliases=["encode dataset"],
+            display_name="Make Training Dataset",
+            category="model/training",
+            description="Encode images with VAE and texts with CLIP to create a training dataset of latents and conditionings.",
+            is_experimental=True,
+            is_input_list=True,  # images and texts as lists
+            inputs=[
+                io.Image.Input("images", tooltip="List of images to encode."),
+                io.Vae.Input(
+                    "vae", tooltip="VAE model for encoding images to latents."
+                ),
+                io.Clip.Input(
+                    "clip", tooltip="CLIP model for encoding text to conditioning."
+                ),
+                io.String.Input(
+                    "texts",
+                    optional=True,
+                    tooltip="List of text captions. Can be length n (matching images), 1 (repeated for all), or omitted (uses empty string).",
+                    force_input=True
+                ),
+            ],
+            outputs=[
+                io.Latent.Output(
+                    display_name="latents",
+                    is_output_list=True,
+                    tooltip="List of latent dicts",
+                ),
+                io.Conditioning.Output(
+                    display_name="conditioning",
+                    is_output_list=True,
+                    tooltip="List of conditioning lists",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, images, vae, clip, texts=None):
+        # Extract scalars (vae and clip are single values wrapped in lists)
+        vae = vae[0]
+        clip = clip[0]
+
+        # Handle text list
+        num_images = len(images)
+
+        if texts is None or len(texts) == 0:
+            # Treat as [""] for unconditional training
+            texts = [""]
+
+        if len(texts) == 1 and num_images > 1:
+            # Repeat single text for all images
+            texts = texts * num_images
+        elif len(texts) != num_images:
+            raise ValueError(
+                f"Number of texts ({len(texts)}) does not match number of images ({num_images}). "
+                f"Text list should have length {num_images}, 1, or 0."
+            )
+
+        # Encode images with VAE
+        logging.info(f"Encoding {num_images} images with VAE...")
+        latents_list = []  # list[{"samples": tensor}]
+        for img_tensor in images:
+            # img_tensor is [1, H, W, 3]
+            latent_tensor = vae.encode(img_tensor[:, :, :, :3])
+            latents_list.append({"samples": latent_tensor})
+
+        # Encode texts with CLIP
+        logging.info(f"Encoding {len(texts)} texts with CLIP...")
+        conditioning_list = []  # list[list[cond]]
+        for text in texts:
+            if text == "":
+                cond = clip.encode_from_tokens_scheduled(clip.tokenize(""))
+            else:
+                tokens = clip.tokenize(text)
+                cond = clip.encode_from_tokens_scheduled(tokens)
+            conditioning_list.append(cond)
+
+        logging.info(
+            f"Created dataset with {len(latents_list)} latents and {len(conditioning_list)} conditioning."
+        )
+        return io.NodeOutput(latents_list, conditioning_list)
+
+
+class SaveTrainingDataset(io.ComfyNode):
+    """Save encoded training dataset (latents + conditioning) to disk."""
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveTrainingDataset",
+            search_aliases=["export dataset", "save dataset"],
+            display_name="Save Training Dataset",
+            category="model/training",
+            description="Save encoded training dataset (latents + conditioning) to disk for efficient loading during training.",
+            is_experimental=True,
+            is_output_node=True,
+            is_input_list=True,  # Receive lists
+            inputs=[
+                io.Latent.Input(
+                    "latents",
+                    tooltip="List of latent dicts from MakeTrainingDataset.",
+                ),
+                io.Conditioning.Input(
+                    "conditioning",
+                    tooltip="List of conditioning lists from MakeTrainingDataset.",
+                ),
+                io.String.Input(
+                    "folder_name",
+                    default="training_dataset",
+                    tooltip="Name of folder to save the dataset into, inside the datasets directory. Subfolders like 'project/run1' are allowed.",
+                ),
+                io.Int.Input(
+                    "shard_size",
+                    default=1000,
+                    min=1,
+                    max=100000,
+                    tooltip="Number of samples per shard file.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[],
+        )
+
+    @classmethod
+    def execute(cls, latents, conditioning, folder_name, shard_size):
+        # Extract scalars
+        folder_name = folder_name[0]
+        shard_size = shard_size[0]
+
+        # latents: list[{"samples": tensor}]
+        # conditioning: list[list[cond]]
+
+        # Validate lengths match
+        if len(latents) != len(conditioning):
+            raise ValueError(
+                f"Number of latents ({len(latents)}) does not match number of conditions ({len(conditioning)}). "
+                f"Something went wrong in dataset preparation."
+            )
+
+        # Create output directory (inside the datasets root, traversal-safe)
+        output_dir = get_dataset_save_dir(folder_name)
+        os.makedirs(output_dir, exist_ok=True)
+
+        # Prepare data pairs
+        num_samples = len(latents)
+        num_shards = (num_samples + shard_size - 1) // shard_size  # Ceiling division
+
+        logging.info(
+            f"Saving {num_samples} samples to {num_shards} shards in {output_dir}..."
+        )
+
+        # Save data in shards
+        for shard_idx in range(num_shards):
+            start_idx = shard_idx * shard_size
+            end_idx = min(start_idx + shard_size, num_samples)
+
+            # Get shard data (list of latent dicts and conditioning lists)
+            shard_data = {
+                "latents": latents[start_idx:end_idx],
+                "conditioning": conditioning[start_idx:end_idx],
+            }
+
+            # Save shard
+            shard_filename = f"shard_{shard_idx:04d}.pkl"
+            shard_path = os.path.join(output_dir, shard_filename)
+
+            with open(shard_path, "wb") as f:
+                torch.save(shard_data, f)
+
+            logging.info(
+                f"Saved shard {shard_idx + 1}/{num_shards}: {shard_filename} ({end_idx - start_idx} samples)"
+            )
+
+        # Save metadata
+        metadata = {
+            "num_samples": num_samples,
+            "num_shards": num_shards,
+            "shard_size": shard_size,
+        }
+        metadata_path = os.path.join(output_dir, "metadata.json")
+        with open(metadata_path, "w") as f:
+            json.dump(metadata, f, indent=2)
+
+        logging.info(f"Successfully saved {num_samples} samples to {output_dir}.")
+        return io.NodeOutput()
+
+
+class LoadTrainingDataset(io.ComfyNode):
+    """Load encoded training dataset from disk."""
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadTrainingDataset",
+            search_aliases=["import dataset", "training data"],
+            display_name="Load Training Dataset",
+            category="model/training",
+            description="Load encoded training dataset (latents + conditioning) from disk for use in training.",
+            is_experimental=True,
+            inputs=[
+                io.Combo.Input(
+                    "folder_name",
+                    options=list_dataset_folders(),
+                    tooltip="Saved dataset to load, from the datasets directory.",
+                ),
+            ],
+            outputs=[
+                io.Latent.Output(
+                    display_name="latents",
+                    is_output_list=True,
+                    tooltip="List of latent dicts",
+                ),
+                io.Conditioning.Output(
+                    display_name="conditioning",
+                    is_output_list=True,
+                    tooltip="List of conditioning lists",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, folder_name):
+        # Get dataset directory (searched across all dataset roots, traversal-safe)
+        dataset_dir = get_dataset_dir(folder_name)
+
+        # Find all shard files
+        shard_files = sorted(
+            [
+                f
+                for f in os.listdir(dataset_dir)
+                if f.startswith("shard_") and f.endswith(".pkl")
+            ]
+        )
+
+        if not shard_files:
+            raise ValueError(f"No shard files found in {dataset_dir}")
+
+        logging.info(f"Loading {len(shard_files)} shards from {dataset_dir}...")
+
+        # Load all shards
+        all_latents = []  # list[{"samples": tensor}]
+        all_conditioning = []  # list[list[cond]]
+
+        for shard_file in shard_files:
+            shard_path = os.path.join(dataset_dir, shard_file)
+
+            with open(shard_path, "rb") as f:
+                shard_data = torch.load(f, weights_only=True)
+
+            all_latents.extend(shard_data["latents"])
+            all_conditioning.extend(shard_data["conditioning"])
+
+            logging.info(f"Loaded {shard_file}: {len(shard_data['latents'])} samples")
+
+        logging.info(
+            f"Successfully loaded {len(all_latents)} samples from {dataset_dir}."
+        )
+        return io.NodeOutput(all_latents, all_conditioning)
+
+
+# ========== Extension Setup ==========
+
+
+class DatasetExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            # Data loading/saving nodes
+            LoadImageDataSetFromFolderNode,
+            LoadImageTextDataSetFromFolderNode,
+            SaveImageDataSetToFolderNode,
+            SaveImageTextDataSetToFolderNode,
+            # Video data loading nodes
+            LoadVideoDataSetFromFolderNode,
+            LoadVideoTextDataSetFromFolderNode,
+            # Image transform nodes (auto-handle video via per-frame processing)
+            ResizeImagesByShorterEdgeNode,
+            ResizeImagesByLongerEdgeNode,
+            CenterCropImagesNode,
+            RandomCropImagesNode,
+            NormalizeImagesNode,
+            AdjustBrightnessNode,
+            AdjustContrastNode,
+            ShuffleDatasetNode,
+            ShuffleImageTextDatasetNode,
+            # Video processing nodes (lazy VideoInput in/out)
+            VideoFrameSampleNode,
+            VideoTemporalCropNode,
+            VideoRandomTemporalCropNode,
+            ShuffleVideoDatasetNode,
+            ShuffleVideoTextDatasetNode,
+            # Text transform nodes
+            TextToLowercaseNode,
+            TextToUppercaseNode,
+            TruncateTextNode,
+            AddTextPrefixNode,
+            AddTextSuffixNode,
+            ReplaceTextNode,
+            StripWhitespaceNode,
+            # Group processing examples
+            ImageDeduplicationNode,
+            ImageGridNode,
+            MergeImageListsNode,
+            MergeTextListsNode,
+            # Training dataset nodes
+            MakeTrainingDataset,
+            SaveTrainingDataset,
+            LoadTrainingDataset,
+            ResolutionBucket,
+        ]
+
+
+async def comfy_entrypoint() -> DatasetExtension:
+    return DatasetExtension()
diff --git a/comfy_extras/nodes_depth_anything_3.py b/comfy_extras/nodes_depth_anything_3.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d797546b82965da0424dea583f5a13cef22194c
--- /dev/null
+++ b/comfy_extras/nodes_depth_anything_3.py
@@ -0,0 +1,681 @@
+"""ComfyUI nodes for Depth Anything 3.
+Model capability matrix:
+
+Variant               head_type  has_sky  has_conf  cam_dec
+DA3-Small             dualdpt    False    True      yes
+DA3-Base              dualdpt    False    True      yes
+DA3-Mono-Large        dpt        True     False     no
+DA3-Metric-Large      dpt        True     False     no  (raw output is metres)
+"""
+
+from __future__ import annotations
+
+import logging
+from typing_extensions import override
+
+import torch
+
+import comfy.model_management as mm
+import comfy.sd
+import folder_paths
+from comfy.ldm.colormap import turbo as _turbo
+from comfy.ldm.depth_anything_3 import preprocess as da3_preprocess
+from comfy_api.latest import ComfyExtension, Types, io
+from comfy.ldm.moge.geometry import triangulate_grid_mesh
+
+DA3ModelType = io.Custom("DA3_MODEL")
+DA3Geometry = io.Custom("DA3_GEOMETRY")
+DA3PointCloud = io.Custom("DA3_POINT_CLOUD")
+
+# DA3_GEOMETRY is a dict with these optional keys (absent when the upstream model didn't produce them):
+#
+# Per-frame tensors - B = batch size in mono mode; B = S (number of views) in multi-view mode.
+#   "depth":       torch.Tensor (B, H, W)         -- raw model depth (always present; matches MoGe convention)
+#   "image":       torch.Tensor (B, H, W, 3)      -- source image in [0, 1], CPU (always present)
+#   "mode":        str                            -- "mono" or "multiview" (always present)
+#   "sky":         torch.Tensor (B, H, W)         -- sky probability in [0, 1] (Mono/Metric variants only)
+#   "confidence":  torch.Tensor (B, H, W)         -- raw model confidence output (Small/Base variants only)
+#
+# Multi-view only - S = number of views; the leading 1 is the scene dimension from the model.
+#   "extrinsics":  torch.Tensor (1, S, 3, 4)      -- world-to-camera [R|t] matrices
+#   "intrinsics":  torch.Tensor (1, S, 3, 3)      -- pixel-space intrinsics
+#
+# DA3_POINT_CLOUD is a dict:
+#   "points":     torch.Tensor (N, 3)  -- 3-D coords in glTF convention (Y-up, Z-back)
+#   "colors":     torch.Tensor (N, 3)  -- RGB in [0, 1], or None
+#   "confidence": torch.Tensor (N,)    -- raw confidence per point, or None
+
+
+def _da3_unproject(depth: torch.Tensor, K: torch.Tensor) -> torch.Tensor:
+    """Pixel-space K⁻¹ unprojection: (H,W) depth → (H,W,3) point map in OpenCV space."""
+    H, W = depth.shape
+    u = torch.arange(W, dtype=torch.float32, device=depth.device)
+    v = torch.arange(H, dtype=torch.float32, device=depth.device)
+    u, v = torch.meshgrid(u, v, indexing='xy')             # both (H, W)
+    pix = torch.stack([u, v, torch.ones_like(u)], dim=-1)  # (H, W, 3)
+    rays = torch.einsum('ij,hwj->hwi', torch.linalg.inv(K.to(depth.device)), pix)
+    return rays * depth.unsqueeze(-1)                       # (H, W, 3)
+
+
+def _da3_default_K(H: int, W: int) -> torch.Tensor:
+    """Fallback ~60° FOV pinhole K for mono-mode DA3 (no intrinsics in geometry)."""
+    fx = fy = float(W) * 0.7
+    return torch.tensor([[fx, 0.0, (W - 1) / 2.0],
+                         [0.0, fy, (H - 1) / 2.0],
+                         [0.0, 0.0, 1.0]], dtype=torch.float32)
+
+
+def _da3_get_K(geometry: dict, b: int, H: int, W: int) -> torch.Tensor:
+    """Return pixel-space K for batch element b, falling back to a default estimate."""
+    if "intrinsics" in geometry:
+        # shape (1, S, 3, 3) - leading scene dimension from the multiview head
+        return geometry["intrinsics"][0, b].float()
+    logging.getLogger("comfy").warning(
+        "DA3_GEOMETRY has no intrinsics (mono-mode model). "
+        "Using a ~60° FOV estimate; 3-D reconstruction may be inaccurate."
+    )
+    return _da3_default_K(H, W)
+
+
+def _da3_get_extrinsic(geometry: dict, b: int) -> torch.Tensor | None:
+    """Return the world-to-camera extrinsic for batch element b, or None in mono mode.
+
+    The model outputs (1, S, 3, 4) [R|t] matrices; the fallback identity is (4, 4).
+    _da3_apply_extrinsic handles both shapes via [:3, :3] / [:3, 3] slicing.
+    """
+    if "extrinsics" not in geometry:
+        return None
+    return geometry["extrinsics"][0, b].float()
+
+
+def _da3_apply_extrinsic(points_cam: torch.Tensor, E: torch.Tensor) -> torch.Tensor:
+    """Transform (H,W,3) OpenCV camera-space points to world space."""
+    E = E.to(points_cam.device).float()
+    if not torch.isfinite(E).all():
+        logging.getLogger("comfy").warning(
+            "DA3 extrinsic matrix contains non-finite values (pose estimation may have failed). "
+            "Falling back to camera-space coordinates."
+        )
+        return points_cam
+    H, W, _ = points_cam.shape
+    R = E[:3, :3]           # (3, 3) rotation
+    t = E[:3, 3]            # (3,)   translation
+    R_inv = R.T             # rotation inverse = transpose for orthogonal R
+    t_inv = -(R_inv @ t)    # (3,)
+    pts = points_cam.reshape(-1, 3)                 # (N, 3)
+    pts_world = pts @ R_inv.T + t_inv               # (N, 3)
+    return pts_world.reshape(H, W, 3)
+
+
+def _normalize_confidence(conf: torch.Tensor) -> torch.Tensor:
+    """Map raw confidence to [0, 1] per image."""
+    B = conf.shape[0]
+    out = []
+    for i in range(B):
+        c = conf[i]
+        c_min, c_max = c.min(), c.max()
+        out.append((c - c_min) / (c_max - c_min) if c_max > c_min else torch.ones_like(c))
+    return torch.stack(out, dim=0)
+
+
+def _da3_build_mask(geometry: dict, b: int, H: int, W: int, confidence_threshold: float, use_sky_mask: bool) -> torch.Tensor:
+    """Build (H,W) bool keep-mask from sky probability and confidence."""
+    mask = torch.ones(H, W, dtype=torch.bool)
+    if use_sky_mask and "sky" in geometry:
+        mask = mask & (geometry["sky"][b] < 0.5)
+    if "confidence" in geometry and confidence_threshold > 0.0:
+        conf_norm = _normalize_confidence(geometry["confidence"][b:b + 1])[0]
+        mask = mask & (conf_norm >= confidence_threshold)
+    return mask
+
+
+class LoadDA3Model(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadDA3Model",
+            display_name="Load Depth Anything 3",
+            category="model/loaders",
+            inputs=[
+                io.Combo.Input(
+                    "model_name",
+                    options=folder_paths.get_filename_list("geometry_estimation"),
+                ),
+                io.Combo.Input(
+                    "weight_dtype",
+                    options=["default", "fp16", "bf16", "fp32"],
+                    default="default",
+                ),
+            ],
+            outputs=[DA3ModelType.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model_name, weight_dtype) -> io.NodeOutput:
+        model_options = {}
+        if weight_dtype == "fp16":
+            model_options["dtype"] = torch.float16
+        elif weight_dtype == "bf16":
+            model_options["dtype"] = torch.bfloat16
+        elif weight_dtype == "fp32":
+            model_options["dtype"] = torch.float32
+
+        path = folder_paths.get_full_path_or_raise("geometry_estimation", model_name)
+        model = comfy.sd.load_diffusion_model(path, model_options=model_options)
+        return io.NodeOutput(model)
+
+
+def _run_da3(model_patcher, image: torch.Tensor, process_res: int, method: str = "upper_bound_resize"):
+    """Run DA3 on (B,H,W,3), returns depth/conf/sky at original resolution (or None)."""
+    assert image.ndim == 4 and image.shape[-1] == 3, f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
+
+    B, H, W, _ = image.shape
+    mm.load_model_gpu(model_patcher)
+    diffusion = model_patcher.model.diffusion_model
+    device = mm.get_torch_device()
+    dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32
+
+    depths, confs, skies = [], [], []
+    for i in range(B):
+        single = image[i:i + 1].to(device)
+        x = da3_preprocess.preprocess_image(single, process_res=process_res, method=method)
+        x = x.to(dtype=dtype)
+        with torch.no_grad():
+            out = diffusion(x)
+
+        depth_lr = out["depth"]
+        depth_full = torch.nn.functional.interpolate(
+            depth_lr.unsqueeze(1).float(), size=(H, W),
+            mode="bilinear", align_corners=False,
+        ).squeeze(1).cpu()
+        depths.append(depth_full)
+
+        if "depth_conf" in out:
+            conf_full = torch.nn.functional.interpolate(
+                out["depth_conf"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+            confs.append(conf_full)
+        if "sky" in out:
+            sky_full = torch.nn.functional.interpolate(
+                out["sky"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+            skies.append(sky_full)
+
+    depth = torch.cat(depths, dim=0)
+    confidence = torch.cat(confs, dim=0) if confs else None
+    sky = torch.cat(skies, dim=0) if skies else None
+    return depth, confidence, sky
+
+
+class DA3Inference(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3Inference",
+            search_aliases=["depth", "geometry", "da3", "depth anything", "monocular", "pointmap", "sky", "3d", "metric depth", "disparity"],
+            display_name="Run Depth Anything 3",
+            category="image/geometry estimation",
+            description="Run Depth Anything 3 on an image. In multi-view mode each image is treated as a separate view of the same scene.",
+            inputs=[
+                DA3ModelType.Input("da3_model"),
+                io.Image.Input("image"),
+                io.Int.Input("resolution", default=504, min=140, max=2520, step=14,
+                    tooltip="Resolution the model runs at (longest side, multiple of 14).\n"
+                        "Lower = faster / less VRAM.\n"
+                        "Higher = more detail.\n"
+                        "Output is upsampled back to the original size."),
+                io.Combo.Input("resize_method", options=["upper_bound_resize", "lower_bound_resize"], default="upper_bound_resize",
+                    tooltip="upper_bound_resize: scale so the longest side = resolution (caps memory, default).\n"
+                        "lower_bound_resize: scale so the shortest side = resolution (preserves more detail on tall/wide images, uses more memory)."),
+                io.DynamicCombo.Input("mode", tooltip="mono: single view image (works with any model variant).\n"
+                    "multiview: all images processed together for geometric consistency + camera pose (for Small/Base models only).",
+                    options=[
+                        io.DynamicCombo.Option("mono", []),
+                        io.DynamicCombo.Option("multiview", [
+                        io.Combo.Input("ref_view_strategy", options=["saddle_balanced", "saddle_sim_range", "first", "middle"], default="saddle_balanced",
+                            tooltip="Which view acts as the geometric anchor.\n"
+                                "- saddle_balanced: the view most 'average' across all others (best general choice).\n"
+                                "- saddle_sim_range: the view most visually distinct from the others.\n"
+                                "- first / middle: fixed positional picks."),
+                        io.Combo.Input("pose_method", options=["cam_dec", "ray_pose"], default="cam_dec",
+                            tooltip="How the camera field-of-view is estimated (for Small/Base models only).\n"
+                                "- cam_dec: learned from image features.\n"
+                                "- ray_pose: derived geometrically from the model's 3D ray output.\n"
+                                "Affects perspective correctness of the 3D output. Try both if results look distorted."),
+                    ]),
+                ]),
+            ],
+            outputs=[
+                DA3Geometry.Output("da3_geometry", tooltip="Dictionary of non-normalized tensors.\n"
+                    "Always has the keys: depth, image, mode.\n"
+                    "Optional keys: sky (for Mono/Metric), confidence (for Small/Base), extrinsics + intrinsics (for multi-view)."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, da3_model, image, resolution, resize_method, mode) -> io.NodeOutput:
+        mode_val = mode["mode"]  # "mono" or "multiview"
+
+        if mode_val == "mono":
+            return cls._execute_mono(da3_model, image, resolution, resize_method)
+
+        # Capability checks for multi-view mode.
+        diffusion = da3_model.model.diffusion_model
+        pose_method = mode["pose_method"]
+        ref_view_strategy = mode["ref_view_strategy"]
+
+        has_cam_dec = diffusion.cam_dec is not None
+        has_dualdpt = diffusion.head_type == "dualdpt"
+
+        if not has_cam_dec and not has_dualdpt:
+            raise ValueError(
+                "multi-view mode requires Small or Base model. The loaded model "
+                f"(head_type='{diffusion.head_type}') does not support cross-view "
+                "attention or camera pose estimation. Switch mode to 'mono', or "
+                "load Small or Base model for mult-view."
+            )
+
+        if pose_method == "cam_dec" and not has_cam_dec:
+            raise ValueError(
+                "pose_method='cam_dec' requires a camera decoder, but the loaded "
+                f"model (head_type='{diffusion.head_type}') does not have one. "
+                "Use pose_method='ray_pose' instead."
+            )
+        if pose_method == "ray_pose" and not has_dualdpt:
+            raise ValueError(
+                "pose_method='ray_pose' requires a DualDPT head, but the loaded "
+                f"model has a '{diffusion.head_type}' head. "
+                "Use pose_method='cam_dec' instead."
+            )
+
+        return cls._execute_multiview(
+            da3_model, image, resolution, resize_method,
+            ref_view_strategy, pose_method,
+        )
+
+    @classmethod
+    def _execute_mono(cls, model, image, resolution, resize_method) -> io.NodeOutput:
+        depth, confidence, sky = _run_da3(model, image, resolution, method=resize_method)
+
+        geometry: dict = {
+            "depth": depth.contiguous(),
+            "image": image[..., :3].cpu(),
+            "mode": "mono",
+        }
+        if sky is not None:
+            geometry["sky"] = sky.contiguous()
+        if confidence is not None:
+            geometry["confidence"] = confidence.contiguous()
+        return io.NodeOutput(geometry)
+
+    @classmethod
+    def _execute_multiview(cls, model, image, resolution, resize_method, ref_view_strategy, pose_method) -> io.NodeOutput:
+        assert image.ndim == 4 and image.shape[-1] == 3, \
+            f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
+        S, H, W, _ = image.shape
+
+        mm.load_model_gpu(model)
+        diffusion = model.model.diffusion_model
+        device = mm.get_torch_device()
+        dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32
+
+        # All views in a single forward pass: (1, S, 3, H', W').
+        x = image.to(device)
+        x = da3_preprocess.preprocess_image(x, process_res=resolution, method=resize_method)
+        x = x.to(dtype=dtype).unsqueeze(0)
+
+        use_ray_pose = (pose_method == "ray_pose")
+        with torch.no_grad():
+            out = diffusion(x, use_ray_pose=use_ray_pose, ref_view_strategy=ref_view_strategy)
+
+        depth = torch.nn.functional.interpolate(
+            out["depth"].float().unsqueeze(1), size=(H, W),
+            mode="bilinear", align_corners=False,
+        ).squeeze(1).cpu()
+
+        sky = None
+        if "sky" in out:
+            sky = torch.nn.functional.interpolate(
+                out["sky"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+
+        if "extrinsics" in out and "intrinsics" in out:
+            extrinsics = out["extrinsics"].float().cpu()
+            intrinsics = out["intrinsics"].float().cpu()
+        else:
+            extrinsics = torch.eye(4)[None, None].expand(1, S, 4, 4).clone()
+            intrinsics = torch.eye(3)[None, None].expand(1, S, 3, 3).clone()
+
+        geometry: dict = {
+            "depth": depth.contiguous(),
+            "image": image[..., :3].cpu(),
+            "mode": "multiview",
+            "extrinsics": extrinsics.contiguous(),
+            "intrinsics": intrinsics.contiguous(),
+        }
+        if sky is not None:
+            geometry["sky"] = sky.contiguous()
+        if "depth_conf" in out:
+            conf = torch.nn.functional.interpolate(
+                out["depth_conf"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+            geometry["confidence"] = conf.contiguous()
+        return io.NodeOutput(geometry)
+
+
+class DA3Render(io.ComfyNode):
+    """Render a visualization from a DA3_GEOMETRY packet."""
+
+    _DEPTH_RENDER_INPUTS = [
+        io.Combo.Input("normalization",
+            options=["v2_style", "min_max", "raw"],
+            default="v2_style",
+            tooltip="- v2_style: mean/std normalisation for perceptually balanced results (default).\n"
+                "- min_max: stretches the full depth range to [0, 1] for maximum contrast.\n"
+                "- raw: no scaling,preserves metric units for Metric model."),
+        io.Boolean.Input("apply_sky_clip", default=False,
+            tooltip="Clip sky-region depth to the 99th percentile of foreground depth before normalisation. "
+                "Requires a sky key in the da3_geometry input (for Mono/Metric models only)."),
+    ]
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3Render",
+            display_name="Render Depth Anything 3",
+            category="image/geometry estimation",
+            description="Render a depth map, confidence map, or sky mask from Depth Anything 3 geometry data.",
+            inputs=[
+                DA3Geometry.Input("da3_geometry"),
+                io.DynamicCombo.Input("output",
+                    tooltip="- depth: normalised greyscale depth image.\n"
+                        "- depth_colored: depth mapped through the Turbo colormap.\n"
+                        "- sky_mask: sky probability in [0, 1] (for Mono/Metric models only).\n"
+                        "- confidence: normalised depth confidence (for Small/Base models only).",
+                options=[
+                    io.DynamicCombo.Option("depth", cls._DEPTH_RENDER_INPUTS),
+                    io.DynamicCombo.Option("depth_colored", cls._DEPTH_RENDER_INPUTS),
+                    io.DynamicCombo.Option("sky_mask", [
+                        io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the sky mask."),
+                    ]),
+                    io.DynamicCombo.Option("confidence", [
+                        io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the confidence map."),
+                    ]),
+                ]),
+            ],
+            outputs=[io.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, da3_geometry, output) -> io.NodeOutput:
+        output_val = output["output"]
+
+        if output_val in ("depth", "depth_colored"):
+            normalization = output["normalization"]
+            apply_sky_clip = output["apply_sky_clip"]
+            if apply_sky_clip and "sky" not in da3_geometry:
+                raise ValueError(
+                    "apply_sky_clip=True requires a sky tensor in the da3_geometry input, but none is present. "
+                    "Run with Mono/Metric models or set apply_sky_clip=False."
+                )
+            depth = da3_geometry["depth"]
+            sky = da3_geometry.get("sky")
+            if apply_sky_clip and sky is not None:
+                depth = torch.stack([
+                    da3_preprocess.apply_sky_aware_clip(depth[i], sky[i])
+                    for i in range(depth.shape[0])
+                ], dim=0)
+            grey = cls._depth_to_image(depth, sky, normalization)  # (B,H,W,3) greyscale
+            result = _turbo(grey[..., 0]) if output_val == "depth_colored" else grey
+
+        elif output_val == "sky_mask":
+            if "sky" not in da3_geometry:
+                raise ValueError("geometry has no sky output; run with Mono/Metric models.")
+            sky = da3_geometry["sky"]
+            if output["colored"]:
+                result = _turbo(sky)
+            else:
+                result = sky.unsqueeze(-1).expand(*sky.shape, 3).contiguous()
+
+        elif output_val == "confidence":
+            if "confidence" not in da3_geometry:
+                raise ValueError("da3_geometry has no confidence output; run with Small/Base models.")
+            conf = _normalize_confidence(da3_geometry["confidence"])
+            if output["colored"]:
+                result = _turbo(conf)
+            else:
+                result = conf.unsqueeze(-1).expand(*conf.shape, 3).contiguous()
+
+        else:
+            raise ValueError(f"Unknown output mode: {output_val}")
+
+        return io.NodeOutput(result.float())
+
+    @staticmethod
+    def _depth_to_image(depth: torch.Tensor, sky_for_norm: torch.Tensor | None, normalization: str) -> torch.Tensor:
+        """Normalise depth and pack as an (B,H,W,3) image tensor."""
+
+        N = depth.shape[0]
+        if normalization == "v2_style":
+            norm = torch.stack([
+                da3_preprocess.normalize_depth_v2_style(
+                    depth[i], sky_for_norm[i] if sky_for_norm is not None else None)
+                for i in range(N)
+            ], dim=0)
+        elif normalization == "min_max":
+            norm = da3_preprocess.normalize_depth_min_max(depth)
+        else:
+            norm = depth
+
+        out = norm.unsqueeze(-1).repeat(1, 1, 1, 3)
+        if normalization != "raw":
+            out = out.clamp(0.0, 1.0)
+        return out.contiguous()
+
+
+class DA3GeometryToMesh(io.ComfyNode):
+    """Convert a DA3_GEOMETRY packet into a Types.MESH by unprojecting depth and triangulating."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3GeometryToMesh",
+            search_aliases=["da3", "depth anything", "mesh", "geometry", "3d", "triangulate"],
+            display_name="Convert DA3 Geometry to Mesh",
+            category="image/geometry estimation",
+            description="Convert a depth map into a triangulated 3D mesh.",
+            inputs=[
+                DA3Geometry.Input("da3_geometry"),
+                io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert. Per-image vertex counts differ so batches cannot be stacked."),
+                io.Int.Input("decimation", default=1, min=1, max=8, tooltip="Vertex stride. 1 = full resolution, 2 = half, etc."),
+                io.Float.Input("discontinuity_threshold", default=0.04, min=0.0, max=1.0, step=0.01, tooltip="Drop triangles whose 3x3 depth span exceeds this fraction. 0 = off."),
+                io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
+                    tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all, 1 = keep only the single most confident pixel). "
+                        "Used when the geometry has a confidence map (Small/Base models)."),
+                io.Boolean.Input("use_sky_mask", default=True, tooltip="Exclude sky-probability pixels (sky >= 0.5) from the mesh. Used when the geometry has a sky map (Mono/Metric models)."),
+                io.Boolean.Input("texture", default=True, tooltip="Use the source image as a base color texture."),
+            ],
+            outputs=[io.Mesh.Output()],
+        )
+
+    @classmethod
+    def execute(cls, da3_geometry, batch_index, decimation, discontinuity_threshold, confidence_threshold, use_sky_mask, texture) -> io.NodeOutput:
+        depth_all = da3_geometry["depth"]   # (B, H, W)
+        B = depth_all.shape[0]
+        if batch_index >= B:
+            raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.")
+
+        depth = depth_all[batch_index]      # (H, W)
+        H, W = depth.shape
+
+        # NaN/inf depth would propagate silently through unproject and produce an
+        # empty mesh; replace them with 0 here so those pixels are later excluded
+        # by the isfinite check inside triangulate_grid_mesh.
+        depth = depth.clone()
+        n_bad = (~torch.isfinite(depth)).sum().item()
+        if n_bad:
+            logging.getLogger("comfy").warning(
+                f"DA3GeometryToMesh: depth[{batch_index}] has {n_bad} non-finite pixels "
+                f"({100*n_bad/(H*W):.1f}%) - zeroed before unproject."
+            )
+        depth[~torch.isfinite(depth)] = 0.0
+        logging.getLogger("comfy").debug(
+            f"DA3GeometryToMesh: depth[{batch_index}] range "
+            f"[{depth.min():.4g}, {depth.max():.4g}], mean={depth.mean():.4g}"
+        )
+
+        K = _da3_get_K(da3_geometry, batch_index, H, W)
+        points = _da3_unproject(depth, K)   # (H, W, 3) in OpenCV camera space
+
+        # Apply world-to-camera inverse so multi-view frames share a common world frame.
+        E = _da3_get_extrinsic(da3_geometry, batch_index)
+        if E is not None:
+            points = _da3_apply_extrinsic(points, E)
+
+        # Mask invalid pixels by setting them to inf so triangulate_grid_mesh skips them.
+        mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask)
+        # Also exclude pixels where depth was invalid.
+        mask = mask & (depth_all[batch_index] > 0) & torch.isfinite(depth_all[batch_index])
+        points = points.clone()
+        points[~mask] = float('inf')
+
+        verts, faces, uvs = triangulate_grid_mesh(
+            points,
+            decimation=decimation,
+            discontinuity_threshold=discontinuity_threshold,
+            depth=depth,
+        )
+        if verts.shape[0] == 0 or faces.shape[0] == 0:
+            raise ValueError(
+                "DA3GeometryToMesh produced an empty mesh. "
+                "Try raising discontinuity_threshold, lowering confidence_threshold, "
+                "or disabling use_sky_mask."
+            )
+
+        # OpenCV (X right, Y down, Z forward) → glTF (X right, Y up, Z back).
+        # Same transform as MoGePointMapToMesh perspective branch.
+        verts = verts * torch.tensor([1.0, -1.0, -1.0], dtype=verts.dtype)
+        faces = faces[:, [0, 2, 1]].contiguous()
+
+        tex = da3_geometry["image"][batch_index:batch_index + 1] if texture else None
+        mesh = Types.MESH(
+            vertices=verts.unsqueeze(0),
+            faces=faces.unsqueeze(0),
+            uvs=uvs.unsqueeze(0),
+            texture=tex,
+        )
+        return io.NodeOutput(mesh)
+
+
+class DA3GeometryToPointCloud(io.ComfyNode):
+    """Unproject a DA3_GEOMETRY depth map into a filtered DA3_POINT_CLOUD."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3GeometryToPointCloud",
+            search_aliases=["da3", "depth anything", "point cloud", "pointcloud", "3d", "geometry"],
+            display_name="Convert DA3 Geometry to Point Cloud",
+            category="image/geometry estimation",
+            description="Convert a depth map into a 3D point cloud.",
+            inputs=[
+                DA3Geometry.Input("da3_geometry"),
+                io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert."),
+                io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
+                    tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all). Used when the geometry has a confidence map (Small/Base models)."),
+                io.Boolean.Input("use_sky_mask", default=True,
+                    tooltip="Exclude sky-probability pixels (sky >= 0.5). Used when the geometry has a sky map (Mono/Metric models)."),
+                io.Int.Input("downsample", default=1, min=1, max=16,
+                    tooltip="Take every Nth pixel (1 = full resolution). Higher values give fewer points and faster processing."),
+            ],
+            # TODO: add a proper PointCloud output type
+            outputs=[DA3PointCloud.Output(display_name="point_cloud")],
+        )
+
+    @classmethod
+    def execute(cls, da3_geometry, batch_index, confidence_threshold, use_sky_mask, downsample) -> io.NodeOutput:
+        depth_all = da3_geometry["depth"]   # (B, H, W)
+        B = depth_all.shape[0]
+        if batch_index >= B:
+            raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.")
+
+        depth = depth_all[batch_index].clone()  # (H, W)
+        depth[~torch.isfinite(depth)] = 0.0
+        H, W = depth.shape
+
+        K = _da3_get_K(da3_geometry, batch_index, H, W)
+
+        if downsample > 1:
+            depth = depth[::downsample, ::downsample].contiguous()
+            # Scale intrinsics to the downsampled grid.
+            K = K.clone()
+            K[0, :] /= downsample
+            K[1, :] /= downsample
+
+        H_ds, W_ds = depth.shape
+        points = _da3_unproject(depth, K)   # (H_ds, W_ds, 3) in OpenCV camera space
+
+        # Apply world-to-camera inverse so multi-view frames share a common world frame.
+        E = _da3_get_extrinsic(da3_geometry, batch_index)
+        if E is not None:
+            points = _da3_apply_extrinsic(points, E)
+
+        # Rebuild mask at downsampled resolution.
+        mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask)
+        if downsample > 1:
+            mask = mask[::downsample, ::downsample]
+
+        mask = mask & torch.isfinite(depth)
+
+        # OpenCV → glTF: flip Y and Z.
+        points_gltf = points.clone()
+        points_gltf[..., 1] *= -1.0
+        points_gltf[..., 2] *= -1.0
+
+        pts_flat = points_gltf.reshape(-1, 3)[mask.reshape(-1)]
+
+        colors_flat = None
+        if "image" in da3_geometry:
+            img = da3_geometry["image"][batch_index]     # (H, W, 3)
+            if downsample > 1:
+                img = img[::downsample, ::downsample]
+            colors_flat = img.reshape(-1, 3)[mask.reshape(-1)]
+
+        conf_flat = None
+        if "confidence" in da3_geometry:
+            conf = da3_geometry["confidence"][batch_index]   # (H, W)
+            if downsample > 1:
+                conf = conf[::downsample, ::downsample]
+            conf_flat = conf.reshape(-1)[mask.reshape(-1)]
+
+        if pts_flat.shape[0] == 0:
+            raise ValueError(
+                "DA3GeometryToPointCloud produced zero points after filtering. "
+                "Try lowering confidence_threshold or disabling use_sky_mask."
+            )
+
+        return io.NodeOutput({
+            "points": pts_flat,
+            "colors": colors_flat,
+            "confidence": conf_flat,
+        })
+
+
+class DA3Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LoadDA3Model,
+            DA3Inference,
+            DA3Render,
+            DA3GeometryToMesh,
+            # DA3GeometryToPointCloud,  # Keep this commented out for now until we have a proper PointCloud output type
+        ]
+
+
+async def comfy_entrypoint() -> DA3Extension:
+    return DA3Extension()
diff --git a/comfy_extras/nodes_differential_diffusion.py b/comfy_extras/nodes_differential_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd487cdef3e04835f8a5038d6bded37cca342ddf
--- /dev/null
+++ b/comfy_extras/nodes_differential_diffusion.py
@@ -0,0 +1,73 @@
+# code adapted from https://github.com/exx8/differential-diffusion
+
+from typing_extensions import override
+
+import torch
+from comfy_api.latest import ComfyExtension, io
+
+
+class DifferentialDiffusion(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DifferentialDiffusion",
+            search_aliases=["inpaint gradient", "variable denoise strength"],
+            display_name="Differential Diffusion",
+            category="experimental",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input(
+                    "strength",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    optional=True,
+                ),
+            ],
+            outputs=[io.Model.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, strength=1.0) -> io.NodeOutput:
+        model = model.clone()
+        model.set_model_denoise_mask_function(lambda *args, **kwargs: cls.forward(*args, **kwargs, strength=strength))
+        return io.NodeOutput(model)
+
+    @classmethod
+    def forward(cls, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict, strength: float):
+        model = extra_options["model"]
+        step_sigmas = extra_options["sigmas"]
+        sigma_to = model.inner_model.model_sampling.sigma_min
+        if step_sigmas[-1] > sigma_to:
+            sigma_to = step_sigmas[-1]
+        sigma_from = step_sigmas[0]
+
+        ts_from = model.inner_model.model_sampling.timestep(sigma_from)
+        ts_to = model.inner_model.model_sampling.timestep(sigma_to)
+        current_ts = model.inner_model.model_sampling.timestep(sigma[0])
+
+        threshold = (current_ts - ts_to) / (ts_from - ts_to)
+
+        # Generate the binary mask based on the threshold
+        binary_mask = (denoise_mask >= threshold).to(denoise_mask.dtype)
+
+        # Blend binary mask with the original denoise_mask using strength
+        if strength and strength < 1:
+            blended_mask = strength * binary_mask + (1 - strength) * denoise_mask
+            return blended_mask
+        else:
+            return binary_mask
+
+
+class DifferentialDiffusionExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            DifferentialDiffusion,
+        ]
+
+
+async def comfy_entrypoint() -> DifferentialDiffusionExtension:
+    return DifferentialDiffusionExtension()
diff --git a/comfy_extras/nodes_easycache.py b/comfy_extras/nodes_easycache.py
new file mode 100644
index 0000000000000000000000000000000000000000..db0943599e56473172d3e0ef7db02912c164d559
--- /dev/null
+++ b/comfy_extras/nodes_easycache.py
@@ -0,0 +1,530 @@
+from __future__ import annotations
+from typing import TYPE_CHECKING, Union
+from comfy_api.latest import io, ComfyExtension
+import comfy.patcher_extension
+import logging
+import torch
+import comfy.model_patcher
+if TYPE_CHECKING:
+    from uuid import UUID
+
+
+def _extract_tensor(data, output_channels):
+    """Extract tensor from data, handling both single tensors and lists."""
+    if isinstance(data, list):
+        # LTX2 AV tensors: [video, audio]
+        return data[0][:, :output_channels], data[1][:, :output_channels]
+    return data[:, :output_channels], None
+
+
+def easycache_forward_wrapper(executor, *args, **kwargs):
+    # get values from args
+    transformer_options: dict[str] = args[-1]
+    if not isinstance(transformer_options, dict):
+        transformer_options = kwargs.get("transformer_options")
+        if not transformer_options:
+            transformer_options = args[-2]
+    easycache: EasyCacheHolder = transformer_options["easycache"]
+    x, ax = _extract_tensor(args[0], easycache.output_channels)
+    sigmas = transformer_options["sigmas"]
+    uuids = transformer_options["uuids"]
+    if sigmas is not None and easycache.is_past_end_timestep(sigmas):
+        return executor(*args, **kwargs)
+    # prepare next x_prev
+    has_first_cond_uuid = easycache.has_first_cond_uuid(uuids)
+    next_x_prev = x
+    input_change = None
+    do_easycache = easycache.should_do_easycache(sigmas)
+    if do_easycache:
+        easycache.check_metadata(x)
+        # if there isn't a cache diff for current conds, we cannot skip this step
+        can_apply_cache_diff = easycache.can_apply_cache_diff(uuids)
+        # if first cond marked this step for skipping, skip it and use appropriate cached values
+        if easycache.skip_current_step and can_apply_cache_diff:
+            if easycache.verbose:
+                logging.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}")
+            result = easycache.apply_cache_diff(x, uuids)
+            if ax is not None:
+                result_audio = easycache.apply_cache_diff(ax, uuids, is_audio=True)
+                return [result, result_audio]
+            return result
+        if easycache.initial_step:
+            easycache.first_cond_uuid = uuids[0]
+            has_first_cond_uuid = easycache.has_first_cond_uuid(uuids)
+            easycache.initial_step = False
+        if has_first_cond_uuid:
+            if easycache.has_x_prev_subsampled():
+                input_change = (easycache.subsample(x, uuids, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean()
+            if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate():
+                approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
+                easycache.cumulative_change_rate += approx_output_change_rate
+                if easycache.cumulative_change_rate < easycache.reuse_threshold and can_apply_cache_diff:
+                    if easycache.verbose:
+                        logging.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
+                    # other conds should also skip this step, and instead use their cached values
+                    easycache.skip_current_step = True
+                    result = easycache.apply_cache_diff(x, uuids)
+                    if ax is not None:
+                        result_audio = easycache.apply_cache_diff(ax, uuids, is_audio=True)
+                        return [result, result_audio]
+                    return result
+                else:
+                    if easycache.verbose:
+                        logging.info(f"EasyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
+                    easycache.cumulative_change_rate = 0.0
+
+    full_output: torch.Tensor = executor(*args, **kwargs)
+    output, audio_output = _extract_tensor(full_output, easycache.output_channels)
+    if has_first_cond_uuid and easycache.has_output_prev_norm():
+        output_change = (easycache.subsample(output, uuids, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean()
+        if easycache.verbose:
+            output_change_rate = output_change / easycache.output_prev_norm
+            easycache.output_change_rates.append(output_change_rate.item())
+        if easycache.has_relative_transformation_rate():
+            approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
+            easycache.approx_output_change_rates.append(approx_output_change_rate.item())
+            if easycache.verbose:
+                logging.info(f"EasyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}")
+        if input_change is not None:
+            easycache.relative_transformation_rate = output_change / input_change
+        if easycache.verbose:
+            logging.info(f"EasyCache [verbose] - output_change_rate: {output_change_rate}")
+    # TODO: allow cache_diff to be offloaded
+    easycache.update_cache_diff(output, next_x_prev, uuids)
+    if audio_output is not None:
+        easycache.update_cache_diff(audio_output, ax, uuids, is_audio=True)
+    if has_first_cond_uuid:
+        easycache.x_prev_subsampled = easycache.subsample(next_x_prev, uuids)
+        easycache.output_prev_subsampled = easycache.subsample(output, uuids)
+        easycache.output_prev_norm = output.flatten().abs().mean()
+        if easycache.verbose:
+            logging.info(f"EasyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
+    return full_output
+
+def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
+    # get values from args
+    timestep: float = args[1]
+    model_options: dict[str] = args[2]
+    easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"]
+    if easycache.is_past_end_timestep(timestep):
+        return executor(*args, **kwargs)
+    x: torch.Tensor = args[0][:, :easycache.output_channels]
+    # prepare next x_prev
+    next_x_prev = x
+    input_change = None
+    do_easycache = easycache.should_do_easycache(timestep)
+    if do_easycache:
+        easycache.check_metadata(x)
+        if easycache.has_x_prev_subsampled():
+            if easycache.has_x_prev_subsampled():
+                input_change = (easycache.subsample(x, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean()
+            if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate():
+                approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
+                easycache.cumulative_change_rate += approx_output_change_rate
+                if easycache.cumulative_change_rate < easycache.reuse_threshold:
+                    if easycache.verbose:
+                        logging.info(f"LazyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
+                    # other conds should also skip this step, and instead use their cached values
+                    easycache.skip_current_step = True
+                    return easycache.apply_cache_diff(x)
+                else:
+                    if easycache.verbose:
+                        logging.info(f"LazyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
+                    easycache.cumulative_change_rate = 0.0
+    output: torch.Tensor = executor(*args, **kwargs)
+    if easycache.has_output_prev_norm():
+        output_change = (easycache.subsample(output, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean()
+        if easycache.verbose:
+            output_change_rate = output_change / easycache.output_prev_norm
+            easycache.output_change_rates.append(output_change_rate.item())
+        if easycache.has_relative_transformation_rate():
+            approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
+            easycache.approx_output_change_rates.append(approx_output_change_rate.item())
+            if easycache.verbose:
+                logging.info(f"LazyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}")
+        if input_change is not None:
+            easycache.relative_transformation_rate = output_change / input_change
+        if easycache.verbose:
+            logging.info(f"LazyCache [verbose] - output_change_rate: {output_change_rate}")
+    # TODO: allow cache_diff to be offloaded
+    easycache.update_cache_diff(output, next_x_prev)
+    easycache.x_prev_subsampled = easycache.subsample(next_x_prev)
+    easycache.output_prev_subsampled = easycache.subsample(output)
+    easycache.output_prev_norm = output.flatten().abs().mean()
+    if easycache.verbose:
+        logging.info(f"LazyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
+    return output
+
+def easycache_calc_cond_batch_wrapper(executor, *args, **kwargs):
+    model_options = args[-1]
+    easycache: EasyCacheHolder = model_options["transformer_options"]["easycache"]
+    easycache.skip_current_step = False
+    # TODO: check if first_cond_uuid is active at this timestep; otherwise, EasyCache needs to be partially reset
+    return executor(*args, **kwargs)
+
+def easycache_sample_wrapper(executor, *args, **kwargs):
+    """
+    This OUTER_SAMPLE wrapper makes sure easycache is prepped for current run, and all memory usage is cleared at the end.
+    """
+    try:
+        guider = executor.class_obj
+        orig_model_options = guider.model_options
+        guider.model_options = comfy.model_patcher.create_model_options_clone(orig_model_options)
+        # clone and prepare timesteps
+        guider.model_options["transformer_options"]["easycache"] = guider.model_options["transformer_options"]["easycache"].clone().prepare_timesteps(guider.model_patcher.model.model_sampling)
+        easycache: Union[EasyCacheHolder, LazyCacheHolder] = guider.model_options['transformer_options']['easycache']
+        logging.info(f"{easycache.name} enabled - threshold: {easycache.reuse_threshold}, start_percent: {easycache.start_percent}, end_percent: {easycache.end_percent}")
+        return executor(*args, **kwargs)
+    finally:
+        easycache = guider.model_options['transformer_options']['easycache']
+        output_change_rates = easycache.output_change_rates
+        approx_output_change_rates = easycache.approx_output_change_rates
+        if easycache.verbose:
+            logging.info(f"{easycache.name} [verbose] - output_change_rates {len(output_change_rates)}: {output_change_rates}")
+            logging.info(f"{easycache.name} [verbose] - approx_output_change_rates {len(approx_output_change_rates)}: {approx_output_change_rates}")
+        total_steps = len(args[3])-1
+        # catch division by zero for log statement; sucks to crash after all sampling is done
+        try:
+            speedup = total_steps/(total_steps-easycache.total_steps_skipped)
+        except ZeroDivisionError:
+            speedup = 1.0
+        logging.info(f"{easycache.name} - skipped {easycache.total_steps_skipped}/{total_steps} steps ({speedup:.2f}x speedup).")
+        easycache.reset()
+        guider.model_options = orig_model_options
+
+
+class EasyCacheHolder:
+    def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False, output_channels: int=None):
+        self.name = "EasyCache"
+        self.reuse_threshold = reuse_threshold
+        self.start_percent = start_percent
+        self.end_percent = end_percent
+        self.subsample_factor = subsample_factor
+        self.offload_cache_diff = offload_cache_diff
+        self.verbose = verbose
+        # timestep values
+        self.start_t = 0.0
+        self.end_t = 0.0
+        # control values
+        self.relative_transformation_rate: float = None
+        self.cumulative_change_rate = 0.0
+        self.initial_step = True
+        self.skip_current_step = False
+        # cache values
+        self.first_cond_uuid = None
+        self.x_prev_subsampled: torch.Tensor = None
+        self.output_prev_subsampled: torch.Tensor = None
+        self.output_prev_norm: torch.Tensor = None
+        self.uuid_cache_diffs: dict[UUID, torch.Tensor] = {}
+        self.uuid_cache_diffs_audio: dict[UUID, torch.Tensor] = {}
+        self.output_change_rates = []
+        self.approx_output_change_rates = []
+        self.total_steps_skipped = 0
+        # how to deal with mismatched dims
+        self.allow_mismatch = True
+        self.cut_from_start = True
+        self.state_metadata = None
+        self.output_channels = output_channels
+
+    def is_past_end_timestep(self, timestep: float) -> bool:
+        return not (timestep[0] > self.end_t).item()
+
+    def should_do_easycache(self, timestep: float) -> bool:
+        return (timestep[0] <= self.start_t).item()
+
+    def has_x_prev_subsampled(self) -> bool:
+        return self.x_prev_subsampled is not None
+
+    def has_output_prev_subsampled(self) -> bool:
+        return self.output_prev_subsampled is not None
+
+    def has_output_prev_norm(self) -> bool:
+        return self.output_prev_norm is not None
+
+    def has_relative_transformation_rate(self) -> bool:
+        return self.relative_transformation_rate is not None
+
+    def prepare_timesteps(self, model_sampling):
+        self.start_t = model_sampling.percent_to_sigma(self.start_percent)
+        self.end_t = model_sampling.percent_to_sigma(self.end_percent)
+        return self
+
+    def subsample(self, x: torch.Tensor, uuids: list[UUID], clone: bool = True) -> torch.Tensor:
+        batch_offset = x.shape[0] // len(uuids)
+        uuid_idx = uuids.index(self.first_cond_uuid)
+        if self.subsample_factor > 1:
+            to_return = x[uuid_idx*batch_offset:(uuid_idx+1)*batch_offset, ..., ::self.subsample_factor, ::self.subsample_factor]
+            if clone:
+                return to_return.clone()
+            return to_return
+        to_return = x[uuid_idx*batch_offset:(uuid_idx+1)*batch_offset, ...]
+        if clone:
+            return to_return.clone()
+        return to_return
+
+    def can_apply_cache_diff(self, uuids: list[UUID]) -> bool:
+        return all(uuid in self.uuid_cache_diffs for uuid in uuids)
+
+    def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID], is_audio: bool = False):
+        if self.first_cond_uuid in uuids and not is_audio:
+            self.total_steps_skipped += 1
+        cache_diffs = self.uuid_cache_diffs_audio if is_audio else self.uuid_cache_diffs
+        batch_offset = x.shape[0] // len(uuids)
+        for i, uuid in enumerate(uuids):
+            # slice out only what is relevant to this cond
+            batch_slice = [slice(i*batch_offset,(i+1)*batch_offset)]
+            # if cached dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
+            if x.shape[1:] != cache_diffs[uuid].shape[1:]:
+                if not self.allow_mismatch:
+                    raise ValueError(f"Cached dims {self.uuid_cache_diffs[uuid].shape} don't match x dims {x.shape} - this is no good")
+                slicing = []
+                skip_this_dim = True
+                for dim_u, dim_x in zip(cache_diffs[uuid].shape, x.shape):
+                    if skip_this_dim:
+                        skip_this_dim = False
+                        continue
+                    if dim_u != dim_x:
+                        if self.cut_from_start:
+                            slicing.append(slice(dim_x-dim_u, None))
+                        else:
+                            slicing.append(slice(None, dim_u))
+                    else:
+                        slicing.append(slice(None))
+                batch_slice = batch_slice + slicing
+            x[tuple(batch_slice)] += cache_diffs[uuid].to(x.device)
+        return x
+
+    def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID], is_audio: bool = False):
+        cache_diffs = self.uuid_cache_diffs_audio if is_audio else self.uuid_cache_diffs
+        # if output dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
+        if output.shape[1:] != x.shape[1:]:
+            if not self.allow_mismatch:
+                raise ValueError(f"Output dims {output.shape} don't match x dims {x.shape} - this is no good")
+            slicing = []
+            skip_dim = True
+            for dim_o, dim_x in zip(output.shape, x.shape):
+                if not skip_dim and dim_o != dim_x:
+                    if self.cut_from_start:
+                        slicing.append(slice(dim_x-dim_o, None))
+                    else:
+                        slicing.append(slice(None, dim_o))
+                else:
+                    slicing.append(slice(None))
+                skip_dim = False
+            x = x[tuple(slicing)]
+        diff = output - x
+        batch_offset = diff.shape[0] // len(uuids)
+        for i, uuid in enumerate(uuids):
+            cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...]
+
+    def has_first_cond_uuid(self, uuids: list[UUID]) -> bool:
+        return self.first_cond_uuid in uuids
+
+    def check_metadata(self, x: torch.Tensor) -> bool:
+        metadata = (x.device, x.dtype, x.shape[1:])
+        if self.state_metadata is None:
+            self.state_metadata = metadata
+            return True
+        if metadata == self.state_metadata:
+            return True
+        logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
+        self.reset()
+        return False
+
+    def reset(self):
+        self.relative_transformation_rate = 0.0
+        self.cumulative_change_rate = 0.0
+        self.initial_step = True
+        self.skip_current_step = False
+        self.output_change_rates = []
+        self.first_cond_uuid = None
+        del self.x_prev_subsampled
+        self.x_prev_subsampled = None
+        del self.output_prev_subsampled
+        self.output_prev_subsampled = None
+        del self.output_prev_norm
+        self.output_prev_norm = None
+        del self.uuid_cache_diffs
+        self.uuid_cache_diffs = {}
+        del self.uuid_cache_diffs_audio
+        self.uuid_cache_diffs_audio = {}
+        self.total_steps_skipped = 0
+        self.state_metadata = None
+        return self
+
+    def clone(self):
+        return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose, output_channels=self.output_channels)
+
+
+class EasyCacheNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="EasyCache",
+            display_name="EasyCache",
+            description="Native EasyCache implementation.",
+            category="advanced/debug",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model", tooltip="The model to add EasyCache to."),
+                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
+                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache.", advanced=True),
+                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache.", advanced=True),
+                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
+            ],
+            outputs=[
+                io.Model.Output(tooltip="The model with EasyCache."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
+        model = model.clone()
+        model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose, output_channels=model.model.latent_format.latent_channels)
+        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "easycache", easycache_sample_wrapper)
+        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, "easycache", easycache_calc_cond_batch_wrapper)
+        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "easycache", easycache_forward_wrapper)
+        return io.NodeOutput(model)
+
+
+class LazyCacheHolder:
+    def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False, output_channels: int=None):
+        self.name = "LazyCache"
+        self.reuse_threshold = reuse_threshold
+        self.start_percent = start_percent
+        self.end_percent = end_percent
+        self.subsample_factor = subsample_factor
+        self.offload_cache_diff = offload_cache_diff
+        self.verbose = verbose
+        # timestep values
+        self.start_t = 0.0
+        self.end_t = 0.0
+        # control values
+        self.relative_transformation_rate: float = None
+        self.cumulative_change_rate = 0.0
+        self.initial_step = True
+        # cache values
+        self.x_prev_subsampled: torch.Tensor = None
+        self.output_prev_subsampled: torch.Tensor = None
+        self.output_prev_norm: torch.Tensor = None
+        self.cache_diff: torch.Tensor = None
+        self.output_change_rates = []
+        self.approx_output_change_rates = []
+        self.total_steps_skipped = 0
+        self.state_metadata = None
+        self.output_channels = output_channels
+
+    def has_cache_diff(self) -> bool:
+        return self.cache_diff is not None
+
+    def is_past_end_timestep(self, timestep: float) -> bool:
+        return not (timestep[0] > self.end_t).item()
+
+    def should_do_easycache(self, timestep: float) -> bool:
+        return (timestep[0] <= self.start_t).item()
+
+    def has_x_prev_subsampled(self) -> bool:
+        return self.x_prev_subsampled is not None
+
+    def has_output_prev_subsampled(self) -> bool:
+        return self.output_prev_subsampled is not None
+
+    def has_output_prev_norm(self) -> bool:
+        return self.output_prev_norm is not None
+
+    def has_relative_transformation_rate(self) -> bool:
+        return self.relative_transformation_rate is not None
+
+    def prepare_timesteps(self, model_sampling):
+        self.start_t = model_sampling.percent_to_sigma(self.start_percent)
+        self.end_t = model_sampling.percent_to_sigma(self.end_percent)
+        return self
+
+    def subsample(self, x: torch.Tensor, clone: bool = True) -> torch.Tensor:
+        if self.subsample_factor > 1:
+            to_return = x[..., ::self.subsample_factor, ::self.subsample_factor]
+            if clone:
+                return to_return.clone()
+            return to_return
+        if clone:
+            return x.clone()
+        return x
+
+    def apply_cache_diff(self, x: torch.Tensor):
+        self.total_steps_skipped += 1
+        return x + self.cache_diff.to(x.device)
+
+    def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor):
+        self.cache_diff = output - x
+
+    def check_metadata(self, x: torch.Tensor) -> bool:
+        metadata = (x.device, x.dtype, x.shape)
+        if self.state_metadata is None:
+            self.state_metadata = metadata
+            return True
+        if metadata == self.state_metadata:
+            return True
+        logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
+        self.reset()
+        return False
+
+    def reset(self):
+        self.relative_transformation_rate = 0.0
+        self.cumulative_change_rate = 0.0
+        self.initial_step = True
+        self.output_change_rates = []
+        self.approx_output_change_rates = []
+        del self.cache_diff
+        self.cache_diff = None
+        del self.x_prev_subsampled
+        self.x_prev_subsampled = None
+        del self.output_prev_subsampled
+        self.output_prev_subsampled = None
+        del self.output_prev_norm
+        self.output_prev_norm = None
+        self.total_steps_skipped = 0
+        self.state_metadata = None
+        return self
+
+    def clone(self):
+        return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose, output_channels=self.output_channels)
+
+class LazyCacheNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LazyCache",
+            display_name="LazyCache",
+            description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.",
+            category="advanced/debug",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model", tooltip="The model to add LazyCache to."),
+                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
+                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache.", advanced=True),
+                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache.", advanced=True),
+                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
+            ],
+            outputs=[
+                io.Model.Output(tooltip="The model with LazyCache."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
+        model = model.clone()
+        model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose, output_channels=model.model.latent_format.latent_channels)
+        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "lazycache", easycache_sample_wrapper)
+        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "lazycache", lazycache_predict_noise_wrapper)
+        return io.NodeOutput(model)
+
+
+class EasyCacheExtension(ComfyExtension):
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EasyCacheNode,
+            LazyCacheNode,
+        ]
+
+def comfy_entrypoint():
+    return EasyCacheExtension()
diff --git a/comfy_extras/nodes_edit_model.py b/comfy_extras/nodes_edit_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..32905b8939caec4ecdb11b150dfc328a80dd741a
--- /dev/null
+++ b/comfy_extras/nodes_edit_model.py
@@ -0,0 +1,39 @@
+import node_helpers
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class ReferenceLatent(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ReferenceLatent",
+            display_name="Set Reference Latent",
+            category="model/conditioning",
+            description="This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images.",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+                io.Latent.Input("latent", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, conditioning, latent=None) -> io.NodeOutput:
+        if latent is not None:
+            conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [latent["samples"]]}, append=True)
+        return io.NodeOutput(conditioning)
+
+
+class EditModelExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            ReferenceLatent,
+        ]
+
+
+def comfy_entrypoint() -> EditModelExtension:
+    return EditModelExtension()
diff --git a/comfy_extras/nodes_eps.py b/comfy_extras/nodes_eps.py
new file mode 100644
index 0000000000000000000000000000000000000000..0086855cccb4266a4a84b320c1cdcde1389d576f
--- /dev/null
+++ b/comfy_extras/nodes_eps.py
@@ -0,0 +1,172 @@
+import torch
+from typing_extensions import override
+
+from comfy.k_diffusion.sampling import sigma_to_half_log_snr
+from comfy_api.latest import ComfyExtension, io
+
+
+class EpsilonScaling(io.ComfyNode):
+    """
+    Implements the Epsilon Scaling method from 'Elucidating the Exposure Bias in Diffusion Models'
+    (https://arxiv.org/abs/2308.15321v6).
+
+    This method mitigates exposure bias by scaling the predicted noise during sampling,
+    which can significantly improve sample quality. This implementation uses the "uniform schedule"
+    recommended by the paper for its practicality and effectiveness.
+    """
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Epsilon Scaling",
+            category="model/patch/unet",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input(
+                    "scaling_factor",
+                    default=1.005,
+                    min=0.5,
+                    max=1.5,
+                    step=0.001,
+                    display_mode=io.NumberDisplay.number,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, scaling_factor) -> io.NodeOutput:
+        # Prevent division by zero, though the UI's min value should prevent this.
+        if scaling_factor == 0:
+            scaling_factor = 1e-9
+
+        def epsilon_scaling_function(args):
+            """
+            This function is applied after the CFG guidance has been calculated.
+            It recalculates the denoised latent by scaling the predicted noise.
+            """
+            denoised = args["denoised"]
+            x = args["input"]
+
+            noise_pred = x - denoised
+
+            scaled_noise_pred = noise_pred / scaling_factor
+
+            new_denoised = x - scaled_noise_pred
+
+            return new_denoised
+
+        # Clone the model patcher to avoid modifying the original model in place
+        model_clone = model.clone()
+
+        model_clone.set_model_sampler_post_cfg_function(epsilon_scaling_function)
+
+        return io.NodeOutput(model_clone)
+
+
+def compute_tsr_rescaling_factor(
+    snr: torch.Tensor, tsr_k: float, tsr_variance: float
+) -> torch.Tensor:
+    """Compute the rescaling score ratio in Temporal Score Rescaling.
+
+    See equation (6) in https://arxiv.org/pdf/2510.01184v1.
+    """
+    posinf_mask = torch.isposinf(snr)
+    rescaling_factor = (snr * tsr_variance + 1) / (snr * tsr_variance / tsr_k + 1)
+    return torch.where(posinf_mask, tsr_k, rescaling_factor) # when snr → inf, r = tsr_k
+
+
+class TemporalScoreRescaling(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TemporalScoreRescaling",
+            display_name="TSR - Temporal Score Rescaling",
+            category="model/patch/unet",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input(
+                    "tsr_k",
+                    tooltip=(
+                        "Controls the rescaling strength.\n"
+                        "Lower k produces more detailed results; higher k produces smoother results in image generation. Setting k = 1 disables rescaling."
+                    ),
+                    default=0.95,
+                    min=0.01,
+                    max=100.0,
+                    step=0.001,
+                    display_mode=io.NumberDisplay.number,
+                    advanced=True,
+                ),
+                io.Float.Input(
+                    "tsr_sigma",
+                    tooltip=(
+                        "Controls how early rescaling takes effect.\n"
+                        "Larger values take effect earlier."
+                    ),
+                    default=1.0,
+                    min=0.01,
+                    max=100.0,
+                    step=0.001,
+                    display_mode=io.NumberDisplay.number,
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                io.Model.Output(
+                    display_name="patched_model",
+                ),
+            ],
+            description=(
+                "[Post-CFG Function]\n"
+                "TSR - Temporal Score Rescaling (2510.01184)\n\n"
+                "Rescaling the model's score or noise to steer the sampling diversity.\n"
+            ),
+        )
+
+    @classmethod
+    def execute(cls, model, tsr_k, tsr_sigma) -> io.NodeOutput:
+        tsr_variance = tsr_sigma**2
+
+        def temporal_score_rescaling(args):
+            denoised = args["denoised"]
+            x = args["input"]
+            sigma = args["sigma"]
+            curr_model = args["model"]
+
+            # No rescaling (r = 1) or no noise
+            if tsr_k == 1 or sigma == 0:
+                return denoised
+
+            model_sampling = curr_model.current_patcher.get_model_object("model_sampling")
+            half_log_snr = sigma_to_half_log_snr(sigma, model_sampling)
+            snr = (2 * half_log_snr).exp()
+
+            # No rescaling needed (r = 1)
+            if snr == 0:
+                return denoised
+
+            rescaling_r = compute_tsr_rescaling_factor(snr, tsr_k, tsr_variance)
+
+            # Derived from scaled_denoised = (x - r * sigma * noise) / alpha
+            alpha = sigma * half_log_snr.exp()
+            return torch.lerp(x / alpha, denoised, rescaling_r)
+
+        m = model.clone()
+        m.set_model_sampler_post_cfg_function(temporal_score_rescaling)
+        return io.NodeOutput(m)
+
+
+class EpsilonScalingExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EpsilonScaling,
+            TemporalScoreRescaling,
+        ]
+
+
+async def comfy_entrypoint() -> EpsilonScalingExtension:
+    return EpsilonScalingExtension()
diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py
new file mode 100644
index 0000000000000000000000000000000000000000..66235ae582116924b243d415c549aa22a3ffe790
--- /dev/null
+++ b/comfy_extras/nodes_flux.py
@@ -0,0 +1,319 @@
+import node_helpers
+import comfy.utils
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+import comfy.model_management
+import torch
+import math
+import nodes
+import comfy.ldm.flux.math
+
+class CLIPTextEncodeFlux(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeFlux",
+            category="model/conditioning/flux",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
+                io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
+                io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, clip_l, t5xxl, guidance) -> io.NodeOutput:
+        tokens = clip.tokenize(clip_l)
+        tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
+
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}))
+
+    encode = execute  # TODO: remove
+
+class EmptyFlux2LatentImage(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyFlux2LatentImage",
+            display_name="Empty Flux 2 Latent",
+            category="model/latent/flux",
+            inputs=[
+                io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 128, height // 16, width // 16], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples": latent})
+
+class FluxGuidance(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FluxGuidance",
+            category="model/conditioning/flux",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+                io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, conditioning, guidance) -> io.NodeOutput:
+        c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
+        return io.NodeOutput(c)
+
+    append = execute  # TODO: remove
+
+
+class FluxDisableGuidance(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FluxDisableGuidance",
+            category="model/conditioning/flux",
+            description="This node completely disables the guidance embed on Flux and Flux like models",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, conditioning) -> io.NodeOutput:
+        c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
+        return io.NodeOutput(c)
+
+    append = execute  # TODO: remove
+
+
+PREFERRED_KONTEXT_RESOLUTIONS = [
+    (672, 1568),
+    (688, 1504),
+    (720, 1456),
+    (752, 1392),
+    (800, 1328),
+    (832, 1248),
+    (880, 1184),
+    (944, 1104),
+    (1024, 1024),
+    (1104, 944),
+    (1184, 880),
+    (1248, 832),
+    (1328, 800),
+    (1392, 752),
+    (1456, 720),
+    (1504, 688),
+    (1568, 672),
+]
+
+
+class FluxKontextImageScale(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FluxKontextImageScale",
+            category="model/conditioning/flux",
+            description="This node resizes the image to one that is more optimal for flux kontext.",
+            inputs=[
+                io.Image.Input("image"),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image) -> io.NodeOutput:
+        width = image.shape[2]
+        height = image.shape[1]
+        aspect_ratio = width / height
+        _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERRED_KONTEXT_RESOLUTIONS)
+        image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
+        return io.NodeOutput(image)
+
+    scale = execute  # TODO: remove
+
+
+class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FluxKontextMultiReferenceLatentMethod",
+            display_name="Edit Model Reference Method",
+            category="model/conditioning/flux",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+                io.Combo.Input(
+                    "reference_latents_method",
+                    options=["offset", "index", "uxo/uno", "index_timestep_zero"],
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, conditioning, reference_latents_method) -> io.NodeOutput:
+        if "uxo" in reference_latents_method or "uso" in reference_latents_method:
+            reference_latents_method = "uxo"
+        c = node_helpers.conditioning_set_values(conditioning, {"reference_latents_method": reference_latents_method})
+        return io.NodeOutput(c)
+
+    append = execute  # TODO: remove
+
+
+def generalized_time_snr_shift(t, mu: float, sigma: float):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+
+def compute_empirical_mu(image_seq_len: int, num_steps: int) -> float:
+    a1, b1 = 8.73809524e-05, 1.89833333
+    a2, b2 = 0.00016927, 0.45666666
+
+    if image_seq_len > 4300:
+        mu = a2 * image_seq_len + b2
+        return float(mu)
+
+    m_200 = a2 * image_seq_len + b2
+    m_10 = a1 * image_seq_len + b1
+
+    a = (m_200 - m_10) / 190.0
+    b = m_200 - 200.0 * a
+    mu = a * num_steps + b
+
+    return float(mu)
+
+
+def get_schedule(num_steps: int, image_seq_len: int) -> list[float]:
+    mu = compute_empirical_mu(image_seq_len, num_steps)
+    timesteps = torch.linspace(1, 0, num_steps + 1)
+    timesteps = generalized_time_snr_shift(timesteps, mu, 1.0)
+    return timesteps
+
+
+class Flux2Scheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Flux2Scheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Int.Input("steps", default=20, min=1, max=4096),
+                io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=1),
+                io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[
+                io.Sigmas.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, steps, width, height) -> io.NodeOutput:
+        seq_len = (width * height / (16 * 16))
+        sigmas = get_schedule(steps, round(seq_len))
+        return io.NodeOutput(sigmas)
+
+class KV_Attn_Input:
+    def __init__(self):
+        self.cache = {}
+
+    def __call__(self, q, k, v, extra_options, **kwargs):
+        reference_image_num_tokens = extra_options.get("reference_image_num_tokens", [])
+        if len(reference_image_num_tokens) == 0:
+            return {}
+
+        ref_toks = sum(reference_image_num_tokens)
+        cache_key = "{}_{}".format(extra_options["block_type"], extra_options["block_index"])
+        if cache_key in self.cache:
+            kk, vv = self.cache[cache_key]
+
+            # Fix batch size changing.
+            kk = comfy.utils.repeat_to_batch_size(kk, k.shape[0])
+            vv = comfy.utils.repeat_to_batch_size(vv, v.shape[0])
+
+            self.set_cache = False
+            return {"q": q, "k": torch.cat((k, kk), dim=2), "v": torch.cat((v, vv), dim=2)}
+
+        self.cache[cache_key] = (k[:, :, -ref_toks:].clone(), v[:, :, -ref_toks:].clone())
+        self.set_cache = True
+        return {"q": q, "k": k, "v": v}
+
+    def cleanup(self):
+        self.cache = {}
+
+
+class FluxKVCache(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="FluxKVCache",
+            display_name="Flux KV Cache",
+            description="Enables KV Cache optimization for reference images on Flux family models.",
+            category="experimental",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model", tooltip="The model to use KV Cache on."),
+            ],
+            outputs=[
+                io.Model.Output(tooltip="The patched model with KV Cache enabled."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model: io.Model.Type) -> io.NodeOutput:
+        m = model.clone()
+        input_patch_obj = KV_Attn_Input()
+
+        def model_input_patch(inputs):
+            if len(input_patch_obj.cache) > 0:
+                ref_image_tokens = sum(inputs["transformer_options"].get("reference_image_num_tokens", []))
+                if ref_image_tokens > 0:
+                    img = inputs["img"]
+                    inputs["img"] = img[:, :-ref_image_tokens]
+            return inputs
+
+        m.set_model_attn1_patch(input_patch_obj)
+        m.set_model_post_input_patch(model_input_patch)
+        if hasattr(model.model.diffusion_model, "params"):
+            m.add_object_patch("diffusion_model.params.default_ref_method", "index_timestep_zero")
+        else:
+            m.add_object_patch("diffusion_model.default_ref_method", "index_timestep_zero")
+
+        return io.NodeOutput(m)
+
+class FluxExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeFlux,
+            FluxGuidance,
+            FluxDisableGuidance,
+            FluxKontextImageScale,
+            FluxKontextMultiReferenceLatentMethod,
+            EmptyFlux2LatentImage,
+            Flux2Scheduler,
+            FluxKVCache,
+        ]
+
+
+async def comfy_entrypoint() -> FluxExtension:
+    return FluxExtension()
diff --git a/comfy_extras/nodes_frame_interpolation.py b/comfy_extras/nodes_frame_interpolation.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f72f6d1f8ab11af7e76e0f067159ae935925c06
--- /dev/null
+++ b/comfy_extras/nodes_frame_interpolation.py
@@ -0,0 +1,208 @@
+import torch
+from tqdm import tqdm
+from typing_extensions import override
+
+import comfy.model_patcher
+import comfy.utils
+import folder_paths
+from comfy import model_management
+from comfy_extras.frame_interpolation_models.ifnet import IFNet, detect_rife_config
+from comfy_extras.frame_interpolation_models.film_net import FILMNet
+from comfy_api.latest import ComfyExtension, io
+
+FrameInterpolationModel = io.Custom("INTERP_MODEL")
+
+
+class FrameInterpolationModelLoader(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FrameInterpolationModelLoader",
+            display_name="Load Frame Interpolation Model",
+            category="model/loaders",
+            inputs=[
+                io.Combo.Input("model_name", options=folder_paths.get_filename_list("frame_interpolation"),
+                               tooltip="Select a frame interpolation model to load. Models must be placed in the 'frame_interpolation' folder."),
+            ],
+            outputs=[
+                FrameInterpolationModel.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_name) -> io.NodeOutput:
+        model_path = folder_paths.get_full_path_or_raise("frame_interpolation", model_name)
+        sd = comfy.utils.load_torch_file(model_path, safe_load=True)
+
+        model = cls._detect_and_load(sd)
+        dtype = torch.float16 if model_management.should_use_fp16(model_management.get_torch_device()) else torch.float32
+        model.eval().to(dtype)
+        patcher = comfy.model_patcher.CoreModelPatcher(
+            model,
+            load_device=model_management.get_torch_device(),
+            offload_device=model_management.unet_offload_device(),
+        )
+        return io.NodeOutput(patcher)
+
+    @classmethod
+    def _detect_and_load(cls, sd):
+        # Try FILM
+        if "extract.extract_sublevels.convs.0.0.conv.weight" in sd:
+            model = FILMNet()
+            model.load_state_dict(sd)
+            return model
+
+        # Try RIFE (needs key remapping for raw checkpoints)
+        sd = comfy.utils.state_dict_prefix_replace(sd, {"module.": "", "flownet.": ""})
+        key_map = {}
+        for k in sd:
+            for i in range(5):
+                if k.startswith(f"block{i}."):
+                    key_map[k] = f"blocks.{i}.{k[len(f'block{i}.'):]}"
+        if key_map:
+            sd = {key_map.get(k, k): v for k, v in sd.items()}
+        sd = {k: v for k, v in sd.items() if not k.startswith(("teacher.", "caltime."))}
+
+        try:
+            head_ch, channels = detect_rife_config(sd)
+        except (KeyError, ValueError):
+            raise ValueError("Unrecognized frame interpolation model format")
+        model = IFNet(head_ch=head_ch, channels=channels)
+        model.load_state_dict(sd)
+        return model
+
+
+class FrameInterpolate(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FrameInterpolate",
+            display_name="Run Frame Interpolation Model",
+            category="video",
+            search_aliases=["rife", "film", "frame interpolation", "slow motion", "interpolate frames", "vfi"],
+            inputs=[
+                FrameInterpolationModel.Input("interp_model"),
+                io.Image.Input("images"),
+                io.Int.Input("multiplier", default=2, min=2, max=16),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, interp_model, images, multiplier) -> io.NodeOutput:
+        offload_device = model_management.intermediate_device()
+
+        num_frames = images.shape[0]
+        if num_frames < 2 or multiplier < 2:
+            return io.NodeOutput(images)
+
+        device = interp_model.load_device
+        dtype = interp_model.model_dtype()
+        inference_model = interp_model.model
+        activation_mem = inference_model.memory_used_forward(images.shape, dtype)
+        model_management.load_models_gpu([interp_model], memory_required=activation_mem)
+        align = getattr(inference_model, "pad_align", 1)
+        H, W = images.shape[1], images.shape[2]
+
+        # Prepare a single padded frame on device for determining output dimensions
+        def prepare_frame(idx):
+            frame = images[idx:idx + 1].movedim(-1, 1).to(dtype=dtype, device=device)
+            if align > 1:
+                from comfy.ldm.common_dit import pad_to_patch_size
+                frame = pad_to_patch_size(frame, (align, align), padding_mode="reflect")
+            return frame
+
+        # Count total interpolation passes for progress bar
+        total_pairs = num_frames - 1
+        num_interp = multiplier - 1
+        total_steps = total_pairs * num_interp
+        pbar = comfy.utils.ProgressBar(total_steps)
+        tqdm_bar = tqdm(total=total_steps, desc="Frame interpolation")
+
+        batch = num_interp  # reduced on OOM and persists across pairs (same resolution = same limit)
+        t_values = [t / multiplier for t in range(1, multiplier)]
+
+        out_dtype = model_management.intermediate_dtype()
+        total_out_frames = total_pairs * multiplier + 1
+        result = torch.empty((total_out_frames, 3, H, W), dtype=out_dtype, device=offload_device)
+        result[0] = images[0].movedim(-1, 0).to(out_dtype)
+        out_idx = 1
+
+        # Pre-compute timestep tensor on device (padded dimensions needed)
+        sample = prepare_frame(0)
+        pH, pW = sample.shape[2], sample.shape[3]
+        ts_full = torch.tensor(t_values, device=device, dtype=dtype).reshape(num_interp, 1, 1, 1)
+        ts_full = ts_full.expand(-1, 1, pH, pW)
+        del sample
+
+        multi_fn = getattr(inference_model, "forward_multi_timestep", None)
+        feat_cache = {}
+        prev_frame = None
+
+        try:
+            for i in range(total_pairs):
+                img0_single = prev_frame if prev_frame is not None else prepare_frame(i)
+                img1_single = prepare_frame(i + 1)
+                prev_frame = img1_single
+
+                # Cache features: img1 of pair N becomes img0 of pair N+1
+                feat_cache["img0"] = feat_cache.pop("next") if "next" in feat_cache else inference_model.extract_features(img0_single)
+                feat_cache["img1"] = inference_model.extract_features(img1_single)
+                feat_cache["next"] = feat_cache["img1"]
+
+                used_multi = False
+                if multi_fn is not None:
+                    # Models with timestep-independent flow can compute it once for all timesteps
+                    try:
+                        mids = multi_fn(img0_single, img1_single, t_values, cache=feat_cache)
+                        result[out_idx:out_idx + num_interp] = mids[:, :, :H, :W].to(out_dtype)
+                        out_idx += num_interp
+                        pbar.update(num_interp)
+                        tqdm_bar.update(num_interp)
+                        used_multi = True
+                    except model_management.OOM_EXCEPTION:
+                        model_management.soft_empty_cache()
+                        multi_fn = None  # fall through to single-timestep path
+
+                if not used_multi:
+                    j = 0
+                    while j < num_interp:
+                        b = min(batch, num_interp - j)
+                        try:
+                            img0 = img0_single.expand(b, -1, -1, -1)
+                            img1 = img1_single.expand(b, -1, -1, -1)
+                            mids = inference_model(img0, img1, timestep=ts_full[j:j + b], cache=feat_cache)
+                            result[out_idx:out_idx + b] = mids[:, :, :H, :W].to(out_dtype)
+                            out_idx += b
+                            pbar.update(b)
+                            tqdm_bar.update(b)
+                            j += b
+                        except model_management.OOM_EXCEPTION:
+                            if batch <= 1:
+                                raise
+                            batch = max(1, batch // 2)
+                            model_management.soft_empty_cache()
+
+                result[out_idx] = images[i + 1].movedim(-1, 0).to(out_dtype)
+                out_idx += 1
+        finally:
+            tqdm_bar.close()
+
+        # BCHW -> BHWC
+        result = result.movedim(1, -1).clamp_(0.0, 1.0)
+        return io.NodeOutput(result)
+
+
+class FrameInterpolationExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            FrameInterpolationModelLoader,
+            FrameInterpolate,
+        ]
+
+
+async def comfy_entrypoint() -> FrameInterpolationExtension:
+    return FrameInterpolationExtension()
diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py
new file mode 100644
index 0000000000000000000000000000000000000000..61434badef57fa1b911ae6ac3923c99ad6f82c3d
--- /dev/null
+++ b/comfy_extras/nodes_freelunch.py
@@ -0,0 +1,138 @@
+#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)
+
+import torch
+import logging
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO
+
+def Fourier_filter(x, threshold, scale):
+    # FFT
+    x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))
+    x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))
+
+    B, C, H, W = x_freq.shape
+    mask = torch.ones((B, C, H, W), device=x.device)
+
+    crow, ccol = H // 2, W //2
+    mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
+    x_freq = x_freq * mask
+
+    # IFFT
+    x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))
+    x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real
+
+    return x_filtered.to(x.dtype)
+
+
+class FreeU(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="FreeU",
+            category="model/patch/unet",
+            inputs=[
+                IO.Model.Input("model"),
+                IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
+            ],
+            outputs=[
+                IO.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, b1, b2, s1, s2) -> IO.NodeOutput:
+        model_channels = model.model.model_config.unet_config["model_channels"]
+        scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
+        on_cpu_devices = {}
+
+        def output_block_patch(h, hsp, transformer_options):
+            scale = scale_dict.get(int(h.shape[1]), None)
+            if scale is not None:
+                h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0]
+                if hsp.device not in on_cpu_devices:
+                    try:
+                        hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
+                    except:
+                        logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
+                        on_cpu_devices[hsp.device] = True
+                        hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
+                else:
+                    hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
+
+            return h, hsp
+
+        m = model.clone()
+        m.set_model_output_block_patch(output_block_patch)
+        return IO.NodeOutput(m)
+
+    patch = execute  # TODO: remove
+
+
+class FreeU_V2(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="FreeU_V2",
+            category="model/patch/unet",
+            inputs=[
+                IO.Model.Input("model"),
+                IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
+            ],
+            outputs=[
+                IO.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, b1, b2, s1, s2) -> IO.NodeOutput:
+        model_channels = model.model.model_config.unet_config["model_channels"]
+        scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
+        on_cpu_devices = {}
+
+        def output_block_patch(h, hsp, transformer_options):
+            scale = scale_dict.get(int(h.shape[1]), None)
+            if scale is not None:
+                hidden_mean = h.mean(1).unsqueeze(1)
+                B = hidden_mean.shape[0]
+                hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
+                hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
+                hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)
+
+                h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1)
+
+                if hsp.device not in on_cpu_devices:
+                    try:
+                        hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
+                    except:
+                        logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
+                        on_cpu_devices[hsp.device] = True
+                        hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
+                else:
+                    hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
+
+            return h, hsp
+
+        m = model.clone()
+        m.set_model_output_block_patch(output_block_patch)
+        return IO.NodeOutput(m)
+
+    patch = execute  # TODO: remove
+
+
+class FreelunchExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            FreeU,
+            FreeU_V2,
+        ]
+
+
+async def comfy_entrypoint() -> FreelunchExtension:
+    return FreelunchExtension()
diff --git a/comfy_extras/nodes_fresca.py b/comfy_extras/nodes_fresca.py
new file mode 100644
index 0000000000000000000000000000000000000000..533bab56e1ed1c60b84ad4865fe27b18113ede6b
--- /dev/null
+++ b/comfy_extras/nodes_fresca.py
@@ -0,0 +1,115 @@
+# Code based on https://github.com/WikiChao/FreSca (MIT License)
+import torch
+import torch.fft as fft
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
+    """
+    Apply frequency-dependent scaling to an image tensor using Fourier transforms.
+
+    Parameters:
+        x:           Input tensor of shape (..., H, W)
+        scale_low:   Scaling factor for low-frequency components (default: 1.0)
+        scale_high:  Scaling factor for high-frequency components (default: 1.5)
+        freq_cutoff: Number of frequency indices around center to consider as low-frequency (default: 20)
+
+    Returns:
+        x_filtered: Filtered version of x in spatial domain with frequency-specific scaling applied.
+    """
+    # Preserve input dtype and device
+    dtype, device = x.dtype, x.device
+
+    # Convert to float32 for FFT computations
+    x = x.to(torch.float32)
+
+    # 1) Apply FFT and shift low frequencies to center
+    x_freq = fft.fftn(x, dim=(-2, -1))
+    x_freq = fft.fftshift(x_freq, dim=(-2, -1))
+
+    # Initialize mask with high-frequency scaling factor
+    mask = torch.ones(x_freq.shape, device=device) * scale_high
+    m = mask
+    for d in range(2):
+        dim = len(x_freq.shape) - 2 + d
+        cc = x_freq.shape[dim] // 2
+        f_c = min(freq_cutoff, cc)
+        m = m.narrow(dim, cc - f_c, f_c * 2)
+
+    # Apply low-frequency scaling factor to center region
+    m[:] = scale_low
+
+    # 3) Apply frequency-specific scaling
+    x_freq = x_freq * mask
+
+    # 4) Convert back to spatial domain
+    x_freq = fft.ifftshift(x_freq, dim=(-2, -1))
+    x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real
+
+    # 5) Restore original dtype
+    x_filtered = x_filtered.to(dtype)
+
+    return x_filtered
+
+
+class FreSca(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FreSca",
+            search_aliases=["frequency guidance"],
+            display_name="FreSca",
+            category="experimental",
+            description="Applies frequency-dependent scaling to the guidance",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,
+                               tooltip="Scaling factor for low-frequency components", advanced=True),
+                io.Float.Input("scale_high", default=1.25, min=0, max=10, step=0.01,
+                               tooltip="Scaling factor for high-frequency components", advanced=True),
+                io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1,
+                             tooltip="Number of frequency indices around center to consider as low-frequency", advanced=True),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, scale_low, scale_high, freq_cutoff):
+        def custom_cfg_function(args):
+            conds_out = args["conds_out"]
+            if len(conds_out) <= 1 or None in args["conds"][:2]:
+                return conds_out
+            cond = conds_out[0]
+            uncond = conds_out[1]
+
+            guidance = cond - uncond
+            filtered_guidance = Fourier_filter(
+                guidance,
+                scale_low=scale_low,
+                scale_high=scale_high,
+                freq_cutoff=freq_cutoff,
+            )
+            filtered_cond = filtered_guidance + uncond
+
+            return [filtered_cond, uncond] + conds_out[2:]
+
+        m = model.clone()
+        m.set_model_sampler_pre_cfg_function(custom_cfg_function)
+
+        return io.NodeOutput(m)
+
+
+class FreScaExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            FreSca,
+        ]
+
+
+async def comfy_entrypoint() -> FreScaExtension:
+    return FreScaExtension()
diff --git a/comfy_extras/nodes_gaussian_splat.py b/comfy_extras/nodes_gaussian_splat.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcb72f8fcf3a9128b39a26bf2bdc427032a1339a
--- /dev/null
+++ b/comfy_extras/nodes_gaussian_splat.py
@@ -0,0 +1,1673 @@
+# Generic utility nodes for the SPLAT type (3D gaussian splats)
+
+import gzip
+import logging
+import math
+import struct
+from io import BytesIO
+
+import numpy as np
+import torch
+from typing_extensions import override
+from scipy.ndimage import map_coordinates, minimum as _ndi_minimum, maximum as _ndi_maximum
+from scipy.sparse import coo_matrix
+from scipy.sparse.csgraph import connected_components
+
+import comfy.model_management
+import comfy.utils
+from comfy_api.latest import ComfyExtension, IO, Types
+from comfy_extras.nodes_save_3d import pack_variable_mesh_batch
+from server import PromptServer
+
+_C0 = 0.28209479177387814  # SH band-0 constant: DC coefficient -> base RGB
+
+
+def _srgb_to_linear(c):
+    return torch.where(c <= 0.04045, c / 12.92, ((c.clamp_min(0) + 0.055) / 1.055) ** 2.4)
+
+
+def _linear_to_srgb(c):
+    return torch.where(c <= 0.0031308, c * 12.92, 1.055 * c.clamp_min(0) ** (1 / 2.4) - 0.055)
+
+
+def _real_len(g: Types.SPLAT, i: int) -> int:
+    # Real splat count of batch item i (honors variable-length `counts`).
+    return int(g.counts[i].item()) if g.counts is not None else g.positions.shape[1]
+
+
+def _hex_to_rgb(h: str) -> tuple[float, float, float]:
+    # "#RRGGBB" -> (r,g,b) in [0,1]; falls back to black.
+    h = h.lstrip("#")
+    if len(h) != 6:
+        return (0.0, 0.0, 0.0)
+    return tuple(int(h[i:i + 2], 16) / 255.0 for i in (0, 2, 4))
+
+
+def _quantile(x, q):
+    # torch.quantile errors above 2**24 elements; stride-subsample large inputs for the estimate.
+    lim = 1 << 24
+    if x.numel() > lim:
+        x = x[:: x.numel() // lim + 1]
+    return torch.quantile(x, q)
+
+
+def _gaussian_ply_bytes(positions, scales, rotations, opacities, sh) -> bytes:
+    """Serialize render-ready gaussian tensors as a binary 3DGS .ply.
+
+    positions (N,3) world; scales (N,3) linear; rotations (N,4) quat wxyz; opacities (N,1) in [0,1];
+    sh (N,K,3) SH coefficients. Activated values are inverted to the standard 3D gaussian splat storage convention
+    (log scale, logit opacity).
+    """
+    xyz = positions.cpu().numpy().astype(np.float32)
+    n = xyz.shape[0]
+    if n == 0:
+        raise ValueError("SplatToFile3D: gaussian is empty")
+    normals = np.zeros_like(xyz)
+    f = sh.cpu().numpy().astype(np.float32)                  # (N, K, 3)
+    f_dc = f[:, 0, :]                                        # (N, 3)
+    f_rest = f[:, 1:, :].transpose(0, 2, 1).reshape(n, -1)   # (N, 3*(K-1)) channel-major
+    op = opacities.cpu().numpy().astype(np.float32).reshape(n, 1).clip(1e-6, 1 - 1e-6)
+    op = np.log(op / (1.0 - op))                             # inverse sigmoid (logit)
+    scale = np.log(scales.cpu().numpy().astype(np.float32).clip(min=1e-8))
+    rot = rotations.cpu().numpy().astype(np.float32)         # (N, 4)
+
+    attrs = (['x', 'y', 'z', 'nx', 'ny', 'nz']
+             + [f'f_dc_{i}' for i in range(3)]
+             + [f'f_rest_{i}' for i in range(f_rest.shape[1])]
+             + ['opacity'] + [f'scale_{i}' for i in range(3)] + [f'rot_{i}' for i in range(4)])
+    elements = np.empty(n, dtype=[(a, 'f4') for a in attrs])
+    elements[:] = list(map(tuple, np.concatenate([xyz, normals, f_dc, f_rest, op, scale, rot], axis=1)))
+
+    header = "ply\nformat binary_little_endian 1.0\n" + f"element vertex {n}\n"
+    header += "".join(f"property float {a}\n" for a in attrs) + "end_header\n"
+    return header.encode('ascii') + elements.tobytes()
+
+
+# .ksplat (mkkellogg SplatBuffer) level 0, SH degree 0: 4096-byte header, one 1024-byte section header,
+# then N 44-byte records. Bucketing/quantization only exist at levels >= 1. See SplatBuffer.js.
+_KSPLAT_HEADER_BYTES = 4096
+_KSPLAT_SECTION_HEADER_BYTES = 1024
+_KSPLAT_BYTES_PER_SPLAT = 44  # center 12 + scale 12 + rotation 16 + color(RGBA u8) 4
+_KSPLAT_VERSION = (0, 1)      # SplatBuffer CurrentMajor/MinorVersion
+
+
+def _gaussian_ksplat_bytes(positions, scales, rotations, opacities, sh) -> bytes:
+    """Serialize gaussian tensors as a level-0, SH degree-0 .ksplat (linear scale, opacity in color alpha).
+
+    positions (N,3) world; scales (N,3) linear; rotations (N,4) wxyz; opacities (N,1) in [0,1]; sh (N,K,3).
+    """
+    xyz = positions.cpu().numpy().astype(np.float32)
+    n = xyz.shape[0]
+    if n == 0:
+        raise ValueError("SplatToFile3D: gaussian is empty")
+    scale = scales.cpu().numpy().astype(np.float32)
+    rot = rotations.cpu().numpy().astype(np.float32)                      # wxyz, mirrors the .ply rot order
+    rot = rot / np.linalg.norm(rot, axis=1, keepdims=True).clip(1e-12)
+    rgb = np.clip(sh[:, 0, :].cpu().numpy().astype(np.float32) * _C0 + 0.5, 0, 1)
+    op = opacities.cpu().numpy().astype(np.float32).reshape(n, 1).clip(0, 1)
+    rgba = np.round(np.concatenate([rgb, op], axis=1) * 255.0).astype(np.uint8)   # (N, 4) RGBA
+
+    # 44-byte record: float center(3) + scale(3) + rot(4), then uint8 rgba(4).
+    floats = np.concatenate([xyz, scale, rot], axis=1).astype('<f4')     # (N, 10)
+    rec = np.empty(n, dtype=[('f', '<f4', 10), ('c', 'u1', 4)])
+    rec['f'] = floats
+    rec['c'] = rgba
+    splat_data = rec.tobytes()
+
+    header = bytearray(_KSPLAT_HEADER_BYTES)
+    header[0] = _KSPLAT_VERSION[0]
+    header[1] = _KSPLAT_VERSION[1]
+    struct.pack_into('<I', header, 4, 1)        # maxSectionCount
+    struct.pack_into('<I', header, 8, 1)        # sectionCount
+    struct.pack_into('<I', header, 12, n)       # maxSplatCount
+    struct.pack_into('<I', header, 16, n)       # splatCount
+    struct.pack_into('<H', header, 20, 0)       # compressionLevel
+    struct.pack_into('<fff', header, 24, 0.0, 0.0, 0.0)   # sceneCenter
+    struct.pack_into('<ff', header, 36, 0.0, 0.0)         # min/max SH coeff (unused at degree 0)
+
+    section = bytearray(_KSPLAT_SECTION_HEADER_BYTES)
+    struct.pack_into('<I', section, 0, n)       # splatCount
+    struct.pack_into('<I', section, 4, n)       # maxSplatCount
+    # offsets 8..24: bucketSize/bucketCount/bucketBlockSize/bucketStorageSizeBytes/compressionScaleRange = 0
+    struct.pack_into('<I', section, 28, n * _KSPLAT_BYTES_PER_SPLAT)   # storageSizeBytes
+    struct.pack_into('<I', section, 32, 0)      # fullBucketCount
+    struct.pack_into('<I', section, 36, 0)      # partiallyFilledBucketCount
+    struct.pack_into('<H', section, 40, 0)      # sphericalHarmonicsDegree
+
+    return bytes(header) + bytes(section) + splat_data
+
+
+# .spz (Niantic) version 2, gzip-wrapped, SH degree 0: a 16-byte header then per-attribute arrays
+# (positions 24-bit fixed point, then 1B alpha, 3B color, 3B scale, 3B rotation per splat). The
+# quantizations below invert spark's SpzReader decode formulas.
+_SPZ_MAGIC = 0x5053474E               # "NGSP"
+_SPZ_VERSION = 2
+_SPZ_FRACTIONAL_BITS = 12             # position fixed-point precision (~0.24mm at unit scale)
+_SPZ_COLOR_SCALE = _C0 / 0.15         # contrast factor applied when decoding color bytes
+
+
+def _gaussian_spz_bytes(positions, scales, rotations, opacities, sh) -> bytes:
+    """Serialize gaussian tensors as a gzip-compressed .spz (Niantic v2, SH degree 0, base color only).
+
+    positions (N,3) world; scales (N,3) linear; rotations (N,4) wxyz; opacities (N,1) in [0,1]; sh (N,K,3).
+    """
+    xyz = positions.cpu().numpy().astype(np.float32)
+    n = xyz.shape[0]
+    if n == 0:
+        raise ValueError("SplatToFile3D: gaussian is empty")
+
+    # Positions: fixed point, masked to 24 bits, little-endian 3-byte words.
+    fixed = 1 << _SPZ_FRACTIONAL_BITS
+    qi = np.clip(np.round(xyz * fixed), -(1 << 23), (1 << 23) - 1).astype(np.int32)
+    qu = (qi & 0xFFFFFF).astype(np.uint32)
+    pos = np.stack([qu & 0xFF, (qu >> 8) & 0xFF, (qu >> 16) & 0xFF], axis=-1).reshape(n, 9).astype(np.uint8)
+
+    alpha = np.round(opacities.cpu().numpy().astype(np.float32).reshape(n) * 255.0).clip(0, 255).astype(np.uint8)
+
+    rgb = sh[:, 0, :].cpu().numpy().astype(np.float32) * _C0 + 0.5
+    col = np.round(((rgb - 0.5) / _SPZ_COLOR_SCALE + 0.5) * 255.0).clip(0, 255).astype(np.uint8)   # (N,3)
+
+    sln = np.log(scales.cpu().numpy().astype(np.float32).clip(min=1e-9))
+    scb = np.round((sln + 10.0) * 16.0).clip(0, 255).astype(np.uint8)     # (N,3) inverts exp(b/16-10)
+
+    rot = rotations.cpu().numpy().astype(np.float32)                      # wxyz
+    rot = rot / np.linalg.norm(rot, axis=1, keepdims=True).clip(1e-12)
+    rot[rot[:, 0] < 0] *= -1.0                                            # canonical w >= 0 (w dropped on decode)
+    rotb = np.round((rot[:, 1:4] + 1.0) * 127.5).clip(0, 255).astype(np.uint8)   # (N,3) x,y,z
+
+    header = bytearray(16)
+    struct.pack_into('<I', header, 0, _SPZ_MAGIC)
+    struct.pack_into('<I', header, 4, _SPZ_VERSION)
+    struct.pack_into('<I', header, 8, n)
+    header[12] = 0                       # shDegree
+    header[13] = _SPZ_FRACTIONAL_BITS
+    header[14] = 0                       # flags
+    header[15] = 0                       # reserved
+
+    raw = (bytes(header) + pos.tobytes() + alpha.tobytes()
+           + col.tobytes() + scb.tobytes() + rotb.tobytes())
+    return gzip.compress(raw)
+
+
+# ---- Readers: splat file bytes -> (positions, scales linear, rotations wxyz, opacities [0,1], sh (N,K,3)) ----
+# Inverse of the writers above and of spark's loaders. ksplat/splat/spz carry base color only (SH degree 0
+# -> K=1); .ply round-trips full SH. None of the formats flip axes, so import is the identity of export.
+_PLY_DTYPES = {'char': 'i1', 'uchar': 'u1', 'short': 'i2', 'ushort': 'u2', 'int': 'i4', 'uint': 'u4',
+               'float': 'f4', 'double': 'f8', 'int8': 'i1', 'uint8': 'u1', 'int16': 'i2', 'uint16': 'u2',
+               'int32': 'i4', 'uint32': 'u4', 'float32': 'f4', 'float64': 'f8'}
+_KSPLAT_COMPRESSION = {  # level -> (bytesPerCenter, scale, rotation, color, shComponent, defaultScaleRange)
+    0: (12, 12, 16, 4, 4, 1), 1: (6, 6, 8, 4, 2, 32767), 2: (6, 6, 8, 4, 1, 32767)}
+_KSPLAT_SH_COMPONENTS = {0: 0, 1: 9, 2: 24, 3: 45}
+
+
+def _rgb_to_sh_dc(rgb):
+    return ((np.asarray(rgb, np.float32) - 0.5) / _C0)[:, None, :]   # (N,3) base color -> (N,1,3) SH DC
+
+
+def _norm_quat(q):
+    return q / np.linalg.norm(q, axis=1, keepdims=True).clip(1e-12)
+
+
+def _parse_ply_gaussian(data: bytes):
+    end = data.find(b'end_header')
+    if end < 0:
+        raise ValueError("File3DToSplat: not a PLY (missing end_header)")
+    header = data[:end].decode('ascii', 'replace')
+    body = end + len(b'end_header')
+    body += 2 if data[body:body + 2] == b'\r\n' else 1
+    count, props, in_vertex = 0, [], False
+    for line in header.splitlines():
+        p = line.split()
+        if not p:
+            continue
+        if p[0] == 'format' and p[1] != 'binary_little_endian':
+            raise ValueError(f"File3DToSplat: unsupported PLY format '{p[1]}' (need binary_little_endian)")
+        if p[0] == 'element':
+            in_vertex = p[1] == 'vertex'
+            if in_vertex:
+                count = int(p[2])
+        elif p[0] == 'property' and in_vertex:
+            if p[1] == 'list':
+                raise ValueError("File3DToSplat: PLY vertex has list properties (unsupported)")
+            props.append((p[2], '<' + _PLY_DTYPES[p[1]]))
+    arr = np.frombuffer(data, np.dtype(props), count=count, offset=body)
+    names = arr.dtype.names
+    c = lambda k: arr[k].astype(np.float32)
+    n = count
+
+    xyz = np.stack([c('x'), c('y'), c('z')], 1)
+    if 'scale_0' in names:
+        scale = np.exp(np.stack([c('scale_0'), c('scale_1'), c('scale_2')], 1))   # 3DGS stores log scale
+    else:
+        scale = np.full((n, 3), 0.01, np.float32)
+    if 'rot_0' in names:
+        rot = _norm_quat(np.stack([c('rot_0'), c('rot_1'), c('rot_2'), c('rot_3')], 1))   # wxyz
+    else:
+        rot = np.tile(np.array([1, 0, 0, 0], np.float32), (n, 1))
+    opacity = 1.0 / (1.0 + np.exp(-c('opacity'))) if 'opacity' in names else np.ones(n, np.float32)
+
+    if 'f_dc_0' in names:
+        dc = np.stack([c('f_dc_0'), c('f_dc_1'), c('f_dc_2')], 1)                 # (N,3)
+        rest = sorted((k for k in names if k.startswith('f_rest_')), key=lambda s: int(s.split('_')[-1]))
+        if rest:
+            r = np.stack([c(k) for k in rest], 1)                                 # (N, 3*(K-1)) channel-major
+            kk = r.shape[1] // 3 + 1
+            r = r.reshape(n, 3, kk - 1).transpose(0, 2, 1)                        # -> (N, K-1, 3)
+            sh = np.concatenate([dc[:, None, :], r], 1)
+        else:
+            sh = dc[:, None, :]
+    elif 'red' in names:
+        sh = _rgb_to_sh_dc(np.stack([c('red'), c('green'), c('blue')], 1) / 255.0)
+    else:
+        sh = np.zeros((n, 1, 3), np.float32)
+    return xyz, scale, rot, opacity, sh
+
+
+def _parse_splat_gaussian(data: bytes):
+    # antimatter15 .splat: 32-byte records (f32 xyz, f32 scale, u8 rgba, u8 quat as (b-128)/128 wxyz).
+    if len(data) % 32 != 0:
+        raise ValueError("File3DToSplat: .splat size is not a multiple of 32 bytes")
+    rec = np.frombuffer(data, np.dtype([('xyz', '<f4', 3), ('scale', '<f4', 3),
+                                        ('rgba', 'u1', 4), ('quat', 'u1', 4)]))
+    rgba = rec['rgba'].astype(np.float32) / 255.0
+    rot = _norm_quat((rec['quat'].astype(np.float32) - 128.0) / 128.0)            # wxyz
+    return (rec['xyz'].astype(np.float32), rec['scale'].astype(np.float32), rot,
+            rgba[:, 3].copy(), _rgb_to_sh_dc(rgba[:, :3]))
+
+
+def _parse_ksplat_gaussian(data: bytes):
+    # mkkellogg SplatBuffer: 4096-byte header, N section headers, then per-section splat data. Supports
+    # levels 0 (float) / 1 (half + bucketed positions) / 2 (half, uint8 SH). SH is skipped (base color kept).
+    if data[0] != 0:
+        raise ValueError(f"File3DToSplat: unsupported .ksplat version {data[0]}.{data[1]}")
+    max_sections = struct.unpack_from('<I', data, 4)[0]
+    level = struct.unpack_from('<H', data, 20)[0]
+    if level not in _KSPLAT_COMPRESSION:
+        raise ValueError(f"File3DToSplat: invalid .ksplat compression level {level}")
+    bc, bs, br, bcol, bshc, default_range = _KSPLAT_COMPRESSION[level]
+
+    parts = []
+    base = 4096 + max_sections * 1024
+    for s in range(max_sections):
+        so = 4096 + s * 1024
+        cnt = struct.unpack_from('<I', data, so + 0)[0]
+        sec_max = struct.unpack_from('<I', data, so + 4)[0]
+        bucket_size = struct.unpack_from('<I', data, so + 8)[0]
+        bucket_count = struct.unpack_from('<I', data, so + 12)[0]
+        block_size = struct.unpack_from('<f', data, so + 16)[0]
+        bucket_store = struct.unpack_from('<H', data, so + 20)[0]
+        scale_range = struct.unpack_from('<I', data, so + 24)[0] or default_range
+        full_buckets = struct.unpack_from('<I', data, so + 32)[0]
+        partial_buckets = struct.unpack_from('<I', data, so + 36)[0]
+        sh_components = _KSPLAT_SH_COMPONENTS.get(struct.unpack_from('<H', data, so + 40)[0], 0)
+        bytes_per_splat = bc + bs + br + bcol + sh_components * bshc
+        meta_bytes = partial_buckets * 4
+        buckets_store = bucket_store * bucket_count + meta_bytes
+        data_base = base + buckets_store
+
+        if cnt > 0:
+            ct, ft = ('<f4', '<f4') if level == 0 else ('<u2', '<f2')
+            fields = [('center', ct, 3), ('scale', ft, 3), ('rot', ft, 4), ('color', 'u1', 4)]
+            if sh_components:
+                fields.append(('sh', '<f2' if level == 1 else ('<f4' if level == 0 else 'u1'), sh_components))
+            rec = np.frombuffer(data, np.dtype(fields), count=cnt, offset=data_base)
+            colf = rec['color'].astype(np.float32) / 255.0
+            rot = _norm_quat(rec['rot'].astype(np.float32))                       # wxyz
+            scale = rec['scale'].astype(np.float32)
+            if level == 0:
+                xyz = rec['center'].astype(np.float32)
+            else:
+                buckets = np.frombuffer(data, '<f4', count=bucket_count * 3, offset=base + meta_bytes).reshape(-1, 3)
+                idx = np.empty(cnt, np.int64)
+                full_splats = full_buckets * bucket_size
+                nf = min(full_splats, cnt)
+                idx[:nf] = np.arange(nf) // bucket_size
+                if cnt > full_splats:
+                    lengths = np.frombuffer(data, '<u4', count=partial_buckets, offset=base)
+                    idx[full_splats:] = np.repeat(full_buckets + np.arange(partial_buckets), lengths)[:cnt - full_splats]
+                xyz = (rec['center'].astype(np.float32) - scale_range) * (block_size / 2.0 / scale_range) + buckets[idx]
+            parts.append((xyz, scale, rot, colf[:, 3].copy(), _rgb_to_sh_dc(colf[:, :3])))
+        base += bytes_per_splat * sec_max + buckets_store
+
+    if not parts:
+        raise ValueError("File3DToSplat: .ksplat has no splats")
+    return tuple(np.concatenate([p[i] for p in parts]) for i in range(5))
+
+
+def _parse_spz_gaussian(data: bytes):
+    # Niantic .spz (gzip-wrapped), versions 1-3. Base color only (SH skipped). See spark's SpzReader.
+    raw = gzip.decompress(data)
+    if struct.unpack_from('<I', raw, 0)[0] != _SPZ_MAGIC:
+        raise ValueError("File3DToSplat: invalid .spz (bad magic)")
+    version = struct.unpack_from('<I', raw, 4)[0]
+    n = struct.unpack_from('<I', raw, 8)[0]
+    frac_bits = raw[13]
+    off = 16
+
+    if version == 1:
+        xyz = np.frombuffer(raw, '<f2', count=n * 3, offset=off).astype(np.float32).reshape(n, 3)
+        off += n * 6
+    elif version in (2, 3):
+        b = np.frombuffer(raw, np.uint8, count=n * 9, offset=off).reshape(n, 3, 3).astype(np.int64)
+        v = (b[..., 2] << 16) | (b[..., 1] << 8) | b[..., 0]
+        v = np.where(v & 0x800000, v - 0x1000000, v)                             # sign-extend 24-bit
+        xyz = (v / (1 << frac_bits)).astype(np.float32)
+        off += n * 9
+    else:
+        raise ValueError(f"File3DToSplat: unsupported .spz version {version}")
+
+    alpha = np.frombuffer(raw, np.uint8, count=n, offset=off).astype(np.float32) / 255.0
+    off += n
+    cb = np.frombuffer(raw, np.uint8, count=n * 3, offset=off).reshape(n, 3).astype(np.float32)
+    off += n * 3
+    rgb = (cb / 255.0 - 0.5) * _SPZ_COLOR_SCALE + 0.5
+    sb = np.frombuffer(raw, np.uint8, count=n * 3, offset=off).reshape(n, 3).astype(np.float32)
+    off += n * 3
+    scale = np.exp(sb / 16.0 - 10.0)
+
+    if version == 3:                                                             # smallest-three quaternion
+        qb = np.frombuffer(raw, np.uint8, count=n * 4, offset=off).reshape(n, 4).astype(np.int64)
+        combined = qb[:, 0] | (qb[:, 1] << 8) | (qb[:, 2] << 16) | (qb[:, 3] << 24)
+        largest = (combined >> 30) & 3
+        q = np.zeros((n, 4), np.float32)                                         # x,y,z,w
+        remaining, sumsq = combined.copy(), np.zeros(n, np.float64)
+        for comp in (3, 2, 1, 0):
+            active = comp != largest
+            value = (remaining & 0x1FF).astype(np.float64)
+            sign = (remaining >> 9) & 1
+            remaining = np.where(active, remaining >> 10, remaining)
+            val = (1.0 / math.sqrt(2)) * (value / 0x1FF)
+            val = np.where(sign == 1, -val, val)
+            q[active, comp] = val[active]
+            sumsq += np.where(active, val * val, 0.0)
+        q[np.arange(n), largest] = np.sqrt(np.clip(1.0 - sumsq, 0, None))
+        rot = _norm_quat(np.stack([q[:, 3], q[:, 0], q[:, 1], q[:, 2]], 1))      # xyzw -> wxyz
+    else:
+        qb = np.frombuffer(raw, np.uint8, count=n * 3, offset=off).reshape(n, 3).astype(np.float32)
+        xq = qb / 127.5 - 1.0
+        w = np.sqrt(np.clip(1.0 - (xq ** 2).sum(1), 0, None))
+        rot = _norm_quat(np.concatenate([w[:, None], xq], 1))                    # wxyz
+    return xyz, scale, rot, alpha, _rgb_to_sh_dc(rgb)
+
+
+_GAUSSIAN_PARSERS = {"ply": _parse_ply_gaussian, "splat": _parse_splat_gaussian,
+                     "ksplat": _parse_ksplat_gaussian, "spz": _parse_spz_gaussian}
+
+
+def _detect_splat_format(data: bytes) -> str:
+    if data[:3] == b'ply':
+        return "ply"
+    if data[:2] == b'\x1f\x8b':            # gzip -> spz
+        return "spz"
+    if len(data) >= 2 and data[0] == 0 and data[1] >= 1:   # ksplat version 0.x header
+        return "ksplat"
+    if len(data) % 32 == 0:
+        return "splat"
+    raise ValueError("File3DToSplat: could not determine splat format from contents")
+
+
+def _gaussian_item(g: Types.SPLAT, i: int, device):
+    # Slice batch item i to its real length, as float32 torch tensors on `device` (SH DC -> base RGB).
+    end = _real_len(g, i)
+    to = lambda a: a.to(device=device, dtype=torch.float32)
+    xyz = to(g.positions[i, :end])
+    rgb = (to(g.sh[i, :end, 0, :]) * _C0 + 0.5).clamp(0, 1)
+    opacity = to(g.opacities[i, :end]).reshape(-1)
+    scale = to(g.scales[i, :end])
+    rot = to(g.rotations[i, :end])
+    return xyz, rgb, opacity, scale, rot
+
+
+def _quat_to_mat(q):
+    # q: (N, 4) wxyz, normalized -> (N, 3, 3)
+    q = q / q.norm(dim=-1, keepdim=True).clamp_min(1e-12)
+    w, x, y, z = q.unbind(-1)
+    return torch.stack([
+        1 - 2 * (y * y + z * z), 2 * (x * y - w * z), 2 * (x * z + w * y),
+        2 * (x * y + w * z), 1 - 2 * (x * x + z * z), 2 * (y * z - w * x),
+        2 * (x * z - w * y), 2 * (y * z + w * x), 1 - 2 * (x * x + y * y),
+    ], dim=-1).reshape(-1, 3, 3)
+
+
+def _quat_mul(a, b):
+    # Hamilton product a (x) b, wxyz.
+    aw, ax, ay, az = a.unbind(-1)
+    bw, bx, by, bz = b.unbind(-1)
+    return torch.stack([
+        aw * bw - ax * bx - ay * by - az * bz,
+        aw * bx + ax * bw + ay * bz - az * by,
+        aw * by - ax * bz + ay * bw + az * bx,
+        aw * bz + ax * by - ay * bx + az * bw,
+    ], dim=-1)
+
+
+def _euler_to_quat(rx, ry, rz):
+    # Degrees, applied as Rz @ Ry @ Rx (rotate about X, then Y, then Z in world). Returns wxyz.
+    c, s = np.cos(np.radians([rx, ry, rz]) / 2.0), np.sin(np.radians([rx, ry, rz]) / 2.0)
+    qx = torch.tensor([c[0], s[0], 0.0, 0.0], dtype=torch.float32)
+    qy = torch.tensor([c[1], 0.0, s[1], 0.0], dtype=torch.float32)
+    qz = torch.tensor([c[2], 0.0, 0.0, s[2]], dtype=torch.float32)
+    return _quat_mul(_quat_mul(qz, qy), qx)
+
+
+def _mat_to_quat(m):
+    # Rotation matrix (..., 3, 3) -> quaternion (..., 4) wxyz. Batched; builds the four candidate quaternions
+    # and keeps the one with the largest component (numerically stable across all rotations).
+    m00, m11, m22 = m[..., 0, 0], m[..., 1, 1], m[..., 2, 2]
+    m21, m12 = m[..., 2, 1], m[..., 1, 2]
+    m02, m20 = m[..., 0, 2], m[..., 2, 0]
+    m10, m01 = m[..., 1, 0], m[..., 0, 1]
+    q2 = torch.stack([1 + m00 + m11 + m22, 1 + m00 - m11 - m22,
+                      1 - m00 + m11 - m22, 1 - m00 - m11 + m22], -1)   # 4 * (w^2, x^2, y^2, z^2)
+    cand = torch.stack([
+        torch.stack([q2[..., 0], m21 - m12, m02 - m20, m10 - m01], -1),
+        torch.stack([m21 - m12, q2[..., 1], m10 + m01, m02 + m20], -1),
+        torch.stack([m02 - m20, m10 + m01, q2[..., 2], m12 + m21], -1),
+        torch.stack([m10 - m01, m02 + m20, m12 + m21, q2[..., 3]], -1),
+    ], -2)                                                            # (...,4,4) candidates, rows = wxyz
+    sel = q2.argmax(-1)
+    q = torch.gather(cand, -2, sel[..., None, None].expand(sel.shape + (1, 4)))[..., 0, :]
+    return q / q.norm(dim=-1, keepdim=True).clamp_min(1e-12)
+
+
+class SplatToFile3D(IO.ComfyNode):
+    FORMAT_WRITERS = {
+        "ply": _gaussian_ply_bytes,
+        "ksplat": _gaussian_ksplat_bytes,
+        "spz": _gaussian_spz_bytes,
+    }
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SplatToFile3D",
+            display_name="Create 3D File (from Splat)",
+            search_aliases=["gaussian to ply", "splat to file", "export gaussian"],
+            category="3d/splat",
+            description="Serialize a gaussian splat to a File3D object for Save / Preview 3D nodes. "
+                        "Supports one item per batch only.",
+            inputs=[
+                IO.Splat.Input("splat"),
+                IO.Combo.Input("format", options=list(cls.FORMAT_WRITERS),  # TODO: add "splat" when we have a writer for it
+                               tooltip="ply: standard 3D Gaussian Splat with full spherical harmonics. "
+                                       "ksplat: mkkellogg SplatBuffer (level 0, uncompressed), base color only "
+                                       "spz: Niantic gzip-compressed (~10x smaller), base color only "
+                                       ),
+            ],
+            outputs=[IO.File3DSplatAny.Output(display_name="model_3d")],
+        )
+
+    @classmethod
+    def execute(cls, splat, format="ply") -> IO.NodeOutput:
+        writer = cls.FORMAT_WRITERS.get(format)
+        if writer is None:
+            raise ValueError(f"Unsupported splat format: {format!r}")
+
+        if splat.positions.shape[0] > 1:
+            logging.warning("SplatToFile3D supports one item per batch only. Got %d; using first.", splat.positions.shape[0])
+        end = _real_len(splat, 0)
+        data = writer(splat.positions[0, :end], splat.scales[0, :end],
+                      splat.rotations[0, :end], splat.opacities[0, :end], splat.sh[0, :end])
+        return IO.NodeOutput(Types.File3D(BytesIO(data), file_format=format))
+
+
+class File3DToSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="File3DToSplat",
+            display_name="Get Splat",
+            search_aliases=["load splat", "ply to splat", "import splat", "file to splat"],
+            category="3d/splat",
+            description="Parse a splat File3D into a gaussian splat. Inverse of Create 3D File (from Splat). "
+                        "Supported format:  PLY, SPLAT, KSPLAT, SPZ. PLY carries full spherical harmonics, "
+                        "the other formats are base color only. Format is auto-detected from the file contents.",
+            inputs=[
+                IO.MultiType.Input(
+                    IO.File3DAny.Input("model_3d"),
+                    types=[IO.File3DSplatAny, IO.File3DPLY, IO.File3DSPLAT, IO.File3DKSPLAT, IO.File3DSPZ],
+                    tooltip="A gaussian splat 3D file",
+                ),
+            ],
+            outputs=[IO.Splat.Output(display_name="splat")],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D) -> IO.NodeOutput:
+        data = model_3d.get_bytes()
+        fmt = (model_3d.format or "").lower()
+        parser = _GAUSSIAN_PARSERS.get(fmt) or _GAUSSIAN_PARSERS[_detect_splat_format(data)]
+        xyz, scale, rot, opacity, sh = parser(data)
+
+        t = lambda a: torch.from_numpy(np.ascontiguousarray(a)).float()
+        splat = Types.SPLAT(
+            t(xyz)[None],                              # (1, N, 3)
+            t(scale)[None],                            # (1, N, 3) linear
+            t(rot)[None],                              # (1, N, 4) wxyz
+            t(opacity).reshape(1, -1, 1),              # (1, N, 1)
+            t(sh)[None],                               # (1, N, K, 3)
+        )
+        return IO.NodeOutput(splat)
+
+
+def _view_matrix_t(yaw_deg, pitch_deg, device):
+    y, p = math.radians(yaw_deg), math.radians(pitch_deg)
+    cy, sy, cp, sp = math.cos(y), math.sin(y), math.cos(p), math.sin(p)
+    Ry = torch.tensor([[cy, 0, sy], [0, 1, 0], [-sy, 0, cy]], device=device)
+    Rx = torch.tensor([[1, 0, 0], [0, cp, -sp], [0, sp, cp]], device=device)
+    return Rx @ Ry
+
+
+def _camera_basis(camera_info, dev):
+    # Look-at basis in the splat frame, named by their projection rows: right = image +x, up = image +y
+    # (down, since yflip=1), fwd = view/depth axis (eye -> scene). Load3D is three.js (right-handed, Y-up,
+    # camera looks down -Z); the splat is 3DGS (Y-down, Z-forward). World -> splat is a 180 deg rotation
+    # about X: (x, y, z) -> (x, -y, -z) (det +1, no mirror, no axis swap).
+    pos, tgt = camera_info.get("position", {}), camera_info.get("target", {})
+    m = lambda d: torch.tensor([float(d.get("x", 0.0)), -float(d.get("y", 0.0)), -float(d.get("z", 0.0))], device=dev)
+    eye, target = m(pos), m(tgt)
+    mv = lambda v: torch.stack([v[0], -v[1], -v[2]])             # same world->splat map, for direction vectors
+    n = lambda v: v / v.norm().clamp_min(1e-8)
+    q = camera_info.get("quaternion")
+    if q:                                                        # exact camera world rotation (incl. roll)
+        qwxyz = torch.tensor([float(q.get("w", 1.0)), float(q.get("x", 0.0)),
+                              float(q.get("y", 0.0)), float(q.get("z", 0.0))], device=dev)
+        R = _quat_to_mat(qwxyz[None])[0]                         # columns = camera world axes; looks down local -Z
+        right = n(mv(R[:, 0]))                                   # camera +X -> image right
+        up = n(mv(-R[:, 1]))                                     # camera +Y is image up; image-down row is its negative
+        fwd = n(mv(-R[:, 2]))                                    # camera looks down local -Z -> view direction
+        return eye, target, right, up, fwd
+    fwd = n(target - eye)                                        # no quaternion: orbit-consistent, roll-free
+    yaw = math.degrees(math.atan2(-float(fwd[0]), float(fwd[2])))
+    pitch = math.degrees(math.asin(max(-1.0, min(1.0, float(fwd[1])))))
+    W = _view_matrix_t(yaw, pitch, dev)
+    return eye, target, W[0], W[1], W[2]
+
+
+def _lookat_quat_wxyz(position, target, dev):
+    # three.js lookAt in world frame: camera local +Z = (eye - target), up = world +Y. Returns wxyz.
+    z = position - target
+    z = z / z.norm().clamp_min(1e-8)
+    up0 = torch.tensor([0.0, 1.0, 0.0], device=dev)
+    if z.dot(up0).abs() > 0.999:                                # looking straight up/down
+        up0 = torch.tensor([0.0, 0.0, 1.0], device=dev)
+    x = torch.linalg.cross(up0, z)
+    x = x / x.norm().clamp_min(1e-8)
+    y = torch.linalg.cross(z, x)
+    R = torch.stack([x, y, z], dim=1)                           # columns = camera world axes
+    return _mat_to_quat(R[None])[0]
+
+
+def _lookat_camera_info(position, target, fov, dev, zoom=1.0, camera_type="perspective", roll=0.0):
+    # Build a camera_info from a world-space (right-handed, Y-up) eye + look-at target; up = world +Y.
+    pos = torch.as_tensor(position, dtype=torch.float32, device=dev)
+    tgt = torch.as_tensor(target, dtype=torch.float32, device=dev)
+    q = _lookat_quat_wxyz(pos, tgt, dev)
+    if roll:                                                    # roll about the view axis (camera local Z)
+        a = math.radians(roll)
+        qz = torch.tensor([math.cos(a / 2), 0.0, 0.0, math.sin(a / 2)], device=dev)
+        q = _quat_mul(q[None], qz[None])[0]
+    xyz = lambda v: {"x": float(v[0]), "y": float(v[1]), "z": float(v[2])}
+    return {"position": xyz(pos), "target": xyz(tgt),
+            "quaternion": {"x": float(q[1]), "y": float(q[2]), "z": float(q[3]), "w": float(q[0])},
+            "fov": float(fov), "cameraType": str(camera_type), "zoom": float(zoom)}
+
+
+def _quat_camera_info(position, quat_xyzw, fov, dev, zoom=1.0, camera_type="perspective"):
+    # camera_info from an explicit world position + camera-rotation quaternion (three.js: looks down local -Z).
+    pos = torch.as_tensor(position, dtype=torch.float32, device=dev)
+    qx, qy, qz, qw = (float(c) for c in quat_xyzw)
+    qwxyz = torch.tensor([qw, qx, qy, qz], dtype=torch.float32, device=dev)
+    qwxyz = qwxyz / qwxyz.norm().clamp_min(1e-8)
+    R = _quat_to_mat(qwxyz[None])[0]
+    tgt = pos - R[:, 2]                                         # look one unit down local -Z
+    xyz = lambda v: {"x": float(v[0]), "y": float(v[1]), "z": float(v[2])}
+    return {"position": xyz(pos), "target": xyz(tgt),
+            "quaternion": {"x": float(qwxyz[1]), "y": float(qwxyz[2]), "z": float(qwxyz[3]), "w": float(qwxyz[0])},
+            "fov": float(fov), "cameraType": str(camera_type), "zoom": float(zoom)}
+
+
+def _orbit_camera_info(yaw, pitch, distance, fov, pivot_splat, dev):
+    # Orbit helper for RenderSplat's default camera: yaw/pitch about `pivot_splat` (splat frame) at `distance`.
+    # World<->splat is the (x,-y,-z) map, so _camera_basis recovers exactly _view_matrix_t(yaw, pitch).
+    y, p = math.radians(yaw), math.radians(pitch)
+    cy, sy, cp, sp = math.cos(y), math.sin(y), math.cos(p), math.sin(p)
+    fwd_splat = torch.tensor([-cp * sy, sp, cp * cy], device=dev)    # == _view_matrix_t(yaw, pitch)[2]
+    m = lambda v: torch.stack([v[0], -v[1], -v[2]])                  # splat<->world (its own inverse)
+    return _lookat_camera_info(m(pivot_splat - distance * fwd_splat), m(pivot_splat), fov, dev)
+
+
+def _orbit_camera_info_yaw(camera_info, angle_deg, dev):
+    # Turntable: rigidly rotate a camera_info about world +Y around its target by angle_deg. Returns a new dict.
+    a = math.radians(angle_deg)
+    ca, sa = math.cos(a), math.sin(a)
+    v = lambda d: torch.tensor([float(d.get("x", 0.0)), float(d.get("y", 0.0)), float(d.get("z", 0.0))], device=dev)
+    pos, tgt = v(camera_info.get("position", {})), v(camera_info.get("target", {}))
+    Ry = torch.tensor([[ca, 0.0, sa], [0.0, 1.0, 0.0], [-sa, 0.0, ca]], device=dev)
+    new_pos = tgt + Ry @ (pos - tgt)
+    q = camera_info.get("quaternion") or {}
+    qcur = torch.tensor([float(q.get("w", 1.0)), float(q.get("x", 0.0)),
+                         float(q.get("y", 0.0)), float(q.get("z", 0.0))], device=dev)
+    qy = torch.tensor([math.cos(a / 2), 0.0, math.sin(a / 2), 0.0], device=dev)   # world +Y rotation
+    qn = _quat_mul(qy[None], qcur[None])[0]
+    xyz = lambda t: {"x": float(t[0]), "y": float(t[1]), "z": float(t[2])}
+    return {**camera_info, "position": xyz(new_pos),
+            "quaternion": {"x": float(qn[1]), "y": float(qn[2]), "z": float(qn[3]), "w": float(qn[0])}}
+
+
+def _gauss_blur(x, sigma, dev):
+    # Separable Gaussian blur of (1, C, H, W). Used to denoise the screen-space normal map.
+    r = max(1, int(round(3 * sigma)))
+    k = torch.exp(-0.5 * (torch.arange(-r, r + 1, device=dev, dtype=torch.float32) / sigma) ** 2)
+    k = k / k.sum()
+    c = x.shape[1]
+    x = torch.nn.functional.conv2d(x, k.view(1, 1, 1, -1).expand(c, 1, 1, -1), padding=(0, r), groups=c)
+    x = torch.nn.functional.conv2d(x, k.view(1, 1, -1, 1).expand(c, 1, -1, 1), padding=(r, 0), groups=c)
+    return x
+
+
+def _render_gaussian(xyz, rgb, opacity, scale, rot, width, height, splat_scale, bg, camera_info,
+                     sharpen=1.0, headlight_shading=0.0, render_style="color"):
+    # Perspective-correct anisotropic gaussian splat rasterizer. Each splat is weighted by its 3D Gaussian's
+    # peak along each pixel's ray (AAA / Hahlbohm), composited front-to-back across depth slabs. `render_style`
+    # selects the image: color / clay / depth / normal. Returns (image HxWx3, coverage mask HxW) on CPU.
+    dev = comfy.model_management.get_torch_device()
+    t = lambda a: torch.as_tensor(a, dtype=torch.float32, device=dev)
+    idev, idtype = comfy.model_management.intermediate_device(), comfy.model_management.intermediate_dtype()
+    xyz, rgb, opacity = t(xyz), t(rgb).clamp(0, 1), t(opacity).reshape(-1)
+    scale, rot = t(scale) * float(splat_scale), t(rot)
+    do_linear = render_style == "color"  # colour blends in linear light, re-encoded at the end
+    if do_linear:
+        rgb = _srgb_to_linear(rgb)
+    flat = width * height
+    bg_t = t(bg)
+    bg_comp = _srgb_to_linear(bg_t) if do_linear else bg_t  # background blended in the same space as the splats
+    need_depth = render_style == "depth"
+    need_normal = render_style in ("normal", "clay") or headlight_shading > 0
+
+    def background_only():  # no splats to rasterize -> just the background + empty mask
+        img = bg_t.expand(height, width, 3) if render_style == "color" else torch.zeros(height, width, 3, device=dev)
+        return img.to(idev, idtype), torch.zeros(height, width, device=idev, dtype=idtype)
+
+    if xyz.shape[0] == 0:  # empty input (e.g. all culled by opacity_threshold)
+        return background_only()
+
+    eye, target, right, up, fwd = _camera_basis(camera_info, dev)  # all camera state comes from camera_info
+    W = torch.stack([right, up, fwd], 0)                           # rows = camera axes (world -> camera)
+    cam = (xyz - eye) @ W.T
+    fov = float(camera_info.get("fov", 0) or 0) or 35.0
+    zoom = float(camera_info.get("zoom", 1.0) or 1.0)              # three.js digital zoom: scales the focal length
+    is_ortho = str(camera_info.get("cameraType", "")).lower().startswith("ortho")
+    xc, yc, zc = cam.unbind(-1)
+
+    keep = zc > 1e-2
+    xc, yc, zc, rgb, opacity, scale, rot = (a[keep] for a in (xc, yc, zc, rgb, opacity, scale, rot))
+    if xc.shape[0] == 0:  # nothing in front of the camera -> background only
+        return background_only()
+    if render_style == "clay":
+        rgb = torch.full_like(rgb, 0.75)  # neutral albedo -> shading shows pure geometry
+
+    f = (min(width, height) / 2) / math.tan(math.radians(fov) / 2) * zoom  # fov over the smaller axis, x camera zoom
+    cx0, cy0 = width / 2, height / 2
+
+    # Camera-space 3D covariance per splat: Sigma = (W Rq) diag(scale^2) (W Rq)^T, plus a tiny relative
+    # regularizer for a stable inverse (a pixel-size Mip low-pass would over-thicken flat surfels and blur).
+    Mw = W[None] @ _quat_to_mat(rot)  # (N,3,3) world -> camera
+    cam_cov = (Mw * scale.square()[:, None, :]) @ Mw.transpose(1, 2)
+    cam_cov = cam_cov + (cam_cov.diagonal(dim1=-2, dim2=-1).mean(-1) * 1e-3)[:, None, None] * torch.eye(3, device=dev)
+
+    # Perspective-correct weighting: peak of the 3D Gaussian along each pixel ray. Precompute Si, Si@mu, mu^T Si mu.
+    mu = torch.stack([xc, yc, zc], -1)
+    si = torch.linalg.inv(cam_cov)
+    simu = (si @ mu[:, :, None])[:, :, 0]  # (N,3)
+    musimu = (mu * simu).sum(-1)           # (N,)
+    s00, s01, s02 = si[:, 0, 0], si[:, 0, 1], si[:, 0, 2]
+    s11, s12, s22 = si[:, 1, 1], si[:, 1, 2], si[:, 2, 2]
+    simu0, simu1, simu2 = simu.unbind(-1)
+    if need_normal:  # surfel normal = thinnest axis, oriented toward camera
+        nrm = Mw[torch.arange(Mw.shape[0], device=dev), :, scale.argmin(-1)]  # (N,3) camera-space normal
+        nrm = nrm * torch.where(nrm[:, 2:3] > 0, -1.0, 1.0)                   # flip so nz <= 0 (faces camera)
+
+    # Screen centre (exact) + footprint radius from the affine 2D projection (used only to size the kernel).
+    # The image is +y-down, so the projection's y row is unflipped - it matches the splat frame's +Y.
+    jm = torch.zeros(xc.shape[0], 2, 3, device=dev)
+    if is_ortho:                                              # parallel projection: screen = s * (xc, yc)
+        s = f / float((target - eye).norm().clamp_min(1e-6))  # pixels per world unit at the target plane
+        cx, cy = cx0 + s * xc, cy0 + s * yc
+        jm[:, 0, 0] = s
+        jm[:, 1, 1] = s
+    else:  # perspective: screen = f * (xc, yc) / zc
+        invz = 1.0 / zc
+        cx, cy = cx0 + f * xc * invz, cy0 + f * yc * invz
+        jm[:, 0, 0], jm[:, 0, 2] = f * invz, -f * xc * invz.square()
+        jm[:, 1, 1], jm[:, 1, 2] = f * invz, -f * yc * invz.square()
+    cov2 = jm @ cam_cov @ jm.transpose(1, 2)
+    a, b, c = cov2[:, 0, 0], cov2[:, 0, 1], cov2[:, 1, 1]
+    max_eig = (a + c) * 0.5 + (((a - c) * 0.5).square() + b * b).clamp_min(0).sqrt()
+    radius = 3.0 * max_eig.clamp_min(1e-8).sqrt()
+    K = int(min(max(24, min(width, height) // 16), max(2, math.ceil(_quantile(radius, 0.995).item()))))
+
+    # Per-splat kernel size: bucket splats by radius into a coarse ladder of window sizes (global K stays the cap) so
+    # small splats (the bulk of it) use a small window.
+    levels = [L for L in (16, 64, 256) if L < K] + [K]
+    levels_t = torch.tensor(levels, device=dev, dtype=torch.float32)
+    grids = []
+    for L in levels:
+        rng = torch.arange(-L, L + 1, device=dev, dtype=torch.float32)
+        gy, gx = torch.meshgrid(rng, rng, indexing="ij")
+        grids.append((gx.reshape(-1), gy.reshape(-1)))
+    blevel = torch.bucketize(radius * (4.0 / 3.0), levels_t).clamp_(max=len(levels) - 1)  # window >= ~4 sigma
+
+    n = zc.shape[0]
+    ns = int(min(256, max(1, n // 1000)))                      # depth slabs: 1 per ~1000 splats, capped
+    nl = len(levels)
+    order = torch.argsort(zc)                                  # front (small zc) -> back -> defines the slabs
+    bounds = torch.linspace(0, n, ns + 1, device=dev).round().long()
+    rank = torch.empty(n, dtype=torch.long, device=dev)
+    rank[order] = torch.arange(n, device=dev)                  # depth rank of each splat
+    slab_id = (torch.searchsorted(bounds, rank, right=True) - 1).clamp_(0, ns - 1)
+    key = slab_id * nl + blevel                                # group by slab, then kernel level (order-free within)
+    order = torch.argsort(key)
+    key = key[order]
+
+    cxr, cyr = cx[order].round(), cy[order].round()
+    s00, s01, s02 = s00[order], s01[order], s02[order]
+    s11, s12, s22 = s11[order], s12[order], s22[order]
+    s01b, s02b, s12b = s01 * 2, s02 * 2, s12 * 2               # doubled cross terms for the fused quadratic forms
+    simu0, simu1, simu2, musimu = simu0[order], simu1[order], simu2[order], musimu[order]
+    opacity, rgb = opacity[order], rgb[order]
+    zc_o = zc[order] if need_depth else None
+    nrm_o = nrm[order] if need_normal else None
+    mux_o, muy_o, muz_o = (xc[order], yc[order], zc[order]) if is_ortho else (None, None, None)
+
+    # Pack the per-splat scalars into one tensor so each chunk slices once
+    common = [cxr, cyr, s00, s11, s22, s01b, s02b, s12b, opacity]
+    pstack = torch.stack(common + ([s02, s12, mux_o, muy_o, muz_o] if is_ortho else [simu0, simu1, simu2, musimu]))
+
+    # Precompute the (slab, level) run table on-GPU and pull it to the CPU once
+    starts = torch.cat([torch.zeros(1, dtype=torch.long, device=dev), (key[1:] != key[:-1]).nonzero().flatten() + 1])
+    ks = key[starts]
+    run_lo = starts.tolist() + [n]
+    run_lev = (ks % nl).tolist()
+    run_slab = torch.div(ks, nl, rounding_mode="floor").tolist()
+    slab_runs = [[] for _ in range(ns)]
+    for r in range(len(run_lev)):
+        slab_runs[run_slab[r]].append((run_lo[r], run_lo[r + 1], run_lev[r]))
+
+    def splat(lo, hi, ox, oy):  # -> pixel idx (m,M), alpha (m,M); weight = 3D Gaussian peak along each pixel's ray
+        cols = pstack[:, lo:hi, None].unbind(0)
+        cxr_, cyr_, a00, a11, a22, b01, b02, b12, opa = cols[:9]   # a* = Si components; b* = 2 * cross terms
+        px = cxr_ + ox[None, :]
+        py = cyr_ + oy[None, :]
+        valid = (px >= 0) & (px < width) & (py >= 0) & (py < height)
+        if is_ortho:  # parallel ray (0,0,1) from screen point (X, Y, 0); rz constant per splat
+            c02, c12, mx, my, mz = cols[9:]
+            rx = (px - cx0) / s - mx
+            ry = (py - cy0) / s - my
+            rz = -mz
+            a22rz = a22 * rz
+            inx = torch.addcmul(b02 * rz, a00, rx).addcmul_(b01, ry)          # a00 rx + b01 ry + b02 rz
+            rSr = torch.addcmul(a22rz * rz, rx, inx).addcmul_(ry, torch.addcmul(b12 * rz, a11, ry))
+            dsr = torch.addcmul(a22rz, c02, rx).addcmul_(c12, ry)
+            q = torch.addcdiv(rSr, dsr * dsr, a22.clamp_min(1e-12), value=-1).clamp_min_(0)
+        else:  # perspective ray (dx,dy,1) through the camera origin
+            su0, su1, su2, mus = cols[9:]
+            dx, dy = (px - cx0) / f, (py - cy0) / f
+            dsid = torch.addcmul(a22, dx, torch.addcmul(b02, a00, dx))        # a22 + dx*(a00 dx + b02)
+            dsid = dsid.addcmul_(dy, torch.addcmul(b12, a11, dy))             # + dy*(a11 dy + b12)
+            dsid = dsid.addcmul_(b01 * dx, dy)                               # + (2 s01) dx dy
+            dsimu = torch.addcmul(su2, dx, su0).addcmul_(dy, su1)
+            q = torch.addcdiv(mus, dsimu * dsimu, dsid.clamp_min(1e-12), value=-1).clamp_min_(0)
+        alpha = (opa * torch.exp(-0.5 * q) * valid).clamp_(0, 0.999)
+        idx = py.long().clamp(0, height - 1) * width + px.long().clamp(0, width - 1)
+        return idx, alpha
+
+    # Front-to-back compositing over the depth slabs set up above. Within a slab the accumulation is a pure
+    # sum (order-independent), so splats are grouped by kernel level and each level uses its own tight window.
+    sharp = sharpen != 1.0  # winner-take-more colour blend: dominant splat shows more
+    cacc = torch.zeros((flat, 3), device=dev)
+    trans = torch.ones((flat,), device=dev)
+    a_buf = torch.zeros((flat,), device=dev)                            # sum alpha -> colour/depth/normal weight (alpha-weighted mean)
+    tau_buf = torch.zeros((flat,), device=dev)                          # sum -ln(1-alpha) -> slab opacity = 1-prod(1-alpha)
+    crgb = torch.zeros((flat, 3), device=dev)                           # sum alpha^p * rgb -> slab colour
+    wbuf = torch.zeros((flat,), device=dev) if sharp else None          # sum alpha^p -> colour normalizer (sharp only)
+    dacc = torch.zeros((flat,), device=dev) if need_depth else None     # front-weighted depth
+    nacc = torch.zeros((flat, 3), device=dev) if need_normal else None  # front-weighted camera-space normal
+    zslab = torch.zeros((flat,), device=dev) if need_depth else None
+    nslab = torch.zeros((flat, 3), device=dev) if need_normal else None
+    stale = 0  # consecutive fully-occluded slabs -> early-out
+    for si in range(ns):
+        runs = slab_runs[si]
+        if not runs:
+            continue
+        a_buf.zero_()
+        tau_buf.zero_()
+        crgb.zero_()
+        if sharp:
+            wbuf.zero_()
+        if need_depth:
+            zslab.zero_()
+        if need_normal:
+            nslab.zero_()
+        for r_lo, r_hi, li in runs:            # contiguous same-kernel-level runs in this slab
+            ox, oy = grids[li]
+            ch = max(2048, 10_000_000 // ox.shape[0])          # splats/chunk, bounded by this level's kernel size
+            for lo in range(r_lo, r_hi, ch):
+                hi = min(lo + ch, r_hi)
+                idx, alpha = splat(lo, hi, ox, oy)
+                idx, af = idx.reshape(-1), alpha.reshape(-1)
+                a_buf.index_add_(0, idx, af)
+                tau_buf.index_add_(0, idx, (-torch.log1p(-alpha)).reshape(-1))      # -ln(1-alpha), correct opacity merge
+                apw = alpha.pow(sharpen) if sharp else alpha                        # bias colour toward the highest-alpha splat
+                crgb.index_add_(0, idx, (apw[:, :, None] * rgb[lo:hi, None, :]).reshape(-1, 3))
+                if sharp:
+                    wbuf.index_add_(0, idx, apw.reshape(-1))
+                if need_depth:
+                    zslab.index_add_(0, idx, (alpha * zc_o[lo:hi, None]).reshape(-1))
+                if need_normal:
+                    nslab.index_add_(0, idx, (alpha[:, :, None] * nrm_o[lo:hi, None, :]).reshape(-1, 3))
+        slab_a = 1 - torch.exp(-tau_buf)   # 1 - prod(1-alpha): true opacity of the slab's splats
+        front = trans * slab_a
+        denom = wbuf if sharp else a_buf
+        cacc.addcmul_(front[:, None], crgb / denom.clamp_min(1e-8)[:, None])  # cacc += front * (crgb/denom)
+        if need_depth or need_normal:
+            ainv = a_buf.clamp_min(1e-8)   # alpha-weighted-mean normalizer (depth/normal only)
+            if need_depth:
+                dacc.addcmul_(front, zslab / ainv)
+            if need_normal:
+                nacc.addcmul_(front[:, None], nslab / ainv[:, None])
+        trans.mul_(1 - slab_a)
+        if si % 8 == 7:                    # checkpoint every 8 slabs (a per-slab GPU sync would cost more)
+            if float(front.max()) < 1e-3:  # this checkpoint slab is fully occluded by what is in front
+                stale += 1
+                if stale >= 2:             # two occluded checkpoints running -> the rest are too -> stop
+                    break
+            else:
+                stale = 0
+
+    cov = 1 - trans
+    covg = cov.reshape(height, width)
+    covm = covg > 0.5 if render_style in ("depth", "normal") else None  # silhouette mask (depth/normal styles only)
+    depth_map = (dacc / cov.clamp_min(1e-6)).reshape(height, width) if need_depth else None
+    nrm_map = None
+    if need_normal:
+        # Per-splat surfel normals are jittery, so do a masked blur
+        nb = nacc.reshape(height, width, 3).permute(2, 0, 1)[None]
+        cb = cov.reshape(1, 1, height, width)
+        nb, cb = _gauss_blur(nb, 1.2, dev), _gauss_blur(cb, 1.2, dev)
+        normal = (nb / cb.clamp_min(1e-6))[0].permute(1, 2, 0)
+        nrm_map = normal / normal.norm(dim=-1, keepdim=True).clamp_min(1e-6)
+
+    if render_style == "depth":  # near = bright, far = dark, 0 off-object
+        d = torch.zeros(height, width, device=dev)
+        if bool(covm.any()):
+            lo, hi = depth_map[covm].min(), depth_map[covm].max()
+            d = torch.where(covm, ((hi - depth_map) / (hi - lo).clamp_min(1e-6)).clamp(0, 1), d)
+        img = d[:, :, None].expand(height, width, 3)
+    elif render_style == "normal":  # OpenGL normal map: +X right, +Y up, +Z to viewer
+        enc = (nrm_map * t([1.0, -1.0, -1.0]) * 0.5 + 0.5).clamp(0, 1)
+        img = enc * covm[:, :, None]
+    else:  # color / clay
+        img = cacc.reshape(height, width, 3)
+        if render_style == "clay":  # studio key light + ambient -> sculpted matte look
+            kl = t([-0.4, -0.7, -0.6])  # key from screen upper-left, angled toward the viewer
+            kl = kl / kl.norm()
+            hl = (0.5 * (nrm_map * kl).sum(-1) + 0.5).clamp(0, 1)  # half-Lambert: soft terminator, no harsh dark side
+            img = img * (0.35 + 0.65 * hl * hl)[:, :, None]        # ambient floor + diffuse key
+        elif headlight_shading > 0:  # camera headlight: darken faces turned from view
+            k = float(headlight_shading)
+            ndotl = (-nrm_map[:, :, 2]).clamp(0, 1)
+            img = img * (1 - 0.6 * k + 0.6 * k * ndotl)[:, :, None]
+        img = img.addcmul_(trans.reshape(height, width, 1), bg_comp)
+        if do_linear:  # back to display space after linear compositing
+            img = _linear_to_srgb(img)
+    return img.clamp(0, 1).to(idev, idtype), covg.clamp(0, 1).to(idev, idtype)
+
+
+class RenderSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RenderSplat",
+            display_name="Render Splat",
+            search_aliases=["splat to image", "render splat", "gaussian turntable"],
+            category="3d/splat",
+            description="Render a gaussian splat as an image with an anisotropic EWA rasterizer (oriented "
+                        "elliptical splats, antialiased, depth-sorted front-to-back). The camera comes from a "
+                        "camera_info input (Load / Preview 3D, or a Create Camera Info node); leave it empty to "
+                        "auto-frame the splat. Set frames greater than 1 for a turntable batch of images to feed a Video node.",
+            inputs=[
+                IO.Splat.Input("splat"),
+                IO.Int.Input("width", default=1024, min=64, max=2048, step=8),
+                IO.Int.Input("height", default=1024, min=64, max=2048, step=8),
+                IO.Int.Input("frames", default=1, min=-240, max=240,
+                             tooltip="-1, 0, 1 = single still image; >1 = turntable, the camera orbits over a full "
+                                     "360 turn (works with any camera_info). Negative value orbits the other way."),
+                IO.Float.Input("splat_scale", default=1.0, min=0.1, max=5.0, step=0.05, advanced=True,
+                               tooltip="Multiplier on each splat's projected footprint (lower = crisper points, "
+                                       "higher = softer/fuller surface)."),
+                IO.Float.Input("sharpen", default=2.0, min=1.0, max=8.0, step=0.5,
+                               tooltip="Sharpen overlapping splats: 1.0 = physically-correct blend; higher biases "
+                                       "each pixel toward its dominant (nearest) splat for crisper texture, without "
+                                       "shrinking splats or opening gaps. Non-physical above 1."),
+                IO.Float.Input("headlight_shading", default=0.0, min=0.0, max=3.0, step=0.05, advanced=True,
+                               tooltip="Diffuse shading from a light at the camera (headlight), using the splat surfel "
+                                       "normals: darkens surfaces that turn away from view to reveal form/curvature. "
+                                       "0 = flat albedo, 1 = strongest shading."),
+                IO.Float.Input("opacity_threshold", default=0.0, min=0.0, max=1.0, step=0.01, advanced=True,
+                               tooltip="Cull gaussians with opacity below this (removes faint floaters)."),
+                IO.Combo.Input("render_style", options=["color", "clay", "depth", "normal"],
+                               tooltip="What the image output shows: color, clay (neutral-albedo shaded), "
+                                       "depth (near=bright), normal (OpenGL normal map)."),
+                IO.Color.Input("background", default="#000000"),
+                IO.Image.Input("bg_image", optional=True,
+                               tooltip="Optional background plate composited behind the splat (overrides the solid "
+                                       "background colour). Resized to the render size; a batch is used per frame, "
+                                       "a single image for all. color/clay only."),
+                IO.Load3DCamera.Input("camera_info", optional=True,
+                                      tooltip="Camera to render from - a Load3D / Preview3D camera or a Create Camera "
+                                              "Info node. If empty, the splat is auto-framed from a default 3/4 view."),
+            ],
+            outputs=[IO.Image.Output(display_name="image"), IO.Mask.Output(display_name="mask")],
+        )
+
+    @classmethod
+    def execute(cls, splat, width, height, frames, splat_scale, sharpen, headlight_shading,
+                opacity_threshold, background, render_style, camera_info=None, bg_image=None) -> IO.NodeOutput:
+        bg = _hex_to_rgb(background)
+        bg_imgs = None
+        if bg_image is not None:  # resize the plate(s) to the render size: (B,H,W,3)
+            bi = bg_image[... , :3].movedim(-1, 1)  # (B,3,H,W)
+            bi = comfy.utils.common_upscale(bi, width, height, "bicubic", "disabled")
+            bg_imgs = bi.movedim(1, -1).clamp(0, 1)
+        n_frames = abs(int(frames)) or 1         # magnitude = frame count (0 -> single still)
+        orbit_dir = -1.0 if frames < 0 else 1.0  # sign = orbit direction
+        imgs, masks = [], []
+        device = comfy.model_management.get_torch_device()
+        total = splat.positions.shape[0] * n_frames
+        pbar = comfy.utils.ProgressBar(total) if total > 1 else None
+        k = 0
+        for i in range(splat.positions.shape[0]):
+            xyz, rgb, opacity, scale, rot = _gaussian_item(splat, i, device)
+            if opacity_threshold > 0:
+                keep = opacity >= opacity_threshold
+                xyz, rgb, opacity, scale, rot = xyz[keep], rgb[keep], opacity[keep], scale[keep], rot[keep]
+            base_cam = camera_info
+            if base_cam is None:  # no camera -> default 3/4 view, auto-framed on the splat
+                center = xyz.mean(0) if xyz.shape[0] else torch.zeros(3, device=device)
+                extent = (_quantile((xyz - center).norm(dim=-1), 0.99).clamp_min(1e-4) if xyz.shape[0]
+                          else torch.tensor(1.0, device=device))
+                dist = float(extent / (math.tan(math.radians(35.0) / 2) * 0.9))
+                base_cam = _orbit_camera_info(35.0, 30.0, dist, 35.0, center, device)
+            for fr in range(n_frames):
+                cam_fr = (base_cam if n_frames == 1
+                          else _orbit_camera_info_yaw(base_cam, orbit_dir * 360.0 * fr / n_frames, device))
+                bg_k = bg_imgs[k % bg_imgs.shape[0]] if bg_imgs is not None else bg   # per-frame plate, or solid colour
+                img, mask = _render_gaussian(xyz, rgb, opacity, scale, rot, width, height, splat_scale, bg_k, cam_fr,
+                                             sharpen=sharpen, headlight_shading=headlight_shading,
+                                             render_style=render_style)
+                imgs.append(img)
+                masks.append(mask)
+                k += 1
+                if pbar is not None:
+                    pbar.update(1)
+        return IO.NodeOutput(torch.stack(imgs), torch.stack(masks))
+
+
+class CreateCameraInfo(IO.ComfyNode):  # TODO: move to better file
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="CreateCameraInfo",
+            display_name="Create Camera Info",
+            search_aliases=["camera position", "make camera info", "orbit camera", "look at camera"],
+            category="3d",
+            description="Build a camera_info"
+                        "Mode 'orbit' aims with yaw/pitch/distance around the target; "
+                        "'look_at' places the camera at world position. Coordinates are the viewer's world space (right-handed,Y-up).",
+            inputs=[
+                IO.DynamicCombo.Input("mode", options=[
+                    IO.DynamicCombo.Option("orbit", [
+                        IO.Float.Input("yaw", default=35.0, min=-360.0, max=360.0, step=1.0),
+                        IO.Float.Input("pitch", default=30.0, min=-89.0, max=89.0, step=1.0),
+                        IO.Float.Input("distance", default=4.0, min=0.01, max=1000.0, step=0.01,
+                                       tooltip="Camera distance from the target."),
+                    ]),
+                    IO.DynamicCombo.Option("look_at", [
+                        IO.Float.Input("position_x", default=4.0, min=-1000.0, max=1000.0, step=0.01,
+                                       tooltip="Camera position in world space (right-handed, Y-up)."),
+                        IO.Float.Input("position_y", default=4.0, min=-1000.0, max=1000.0, step=0.01),
+                        IO.Float.Input("position_z", default=4.0, min=-1000.0, max=1000.0, step=0.01),
+                    ]),
+                    IO.DynamicCombo.Option("quaternion", [
+                        IO.Float.Input("position_x", default=4.0, min=-1000.0, max=1000.0, step=0.01,
+                                       tooltip="Camera position in world space (right-handed, Y-up)."),
+                        IO.Float.Input("position_y", default=4.0, min=-1000.0, max=1000.0, step=0.01),
+                        IO.Float.Input("position_z", default=4.0, min=-1000.0, max=1000.0, step=0.01),
+                        IO.Float.Input("quat_x", default=0.0, min=-1.0, max=1.0, step=0.001),
+                        IO.Float.Input("quat_y", default=0.0, min=-1.0, max=1.0, step=0.001),
+                        IO.Float.Input("quat_z", default=0.0, min=-1.0, max=1.0, step=0.001),
+                        IO.Float.Input("quat_w", default=1.0, min=-1.0, max=1.0, step=0.001,
+                                       tooltip="Camera world-rotation quaternion (three.js: looks down local -Z). Normalized for you."),
+                    ]),
+                ], tooltip="How to define the camera: orbit angles, an explicit position, or a position + quaternion."),
+                IO.Float.Input("target_x", default=0.0, min=-1000.0, max=1000.0, step=0.01, advanced=True,
+                               tooltip="Look-at point (orbit pivot / aim). In orbit mode, move it to pan/translate the "
+                                       "whole camera. Ignored in quaternion mode. Defaults to the origin."),
+                IO.Float.Input("target_y", default=0.0, min=-1000.0, max=1000.0, step=0.01, advanced=True),
+                IO.Float.Input("target_z", default=0.0, min=-1000.0, max=1000.0, step=0.01, advanced=True),
+                IO.Float.Input("roll", default=0.0, min=-180.0, max=180.0, step=1.0,
+                               tooltip="Camera roll about the view axis, degrees."),
+                IO.Float.Input("fov", default=35.0, min=1.0, max=120.0, step=1.0,
+                               tooltip="Vertical field of view in degrees."),
+                IO.Float.Input("zoom", default=1.0, min=0.01, max=100.0, step=0.01,
+                               tooltip="Digital zoom (focal-length multiplier). >1 zooms in without moving the camera."),
+                IO.Combo.Input("camera_type", options=["perspective", "orthographic"],
+                               tooltip="Projection used by Render Splat: perspective (foreshortening) or orthographic (parallel)."),
+            ],
+            outputs=[IO.Load3DCamera.Output(display_name="camera_info")],
+        )
+
+    @classmethod
+    def execute(cls, mode, target_x, target_y, target_z, roll, fov, zoom=1.0, camera_type="perspective") -> IO.NodeOutput:
+        dev = comfy.model_management.get_torch_device()
+        kind = mode["mode"]
+        if kind == "quaternion":  # explicit world position + camera rotation
+            position = [mode["position_x"], mode["position_y"], mode["position_z"]]
+            quat = [mode["quat_x"], mode["quat_y"], mode["quat_z"], mode["quat_w"]]
+            return IO.NodeOutput(_quat_camera_info(position, quat, fov, dev, zoom=zoom, camera_type=camera_type))
+        target = [target_x, target_y, target_z]  # orbit pivot / aim; move it to pan the whole camera
+        if kind == "orbit":  # yaw/pitch/distance about the target (world Y-up)
+            y, p = math.radians(mode["yaw"]), math.radians(mode["pitch"])
+            cy, sy, cp, sp = math.cos(y), math.sin(y), math.cos(p), math.sin(p)
+            d = mode["distance"]
+            position = [target_x + d * cp * sy, target_y + d * sp, target_z + d * cp * cy]
+        else:  # look_at: explicit world-space camera position
+            position = [mode["position_x"], mode["position_y"], mode["position_z"]]
+        return IO.NodeOutput(_lookat_camera_info(position, target, fov, dev, zoom=zoom, camera_type=camera_type, roll=roll))
+
+
+class TransformSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TransformSplat",
+            display_name="Transform Splat",
+            search_aliases=["move splat", "rotate splat", "scale splat", "gaussian transform"],
+            category="3d/splat",
+            description="Translate, rotate, and scale a gaussian splat. "
+                        "Non-uniform scale also reshapes every individual splat, slower process.",
+            inputs=[
+                IO.Splat.Input("splat"),
+                IO.Float.Input("translate_x", default=0.0, min=-100.0, max=100.0, step=0.01),
+                IO.Float.Input("translate_y", default=0.0, min=-100.0, max=100.0, step=0.01),
+                IO.Float.Input("translate_z", default=0.0, min=-100.0, max=100.0, step=0.01),
+                IO.Float.Input("rotate_x", default=0.0, min=-360.0, max=360.0, step=1.0),
+                IO.Float.Input("rotate_y", default=0.0, min=-360.0, max=360.0, step=1.0),
+                IO.Float.Input("rotate_z", default=0.0, min=-360.0, max=360.0, step=1.0),
+                IO.Float.Input("scale_x", default=1.0, min=0.01, max=100.0, step=0.01),
+                IO.Float.Input("scale_y", default=1.0, min=0.01, max=100.0, step=0.01),
+                IO.Float.Input("scale_z", default=1.0, min=0.01, max=100.0, step=0.01),
+            ],
+            outputs=[IO.Splat.Output(display_name="splat")],
+        )
+
+    @classmethod
+    def execute(cls, splat, translate_x, translate_y, translate_z,
+                rotate_x, rotate_y, rotate_z, scale_x, scale_y, scale_z) -> IO.NodeOutput:
+        pos = splat.positions
+        dev, dt = pos.device, pos.dtype
+        q_rot = _euler_to_quat(rotate_x, rotate_y, rotate_z).to(device=dev, dtype=dt)
+        R = _quat_to_mat(q_rot[None])[0]                            # (3, 3) node rotation
+        D = torch.tensor([scale_x, scale_y, scale_z], dtype=dt, device=dev)
+        A = D[:, None] * R                                          # diag(D) @ R: per-axis scale after rotation
+        t = torch.tensor([translate_x, translate_y, translate_z], dtype=dt, device=dev)
+
+        positions = pos @ A.T + t                                   # rotate, scale per-axis, then translate
+        if scale_x == scale_y == scale_z:                           # uniform: rotation/scale factor out cleanly
+            scales = splat.scales * scale_x
+            rotations = _quat_mul(q_rot.expand_as(splat.rotations), splat.rotations)
+            rotations = rotations / rotations.norm(dim=-1, keepdim=True).clamp_min(1e-12)
+        else:                                                       # non-uniform: transform Sigma = A R s^2 R^T A^T, re-extract
+            rg = _quat_to_mat(splat.rotations.reshape(-1, 4))       # (M,3,3) per-splat rotation
+            s2 = splat.scales.reshape(-1, 3).square()
+            cov = (rg * s2[:, None, :]) @ rg.transpose(-1, -2)      # Sigma
+            cov = A @ cov @ A.T                                     # A Sigma A^T (A broadcast over splats)
+            lam, V = torch.linalg.eigh(cov)                         # symmetric -> eigenvalues (asc), orthonormal axes
+            V = V * torch.where(torch.linalg.det(V) < 0, -1.0, 1.0)[..., None, None]   # keep a proper rotation
+            scales = lam.clamp_min(0).sqrt().reshape(splat.scales.shape)
+            rotations = _mat_to_quat(V).reshape(splat.rotations.shape)
+        out = Types.SPLAT(positions, scales, rotations, splat.opacities, splat.sh,
+                             counts=getattr(splat, "counts", None))
+        return IO.NodeOutput(out)
+
+
+class GetSplatCount(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GetSplatCount",
+            display_name="Get Splat Count",
+            search_aliases=["splat count", "gaussian count", "number of splats", "splat info"],
+            category="3d/splat",
+            description="Returns the number of splats summed across the batch.",
+            inputs=[IO.Splat.Input("splat")],
+            outputs=[IO.Splat.Output(display_name="splat"),
+                     IO.Int.Output(display_name="count"),
+                     ],
+            hidden=[IO.Hidden.unique_id],
+        )
+
+    @classmethod
+    def execute(cls, splat) -> IO.NodeOutput:
+        count = sum(_real_len(splat, i) for i in range(splat.positions.shape[0]))
+        if cls.hidden.unique_id:  # show the count inline on the node
+            PromptServer.instance.send_progress_text(f"{count:,} splats", cls.hidden.unique_id)
+        return IO.NodeOutput(splat, count)
+
+
+def _pad_stack(items, n):
+    # Stack a list of (Lᵢ, *tail) tensors into (B, n, *tail), zero-padding each row up to n.
+    tail = items[0].shape[1:]
+    out = items[0].new_zeros((len(items), n, *tail))
+    for i, t in enumerate(items):
+        out[i, :t.shape[0]] = t
+    return out
+
+
+def _merge_gaussians(gaussians: list) -> Types.SPLAT:
+    # Concatenate SPLAT batches along the splat dimension (per item), padding SH to the highest degree.
+    gs = [g for g in gaussians if g is not None]
+    if not gs:
+        raise ValueError("MergeSplat: no gaussians to merge")
+    b = gs[0].positions.shape[0]
+    for g in gs:
+        if g.positions.shape[0] != b:
+            raise ValueError(f"MergeSplat: batch size mismatch ({b} vs {g.positions.shape[0]}).")
+    max_k = max(g.sh.shape[2] for g in gs)
+
+    pos_b, scl_b, rot_b, op_b, sh_b, lengths = [], [], [], [], [], []
+    for i in range(b):
+        pos_i, scl_i, rot_i, op_i, sh_i = [], [], [], [], []
+        for g in gs:
+            end = _real_len(g, i)
+            pos_i.append(g.positions[i, :end])
+            scl_i.append(g.scales[i, :end])
+            rot_i.append(g.rotations[i, :end])
+            op_i.append(g.opacities[i, :end])
+            sh = g.sh[i, :end]       # (end, K, 3)
+            if sh.shape[1] < max_k:  # zero-pad lower-degree SH
+                sh = torch.cat([sh, sh.new_zeros(sh.shape[0], max_k - sh.shape[1], sh.shape[2])], dim=1)
+            sh_i.append(sh)
+        pos_b.append(torch.cat(pos_i))
+        scl_b.append(torch.cat(scl_i))
+        rot_b.append(torch.cat(rot_i))
+        op_b.append(torch.cat(op_i))
+        sh_b.append(torch.cat(sh_i))
+        lengths.append(pos_b[-1].shape[0])
+
+    n = max(lengths)
+    counts = None
+    if len(set(lengths)) > 1:
+        counts = torch.tensor(lengths, device=gs[0].positions.device, dtype=torch.int64)
+    return Types.SPLAT(_pad_stack(pos_b, n), _pad_stack(scl_b, n), _pad_stack(rot_b, n),
+                          _pad_stack(op_b, n), _pad_stack(sh_b, n), counts=counts)
+
+
+class MergeSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        # Autogrow: a splat0/splat1/... input list that grows a fresh slot as you connect splats.
+        splats = IO.Autogrow.TemplatePrefix(IO.Splat.Input("splat"), prefix="splat", min=2, max=32)
+        return IO.Schema(
+            node_id="MergeSplat",
+            display_name="Merge Splats",
+            search_aliases=["union splat", "densify gaussian", "combine splat", "merge gaussian"],
+            category="3d/splat",
+            description="Concatenate any number of gaussian splats into one. Unioning several decodes of the same "
+                        "latent at different seeds densifies the surface, this can improve surface quality when meshing.",
+            inputs=[IO.Autogrow.Input("splats", template=splats)],
+            outputs=[IO.Splat.Output(display_name="splat")],
+        )
+
+    @classmethod
+    def execute(cls, splats: IO.Autogrow.Type) -> IO.NodeOutput:
+        gs = [v for v in splats.values() if v is not None]
+        if not gs:
+            raise ValueError("MergeSplat: connect at least one splat.")
+        return IO.NodeOutput(_merge_gaussians(gs))
+
+
+def _inverse_covariance(scale, quat):
+    # Per-splat Sigma^-1 = R diag(1/s^2) R^T. scale (N,3) linear std, quat (N,4) wxyz -> (N,3,3).
+    q = quat / quat.norm(dim=1, keepdim=True).clamp_min(1e-12)
+    w, x, y, z = q.unbind(-1)
+    R = torch.stack([
+        1 - 2 * (y * y + z * z), 2 * (x * y - w * z), 2 * (x * z + w * y),
+        2 * (x * y + w * z), 1 - 2 * (x * x + z * z), 2 * (y * z - w * x),
+        2 * (x * z - w * y), 2 * (y * z + w * x), 1 - 2 * (x * x + y * y),
+    ], dim=1).reshape(-1, 3, 3)
+    inv_s2 = 1.0 / scale.clamp_min(1e-8) ** 2                       # (N, 3)
+    return torch.einsum("nij,nj,nkj->nik", R, inv_s2, R)
+
+
+def _splat_density(xyz, opacity, scale, quat, rgb, res, kernel, device, color_sharpen=1.0, chunk=4096, progress=None,
+                   col_dtype=torch.float16):
+    # Splat each gaussian as its oriented-covariance disk (3-sigma, opacity-weighted) into a density grid,
+    # plus a colour volume. Each gaussian uses a voxel window sized to its OWN 3-sigma (capped at `kernel`).
+    # Colour is weighted by w^color_sharpen: >1 biases each voxel toward its dominant gaussian (crisper
+    # texture). Returns (density, colour numerator, colour normaliser, origin, voxel).
+    pad = 4.0 * scale.median()
+    lo = xyz.amin(0) - pad
+    hi = xyz.amax(0) + pad
+    voxel = ((hi - lo).max() / res).clamp_min(1e-8)
+    dx, dy, dz = (torch.ceil((hi - lo) / voxel).long() + 1).tolist()
+
+    sinv = _inverse_covariance(scale, quat)
+    kreq = torch.ceil(3.0 * scale.amax(-1) / voxel).long().clamp(1, int(kernel))  # per-gaussian half-width
+    sharp = color_sharpen != 1.0
+    vol = torch.zeros(dx * dy * dz, device=device)                                          # Sum(w) density (surface)
+    colvol = torch.zeros(dx * dy * dz, 3, device=device, dtype=col_dtype)                   # Sum(w^p * rgb) colour numerator
+    wcol = torch.zeros(dx * dy * dz, device=device, dtype=col_dtype) if sharp else None     # Sum(w^p) normaliser (p>1)
+    n, done = xyz.shape[0], 0
+    for k in range(1, int(kernel) + 1):
+        sel = (kreq == k).nonzero(as_tuple=True)[0]
+        if sel.numel() == 0:
+            continue
+        rng = torch.arange(-k, k + 1, device=device, dtype=torch.float32)
+        off = torch.stack(torch.meshgrid(rng, rng, rng, indexing="ij"), -1).reshape(-1, 3)  # (M, 3)
+        for st in range(0, sel.numel(), chunk):
+            gi = sel[st:st + chunk]
+            cc = xyz[gi]
+            idx = ((cc - lo) / voxel).round()[:, None, :] + off[None]      # (b, M, 3) voxel coords
+            d = (lo + idx * voxel) - cc[:, None, :]                        # world offset to voxel center
+            quad = torch.einsum("bmi,bij,bmj->bm", d, sinv[gi], d)
+            wgt = opacity[gi, None] * torch.exp(-0.5 * quad)
+            wgt = torch.where(quad < 9.0, wgt, torch.zeros_like(wgt))      # clip beyond 3 sigma
+            ii = idx.long()
+            ix = ii[..., 0].clamp(0, dx - 1)
+            iy = ii[..., 1].clamp(0, dy - 1)
+            iz = ii[..., 2].clamp(0, dz - 1)
+            flat = (ix * (dy * dz) + iy * dz + iz).reshape(-1)
+            vol.index_add_(0, flat, wgt.reshape(-1))
+            wp = wgt.pow(color_sharpen) if sharp else wgt                  # winner-take-more colour weight
+            colvol.index_add_(0, flat, (wp[..., None] * rgb[gi, None, :]).reshape(-1, 3).to(col_dtype))
+            if sharp:
+                wcol.index_add_(0, flat, wp.reshape(-1).to(col_dtype))
+            done += gi.numel()
+            if progress is not None:
+                progress(min(1.0, done / max(1, n)))
+    colnorm = (wcol if sharp else vol).reshape(dx, dy, dz)                 # p==1 -> Sum(w) == density
+    return vol.reshape(dx, dy, dz), colvol.reshape(dx, dy, dz, 3), colnorm, lo.cpu().numpy(), float(voxel)
+
+
+def _connected_components_gpu(faces, nv):
+    # FastSV connected components: grandparent hooking + shortcutting, ~O(log nv) iterations.
+    # Returns per-vertex component labels (min node id, not densified).
+    a = torch.cat([faces[:, 0], faces[:, 1]])                              # 2F edge endpoints: (v0,v1),(v1,v2)
+    b = torch.cat([faces[:, 1], faces[:, 2]])
+    f = torch.arange(nv, device=faces.device)
+    while True:
+        gp = f[f]                                                          # grandparent
+        ga, gb = gp[a], gp[b]
+        new = f.clone()
+        new.scatter_reduce_(0, f[a], gb, "amin", include_self=True)        # stochastic hooking onto roots
+        new.scatter_reduce_(0, f[b], ga, "amin", include_self=True)
+        new.scatter_reduce_(0, a, gb, "amin", include_self=True)           # aggressive hooking, both directions
+        new.scatter_reduce_(0, b, ga, "amin", include_self=True)
+        new = new[new]                                                     # shortcut (path compression)
+        if torch.equal(new, f):
+            return f
+        f = new
+
+
+def _clean_components_gpu(verts, faces, min_verts, device):
+    # GPU port of _clean_components: FastSV components + scatter reductions. Byte-identical to the numpy path
+    vt = torch.as_tensor(verts, device=device)
+    ft = torch.as_tensor(faces, device=device)
+    nv = vt.shape[0]
+    _, label = torch.unique(_connected_components_gpu(ft, nv), return_inverse=True)  # dense 0..ncomp-1
+    ncomp = int(label.max()) + 1
+    flabel = label[ft[:, 0]]                                                         # component id per face
+    keep = torch.bincount(label, minlength=ncomp) >= min_verts                       # per-component vertex-count gate
+    if int(keep.sum()) > 1:
+        fcount = torch.bincount(flabel, minlength=ncomp)
+        largest = int(torch.where(keep, fcount, fcount.new_tensor(-1)).argmax())
+        v0, v1, v2 = vt[ft[:, 0]], vt[ft[:, 1]], vt[ft[:, 2]]
+        cvol = torch.zeros(ncomp, device=device).scatter_add_(0, flabel, (v0 * torch.linalg.cross(v1, v2)).sum(-1))
+        idx3 = label[:, None].expand(-1, 3)  # per-component vertex bbox
+        cmin = torch.full((ncomp, 3), float("inf"), device=device).scatter_reduce_(0, idx3, vt, "amin", include_self=True)
+        cmax = torch.full((ncomp, 3), float("-inf"), device=device).scatter_reduce_(0, idx3, vt, "amax", include_self=True)
+        tol = 1e-4 * (cmax[largest] - cmin[largest]).max()
+        enclosed = (cmin >= cmin[largest] - tol).all(1) & (cmax <= cmax[largest] + tol).all(1)
+        inner = enclosed & (torch.sign(cvol) != torch.sign(cvol[largest])) & (torch.arange(ncomp, device=device) != largest)
+        keep &= ~inner
+    faces_k = ft[keep[flabel]]
+    if faces_k.shape[0] == 0:
+        return verts[:0], faces[:0]
+    used = torch.unique(faces_k)  # sorted, matches np.unique
+    remap = torch.full((nv,), -1, dtype=torch.int64, device=device)
+    remap[used] = torch.arange(used.shape[0], device=device)
+    return vt[used].cpu().numpy(), remap[faces_k].cpu().numpy()
+
+
+def _clean_components(verts, faces, min_verts, device=None):
+    # Drop floaters (components with < min_verts vertices) and inner shells - the surfel shell density
+    # extracts a double wall (outer + inner cavity surface). GPU path (FastSV CC + scatter reductions, ~13x
+    # faster) when an accelerator has headroom; else numpy/scipy. Both produce byte-identical output.
+    if device is not None and not comfy.model_management.is_device_cpu(device) and \
+            comfy.model_management.get_free_memory(device) > 10 * faces.size * 8:   # peak ~8.4x faces bytes
+        return _clean_components_gpu(verts, faces, min_verts, device)
+    nv = len(verts)
+    e = np.concatenate([faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [0, 2]]], 0)
+    ncomp, label = connected_components(coo_matrix((np.ones(len(e)), (e[:, 0], e[:, 1])), shape=(nv, nv)), directed=False)
+    flabel = label[faces[:, 0]]                              # component id per face
+    keep = np.bincount(label, minlength=ncomp) >= min_verts  # per-component vertex-count gate
+    if keep.sum() > 1:
+        fcount = np.bincount(flabel, minlength=ncomp)
+        largest = np.where(keep, fcount, -1).argmax()
+        v0, v1, v2 = verts[faces[:, 0]], verts[faces[:, 1]], verts[faces[:, 2]]
+        cvol = np.bincount(flabel, weights=np.einsum("ij,ij->i", v0, np.cross(v1, v2)), minlength=ncomp)  # 6*signed vol
+        cidx = np.arange(ncomp)                                  # per-component vertex bbox via ndimage (~6x faster than ufunc.at)
+        cmin = np.stack([_ndi_minimum(verts[:, a], label, cidx) for a in range(3)], 1)
+        cmax = np.stack([_ndi_maximum(verts[:, a], label, cidx) for a in range(3)], 1)
+        tol = 1e-4 * (cmax[largest] - cmin[largest]).max()
+        enclosed = (cmin >= cmin[largest] - tol).all(1) & (cmax <= cmax[largest] + tol).all(1)
+        inner = enclosed & (np.sign(cvol) != np.sign(cvol[largest])) & (np.arange(ncomp) != largest)
+        keep &= ~inner
+    faces = faces[keep[flabel]]
+    if len(faces) == 0:
+        return verts[:0], faces
+    used = np.unique(faces)
+    remap = np.full(nv, -1, np.int64)
+    remap[used] = np.arange(len(used))
+    return verts[used], remap[faces]
+
+
+def _surface_nets(vol, level, voxel, origin, device):
+    # Vectorized Surface Nets: one dual vertex per sign-changing cell at its edge-crossing mean, quads wound CCW-outward.
+    # Returns verts (V,3), faces (F,3).
+    vol = vol.to(device=device, dtype=torch.float32)
+    dx, dy, dz = vol.shape
+    origin_t = torch.as_tensor(origin, device=device, dtype=torch.float32)
+    empty = (np.zeros((0, 3), np.float32), np.zeros((0, 3), np.int64))
+    if dx < 2 or dy < 2 or dz < 2:
+        return empty
+
+    # Active = cells whose 8 corners aren't all in/all out.
+    inside = vol >= level  # (dx,dy,dz) bool
+    cs8 = [inside[ox:ox + dx - 1, oy:oy + dy - 1, oz:oz + dz - 1]
+           for ox, oy, oz in ((0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0),
+                              (0, 0, 1), (1, 0, 1), (0, 1, 1), (1, 1, 1))]
+    any_in = cs8[0] | cs8[1] | cs8[2] | cs8[3] | cs8[4] | cs8[5] | cs8[6] | cs8[7]
+    all_in = cs8[0] & cs8[1] & cs8[2] & cs8[3] & cs8[4] & cs8[5] & cs8[6] & cs8[7]
+    active = any_in & ~all_in  # (cx,cy,cz) straddling cells
+    nv = int(active.sum())
+    if nv == 0:
+        return empty
+
+    # Active cells only (a thin shell): each dual vertex = mean of its 12 edges' zero-crossings.
+    del any_in, all_in, cs8                                             # corner bool grids no longer needed
+    ac = active.nonzero(as_tuple=False)                                 # (nv,3) cell min-corner indices
+    offs = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0],
+                         [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]], device=device)
+    offf = offs.to(torch.float32)
+    edges = torch.tensor([[0, 1], [0, 2], [0, 4], [1, 3], [1, 5], [2, 3],
+                          [2, 6], [3, 7], [4, 5], [4, 6], [5, 7], [6, 7]], device=device)
+    e0, e1 = edges[:, 0], edges[:, 1]
+    oe0, oe1 = offf[e0], offf[e1]                                       # (12,3) edge endpoints
+
+    cstep = 1 << 18                                                     # chunk to bound peak memory (CPU RAM too)
+    loc = []
+    for st in range(0, nv, cstep):
+        ci = ac[st:st + cstep, None, :] + offs[None]                    # (m,8,3)
+        cval = vol[ci[..., 0], ci[..., 1], ci[..., 2]]                  # (m,8) corner values
+        csl = cval >= level
+        v0, v1 = cval[:, e0], cval[:, e1]                               # (m,12)
+        cross = (csl[:, e0] != csl[:, e1])[..., None].to(torch.float32)
+        denom = v1 - v0
+        t = torch.where(denom.abs() > 1e-12, (level - v0) / denom, torch.full_like(denom, 0.5)).clamp(0, 1)
+        pts = torch.lerp(oe0, oe1, t[..., None])                        # (m,12,3) local crossings (fused interp)
+        loc.append((pts * cross).sum(1) / cross.sum(1).clamp_min(1.0))  # (m,3) in [0,1]
+    local = torch.cat(loc, 0) if len(loc) > 1 else loc[0]               # (nv,3)
+    verts = origin_t + (ac.to(torch.float32) + local) * voxel           # world space
+    del loc, local, ac
+
+    vid = torch.full((dx - 1, dy - 1, dz - 1), -1, dtype=torch.int32, device=device)
+    vid[active] = torch.arange(nv, dtype=torch.int32, device=device)
+    del active
+
+    # Each straddling grid edge -> one quad from its 4 cells; `sol` (low-end sign) picks outward winding.
+    faces = []
+
+    def emit(cr, sol, a, b, d, c):
+        valid = cr & (a >= 0) & (b >= 0) & (c >= 0) & (d >= 0)
+        if not bool(valid.any()):
+            return
+        a, b, c, d, sol = a[valid], b[valid], c[valid], d[valid], sol[valid]
+        p2, p4 = torch.where(sol, b, c), torch.where(sol, c, b)  # reverse quad winding where ~sol
+        faces.append(torch.stack([a, p2, d], 1))
+        faces.append(torch.stack([a, d, p4], 1))
+
+    a = inside[0:dx - 1, 1:dy - 1, 1:dz - 1]
+    emit(a != inside[1:dx, 1:dy - 1, 1:dz - 1], a,
+         vid[:, 0:dy - 2, 0:dz - 2], vid[:, 1:dy - 1, 0:dz - 2],
+         vid[:, 1:dy - 1, 1:dz - 1], vid[:, 0:dy - 2, 1:dz - 1])
+    a = inside[1:dx - 1, 0:dy - 1, 1:dz - 1]
+    emit(a != inside[1:dx - 1, 1:dy, 1:dz - 1], a,
+         vid[0:dx - 2, :, 0:dz - 2], vid[0:dx - 2, :, 1:dz - 1],
+         vid[1:dx - 1, :, 1:dz - 1], vid[1:dx - 1, :, 0:dz - 2])
+    a = inside[1:dx - 1, 1:dy - 1, 0:dz - 1]
+    emit(a != inside[1:dx - 1, 1:dy - 1, 1:dz], a,
+         vid[0:dx - 2, 0:dy - 2, :], vid[1:dx - 1, 0:dy - 2, :],
+         vid[1:dx - 1, 1:dy - 1, :], vid[0:dx - 2, 1:dy - 1, :])
+
+    if not faces:
+        return empty
+    return verts.cpu().numpy().astype(np.float32), torch.cat(faces, 0).cpu().numpy().astype(np.int64)
+
+
+def _otsu_level(values, bins=256):
+    # Otsu threshold: the density value that best splits inside/outside (max between-class variance).
+    hist, edges = np.histogram(values, bins=bins)
+    hist = hist.astype(np.float64)
+    centers = (edges[:-1] + edges[1:]) * 0.5
+    w = np.cumsum(hist)  # background-class weight at each split
+    mu = np.cumsum(hist * centers)
+    wf = w[-1] - w  # foreground-class weight
+    mb = mu / np.where(w > 0, w, 1.0)
+    mf = (mu[-1] - mu) / np.where(wf > 0, wf, 1.0)
+    var_b = w * wf * (mb - mf) ** 2  # between-class variance
+    var_b[(w <= 0) | (wf <= 0)] = -1.0
+    return float(centers[int(np.argmax(var_b))])
+
+
+def _taubin_smooth(verts, faces, iters, lam=0.5, mu=-0.53):
+    # Taubin lambda|mu smoothing: low-pass the mesh surface without the shrinkage of a Laplacian blur
+    # (the mu inflation pass cancels the lambda pass's volume loss). Uniform (umbrella) weights.
+    if iters <= 0 or len(verts) == 0 or len(faces) == 0:
+        return verts
+    nv = len(verts)
+    e = np.concatenate([faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [0, 2]]], 0)
+    e = np.concatenate([e, e[:, ::-1]], 0)  # symmetric adjacency
+    adj = coo_matrix((np.ones(len(e), np.float32), (e[:, 0], e[:, 1])), shape=(nv, nv)).tocsr()
+    adj.data[:] = 1.0
+    deg = np.clip(np.asarray(adj.sum(1)).ravel(), 1.0, None).astype(np.float32)[:, None]
+    v = verts.astype(np.float32)  # fp32 matvec: ~2x faster, sub-micron drift on unit-scale verts
+    for _ in range(int(iters)):
+        for fac in (lam, mu):
+            v = v + np.float32(fac) * ((adj @ v) / deg - v)  # fac * (mean(neighbours) - v)
+    return np.ascontiguousarray(v)
+
+
+def _sample_vertex_colours_gpu(colvol, colnorm, verts, origin, voxel, device):
+    # GPU trilinear sampling of the colour numerator (3ch) and normaliser (1ch) at vertex grid-coords
+    # reproduces scipy map_coordinates(order=1, mode='nearest'). Returns col (V,3) numpy.
+    dx, dy, dz = colnorm.shape
+    vt = torch.as_tensor(verts, device=device, dtype=torch.float32)
+    org = torch.as_tensor(origin, device=device, dtype=torch.float32)
+    gi = (vt - org) / voxel                                                   # (V,3) grid-index coords (x,y,z)
+    size = torch.tensor([dx, dy, dz], device=device, dtype=torch.float32)
+    g = 2.0 * gi / (size - 1).clamp_min(1.0) - 1.0                            # -> [-1,1] (align_corners)
+    grid = torch.stack([g[:, 2], g[:, 1], g[:, 0]], -1)[None, None, None]     # (1,1,1,V,3): grid_sample order (W=z,H=y,D=x)
+
+    def samp(v):                                                             # (dx,dy,dz,C) cpu fp16 -> (C,V) fp32 on device
+        inp = v.to(device).permute(3, 0, 1, 2)[None].float()
+        o = torch.nn.functional.grid_sample(inp, grid, mode="bilinear", padding_mode="border", align_corners=True)
+        return o[0, :, 0, 0, :]
+    num = samp(colvol)                                                        # (3,V)
+    den = samp(colnorm[..., None])                                            # (1,V)
+    return (num / den.clamp_min(1e-8)).T.cpu().numpy()                        # (V,3)
+
+
+def _gaussian_to_mesh(g: Types.SPLAT, i, res, kernel, taubin, level_bias, min_component, min_opacity, color_sharpen, device, progress=None):
+    # Mesh one splat: density + colour grids -> Surface Nets -> floater removal -> Taubin smoothing ->
+    # volume-sampled colours. Returns (verts, faces int64, colors in [0,1]), or None if no surface.
+    rep = progress if progress is not None else (lambda *_: None)
+
+    end = _real_len(g, i)
+    xyz = g.positions[i, :end].to(device=device, dtype=torch.float32)
+    scale = g.scales[i, :end].to(device=device, dtype=torch.float32)
+    quat = g.rotations[i, :end].to(device=device, dtype=torch.float32)
+    opacity = g.opacities[i, :end].reshape(-1).to(device=device, dtype=torch.float32)
+    rgb = (g.sh[i, :end, 0, :].to(device=device, dtype=torch.float32) * _C0 + 0.5).clamp(0, 1)
+
+    keep = opacity >= min_opacity
+    xyz, scale, quat, opacity, rgb = xyz[keep], scale[keep], quat[keep], opacity[keep], rgb[keep]
+    if xyz.shape[0] == 0:
+        return None
+
+    vol, colvol, colnorm, origin, voxel = _splat_density(xyz, opacity, scale, quat, rgb, res, kernel, device,
+                                                         color_sharpen=color_sharpen,
+                                                         progress=lambda f: rep(0.25 * f))   # density build: 0 -> 25%
+    # Colour: sample on the GPU (grid_sample) when there's headroom
+    colour_gpu = not comfy.model_management.is_device_cpu(device) and comfy.model_management.get_free_memory(device) > 6 * vol.numel() * 4
+    if colour_gpu:
+        colvol_cpu, colnorm_cpu = colvol.cpu(), colnorm.half().cpu()  # park colours (fp16) off-GPU during meshing
+        colvol_np = colnorm_np = None
+    else:
+        colvol_np = colvol.cpu().numpy().astype(np.float32)  # Sum(w^p * rgb) colour numerator (fp16 grid -> fp32)
+        colnorm_np = colnorm.cpu().numpy().astype(np.float32)  # Sum(w^p) colour normaliser
+    del colvol, colnorm                                      # free the colour grids before iso-surfacing
+    rep(0.40)
+
+    vmin, vmax = float(vol.min()), float(vol.max())
+    occ = vol[vol > vmax * 1e-3]                             # occupied voxels (skip the empty-space peak)
+    if occ.numel() == 0:
+        return None
+    # Otsu picks the inside/outside split principledly; `level_bias` nudges it (1.0 = auto). Clamp strictly
+    # inside the data range so a bias can't push the iso off the histogram.
+    level = min(max(_otsu_level(occ.cpu().numpy()) * level_bias, vmin + 1e-6 * (vmax - vmin)),
+                vmax - 1e-6 * (vmax - vmin))
+
+    # Iso-surface on the accelerator when there's headroom: ~15x faster than CPU, identical output. Chunked
+    # Surface Nets peaks at ~3-3.5x the density grid, so fall back to CPU for large grids / tight VRAM.
+    sn_dev = device
+    if not comfy.model_management.is_device_cpu(device) and comfy.model_management.get_free_memory(device) < 6 * vol.numel() * 4:
+        sn_dev = torch.device("cpu")
+        vol = vol.cpu()
+    verts, faces = _surface_nets(vol, level, voxel, origin, sn_dev)
+    del vol
+    rep(0.55)
+    if min_component > 0 and len(faces) > 0:
+        verts, faces = _clean_components(verts, faces, min_component, device)
+    if len(verts) == 0 or len(faces) == 0:
+        return None
+
+    # Taubin smooths the blocky iso without shrinking it (unlike blurring the density, which rounds features).
+    verts = _taubin_smooth(verts, faces, taubin)
+    rep(0.7)
+
+    # Colour each vertex from the co-splatted colour volume: trilinearly sample the numerator Sum(w^p*rgb)
+    # and normaliser Sum(w^p) separately, then divide. Normalising AFTER interpolation keeps zero-density
+    # edge voxels from pulling colours toward black, and matches the gaussians that formed the surface.
+    if colour_gpu:
+        col = _sample_vertex_colours_gpu(colvol_cpu, colnorm_cpu, verts, origin, voxel, device)
+    else:
+        coords = ((verts - origin) / voxel).T  # (3, V) grid-index coords, matching volume axes
+        num = np.stack([map_coordinates(colvol_np[..., c], coords, order=1, mode="nearest") for c in range(3)], -1)
+        den = map_coordinates(colnorm_np, coords, order=1, mode="nearest")
+        col = num / np.clip(den, 1e-8, None)[:, None]
+    rep(1.0)
+
+    # The unlit material's COLOR_0 is linear and the viewer sRGB-encodes it on output; the splat colours
+    # are display (sRGB) values, so convert sRGB -> linear here to land at the same brightness as the splat.
+    col = np.clip(col, 0, 1)
+    col = np.where(col <= 0.04045, col / 12.92, ((col + 0.055) / 1.055) ** 2.4).astype(np.float32)
+
+    # Splat +Y is glTF's -Y: rotate 180 deg about X (negate Y,Z) to land upright. Proper rotation, so
+    # winding is kept; done after colouring (which works in the splat frame).
+    verts = np.ascontiguousarray(verts * np.array([1.0, -1.0, -1.0], dtype=np.float32))
+    return (torch.from_numpy(verts), torch.from_numpy(faces), torch.from_numpy(col))
+
+
+class SplatToMesh(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SplatToMesh",
+            display_name="Extract Mesh from Splat",
+            search_aliases=["splat to mesh", "gaussian surface nets", "splat surface", "mesh splat"],
+            category="3d/splat",
+            description="Extract a coloured mesh from a gaussian splat.",
+            inputs=[
+                IO.Splat.Input("splat"),
+                IO.Int.Input("resolution", default=384, min=64, max=768, step=16,
+                             tooltip="Density-grid resolution along the longest axis. Higher = finer surface, "
+                                     "more VRAM/time (grows with resolution^3)."),
+                IO.Int.Input("kernel", default=5, min=1, max=8,
+                             tooltip="Max splat half-width in voxels. Each gaussian is rasterized over a window "
+                                     "sized to its own 3-sigma, capped here - small surfels stay cheap, large ones "
+                                     "aren't truncated. Raise if sparse splats leave gaps."),
+                IO.Int.Input("smooth", default=0, min=0, max=60, advanced = True,
+                             tooltip="Taubin mesh-smoothing iterations. Smooths the surface without shrinking it "
+                                     "(volume-preserving), unlike blurring the density. 0 = raw surface."),
+                IO.Float.Input("level", default=0.4, min=0.0, max=2.0, step=0.01,
+                               tooltip="Iso-surface level. Auto-picked by Otsu; this biases it (1.0 = auto, lower = "
+                                       "fatter/more-connected surface, higher = thinner/tighter)."),
+                IO.Int.Input("min_component", default=500, min=0, max=100000, step=50, advanced=True,
+                             tooltip="Drop connected components smaller than this many vertices (0 = keep all). "
+                                     "Removes detached floater blobs and the inner shell of the double wall."),
+                IO.Float.Input("min_opacity", default=0.02, min=0.0, max=1.0, step=0.01, advanced=True,
+                               tooltip="Ignore gaussians fainter than this before meshing."),
+                IO.Float.Input("color_sharpen", default=2.0, min=1.0, max=8.0, step=0.5,
+                               tooltip="Crisp up the vertex texture: 1.0 = physically-correct blend; higher biases "
+                                       "each voxel's colour toward its dominant gaussian instead of averaging "
+                                       "neighbours (de-smears the texture). Colour only - geometry is unchanged."),
+            ],
+            outputs=[IO.Mesh.Output(display_name="mesh")],
+        )
+
+    @classmethod
+    def execute(cls, splat, resolution, kernel, smooth, level, min_component, min_opacity, color_sharpen) -> IO.NodeOutput:
+        device = comfy.model_management.get_torch_device()
+        b = splat.positions.shape[0]
+        prec = 1000  # each splat owns a 0..prec block of the bar; its callback advances within that block
+        pbar = comfy.utils.ProgressBar(b * prec)
+
+        verts_l, faces_l, colors_l = [], [], []
+        for i in range(b):
+            cb = lambda f, base=i * prec: pbar.update_absolute(base + int(min(max(f, 0.0), 1.0) * prec))
+            res = _gaussian_to_mesh(splat, i, resolution, kernel, smooth, level, min_component, min_opacity, color_sharpen, device, cb)
+            if res is None:
+                logging.warning("SplatToMesh: splat %d produced no surface; emitting an empty mesh.", i)
+                v, f, c = torch.zeros((0, 3)), torch.zeros((0, 3), dtype=torch.int64), torch.zeros((0, 3))
+            else:
+                v, f, c = res
+            verts_l.append(v)
+            faces_l.append(f)
+            colors_l.append(c)
+            pbar.update_absolute((i + 1) * prec)  # snap to block end (covers empty / early-out splats)
+        # unlit: render flat (emissive-like) so SaveGLB matches the splat instead of lighting/washing it.
+        return IO.NodeOutput(pack_variable_mesh_batch(verts_l, faces_l, colors=colors_l, unlit=True))
+
+
+class GaussianExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [SplatToFile3D, File3DToSplat, RenderSplat, CreateCameraInfo, TransformSplat,
+                GetSplatCount, MergeSplat, SplatToMesh]
+
+
+async def comfy_entrypoint() -> GaussianExtension:
+    return GaussianExtension()
diff --git a/comfy_extras/nodes_gits.py b/comfy_extras/nodes_gits.py
new file mode 100644
index 0000000000000000000000000000000000000000..46b095f515dc75e0d1b04c07927201bffd7cf00e
--- /dev/null
+++ b/comfy_extras/nodes_gits.py
@@ -0,0 +1,382 @@
+# from https://github.com/zju-pi/diff-sampler/tree/main/gits-main
+import numpy as np
+import torch
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+def loglinear_interp(t_steps, num_steps):
+    """
+    Performs log-linear interpolation of a given array of decreasing numbers.
+    """
+    xs = np.linspace(0, 1, len(t_steps))
+    ys = np.log(t_steps[::-1])
+
+    new_xs = np.linspace(0, 1, num_steps)
+    new_ys = np.interp(new_xs, xs, ys)
+
+    interped_ys = np.exp(new_ys)[::-1].copy()
+    return interped_ys
+
+NOISE_LEVELS = {
+    0.80: [
+        [14.61464119, 7.49001646, 0.02916753],
+        [14.61464119, 11.54541874, 6.77309084, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
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+        [14.61464119, 1.56271636, 0.57119018, 0.19894916, 0.02916753],
+        [14.61464119, 1.61558151, 0.69515091, 0.29807833, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.83188516, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.162866, 0.64427125, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.36964464, 0.803307, 0.50118381, 0.32104823, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.41535246, 0.83188516, 0.54755926, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 0.95350921, 0.64427125, 0.45573691, 0.32104823, 0.22545385, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.61558151, 1.01931262, 0.72133851, 0.52423614, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.61558151, 1.01931262, 0.72133851, 0.52423614, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.61558151, 1.05362725, 0.74807048, 0.54755926, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.4783645, 0.38853383, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.45070267, 1.51179266, 1.01931262, 0.74807048, 0.57119018, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+    1.40: [
+        [14.61464119, 0.59516323, 0.02916753],
+        [14.61464119, 0.95350921, 0.34370604, 0.02916753],
+        [14.61464119, 1.08895338, 0.43325692, 0.13792117, 0.02916753],
+        [14.61464119, 1.56271636, 0.64427125, 0.27464288, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.803307, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
+        [14.61464119, 2.05039096, 0.95350921, 0.54755926, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.72133851, 0.43325692, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.52423614, 0.36617002, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.41535246, 0.86115354, 0.59516323, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.4783645, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.72133851, 0.54755926, 0.43325692, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.05362725, 0.74807048, 0.57119018, 0.45573691, 0.38853383, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.43325692, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+    1.45: [
+        [14.61464119, 0.59516323, 0.02916753],
+        [14.61464119, 0.803307, 0.25053367, 0.02916753],
+        [14.61464119, 0.95350921, 0.34370604, 0.09824532, 0.02916753],
+        [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
+        [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 1.91321158, 0.95350921, 0.57119018, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.19988537, 1.08895338, 0.64427125, 0.41087446, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.72133851, 0.54755926, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+    1.50: [
+        [14.61464119, 0.54755926, 0.02916753],
+        [14.61464119, 0.803307, 0.25053367, 0.02916753],
+        [14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753],
+        [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
+        [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+}
+
+class GITSScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="GITSScheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05, advanced=True),
+                io.Int.Input("steps", default=10, min=2, max=1000),
+                io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Sigmas.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, coeff, steps, denoise):
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return io.NodeOutput(torch.FloatTensor([]))
+            total_steps = round(steps * denoise)
+
+        if steps <= 20:
+            sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:]
+        else:
+            sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:]
+            sigmas = loglinear_interp(sigmas, steps + 1)
+
+        sigmas = sigmas[-(total_steps + 1):]
+        sigmas[-1] = 0
+        return io.NodeOutput(torch.FloatTensor(sigmas))
+
+
+class GITSSchedulerExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            GITSScheduler,
+        ]
+
+
+async def comfy_entrypoint() -> GITSSchedulerExtension:
+    return GITSSchedulerExtension()
diff --git a/comfy_extras/nodes_glsl.py b/comfy_extras/nodes_glsl.py
new file mode 100644
index 0000000000000000000000000000000000000000..256e1034dee4b99ff85904266ef894547512b645
--- /dev/null
+++ b/comfy_extras/nodes_glsl.py
@@ -0,0 +1,846 @@
+import os
+import sys
+import re
+import ctypes
+import logging
+from typing import TypedDict
+
+import numpy as np
+import torch
+
+import nodes
+import comfy_angle
+from comfy_api.latest import ComfyExtension, io, ui
+from typing_extensions import override
+
+logger = logging.getLogger(__name__)
+
+
+def _preload_angle():
+    egl_path = comfy_angle.get_egl_path()
+    gles_path = comfy_angle.get_glesv2_path()
+
+    if sys.platform == "win32":
+        angle_dir = comfy_angle.get_lib_dir()
+        os.add_dll_directory(angle_dir)
+        os.environ["PATH"] = angle_dir + os.pathsep + os.environ.get("PATH", "")
+
+    mode = 0 if sys.platform == "win32" else ctypes.RTLD_GLOBAL
+    ctypes.CDLL(str(egl_path), mode=mode)
+    ctypes.CDLL(str(gles_path), mode=mode)
+
+
+# Pre-load ANGLE *before* any PyOpenGL import so that the EGL platform
+# plugin picks up ANGLE's libEGL / libGLESv2 instead of system libs.
+_preload_angle()
+os.environ.setdefault("PYOPENGL_PLATFORM", "egl")
+
+
+import OpenGL
+OpenGL.USE_ACCELERATE = False
+
+
+def _patch_find_library():
+    """PyOpenGL's EGL platform looks for 'EGL' and 'GLESv2' by short name
+    via ctypes.util.find_library, but ANGLE ships as 'libEGL' and
+    'libGLESv2'.  Patch find_library to return the full ANGLE paths so
+    PyOpenGL loads the same libraries we pre-loaded."""
+    if sys.platform == "linux":
+        return
+    import ctypes.util
+    _orig = ctypes.util.find_library
+    def _patched(name):
+        if name == 'EGL':
+            return comfy_angle.get_egl_path()
+        if name == 'GLESv2':
+            return comfy_angle.get_glesv2_path()
+        return _orig(name)
+    ctypes.util.find_library = _patched
+
+
+_patch_find_library()
+
+from OpenGL import EGL
+from OpenGL import GLES3 as gl
+
+class SizeModeInput(TypedDict):
+    size_mode: str
+    width: int
+    height: int
+
+
+MAX_IMAGES = 5      # u_image0-4
+MAX_UNIFORMS = 20   # u_float0-19, u_int0-19
+MAX_BOOLS = 10      # u_bool0-9
+MAX_CURVES = 4      # u_curve0-3 (1D LUT textures)
+MAX_OUTPUTS = 4     # fragColor0-3 (MRT)
+
+# Vertex shader using gl_VertexID trick - no VBO needed.
+# Draws a single triangle that covers the entire screen:
+#
+#     (-1,3)
+#       /|
+#      / |  <- visible area is the unit square from (-1,-1) to (1,1)
+#     /  |     parts outside get clipped away
+# (-1,-1)---(3,-1)
+#
+# v_texCoord is computed from clip space: * 0.5 + 0.5 maps (-1,1) -> (0,1)
+VERTEX_SHADER = """#version 300 es
+out vec2 v_texCoord;
+void main() {
+    vec2 verts[3] = vec2[](vec2(-1, -1), vec2(3, -1), vec2(-1, 3));
+    v_texCoord = verts[gl_VertexID] * 0.5 + 0.5;
+    gl_Position = vec4(verts[gl_VertexID], 0, 1);
+}
+"""
+
+DEFAULT_FRAGMENT_SHADER = """#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+void main() {
+    fragColor0 = texture(u_image0, v_texCoord);
+}
+"""
+
+
+
+def _egl_attribs(*values):
+    """Build an EGL_NONE-terminated EGLint attribute array."""
+    vals = list(values) + [EGL.EGL_NONE]
+    return (ctypes.c_int32 * len(vals))(*vals)
+
+
+# EGL platform extension constants
+EGL_PLATFORM_ANGLE_ANGLE = 0x3202
+EGL_PLATFORM_ANGLE_TYPE_ANGLE = 0x3203
+EGL_PLATFORM_ANGLE_TYPE_VULKAN_ANGLE = 0x3450
+EGL_MESA_PLATFORM_SURFACELESS = 0x31DD
+
+
+_eglGetPlatformDisplayEXT = None
+
+def _get_egl_platform_display_ext(platform, native_display, attribs):
+    """Call eglGetPlatformDisplayEXT via ctypes (extension, not in PyOpenGL)."""
+    global _eglGetPlatformDisplayEXT
+    if _eglGetPlatformDisplayEXT is None:
+        from OpenGL import platform as _plat
+        egl_lib = _plat.PLATFORM.EGL
+        _get_proc = egl_lib.eglGetProcAddress
+        _get_proc.restype = ctypes.c_void_p
+        _get_proc.argtypes = [ctypes.c_char_p]
+        ptr = _get_proc(b"eglGetPlatformDisplayEXT")
+        if not ptr:
+            return None
+        func_type = ctypes.CFUNCTYPE(ctypes.c_void_p, ctypes.c_uint32, ctypes.c_void_p, ctypes.c_void_p)
+        _eglGetPlatformDisplayEXT = func_type(ptr)
+
+    raw = _eglGetPlatformDisplayEXT(platform, native_display, attribs)
+    if not raw:
+        return None
+    return ctypes.cast(raw, EGL.EGLDisplay)
+
+
+def _get_egl_display():
+    """Get an EGL display, trying the default first then ANGLE's Vulkan
+    platform for headless environments without a display server."""
+    failures = []
+
+    # Try the default display first (works when X11/Wayland is available)
+    display = EGL.eglGetDisplay(EGL.EGL_DEFAULT_DISPLAY)
+    if display:
+        major, minor = ctypes.c_int32(0), ctypes.c_int32(0)
+        try:
+            if EGL.eglInitialize(display, ctypes.byref(major), ctypes.byref(minor)):
+                return display, major.value, minor.value
+        except Exception as e:
+            failures.append(f"default: {e}")
+
+    logger.info("Default EGL display unavailable, trying headless fallbacks")
+
+    # Headless fallback strategies, tried in order:
+    headless_strategies = [
+        ("surfaceless", EGL_MESA_PLATFORM_SURFACELESS, None, None),
+        ("ANGLE Vulkan", EGL_PLATFORM_ANGLE_ANGLE, None,
+         _egl_attribs(EGL_PLATFORM_ANGLE_TYPE_ANGLE, EGL_PLATFORM_ANGLE_TYPE_VULKAN_ANGLE)),
+    ]
+
+    for name, platform, native_display, attribs in headless_strategies:
+        display = _get_egl_platform_display_ext(platform, native_display, attribs)
+        if not display:
+            failures.append(f"{name}: eglGetPlatformDisplayEXT returned no display")
+            continue
+        major, minor = ctypes.c_int32(0), ctypes.c_int32(0)
+        try:
+            if EGL.eglInitialize(display, ctypes.byref(major), ctypes.byref(minor)):
+                logger.info(f"Using EGL {name} platform (headless)")
+                return display, major.value, minor.value
+            failures.append(f"{name}: eglInitialize returned false")
+        except Exception as e:
+            failures.append(f"{name}: {e}")
+            continue
+
+    details = "\n".join(f"  - {f}" for f in failures)
+    raise RuntimeError(
+        "Failed to initialize EGL display.\n"
+        "No display server and no headless EGL platform available.\n"
+        f"Tried:\n{details}\n"
+        "Ensure GPU drivers are installed or set DISPLAY for a virtual framebuffer."
+    )
+
+
+def _gl_str(name):
+    """Get an OpenGL string parameter."""
+    v = gl.glGetString(name)
+    if not v:
+        return "Unknown"
+    if isinstance(v, bytes):
+        return v.decode(errors="replace")
+    return ctypes.string_at(v).decode(errors="replace")
+
+
+def _detect_output_count(source: str) -> int:
+    """Detect how many fragColor outputs are used in the shader.
+
+    Returns the count of outputs needed (1 to MAX_OUTPUTS).
+    """
+    matches = re.findall(r"fragColor(\d+)", source)
+    if not matches:
+        return 1  # Default to 1 output if none found
+    max_index = max(int(m) for m in matches)
+    return min(max_index + 1, MAX_OUTPUTS)
+
+
+def _detect_pass_count(source: str) -> int:
+    """Detect multi-pass rendering from #pragma passes N directive.
+
+    Returns the number of passes (1 if not specified).
+    """
+    match = re.search(r'#pragma\s+passes\s+(\d+)', source)
+    if match:
+        return max(1, int(match.group(1)))
+    return 1
+
+
+class GLContext:
+    """Manages an OpenGL ES 3.0 context via EGL/ANGLE (singleton)."""
+
+    _instance = None
+    _initialized = False
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self):
+        if GLContext._initialized:
+            return
+
+        import time
+        start = time.perf_counter()
+
+        self._display = None
+        self._surface = None
+        self._context = None
+        self._vao = None
+
+        try:
+            self._display, self._egl_major, self._egl_minor = _get_egl_display()
+
+            if not EGL.eglBindAPI(EGL.EGL_OPENGL_ES_API):
+                raise RuntimeError("eglBindAPI(EGL_OPENGL_ES_API) failed")
+
+            config = EGL.EGLConfig()
+            n_configs = ctypes.c_int32(0)
+            if not EGL.eglChooseConfig(
+                self._display,
+                _egl_attribs(
+                    EGL.EGL_RENDERABLE_TYPE, EGL.EGL_OPENGL_ES3_BIT,
+                    EGL.EGL_SURFACE_TYPE, EGL.EGL_PBUFFER_BIT,
+                    EGL.EGL_RED_SIZE, 8, EGL.EGL_GREEN_SIZE, 8,
+                    EGL.EGL_BLUE_SIZE, 8, EGL.EGL_ALPHA_SIZE, 8,
+                ),
+                ctypes.byref(config), 1, ctypes.byref(n_configs),
+            ) or n_configs.value == 0:
+                raise RuntimeError("eglChooseConfig() failed")
+
+            self._surface = EGL.eglCreatePbufferSurface(
+                self._display, config,
+                _egl_attribs(EGL.EGL_WIDTH, 64, EGL.EGL_HEIGHT, 64),
+            )
+            if not self._surface:
+                raise RuntimeError("eglCreatePbufferSurface() failed")
+
+            self._context = EGL.eglCreateContext(
+                self._display, config, EGL.EGL_NO_CONTEXT,
+                _egl_attribs(EGL.EGL_CONTEXT_CLIENT_VERSION, 3),
+            )
+            if not self._context:
+                raise RuntimeError("eglCreateContext() failed")
+
+            if not EGL.eglMakeCurrent(self._display, self._surface, self._surface, self._context):
+                raise RuntimeError("eglMakeCurrent() failed")
+
+            self._vao = gl.glGenVertexArrays(1)
+            gl.glBindVertexArray(self._vao)
+
+        except Exception:
+            self._cleanup()
+            raise
+
+        elapsed = (time.perf_counter() - start) * 1000
+
+        renderer = _gl_str(gl.GL_RENDERER)
+        vendor = _gl_str(gl.GL_VENDOR)
+        version = _gl_str(gl.GL_VERSION)
+
+        GLContext._initialized = True
+        logger.info(f"GLSL context initialized in {elapsed:.1f}ms - EGL {self._egl_major}.{self._egl_minor}, {renderer} ({vendor}), GL {version}")
+
+    def make_current(self):
+        if not EGL.eglMakeCurrent(self._display, self._surface, self._surface, self._context):
+            err = EGL.eglGetError()
+            raise RuntimeError(f"eglMakeCurrent() failed (EGL error: 0x{err:04X})")
+        if self._vao is not None:
+            gl.glBindVertexArray(self._vao)
+
+    def _cleanup(self):
+        if not self._display:
+            return
+        try:
+            if self._vao is not None:
+                gl.glDeleteVertexArrays(1, [self._vao])
+                self._vao = None
+        except Exception:
+            pass
+        try:
+            EGL.eglMakeCurrent(self._display, EGL.EGL_NO_SURFACE, EGL.EGL_NO_SURFACE, EGL.EGL_NO_CONTEXT)
+        except Exception:
+            pass
+        try:
+            if self._context:
+                EGL.eglDestroyContext(self._display, self._context)
+        except Exception:
+            pass
+        try:
+            if self._surface:
+                EGL.eglDestroySurface(self._display, self._surface)
+        except Exception:
+            pass
+        try:
+            EGL.eglTerminate(self._display)
+        except Exception:
+            pass
+        self._display = None
+
+
+def _compile_shader(source: str, shader_type: int) -> int:
+    """Compile a shader and return its ID."""
+    shader = gl.glCreateShader(shader_type)
+    gl.glShaderSource(shader, source)
+    gl.glCompileShader(shader)
+
+    if not gl.glGetShaderiv(shader, gl.GL_COMPILE_STATUS):
+        error = gl.glGetShaderInfoLog(shader)
+        if isinstance(error, bytes):
+            error = error.decode(errors="replace")
+        gl.glDeleteShader(shader)
+        raise RuntimeError(f"Shader compilation failed:\n{error}")
+
+    return shader
+
+
+def _create_program(vertex_source: str, fragment_source: str) -> int:
+    """Create and link a shader program."""
+    vertex_shader = _compile_shader(vertex_source, gl.GL_VERTEX_SHADER)
+    try:
+        fragment_shader = _compile_shader(fragment_source, gl.GL_FRAGMENT_SHADER)
+    except RuntimeError:
+        gl.glDeleteShader(vertex_shader)
+        raise
+
+    program = gl.glCreateProgram()
+    gl.glAttachShader(program, vertex_shader)
+    gl.glAttachShader(program, fragment_shader)
+    gl.glLinkProgram(program)
+
+    gl.glDeleteShader(vertex_shader)
+    gl.glDeleteShader(fragment_shader)
+
+    if not gl.glGetProgramiv(program, gl.GL_LINK_STATUS):
+        error = gl.glGetProgramInfoLog(program)
+        if isinstance(error, bytes):
+            error = error.decode(errors="replace")
+        gl.glDeleteProgram(program)
+        raise RuntimeError(f"Program linking failed:\n{error}")
+
+    return program
+
+
+def _render_shader_batch(
+    fragment_code: str,
+    width: int,
+    height: int,
+    image_batches: list[list[np.ndarray]],
+    floats: list[float],
+    ints: list[int],
+    bools: list[bool] | None = None,
+    curves: list[np.ndarray] | None = None,
+) -> list[list[np.ndarray]]:
+    """
+    Render a fragment shader for multiple batches efficiently.
+
+    Compiles shader once, reuses framebuffer/textures across batches.
+    Supports multi-pass rendering via #pragma passes N directive.
+
+    Args:
+        fragment_code: User's fragment shader code
+        width: Output width
+        height: Output height
+        image_batches: List of batches, each batch is a list of input images (H, W, C) float32 [0,1]
+        floats: List of float uniforms
+        ints: List of int uniforms
+        bools: List of bool uniforms (passed as int 0/1 to GLSL bool uniforms)
+        curves: List of 1D LUT arrays (float32) of arbitrary size for u_curve0-N
+
+    Returns:
+        List of batch outputs, each is a list of output images (H, W, 4) float32 [0,1]
+    """
+    import time
+    start_time = time.perf_counter()
+
+    if not image_batches:
+        return []
+
+    ctx = GLContext()
+    ctx.make_current()
+
+    # Detect how many outputs the shader actually uses
+    num_outputs = _detect_output_count(fragment_code)
+
+    # Detect multi-pass rendering
+    num_passes = _detect_pass_count(fragment_code)
+
+    if bools is None:
+        bools = []
+    if curves is None:
+        curves = []
+
+    # Track resources for cleanup
+    program = None
+    fbo = None
+    output_textures = []
+    input_textures = []
+    curve_textures = []
+    ping_pong_textures = []
+    ping_pong_fbos = []
+
+    num_inputs = len(image_batches[0])
+
+    try:
+        # Compile shaders (once for all batches)
+        try:
+            program = _create_program(VERTEX_SHADER, fragment_code)
+        except RuntimeError:
+            logger.error(f"Fragment shader:\n{fragment_code}")
+            raise
+
+        gl.glUseProgram(program)
+
+        # Create framebuffer with only the needed color attachments
+        fbo = gl.glGenFramebuffers(1)
+        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
+
+        draw_buffers = []
+        for i in range(num_outputs):
+            tex = gl.glGenTextures(1)
+            output_textures.append(tex)
+            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
+            gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, width, height, 0, gl.GL_RGBA, gl.GL_FLOAT, None)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+            gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0 + i, gl.GL_TEXTURE_2D, tex, 0)
+            draw_buffers.append(gl.GL_COLOR_ATTACHMENT0 + i)
+
+        gl.glDrawBuffers(num_outputs, draw_buffers)
+
+        if gl.glCheckFramebufferStatus(gl.GL_FRAMEBUFFER) != gl.GL_FRAMEBUFFER_COMPLETE:
+            raise RuntimeError("Framebuffer is not complete")
+
+        # Create ping-pong resources for multi-pass rendering
+        if num_passes > 1:
+            for _ in range(2):
+                pp_tex = gl.glGenTextures(1)
+                ping_pong_textures.append(pp_tex)
+                gl.glBindTexture(gl.GL_TEXTURE_2D, pp_tex)
+                gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, width, height, 0, gl.GL_RGBA, gl.GL_FLOAT, None)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
+
+                pp_fbo = gl.glGenFramebuffers(1)
+                ping_pong_fbos.append(pp_fbo)
+                gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, pp_fbo)
+                gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_TEXTURE_2D, pp_tex, 0)
+                gl.glDrawBuffers(1, [gl.GL_COLOR_ATTACHMENT0])
+
+                if gl.glCheckFramebufferStatus(gl.GL_FRAMEBUFFER) != gl.GL_FRAMEBUFFER_COMPLETE:
+                    raise RuntimeError("Ping-pong framebuffer is not complete")
+
+        # Create input textures (reused for all batches)
+        for i in range(num_inputs):
+            tex = gl.glGenTextures(1)
+            input_textures.append(tex)
+            gl.glActiveTexture(gl.GL_TEXTURE0 + i)
+            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
+
+            loc = gl.glGetUniformLocation(program, f"u_image{i}")
+            if loc >= 0:
+                gl.glUniform1i(loc, i)
+
+        # Set static uniforms (once for all batches)
+        loc = gl.glGetUniformLocation(program, "u_resolution")
+        if loc >= 0:
+            gl.glUniform2f(loc, float(width), float(height))
+
+        for i, v in enumerate(floats):
+            loc = gl.glGetUniformLocation(program, f"u_float{i}")
+            if loc >= 0:
+                gl.glUniform1f(loc, v)
+
+        for i, v in enumerate(ints):
+            loc = gl.glGetUniformLocation(program, f"u_int{i}")
+            if loc >= 0:
+                gl.glUniform1i(loc, v)
+
+        for i, v in enumerate(bools):
+            loc = gl.glGetUniformLocation(program, f"u_bool{i}")
+            if loc >= 0:
+                gl.glUniform1i(loc, 1 if v else 0)
+
+        # Create 1D LUT textures for curves (bound after image texture units)
+        for i, lut in enumerate(curves):
+            tex = gl.glGenTextures(1)
+            curve_textures.append(tex)
+            unit = MAX_IMAGES + i
+            gl.glActiveTexture(gl.GL_TEXTURE0 + unit)
+            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
+            gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_R32F, len(lut), 1, 0, gl.GL_RED, gl.GL_FLOAT, lut)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
+
+            loc = gl.glGetUniformLocation(program, f"u_curve{i}")
+            if loc >= 0:
+                gl.glUniform1i(loc, unit)
+
+        # Get u_pass uniform location for multi-pass
+        pass_loc = gl.glGetUniformLocation(program, "u_pass")
+
+        gl.glViewport(0, 0, width, height)
+        gl.glDisable(gl.GL_BLEND)  # Ensure no alpha blending - write output directly
+
+        # Process each batch
+        all_batch_outputs = []
+        for images in image_batches:
+            # Update input textures with this batch's images
+            for i, img in enumerate(images):
+                gl.glActiveTexture(gl.GL_TEXTURE0 + i)
+                gl.glBindTexture(gl.GL_TEXTURE_2D, input_textures[i])
+
+                # Flip vertically for GL coordinates, ensure RGBA
+                h, w, c = img.shape
+                if c == 3:
+                    img_upload = np.empty((h, w, 4), dtype=np.float32)
+                    img_upload[:, :, :3] = img[::-1, :, :]
+                    img_upload[:, :, 3] = 1.0
+                else:
+                    img_upload = np.ascontiguousarray(img[::-1, :, :])
+
+                gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, w, h, 0, gl.GL_RGBA, gl.GL_FLOAT, img_upload)
+
+            if num_passes == 1:
+                # Single pass - render directly to output FBO
+                gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
+                if pass_loc >= 0:
+                    gl.glUniform1i(pass_loc, 0)
+                gl.glClearColor(0, 0, 0, 0)
+                gl.glClear(gl.GL_COLOR_BUFFER_BIT)
+                gl.glDrawArrays(gl.GL_TRIANGLES, 0, 3)
+            else:
+                # Multi-pass rendering with ping-pong
+                for p in range(num_passes):
+                    is_last_pass = (p == num_passes - 1)
+
+                    # Set pass uniform
+                    if pass_loc >= 0:
+                        gl.glUniform1i(pass_loc, p)
+
+                    if is_last_pass:
+                        # Last pass renders to the main output FBO
+                        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
+                    else:
+                        # Intermediate passes render to ping-pong FBO
+                        target_fbo = ping_pong_fbos[p % 2]
+                        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, target_fbo)
+
+                    # Set input texture for this pass
+                    gl.glActiveTexture(gl.GL_TEXTURE0)
+                    if p == 0:
+                        # First pass reads from original input
+                        gl.glBindTexture(gl.GL_TEXTURE_2D, input_textures[0])
+                    else:
+                        # Subsequent passes read from previous pass output
+                        source_tex = ping_pong_textures[(p - 1) % 2]
+                        gl.glBindTexture(gl.GL_TEXTURE_2D, source_tex)
+
+                    gl.glClearColor(0, 0, 0, 0)
+                    gl.glClear(gl.GL_COLOR_BUFFER_BIT)
+                    gl.glDrawArrays(gl.GL_TRIANGLES, 0, 3)
+
+            # Read back outputs for this batch
+            gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
+            batch_outputs = []
+            for i in range(num_outputs):
+                gl.glReadBuffer(gl.GL_COLOR_ATTACHMENT0 + i)
+                buf = np.empty((height, width, 4), dtype=np.float32)
+                gl.glReadPixels(0, 0, width, height, gl.GL_RGBA, gl.GL_FLOAT, buf)
+                batch_outputs.append(buf[::-1, :, :].copy())
+
+            # Pad with black images for unused outputs
+            black_img = np.zeros((height, width, 4), dtype=np.float32)
+            for _ in range(num_outputs, MAX_OUTPUTS):
+                batch_outputs.append(black_img)
+
+            all_batch_outputs.append(batch_outputs)
+
+        elapsed = (time.perf_counter() - start_time) * 1000
+        num_batches = len(image_batches)
+        pass_info = f", {num_passes} passes" if num_passes > 1 else ""
+        logger.info(f"GLSL shader executed in {elapsed:.1f}ms ({num_batches} batch{'es' if num_batches != 1 else ''}, {width}x{height}{pass_info})")
+
+        return all_batch_outputs
+
+    finally:
+        # Unbind before deleting
+        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, 0)
+        gl.glUseProgram(0)
+
+        if input_textures:
+            gl.glDeleteTextures(len(input_textures), input_textures)
+        if curve_textures:
+            gl.glDeleteTextures(len(curve_textures), curve_textures)
+        if output_textures:
+            gl.glDeleteTextures(len(output_textures), output_textures)
+        if ping_pong_textures:
+            gl.glDeleteTextures(len(ping_pong_textures), ping_pong_textures)
+        if fbo is not None:
+            gl.glDeleteFramebuffers(1, [fbo])
+        if ping_pong_fbos:
+            gl.glDeleteFramebuffers(len(ping_pong_fbos), ping_pong_fbos)
+        if program is not None:
+            gl.glDeleteProgram(program)
+
+class GLSLShader(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        image_template = io.Autogrow.TemplatePrefix(
+            io.Image.Input("image"),
+            prefix="image",
+            min=1,
+            max=MAX_IMAGES,
+        )
+
+        float_template = io.Autogrow.TemplatePrefix(
+            io.Float.Input("float", default=0.0),
+            prefix="u_float",
+            min=0,
+            max=MAX_UNIFORMS,
+        )
+
+        int_template = io.Autogrow.TemplatePrefix(
+            io.Int.Input("int", default=0),
+            prefix="u_int",
+            min=0,
+            max=MAX_UNIFORMS,
+        )
+
+        bool_template = io.Autogrow.TemplatePrefix(
+            io.Boolean.Input("bool", default=False),
+            prefix="u_bool",
+            min=0,
+            max=MAX_BOOLS,
+        )
+
+        curve_template = io.Autogrow.TemplatePrefix(
+            io.Curve.Input("curve"),
+            prefix="u_curve",
+            min=0,
+            max=MAX_CURVES,
+        )
+
+        return io.Schema(
+            node_id="GLSLShader",
+            display_name="GLSL Shader",
+            category="image/shader",
+            description=(
+                "Apply GLSL ES fragment shaders to images. "
+                "u_resolution (vec2) is always available."
+            ),
+            is_experimental=True,
+            has_intermediate_output=True,
+            inputs=[
+                io.String.Input(
+                    "fragment_shader",
+                    default=DEFAULT_FRAGMENT_SHADER,
+                    multiline=True,
+                    tooltip="GLSL fragment shader source code (GLSL ES 3.00 / WebGL 2.0 compatible)",
+                ),
+                io.DynamicCombo.Input(
+                    "size_mode",
+                    options=[
+                        io.DynamicCombo.Option("from_input", []),
+                        io.DynamicCombo.Option(
+                            "custom",
+                            [
+                                io.Int.Input(
+                                    "width",
+                                    default=512,
+                                    min=1,
+                                    max=nodes.MAX_RESOLUTION,
+                                ),
+                                io.Int.Input(
+                                    "height",
+                                    default=512,
+                                    min=1,
+                                    max=nodes.MAX_RESOLUTION,
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Output size: 'from_input' uses first input image dimensions, 'custom' allows manual size",
+                ),
+                io.Autogrow.Input("images", template=image_template, tooltip=f"Images are available as u_image0-{MAX_IMAGES-1} (sampler2D) in the shader code"),
+                io.Autogrow.Input("floats", template=float_template, tooltip=f"Floats are available as u_float0-{MAX_UNIFORMS-1} in the shader code"),
+                io.Autogrow.Input("ints", template=int_template, tooltip=f"Ints are available as u_int0-{MAX_UNIFORMS-1} in the shader code"),
+                io.Autogrow.Input("bools", template=bool_template, tooltip=f"Booleans are available as u_bool0-{MAX_BOOLS-1} (bool) in the shader code"),
+                io.Autogrow.Input("curves", template=curve_template, tooltip=f"Curves are available as u_curve0-{MAX_CURVES-1} (sampler2D, 1D LUT) in the shader code. Sample with texture(u_curve0, vec2(x, 0.5)).r"),
+            ],
+            outputs=[
+                io.Image.Output(display_name="IMAGE0", tooltip="Available via layout(location = 0) out vec4 fragColor0 in the shader code"),
+                io.Image.Output(display_name="IMAGE1", tooltip="Available via layout(location = 1) out vec4 fragColor1 in the shader code"),
+                io.Image.Output(display_name="IMAGE2", tooltip="Available via layout(location = 2) out vec4 fragColor2 in the shader code"),
+                io.Image.Output(display_name="IMAGE3", tooltip="Available via layout(location = 3) out vec4 fragColor3 in the shader code"),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        fragment_shader: str,
+        size_mode: SizeModeInput,
+        images: io.Autogrow.Type,
+        floats: io.Autogrow.Type = None,
+        ints: io.Autogrow.Type = None,
+        bools: io.Autogrow.Type = None,
+        curves: io.Autogrow.Type = None,
+        **kwargs,
+    ) -> io.NodeOutput:
+
+        image_list = [v for v in images.values() if v is not None]
+        float_list = (
+            [v if v is not None else 0.0 for v in floats.values()] if floats else []
+        )
+        int_list = [v if v is not None else 0 for v in ints.values()] if ints else []
+        bool_list = [v if v is not None else False for v in bools.values()] if bools else []
+
+        curve_luts = [v.to_lut().astype(np.float32) for v in curves.values() if v is not None] if curves else []
+
+        if not image_list:
+            raise ValueError("At least one input image is required")
+
+        # Determine output dimensions
+        if size_mode["size_mode"] == "custom":
+            out_width = size_mode["width"]
+            out_height = size_mode["height"]
+        else:
+            out_height, out_width = image_list[0].shape[1:3]
+
+        batch_size = image_list[0].shape[0]
+
+        # Prepare batches
+        image_batches = []
+        for batch_idx in range(batch_size):
+            batch_images = [img_tensor[batch_idx].cpu().numpy().astype(np.float32) for img_tensor in image_list]
+            image_batches.append(batch_images)
+
+        all_batch_outputs = _render_shader_batch(
+            fragment_shader,
+            out_width,
+            out_height,
+            image_batches,
+            float_list,
+            int_list,
+            bool_list,
+            curve_luts,
+        )
+
+        # Collect outputs into tensors
+        all_outputs = [[] for _ in range(MAX_OUTPUTS)]
+        for batch_outputs in all_batch_outputs:
+            for i, out_img in enumerate(batch_outputs):
+                all_outputs[i].append(torch.from_numpy(out_img))
+
+        output_tensors = [torch.stack(all_outputs[i], dim=0) for i in range(MAX_OUTPUTS)]
+        return io.NodeOutput(
+            *output_tensors,
+            ui=cls._build_ui_output(image_list, output_tensors[0]),
+        )
+
+    @classmethod
+    def _build_ui_output(
+        cls, image_list: list[torch.Tensor], output_batch: torch.Tensor
+    ) -> dict[str, list]:
+        """Build UI output with input and output images for client-side shader execution."""
+        input_images_ui = []
+        for img in image_list:
+            input_images_ui.extend(ui.ImageSaveHelper.save_images(
+                img,
+                filename_prefix="GLSLShader_input",
+                folder_type=io.FolderType.temp,
+                cls=None,
+                compress_level=1,
+            ))
+
+        output_images_ui = ui.ImageSaveHelper.save_images(
+            output_batch,
+            filename_prefix="GLSLShader_output",
+            folder_type=io.FolderType.temp,
+            cls=None,
+            compress_level=1,
+        )
+
+        return {"input_images": input_images_ui, "images": output_images_ui}
+
+
+class GLSLExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [GLSLShader]
+
+
+async def comfy_entrypoint() -> GLSLExtension:
+    return GLSLExtension()
diff --git a/comfy_extras/nodes_hidream.py b/comfy_extras/nodes_hidream.py
new file mode 100644
index 0000000000000000000000000000000000000000..0577293e13e4bda59b1fbaee236956aaf2a8b64a
--- /dev/null
+++ b/comfy_extras/nodes_hidream.py
@@ -0,0 +1,76 @@
+from typing_extensions import override
+
+import folder_paths
+import comfy.sd
+import comfy.model_management
+from comfy_api.latest import ComfyExtension, io
+
+
+class QuadrupleCLIPLoader(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="QuadrupleCLIPLoader",
+            display_name="Load CLIP (Quadruple)",
+            category="model/loaders",
+            description="Recipes:\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
+            inputs=[
+                io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
+                io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")),
+                io.Combo.Input("clip_name3", options=folder_paths.get_filename_list("text_encoders")),
+                io.Combo.Input("clip_name4", options=folder_paths.get_filename_list("text_encoders")),
+            ],
+            outputs=[
+                io.Clip.Output(),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip_name1, clip_name2, clip_name3, clip_name4):
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
+        clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
+        clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3)
+        clip_path4 = folder_paths.get_full_path_or_raise("text_encoders", clip_name4)
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3, clip_path4], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return io.NodeOutput(clip)
+
+class CLIPTextEncodeHiDream(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeHiDream",
+            display_name="CLIP Text Encode (HiDream)",
+            search_aliases=["hidream prompt"],
+            category="model/conditioning/hidream",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
+                io.String.Input("clip_g", multiline=True, dynamic_prompts=True),
+                io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
+                io.String.Input("llama", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip, clip_l, clip_g, t5xxl, llama):
+        tokens = clip.tokenize(clip_g)
+        tokens["l"] = clip.tokenize(clip_l)["l"]
+        tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
+        tokens["llama"] = clip.tokenize(llama)["llama"]
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
+
+
+class HiDreamExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            QuadrupleCLIPLoader,
+            CLIPTextEncodeHiDream,
+        ]
+
+
+async def comfy_entrypoint() -> HiDreamExtension:
+    return HiDreamExtension()
diff --git a/comfy_extras/nodes_hidream_o1.py b/comfy_extras/nodes_hidream_o1.py
new file mode 100644
index 0000000000000000000000000000000000000000..17dccdb63cd1be88cee7b46a32caff52ab47e6d6
--- /dev/null
+++ b/comfy_extras/nodes_hidream_o1.py
@@ -0,0 +1,281 @@
+from typing_extensions import override
+
+import torch
+
+import comfy.model_management
+import comfy.patcher_extension
+import node_helpers
+from comfy_api.latest import ComfyExtension, io
+
+
+REFERENCE_IMAGE_INPUT_SLOTS = 100
+
+
+class EmptyHiDreamO1LatentImage(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="EmptyHiDreamO1LatentImage",
+            display_name="Empty HiDream-O1 Latent Image",
+            category="model/latent/hidream",
+            description=(
+                "Empty pixel-space latent for HiDream-O1-Image. The model was "
+                "trained at ~4 megapixels; lower resolutions go off-distribution "
+                "and quality regresses noticeably. Trained resolutions: "
+                "2048x2048, 2304x1728, 1728x2304, 2560x1440, 1440x2560, "
+                "2496x1664, 1664x2496, 3104x1312, 1312x3104, 2304x1792, 1792x2304."
+            ),
+            inputs=[
+                io.Int.Input(id="width", default=2048, min=64, max=4096, step=32),
+                io.Int.Input(id="height", default=2048, min=64, max=4096, step=32),
+                io.Int.Input(id="batch_size", default=1, min=1, max=64),
+            ],
+            outputs=[io.Latent().Output()],
+        )
+
+    @classmethod
+    def execute(cls, *, width: int, height: int, batch_size: int = 1) -> io.NodeOutput:
+        latent = torch.zeros(
+            (batch_size, 3, height, width),
+            device=comfy.model_management.intermediate_device(),
+        )
+        return io.NodeOutput({"samples": latent})
+
+
+class HiDreamO1ReferenceImages(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="HiDreamO1ReferenceImages",
+            display_name="HiDream-O1 Reference Images",
+            category="model/conditioning/hidream",
+            description=(
+                "Attach ordered reference images to positive and negative conditioning."
+            ),
+            search_aliases=["sensenova reference images"],
+            inputs=[
+                io.Conditioning.Input(id="positive"),
+                io.Conditioning.Input(id="negative"),
+                io.Autogrow.Input(
+                    "images",
+                    template=io.Autogrow.TemplateNames(
+                        io.Image.Input("image"),
+                        names=[
+                            f"image_{index}"
+                            for index in range(1, REFERENCE_IMAGE_INPUT_SLOTS + 1)
+                        ],
+                        min=0,
+                    ),
+                    optional=True,
+                    tooltip="Reference images are used in numeric socket order.",
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls, *, positive, negative, images: io.Autogrow.Type = None
+    ) -> io.NodeOutput:
+        images = images or {}
+        ordered_names = [
+            f"image_{index}"
+            for index in range(1, REFERENCE_IMAGE_INPUT_SLOTS + 1)
+            if f"image_{index}" in images
+        ]
+        known_names = set(ordered_names)
+        refs = [images[name] for name in ordered_names]
+        refs.extend(
+            image for name, image in images.items() if name not in known_names
+        )
+        if not refs:
+            return io.NodeOutput(positive, negative)
+        positive = node_helpers.conditioning_set_values(
+            positive, {"reference_latents": refs}, append=True
+        )
+        negative = node_helpers.conditioning_set_values(
+            negative, {"prompt_type": "negative"}
+        )
+        negative = node_helpers.conditioning_set_values(
+            negative, {"reference_latents": refs}, append=True
+        )
+        return io.NodeOutput(positive, negative)
+
+
+class HiDreamO1PatchSeamSmoothing(io.ComfyNode):
+    PATCH_SIZE = 32
+    EDGE_FEATHER = 4
+
+    # Shift presets per (pattern, N). 8-pass = 4-quadrant + 4 quarter-patch offsets.
+    SHIFTS_BY_PATTERN = {
+        ("single_shift", 2): [(0, 0), (16, 16)],
+        ("single_shift", 4): [(0, 0), (16, 0), (0, 16), (16, 16)],
+        ("single_shift", 8): [(0, 0), (16, 0), (0, 16), (16, 16),
+                              (8, 8), (24, 8), (8, 24), (24, 24)],
+        ("symmetric", 2):    [(-8, -8), (8, 8)],
+        ("symmetric", 4):    [(-8, -8), (8, -8), (-8, 8), (8, 8)],
+        ("symmetric", 8):    [(-12, -12), (4, -12), (-12, 4), (4, 4),
+                              (-4, -4), (12, -4), (-4, 12), (12, 12)],
+    }
+    RAMP_LEVELS = {
+        "2":          [2],
+        "4":          [4],
+        "ramp_2_4":   [2, 4],
+        "ramp_2_4_8": [2, 4, 8],
+    }
+
+    @staticmethod
+    def _hann_tile(cy: int, cx: int, size: int = 32) -> torch.Tensor:
+        """size x size Hann tile peaking at (cy, cx) within a patch."""
+        half = size // 2
+        yy = torch.arange(size).view(size, 1)
+        xx = torch.arange(size).view(1, size)
+        dy = ((yy - cy + half) % size) - half
+        dx = ((xx - cx + half) % size) - half
+        return 0.25 * (1 + torch.cos(torch.pi * dy / half)) * (1 + torch.cos(torch.pi * dx / half))
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="HiDreamO1PatchSeamSmoothing",
+            display_name="HiDream-O1 Patch Seam Smoothing",
+            category="model/patch/hidream",
+            is_experimental=True,
+            description=(
+                "Average the model output across multiple shifted patch-grid "
+                "positions during the late portion of sampling. Cancels seams."
+            ),
+            inputs=[
+                io.Model.Input(id="model"),
+                io.Float.Input(id="start_percent", default=0.8, min=0.0, max=1.0, step=0.01,
+                    tooltip="Sampling progress (0=start, 1=end) at which the blend turns ON.",
+                ),
+                io.Float.Input(id="end_percent", default=1.0, min=0.0, max=1.0, step=0.01,
+                    tooltip="Sampling progress at which the blend turns OFF.",
+                ),
+                io.Combo.Input(
+                    id="pattern",
+                    options=["single_shift", "symmetric"],
+                    default="single_shift",
+                    tooltip="Shift layout. single_shift: one pass at the natural patch grid + others offset. symmetric: all passes off-grid, shifts split around origin.",
+                ),
+                io.Combo.Input(
+                    id="passes",
+                    options=["2", "4", "ramp_2_4", "ramp_2_4_8"],
+                    default="2",
+                    tooltip="Number of passes per gated step. 2/4 = fixed. ramp_*: pass count increases as sampling approaches end (more smoothing where seams are most visible).",
+                ),
+                io.Combo.Input(
+                    id="blend",
+                    options=["average", "window", "median"],
+                    default="average",
+                    tooltip="average: equal-weight mean. window: Hann-windowed weighting favoring each pass away from its patch boundaries. median: per-pixel median, rejects wraparound-outlier passes.",
+                ),
+                io.Float.Input(id="strength", default=1.0, min=0.0, max=1.0, step=0.01,
+                    tooltip="Interpolation between the natural-grid pred (0) and the averaged result (1).",
+                ),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def execute(cls, *, model, start_percent: float, end_percent: float, pattern: str, passes: str, blend: str, strength: float) -> io.NodeOutput:
+        if strength <= 0.0 or end_percent <= start_percent:
+            return io.NodeOutput(model)
+
+        P = cls.PATCH_SIZE
+        half = P // 2
+        shift_levels = [cls.SHIFTS_BY_PATTERN[(pattern, n)] for n in cls.RAMP_LEVELS[passes]]
+
+        if blend == "window":
+            window_tile_levels = [
+                torch.stack([cls._hann_tile((half - sy) % P, (half - sx) % P, P) for sy, sx in lst], dim=0)
+                for lst in shift_levels
+            ]
+        else:
+            window_tile_levels = [None] * len(shift_levels)
+
+        m = model.clone()
+        model_sampling = m.get_model_object("model_sampling")
+        multiplier = float(model_sampling.multiplier)
+        start_t = float(model_sampling.percent_to_sigma(start_percent)) * multiplier
+        end_t = float(model_sampling.percent_to_sigma(end_percent)) * multiplier
+
+        edge_ramp_cache: dict = {}
+
+        def get_edge_ramp(H: int, W: int, device, dtype) -> torch.Tensor:
+            key = (H, W, device, dtype)
+            cached = edge_ramp_cache.get(key)
+            if cached is not None:
+                return cached
+            feather = cls.EDGE_FEATHER
+            ys = torch.minimum(torch.arange(H, device=device, dtype=torch.float32),
+                               (H - 1) - torch.arange(H, device=device, dtype=torch.float32))
+            xs = torch.minimum(torch.arange(W, device=device, dtype=torch.float32),
+                               (W - 1) - torch.arange(W, device=device, dtype=torch.float32))
+            y_mask = ((ys - P) / feather).clamp(0, 1)
+            x_mask = ((xs - P) / feather).clamp(0, 1)
+            ramp = (y_mask[:, None] * x_mask[None, :]).to(dtype)
+            edge_ramp_cache[key] = ramp
+            return ramp
+
+        def smoothing_wrapper(executor, *args, **kwargs):
+            x = args[0]
+            t = float(args[1][0])
+            pred = executor(*args, **kwargs)
+            if not (end_t <= t <= start_t):
+                return pred
+            # Pick shift-level by sigma phase across the gated range.
+            if len(shift_levels) == 1:
+                level_idx = 0
+            else:
+                phase = (start_t - t) / max(start_t - end_t, 1e-8)
+                level_idx = min(int(phase * len(shift_levels)), len(shift_levels) - 1)
+            shifts = shift_levels[level_idx]
+            window_tiles = window_tile_levels[level_idx]
+
+            preds = []
+            for sy, sx in shifts:
+                if sy == 0 and sx == 0:
+                    preds.append(pred)
+                    continue
+                x_rolled = torch.roll(x, shifts=(sy, sx), dims=(-2, -1))
+                pred_rolled = executor(x_rolled, *args[1:], **kwargs)
+                preds.append(torch.roll(pred_rolled, shifts=(-sy, -sx), dims=(-2, -1)))
+            stacked = torch.stack(preds, dim=0)  # (N, B, C, H, W)
+            _, _, _, H, W = stacked.shape
+            if blend == "window":
+                N = stacked.shape[0]
+                tiles = window_tiles.to(device=stacked.device, dtype=stacked.dtype)
+                w = tiles.repeat(1, H // P, W // P)[:, :H, :W]
+                sum_w = w.sum(dim=0, keepdim=True)
+                w = torch.where(sum_w < 1e-3, torch.full_like(w, 1.0 / N), w / sum_w.clamp(min=1e-8))
+                avg = (stacked * w[:, None, None, :, :]).sum(dim=0)
+            elif blend == "median":
+                avg = torch.median(stacked, dim=0).values
+            else:
+                avg = stacked.mean(dim=0)
+
+            # Mask out the P-px wraparound contamination strip at each edge.
+            mask = get_edge_ramp(H, W, pred.device, pred.dtype)
+            return pred * (1.0 - mask * strength) + avg * (mask * strength)
+
+        m.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "hidream_o1_patch_seam_smoothing", smoothing_wrapper)
+        return io.NodeOutput(m)
+
+
+class HiDreamO1Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EmptyHiDreamO1LatentImage,
+            HiDreamO1ReferenceImages,
+            HiDreamO1PatchSeamSmoothing,
+        ]
+
+
+async def comfy_entrypoint() -> HiDreamO1Extension:
+    return HiDreamO1Extension()
diff --git a/comfy_extras/nodes_hooks.py b/comfy_extras/nodes_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f2c85428108f8490b036c4c7d7f107f15df919f
--- /dev/null
+++ b/comfy_extras/nodes_hooks.py
@@ -0,0 +1,750 @@
+from __future__ import annotations
+from typing import TYPE_CHECKING, Union
+import logging
+import torch
+from collections.abc import Iterable
+
+if TYPE_CHECKING:
+    from comfy.sd import CLIP
+
+import comfy.hooks
+import comfy.sd
+import comfy.utils
+import folder_paths
+
+###########################################
+# Mask, Combine, and Hook Conditioning
+#------------------------------------------
+class PairConditioningSetProperties:
+    NodeId = 'PairConditioningSetProperties'
+    NodeName = 'Cond Pair Set Props'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "positive_NEW": ("CONDITIONING", ),
+                "negative_NEW": ("CONDITIONING", ),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "set_cond_area": (["default", "mask bounds"],),
+            },
+            "optional": {
+                "mask": ("MASK", ),
+                "hooks": ("HOOKS",),
+                "timesteps": ("TIMESTEPS_RANGE",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    CATEGORY = "advanced/hooks/cond pair"
+    FUNCTION = "set_properties"
+
+    def set_properties(self, positive_NEW, negative_NEW,
+                       strength: float, set_cond_area: str,
+                       mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None):
+        final_positive, final_negative = comfy.hooks.set_conds_props(conds=[positive_NEW, negative_NEW],
+                                                                    strength=strength, set_cond_area=set_cond_area,
+                                                                    mask=mask, hooks=hooks, timesteps_range=timesteps)
+        return (final_positive, final_negative)
+
+class PairConditioningSetPropertiesAndCombine:
+    NodeId = 'PairConditioningSetPropertiesAndCombine'
+    NodeName = 'Cond Pair Set Props Combine'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "positive": ("CONDITIONING", ),
+                "negative": ("CONDITIONING", ),
+                "positive_NEW": ("CONDITIONING", ),
+                "negative_NEW": ("CONDITIONING", ),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "set_cond_area": (["default", "mask bounds"],),
+            },
+            "optional": {
+                "mask": ("MASK", ),
+                "hooks": ("HOOKS",),
+                "timesteps": ("TIMESTEPS_RANGE",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    CATEGORY = "advanced/hooks/cond pair"
+    FUNCTION = "set_properties"
+
+    def set_properties(self, positive, negative, positive_NEW, negative_NEW,
+                       strength: float, set_cond_area: str,
+                       mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None):
+        final_positive, final_negative = comfy.hooks.set_conds_props_and_combine(conds=[positive, negative], new_conds=[positive_NEW, negative_NEW],
+                                                                                strength=strength, set_cond_area=set_cond_area,
+                                                                                mask=mask, hooks=hooks, timesteps_range=timesteps)
+        return (final_positive, final_negative)
+
+class ConditioningSetProperties:
+    NodeId = 'ConditioningSetProperties'
+    NodeName = 'Cond Set Props'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "cond_NEW": ("CONDITIONING", ),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "set_cond_area": (["default", "mask bounds"],),
+            },
+            "optional": {
+                "mask": ("MASK", ),
+                "hooks": ("HOOKS",),
+                "timesteps": ("TIMESTEPS_RANGE",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("CONDITIONING",)
+    CATEGORY = "advanced/hooks/cond single"
+    FUNCTION = "set_properties"
+
+    def set_properties(self, cond_NEW,
+                       strength: float, set_cond_area: str,
+                       mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None):
+        (final_cond,) = comfy.hooks.set_conds_props(conds=[cond_NEW],
+                                                   strength=strength, set_cond_area=set_cond_area,
+                                                   mask=mask, hooks=hooks, timesteps_range=timesteps)
+        return (final_cond,)
+
+class ConditioningSetPropertiesAndCombine:
+    NodeId = 'ConditioningSetPropertiesAndCombine'
+    NodeName = 'Cond Set Props Combine'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "cond": ("CONDITIONING", ),
+                "cond_NEW": ("CONDITIONING", ),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "set_cond_area": (["default", "mask bounds"],),
+            },
+            "optional": {
+                "mask": ("MASK", ),
+                "hooks": ("HOOKS",),
+                "timesteps": ("TIMESTEPS_RANGE",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("CONDITIONING",)
+    CATEGORY = "advanced/hooks/cond single"
+    FUNCTION = "set_properties"
+
+    def set_properties(self, cond, cond_NEW,
+                       strength: float, set_cond_area: str,
+                       mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None):
+        (final_cond,) = comfy.hooks.set_conds_props_and_combine(conds=[cond], new_conds=[cond_NEW],
+                                                               strength=strength, set_cond_area=set_cond_area,
+                                                               mask=mask, hooks=hooks, timesteps_range=timesteps)
+        return (final_cond,)
+
+class PairConditioningCombine:
+    NodeId = 'PairConditioningCombine'
+    NodeName = 'Cond Pair Combine'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "positive_A": ("CONDITIONING",),
+                "negative_A": ("CONDITIONING",),
+                "positive_B": ("CONDITIONING",),
+                "negative_B": ("CONDITIONING",),
+            },
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    CATEGORY = "advanced/hooks/cond pair"
+    FUNCTION = "combine"
+
+    def combine(self, positive_A, negative_A, positive_B, negative_B):
+        final_positive, final_negative = comfy.hooks.set_conds_props_and_combine(conds=[positive_A, negative_A], new_conds=[positive_B, negative_B],)
+        return (final_positive, final_negative,)
+
+class PairConditioningSetDefaultAndCombine:
+    NodeId = 'PairConditioningSetDefaultCombine'
+    NodeName = 'Cond Pair Set Default Combine'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "positive": ("CONDITIONING",),
+                "negative": ("CONDITIONING",),
+                "positive_DEFAULT": ("CONDITIONING",),
+                "negative_DEFAULT": ("CONDITIONING",),
+            },
+            "optional": {
+                "hooks": ("HOOKS",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    CATEGORY = "advanced/hooks/cond pair"
+    FUNCTION = "set_default_and_combine"
+
+    def set_default_and_combine(self, positive, negative, positive_DEFAULT, negative_DEFAULT,
+                                hooks: comfy.hooks.HookGroup=None):
+        final_positive, final_negative = comfy.hooks.set_default_conds_and_combine(conds=[positive, negative], new_conds=[positive_DEFAULT, negative_DEFAULT],
+                                                                                   hooks=hooks)
+        return (final_positive, final_negative)
+
+class ConditioningSetDefaultAndCombine:
+    NodeId = 'ConditioningSetDefaultCombine'
+    NodeName = 'Cond Set Default Combine'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "cond": ("CONDITIONING",),
+                "cond_DEFAULT": ("CONDITIONING",),
+            },
+            "optional": {
+                "hooks": ("HOOKS",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("CONDITIONING",)
+    CATEGORY = "advanced/hooks/cond single"
+    FUNCTION = "set_default_and_combine"
+
+    def set_default_and_combine(self, cond, cond_DEFAULT,
+                                hooks: comfy.hooks.HookGroup=None):
+        (final_conditioning,) = comfy.hooks.set_default_conds_and_combine(conds=[cond], new_conds=[cond_DEFAULT],
+                                                                        hooks=hooks)
+        return (final_conditioning,)
+
+class SetClipHooks:
+    NodeId = 'SetClipHooks'
+    NodeName = 'Set CLIP Hooks'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP",),
+                "apply_to_conds": ("BOOLEAN", {"default": True, "advanced": True}),
+                "schedule_clip": ("BOOLEAN", {"default": False, "advanced": True})
+            },
+            "optional": {
+                "hooks": ("HOOKS",)
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("CLIP",)
+    CATEGORY = "advanced/hooks/clip"
+    FUNCTION = "apply_hooks"
+
+    def apply_hooks(self, clip: CLIP, schedule_clip: bool, apply_to_conds: bool, hooks: comfy.hooks.HookGroup=None):
+        if hooks is not None:
+            clip = clip.clone(disable_dynamic=True)
+            if apply_to_conds:
+                clip.apply_hooks_to_conds = hooks
+            clip.patcher.forced_hooks = hooks.clone()
+            clip.use_clip_schedule = schedule_clip
+            if not clip.use_clip_schedule:
+                clip.patcher.forced_hooks.set_keyframes_on_hooks(None)
+            clip.patcher.register_all_hook_patches(hooks, comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Clip))
+        return (clip,)
+
+class ConditioningTimestepsRange:
+    SEARCH_ALIASES = ["prompt scheduling", "timestep segments", "conditioning phases"]
+    NodeId = 'ConditioningTimestepsRange'
+    NodeName = 'Timesteps Range'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+            },
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("TIMESTEPS_RANGE", "TIMESTEPS_RANGE", "TIMESTEPS_RANGE")
+    RETURN_NAMES = ("TIMESTEPS_RANGE", "BEFORE_RANGE", "AFTER_RANGE")
+    CATEGORY = "advanced/hooks"
+    FUNCTION = "create_range"
+
+    def create_range(self, start_percent: float, end_percent: float):
+        return ((start_percent, end_percent), (0.0, start_percent), (end_percent, 1.0))
+#------------------------------------------
+###########################################
+
+
+###########################################
+# Create Hooks
+#------------------------------------------
+class CreateHookLora:
+    NodeId = 'CreateHookLora'
+    NodeName = 'Create Hook LoRA'
+    def __init__(self):
+        self.loaded_lora = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "lora_name": (folder_paths.get_filename_list("loras"), ),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+            },
+            "optional": {
+                "prev_hooks": ("HOOKS",)
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/create"
+    FUNCTION = "create_hook"
+
+    def create_hook(self, lora_name: str, strength_model: float, strength_clip: float, prev_hooks: comfy.hooks.HookGroup=None):
+        if prev_hooks is None:
+            prev_hooks = comfy.hooks.HookGroup()
+        prev_hooks.clone()
+
+        if strength_model == 0 and strength_clip == 0:
+            return (prev_hooks,)
+
+        lora_path = folder_paths.get_full_path("loras", lora_name)
+        lora = None
+        if self.loaded_lora is not None:
+            if self.loaded_lora[0] == lora_path:
+                lora = self.loaded_lora[1]
+            else:
+                temp = self.loaded_lora
+                self.loaded_lora = None
+                del temp
+
+        if lora is None:
+            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+            self.loaded_lora = (lora_path, lora)
+
+        hooks = comfy.hooks.create_hook_lora(lora=lora, strength_model=strength_model, strength_clip=strength_clip)
+        return (prev_hooks.clone_and_combine(hooks),)
+
+class CreateHookLoraModelOnly(CreateHookLora):
+    NodeId = 'CreateHookLoraModelOnly'
+    NodeName = 'Create Hook LoRA (MO)'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "lora_name": (folder_paths.get_filename_list("loras"), ),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+            },
+            "optional": {
+                "prev_hooks": ("HOOKS",)
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/create"
+    FUNCTION = "create_hook_model_only"
+
+    def create_hook_model_only(self, lora_name: str, strength_model: float, prev_hooks: comfy.hooks.HookGroup=None):
+        return self.create_hook(lora_name=lora_name, strength_model=strength_model, strength_clip=0, prev_hooks=prev_hooks)
+
+class CreateHookModelAsLora:
+    NodeId = 'CreateHookModelAsLora'
+    NodeName = 'Create Hook Model as LoRA'
+
+    def __init__(self):
+        # when not None, will be in following format:
+        # (ckpt_path: str, weights_model: dict, weights_clip: dict)
+        self.loaded_weights = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+            },
+            "optional": {
+                "prev_hooks": ("HOOKS",)
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/create"
+    FUNCTION = "create_hook"
+
+    def create_hook(self, ckpt_name: str, strength_model: float, strength_clip: float,
+                    prev_hooks: comfy.hooks.HookGroup=None):
+        if prev_hooks is None:
+            prev_hooks = comfy.hooks.HookGroup()
+        prev_hooks.clone()
+
+        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        weights_model = None
+        weights_clip = None
+        if self.loaded_weights is not None:
+            if self.loaded_weights[0] == ckpt_path:
+                weights_model = self.loaded_weights[1]
+                weights_clip = self.loaded_weights[2]
+            else:
+                temp = self.loaded_weights
+                self.loaded_weights = None
+                del temp
+
+        if weights_model is None:
+            out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+            weights_model = comfy.hooks.get_patch_weights_from_model(out[0])
+            weights_clip = comfy.hooks.get_patch_weights_from_model(out[1].patcher if out[1] else out[1])
+            self.loaded_weights = (ckpt_path, weights_model, weights_clip)
+
+        hooks = comfy.hooks.create_hook_model_as_lora(weights_model=weights_model, weights_clip=weights_clip,
+                                                      strength_model=strength_model, strength_clip=strength_clip)
+        return (prev_hooks.clone_and_combine(hooks),)
+
+class CreateHookModelAsLoraModelOnly(CreateHookModelAsLora):
+    NodeId = 'CreateHookModelAsLoraModelOnly'
+    NodeName = 'Create Hook Model as LoRA (MO)'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+            },
+            "optional": {
+                "prev_hooks": ("HOOKS",)
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/create"
+    FUNCTION = "create_hook_model_only"
+
+    def create_hook_model_only(self, ckpt_name: str, strength_model: float,
+                               prev_hooks: comfy.hooks.HookGroup=None):
+        return self.create_hook(ckpt_name=ckpt_name, strength_model=strength_model, strength_clip=0.0, prev_hooks=prev_hooks)
+#------------------------------------------
+###########################################
+
+
+###########################################
+# Schedule Hooks
+#------------------------------------------
+class SetHookKeyframes:
+    NodeId = 'SetHookKeyframes'
+    NodeName = 'Set Hook Keyframes'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "hooks": ("HOOKS",),
+            },
+            "optional": {
+                "hook_kf": ("HOOK_KEYFRAMES",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/scheduling"
+    FUNCTION = "set_hook_keyframes"
+
+    def set_hook_keyframes(self, hooks: comfy.hooks.HookGroup, hook_kf: comfy.hooks.HookKeyframeGroup=None):
+        if hook_kf is not None:
+            hooks = hooks.clone()
+            hooks.set_keyframes_on_hooks(hook_kf=hook_kf)
+        return (hooks,)
+
+class CreateHookKeyframe:
+    SEARCH_ALIASES = ["hook scheduling", "strength animation", "timed hook"]
+    NodeId = 'CreateHookKeyframe'
+    NodeName = 'Create Hook Keyframe'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "strength_mult": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+            },
+            "optional": {
+                "prev_hook_kf": ("HOOK_KEYFRAMES",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOK_KEYFRAMES",)
+    RETURN_NAMES = ("HOOK_KF",)
+    CATEGORY = "advanced/hooks/scheduling"
+    FUNCTION = "create_hook_keyframe"
+
+    def create_hook_keyframe(self, strength_mult: float, start_percent: float, prev_hook_kf: comfy.hooks.HookKeyframeGroup=None):
+        if prev_hook_kf is None:
+            prev_hook_kf = comfy.hooks.HookKeyframeGroup()
+        prev_hook_kf = prev_hook_kf.clone()
+        keyframe = comfy.hooks.HookKeyframe(strength=strength_mult, start_percent=start_percent)
+        prev_hook_kf.add(keyframe)
+        return (prev_hook_kf,)
+
+class CreateHookKeyframesInterpolated:
+    SEARCH_ALIASES = ["ease hook strength", "smooth hook transition", "interpolate keyframes"]
+    NodeId = 'CreateHookKeyframesInterpolated'
+    NodeName = 'Create Hook Keyframes Interp.'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "strength_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "interpolation": (comfy.hooks.InterpolationMethod._LIST, ),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "keyframes_count": ("INT", {"default": 5, "min": 2, "max": 100, "step": 1}),
+                "print_keyframes": ("BOOLEAN", {"default": False, "advanced": True}),
+            },
+            "optional": {
+                "prev_hook_kf": ("HOOK_KEYFRAMES",),
+            },
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOK_KEYFRAMES",)
+    RETURN_NAMES = ("HOOK_KF",)
+    CATEGORY = "advanced/hooks/scheduling"
+    FUNCTION = "create_hook_keyframes"
+
+    def create_hook_keyframes(self, strength_start: float, strength_end: float, interpolation: str,
+                              start_percent: float, end_percent: float, keyframes_count: int,
+                              print_keyframes=False, prev_hook_kf: comfy.hooks.HookKeyframeGroup=None):
+        if prev_hook_kf is None:
+            prev_hook_kf = comfy.hooks.HookKeyframeGroup()
+        prev_hook_kf = prev_hook_kf.clone()
+        percents = comfy.hooks.InterpolationMethod.get_weights(num_from=start_percent, num_to=end_percent, length=keyframes_count,
+                                                               method=comfy.hooks.InterpolationMethod.LINEAR)
+        strengths = comfy.hooks.InterpolationMethod.get_weights(num_from=strength_start, num_to=strength_end, length=keyframes_count, method=interpolation)
+
+        is_first = True
+        for percent, strength in zip(percents, strengths):
+            guarantee_steps = 0
+            if is_first:
+                guarantee_steps = 1
+                is_first = False
+            prev_hook_kf.add(comfy.hooks.HookKeyframe(strength=strength, start_percent=percent, guarantee_steps=guarantee_steps))
+            if print_keyframes:
+                logging.info(f"Hook Keyframe - start_percent:{percent} = {strength}")
+        return (prev_hook_kf,)
+
+class CreateHookKeyframesFromFloats:
+    SEARCH_ALIASES = ["batch keyframes", "strength list to keyframes"]
+    NodeId = 'CreateHookKeyframesFromFloats'
+    NodeName = 'Create Hook Keyframes From Floats'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "floats_strength": ("FLOATS", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "print_keyframes": ("BOOLEAN", {"default": False, "advanced": True}),
+            },
+            "optional": {
+                "prev_hook_kf": ("HOOK_KEYFRAMES",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOK_KEYFRAMES",)
+    RETURN_NAMES = ("HOOK_KF",)
+    CATEGORY = "advanced/hooks/scheduling"
+    FUNCTION = "create_hook_keyframes"
+
+    def create_hook_keyframes(self, floats_strength: Union[float, list[float]],
+                              start_percent: float, end_percent: float,
+                              prev_hook_kf: comfy.hooks.HookKeyframeGroup=None, print_keyframes=False):
+        if prev_hook_kf is None:
+            prev_hook_kf = comfy.hooks.HookKeyframeGroup()
+        prev_hook_kf = prev_hook_kf.clone()
+        if type(floats_strength) in (float, int):
+            floats_strength = [float(floats_strength)]
+        elif isinstance(floats_strength, Iterable):
+            pass
+        else:
+            raise Exception(f"floats_strength must be either an iterable input or a float, but was{type(floats_strength).__repr__}.")
+        percents = comfy.hooks.InterpolationMethod.get_weights(num_from=start_percent, num_to=end_percent, length=len(floats_strength),
+                                                               method=comfy.hooks.InterpolationMethod.LINEAR)
+
+        is_first = True
+        for percent, strength in zip(percents, floats_strength):
+            guarantee_steps = 0
+            if is_first:
+                guarantee_steps = 1
+                is_first = False
+            prev_hook_kf.add(comfy.hooks.HookKeyframe(strength=strength, start_percent=percent, guarantee_steps=guarantee_steps))
+            if print_keyframes:
+                logging.info(f"Hook Keyframe - start_percent:{percent} = {strength}")
+        return (prev_hook_kf,)
+#------------------------------------------
+###########################################
+
+
+class SetModelHooksOnCond:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "conditioning": ("CONDITIONING",),
+                "hooks": ("HOOKS",),
+            },
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("CONDITIONING",)
+    CATEGORY = "advanced/hooks/manual"
+    FUNCTION = "attach_hook"
+
+    def attach_hook(self, conditioning, hooks: comfy.hooks.HookGroup):
+        return (comfy.hooks.set_hooks_for_conditioning(conditioning, hooks),)
+
+
+###########################################
+# Combine Hooks
+#------------------------------------------
+class CombineHooks:
+    SEARCH_ALIASES = ["merge hooks"]
+    NodeId = 'CombineHooks2'
+    NodeName = 'Combine Hooks [2]'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+            },
+            "optional": {
+                "hooks_A": ("HOOKS",),
+                "hooks_B": ("HOOKS",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/combine"
+    FUNCTION = "combine_hooks"
+
+    def combine_hooks(self,
+                      hooks_A: comfy.hooks.HookGroup=None,
+                      hooks_B: comfy.hooks.HookGroup=None):
+        candidates = [hooks_A, hooks_B]
+        return (comfy.hooks.HookGroup.combine_all_hooks(candidates),)
+
+class CombineHooksFour:
+    NodeId = 'CombineHooks4'
+    NodeName = 'Combine Hooks [4]'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+            },
+            "optional": {
+                "hooks_A": ("HOOKS",),
+                "hooks_B": ("HOOKS",),
+                "hooks_C": ("HOOKS",),
+                "hooks_D": ("HOOKS",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/combine"
+    FUNCTION = "combine_hooks"
+
+    def combine_hooks(self,
+                      hooks_A: comfy.hooks.HookGroup=None,
+                      hooks_B: comfy.hooks.HookGroup=None,
+                      hooks_C: comfy.hooks.HookGroup=None,
+                      hooks_D: comfy.hooks.HookGroup=None):
+        candidates = [hooks_A, hooks_B, hooks_C, hooks_D]
+        return (comfy.hooks.HookGroup.combine_all_hooks(candidates),)
+
+class CombineHooksEight:
+    NodeId = 'CombineHooks8'
+    NodeName = 'Combine Hooks [8]'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+            },
+            "optional": {
+                "hooks_A": ("HOOKS",),
+                "hooks_B": ("HOOKS",),
+                "hooks_C": ("HOOKS",),
+                "hooks_D": ("HOOKS",),
+                "hooks_E": ("HOOKS",),
+                "hooks_F": ("HOOKS",),
+                "hooks_G": ("HOOKS",),
+                "hooks_H": ("HOOKS",),
+            }
+        }
+
+    EXPERIMENTAL = True
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/combine"
+    FUNCTION = "combine_hooks"
+
+    def combine_hooks(self,
+                      hooks_A: comfy.hooks.HookGroup=None,
+                      hooks_B: comfy.hooks.HookGroup=None,
+                      hooks_C: comfy.hooks.HookGroup=None,
+                      hooks_D: comfy.hooks.HookGroup=None,
+                      hooks_E: comfy.hooks.HookGroup=None,
+                      hooks_F: comfy.hooks.HookGroup=None,
+                      hooks_G: comfy.hooks.HookGroup=None,
+                      hooks_H: comfy.hooks.HookGroup=None):
+        candidates = [hooks_A, hooks_B, hooks_C, hooks_D, hooks_E, hooks_F, hooks_G, hooks_H]
+        return (comfy.hooks.HookGroup.combine_all_hooks(candidates),)
+#------------------------------------------
+###########################################
+
+node_list = [
+    # Create
+    CreateHookLora,
+    CreateHookLoraModelOnly,
+    CreateHookModelAsLora,
+    CreateHookModelAsLoraModelOnly,
+    # Scheduling
+    SetHookKeyframes,
+    CreateHookKeyframe,
+    CreateHookKeyframesInterpolated,
+    CreateHookKeyframesFromFloats,
+    # Combine
+    CombineHooks,
+    CombineHooksFour,
+    CombineHooksEight,
+    # Attach
+    ConditioningSetProperties,
+    ConditioningSetPropertiesAndCombine,
+    PairConditioningSetProperties,
+    PairConditioningSetPropertiesAndCombine,
+    ConditioningSetDefaultAndCombine,
+    PairConditioningSetDefaultAndCombine,
+    PairConditioningCombine,
+    SetClipHooks,
+    # Other
+    ConditioningTimestepsRange,
+]
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+for node in node_list:
+    NODE_CLASS_MAPPINGS[node.NodeId] = node
+    NODE_DISPLAY_NAME_MAPPINGS[node.NodeId] = node.NodeName
diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py
new file mode 100644
index 0000000000000000000000000000000000000000..63b1f6af4bb47dd1a77c550a0cc128c6a29909c3
--- /dev/null
+++ b/comfy_extras/nodes_hunyuan.py
@@ -0,0 +1,438 @@
+import nodes
+import node_helpers
+import torch
+import comfy.model_management
+import comfy.model_patcher
+import comfy.ops
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from comfy.ldm.hunyuan_video.upsampler import HunyuanVideo15SRModel
+from comfy.ldm.lightricks.latent_upsampler import LatentUpsampler
+import folder_paths
+import json
+
+class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeHunyuanDiT",
+            display_name="CLIP Text Encode (Hunyuan Image)",
+            category="model/conditioning/hunyuan image",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("bert", multiline=True, dynamic_prompts=True),
+                io.String.Input("mt5xl", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, bert, mt5xl) -> io.NodeOutput:
+        tokens = clip.tokenize(bert)
+        tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"]
+
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
+
+    encode = execute  # TODO: remove
+
+
+class EmptyHunyuanLatentVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyHunyuanLatentVideo",
+            display_name="Empty HunyuanVideo 1.0 Latent",
+            category="model/latent/hunyuan video",
+            inputs=[
+                io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=25, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
+
+    generate = execute  # TODO: remove
+
+
+class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
+    @classmethod
+    def define_schema(cls):
+        schema = super().define_schema()
+        schema.node_id = "EmptyHunyuanVideo15Latent"
+        schema.display_name = "Empty HunyuanVideo 1.5 Latent"
+        schema.category = "model/latent/hunyuan video"
+        return schema
+
+    @classmethod
+    def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
+        # Using scale factor of 16 instead of 8
+        latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 16})
+
+
+class HunyuanVideo15ImageToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="HunyuanVideo15ImageToVideo",
+            category="model/conditioning/hunyuan video",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=33, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("start_image", optional=True),
+                io.ClipVisionOutput.Input("clip_vision_output", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
+
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+
+            encoded = vae.encode(start_image[:, :, :, :3])
+            concat_latent_image = torch.zeros((latent.shape[0], 32, latent.shape[2], latent.shape[3], latent.shape[4]), device=comfy.model_management.intermediate_device())
+            concat_latent_image[:, :, :encoded.shape[2], :, :] = encoded
+
+            mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
+            mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
+
+            positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
+            negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
+
+        if clip_vision_output is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
+            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return io.NodeOutput(positive, negative, out_latent)
+
+
+class HunyuanVideo15SuperResolution(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="HunyuanVideo15SuperResolution",
+            display_name="Hunyuan Video 1.5 Super Resolution",
+            category="model/conditioning/hunyuan video",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae", optional=True),
+                io.Image.Input("start_image", optional=True),
+                io.ClipVisionOutput.Input("clip_vision_output", optional=True),
+                io.Latent.Input("latent"),
+                io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01, advanced=True),
+
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, latent, noise_augmentation, vae=None, start_image=None, clip_vision_output=None) -> io.NodeOutput:
+        in_latent = latent["samples"]
+        in_channels = in_latent.shape[1]
+        cond_latent = torch.zeros([in_latent.shape[0], in_channels * 2 + 2, in_latent.shape[-3], in_latent.shape[-2], in_latent.shape[-1]], device=comfy.model_management.intermediate_device())
+        cond_latent[:, in_channels + 1 : 2 * in_channels + 1] = in_latent
+        cond_latent[:, 2 * in_channels + 1] = 1
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image.movedim(-1, 1), in_latent.shape[-1] * 16, in_latent.shape[-2] * 16, "bilinear", "center").movedim(1, -1)
+            encoded = vae.encode(start_image[:, :, :, :3])
+            cond_latent[:, :in_channels, :encoded.shape[2], :, :] = encoded
+            cond_latent[:, in_channels + 1, 0] = 1
+
+        positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": cond_latent, "noise_augmentation": noise_augmentation})
+        negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": cond_latent, "noise_augmentation": noise_augmentation})
+        if clip_vision_output is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
+            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
+
+        return io.NodeOutput(positive, negative, latent)
+
+
+class LatentUpscaleModelLoader(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentUpscaleModelLoader",
+            display_name="Load Latent Upscale Model",
+            category="model/loaders",
+            inputs=[
+                io.Combo.Input("model_name", options=folder_paths.get_filename_list("latent_upscale_models")),
+            ],
+            outputs=[
+                io.LatentUpscaleModel.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_name) -> io.NodeOutput:
+        model_path = folder_paths.get_full_path_or_raise("latent_upscale_models", model_name)
+        sd, metadata = comfy.utils.load_torch_file(model_path, safe_load=True, return_metadata=True)
+
+        if "blocks.0.block.0.conv.weight" in sd:
+            config = {
+                "in_channels": sd["in_conv.conv.weight"].shape[1],
+                "out_channels": sd["out_conv.conv.weight"].shape[0],
+                "hidden_channels": sd["in_conv.conv.weight"].shape[0],
+                "num_blocks": len([k for k in sd.keys() if k.startswith("blocks.") and k.endswith(".block.0.conv.weight")]),
+                "global_residual": False,
+            }
+            model_type = "720p"
+            model = HunyuanVideo15SRModel(model_type, config)
+            model.load_sd(sd)
+        elif "up.0.block.0.conv1.conv.weight" in sd:
+            sd = {key.replace("nin_shortcut", "nin_shortcut.conv", 1): value for key, value in sd.items()}
+            config = {
+                "z_channels": sd["conv_in.conv.weight"].shape[1],
+                "out_channels": sd["conv_out.conv.weight"].shape[0],
+                "block_out_channels": tuple(sd[f"up.{i}.block.0.conv1.conv.weight"].shape[0] for i in range(len([k for k in sd.keys() if k.startswith("up.") and k.endswith(".block.0.conv1.conv.weight")]))),
+            }
+            model_type = "1080p"
+            model = HunyuanVideo15SRModel(model_type, config)
+            model.load_sd(sd)
+        elif "post_upsample_res_blocks.0.conv2.bias" in sd:
+            config = json.loads(metadata["config"])
+            model = LatentUpsampler.from_config(config, operations=comfy.ops.disable_weight_init).to(dtype=comfy.model_management.vae_dtype(allowed_dtypes=[torch.bfloat16, torch.float32]))
+            comfy.model_management.archive_model_dtypes(model)
+            model_patcher = comfy.model_patcher.CoreModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
+            model.load_state_dict(sd, assign=model_patcher.is_dynamic())
+            model = model_patcher
+
+        return io.NodeOutput(model)
+
+
+class HunyuanVideo15LatentUpscaleWithModel(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="HunyuanVideo15LatentUpscaleWithModel",
+            display_name="Hunyuan Video 15 Latent Upscale With Model",
+            category="model/latent/hunyhuan video",
+            inputs=[
+                io.LatentUpscaleModel.Input("model"),
+                io.Latent.Input("samples"),
+                io.Combo.Input("upscale_method", options=["nearest-exact", "bilinear", "area", "bicubic", "bislerp"], default="bilinear"),
+                io.Int.Input("width", default=1280, min=0, max=16384, step=8),
+                io.Int.Input("height", default=720, min=0, max=16384, step=8),
+                io.Combo.Input("crop", options=["disabled", "center"]),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, samples, upscale_method, width, height, crop) -> io.NodeOutput:
+        if width == 0 and height == 0:
+            return io.NodeOutput(samples)
+        else:
+            if width == 0:
+                height = max(64, height)
+                width = max(64, round(samples["samples"].shape[-1] * height / samples["samples"].shape[-2]))
+            elif height == 0:
+                width = max(64, width)
+                height = max(64, round(samples["samples"].shape[-2] * width / samples["samples"].shape[-1]))
+            else:
+                width = max(64, width)
+                height = max(64, height)
+            s = comfy.utils.common_upscale(samples["samples"], width // 16, height // 16, upscale_method, crop)
+            s = model.resample_latent(s)
+            return io.NodeOutput({"samples": s.cpu().float()})
+
+
+PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = (
+    "<|start_header_id|>system<|end_header_id|>\n\n<image>\nDescribe the video by detailing the following aspects according to the reference image: "
+    "1. The main content and theme of the video."
+    "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
+    "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
+    "4. background environment, light, style and atmosphere."
+    "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n"
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
+    "<|start_header_id|>assistant<|end_header_id|>\n\n"
+)
+
+class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeHunyuanVideo_ImageToVideo",
+            category="model/conditioning/hunyuan video",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.ClipVisionOutput.Input("clip_vision_output"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Int.Input(
+                    "image_interleave",
+                    default=2,
+                    min=1,
+                    max=512,
+                    tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, clip_vision_output, prompt, image_interleave) -> io.NodeOutput:
+        tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected, image_interleave=image_interleave)
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
+
+    encode = execute  # TODO: remove
+
+
+class HunyuanImageToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="HunyuanImageToVideo",
+            category="model/conditioning/hunyuan video",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=53, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"], advanced=True),
+                io.Image.Input("start_image", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, vae, width, height, length, batch_size, guidance_type, start_image=None) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        out_latent = {}
+
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+
+            concat_latent_image = vae.encode(start_image)
+            mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
+            mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
+
+            if guidance_type == "v1 (concat)":
+                cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask}
+            elif guidance_type == "v2 (replace)":
+                cond = {'guiding_frame_index': 0}
+                latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image
+                out_latent["noise_mask"] = mask
+            elif guidance_type == "custom":
+                cond = {"ref_latent": concat_latent_image}
+
+            positive = node_helpers.conditioning_set_values(positive, cond)
+
+        out_latent["samples"] = latent
+        return io.NodeOutput(positive, out_latent)
+
+    encode = execute  # TODO: remove
+
+
+class EmptyHunyuanImageLatent(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyHunyuanImageLatent",
+            category="model/latent/hunyuan image",
+            inputs=[
+                io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 64, height // 32, width // 32], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples":latent})
+
+    generate = execute  # TODO: remove
+
+
+class HunyuanRefinerLatent(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="HunyuanRefinerLatent",
+            display_name="Hunyuan Latent Refiner",
+            category="model/conditioning/hunyuan video",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Latent.Input("latent"),
+                io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01, advanced=True),
+
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, latent, noise_augmentation) -> io.NodeOutput:
+        latent = latent["samples"]
+        positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
+        negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
+        out_latent = {}
+        out_latent["samples"] = torch.zeros([latent.shape[0], 32, latent.shape[-3], latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput(positive, negative, out_latent)
+
+
+class HunyuanExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeHunyuanDiT,
+            TextEncodeHunyuanVideo_ImageToVideo,
+            EmptyHunyuanLatentVideo,
+            EmptyHunyuanVideo15Latent,
+            HunyuanVideo15ImageToVideo,
+            HunyuanVideo15SuperResolution,
+            HunyuanVideo15LatentUpscaleWithModel,
+            LatentUpscaleModelLoader,
+            HunyuanImageToVideo,
+            EmptyHunyuanImageLatent,
+            HunyuanRefinerLatent,
+        ]
+
+
+async def comfy_entrypoint() -> HunyuanExtension:
+    return HunyuanExtension()
diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f85ea6076e57e9e9fe67796cb318d4690931776
--- /dev/null
+++ b/comfy_extras/nodes_hunyuan3d.py
@@ -0,0 +1,505 @@
+import torch
+from comfy.ldm.modules.diffusionmodules.mmdit import get_1d_sincos_pos_embed_from_grid_torch
+import comfy.model_management
+from comfy_extras.nodes_save_3d import pack_variable_mesh_batch
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO, Types
+from comfy_api.latest._util import MESH, VOXEL  # only for backward compatibility if someone import it from this file (will be removed later) # noqa
+
+
+class EmptyLatentHunyuan3Dv2(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="EmptyLatentHunyuan3Dv2",
+            category="model/latent/hunyuan 3d",
+            inputs=[
+                IO.Int.Input("resolution", default=3072, min=1, max=8192),
+                IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."),
+            ],
+            outputs=[
+                IO.Latent.Output(),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, resolution, batch_size) -> IO.NodeOutput:
+        latent = torch.zeros([batch_size, 64, resolution], device=comfy.model_management.intermediate_device())
+        return IO.NodeOutput({"samples": latent, "type": "hunyuan3dv2"})
+
+    generate = execute  # TODO: remove
+
+
+class Hunyuan3Dv2Conditioning(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Hunyuan3Dv2Conditioning",
+            category="model/conditioning/hunyuan 3d",
+            inputs=[
+                IO.ClipVisionOutput.Input("clip_vision_output"),
+            ],
+            outputs=[
+                IO.Conditioning.Output(display_name="positive"),
+                IO.Conditioning.Output(display_name="negative"),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip_vision_output) -> IO.NodeOutput:
+        embeds = clip_vision_output.last_hidden_state
+        positive = [[embeds, {}]]
+        negative = [[torch.zeros_like(embeds), {}]]
+        return IO.NodeOutput(positive, negative)
+
+    encode = execute  # TODO: remove
+
+
+class Hunyuan3Dv2ConditioningMultiView(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Hunyuan3Dv2ConditioningMultiView",
+            category="model/conditioning/hunyuan 3d",
+            inputs=[
+                IO.ClipVisionOutput.Input("front", optional=True),
+                IO.ClipVisionOutput.Input("left", optional=True),
+                IO.ClipVisionOutput.Input("back", optional=True),
+                IO.ClipVisionOutput.Input("right", optional=True),
+            ],
+            outputs=[
+                IO.Conditioning.Output(display_name="positive"),
+                IO.Conditioning.Output(display_name="negative"),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, front=None, left=None, back=None, right=None) -> IO.NodeOutput:
+        all_embeds = [front, left, back, right]
+        out = []
+        pos_embeds = None
+        for i, e in enumerate(all_embeds):
+            if e is not None:
+                if pos_embeds is None:
+                    pos_embeds = get_1d_sincos_pos_embed_from_grid_torch(e.last_hidden_state.shape[-1], torch.arange(4))
+                out.append(e.last_hidden_state + pos_embeds[i].reshape(1, 1, -1))
+
+        embeds = torch.cat(out, dim=1)
+        positive = [[embeds, {}]]
+        negative = [[torch.zeros_like(embeds), {}]]
+        return IO.NodeOutput(positive, negative)
+
+    encode = execute  # TODO: remove
+
+
+class VAEDecodeHunyuan3D(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VAEDecodeHunyuan3D",
+            category="model/latent/hunyuan 3d",
+            inputs=[
+                IO.Latent.Input("samples"),
+                IO.Vae.Input("vae"),
+                IO.Int.Input("num_chunks", default=8000, min=1000, max=500000, advanced=True),
+                IO.Int.Input("octree_resolution", default=256, min=16, max=512, advanced=True),
+            ],
+            outputs=[
+                IO.Voxel.Output(),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, vae, samples, num_chunks, octree_resolution) -> IO.NodeOutput:
+        voxels = Types.VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution}))
+        return IO.NodeOutput(voxels)
+
+    decode = execute  # TODO: remove
+
+
+def voxel_to_mesh(voxels, threshold=0.5, device=None):
+    if device is None:
+        device = torch.device("cpu")
+    voxels = voxels.to(device)
+
+    binary = (voxels > threshold).float()
+    padded = torch.nn.functional.pad(binary, (1, 1, 1, 1, 1, 1), 'constant', 0)
+
+    D, H, W = binary.shape
+
+    neighbors = torch.tensor([
+        [0, 0, 1],
+        [0, 0, -1],
+        [0, 1, 0],
+        [0, -1, 0],
+        [1, 0, 0],
+        [-1, 0, 0]
+    ], device=device)
+
+    z, y, x = torch.meshgrid(
+        torch.arange(D, device=device),
+        torch.arange(H, device=device),
+        torch.arange(W, device=device),
+        indexing='ij'
+    )
+    voxel_indices = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1)
+
+    solid_mask = binary.flatten() > 0
+    solid_indices = voxel_indices[solid_mask]
+
+    corner_offsets = [
+        torch.tensor([
+            [0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1]
+        ], device=device),
+        torch.tensor([
+            [0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]
+        ], device=device),
+        torch.tensor([
+            [0, 1, 0], [1, 1, 0], [1, 1, 1], [0, 1, 1]
+        ], device=device),
+        torch.tensor([
+            [0, 0, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0]
+        ], device=device),
+        torch.tensor([
+            [1, 0, 1], [1, 1, 1], [1, 1, 0], [1, 0, 0]
+        ], device=device),
+        torch.tensor([
+            [0, 1, 0], [0, 1, 1], [0, 0, 1], [0, 0, 0]
+        ], device=device)
+    ]
+
+    all_vertices = []
+    all_indices = []
+
+    vertex_count = 0
+
+    for face_idx, offset in enumerate(neighbors):
+        neighbor_indices = solid_indices + offset
+
+        padded_indices = neighbor_indices + 1
+
+        is_exposed = padded[
+            padded_indices[:, 0],
+            padded_indices[:, 1],
+            padded_indices[:, 2]
+        ] == 0
+
+        if not is_exposed.any():
+            continue
+
+        exposed_indices = solid_indices[is_exposed]
+
+        corners = corner_offsets[face_idx].unsqueeze(0)
+
+        face_vertices = exposed_indices.unsqueeze(1) + corners
+
+        all_vertices.append(face_vertices.reshape(-1, 3))
+
+        num_faces = exposed_indices.shape[0]
+        face_indices = torch.arange(
+            vertex_count,
+            vertex_count + 4 * num_faces,
+            device=device
+        ).reshape(-1, 4)
+
+        all_indices.append(torch.stack([face_indices[:, 0], face_indices[:, 1], face_indices[:, 2]], dim=1))
+        all_indices.append(torch.stack([face_indices[:, 0], face_indices[:, 2], face_indices[:, 3]], dim=1))
+
+        vertex_count += 4 * num_faces
+
+    if len(all_vertices) > 0:
+        vertices = torch.cat(all_vertices, dim=0)
+        faces = torch.cat(all_indices, dim=0)
+    else:
+        vertices = torch.zeros((1, 3))
+        faces = torch.zeros((1, 3))
+
+    v_min = 0
+    v_max = max(voxels.shape)
+
+    vertices = vertices - (v_min + v_max) / 2
+
+    scale = (v_max - v_min) / 2
+    if scale > 0:
+        vertices = vertices / scale
+
+    vertices = torch.fliplr(vertices)
+    return vertices, faces
+
+def voxel_to_mesh_surfnet(voxels, threshold=0.5, device=None):
+    if device is None:
+        device = torch.device("cpu")
+    voxels = voxels.to(device)
+
+    D, H, W = voxels.shape
+
+    padded = torch.nn.functional.pad(voxels, (1, 1, 1, 1, 1, 1), 'constant', 0)
+    z, y, x = torch.meshgrid(
+        torch.arange(D, device=device),
+        torch.arange(H, device=device),
+        torch.arange(W, device=device),
+        indexing='ij'
+    )
+    cell_positions = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1)
+
+    corner_offsets = torch.tensor([
+        [0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0],
+        [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]
+    ], device=device)
+
+    pos = cell_positions.unsqueeze(1) + corner_offsets.unsqueeze(0)
+    z_idx, y_idx, x_idx = pos.unbind(-1)
+    corner_values = padded[z_idx, y_idx, x_idx]
+
+    corner_signs = corner_values > threshold
+    has_inside = torch.any(corner_signs, dim=1)
+    has_outside = torch.any(~corner_signs, dim=1)
+    contains_surface = has_inside & has_outside
+
+    active_cells = cell_positions[contains_surface]
+    active_signs = corner_signs[contains_surface]
+    active_values = corner_values[contains_surface]
+
+    if active_cells.shape[0] == 0:
+        return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device)
+
+    edges = torch.tensor([
+        [0, 1], [0, 2], [0, 4], [1, 3],
+        [1, 5], [2, 3], [2, 6], [3, 7],
+        [4, 5], [4, 6], [5, 7], [6, 7]
+    ], device=device)
+
+    cell_vertices = {}
+    progress = comfy.utils.ProgressBar(100)
+
+    for edge_idx, (e1, e2) in enumerate(edges):
+        progress.update(1)
+        crossing = active_signs[:, e1] != active_signs[:, e2]
+        if not crossing.any():
+            continue
+
+        cell_indices = torch.nonzero(crossing, as_tuple=True)[0]
+
+        v1 = active_values[cell_indices, e1]
+        v2 = active_values[cell_indices, e2]
+
+        t = torch.zeros_like(v1, device=device)
+        denom = v2 - v1
+        valid = denom != 0
+        t[valid] = (threshold - v1[valid]) / denom[valid]
+        t[~valid] = 0.5
+
+        p1 = corner_offsets[e1].float()
+        p2 = corner_offsets[e2].float()
+
+        intersection = p1.unsqueeze(0) + t.unsqueeze(1) * (p2.unsqueeze(0) - p1.unsqueeze(0))
+
+        for i, point in zip(cell_indices.tolist(), intersection):
+            if i not in cell_vertices:
+                cell_vertices[i] = []
+            cell_vertices[i].append(point)
+
+    # Calculate the final vertices as the average of intersection points for each cell
+    vertices = []
+    vertex_lookup = {}
+
+    vert_progress_mod = round(len(cell_vertices)/50)
+
+    for i, points in cell_vertices.items():
+        if not i % vert_progress_mod:
+            progress.update(1)
+
+        if points:
+            vertex = torch.stack(points).mean(dim=0)
+            vertex = vertex + active_cells[i].float()
+            vertex_lookup[tuple(active_cells[i].tolist())] = len(vertices)
+            vertices.append(vertex)
+
+    if not vertices:
+        return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device)
+
+    final_vertices = torch.stack(vertices)
+
+    inside_corners_mask = active_signs
+    outside_corners_mask = ~active_signs
+
+    inside_counts = inside_corners_mask.sum(dim=1, keepdim=True).float()
+    outside_counts = outside_corners_mask.sum(dim=1, keepdim=True).float()
+
+    inside_pos = torch.zeros((active_cells.shape[0], 3), device=device)
+    outside_pos = torch.zeros((active_cells.shape[0], 3), device=device)
+
+    for i in range(8):
+        mask_inside = inside_corners_mask[:, i].unsqueeze(1)
+        mask_outside = outside_corners_mask[:, i].unsqueeze(1)
+        inside_pos += corner_offsets[i].float().unsqueeze(0) * mask_inside
+        outside_pos += corner_offsets[i].float().unsqueeze(0) * mask_outside
+
+    inside_pos /= inside_counts
+    outside_pos /= outside_counts
+    gradients = inside_pos - outside_pos
+
+    pos_dirs = torch.tensor([
+        [1, 0, 0],
+        [0, 1, 0],
+        [0, 0, 1]
+    ], device=device)
+
+    cross_products = [
+        torch.linalg.cross(pos_dirs[i].float(), pos_dirs[j].float())
+        for i in range(3) for j in range(i+1, 3)
+    ]
+
+    faces = []
+    all_keys = set(vertex_lookup.keys())
+
+    face_progress_mod = round(len(active_cells)/38*3)
+
+    for pair_idx, (i, j) in enumerate([(0,1), (0,2), (1,2)]):
+        dir_i = pos_dirs[i]
+        dir_j = pos_dirs[j]
+        cross_product = cross_products[pair_idx]
+
+        ni_positions = active_cells + dir_i
+        nj_positions = active_cells + dir_j
+        diag_positions = active_cells + dir_i + dir_j
+
+        alignments = torch.matmul(gradients, cross_product)
+
+        valid_quads = []
+        quad_indices = []
+
+        for idx, active_cell in enumerate(active_cells):
+            if not idx % face_progress_mod:
+                progress.update(1)
+            cell_key = tuple(active_cell.tolist())
+            ni_key = tuple(ni_positions[idx].tolist())
+            nj_key = tuple(nj_positions[idx].tolist())
+            diag_key = tuple(diag_positions[idx].tolist())
+
+            if cell_key in all_keys and ni_key in all_keys and nj_key in all_keys and diag_key in all_keys:
+                v0 = vertex_lookup[cell_key]
+                v1 = vertex_lookup[ni_key]
+                v2 = vertex_lookup[nj_key]
+                v3 = vertex_lookup[diag_key]
+
+                valid_quads.append((v0, v1, v2, v3))
+                quad_indices.append(idx)
+
+        for q_idx, (v0, v1, v2, v3) in enumerate(valid_quads):
+            cell_idx = quad_indices[q_idx]
+            if alignments[cell_idx] > 0:
+                faces.append(torch.tensor([v0, v1, v3], device=device, dtype=torch.long))
+                faces.append(torch.tensor([v0, v3, v2], device=device, dtype=torch.long))
+            else:
+                faces.append(torch.tensor([v0, v3, v1], device=device, dtype=torch.long))
+                faces.append(torch.tensor([v0, v2, v3], device=device, dtype=torch.long))
+
+    if faces:
+        faces = torch.stack(faces)
+    else:
+        faces = torch.zeros((0, 3), dtype=torch.long, device=device)
+
+    v_min = 0
+    v_max = max(D, H, W)
+
+    final_vertices = final_vertices - (v_min + v_max) / 2
+
+    scale = (v_max - v_min) / 2
+    if scale > 0:
+        final_vertices = final_vertices / scale
+
+    final_vertices = torch.fliplr(final_vertices)
+
+    return final_vertices, faces
+
+
+class VoxelToMeshBasic(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VoxelToMeshBasic",
+            display_name="Voxel to Mesh (Basic) (DEPRECATED)",
+            category="3d",
+            description="Converts a voxel grid to a mesh.",
+            is_deprecated=True, # This node is superseded by the Voxel To Mesh node
+            inputs=[
+                IO.Voxel.Input("voxel"),
+                IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                IO.Mesh.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, voxel, threshold) -> IO.NodeOutput:
+        vertices = []
+        faces = []
+        for x in voxel.data:
+            v, f = voxel_to_mesh(x, threshold=threshold, device=None)
+            vertices.append(v)
+            faces.append(f)
+
+        if vertices and all(v.shape == vertices[0].shape for v in vertices) and all(f.shape == faces[0].shape for f in faces):
+            return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces)))
+        return IO.NodeOutput(pack_variable_mesh_batch(vertices, faces))
+
+    decode = execute  # TODO: remove
+
+
+class VoxelToMesh(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VoxelToMesh",
+            display_name="Voxel to Mesh",
+            category="3d",
+            description="Converts a voxel grid to a mesh.",
+            inputs=[
+                IO.Voxel.Input("voxel"),
+                IO.Combo.Input("algorithm", options=["surface net", "basic"]),
+                IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                IO.Mesh.Output(),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, voxel, algorithm, threshold) -> IO.NodeOutput:
+        vertices = []
+        faces = []
+
+        if algorithm == "basic":
+            mesh_function = voxel_to_mesh
+        elif algorithm == "surface net":
+            mesh_function = voxel_to_mesh_surfnet
+
+        for x in voxel.data:
+            v, f = mesh_function(x, threshold=threshold, device=None)
+            vertices.append(v)
+            faces.append(f)
+
+        if vertices and all(v.shape == vertices[0].shape for v in vertices) and all(f.shape == faces[0].shape for f in faces):
+            return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces)))
+        return IO.NodeOutput(pack_variable_mesh_batch(vertices, faces))
+
+    decode = execute  # TODO: remove
+
+
+class Hunyuan3dExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            EmptyLatentHunyuan3Dv2,
+            Hunyuan3Dv2Conditioning,
+            Hunyuan3Dv2ConditioningMultiView,
+            VAEDecodeHunyuan3D,
+            VoxelToMeshBasic,
+            VoxelToMesh,
+        ]
+
+
+async def comfy_entrypoint() -> Hunyuan3dExtension:
+    return Hunyuan3dExtension()
diff --git a/comfy_extras/nodes_hypernetwork.py b/comfy_extras/nodes_hypernetwork.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5100599d970c857dd49fd2d56e13c1883312e0b
--- /dev/null
+++ b/comfy_extras/nodes_hypernetwork.py
@@ -0,0 +1,139 @@
+import comfy.utils
+import folder_paths
+import torch
+import logging
+from comfy_api.latest import IO, ComfyExtension
+from typing_extensions import override
+
+
+def load_hypernetwork_patch(path, strength):
+    sd = comfy.utils.load_torch_file(path, safe_load=True)
+    activation_func = sd.get('activation_func', 'linear')
+    is_layer_norm = sd.get('is_layer_norm', False)
+    use_dropout = sd.get('use_dropout', False)
+    activate_output = sd.get('activate_output', False)
+    last_layer_dropout = sd.get('last_layer_dropout', False)
+
+    valid_activation = {
+        "linear": torch.nn.Identity,
+        "relu": torch.nn.ReLU,
+        "leakyrelu": torch.nn.LeakyReLU,
+        "elu": torch.nn.ELU,
+        "swish": torch.nn.Hardswish,
+        "tanh": torch.nn.Tanh,
+        "sigmoid": torch.nn.Sigmoid,
+        "softsign": torch.nn.Softsign,
+        "mish": torch.nn.Mish,
+    }
+
+    if activation_func not in valid_activation:
+        logging.error("Unsupported Hypernetwork format, if you report it I might implement it. {}   {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout))
+        return None
+
+    out = {}
+
+    for d in sd:
+        try:
+            dim = int(d)
+        except:
+            continue
+
+        output = []
+        for index in [0, 1]:
+            attn_weights = sd[dim][index]
+            keys = attn_weights.keys()
+
+            linears = filter(lambda a: a.endswith(".weight"), keys)
+            linears = list(map(lambda a: a[:-len(".weight")], linears))
+            layers = []
+
+            i = 0
+            while i < len(linears):
+                lin_name = linears[i]
+                last_layer = (i == (len(linears) - 1))
+                penultimate_layer = (i == (len(linears) - 2))
+
+                lin_weight = attn_weights['{}.weight'.format(lin_name)]
+                lin_bias = attn_weights['{}.bias'.format(lin_name)]
+                layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0])
+                layer.load_state_dict({"weight": lin_weight, "bias": lin_bias})
+                layers.append(layer)
+                if activation_func != "linear":
+                    if (not last_layer) or (activate_output):
+                        layers.append(valid_activation[activation_func]())
+                if is_layer_norm:
+                    i += 1
+                    ln_name = linears[i]
+                    ln_weight = attn_weights['{}.weight'.format(ln_name)]
+                    ln_bias = attn_weights['{}.bias'.format(ln_name)]
+                    ln = torch.nn.LayerNorm(ln_weight.shape[0])
+                    ln.load_state_dict({"weight": ln_weight, "bias": ln_bias})
+                    layers.append(ln)
+                if use_dropout:
+                    if (not last_layer) and (not penultimate_layer or last_layer_dropout):
+                        layers.append(torch.nn.Dropout(p=0.3))
+                i += 1
+
+            output.append(torch.nn.Sequential(*layers))
+        out[dim] = torch.nn.ModuleList(output)
+
+    class hypernetwork_patch:
+        def __init__(self, hypernet, strength):
+            self.hypernet = hypernet
+            self.strength = strength
+        def __call__(self, q, k, v, extra_options):
+            dim = k.shape[-1]
+            if dim in self.hypernet:
+                hn = self.hypernet[dim]
+                k = k + hn[0](k) * self.strength
+                v = v + hn[1](v) * self.strength
+
+            return q, k, v
+
+        def to(self, device):
+            for d in self.hypernet.keys():
+                self.hypernet[d] = self.hypernet[d].to(device)
+            return self
+
+    return hypernetwork_patch(out, strength)
+
+class HypernetworkLoader(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HypernetworkLoader",
+            display_name="Load Hypernetwork",
+            category="model/loaders",
+            inputs=[
+                IO.Model.Input("model"),
+                IO.Combo.Input("hypernetwork_name", options=folder_paths.get_filename_list("hypernetworks")),
+                IO.Float.Input("strength", default=1.0, min=-10.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                IO.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, hypernetwork_name, strength) -> IO.NodeOutput:
+        hypernetwork_path = folder_paths.get_full_path_or_raise("hypernetworks", hypernetwork_name)
+        model_hypernetwork = model.clone()
+        patch = load_hypernetwork_patch(hypernetwork_path, strength)
+        if patch is not None:
+            model_hypernetwork.set_model_attn1_patch(patch)
+            model_hypernetwork.set_model_attn2_patch(patch)
+        return IO.NodeOutput(model_hypernetwork)
+
+    load_hypernetwork = execute  # TODO: remove
+
+
+class HyperNetworkExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            HypernetworkLoader,
+        ]
+
+
+async def comfy_entrypoint() -> HyperNetworkExtension:
+    return HyperNetworkExtension()
diff --git a/comfy_extras/nodes_hypertile.py b/comfy_extras/nodes_hypertile.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec815d5b7a3cc9a4c288c8765dfe24cc8523f861
--- /dev/null
+++ b/comfy_extras/nodes_hypertile.py
@@ -0,0 +1,98 @@
+#Taken from: https://github.com/tfernd/HyperTile/
+
+import math
+from typing_extensions import override
+from einops import rearrange
+# Use torch rng for consistency across generations
+from torch import randint
+from comfy_api.latest import ComfyExtension, io
+
+def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
+    min_value = min(min_value, value)
+
+    # All big divisors of value (inclusive)
+    divisors = [i for i in range(min_value, value + 1) if value % i == 0]
+
+    ns = [value // i for i in divisors[:max_options]]  # has at least 1 element
+
+    if len(ns) - 1 > 0:
+        idx = randint(low=0, high=len(ns) - 1, size=(1,)).item()
+    else:
+        idx = 0
+
+    return ns[idx]
+
+class HyperTile(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="HyperTile",
+            category="model/patch/unet",
+            inputs=[
+                io.Model.Input("model"),
+                io.Int.Input("tile_size", default=256, min=1, max=2048, advanced=True),
+                io.Int.Input("swap_size", default=2, min=1, max=128, advanced=True),
+                io.Int.Input("max_depth", default=0, min=0, max=10, advanced=True),
+                io.Boolean.Input("scale_depth", default=False, advanced=True),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, tile_size, swap_size, max_depth, scale_depth) -> io.NodeOutput:
+        latent_tile_size = max(32, tile_size) // 8
+        temp = None
+
+        def hypertile_in(q, k, v, extra_options):
+            nonlocal temp
+            model_chans = q.shape[-2]
+            orig_shape = extra_options['original_shape']
+            apply_to = []
+            for i in range(max_depth + 1):
+                apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i)))
+
+            if model_chans in apply_to:
+                shape = extra_options["original_shape"]
+                aspect_ratio = shape[-1] / shape[-2]
+
+                hw = q.size(1)
+                h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
+
+                factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1
+                nh = random_divisor(h, latent_tile_size * factor, swap_size)
+                nw = random_divisor(w, latent_tile_size * factor, swap_size)
+
+                if nh * nw > 1:
+                    q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
+                    temp = (nh, nw, h, w)
+                return q, k, v
+
+            return q, k, v
+        def hypertile_out(out, extra_options):
+            nonlocal temp
+            if temp is not None:
+                nh, nw, h, w = temp
+                temp = None
+                out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
+                out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
+            return out
+
+
+        m = model.clone()
+        m.set_model_attn1_patch(hypertile_in)
+        m.set_model_attn1_output_patch(hypertile_out)
+        return (m, )
+
+
+class HyperTileExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            HyperTile,
+        ]
+
+
+async def comfy_entrypoint() -> HyperTileExtension:
+    return HyperTileExtension()
diff --git a/comfy_extras/nodes_ideogram4.py b/comfy_extras/nodes_ideogram4.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e168e6852279e88422c516e6bafcbcca6886be8
--- /dev/null
+++ b/comfy_extras/nodes_ideogram4.py
@@ -0,0 +1,64 @@
+"""Ideogram 4 sampling helper
+"""
+
+import math
+
+import torch
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+_LOGSNR_MIN = -15.0
+_LOGSNR_MAX = 18.0
+
+
+def _logit_normal_schedule(u, mean, std):
+    # Reference time (0=noise..1=clean) via the probit/ndtri quantile.
+    u = torch.as_tensor(u, dtype=torch.float64)
+    t = 1.0 - torch.special.expit(mean + std * torch.special.ndtri(u))
+    t_min = 1.0 / (1.0 + math.exp(0.5 * _LOGSNR_MAX))
+    t_max = 1.0 / (1.0 + math.exp(0.5 * _LOGSNR_MIN))
+    return t.clamp(t_min, t_max)
+
+
+def ideogram4_sigmas(num_steps, width, height, mu, std):
+    """Descending sigmas (len num_steps+1) for the reference schedule.
+
+    mu + the resolution term form the logSNR shift; std is the spread.
+    """
+    mean = mu + 0.5 * math.log((width * height) / (512 * 512))
+    u = torch.linspace(0.0, 1.0, num_steps + 1, dtype=torch.float64)
+    sigmas = (1.0 - _logit_normal_schedule(u, mean, std)).flip(0)
+    sigmas[-1] = 0.0                                      # clamp leaves ~6e-4; force full denoise
+    return sigmas.to(torch.float32)
+
+
+class Ideogram4Scheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="Ideogram4Scheduler",
+            display_name="Ideogram 4 Scheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Int.Input("steps", default=20, min=1, max=200),
+                io.Int.Input("width", default=1024, min=256, max=8192, step=16),
+                io.Int.Input("height", default=1024, min=256, max=8192, step=16),
+                io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.05),
+                io.Float.Input("std", default=1.75, min=0.1, max=5.0, step=0.05),
+            ],
+            outputs=[io.Sigmas.Output()],
+        )
+
+    @classmethod
+    def execute(cls, steps, width, height, mu, std) -> io.NodeOutput:
+        return io.NodeOutput(ideogram4_sigmas(steps, width, height, mu, std))
+
+
+class Ideogram4Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [Ideogram4Scheduler]
+
+
+async def comfy_entrypoint() -> Ideogram4Extension:
+    return Ideogram4Extension()
diff --git a/comfy_extras/nodes_image_compare.py b/comfy_extras/nodes_image_compare.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d0fcc98da1c131db22a029c47c9b52e5a7d8959
--- /dev/null
+++ b/comfy_extras/nodes_image_compare.py
@@ -0,0 +1,53 @@
+import nodes
+
+from typing_extensions import override
+from comfy_api.latest import IO, ComfyExtension
+
+
+class ImageCompare(IO.ComfyNode):
+    """Compares two images with a slider interface."""
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageCompare",
+            display_name="Compare Images",
+            description="Compares two images side by side with a slider.",
+            category="image",
+            essentials_category="Image Tools",
+            is_output_node=True,
+            inputs=[
+                IO.Image.Input("image_a", optional=True),
+                IO.Image.Input("image_b", optional=True),
+                IO.ImageCompare.Input("compare_view"),
+            ],
+            outputs=[],
+        )
+
+    @classmethod
+    def execute(cls, image_a=None, image_b=None, compare_view=None) -> IO.NodeOutput:
+        result = {"a_images": [], "b_images": []}
+
+        preview_node = nodes.PreviewImage()
+
+        if image_a is not None and len(image_a) > 0:
+            saved = preview_node.save_images(image_a, "comfy.compare.a")
+            result["a_images"] = saved["ui"]["images"]
+
+        if image_b is not None and len(image_b) > 0:
+            saved = preview_node.save_images(image_b, "comfy.compare.b")
+            result["b_images"] = saved["ui"]["images"]
+
+        return IO.NodeOutput(ui=result)
+
+
+class ImageCompareExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            ImageCompare,
+        ]
+
+
+async def comfy_entrypoint() -> ImageCompareExtension:
+    return ImageCompareExtension()
diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcb4e9c5d79feb6d69874d7a1438be590f882233
--- /dev/null
+++ b/comfy_extras/nodes_images.py
@@ -0,0 +1,1787 @@
+import nodes
+import folder_paths
+
+import av
+import json
+
+import os
+import re
+import math
+import numpy as np
+import struct
+import torch
+import logging
+import tempfile
+
+import zlib
+import comfy.utils
+from av.video.reformatter import ColorPrimaries, ColorRange, ColorTrc
+from fractions import Fraction
+from PIL.Image import Exif
+
+from server import PromptServer
+from comfy_api.latest import ComfyExtension, IO, UI
+from comfy.cli_args import args
+from typing_extensions import override
+
+SVG = IO.SVG.Type  # TODO: temporary solution for backward compatibility, will be removed later.
+
+MAX_RESOLUTION = nodes.MAX_RESOLUTION
+
+class ImageCrop(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageCrop",
+            search_aliases=["trim"],
+            display_name="Crop Image (DEPRECATED)",
+            category="image/transform",
+            is_deprecated=True,
+            essentials_category="Image Tools",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, width, height, x, y) -> IO.NodeOutput:
+        x = min(x, image.shape[2] - 1)
+        y = min(y, image.shape[1] - 1)
+        to_x = width + x
+        to_y = height + y
+        img = image[:,y:to_y, x:to_x, :]
+        return IO.NodeOutput(img)
+
+    crop = execute  # TODO: remove
+
+
+class ImageCropV2(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageCropV2",
+            search_aliases=["crop", "cut", "trim"],
+            display_name="Crop Image",
+            category="image/transform",
+            description = "Crop an image to the specified dimensions.",
+            essentials_category="Image Tools",
+            has_intermediate_output=True,
+            inputs=[
+                IO.Image.Input("image"),
+                IO.BoundingBox.Input("crop_region", component="ImageCrop"),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, crop_region) -> IO.NodeOutput:
+        x = crop_region.get("x", 0)
+        y = crop_region.get("y", 0)
+        width = crop_region.get("width", 512)
+        height = crop_region.get("height", 512)
+
+        x = min(x, image.shape[2] - 1)
+        y = min(y, image.shape[1] - 1)
+        to_x = width + x
+        to_y = height + y
+        img = image[:,y:to_y, x:to_x, :]
+        return IO.NodeOutput(img, ui=UI.PreviewImage(img))
+
+
+def _crop_image_with_mask(item_image, item_mask, max_image_size=1024, pad_factor=1.1,
+                          mask_offset=0, mask_threshold=0.05, bg_rgb=(0.0, 0.0, 0.0),
+                          aspect_ratio=1.0):
+    img = item_image.permute(2, 0, 1).unsqueeze(0).cpu().float().clamp(0, 1)
+    mask = item_mask.unsqueeze(0).unsqueeze(0).cpu().float().clamp(0, 1)
+
+    # Detect and correct an inverted mask, only when border and center have opposite polarity.
+    m2d = mask[0, 0]
+    h, w = m2d.shape
+    border = torch.cat([m2d[0, :], m2d[-1, :], m2d[:, 0], m2d[:, -1]])
+    center = m2d[h // 4:h - h // 4, w // 4:w - w // 4]
+    if float(border.mean()) > 0.5 and float(center.mean()) < 0.5:
+        mask = 1.0 - mask
+
+    if mask_offset > 0:
+        r = mask_offset
+        mask = torch.nn.functional.max_pool2d(mask, kernel_size=2 * r + 1, stride=1, padding=r)
+    elif mask_offset < 0:
+        r = -mask_offset
+        mask = 1.0 - torch.nn.functional.max_pool2d(1.0 - mask, kernel_size=2 * r + 1, stride=1, padding=r)
+
+    if mask_threshold > 0.0:
+        mask = torch.where(mask < mask_threshold, torch.zeros_like(mask), mask)
+
+    H, W = img.shape[-2:]
+    if max(H, W) > max_image_size:
+        scale = max_image_size / max(H, W)
+        new_w, new_h = int(W * scale), int(H * scale)
+        img  = comfy.utils.common_upscale(img,  new_w, new_h, "lanczos", "disabled")
+        mask = comfy.utils.common_upscale(mask, new_w, new_h, "lanczos", "disabled")
+        # common_upscale's lanczos path drops the singleton channel dim for masks (utils.py:1062).
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(1)
+        H, W = new_h, new_w
+    scene_size = (W, H)
+
+    alpha_u8 = (mask[0, 0].clamp(0, 1) * 255.0).to(torch.uint8)
+    fg_pixels = (alpha_u8 > 204).nonzero()
+    if fg_pixels.numel() == 0:
+        # Try the inverted mask — auto-invert above may have been too conservative.
+        inv_fg = ((255 - alpha_u8) > 204).nonzero()
+        if inv_fg.numel() > 0:
+            logging.info("Trellis2 preprocess: mask bbox empty, using inverted mask.")
+            mask = 1.0 - mask
+            fg_pixels = inv_fg
+    if fg_pixels.numel() > 0:
+        y_min, x_min = fg_pixels.min(dim=0).values.tolist()
+        y_max, x_max = fg_pixels.max(dim=0).values.tolist()
+        center_y, center_x = (y_min + y_max) / 2.0, (x_min + x_max) / 2.0
+        bw = x_max - x_min + 1
+        bh = y_max - y_min + 1
+        # Grow the bbox so its aspect matches `aspect_ratio` (width/height),
+        # anchored on the max side. Then apply pad_factor.
+        if bw / max(bh, 1) >= aspect_ratio:
+            crop_w = int(bw * pad_factor)
+            crop_h = int(bw / aspect_ratio * pad_factor)
+        else:
+            crop_h = int(bh * pad_factor)
+            crop_w = int(bh * aspect_ratio * pad_factor)
+        half_w, half_h = math.ceil(crop_w / 2), math.ceil(crop_h / 2)
+        crop_x1 = int(center_x - half_w)
+        crop_y1 = int(center_y - half_h)
+        crop_x2 = crop_x1 + 2 * half_w
+        crop_y2 = crop_y1 + 2 * half_h
+    else:
+        logging.warning("Mask for the image is empty; a clean foreground mask is required for best quality.")
+        crop_x1, crop_y1, crop_x2, crop_y2 = 0, 0, W, H
+    crop_bbox = (crop_x1, crop_y1, crop_x2, crop_y2)
+
+    # Zero-pad out-of-bounds slice (PIL.crop semantics).
+    pad_l = max(0, -crop_x1)
+    pad_t = max(0, -crop_y1)
+    pad_r = max(0, crop_x2 - W)
+    pad_b = max(0, crop_y2 - H)
+    if pad_l or pad_t or pad_r or pad_b:
+        img  = torch.nn.functional.pad(img,  (pad_l, pad_r, pad_t, pad_b), value=0.0)
+        mask = torch.nn.functional.pad(mask, (pad_l, pad_r, pad_t, pad_b), value=0.0)
+        crop_x1 += pad_l
+        crop_x2 += pad_l
+        crop_y1 += pad_t
+        crop_y2 += pad_t
+    cropped_img  = img [..., crop_y1:crop_y2, crop_x1:crop_x2]
+    cropped_mask = mask[..., crop_y1:crop_y2, crop_x1:crop_x2]
+
+    bg = torch.tensor(bg_rgb, dtype=cropped_img.dtype, device=cropped_img.device).view(1, 3, 1, 1)
+    composite = (cropped_img * cropped_mask + bg * (1.0 - cropped_mask)).clamp(0, 1)
+    return composite, crop_bbox, scene_size
+
+
+class ImageCropToMask(IO.ComfyNode):
+    """Crop an image to its mask's bounding box (centered square, with pad_factor
+    margin), then composite `img * mask` and resize to a square. Handles OOB crops
+    with zero-padding. Useful for 3D pipelines that expect a centered, background-free
+    subject at a fixed input resolution (Trellis2, Pixal3D, Hunyuan3D, TripoSR, etc.)."""
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageCropToMask",
+            display_name="Crop Image to Mask",
+            category="image/transform",
+            search_aliases=["crop to mask", "mask crop", "crop mask", "mask crop resize", "crop mask resize", "trellis2", "pixal3d"],
+            inputs=[
+                IO.Image.Input("images"),
+                IO.Mask.Input("masks"),
+                IO.Int.Input("width", default=1024, min=64, max=4096, step=8, tooltip="Output width in pixels."),
+                IO.Int.Input("height", default=1024, min=64, max=4096, step=8, tooltip="Output height in pixels."),
+                IO.Float.Input("pad_factor", default=1.0, min=1.0, max=2.0, step=0.01, tooltip="Extra margin around the mask bounding box as a multiplier."),
+                IO.Int.Input("grow_mask", default=0, min=-32, max=32, step=1, tooltip="Grow or shrink the mask by this many pixels before cropping."),
+                IO.Color.Input("background", default="#000000", tooltip="Background color behind the masked subject."),
+            ],
+            outputs=[IO.Image.Output(display_name="images")],
+        )
+
+    @classmethod
+    def execute(cls, images, masks, width, height, pad_factor, grow_mask, background) -> IO.NodeOutput:
+        h = background.lstrip("#")
+        bg_rgb = (int(h[0:2], 16) / 255.0, int(h[2:4], 16) / 255.0, int(h[4:6], 16) / 255.0) if len(h) == 6 else (0.0, 0.0, 0.0)
+        images = images[..., :3]
+        batch_size = images.shape[0]
+        if masks.shape[0] == 1 and batch_size > 1:
+            masks = masks.expand(batch_size, -1, -1)
+        elif masks.shape[0] != batch_size:
+            raise ValueError(f"Mask batch {masks.shape[0]} does not match image batch {batch_size}")
+        if masks.shape[-2:] != images.shape[1:3]:
+            masks = comfy.utils.common_upscale(masks.unsqueeze(1).float(), images.shape[2], images.shape[1], "bilinear", "disabled").squeeze(1)
+
+        out_images = []
+        for b in range(batch_size):
+            composite, _, _ = _crop_image_with_mask(
+                images[b], masks[b], max_image_size=max(width, height), pad_factor=pad_factor,
+                mask_offset=grow_mask, bg_rgb=bg_rgb, aspect_ratio=width / height,
+            )
+            composite = comfy.utils.common_upscale(composite, width, height, "lanczos", "disabled")
+            out_images.append(composite.movedim(-3, -1))
+
+        result = torch.cat(out_images, dim=0).to(
+            device=comfy.model_management.intermediate_device(),
+            dtype=comfy.model_management.intermediate_dtype(),
+        )
+        return IO.NodeOutput(result)
+
+
+class BoundingBox(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="PrimitiveBoundingBox",
+            display_name="Bounding Box",
+            category="utilities/primitive",
+            inputs=[
+                IO.Int.Input("x", default=0, min=0, max=MAX_RESOLUTION),
+                IO.Int.Input("y", default=0, min=0, max=MAX_RESOLUTION),
+                IO.Int.Input("width", default=512, min=1, max=MAX_RESOLUTION),
+                IO.Int.Input("height", default=512, min=1, max=MAX_RESOLUTION),
+            ],
+            outputs=[IO.BoundingBox.Output()],
+        )
+
+    @classmethod
+    def execute(cls, x, y, width, height) -> IO.NodeOutput:
+        return IO.NodeOutput({"x": x, "y": y, "width": width, "height": height})
+
+
+class RepeatImageBatch(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RepeatImageBatch",
+            search_aliases=["duplicate image", "clone image"],
+            display_name="Repeat Image Batch",
+            category="image/batch",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Int.Input("amount", default=1, min=1, max=4096),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, amount) -> IO.NodeOutput:
+        s = image.repeat((amount, 1,1,1))
+        return IO.NodeOutput(s)
+
+    repeat = execute  # TODO: remove
+
+
+class ImageFromBatch(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageFromBatch",
+            search_aliases=["select image", "pick from batch", "extract image"],
+            display_name="Get Image from Batch",
+            category="image/batch",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Int.Input("batch_index", default=0, min=-MAX_RESOLUTION, max=MAX_RESOLUTION),
+                IO.Int.Input("length", default=1, min=1, max=4096),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, batch_index, length) -> IO.NodeOutput:
+        s_in = image
+        if batch_index < 0:
+            batch_index += s_in.shape[0]
+        batch_index = max(0, min(s_in.shape[0] - 1, batch_index))
+        length = min(s_in.shape[0] - batch_index, length)
+        s = s_in[batch_index:batch_index + length].clone()
+        return IO.NodeOutput(s)
+
+    frombatch = execute  # TODO: remove
+
+
+class ImageAddNoise(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageAddNoise",
+            search_aliases=["film grain"],
+            display_name="Add Noise to Image",
+            category="image/filters",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                IO.Float.Input("strength", default=0.5, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, seed, strength) -> IO.NodeOutput:
+        generator = torch.manual_seed(seed)
+        s = torch.clip((image + strength * torch.randn(image.size(), generator=generator, device="cpu").to(image)), min=0.0, max=1.0)
+        return IO.NodeOutput(s)
+
+    repeat = execute  # TODO: remove
+
+
+class SaveAnimatedWEBP(IO.ComfyNode):
+    COMPRESS_METHODS = {"default": 4, "fastest": 0, "slowest": 6}
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAnimatedWEBP",
+            display_name="Save Animated WEBP",
+            category="image",
+            inputs=[
+                IO.Image.Input("images"),
+                IO.String.Input("filename_prefix", default="ComfyUI"),
+                IO.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
+                IO.Boolean.Input("lossless", default=True),
+                IO.Int.Input("quality", default=80, min=0, max=100),
+                IO.Combo.Input("method", options=list(cls.COMPRESS_METHODS.keys())),
+                # "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+            outputs=[IO.Image.Output(display_name="images")]
+        )
+
+    @classmethod
+    def execute(cls, images, fps, filename_prefix, lossless, quality, method, num_frames=0) -> IO.NodeOutput:
+        return IO.NodeOutput(
+            images,
+            ui=UI.ImageSaveHelper.get_save_animated_webp_ui(
+                images=images,
+                filename_prefix=filename_prefix,
+                cls=cls,
+                fps=fps,
+                lossless=lossless,
+                quality=quality,
+                method=cls.COMPRESS_METHODS.get(method)
+            )
+        )
+
+
+class SaveAnimatedPNG(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAnimatedPNG",
+            display_name="Save Animated PNG",
+            category="image",
+            inputs=[
+                IO.Image.Input("images"),
+                IO.String.Input("filename_prefix", default="ComfyUI"),
+                IO.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
+                IO.Int.Input("compress_level", default=4, min=0, max=9, advanced=True),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+            outputs=[IO.Image.Output(display_name="images")]
+        )
+
+    @classmethod
+    def execute(cls, images, fps, compress_level, filename_prefix="ComfyUI") -> IO.NodeOutput:
+        return IO.NodeOutput(
+            images,
+            ui=UI.ImageSaveHelper.get_save_animated_png_ui(
+                images=images,
+                filename_prefix=filename_prefix,
+                cls=cls,
+                fps=fps,
+                compress_level=compress_level,
+            )
+        )
+
+
+class ImageStitch(IO.ComfyNode):
+    """Upstreamed from https://github.com/kijai/ComfyUI-KJNodes"""
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageStitch",
+            search_aliases=["combine images", "join images", "concatenate images", "side by side"],
+            display_name="Stitch Images",
+            description="Stitches image2 to image1 in the specified direction.\n"
+            "If image2 is not provided, returns image1 unchanged.\n"
+            "Optional spacing can be added between images.",
+            category="image/transform",
+            inputs=[
+                IO.Image.Input("image1"),
+                IO.Combo.Input("direction", options=["right", "down", "left", "up"], default="right"),
+                IO.Boolean.Input("match_image_size", default=True),
+                IO.Int.Input("spacing_width", default=0, min=0, max=1024, step=2, advanced=True),
+                IO.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white", advanced=True),
+                IO.Image.Input("image2", optional=True),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image1,
+        direction,
+        match_image_size,
+        spacing_width,
+        spacing_color,
+        image2=None,
+    ) -> IO.NodeOutput:
+        if image2 is None:
+            return IO.NodeOutput(image1)
+
+        # Handle batch size differences
+        if image1.shape[0] != image2.shape[0]:
+            max_batch = max(image1.shape[0], image2.shape[0])
+            if image1.shape[0] < max_batch:
+                image1 = torch.cat(
+                    [image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)]
+                )
+            if image2.shape[0] < max_batch:
+                image2 = torch.cat(
+                    [image2, image2[-1:].repeat(max_batch - image2.shape[0], 1, 1, 1)]
+                )
+
+        # Match image sizes if requested
+        if match_image_size:
+            h1, w1 = image1.shape[1:3]
+            h2, w2 = image2.shape[1:3]
+            aspect_ratio = w2 / h2
+
+            if direction in ["left", "right"]:
+                target_h, target_w = h1, int(h1 * aspect_ratio)
+            else:  # up, down
+                target_w, target_h = w1, int(w1 / aspect_ratio)
+
+            image2 = comfy.utils.common_upscale(
+                image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled"
+            ).movedim(1, -1)
+
+        color_map = {
+            "white": 1.0,
+            "black": 0.0,
+            "red": (1.0, 0.0, 0.0),
+            "green": (0.0, 1.0, 0.0),
+            "blue": (0.0, 0.0, 1.0),
+        }
+
+        color_val = color_map[spacing_color]
+
+        # When not matching sizes, pad to align non-concat dimensions
+        if not match_image_size:
+            h1, w1 = image1.shape[1:3]
+            h2, w2 = image2.shape[1:3]
+            pad_value = 0.0
+            if not isinstance(color_val, tuple):
+                pad_value = color_val
+
+            if direction in ["left", "right"]:
+                # For horizontal concat, pad heights to match
+                if h1 != h2:
+                    target_h = max(h1, h2)
+                    if h1 < target_h:
+                        pad_h = target_h - h1
+                        pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
+                        image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value)
+                    if h2 < target_h:
+                        pad_h = target_h - h2
+                        pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
+                        image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value)
+            else:  # up, down
+                # For vertical concat, pad widths to match
+                if w1 != w2:
+                    target_w = max(w1, w2)
+                    if w1 < target_w:
+                        pad_w = target_w - w1
+                        pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
+                        image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=pad_value)
+                    if w2 < target_w:
+                        pad_w = target_w - w2
+                        pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
+                        image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=pad_value)
+
+        # Ensure same number of channels
+        if image1.shape[-1] != image2.shape[-1]:
+            max_channels = max(image1.shape[-1], image2.shape[-1])
+            if image1.shape[-1] < max_channels:
+                image1 = torch.cat(
+                    [
+                        image1,
+                        torch.ones(
+                            *image1.shape[:-1],
+                            max_channels - image1.shape[-1],
+                            device=image1.device,
+                        ),
+                    ],
+                    dim=-1,
+                )
+            if image2.shape[-1] < max_channels:
+                image2 = torch.cat(
+                    [
+                        image2,
+                        torch.ones(
+                            *image2.shape[:-1],
+                            max_channels - image2.shape[-1],
+                            device=image2.device,
+                        ),
+                    ],
+                    dim=-1,
+                )
+
+        # Add spacing if specified
+        if spacing_width > 0:
+            spacing_width = spacing_width + (spacing_width % 2)  # Ensure even
+
+            if direction in ["left", "right"]:
+                spacing_shape = (
+                    image1.shape[0],
+                    max(image1.shape[1], image2.shape[1]),
+                    spacing_width,
+                    image1.shape[-1],
+                )
+            else:
+                spacing_shape = (
+                    image1.shape[0],
+                    spacing_width,
+                    max(image1.shape[2], image2.shape[2]),
+                    image1.shape[-1],
+                )
+
+            spacing = torch.full(spacing_shape, 0.0, device=image1.device)
+            if isinstance(color_val, tuple):
+                for i, c in enumerate(color_val):
+                    if i < spacing.shape[-1]:
+                        spacing[..., i] = c
+                if spacing.shape[-1] == 4:  # Add alpha
+                    spacing[..., 3] = 1.0
+            else:
+                spacing[..., : min(3, spacing.shape[-1])] = color_val
+                if spacing.shape[-1] == 4:
+                    spacing[..., 3] = 1.0
+
+        # Concatenate images
+        images = [image2, image1] if direction in ["left", "up"] else [image1, image2]
+        if spacing_width > 0:
+            images.insert(1, spacing)
+
+        concat_dim = 2 if direction in ["left", "right"] else 1
+        return IO.NodeOutput(torch.cat(images, dim=concat_dim))
+
+    stitch = execute  # TODO: remove
+
+
+class ResizeAndPadImage(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ResizeAndPadImage",
+            search_aliases=["fit to size"],
+            display_name="Resize And Pad Image",
+            category="image/transform",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Int.Input("target_width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("target_height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Combo.Input("padding_color", options=["white", "black"], advanced=True),
+                IO.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"], advanced=True),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, target_width, target_height, padding_color, interpolation) -> IO.NodeOutput:
+        batch_size, orig_height, orig_width, channels = image.shape
+
+        scale_w = target_width / orig_width
+        scale_h = target_height / orig_height
+        scale = min(scale_w, scale_h)
+
+        new_width = int(orig_width * scale)
+        new_height = int(orig_height * scale)
+
+        image_permuted = image.permute(0, 3, 1, 2)
+
+        resized = comfy.utils.common_upscale(image_permuted, new_width, new_height, interpolation, "disabled")
+
+        pad_value = 0.0 if padding_color == "black" else 1.0
+        padded = torch.full(
+            (batch_size, channels, target_height, target_width),
+            pad_value,
+            dtype=image.dtype,
+            device=image.device
+        )
+
+        y_offset = (target_height - new_height) // 2
+        x_offset = (target_width - new_width) // 2
+
+        padded[:, :, y_offset:y_offset + new_height, x_offset:x_offset + new_width] = resized
+
+        output = padded.permute(0, 2, 3, 1)
+        return IO.NodeOutput(output)
+
+    resize_and_pad = execute  # TODO: remove
+
+
+class SaveSVGNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveSVGNode",
+            search_aliases=["export vector", "save vector graphics"],
+            display_name="Save SVG",
+            description="Save SVG files on disk.",
+            category="image",
+            inputs=[
+                IO.SVG.Input("svg"),
+                IO.String.Input(
+                    "filename_prefix",
+                    default="svg/ComfyUI",
+                    tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
+                ),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+            outputs=[IO.SVG.Output("svg")],
+        )
+
+    @classmethod
+    def execute(cls, svg: IO.SVG.Type, filename_prefix="svg/ComfyUI") -> IO.NodeOutput:
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
+        results: list[UI.SavedResult] = []
+
+        # Prepare metadata JSON
+        metadata_dict = {}
+        if cls.hidden.prompt is not None:
+            metadata_dict["prompt"] = cls.hidden.prompt
+        if cls.hidden.extra_pnginfo is not None:
+            metadata_dict.update(cls.hidden.extra_pnginfo)
+
+        # Convert metadata to JSON string
+        metadata_json = json.dumps(metadata_dict, indent=2) if metadata_dict else None
+
+
+        for batch_number, svg_bytes in enumerate(svg.data):
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.svg"
+
+            # Read SVG content
+            svg_bytes.seek(0)
+            svg_content = svg_bytes.read().decode('utf-8')
+
+            # Inject metadata if available
+            if metadata_json:
+                # Create metadata element with CDATA section
+                metadata_element = f"""  <metadata>
+                <![CDATA[
+            {metadata_json}
+                ]]>
+            </metadata>
+            """
+                # Insert metadata after opening svg tag using regex with a replacement function
+                def replacement(match):
+                    # match.group(1) contains the captured <svg> tag
+                    return match.group(1) + '\n' + metadata_element
+
+                # Apply the substitution
+                svg_content = re.sub(r'(<svg[^>]*>)', replacement, svg_content, flags=re.UNICODE)
+
+            # Write the modified SVG to file
+            with open(os.path.join(full_output_folder, file), 'wb') as svg_file:
+                svg_file.write(svg_content.encode('utf-8'))
+
+            results.append(UI.SavedResult(filename=file, subfolder=subfolder, type=IO.FolderType.output))
+            counter += 1
+        return IO.NodeOutput(svg, ui={"images": results})
+
+
+class GetImageSize(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GetImageSize",
+            search_aliases=["dimensions", "resolution", "image info"],
+            display_name="Get Image Size",
+            description="Returns width and height of the image, and passes it through unchanged.",
+            category="image",
+            inputs=[
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+                IO.Int.Output(display_name="batch_size"),
+            ],
+            hidden=[IO.Hidden.unique_id],
+        )
+
+    @classmethod
+    def execute(cls, image) -> IO.NodeOutput:
+        height = image.shape[1]
+        width = image.shape[2]
+        batch_size = image.shape[0]
+
+        # Send progress text to display size on the node
+        if cls.hidden.unique_id:
+            PromptServer.instance.send_progress_text(f"width: {width}, height: {height}\n batch size: {batch_size}", cls.hidden.unique_id)
+
+        return IO.NodeOutput(width, height, batch_size)
+
+    get_size = execute  # TODO: remove
+
+
+class ImageRotate(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageRotate",
+            display_name="Rotate Image",
+            search_aliases=["turn", "flip orientation"],
+            category="image/transform",
+            essentials_category="Image Tools",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Combo.Input("rotation", options=["none", "90 degrees", "180 degrees", "270 degrees"]),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, rotation) -> IO.NodeOutput:
+        rotate_by = 0
+        if rotation.startswith("90"):
+            rotate_by = 1
+        elif rotation.startswith("180"):
+            rotate_by = 2
+        elif rotation.startswith("270"):
+            rotate_by = 3
+
+        image = torch.rot90(image, k=rotate_by, dims=[2, 1])
+        return IO.NodeOutput(image)
+
+    rotate = execute  # TODO: remove
+
+
+class ImageFlip(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageFlip",
+            search_aliases=["mirror", "reflect"],
+            display_name="Flip Image",
+            category="image/transform",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Combo.Input("flip_method", options=["x-axis: vertically", "y-axis: horizontally"]),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, flip_method) -> IO.NodeOutput:
+        if flip_method.startswith("x"):
+            image = torch.flip(image, dims=[1])
+        elif flip_method.startswith("y"):
+            image = torch.flip(image, dims=[2])
+
+        return IO.NodeOutput(image)
+
+    flip = execute  # TODO: remove
+
+
+class ImageScaleToMaxDimension(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageScaleToMaxDimension",
+            display_name="Scale Image to Max Dimension",
+            category="image/upscaling",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Combo.Input(
+                    "upscale_method",
+                    options=["area", "lanczos", "bilinear", "nearest-exact", "bilinear", "bicubic"],
+                ),
+                IO.Int.Input("largest_size", default=512, min=0, max=MAX_RESOLUTION, step=1),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, upscale_method, largest_size) -> IO.NodeOutput:
+        height = image.shape[1]
+        width = image.shape[2]
+
+        if height > width:
+            width = round((width / height) * largest_size)
+            height = largest_size
+        elif width > height:
+            height = round((height / width) * largest_size)
+            width = largest_size
+        else:
+            height = largest_size
+            width = largest_size
+
+        samples = image.movedim(-1, 1)
+        s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
+        s = s.movedim(1, -1)
+        return IO.NodeOutput(s)
+
+    upscale = execute    # TODO: remove
+
+
+class SplitImageToTileList(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SplitImageToTileList",
+            category="image/batch",
+            search_aliases=["split image", "tile image", "slice image"],
+            display_name="Split Image into List of Tiles",
+            description="Splits an image into a batched list of tiles with a specified overlap.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Int.Input("tile_width", default=1024, min=64, max=MAX_RESOLUTION),
+                IO.Int.Input("tile_height", default=1024, min=64, max=MAX_RESOLUTION),
+                IO.Int.Input("overlap", default=128, min=0, max=4096),
+            ],
+            outputs=[
+                IO.Image.Output(is_output_list=True),
+            ],
+        )
+
+    @staticmethod
+    def get_grid_coords(width, height, tile_width, tile_height, overlap):
+        coords = []
+        stride_x = round(max(tile_width * 0.25, tile_width - overlap))
+        stride_y = round(max(tile_height * 0.25, tile_height - overlap))
+
+        y = 0
+        while y < height:
+            x = 0
+            y_end = min(y + tile_height, height)
+            y_start = max(0, y_end - tile_height)
+
+            while x < width:
+                x_end = min(x + tile_width, width)
+                x_start = max(0, x_end - tile_width)
+
+                coords.append((x_start, y_start, x_end, y_end))
+
+                if x_end >= width:
+                    break
+                x += stride_x
+
+            if y_end >= height:
+                break
+            y += stride_y
+
+        return coords
+
+    @classmethod
+    def execute(cls, image, tile_width, tile_height, overlap):
+        b, h, w, c = image.shape
+        coords = cls.get_grid_coords(w, h, tile_width, tile_height, overlap)
+
+        output_list = []
+        for (x_start, y_start, x_end, y_end) in coords:
+            tile = image[:, y_start:y_end, x_start:x_end, :]
+            output_list.append(tile)
+
+        return IO.NodeOutput(output_list)
+
+
+class ImageMergeTileList(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageMergeTileList",
+            display_name="Merge List of Tiles to Image",
+            category="image/batch",
+            search_aliases=["split image", "tile image", "slice image"],
+            is_input_list=True,
+            inputs=[
+                IO.Image.Input("image_list"),
+                IO.Int.Input("final_width", default=1024, min=64, max=32768),
+                IO.Int.Input("final_height", default=1024, min=64, max=32768),
+                IO.Int.Input("overlap", default=128, min=0, max=4096),
+            ],
+            outputs=[
+                IO.Image.Output(is_output_list=False),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image_list, final_width, final_height, overlap):
+        w = final_width[0]
+        h = final_height[0]
+        ovlp = overlap[0]
+        feather_str = 1.0
+
+        first_tile = image_list[0]
+        b, t_h, t_w, c = first_tile.shape
+        device = first_tile.device
+        dtype = first_tile.dtype
+
+        coords = SplitImageToTileList.get_grid_coords(w, h, t_w, t_h, ovlp)
+
+        canvas = torch.zeros((b, h, w, c), device=device, dtype=dtype)
+        weights = torch.zeros((b, h, w, 1), device=device, dtype=dtype)
+
+        if ovlp > 0:
+            y_w = torch.sin(math.pi * torch.linspace(0, 1, t_h, device=device, dtype=dtype))
+            x_w = torch.sin(math.pi * torch.linspace(0, 1, t_w, device=device, dtype=dtype))
+            y_w = torch.clamp(y_w, min=1e-5)
+            x_w = torch.clamp(x_w, min=1e-5)
+
+            sine_mask = (y_w.unsqueeze(1) * x_w.unsqueeze(0)).unsqueeze(0).unsqueeze(-1)
+            flat_mask = torch.ones_like(sine_mask)
+
+            weight_mask = torch.lerp(flat_mask, sine_mask, feather_str)
+        else:
+            weight_mask = torch.ones((1, t_h, t_w, 1), device=device, dtype=dtype)
+
+        for i, (x_start, y_start, x_end, y_end) in enumerate(coords):
+            if i >= len(image_list):
+                break
+
+            tile = image_list[i]
+
+            region_h = y_end - y_start
+            region_w = x_end - x_start
+
+            real_h = min(region_h, tile.shape[1])
+            real_w = min(region_w, tile.shape[2])
+
+            y_end_actual = y_start + real_h
+            x_end_actual = x_start + real_w
+
+            tile_crop = tile[:, :real_h, :real_w, :]
+            mask_crop = weight_mask[:, :real_h, :real_w, :]
+
+            canvas[:, y_start:y_end_actual, x_start:x_end_actual, :] += tile_crop * mask_crop
+            weights[:, y_start:y_end_actual, x_start:x_end_actual, :] += mask_crop
+
+        weights[weights == 0] = 1.0
+        merged_image = canvas / weights
+
+        return IO.NodeOutput(merged_image)
+
+
+# ---------------------------------------------------------------------------
+# Format specifications
+# ---------------------------------------------------------------------------
+
+# Maps (file_format, bit_depth, num_channels) -> (quantization scale, numpy dtype,
+# av frame pix_fmt, stream pix_fmt). Keeps the encode path declarative instead of branchy.
+_FORMAT_SPECS = {
+    ("png", "8-bit", 1):  {"scale": 255.0,   "dtype": np.uint8,   "frame_fmt": "gray",      "stream_fmt": "gray"},
+    ("png", "8-bit", 3):  {"scale": 255.0,   "dtype": np.uint8,   "frame_fmt": "rgb24",     "stream_fmt": "rgb24"},
+    ("png", "8-bit", 4):  {"scale": 255.0,   "dtype": np.uint8,   "frame_fmt": "rgba",      "stream_fmt": "rgba"},
+    ("png", "16-bit", 1): {"scale": 65535.0, "dtype": np.uint16,  "frame_fmt": "gray16le",  "stream_fmt": "gray16be"},
+    ("png", "16-bit", 3): {"scale": 65535.0, "dtype": np.uint16,  "frame_fmt": "rgb48le",   "stream_fmt": "rgb48be"},
+    ("png", "16-bit", 4): {"scale": 65535.0, "dtype": np.uint16,  "frame_fmt": "rgba64le",  "stream_fmt": "rgba64be"},
+    ("exr", "32-bit float", 1): {"scale": 1.0, "dtype": np.float32, "frame_fmt": "grayf32le",  "stream_fmt": "grayf32le"},
+    ("exr", "32-bit float", 3): {"scale": 1.0, "dtype": np.float32, "frame_fmt": "gbrpf32le",  "stream_fmt": "gbrpf32le"},
+    ("exr", "32-bit float", 4): {"scale": 1.0, "dtype": np.float32, "frame_fmt": "gbrapf32le", "stream_fmt": "gbrapf32le"},
+}
+
+_AVIF_COLOR_PROPERTIES = {
+    "sRGB": (ColorPrimaries.BT709, ColorTrc.IEC61966_2_1, 1),
+    "HDR": (ColorPrimaries.BT2020, ColorTrc.ARIB_STD_B67, 9),
+    "HDR PQ": (ColorPrimaries.BT2020, ColorTrc.SMPTE2084, 9),
+}
+
+
+# ---------------------------------------------------------------------------
+# Color transforms
+# ---------------------------------------------------------------------------
+
+def srgb_to_linear(t: torch.Tensor) -> torch.Tensor:
+    """Inverse sRGB EOTF (IEC 61966-2-1). Operates on RGB channels only;
+    alpha (if present as the 4th channel) is passed through unchanged."""
+    if t.shape[-1] == 4:
+        rgb, alpha = t[..., :3], t[..., 3:]
+        return torch.cat([srgb_to_linear(rgb), alpha], dim=-1)
+
+    # Piecewise: linear toe below 0.04045, gamma curve above.
+    low = t / 12.92
+    high = ((t.clamp(min=0.0) + 0.055) / 1.055) ** 2.4
+    return torch.where(t <= 0.04045, low, high)
+
+
+# HLG OETF constants from BT.2100 Table 5.
+_HLG_A = 0.17883277
+_HLG_B = 0.28466892
+_HLG_C = 0.55991072928   # = 0.5 - a*ln(4*a)
+
+
+def hlg_to_linear(t: torch.Tensor) -> torch.Tensor:
+    """Inverse HLG OETF (BT.2100). Maps a non-linear HLG signal in [0, 1] to
+    *scene*-linear light in [0, 1]. Per BT.2100 Note 5a, this is the correct
+    transform when converting HLG to a linear scene-light representation
+    (rather than display-light, which would also involve the HLG OOTF).
+
+    Operates on RGB channels only; alpha is passed through unchanged."""
+    if t.shape[-1] == 4:
+        rgb, alpha = t[..., :3], t[..., 3:]
+        return torch.cat([hlg_to_linear(rgb), alpha], dim=-1)
+
+    # Piecewise: sqrt branch below 0.5, log branch above.
+    # Clamp the log branch at the 0.5 branch point (not above it) so the
+    # unselected lane stays finite in exp() without altering selected values;
+    # values above 1.0 are allowed and extrapolate naturally.
+    low = (t ** 2) / 3.0
+    high = (torch.exp((t.clamp(min=0.5) - _HLG_C) / _HLG_A) + _HLG_B) / 12.0
+    return torch.where(t <= 0.5, low, high)
+
+
+# ---------------------------------------------------------------------------
+# Metadata injection
+# ---------------------------------------------------------------------------
+
+_PNG_SIGNATURE = b"\x89PNG\r\n\x1a\n"
+
+
+def _png_chunk(chunk_type: bytes, data: bytes) -> bytes:
+    """Build a single PNG chunk: length | type | data | CRC32(type+data)."""
+    crc = zlib.crc32(chunk_type + data) & 0xFFFFFFFF
+    return struct.pack(">I", len(data)) + chunk_type + data + struct.pack(">I", crc)
+
+
+def _png_text_chunk(keyword: str, text: str) -> bytes:
+    """tEXt chunk: latin-1 keyword + NUL + latin-1 text."""
+    payload = keyword.encode("latin-1") + b"\x00" + text.encode("latin-1", errors="replace")
+    return _png_chunk(b"tEXt", payload)
+
+
+def inject_png_metadata(png_bytes: bytes, prompt: dict | None, extra_pnginfo: dict | None) -> bytes:
+    """Insert ComfyUI prompt/workflow as tEXt chunks right after IHDR."""
+    if not png_bytes.startswith(_PNG_SIGNATURE):
+        return png_bytes
+
+    chunks: list[bytes] = []
+    if prompt is not None:
+        chunks.append(_png_text_chunk("prompt", json.dumps(prompt)))
+    if extra_pnginfo:
+        for key, value in extra_pnginfo.items():
+            chunks.append(_png_text_chunk(key, json.dumps(value)))
+    if not chunks:
+        return png_bytes
+
+    # IHDR is always the first chunk; insert ours immediately after it.
+    ihdr_length = struct.unpack(">I", png_bytes[8:12])[0]
+    ihdr_end = 8 + 8 + ihdr_length + 4  # signature + (len+type) + data + crc
+    return png_bytes[:ihdr_end] + b"".join(chunks) + png_bytes[ihdr_end:]
+
+
+# Standard chromaticities (CIE 1931 xy) for the colorspaces this node writes.
+# Each tuple is (Rx, Ry, Gx, Gy, Bx, By, Wx, Wy). All share D65 white point.
+_CHROMATICITIES = {
+    # ITU-R BT.709 / sRGB primaries
+    "Rec.709":  (0.6400, 0.3300, 0.3000, 0.6000, 0.1500, 0.0600, 0.3127, 0.3290),
+    # ITU-R BT.2020 (UHDTV / wide-gamut HDR) primaries
+    "Rec.2020": (0.7080, 0.2920, 0.1700, 0.7970, 0.1310, 0.0460, 0.3127, 0.3290),
+}
+
+
+def _pack_chromaticities(primaries: tuple) -> bytes:
+    """Serialize 8 chromaticity floats into the EXR `chromaticities` payload."""
+    return struct.pack("<8f", *primaries)
+
+
+def _exr_attribute(name: str, attr_type: str, value: bytes) -> bytes:
+    """Serialize one EXR header attribute: name\\0 type\\0 size:int32 value."""
+    return (
+        name.encode("utf-8") + b"\x00"
+        + attr_type.encode("utf-8") + b"\x00"
+        + struct.pack("<i", len(value))
+        + value
+    )
+
+
+def inject_exr_metadata(
+    exr_bytes: bytes,
+    prompt: dict | None,
+    extra_pnginfo: dict | None,
+    colorspace: str | None = None,
+) -> bytes:
+    """Insert ComfyUI metadata and color-space info into an EXR header.
+
+    Color: EXR pixels are linear by convention. The standard way to describe
+    their RGB→XYZ relationship is the `chromaticities` attribute. We pick the
+    primaries that match what the user told us their input was:
+
+      colorspace="sRGB" → Rec. 709 / sRGB primaries (D65)
+      colorspace="HDR"  → Rec. 2020 / BT.2100 primaries (D65)
+
+    Pixels are always converted to linear scene light upstream (sRGB EOTF
+    inverse for sRGB; HLG OETF inverse for HDR), so the file content is
+    scene-linear in the indicated gamut. OpenEXR has no standard transfer-
+    function attribute (the OpenEXR TSC has discussed adding one but it
+    doesn't exist), so we don't invent one — `chromaticities` plus the EXR
+    linear-by-convention rule fully specifies the color.
+
+    Prompt/workflow: written as plain `string` attributes using the same keys
+    (`prompt`, `workflow`, ...) that Comfy uses for PNG tEXt chunks, so the
+    same readers can pull them out symmetrically.
+
+    Implementation note: the chunk-offset table that follows the header stores
+    *absolute* byte offsets into the file. Inserting N bytes into the header
+    means every offset must be incremented by N or the file becomes unreadable.
+    """
+    if len(exr_bytes) < 8 or exr_bytes[:4] != b"\x76\x2f\x31\x01":
+        return exr_bytes
+
+    new_blob = b""
+    if prompt is not None:
+        new_blob += _exr_attribute("prompt", "string", json.dumps(prompt).encode("utf-8"))
+    if extra_pnginfo:
+        for key, value in extra_pnginfo.items():
+            new_blob += _exr_attribute(key, "string", json.dumps(value).encode("utf-8"))
+    if colorspace is not None:
+        # Map each colorspace option to the RGB primaries the linear pixels
+        # are now in. "sRGB" and "linear" both produce Rec. 709 linear; "HDR"
+        # (HLG-encoded Rec. 2020 input) produces Rec. 2020 linear.
+        primaries_name = {
+            "sRGB":   "Rec.709",
+            "linear": "Rec.709",
+            "HDR":    "Rec.2020",
+        }.get(colorspace, "Rec.709")
+        new_blob += _exr_attribute(
+            "chromaticities",
+            "chromaticities",
+            _pack_chromaticities(_CHROMATICITIES[primaries_name]),
+        )
+    if not new_blob:
+        return exr_bytes
+
+    # Walk header attributes to find the terminating null byte, and pick up
+    # dataWindow + compression so we know how many chunks the offset table has.
+    pos = 8  # past magic (4) + version (4)
+    data_window = None
+    compression = 0
+    while pos < len(exr_bytes) and exr_bytes[pos] != 0:
+        name_end = exr_bytes.index(b"\x00", pos)
+        attr_name = exr_bytes[pos:name_end].decode("latin-1", errors="replace")
+        type_end = exr_bytes.index(b"\x00", name_end + 1)
+        attr_type = exr_bytes[name_end + 1:type_end].decode("latin-1", errors="replace")
+        size = struct.unpack("<i", exr_bytes[type_end + 1:type_end + 5])[0]
+        value_start = type_end + 5
+        value = exr_bytes[value_start:value_start + size]
+
+        if attr_name == "dataWindow" and attr_type == "box2i":
+            data_window = struct.unpack("<iiii", value)  # xMin, yMin, xMax, yMax
+        elif attr_name == "compression" and attr_type == "compression":
+            compression = value[0]
+
+        pos = value_start + size
+
+    if data_window is None:
+        return exr_bytes  # required attribute missing — don't risk corrupting
+
+    # Scanlines per chunk by compression, from the OpenEXR spec.
+    scanlines_per_block = {
+        0: 1,   # NO_COMPRESSION
+        1: 1,   # RLE
+        2: 1,   # ZIPS
+        3: 16,  # ZIP
+        4: 32,  # PIZ
+        5: 16,  # PXR24
+        6: 32,  # B44
+        7: 32,  # B44A
+        8: 256, # DWAA
+        9: 256, # DWAB
+    }.get(compression, 1)
+
+    _, y_min, _, y_max = data_window
+    height = y_max - y_min + 1
+    num_chunks = (height + scanlines_per_block - 1) // scanlines_per_block
+
+    header_end = pos  # position of the terminating null byte
+    table_start = header_end + 1
+    pixel_start = table_start + num_chunks * 8
+    delta = len(new_blob)
+
+    old_offsets = struct.unpack(f"<{num_chunks}Q", exr_bytes[table_start:pixel_start])
+    new_table = struct.pack(f"<{num_chunks}Q", *(o + delta for o in old_offsets))
+
+    return (
+        exr_bytes[:header_end]                # header attributes
+        + new_blob                            # our new attributes
+        + exr_bytes[header_end:table_start]   # terminating null byte
+        + new_table                           # shifted offset table
+        + exr_bytes[pixel_start:]             # pixel data, untouched
+    )
+
+
+def _bmff_box(box_type: bytes, payload: bytes) -> bytes:
+    size = 8 + len(payload)
+    if size > 0xFFFFFFFF:
+        raise ValueError("AVIF metadata box is too large.")
+    return struct.pack(">I4s", size, box_type) + payload
+
+
+def _bmff_boxes(data: bytes, start: int, end: int) -> list[tuple[int, int, bytes, int]]:
+    boxes = []
+    pos = start
+    while pos < end:
+        if pos + 8 > end:
+            raise ValueError("Invalid AVIF box structure.")
+        size, box_type = struct.unpack_from(">I4s", data, pos)
+        header_size = 8
+        if size == 1:
+            if pos + 16 > end:
+                raise ValueError("Invalid AVIF extended-size box.")
+            size = struct.unpack_from(">Q", data, pos + 8)[0]
+            header_size = 16
+        elif size == 0:
+            size = end - pos
+        if size < header_size or pos + size > end:
+            raise ValueError("Invalid AVIF box size.")
+        boxes.append((pos, size, box_type, header_size))
+        pos += size
+    return boxes
+
+
+def _avif_exif(metadata: dict) -> bytes:
+    exif = Exif()
+    if "prompt" in metadata:
+        exif[0x0110] = f"prompt:{json.dumps(metadata['prompt'])}"
+    next_tag = 0x010F
+    for key, value in metadata.items():
+        if key == "prompt":
+            continue
+        exif[next_tag] = f"{key}:{json.dumps(value)}"
+        next_tag -= 1
+    return b"\x00\x00\x00\x00" + exif.tobytes()[6:]
+
+
+def _add_avif_exif_item(meta: bytes, exif_offset: int, exif_length: int, offset_delta: int) -> bytes:
+    if meta[4:8] != b"meta" or len(meta) < 12:
+        raise ValueError("AVIF metadata requires a valid meta box.")
+    children = _bmff_boxes(meta, 12, len(meta))
+    child_by_type = {box_type: (pos, size, header_size) for pos, size, box_type, header_size in children}
+    if not all(box_type in child_by_type for box_type in (b"pitm", b"iloc", b"iinf")):
+        raise ValueError("AVIF metadata boxes are incomplete.")
+
+    pitm_pos, pitm_size, _ = child_by_type[b"pitm"]
+    pitm = meta[pitm_pos:pitm_pos + pitm_size]
+    if pitm[8] != 0:
+        raise ValueError("Unsupported AVIF primary-item format.")
+    primary_item_id = struct.unpack_from(">H", pitm, 12)[0]
+
+    iloc_pos, iloc_size, _ = child_by_type[b"iloc"]
+    iloc = bytearray(meta[iloc_pos:iloc_pos + iloc_size])
+    if iloc[8] != 0 or iloc[12] != 0x44 or iloc[13] != 0:
+        raise ValueError("Unsupported AVIF item-location format.")
+    item_count = struct.unpack_from(">H", iloc, 14)[0]
+    cursor = 16
+    item_ids = []
+    for _ in range(item_count):
+        item_id, _, extent_count = struct.unpack_from(">HHH", iloc, cursor)
+        cursor += 6
+        item_ids.append(item_id)
+        for _ in range(extent_count):
+            extent_offset = struct.unpack_from(">I", iloc, cursor)[0]
+            struct.pack_into(">I", iloc, cursor, extent_offset + offset_delta)
+            cursor += 8
+    if cursor != len(iloc):
+        raise ValueError("Unsupported AVIF item-location entries.")
+    exif_item_id = max(item_ids) + 1
+    if exif_item_id > 0xFFFF or exif_offset > 0xFFFFFFFF or exif_length > 0xFFFFFFFF:
+        raise ValueError("AVIF metadata exceeds 32-bit item limits.")
+    struct.pack_into(">H", iloc, 14, item_count + 1)
+    iloc.extend(struct.pack(">HHHII", exif_item_id, 0, 1, exif_offset, exif_length))
+    struct.pack_into(">I", iloc, 0, len(iloc))
+
+    iinf_pos, iinf_size, _ = child_by_type[b"iinf"]
+    iinf = bytearray(meta[iinf_pos:iinf_pos + iinf_size])
+    if iinf[8] != 0:
+        raise ValueError("Unsupported AVIF item-information format.")
+    iinf_count = struct.unpack_from(">H", iinf, 12)[0]
+    struct.pack_into(">H", iinf, 12, iinf_count + 1)
+    infe_payload = b"\x02\x00\x00\x00" + struct.pack(">HH4s", exif_item_id, 0, b"Exif") + b"\x00"
+    iinf.extend(_bmff_box(b"infe", infe_payload))
+    struct.pack_into(">I", iinf, 0, len(iinf))
+
+    cdsc = _bmff_box(b"cdsc", struct.pack(">HHH", exif_item_id, 1, primary_item_id))
+    if b"iref" in child_by_type:
+        iref_pos, iref_size, _ = child_by_type[b"iref"]
+        iref = bytearray(meta[iref_pos:iref_pos + iref_size])
+        if iref[8] != 0:
+            raise ValueError("Unsupported AVIF item-reference format.")
+        iref.extend(cdsc)
+        struct.pack_into(">I", iref, 0, len(iref))
+    else:
+        iref = _bmff_box(b"iref", b"\x00\x00\x00\x00" + cdsc)
+
+    output = bytearray(meta[:12])
+    for pos, size, box_type, _ in children:
+        if box_type == b"iloc":
+            output.extend(iloc)
+        elif box_type == b"iinf":
+            output.extend(iinf)
+            if b"iref" not in child_by_type:
+                output.extend(iref)
+        elif box_type == b"iref":
+            output.extend(iref)
+        else:
+            output.extend(meta[pos:pos + size])
+    struct.pack_into(">I", output, 0, len(output))
+    return bytes(output)
+
+
+def _adjust_avif_chunk_offsets(moov: bytes, offset_delta: int) -> bytes:
+    output = bytearray(moov)
+    containers = {b"moov", b"trak", b"mdia", b"minf", b"stbl"}
+
+    def adjust(start: int, end: int) -> None:
+        for pos, size, box_type, header_size in _bmff_boxes(output, start, end):
+            if box_type in containers:
+                adjust(pos + header_size, pos + size)
+            elif box_type in (b"stco", b"co64"):
+                entry_size = 4 if box_type == b"stco" else 8
+                entry_count = struct.unpack_from(">I", output, pos + header_size + 4)[0]
+                cursor = pos + header_size + 8
+                if cursor + entry_count * entry_size != pos + size:
+                    raise ValueError("Invalid AVIF chunk-offset table.")
+                value_format = ">I" if entry_size == 4 else ">Q"
+                for _ in range(entry_count):
+                    value = struct.unpack_from(value_format, output, cursor)[0]
+                    struct.pack_into(value_format, output, cursor, value + offset_delta)
+                    cursor += entry_size
+
+    adjust(0, len(output))
+    return bytes(output)
+
+
+def _copy_file_bytes(source, destination, size: int) -> None:
+    remaining = size
+    while remaining:
+        chunk = source.read(min(remaining, 1024 * 1024))
+        if not chunk:
+            raise ValueError("Unexpected end of AVIF file.")
+        destination.write(chunk)
+        remaining -= len(chunk)
+
+
+def inject_avif_metadata(path: str, metadata: dict) -> None:
+    """Add a ComfyUI-compatible EXIF item without loading media data into memory."""
+    if not metadata:
+        return
+    exif = _avif_exif(metadata)
+    file_size = os.path.getsize(path)
+    top_level_boxes = []
+    with open(path, "rb") as source:
+        pos = 0
+        while pos < file_size:
+            source.seek(pos)
+            header = source.read(8)
+            if len(header) != 8:
+                raise ValueError("Invalid AVIF box header.")
+            size, box_type = struct.unpack(">I4s", header)
+            header_size = 8
+            size_field = size
+            if size == 1:
+                extended_size = source.read(8)
+                if len(extended_size) != 8:
+                    raise ValueError("Invalid AVIF extended-size box.")
+                size = struct.unpack(">Q", extended_size)[0]
+                header_size = 16
+            elif size == 0:
+                size = file_size - pos
+            if size < header_size or pos + size > file_size:
+                raise ValueError("Invalid AVIF top-level box size.")
+            top_level_boxes.append((pos, size, box_type, header_size, size_field))
+            pos += size
+
+        meta_box = next((box for box in top_level_boxes if box[2] == b"meta"), None)
+        mdat_box = next((box for box in top_level_boxes if box[2] == b"mdat"), None)
+        if meta_box is None or mdat_box is None or mdat_box[0] + mdat_box[1] != file_size or meta_box[0] > mdat_box[0]:
+            raise ValueError("Unsupported AVIF file layout for metadata.")
+        source.seek(meta_box[0])
+        meta = source.read(meta_box[1])
+
+    provisional_meta = _add_avif_exif_item(meta, 0, len(exif), 0)
+    offset_delta = len(provisional_meta) - len(meta)
+    exif_offset = mdat_box[0] + mdat_box[1] + offset_delta
+    updated_meta = _add_avif_exif_item(meta, exif_offset, len(exif), offset_delta)
+
+    fd, temp_path = tempfile.mkstemp(prefix=f".{os.path.basename(path)}.", dir=os.path.dirname(path) or ".")
+    try:
+        with os.fdopen(fd, "wb") as destination, open(path, "rb") as source:
+            for pos, size, box_type, header_size, size_field in top_level_boxes:
+                source.seek(pos)
+                if box_type == b"ftyp":
+                    ftyp = bytearray(source.read(size))
+                    # The frontend metadata reader recognizes the avif major brand; avis remains a compatible sequence brand.
+                    if ftyp[8:12] == b"avis" and b"avif" in ftyp[16:]:
+                        ftyp[8:12] = b"avif"
+                    destination.write(ftyp)
+                elif box_type == b"meta":
+                    destination.write(updated_meta)
+                elif box_type == b"moov":
+                    destination.write(_adjust_avif_chunk_offsets(source.read(size), offset_delta))
+                elif box_type == b"mdat":
+                    new_size = size + len(exif)
+                    if size_field == 1:
+                        destination.write(struct.pack(">I4sQ", 1, b"mdat", new_size))
+                    elif size_field == 0:
+                        destination.write(struct.pack(">I4s", 0, b"mdat"))
+                    elif new_size <= 0xFFFFFFFF:
+                        destination.write(struct.pack(">I4s", new_size, b"mdat"))
+                    else:
+                        raise ValueError("AVIF media-data box exceeds its 32-bit size field.")
+                    source.seek(pos + header_size)
+                    _copy_file_bytes(source, destination, size - header_size)
+                    destination.write(exif)
+                else:
+                    _copy_file_bytes(source, destination, size)
+        os.chmod(temp_path, os.stat(path).st_mode)
+        os.replace(temp_path, path)
+    finally:
+        if os.path.exists(temp_path):
+            os.unlink(temp_path)
+
+
+# ---------------------------------------------------------------------------
+# Encoding
+# ---------------------------------------------------------------------------
+
+def _encode_image(
+    img_tensor: torch.Tensor,
+    file_format: str,
+    bit_depth: str,
+    colorspace: str,
+) -> bytes:
+    """Encode a single HxWxC (or channel-less HxW grayscale) tensor to PNG or
+    EXR bytes in memory. Grayscale is written as single-channel PNG / Y-only EXR.
+
+    For EXR the input is interpreted according to `colorspace` and converted
+    to scene-linear (EXR's convention) before writing:
+
+      "sRGB"   → input is sRGB-encoded Rec. 709; apply inverse sRGB EOTF.
+      "HDR"    → input is HLG-encoded Rec. 2020 (BT.2100); apply inverse HLG
+                 OETF to get scene-linear, per BT.2100 Note 5a.
+      "linear" → input is already scene-linear (Rec. 709 primaries); write
+                 through unchanged. Use this for renderer/compositor output.
+
+    For PNG, colorspace selection does not modify pixels — PNG is delivered
+    sRGB-encoded and there is no PNG path for wide-gamut HDR in this node.
+    """
+    if img_tensor.ndim == 2:
+        img_tensor = img_tensor.unsqueeze(-1)  # Some nodes emit grayscale as (H, W) with no channel dim, mask-style.
+    height, width, num_channels = img_tensor.shape
+
+    spec = _FORMAT_SPECS.get((file_format, bit_depth, num_channels))
+    if spec is None:
+        raise ValueError(
+            f"No {file_format}/{bit_depth} encoder for {num_channels}-channel images: "
+            "supported channel counts are 1 (grayscale), 3 (RGB) and 4 (RGBA)."
+        )
+
+    if spec["dtype"] == np.float32:
+        # EXR path: preserve full range, no clamp.
+        if colorspace == "sRGB":
+            img_tensor = srgb_to_linear(img_tensor)
+        elif colorspace == "HDR":
+            img_tensor = hlg_to_linear(img_tensor)
+        img_np = img_tensor.cpu().numpy().astype(np.float32)
+    else:
+        # PNG path: quantize to integer range.
+        scaled = (img_tensor * spec["scale"]).clamp(0, spec["scale"])
+        img_np = scaled.to(torch.int32).cpu().numpy().astype(spec["dtype"])
+
+    # Encode directly via CodecContext. PyAV's `image2` muxer does NOT write to
+    # BytesIO (it expects a real file path), so we bypass the container entirely.
+    # For single-frame PNG/EXR the raw codec output IS the file.
+    codec = av.CodecContext.create(file_format, "w")
+    codec.width = width
+    codec.height = height
+    codec.pix_fmt = spec["stream_fmt"]
+    codec.time_base = Fraction(1, 1)
+
+    frame = av.VideoFrame.from_ndarray(img_np, format=spec["frame_fmt"])
+    if spec["frame_fmt"] != spec["stream_fmt"]:
+        frame = frame.reformat(format=spec["stream_fmt"])
+    frame.pts = 0
+    frame.time_base = codec.time_base
+
+    packets = list(codec.encode(frame)) + list(codec.encode(None))  # flush with None
+    return b"".join(bytes(p) for p in packets)
+
+
+def _set_avif_color_properties(target, colorspace: str) -> None:
+    color_primaries, color_trc, yuv_colorspace = _AVIF_COLOR_PROPERTIES[colorspace]
+    target.color_primaries = color_primaries
+    target.color_trc = color_trc
+    target.colorspace = yuv_colorspace
+    target.color_range = ColorRange.MPEG
+
+
+def _avif_frame(image: torch.Tensor, bit_depth: str, colorspace: str, pixel_format: str) -> av.VideoFrame:
+    if image.ndim == 2:
+        image = image.unsqueeze(-1)
+    num_channels = image.shape[-1]
+    if num_channels not in (1, 3):
+        raise ValueError("AVIF saving supports 1-channel grayscale and 3-channel RGB images; PyAV's SVT-AV1 encoder does not support alpha.")
+
+    if bit_depth == "10-bit YUV420":
+        image_np = (image * 65535.0).clamp(0, 65535).to(torch.int32).cpu().numpy().astype(np.uint16)
+        frame_format = "gray16le" if num_channels == 1 else "rgb48le"
+    else:
+        image_np = (image * 255.0).clamp(0, 255).to(torch.uint8).cpu().numpy()
+        frame_format = "gray" if num_channels == 1 else "rgb24"
+    if num_channels == 1:
+        image_np = image_np[..., 0]
+
+    frame = av.VideoFrame.from_ndarray(image_np, format=frame_format)
+    frame = frame.reformat(format=pixel_format, dst_colorspace=_AVIF_COLOR_PROPERTIES[colorspace][2])
+    _set_avif_color_properties(frame, colorspace)
+    return frame
+
+
+def _save_avif(
+    images: torch.Tensor,
+    output_path: str,
+    bit_depth: str,
+    colorspace: str,
+    crf: int,
+    fps: float = 1.0,
+    loop_count: int | None = None,
+    metadata: dict | None = None,
+) -> None:
+    if bit_depth == "auto":
+        bit_depth = "10-bit YUV420" if colorspace in ("HDR", "HDR PQ") else "8-bit YUV420"
+    pixel_format = "yuv420p10le" if bit_depth == "10-bit YUV420" else "yuv420p"
+    options = {"loop": str(loop_count)} if loop_count is not None else None
+
+    with av.open(output_path, mode="w", format="avif", options=options) as container:
+        frame_rate = Fraction(round(fps * 1000), 1000)
+        stream = container.add_stream("libsvtav1", rate=frame_rate)
+        stream.width = images.shape[2]
+        stream.height = images.shape[1]
+        stream.pix_fmt = pixel_format
+        stream.options = {"crf": str(crf), "preset": "8"}
+        _set_avif_color_properties(stream.codec_context, colorspace)
+
+        for image in images:
+            for packet in stream.encode(_avif_frame(image, bit_depth, colorspace, pixel_format)):
+                container.mux(packet)
+        for packet in stream.encode(None):
+            container.mux(packet)
+    if metadata:
+        inject_avif_metadata(output_path, metadata)
+
+
+# ---------------------------------------------------------------------------
+# Node
+# ---------------------------------------------------------------------------
+
+class SaveImageAdvanced(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveImageAdvanced",
+            search_aliases=["save", "save image", "export image", "output image", "write image"],
+            display_name="Save Image (Advanced)",
+            description="Saves the input images to your ComfyUI output directory.",
+            category="image",
+            essentials_category="Basics",
+            inputs=[
+                IO.Image.Input("images", tooltip="The images to save."),
+                IO.String.Input(
+                    "filename_prefix",
+                    default="ComfyUI",
+                    tooltip=("The prefix for the file to save. May include formatting tokens such as %date:yyyy-MM-dd% or %Empty Latent Image.width%."),
+                ),
+                IO.DynamicCombo.Input(
+                    "format",
+                    options=[
+                        IO.DynamicCombo.Option("png", [
+                            IO.Combo.Input("bit_depth", options=["8-bit", "16-bit"], default="8-bit", advanced=True),
+                            IO.Combo.Input("input_color_space", options=["sRGB"], default="sRGB", advanced=True),
+                        ]),
+                        IO.DynamicCombo.Option("exr", [
+                            IO.Combo.Input("bit_depth", options=["32-bit float"], default="32-bit float", advanced=True),
+                            IO.Combo.Input(
+                                "input_color_space",
+                                options=["sRGB", "HDR", "linear"],
+                                default="sRGB",
+                                advanced=True,
+                                tooltip=(
+                                    "Colorspace of the input tensor. The EXR is always written as scene-linear in the matching gamut.\n"
+                                    "sRGB — input is sRGB-encoded Rec.709; the inverse sRGB EOTF is applied.\n"
+                                    "HDR — input is HLG-encoded Rec.2020 (BT.2100); the inverse HLG OETF is applied to get scene-linear light.\n"
+                                    "linear — input is already scene-linear (Rec.709 primaries); written through unchanged. Use this for renderer/compositor output."
+                                ),
+                            ),
+                        ]),
+                        IO.DynamicCombo.Option("avif", [
+                            IO.Combo.Input(
+                                "bit_depth",
+                                options=["auto", "8-bit YUV420", "10-bit YUV420"],
+                                default="auto",
+                                advanced=True,
+                                tooltip="Auto uses 8-bit YUV420 for sRGB and 10-bit YUV420 for HDR.",
+                            ),
+                            IO.Combo.Input(
+                                "input_color_space",
+                                options=["sRGB", "HDR", "HDR PQ"],
+                                default="sRGB",
+                                advanced=True,
+                                tooltip="Colorspace of the input images. HDR selects BT.2020/HLG and HDR PQ selects BT.2020/PQ.",
+                            ),
+                            IO.Int.Input(
+                                "crf",
+                                default=18,
+                                min=1,
+                                max=63,
+                                advanced=True,
+                                tooltip="Lower values produce higher quality and larger files.",
+                            ),
+                            IO.DynamicCombo.Input(
+                                "save_mode",
+                                display_name="save mode",
+                                options=[
+                                    IO.DynamicCombo.Option("still images", []),
+                                    IO.DynamicCombo.Option("animated", [
+                                        IO.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
+                                        IO.Int.Input(
+                                            "loop_count",
+                                            default=0,
+                                            min=0,
+                                            max=1000,
+                                            advanced=True,
+                                            tooltip="Number of times to loop the animation. 0 loops forever.",
+                                        ),
+                                    ]),
+                                ],
+                            ),
+                        ]),
+                    ],
+                    tooltip="The file format in which to save the image.",
+                ),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+            outputs=[IO.Image.Output(display_name="images")]
+        )
+
+    @classmethod
+    def execute(cls, images, filename_prefix: str, format: dict) -> IO.NodeOutput:
+        file_format = format["format"]
+        bit_depth = format["bit_depth"]
+        colorspace = format.get("input_color_space", "sRGB")
+
+        output_dir = folder_paths.get_output_directory()
+        full_output_folder, filename, counter, subfolder, filename_prefix = (
+            folder_paths.get_save_image_path(
+                filename_prefix, output_dir, images[0].shape[1], images[0].shape[0]
+            )
+        )
+
+        prompt = cls.hidden.prompt
+        extra_pnginfo = cls.hidden.extra_pnginfo
+        write_metadata = not args.disable_metadata
+
+        results = []
+        if file_format == "avif":
+            metadata = None
+            if write_metadata:
+                metadata = {}
+                if prompt is not None:
+                    metadata["prompt"] = prompt
+                if extra_pnginfo:
+                    metadata.update(extra_pnginfo)
+            save_mode = format["save_mode"]
+            animated = save_mode["save_mode"] == "animated"
+            batches = [images] if animated else images.unsqueeze(1)
+            for batch_number, batch in enumerate(batches):
+                name = filename.replace("%batch_num%", str(batch_number))
+                file = f"{name}_{counter:05}.avif"
+                _save_avif(
+                    batch,
+                    os.path.join(full_output_folder, file),
+                    bit_depth,
+                    colorspace,
+                    format["crf"],
+                    fps=save_mode.get("fps", 1.0),
+                    loop_count=save_mode.get("loop_count") if animated else None,
+                    metadata=metadata,
+                )
+                results.append({"filename": file, "subfolder": subfolder, "type": "output"})
+                counter += 1
+            ui = {"images": results}
+            if animated and len(images) > 1:
+                ui["animated"] = (True,)
+            return IO.NodeOutput(images, ui=ui)
+
+        for batch_number, image in enumerate(images):
+            encoded = _encode_image(image, file_format, bit_depth, colorspace)
+
+            if write_metadata:
+                if file_format == "png":
+                    encoded = inject_png_metadata(encoded, prompt, extra_pnginfo)
+                elif file_format == "exr":
+                    encoded = inject_exr_metadata(encoded, prompt, extra_pnginfo, colorspace)
+
+            name = filename.replace("%batch_num%", str(batch_number))
+            file = f"{name}_{counter:05}.{file_format}"
+            with open(os.path.join(full_output_folder, file), "wb") as f:
+                f.write(encoded)
+
+            results.append({"filename": file, "subfolder": subfolder, "type": "output"})
+            counter += 1
+
+        return IO.NodeOutput(images, ui={"images": results})
+
+
+class ImagesExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            ImageCrop,
+            ImageCropV2,
+            ImageCropToMask,
+            BoundingBox,
+            RepeatImageBatch,
+            ImageFromBatch,
+            ImageAddNoise,
+            SaveAnimatedWEBP,
+            SaveAnimatedPNG,
+            SaveImageAdvanced,
+            SaveSVGNode,
+            ImageStitch,
+            ResizeAndPadImage,
+            GetImageSize,
+            ImageRotate,
+            ImageFlip,
+            ImageScaleToMaxDimension,
+            SplitImageToTileList,
+            ImageMergeTileList,
+        ]
+
+
+async def comfy_entrypoint() -> ImagesExtension:
+    return ImagesExtension()
diff --git a/comfy_extras/nodes_ip2p.py b/comfy_extras/nodes_ip2p.py
new file mode 100644
index 0000000000000000000000000000000000000000..2226fa68277dc7f18f340e60b79d5e0ba02a6cb5
--- /dev/null
+++ b/comfy_extras/nodes_ip2p.py
@@ -0,0 +1,63 @@
+import torch
+
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class InstructPixToPixConditioning(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="InstructPixToPixConditioning",
+            category="model/conditioning/instructpix2pix",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Image.Input("pixels"),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, pixels, vae) -> io.NodeOutput:
+        x = (pixels.shape[1] // 8) * 8
+        y = (pixels.shape[2] // 8) * 8
+
+        if pixels.shape[1] != x or pixels.shape[2] != y:
+            x_offset = (pixels.shape[1] % 8) // 2
+            y_offset = (pixels.shape[2] % 8) // 2
+            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+
+        concat_latent = vae.encode(pixels)
+
+        out_latent = {}
+        out_latent["samples"] = torch.zeros_like(concat_latent)
+
+        out = []
+        for conditioning in [positive, negative]:
+            c = []
+            for t in conditioning:
+                d = t[1].copy()
+                d["concat_latent_image"] = concat_latent
+                n = [t[0], d]
+                c.append(n)
+            out.append(c)
+        return io.NodeOutput(out[0], out[1], out_latent)
+
+
+class InstructPix2PixExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            InstructPixToPixConditioning,
+        ]
+
+
+async def comfy_entrypoint() -> InstructPix2PixExtension:
+    return InstructPix2PixExtension()
+
diff --git a/comfy_extras/nodes_joyimage.py b/comfy_extras/nodes_joyimage.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc7bc820837b1c29bf65064fefc0bca7ef14c13b
--- /dev/null
+++ b/comfy_extras/nodes_joyimage.py
@@ -0,0 +1,102 @@
+from typing_extensions import override
+
+import comfy.utils
+import node_helpers
+from comfy_api.latest import ComfyExtension, io
+
+
+# fmt: off
+BUCKETS_1024 = [
+    (512, 1792), (512, 1856), (512, 1920), (512, 1984), (512, 2048),
+    (576, 1600), (576, 1664), (576, 1728), (576, 1792),
+    (640, 1472), (640, 1536), (640, 1600),
+    (704, 1344), (704, 1408), (704, 1472),
+    (768, 1216), (768, 1280), (768, 1344),
+    (832, 1152), (832, 1216),
+    (896, 1088), (896, 1152),
+    (960, 1024), (960, 1088),
+    (1024, 960), (1024, 1024),
+    (1088, 896), (1088, 960),
+    (1152, 832), (1152, 896),
+    (1216, 768), (1216, 832),
+    (1280, 768),
+    (1344, 704), (1344, 768),
+    (1408, 704),
+    (1472, 640), (1472, 704),
+    (1536, 640),
+    (1600, 576), (1600, 640),
+    (1664, 576),
+    (1728, 576),
+    (1792, 512), (1792, 576),
+    (1856, 512),
+    (1920, 512),
+    (1984, 512),
+    (2048, 512),
+]
+# fmt: on
+
+
+def _find_best_bucket(height: int, width: int) -> tuple[int, int]:
+    target_ratio = height / width
+    return min(BUCKETS_1024, key=lambda hw: abs(hw[0] / hw[1] - target_ratio))
+
+
+def _resize_reference(image):
+    if image.shape[0] != 1:
+        raise ValueError("JoyImage reference inputs must contain one image each")
+    samples = image.movedim(-1, 1)
+    bucket_h, bucket_w = _find_best_bucket(samples.shape[2], samples.shape[3])
+    resized = comfy.utils.common_upscale(samples, bucket_w, bucket_h, "bilinear", "center")
+    return resized.movedim(1, -1)[:, :, :, :3]
+
+
+def _encode(clip, prompt, vae, images):
+    resized_images = [_resize_reference(image) for image in images]
+    conditioning = clip.encode_from_tokens_scheduled(clip.tokenize(prompt, images=resized_images))
+    if vae is not None and resized_images:
+        ref_latents = [vae.encode(image) for image in resized_images]
+        conditioning = node_helpers.conditioning_set_values(
+            conditioning, {"reference_latents": ref_latents}, append=True,
+        )
+    return conditioning
+
+
+class TextEncodeJoyImageEdit(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        image_template = io.Autogrow.TemplatePrefix(
+            io.Image.Input("image"),
+            prefix="image",
+            min=0,
+            max=6,
+        )
+        return io.Schema(
+            node_id="TextEncodeJoyImageEdit",
+            category="model/conditioning/joyimage",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Vae.Input("vae", optional=True),
+                io.Autogrow.Input("images", template=image_template, optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, vae=None, images: io.Autogrow.Type = None) -> io.NodeOutput:
+        images = images or {}
+        return io.NodeOutput(_encode(clip, prompt, vae, list(images.values())))
+
+
+class JoyImageExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TextEncodeJoyImageEdit,
+        ]
+
+
+async def comfy_entrypoint() -> JoyImageExtension:
+    return JoyImageExtension()
diff --git a/comfy_extras/nodes_json_prompt.py b/comfy_extras/nodes_json_prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..11ec391aab208517d85ca6619c82b1cea461ff71
--- /dev/null
+++ b/comfy_extras/nodes_json_prompt.py
@@ -0,0 +1,77 @@
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+from comfy_extras.color_util import normalize_palette
+
+
+class BuildJsonPromptIdeogram(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        color_palette = io.Colors.Input(
+            "color_palette",
+            socketless=False,
+            tooltip="Hex color codes that steer the image's dominant colors. Up to 16 entries.",
+        )
+        return io.Schema(
+            node_id="BuildJsonPromptIdeogram",
+            display_name="Build JSON Prompt (Ideogram)",
+            category="text",
+            description="Build a JSON prompt for the Ideogram 4 model.",
+            inputs=[
+                io.Array.Input("element", tooltip="Prompt elements from the node Create Bounding Boxes."),
+                io.String.Input("high_level_description", multiline=True, default="",
+                                tooltip="Optional description of the image in one or two sentences. Strongly recommended."),
+                io.String.Input("background", multiline=True, default="",
+                                tooltip="Mandatory description of the image background or environment."),
+                io.DynamicCombo.Input("style", options=[
+                    io.DynamicCombo.Option("none", []),
+                    io.DynamicCombo.Option("photo", [io.String.Input("photo", default="", tooltip="Camera or lens details for photographic outputs (e.g. 35mm, f/1.4, bokeh).")]),
+                    io.DynamicCombo.Option("art_style", [io.String.Input("art_style", default="", tooltip="Art style description (e.g. flat vector illustration, bold outlines).")]),
+                ]),
+                io.String.Input("aesthetics", default="", tooltip="Mandatory aesthetic keywords (e.g. moody, cinematic, desaturated)."),
+                io.String.Input("lighting", default="", tooltip="Mandatory lighting description (e.g. golden hour, rim light, dramatic shadows)."),
+                io.String.Input("medium", default="", tooltip="Mandatory medium type (e.g. photograph, illustration, 3d_render, painting, graphic_design). When style = photo, set to photograph."),
+                color_palette,
+            ],
+            outputs=[io.Dict.Output(display_name="prompt")],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, element, style, high_level_description="", background="",
+                aesthetics="", lighting="", medium="", color_palette=None) -> io.NodeOutput:
+        elements = element if isinstance(element, list) else []
+        kind = style.get("style", "none") if isinstance(style, dict) else "none"
+        photo = style.get("photo", "") if isinstance(style, dict) else ""
+        art_style = style.get("art_style", "") if isinstance(style, dict) else ""
+        palette = normalize_palette(color_palette or [])
+
+        caption: dict = {}
+        if high_level_description.strip():
+            caption["high_level_description"] = high_level_description
+        if kind != "none":
+            style_desc: dict = {"aesthetics": aesthetics, "lighting": lighting}
+            if kind == "photo":
+                style_desc["photo"] = photo
+                style_desc["medium"] = medium
+            else:
+                style_desc["medium"] = medium
+                style_desc["art_style"] = art_style
+            if palette:
+                style_desc["color_palette"] = palette
+            caption["style_description"] = style_desc
+        caption["compositional_deconstruction"] = {
+            "background": background,
+            "elements": elements,
+        }
+        return io.NodeOutput(caption)
+
+
+class JsonPromptExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [BuildJsonPromptIdeogram]
+
+
+async def comfy_entrypoint() -> JsonPromptExtension:
+    return JsonPromptExtension()
diff --git a/comfy_extras/nodes_kandinsky5.py b/comfy_extras/nodes_kandinsky5.py
new file mode 100644
index 0000000000000000000000000000000000000000..94503cbe05d02f87f2d53bce13f4bf17dbcae3ba
--- /dev/null
+++ b/comfy_extras/nodes_kandinsky5.py
@@ -0,0 +1,138 @@
+import nodes
+import node_helpers
+import torch
+import comfy.model_management
+import comfy.utils
+
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class Kandinsky5ImageToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Kandinsky5ImageToVideo",
+            category="model/conditioning/kandinsky",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=768, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=512, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("start_image", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent", tooltip="Empty video latent"),
+                io.Latent.Output(display_name="cond_latent", tooltip="Clean encoded start images, used to replace the noisy start of the model output latents"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        cond_latent_out = {}
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            encoded = vae.encode(start_image[:, :, :, :3])
+            cond_latent_out["samples"] = encoded
+
+            mask = torch.ones((1, 1, latent.shape[2], latent.shape[-2], latent.shape[-1]), device=start_image.device, dtype=start_image.dtype)
+            mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
+
+            positive = node_helpers.conditioning_set_values(positive, {"time_dim_replace": encoded, "concat_mask": mask})
+            negative = node_helpers.conditioning_set_values(negative, {"time_dim_replace": encoded, "concat_mask": mask})
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return io.NodeOutput(positive, negative, out_latent, cond_latent_out)
+
+
+def adaptive_mean_std_normalization(source, reference, clump_mean_low=0.3, clump_mean_high=0.35, clump_std_low=0.35, clump_std_high=0.5):
+    source_mean = source.mean(dim=(1, 3, 4), keepdim=True)  # mean over C, H, W
+    source_std = source.std(dim=(1, 3, 4), keepdim=True)    # std over C, H, W
+
+    reference_mean = torch.clamp(reference.mean(), source_mean - clump_mean_low, source_mean + clump_mean_high)
+    reference_std = torch.clamp(reference.std(), source_std - clump_std_low, source_std + clump_std_high)
+
+    # normalization
+    normalized = (source - source_mean) / (source_std + 1e-8)
+    normalized = normalized * reference_std + reference_mean
+
+    return normalized
+
+
+class NormalizeVideoLatentStart(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="NormalizeVideoLatentStart",
+            category="model/conditioning",
+            description="Normalizes the initial frames of a video latent to match the mean and standard deviation of subsequent reference frames. Helps reduce differences between the starting frames and the rest of the video.",
+            inputs=[
+                io.Latent.Input("latent"),
+                io.Int.Input("start_frame_count", default=4, min=1, max=nodes.MAX_RESOLUTION, step=1, tooltip="Number of latent frames to normalize, counted from the start"),
+                io.Int.Input("reference_frame_count", default=5, min=1, max=nodes.MAX_RESOLUTION, step=1, tooltip="Number of latent frames after the start frames to use as reference"),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, latent, start_frame_count, reference_frame_count) -> io.NodeOutput:
+        if latent["samples"].shape[2] <= 1:
+            return io.NodeOutput(latent)
+        s = latent.copy()
+        samples = latent["samples"].clone()
+
+        first_frames = samples[:, :, :start_frame_count]
+        reference_frames_data = samples[:, :, start_frame_count:start_frame_count+min(reference_frame_count, samples.shape[2]-1)]
+        normalized_first_frames = adaptive_mean_std_normalization(first_frames, reference_frames_data)
+
+        samples[:, :, :start_frame_count] = normalized_first_frames
+        s["samples"] = samples
+        return io.NodeOutput(s)
+
+
+class CLIPTextEncodeKandinsky5(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeKandinsky5",
+            display_name="CLIP Text Encode (Kandinsky 5)",
+            search_aliases=["kandinsky prompt"],
+            category="model/conditioning/kandinsky",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
+                io.String.Input("qwen25_7b", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, clip_l, qwen25_7b) -> io.NodeOutput:
+        tokens = clip.tokenize(clip_l)
+        tokens["qwen25_7b"] = clip.tokenize(qwen25_7b)["qwen25_7b"]
+
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
+
+
+class Kandinsky5Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            Kandinsky5ImageToVideo,
+            NormalizeVideoLatentStart,
+            CLIPTextEncodeKandinsky5,
+        ]
+
+async def comfy_entrypoint() -> Kandinsky5Extension:
+    return Kandinsky5Extension()
diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7cc1c3b01d8226de3976a968e873c64c48d6f70
--- /dev/null
+++ b/comfy_extras/nodes_latent.py
@@ -0,0 +1,506 @@
+import comfy.utils
+import comfy_extras.nodes_post_processing
+import torch
+import nodes
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+import logging
+import math
+
+def reshape_latent_to(target_shape, latent, repeat_batch=True):
+    if latent.shape[1:] != target_shape[1:]:
+        latent = comfy.utils.common_upscale(latent, target_shape[-1], target_shape[-2], "bilinear", "center")
+    if repeat_batch:
+        return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
+    else:
+        return latent
+
+
+class LatentAdd(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentAdd",
+            search_aliases=["combine latents", "sum latents"],
+            category="model/latent/advanced",
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples1, samples2) -> io.NodeOutput:
+        samples_out = samples1.copy()
+
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+
+        s2 = reshape_latent_to(s1.shape, s2)
+        samples_out["samples"] = s1 + s2
+        return io.NodeOutput(samples_out)
+
+class LatentSubtract(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentSubtract",
+            search_aliases=["difference latent", "remove features"],
+            category="model/latent/advanced",
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples1, samples2) -> io.NodeOutput:
+        samples_out = samples1.copy()
+
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+
+        s2 = reshape_latent_to(s1.shape, s2)
+        samples_out["samples"] = s1 - s2
+        return io.NodeOutput(samples_out)
+
+class LatentMultiply(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentMultiply",
+            search_aliases=["scale latent", "amplify latent", "latent gain"],
+            category="model/latent/advanced",
+            inputs=[
+                io.Latent.Input("samples"),
+                io.Float.Input("multiplier", default=1.0, min=-10.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples, multiplier) -> io.NodeOutput:
+        samples_out = samples.copy()
+
+        s1 = samples["samples"]
+        samples_out["samples"] = s1 * multiplier
+        return io.NodeOutput(samples_out)
+
+class LatentInterpolate(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentInterpolate",
+            search_aliases=["blend latent", "mix latent", "lerp latent", "transition"],
+            category="model/latent/advanced",
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+                io.Float.Input("ratio", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples1, samples2, ratio) -> io.NodeOutput:
+        samples_out = samples1.copy()
+
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+
+        s2 = reshape_latent_to(s1.shape, s2)
+
+        m1 = torch.linalg.vector_norm(s1, dim=(1))
+        m2 = torch.linalg.vector_norm(s2, dim=(1))
+
+        s1 = torch.nan_to_num(s1 / m1)
+        s2 = torch.nan_to_num(s2 / m2)
+
+        t = (s1 * ratio + s2 * (1.0 - ratio))
+        mt = torch.linalg.vector_norm(t, dim=(1))
+        st = torch.nan_to_num(t / mt)
+
+        samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
+        return io.NodeOutput(samples_out)
+
+class LatentConcat(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentConcat",
+            search_aliases=["join latents", "stitch latents"],
+            category="model/latent/advanced",
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+                io.Combo.Input("dim", options=["x", "-x", "y", "-y", "t", "-t"]),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples1, samples2, dim) -> io.NodeOutput:
+        samples_out = samples1.copy()
+
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+        s2 = comfy.utils.repeat_to_batch_size(s2, s1.shape[0])
+
+        if "-" in dim:
+            c = (s2, s1)
+        else:
+            c = (s1, s2)
+
+        if "x" in dim:
+            dim = -1
+        elif "y" in dim:
+            dim = -2
+        elif "t" in dim:
+            dim = -3
+
+        samples_out["samples"] = torch.cat(c, dim=dim)
+        return io.NodeOutput(samples_out)
+
+class LatentCut(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentCut",
+            search_aliases=["crop latent", "slice latent", "extract region"],
+            category="model/latent/advanced",
+            inputs=[
+                io.Latent.Input("samples"),
+                io.Combo.Input("dim", options=["x", "y", "t"]),
+                io.Int.Input("index", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
+                io.Int.Input("amount", default=1, min=1, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples, dim, index, amount) -> io.NodeOutput:
+        samples_out = samples.copy()
+
+        s1 = samples["samples"]
+
+        if "x" in dim:
+            dim = s1.ndim - 1
+        elif "y" in dim:
+            dim = s1.ndim - 2
+        elif "t" in dim:
+            dim = s1.ndim - 3
+
+        if index >= 0:
+            index = min(index, s1.shape[dim] - 1)
+            amount = min(s1.shape[dim] - index, amount)
+        else:
+            index = max(index, -s1.shape[dim])
+            amount = min(-index, amount)
+
+        samples_out["samples"] = torch.narrow(s1, dim, index, amount)
+        return io.NodeOutput(samples_out)
+
+class LatentCutToBatch(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentCutToBatch",
+            search_aliases=["slice to batch", "split latent", "tile latent"],
+            category="model/latent/advanced",
+            inputs=[
+                io.Latent.Input("samples"),
+                io.Combo.Input("dim", options=["t", "x", "y"]),
+                io.Int.Input("slice_size", default=1, min=1, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples, dim, slice_size) -> io.NodeOutput:
+        samples_out = samples.copy()
+
+        s1 = samples["samples"]
+
+        if "x" in dim:
+            dim = s1.ndim - 1
+        elif "y" in dim:
+            dim = s1.ndim - 2
+        elif "t" in dim:
+            dim = s1.ndim - 3
+
+        if dim < 2:
+            return io.NodeOutput(samples)
+
+        s = s1.movedim(dim, 1)
+        if s.shape[1] < slice_size:
+            slice_size = s.shape[1]
+        elif s.shape[1] % slice_size != 0:
+            s = s[:, :math.floor(s.shape[1] / slice_size) * slice_size]
+        new_shape = [-1, slice_size] + list(s.shape[2:])
+        samples_out["samples"] = s.reshape(new_shape).movedim(1, dim)
+        return io.NodeOutput(samples_out)
+
+class LatentBatch(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentBatch",
+            search_aliases=["combine latents", "merge latents", "join latents"],
+            display_name="Batch Latents (DEPRECATED)",
+            category="model/latent/batch",
+            is_deprecated=True,
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples1, samples2) -> io.NodeOutput:
+        samples_out = samples1.copy()
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+
+        s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False)
+        s = torch.cat((s1, s2), dim=0)
+        samples_out["samples"] = s
+        samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])
+        return io.NodeOutput(samples_out)
+
+class LatentBatchSeedBehavior(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentBatchSeedBehavior",
+            category="model/latent/advanced",
+            inputs=[
+                io.Latent.Input("samples"),
+                io.Combo.Input("seed_behavior", options=["random", "fixed"], default="fixed"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples, seed_behavior) -> io.NodeOutput:
+        samples_out = samples.copy()
+        latent = samples["samples"]
+        if seed_behavior == "random":
+            if 'batch_index' in samples_out:
+                samples_out.pop('batch_index')
+        elif seed_behavior == "fixed":
+            batch_number = samples_out.get("batch_index", [0])[0]
+            samples_out["batch_index"] = [batch_number] * latent.shape[0]
+
+        return io.NodeOutput(samples_out)
+
+class LatentApplyOperation(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentApplyOperation",
+            search_aliases=["transform latent"],
+            category="model/latent/advanced/operations",
+            is_experimental=True,
+            inputs=[
+                io.Latent.Input("samples"),
+                io.LatentOperation.Input("operation"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples, operation) -> io.NodeOutput:
+        samples_out = samples.copy()
+
+        s1 = samples["samples"]
+        samples_out["samples"] = operation(latent=s1)
+        return io.NodeOutput(samples_out)
+
+class LatentApplyOperationCFG(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentApplyOperationCFG",
+            category="model/latent/advanced/operations",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.LatentOperation.Input("operation"),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, operation) -> io.NodeOutput:
+        m = model.clone()
+
+        def pre_cfg_function(args):
+            conds_out = args["conds_out"]
+            if len(conds_out) == 2:
+                conds_out[0] = operation(latent=(conds_out[0] - conds_out[1])) + conds_out[1]
+            else:
+                conds_out[0] = operation(latent=conds_out[0])
+            return conds_out
+
+        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
+        return io.NodeOutput(m)
+
+class LatentOperationTonemapReinhard(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentOperationTonemapReinhard",
+            search_aliases=["hdr latent"],
+            category="model/latent/advanced/operations",
+            is_experimental=True,
+            inputs=[
+                io.Float.Input("multiplier", default=1.0, min=0.0, max=100.0, step=0.01),
+            ],
+            outputs=[
+                io.LatentOperation.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, multiplier) -> io.NodeOutput:
+        def tonemap_reinhard(latent, **kwargs):
+            latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
+            normalized_latent = latent / latent_vector_magnitude
+
+            dims = list(range(1, latent_vector_magnitude.ndim))
+            mean = torch.mean(latent_vector_magnitude, dim=dims, keepdim=True)
+            std = torch.std(latent_vector_magnitude, dim=dims, keepdim=True)
+
+            top = (std * 5 + mean) * multiplier
+
+            #reinhard
+            latent_vector_magnitude *= (1.0 / top)
+            new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0)
+            new_magnitude *= top
+
+            return normalized_latent * new_magnitude
+        return io.NodeOutput(tonemap_reinhard)
+
+class LatentOperationSharpen(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentOperationSharpen",
+            category="model/latent/advanced/operations",
+            is_experimental=True,
+            inputs=[
+                io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1, advanced=True),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1, advanced=True),
+                io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01, advanced=True),
+            ],
+            outputs=[
+                io.LatentOperation.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, sharpen_radius, sigma, alpha) -> io.NodeOutput:
+        def sharpen(latent, **kwargs):
+            luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None]
+            normalized_latent = latent / luminance
+            channels = latent.shape[1]
+
+            kernel_size = sharpen_radius * 2 + 1
+            kernel = comfy_extras.nodes_post_processing.gaussian_kernel(kernel_size, sigma, device=luminance.device)
+            center = kernel_size // 2
+
+            kernel *= alpha * -10
+            kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
+
+            padded_image = torch.nn.functional.pad(normalized_latent, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect')
+            sharpened = torch.nn.functional.conv2d(padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]
+
+            return luminance * sharpened
+        return io.NodeOutput(sharpen)
+
+class ReplaceVideoLatentFrames(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ReplaceVideoLatentFrames",
+            display_name="Replace Video Latent Frames",
+            category="model/latent/batch",
+            inputs=[
+                io.Latent.Input("destination", tooltip="The destination latent where frames will be replaced."),
+                io.Latent.Input("source", optional=True, tooltip="The source latent providing frames to insert into the destination latent. If not provided, the destination latent is returned unchanged."),
+                io.Int.Input("index", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1, tooltip="The starting latent frame index in the destination latent where the source latent frames will be placed. Negative values count from the end."),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, destination, index, source=None) -> io.NodeOutput:
+        if source is None:
+            return io.NodeOutput(destination)
+        dest_frames = destination["samples"].shape[2]
+        source_frames = source["samples"].shape[2]
+        if index < 0:
+            index = dest_frames + index
+        if index > dest_frames:
+            logging.warning(f"ReplaceVideoLatentFrames: Index {index} is out of bounds for destination latent frames {dest_frames}.")
+            return io.NodeOutput(destination)
+        if index + source_frames > dest_frames:
+            logging.warning(f"ReplaceVideoLatentFrames: Source latent frames {source_frames} do not fit within destination latent frames {dest_frames} at the specified index {index}.")
+            return io.NodeOutput(destination)
+        s = source.copy()
+        s_source = source["samples"]
+        s_destination = destination["samples"].clone()
+        s_destination[:, :, index:index + s_source.shape[2]] = s_source
+        s["samples"] = s_destination
+        return io.NodeOutput(s)
+
+class LatentExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LatentAdd,
+            LatentSubtract,
+            LatentMultiply,
+            LatentInterpolate,
+            LatentConcat,
+            LatentCut,
+            LatentCutToBatch,
+            LatentBatch,
+            LatentBatchSeedBehavior,
+            LatentApplyOperation,
+            LatentApplyOperationCFG,
+            LatentOperationTonemapReinhard,
+            LatentOperationSharpen,
+            ReplaceVideoLatentFrames
+        ]
+
+
+async def comfy_entrypoint() -> LatentExtension:
+    return LatentExtension()
diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a377d2019e94a4721effcc7a6c8d45d1e40627a
--- /dev/null
+++ b/comfy_extras/nodes_load_3d.py
@@ -0,0 +1,400 @@
+import nodes
+import folder_paths
+import os
+import uuid
+
+from typing_extensions import override
+from comfy_api.latest import IO, UI, ComfyExtension, InputImpl, Types
+
+from pathlib import Path
+
+
+def normalize_path(path):
+    return path.replace('\\', '/')
+
+class Load3D(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        input_dir = os.path.join(folder_paths.get_input_directory(), "3d")
+
+        os.makedirs(input_dir, exist_ok=True)
+
+        input_path = Path(input_dir)
+        base_path = Path(folder_paths.get_input_directory())
+
+        files = [
+            normalize_path(str(file_path.relative_to(base_path)))
+            for file_path in input_path.rglob("*")
+            if file_path.suffix.lower() in {'.gltf', '.glb', '.obj', '.fbx', '.stl', '.spz', '.splat', '.ply', '.ksplat'}
+        ]
+        return IO.Schema(
+            node_id="Load3D",
+            display_name="Load 3D & Animation",
+            category="3d",
+            essentials_category="Basics",
+            is_experimental=True,
+            inputs=[
+                IO.Combo.Input("model_file", options=["none"] + sorted(files), upload=IO.UploadType.model),
+                IO.Load3D.Input("image"),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.Image.Output(display_name="image"),
+                IO.Mask.Output(display_name="mask"),
+                IO.String.Output(display_name="mesh_path"),
+                IO.Image.Output(display_name="normal"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Video.Output(display_name="recording_video"),
+                IO.File3DAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+            ],
+        )
+
+    @classmethod
+    def validate_inputs(cls, model_file, **kwargs) -> bool | str:
+        if not model_file or model_file == "none":
+            return True
+        if not folder_paths.exists_annotated_filepath(model_file):
+            return f"Invalid 3D model file: {model_file}"
+        return True
+
+    @classmethod
+    def execute(cls, model_file, image, **kwargs) -> IO.NodeOutput:
+        load_image_node = nodes.LoadImage()
+        output_image, ignore_mask = load_image_node.load_image(image=image['image'])
+        ignore_image, output_mask = load_image_node.load_image(image=image['mask'])
+        normal_image, ignore_mask2 = load_image_node.load_image(image=image['normal'])
+
+        video = None
+
+        if image['recording'] != "":
+            recording_video_path = folder_paths.get_annotated_filepath(image['recording'])
+
+            video = InputImpl.VideoFromFile(recording_video_path)
+
+        file_3d = None
+        mesh_path = ""
+        if model_file and model_file != "none":
+            file_3d = Types.File3D(folder_paths.get_annotated_filepath(model_file))
+            mesh_path = model_file
+        model_3d_info = image.get('model_3d_info', [])
+        return IO.NodeOutput(output_image, output_mask, mesh_path, normal_image, image['camera_info'], video, file_3d, model_3d_info)
+
+    process = execute  # TODO: remove
+
+
+class Preview3D(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Preview3D",
+            search_aliases=["view mesh", "3d viewer"],
+            display_name="Preview 3D & Animation",
+            category="3d",
+            description="Preview a 3D model file without saving it to the ComfyUI output directory.",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    IO.String.Input("model_file", default="", multiline=False),
+                    types=[
+                        IO.File3DGLB,
+                        IO.File3DGLTF,
+                        IO.File3DFBX,
+                        IO.File3DOBJ,
+                        IO.File3DSTL,
+                        IO.File3DUSDZ,
+                        IO.File3DAny,
+                    ],
+                    tooltip="3D model file or path string",
+                ),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Image.Input("bg_image", optional=True, advanced=True),
+            ],
+            outputs=[],
+        )
+
+    @classmethod
+    def execute(cls, model_file: str | Types.File3D, **kwargs) -> IO.NodeOutput:
+        if isinstance(model_file, Types.File3D):
+            filename = f"preview3d_{uuid.uuid4().hex}.{model_file.format}"
+            model_file.save_to(os.path.join(folder_paths.get_output_directory(), filename))
+        else:
+            filename = model_file
+        camera_info = kwargs.get("camera_info", None)
+        bg_image = kwargs.get("bg_image", None)
+        return IO.NodeOutput(ui=UI.PreviewUI3D(filename, camera_info, bg_image=bg_image))
+
+    process = execute  # TODO: remove
+
+
+class Preview3DAdvanced(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Preview3DAdvanced",
+            display_name="Preview 3D (Advanced)",
+            search_aliases=["preview 3d", "3d viewer", "view mesh", "frame 3d", "3d camera output"],
+            category="3d",
+            description="Preview a 3D model file without saving it to the ComfyUI output directory.",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DGLB,
+                        IO.File3DGLTF,
+                        IO.File3DFBX,
+                        IO.File3DOBJ,
+                        IO.File3DSTL,
+                        IO.File3DUSDZ,
+                        IO.File3DAny,
+                    ],
+                    tooltip="3D model file from an upstream 3D node.",
+                ),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
+        filename = f"preview3d_advanced_{uuid.uuid4().hex}.{model_3d.format}"
+        model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
+
+        viewport_state = viewport_state if isinstance(viewport_state, dict) else {}
+        camera_info_input = kwargs.get("camera_info", None)
+        camera_info = camera_info_input if camera_info_input is not None else viewport_state.get('camera_info')
+        model_3d_info_input = kwargs.get("model_3d_info", None)
+        model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
+        return IO.NodeOutput(
+            model_3d,
+            model_3d_info,
+            camera_info,
+            width,
+            height,
+            ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info),
+        )
+
+
+class PreviewGaussianSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="PreviewGaussianSplat",
+            display_name="Preview Splat",
+            category="3d",
+            description="Preview a gaussian splat 3D file without saving it to the ComfyUI output directory.",
+            is_experimental=True,
+            is_output_node=True,
+            search_aliases=[
+                "view splat",
+                "view gaussian",
+                "view gaussian splat",
+                "preview gaussian",
+                "preview gaussian splat",
+                "view 3dgs",
+                "preview 3dgs",
+                "preview ply",
+                "preview spz",
+                "preview splat",
+                "preview ksplat",
+            ],
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DSplatAny,
+                        IO.File3DPLY,
+                        IO.File3DSPLAT,
+                        IO.File3DSPZ,
+                        IO.File3DKSPLAT,
+                    ],
+                    tooltip="A gaussian splat 3D file.",
+                ),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DSplatAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
+        filename = f"preview_splat_{uuid.uuid4().hex}.{model_3d.format}"
+        model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
+
+        viewport_state = viewport_state if isinstance(viewport_state, dict) else {}
+        camera_info_input = kwargs.get("camera_info", None)
+        camera_info = camera_info_input if camera_info_input is not None else viewport_state.get('camera_info')
+        model_3d_info_input = kwargs.get("model_3d_info", None)
+        model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
+        return IO.NodeOutput(
+            model_3d,
+            model_3d_info,
+            camera_info,
+            width,
+            height,
+            ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info),
+        )
+
+
+class PreviewPointCloud(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="PreviewPointCloud",
+            display_name="Preview Point Cloud",
+            category="3d",
+            description="Preview a point cloud 3D file without saving it to the ComfyUI output directory.",
+            is_experimental=True,
+            is_output_node=True,
+            search_aliases=[
+                "view point cloud",
+                "view pointcloud",
+                "preview point cloud",
+                "preview pointcloud",
+                "preview ply",
+            ],
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DPointCloudAny,
+                        IO.File3DPLY,
+                    ],
+                    tooltip="Point cloud file (.ply)",
+                ),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DPointCloudAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
+        filename = f"preview_pointcloud_{uuid.uuid4().hex}.{model_3d.format}"
+        model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
+
+        viewport_state = viewport_state if isinstance(viewport_state, dict) else {}
+        camera_info_input = kwargs.get("camera_info", None)
+        camera_info = camera_info_input if camera_info_input is not None else viewport_state.get('camera_info')
+        model_3d_info_input = kwargs.get("model_3d_info", None)
+        model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
+        return IO.NodeOutput(
+            model_3d,
+            model_3d_info,
+            camera_info,
+            width,
+            height,
+            ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info),
+        )
+
+
+MESH_EXTENSIONS = {'.gltf', '.glb', '.obj', '.fbx', '.stl'}
+
+
+class Load3DAdvanced(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        input_dir = os.path.join(folder_paths.get_input_directory(), "3d")
+        os.makedirs(input_dir, exist_ok=True)
+
+        input_path = Path(input_dir)
+        base_path = Path(folder_paths.get_input_directory())
+
+        files = [
+            normalize_path(str(file_path.relative_to(base_path)))
+            for file_path in input_path.rglob("*")
+            if file_path.suffix.lower() in MESH_EXTENSIONS
+        ]
+        return IO.Schema(
+            node_id="Load3DAdvanced",
+            display_name="Load 3D (Advanced)",
+            category="3d",
+            search_aliases=[
+                "load mesh",
+                "load gltf",
+                "load glb",
+                "load obj",
+                "load fbx",
+                "load stl",
+            ],
+            is_experimental=True,
+            inputs=[
+                IO.Combo.Input("model_file", options=["none"] + sorted(files), upload=IO.UploadType.model),
+                IO.Load3D.Input("viewport_state"),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def validate_inputs(cls, model_file, **kwargs) -> bool | str:
+        if not model_file or model_file == "none":
+            return True
+        if not folder_paths.exists_annotated_filepath(model_file):
+            return f"Invalid 3D model file: {model_file}"
+        return True
+
+    @classmethod
+    def execute(cls, model_file, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
+        file_3d = None
+        if model_file and model_file != "none":
+            file_3d = Types.File3D(folder_paths.get_annotated_filepath(model_file))
+        viewport_state = viewport_state if isinstance(viewport_state, dict) else {}
+        model_3d_info = viewport_state.get('model_3d_info', [])
+        return IO.NodeOutput(file_3d, model_3d_info, viewport_state.get('camera_info'), width, height)
+
+
+class Load3DExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            Load3D,
+            Load3DAdvanced,
+            Preview3D,
+            Preview3DAdvanced,
+            PreviewGaussianSplat,
+            PreviewPointCloud,
+        ]
+
+
+async def comfy_entrypoint() -> Load3DExtension:
+    return Load3DExtension()
diff --git a/comfy_extras/nodes_logic.py b/comfy_extras/nodes_logic.py
new file mode 100644
index 0000000000000000000000000000000000000000..88b42dacd842e1366b6689dca61785f2ccb9b4be
--- /dev/null
+++ b/comfy_extras/nodes_logic.py
@@ -0,0 +1,353 @@
+from typing import TypedDict
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from comfy_api.latest import _io
+
+# sentinel for missing inputs
+MISSING = object()
+
+
+class NotNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ComfyNotNode",
+            display_name="Not",
+            category="utilities/logic",
+            description="Logical NOT operation. Returns true if the value is falsy. Uses Python's rules for truthiness.",
+            search_aliases=["invert", "toggle", "negate", "flip boolean"],
+            inputs=[
+                io.AnyType.Input("value"),
+            ],
+            outputs=[
+                io.Boolean.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, value) -> io.NodeOutput:
+        return io.NodeOutput(not value)
+
+
+class AndNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.Autogrow.TemplatePrefix(
+            input=io.AnyType.Input("value"),
+            prefix="value",
+            min=1,
+        )
+        return io.Schema(
+            node_id="ComfyAndNode",
+            display_name="And",
+            category="utilities/logic",
+            description="Logical AND operation. Returns true if all of the values are truthy. Uses Python's rules for truthiness.",
+            search_aliases=["all", "every"],
+            inputs=[
+                io.Autogrow.Input("values", template=template),
+            ],
+            outputs=[
+                io.Boolean.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, values: io.Autogrow.Type) -> io.NodeOutput:
+        return io.NodeOutput(all(values.values()))
+
+
+class OrNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.Autogrow.TemplatePrefix(
+            input=io.AnyType.Input("value"),
+            prefix="value",
+            min=1,
+        )
+        return io.Schema(
+            node_id="ComfyOrNode",
+            display_name="Or",
+            category="utilities/logic",
+            description="Logical OR operation. Returns true if any of the values are truthy. Uses Python's rules for truthiness.",
+            search_aliases=["any", "some"],
+            inputs=[
+                io.Autogrow.Input("values", template=template),
+            ],
+            outputs=[
+                io.Boolean.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, values: io.Autogrow.Type) -> io.NodeOutput:
+        return io.NodeOutput(any(values.values()))
+
+
+class SwitchNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("switch")
+        return io.Schema(
+            node_id="ComfySwitchNode",
+            search_aliases=["if", "then", "switch", "conditional", "branch"],
+            display_name="If/Else Switch",
+            category="utilities/logic",
+            is_experimental=True,
+            inputs=[
+                io.Boolean.Input("switch"),
+                io.MatchType.Input("on_false", template=template, lazy=True),
+                io.MatchType.Input("on_true", template=template, lazy=True),
+            ],
+            outputs=[
+                io.MatchType.Output(template=template, display_name="output"),
+            ],
+        )
+
+    @classmethod
+    def check_lazy_status(cls, switch, on_false=None, on_true=None):
+        if switch and on_true is None:
+            return ["on_true"]
+        if not switch and on_false is None:
+            return ["on_false"]
+
+    @classmethod
+    def execute(cls, switch, on_true, on_false) -> io.NodeOutput:
+        return io.NodeOutput(on_true if switch else on_false)
+
+
+class SoftSwitchNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("switch")
+        return io.Schema(
+            node_id="ComfySoftSwitchNode",
+            display_name="Soft Switch",
+            category="utilities/logic",
+            is_experimental=True,
+            inputs=[
+                io.Boolean.Input("switch"),
+                io.MatchType.Input("on_false", template=template, lazy=True, optional=True),
+                io.MatchType.Input("on_true", template=template, lazy=True, optional=True),
+            ],
+            outputs=[
+                io.MatchType.Output(template=template, display_name="output"),
+            ],
+        )
+
+    @classmethod
+    def check_lazy_status(cls, switch, on_false=MISSING, on_true=MISSING):
+        # We use MISSING instead of None, as None is passed for connected-but-unevaluated inputs.
+        # This trick allows us to ignore the value of the switch and still be able to run execute().
+
+        # One of the inputs may be missing, in which case we need to evaluate the other input
+        if on_false is MISSING:
+            return ["on_true"]
+        if on_true is MISSING:
+            return ["on_false"]
+        # Normal lazy switch operation
+        if switch and on_true is None:
+            return ["on_true"]
+        if not switch and on_false is None:
+            return ["on_false"]
+
+    @classmethod
+    def validate_inputs(cls, switch, on_false=MISSING, on_true=MISSING):
+        # This check happens before check_lazy_status(), so we can eliminate the case where
+        # both inputs are missing.
+        if on_false is MISSING and on_true is MISSING:
+            return "At least one of on_false or on_true must be connected to Switch node"
+        return True
+
+    @classmethod
+    def execute(cls, switch, on_true=MISSING, on_false=MISSING) -> io.NodeOutput:
+        if on_true is MISSING:
+            return io.NodeOutput(on_false)
+        if on_false is MISSING:
+            return io.NodeOutput(on_true)
+        return io.NodeOutput(on_true if switch else on_false)
+
+
+class CustomComboNode(io.ComfyNode):
+    """
+    Frontend node that allows user to write their own options for a combo.
+    This is here to make sure the node has a backend-representation to avoid some annoyances.
+    """
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CustomCombo",
+            display_name="Custom Combo",
+            category="utilities",
+            is_experimental=True,
+            inputs=[io.Combo.Input("choice", options=[])],
+            outputs=[
+                io.String.Output(display_name="STRING"),
+                io.Int.Output(display_name="INDEX"),
+            ],
+            accept_all_inputs=True,
+        )
+
+    @classmethod
+    def validate_inputs(cls, choice: io.Combo.Type, index: int = 0, **kwargs) -> bool:
+        # NOTE: DO NOT DO THIS unless you want to skip validation entirely on the node's inputs.
+        # I am doing that here because the widgets (besides the combo dropdown) on this node are fully frontend defined.
+        # I need to skip checking that the chosen combo option is in the options list, since those are defined by the user.
+        return True
+
+    @classmethod
+    def execute(cls, choice: io.Combo.Type, index: int = 0, **kwargs) -> io.NodeOutput:
+        return io.NodeOutput(choice, index)
+
+
+class DCTestNode(io.ComfyNode):
+    class DCValues(TypedDict):
+        combo: str
+        string: str
+        integer: int
+        image: io.Image.Type
+        subcombo: dict[str]
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DCTestNode",
+            display_name="DCTest",
+            category="utilities/logic",
+            is_output_node=True,
+            inputs=[io.DynamicCombo.Input("combo", options=[
+                io.DynamicCombo.Option("option1", [io.String.Input("string")]),
+                io.DynamicCombo.Option("option2", [io.Int.Input("integer")]),
+                io.DynamicCombo.Option("option3", [io.Image.Input("image")]),
+                io.DynamicCombo.Option("option4", [
+                    io.DynamicCombo.Input("subcombo", options=[
+                        io.DynamicCombo.Option("opt1", [io.Float.Input("float_x"), io.Float.Input("float_y")]),
+                        io.DynamicCombo.Option("opt2", [io.Mask.Input("mask1", optional=True)]),
+                    ])
+                ])]
+            )],
+            outputs=[io.AnyType.Output()],
+        )
+
+    @classmethod
+    def execute(cls, combo: DCValues) -> io.NodeOutput:
+        combo_val = combo["combo"]
+        if combo_val == "option1":
+            return io.NodeOutput(combo["string"])
+        elif combo_val == "option2":
+            return io.NodeOutput(combo["integer"])
+        elif combo_val == "option3":
+            return io.NodeOutput(combo["image"])
+        elif combo_val == "option4":
+            return io.NodeOutput(f"{combo['subcombo']}")
+        else:
+            raise ValueError(f"Invalid combo: {combo_val}")
+
+
+class AutogrowNamesTestNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = _io.Autogrow.TemplateNames(input=io.Float.Input("float"), names=["a", "b", "c"])
+        return io.Schema(
+            node_id="AutogrowNamesTestNode",
+            display_name="AutogrowNamesTest",
+            category="utilities/logic",
+            inputs=[
+                _io.Autogrow.Input("autogrow", template=template)
+            ],
+            outputs=[io.String.Output()],
+        )
+
+    @classmethod
+    def execute(cls, autogrow: _io.Autogrow.Type) -> io.NodeOutput:
+        vals = list(autogrow.values())
+        combined = ",".join([str(x) for x in vals])
+        return io.NodeOutput(combined)
+
+class AutogrowPrefixTestNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = _io.Autogrow.TemplatePrefix(input=io.Float.Input("float"), prefix="float", min=1, max=10)
+        return io.Schema(
+            node_id="AutogrowPrefixTestNode",
+            display_name="AutogrowPrefixTest",
+            category="utilities/logic",
+            inputs=[
+                _io.Autogrow.Input("autogrow", template=template)
+            ],
+            outputs=[io.String.Output()],
+        )
+
+    @classmethod
+    def execute(cls, autogrow: _io.Autogrow.Type) -> io.NodeOutput:
+        vals = list(autogrow.values())
+        combined = ",".join([str(x) for x in vals])
+        return io.NodeOutput(combined)
+
+class ComboOutputTestNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ComboOptionTestNode",
+            display_name="ComboOptionTest",
+            category="utilities/logic",
+            inputs=[io.Combo.Input("combo", options=["option1", "option2", "option3"]),
+                    io.Combo.Input("combo2", options=["option4", "option5", "option6"])],
+            outputs=[io.Combo.Output(), io.Combo.Output()],
+        )
+
+    @classmethod
+    def execute(cls, combo: io.Combo.Type, combo2: io.Combo.Type) -> io.NodeOutput:
+        return io.NodeOutput(combo, combo2)
+
+class ConvertStringToComboNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ConvertStringToComboNode",
+            search_aliases=["string to dropdown", "text to combo"],
+            display_name="Convert String to Combo",
+            category="utilities/logic",
+            inputs=[io.String.Input("string")],
+            outputs=[io.Combo.Output()],
+        )
+
+    @classmethod
+    def execute(cls, string: str) -> io.NodeOutput:
+        return io.NodeOutput(string)
+
+class InvertBooleanNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="InvertBooleanNode",
+            search_aliases=["not", "toggle", "negate", "flip boolean"],
+            display_name="Invert Boolean",
+            category="utilities/logic",
+            inputs=[io.Boolean.Input("boolean")],
+            outputs=[io.Boolean.Output()],
+        )
+
+    @classmethod
+    def execute(cls, boolean: bool) -> io.NodeOutput:
+        return io.NodeOutput(not boolean)
+
+class LogicExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SwitchNode,
+            CustomComboNode,
+            NotNode,
+            AndNode,
+            OrNode,
+            # SoftSwitchNode,
+            # ConvertStringToComboNode,
+            # DCTestNode,
+            # AutogrowNamesTestNode,
+            # AutogrowPrefixTestNode,
+            # ComboOutputTestNode,
+            # InvertBooleanNode,
+        ]
+
+async def comfy_entrypoint() -> LogicExtension:
+    return LogicExtension()
diff --git a/comfy_extras/nodes_lora_debug.py b/comfy_extras/nodes_lora_debug.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca61fdc68f3b0006b34432e8b9439ef1a6f1009d
--- /dev/null
+++ b/comfy_extras/nodes_lora_debug.py
@@ -0,0 +1,79 @@
+import folder_paths
+import comfy.utils
+import comfy.sd
+
+
+class LoraLoaderBypass:
+    """
+    Apply LoRA in bypass mode without modifying base model weights.
+
+    Bypass mode computes: output = base_forward(x) + lora_path(x)
+    This is useful for training and when model weights are offloaded.
+    """
+
+    def __init__(self):
+        self.loaded_lora = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
+                "clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
+                "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
+                "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP")
+    OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
+    FUNCTION = "load_lora"
+
+    CATEGORY = "model/loaders"
+    DESCRIPTION = "Apply LoRA in bypass mode. Unlike regular LoRA, this doesn't modify model weights - instead it injects the LoRA computation during forward pass. Useful for training scenarios."
+    EXPERIMENTAL = True
+
+    def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
+        if strength_model == 0 and strength_clip == 0:
+            return (model, clip)
+
+        lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
+        lora = None
+        if self.loaded_lora is not None:
+            if self.loaded_lora[0] == lora_path:
+                lora = self.loaded_lora[1]
+            else:
+                self.loaded_lora = None
+
+        if lora is None:
+            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+            self.loaded_lora = (lora_path, lora)
+
+        model_lora, clip_lora = comfy.sd.load_bypass_lora_for_models(model, clip, lora, strength_model, strength_clip)
+        return (model_lora, clip_lora)
+
+
+class LoraLoaderBypassModelOnly(LoraLoaderBypass):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "lora_name": (folder_paths.get_filename_list("loras"), ),
+                              "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_lora_model_only"
+
+    def load_lora_model_only(self, model, lora_name, strength_model):
+        return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
+
+
+NODE_CLASS_MAPPINGS = {
+    "LoraLoaderBypass": LoraLoaderBypass,
+    "LoraLoaderBypassModelOnly": LoraLoaderBypassModelOnly,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoraLoaderBypass": "Load LoRA (Bypass) (For debugging)",
+    "LoraLoaderBypassModelOnly": "Load LoRA (Bypass, Model Only) (for debugging)",
+}
diff --git a/comfy_extras/nodes_lora_extract.py b/comfy_extras/nodes_lora_extract.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cc7e9ec7ad39f460cdbeefcfd8351f434c68477
--- /dev/null
+++ b/comfy_extras/nodes_lora_extract.py
@@ -0,0 +1,145 @@
+import torch
+import comfy.model_management
+import comfy.utils
+import folder_paths
+import os
+import logging
+from enum import Enum
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from tqdm.auto import trange
+
+CLAMP_QUANTILE = 0.99
+
+def extract_lora(diff, rank):
+    conv2d = (len(diff.shape) == 4)
+    kernel_size = None if not conv2d else diff.size()[2:4]
+    conv2d_3x3 = conv2d and kernel_size != (1, 1)
+    out_dim, in_dim = diff.size()[0:2]
+    rank = min(rank, in_dim, out_dim)
+
+    if conv2d:
+        if conv2d_3x3:
+            diff = diff.flatten(start_dim=1)
+        else:
+            diff = diff.squeeze()
+
+
+    U, S, Vh = torch.linalg.svd(diff.float())
+    U = U[:, :rank]
+    S = S[:rank]
+    U = U @ torch.diag(S)
+    Vh = Vh[:rank, :]
+
+    dist = torch.cat([U.flatten(), Vh.flatten()])
+    hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+    low_val = -hi_val
+
+    U = U.clamp(low_val, hi_val)
+    Vh = Vh.clamp(low_val, hi_val)
+    if conv2d:
+        U = U.reshape(out_dim, rank, 1, 1)
+        Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
+    return (U, Vh)
+
+class LORAType(Enum):
+    STANDARD = 0
+    FULL_DIFF = 1
+
+LORA_TYPES = {"standard": LORAType.STANDARD,
+              "full_diff": LORAType.FULL_DIFF}
+
+def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, bias_diff=False):
+    comfy.model_management.load_models_gpu([model_diff])
+    sd = model_diff.model_state_dict(filter_prefix=prefix_model)
+
+    sd_keys = list(sd.keys())
+    for index in trange(len(sd_keys), unit="weight"):
+        k = sd_keys[index]
+        op_keys = sd_keys[index].rsplit('.', 1)
+        if len(op_keys) < 2 or op_keys[1] not in ["weight", "bias"] or (op_keys[1] == "bias" and not bias_diff):
+            continue
+        op = comfy.utils.get_attr(model_diff.model, op_keys[0])
+        if hasattr(op, "comfy_cast_weights") and not getattr(op, "comfy_patched_weights", False):
+            weight_diff = model_diff.patch_weight_to_device(k, model_diff.load_device, return_weight=True)
+        else:
+            weight_diff = sd[k]
+
+        if op_keys[1] == "weight":
+            if lora_type == LORAType.STANDARD:
+                if weight_diff.ndim < 2:
+                    if bias_diff:
+                        output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
+                    continue
+                try:
+                    out = extract_lora(weight_diff, rank)
+                    output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().half().cpu()
+                    output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu()
+                except:
+                    logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k))
+            elif lora_type == LORAType.FULL_DIFF:
+                output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
+
+        elif bias_diff and op_keys[1] == "bias":
+            output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = weight_diff.contiguous().half().cpu()
+    return output_sd
+
+class LoraSave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoraSave",
+            search_aliases=["export lora"],
+            display_name="Extract and Save Lora",
+            category="experimental",
+            inputs=[
+                io.String.Input("filename_prefix", default="loras/ComfyUI_extracted_lora"),
+                io.Int.Input("rank", default=8, min=1, max=4096, step=1, advanced=True),
+                io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys()), advanced=True),
+                io.Boolean.Input("bias_diff", default=True, advanced=True),
+                io.Model.Input(
+                    "model_diff",
+                    tooltip="The ModelSubtract output to be converted to a lora.",
+                    optional=True,
+                ),
+                io.Clip.Input(
+                  "text_encoder_diff",
+                    tooltip="The CLIPSubtract output to be converted to a lora.",
+                    optional=True,
+                ),
+            ],
+            is_experimental=True,
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None) -> io.NodeOutput:
+        if model_diff is None and text_encoder_diff is None:
+            return io.NodeOutput()
+
+        lora_type = LORA_TYPES.get(lora_type)
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
+
+        output_sd = {}
+        if model_diff is not None:
+            output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, bias_diff=bias_diff)
+        if text_encoder_diff is not None:
+            output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, lora_type, bias_diff=bias_diff)
+
+        output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+        comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
+        return io.NodeOutput()
+
+
+class LoraSaveExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LoraSave,
+        ]
+
+
+async def comfy_entrypoint() -> LoraSaveExtension:
+    return LoraSaveExtension()
diff --git a/comfy_extras/nodes_lotus.py b/comfy_extras/nodes_lotus.py
new file mode 100644
index 0000000000000000000000000000000000000000..084cc56638fec345989e8241487fa50a1a11c1b6
--- /dev/null
+++ b/comfy_extras/nodes_lotus.py
@@ -0,0 +1,39 @@
+from typing_extensions import override
+
+import torch
+import comfy.model_management as mm
+from comfy_api.latest import ComfyExtension, io
+
+
+class LotusConditioning(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LotusConditioning",
+            category="model/conditioning/lotus",
+            inputs=[],
+            outputs=[io.Conditioning.Output(display_name="conditioning")],
+        )
+
+    @classmethod
+    def execute(cls) -> io.NodeOutput:
+        device = mm.get_torch_device()
+        #lotus uses a frozen encoder and null conditioning, i'm just inlining the results of that operation since it doesn't change
+        #and getting parity with the reference implementation would otherwise require inference and 800mb of tensors
+        prompt_embeds = torch.tensor([[[-0.3134765625, -0.447509765625, -0.00823974609375, -0.22802734375, 0.1785888671875, -0.2342529296875, -0.2188720703125, -0.0089111328125, -0.31396484375, 0.196533203125, -0.055877685546875, -0.3828125, -0.0965576171875, 0.0073394775390625, -0.284423828125, 0.07470703125, -0.086181640625, -0.211181640625, 0.0599365234375, 0.10693359375, 0.0007929801940917969, -0.78076171875, -0.382568359375, -0.1851806640625, -0.140625, -0.0936279296875, -0.1229248046875, -0.152099609375, -0.203857421875, -0.2349853515625, -0.2437744140625, -0.10858154296875, -0.08990478515625, 0.08892822265625, -0.2391357421875, -0.1611328125, -0.427978515625, -0.1336669921875, -0.27685546875, -0.1781005859375, -0.3857421875, 0.251953125, -0.055999755859375, -0.0712890625, -0.00130462646484375, 0.033477783203125, -0.26416015625, 0.07171630859375, -0.0090789794921875, -0.2025146484375, -0.2763671875, -0.09869384765625, -0.45751953125, -0.23095703125, 0.004528045654296875, 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-0.09381103515625, -0.7216796875, 1.37890625, -0.398193359375, -0.3095703125, -1.4912109375, 0.96630859375, 0.43798828125, 0.62255859375, 0.0213470458984375, 0.235595703125, -1.2958984375, 0.0157318115234375, -0.810546875, 1.9736328125, -0.2462158203125, 0.720703125, 0.822265625, -0.755859375, -0.658203125, 0.344482421875, -2.892578125, -0.282470703125, 1.2529296875, -0.294189453125, 0.6748046875, -0.80859375, 0.9287109375, 1.27734375, -1.71875, -0.166015625, 0.47412109375, -0.41259765625, -1.3681640625, -0.978515625, -0.77978515625, -1.044921875, -0.90380859375, -0.08184814453125, -0.86181640625, -0.10772705078125, -0.299560546875, -0.4306640625, -0.47119140625, 0.95703125, 1.107421875, 0.91796875, 0.76025390625, 0.7392578125, -0.09161376953125, -0.7392578125, 0.9716796875, -0.395751953125, -0.75390625, -0.164306640625, -0.087646484375, 0.028564453125, -0.91943359375, -0.66796875, 2.486328125, 0.427734375, 0.626953125, 0.474853515625, 0.0926513671875, 0.830078125, -0.6923828125, 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-1.6064453125, -0.353515625, -0.484130859375, -0.6044921875, -0.455810546875, 0.95849609375, 1.3671875, 0.544921875, 0.560546875, 0.34521484375, -0.6513671875, -0.410400390625, -0.2021484375, -0.1656494140625, 0.073486328125, 0.84716796875, -1.7998046875, -1.0126953125, -0.1324462890625, 0.95849609375, -0.669921875, -0.79052734375, -2.193359375, -0.42529296875, -1.7275390625, -1.04296875, 0.716796875, -0.4423828125, -1.193359375, 0.61572265625, -1.5224609375, 0.62890625, -0.705078125, 0.677734375, -0.213134765625, -1.6748046875, -1.087890625, -0.65185546875, -1.1337890625, 2.314453125, -0.352783203125, -0.27001953125, -2.01953125, -1.2685546875, 0.308837890625, -0.280517578125, -1.3798828125, -1.595703125, 0.642578125, 1.693359375, -0.82470703125, -1.255859375, 0.57373046875, 1.5859375, 1.068359375, -0.876953125, 0.370849609375, 1.220703125, 0.59765625, 0.007602691650390625, 0.09326171875, -0.9521484375, -0.024932861328125, -0.94775390625, -0.299560546875, -0.002536773681640625, 1.41796875, -0.06903076171875, -1.5927734375, 0.353515625, 3.63671875, -0.765625, -1.1142578125, 0.4287109375, -0.86865234375, -0.9267578125, -0.21826171875, -1.10546875, 0.29296875, -0.225830078125, 0.5400390625, -0.45556640625, -0.68701171875, -0.79150390625, -1.0810546875, 0.25439453125, -1.2998046875, -0.494140625, -0.1510009765625, 1.5615234375, -0.4248046875, -0.486572265625, 0.45458984375, 0.047637939453125, -0.11639404296875, 0.057403564453125, 0.130126953125, -0.10125732421875, -0.56201171875, 1.4765625, -1.7451171875, 1.34765625, -0.45703125, 0.873046875, -0.056121826171875, -0.8876953125, -0.986328125, 1.5654296875, 0.49853515625, 0.55859375, -0.2198486328125, 0.62548828125, 0.2734375, -0.63671875, -0.41259765625, -1.2705078125, 0.0665283203125, 1.3369140625, 0.90283203125, -0.77685546875, -1.5, -1.8525390625, -1.314453125, -0.86767578125, -0.331787109375, 0.1590576171875, 0.94775390625, -0.1771240234375, 1.638671875, -2.17578125, 0.58740234375, 0.424560546875, -0.3466796875, 0.642578125, 0.473388671875, 0.96435546875, 1.38671875, -0.91357421875, 1.0361328125, -0.67333984375, 1.5009765625]]]).to(device)
+
+        cond = [[prompt_embeds, {}]]
+
+        return io.NodeOutput(cond)
+
+
+class LotusExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LotusConditioning,
+        ]
+
+
+async def comfy_entrypoint() -> LotusExtension:
+    return LotusExtension()
diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e80ca72118a14bb00d3d5e85aa39236ac937548
--- /dev/null
+++ b/comfy_extras/nodes_lt.py
@@ -0,0 +1,1202 @@
+import nodes
+import node_helpers
+import torch
+import torchaudio
+import comfy.ldm.lightricks.duration_head
+import comfy.model_management
+import comfy.model_sampling
+import comfy.samplers
+import comfy.utils
+import logging
+import math
+import re
+import numpy as np
+import av
+from io import BytesIO
+from typing_extensions import override
+from comfy.ldm.lightricks.symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
+from comfy_api.latest import ComfyExtension, io
+
+ICLoRAParameters = io.Custom("IC_LORA_PARAMETERS")
+
+
+class GetICLoRAParameters(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="GetICLoRAParameters",
+            display_name="Get IC-LoRA Parameters",
+            description="Extracts IC-LoRA parameters from the safetensors metadata of a LoRA-loaded "
+                        "model and outputs them for LTXVAddGuide (eg. reference_downscale_factor).",
+            category="model/conditioning/ltxv",
+            search_aliases=["ic-lora", "ic lora", "iclora", "downscale factor", "reference downscale"],
+            inputs=[
+                io.Model.Input(
+                    "iclora_model",
+                    tooltip="Direct output from a LoRA Loader for the specific IC-LoRA "
+                            "from which to extract the metadata.",
+                ),
+            ],
+            outputs=[
+                ICLoRAParameters.Output(
+                    "iclora_parameters",
+                    tooltip="IC-LoRA parameters extracted from the LoRA metadata "
+                            "(eg. reference_downscale_factor). Connect to LTXVAddGuide "
+                            "if the LoRA requires special handling of the guides.",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, iclora_model) -> io.NodeOutput:
+        metadata = iclora_model.get_attachment("lora_metadata")
+        factor = 1
+        if metadata:
+            try:
+                factor = max(1, round(float(next(v for k, v in metadata.items() if k.endswith("reference_downscale_factor")))))
+            except (StopIteration, TypeError, ValueError):
+                factor = 1
+        parameters = {"reference_downscale_factor": factor}
+        return io.NodeOutput(parameters)
+
+
+class EmptyLTXVLatentVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyLTXVLatentVideo",
+            category="model/latent/ltxv",
+            inputs=[
+                io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("length", default=97, min=1, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 32})
+
+    generate = execute  # TODO: remove
+
+class LTXVImgToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVImgToVideo",
+            category="model/conditioning/ltxv",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Image.Input("image"),
+                io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("length", default=97, min=9, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Float.Input("strength", default=1.0, min=0.0, max=1.0),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, image, vae, width, height, length, batch_size, strength) -> io.NodeOutput:
+        pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+        encode_pixels = pixels[:, :, :, :3]
+        t = vae.encode(encode_pixels)
+
+        latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
+        latent[:, :, :t.shape[2]] = t
+
+        conditioning_latent_frames_mask = torch.ones(
+            (batch_size, 1, latent.shape[2], 1, 1),
+            dtype=torch.float32,
+            device=latent.device,
+        )
+        conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength
+
+        return io.NodeOutput(positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask})
+
+    generate = execute  # TODO: remove
+
+
+class LTXVImgToVideoInplace(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVImgToVideoInplace",
+            category="model/conditioning/ltxv",
+            inputs=[
+                io.Vae.Input("vae"),
+                io.Image.Input("image"),
+                io.Latent.Input("latent"),
+                io.Float.Input("strength", default=1.0, min=0.0, max=1.0),
+                io.Boolean.Input("bypass", default=False, tooltip="Bypass the conditioning.")
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, vae, image, latent, strength, bypass=False) -> io.NodeOutput:
+        if bypass:
+            return (latent,)
+
+        samples = latent["samples"].clone()
+        _, height_scale_factor, width_scale_factor = (
+            vae.downscale_index_formula
+        )
+
+        _, _, _, latent_height, latent_width = samples.shape
+        width = latent_width * width_scale_factor
+        height = latent_height * height_scale_factor
+
+        if image.shape[1] != height or image.shape[2] != width:
+            pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+        else:
+            pixels = image
+        encode_pixels = pixels[:, :, :, :3]
+        t = vae.encode(encode_pixels)
+
+        samples[:, :, :t.shape[2]] = t
+
+        conditioning_latent_frames_mask = get_noise_mask(latent)
+        conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength
+
+        return io.NodeOutput({"samples": samples, "noise_mask": conditioning_latent_frames_mask})
+
+    generate = execute  # TODO: remove
+
+
+def _append_guide_attention_entry(positive, negative, pre_filter_count, latent_shape, strength=1.0, attention_mask=None):
+    """Append a guide_attention_entry to both positive and negative conditioning.
+
+    Each entry tracks one guide reference for per-reference attention control.
+    Entries are derived independently from each conditioning to avoid cross-contamination.
+    """
+    new_entry = {
+        "pre_filter_count": pre_filter_count,
+        "strength": strength,
+        "pixel_mask": attention_mask.unsqueeze(0).unsqueeze(0) if attention_mask is not None else None,  # reshape to (1, 1, F, H, W)
+        "latent_shape": latent_shape,
+    }
+
+    results = []
+    for cond in (positive, negative):
+        # Read existing entries from this specific conditioning
+        existing = []
+        for t in cond:
+            found = t[1].get("guide_attention_entries", None)
+            if found is not None:
+                existing = found
+                break
+        # Shallow copy only and append (pixel_mask is never mutated).
+        entries = [*existing, new_entry]
+        results.append(node_helpers.conditioning_set_values(
+            cond, {"guide_attention_entries": entries}
+        ))
+    return results[0], results[1]
+
+
+def conditioning_get_any_value(conditioning, key, default=None):
+    for t in conditioning:
+        if key in t[1]:
+            return t[1][key]
+    return default
+
+
+def get_noise_mask(latent):
+    noise_mask = latent.get("noise_mask", None)
+    latent_image = latent["samples"]
+    if noise_mask is None:
+        batch_size, _, latent_length, _, _ = latent_image.shape
+        noise_mask = torch.ones(
+            (batch_size, 1, latent_length, 1, 1),
+            dtype=torch.float32,
+            device=latent_image.device,
+        )
+    else:
+        noise_mask = noise_mask.clone()
+    return noise_mask
+
+def get_keyframe_idxs(cond, latent_shape=None):
+    keyframe_idxs = conditioning_get_any_value(cond, "keyframe_idxs", None)
+    if keyframe_idxs is None:
+        return None, 0
+    # Get number of keyframes from latent_shape or guide_attention_entries if available
+    if latent_shape is not None and len(latent_shape) == 5:
+        tokens_per_frame = latent_shape[-2] * latent_shape[-1]
+        num_keyframes = keyframe_idxs.shape[2] // tokens_per_frame
+        return keyframe_idxs, num_keyframes
+    entries = conditioning_get_any_value(cond, "guide_attention_entries", None)
+    if entries:
+        num_keyframes = sum(e["latent_shape"][0] for e in entries)
+        return keyframe_idxs, num_keyframes
+    # fallback, may under-count if keyframes share t-start
+    # keyframe_idxs contains start/end positions (last dimension), checking for unqiue values only for start
+    num_keyframes = torch.unique(keyframe_idxs[:, 0, :, 0]).shape[0]
+    return keyframe_idxs, num_keyframes
+
+class LTXVAddGuide(io.ComfyNode):
+    PATCHIFIER = SymmetricPatchifier(1, start_end=True)
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVAddGuide",
+            category="model/conditioning/ltxv",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Latent.Input("latent"),
+                io.Image.Input(
+                    "image",
+                    tooltip="Image or video to condition the latent video on. Must be 8*n + 1 frames. "
+                            "If the video is not 8*n + 1 frames, it will be cropped to the nearest 8*n + 1 frames.",
+                ),
+                io.Int.Input(
+                    "frame_idx",
+                    default=0,
+                    min=-9999,
+                    max=9999,
+                    tooltip="Frame index to start the conditioning at. "
+                            "For single-frame images or videos with 1-8 frames, any frame_idx value is acceptable. "
+                            "For videos with 9+ frames, frame_idx must be divisible by 8, otherwise it will be rounded "
+                            "down to the nearest multiple of 8. Negative values are counted from the end of the video.",
+                ),
+                io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Mask.Input(
+                    "attention_mask",
+                    optional=True,
+                    tooltip="Optional pixel-space spatial mask. Controls per-region "
+                            "conditioning influence via self-attention, multiplied by strength.",
+                ),
+                ICLoRAParameters.Input(
+                    "iclora_parameters",
+                    optional=True,
+                    tooltip="Optional IC-LoRA parameters from a Get IC-LoRA Parameters node. "
+                            "Used for adjusting guide processing as required by certain IC-LoRAs "
+                            "(eg. those with a reference_downscale_factor > 1). "
+                            "When chained, each LTXVAddGuide uses only the parameters connected to it.",
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def encode(cls, vae, latent_width, latent_height, images, scale_factors, latent_downscale_factor=1):
+        time_scale_factor, width_scale_factor, height_scale_factor = scale_factors
+        images = images[:(images.shape[0] - 1) // time_scale_factor * time_scale_factor + 1]
+        target_width = int(latent_width * width_scale_factor / latent_downscale_factor)
+        target_height = int(latent_height * height_scale_factor / latent_downscale_factor)
+        pixels = comfy.utils.common_upscale(images.movedim(-1, 1), target_width, target_height, "bilinear", crop="center").movedim(1, -1)
+        encode_pixels = pixels[:, :, :, :3]
+        t = vae.encode(encode_pixels)
+        return encode_pixels, t
+
+    @classmethod
+    def dilate_latent(cls, guide_latent, latent_downscale_factor):
+        if latent_downscale_factor <= 1:
+            return guide_latent, None
+        scale = int(latent_downscale_factor)
+        dilated_shape = guide_latent.shape[:3] + (guide_latent.shape[3] * scale, guide_latent.shape[4] * scale)
+        dilated = torch.zeros(dilated_shape, device=guide_latent.device, dtype=guide_latent.dtype)
+        dilated[..., ::scale, ::scale] = guide_latent
+        dilated_mask = torch.full(
+            (dilated.shape[0], 1, dilated.shape[2], dilated.shape[3], dilated.shape[4]),
+            -1.0, device=guide_latent.device, dtype=guide_latent.dtype,
+        )
+        dilated_mask[..., ::scale, ::scale] = 1.0
+        return dilated, dilated_mask
+
+    @classmethod
+    def get_reference_downscale_factor(cls, iclora_parameters):
+        if not iclora_parameters:
+            return 1
+        try:
+            factor = max(1, round(float(iclora_parameters.get("reference_downscale_factor", 1))))
+        except (TypeError, ValueError):
+            factor = 1
+        return factor
+
+    @classmethod
+    def get_latent_index(cls, cond, latent_length, guide_length, frame_idx, scale_factors, latent_shape=None):
+        time_scale_factor, _, _ = scale_factors
+        _, num_keyframes = get_keyframe_idxs(cond, latent_shape)
+        latent_count = latent_length - num_keyframes
+        frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * time_scale_factor + 1 + frame_idx, 0)
+        if guide_length > 1 and frame_idx != 0:
+            frame_idx = (frame_idx - 1) // time_scale_factor * time_scale_factor + 1 # frame index - 1 must be divisible by 8 or frame_idx == 0
+
+        latent_idx = (frame_idx + time_scale_factor - 1) // time_scale_factor
+
+        return frame_idx, latent_idx
+
+    @classmethod
+    def add_keyframe_index(cls, cond, frame_idx, guiding_latent, scale_factors, latent_downscale_factor=1, causal_fix=None):
+        keyframe_idxs, _ = get_keyframe_idxs(cond)
+        _, latent_coords = cls.PATCHIFIER.patchify(guiding_latent)
+        if causal_fix is None:
+            causal_fix = frame_idx == 0 or guiding_latent.shape[2] == 1
+        pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, causal_fix=causal_fix)
+        pixel_coords[:, 0] += frame_idx
+
+        # The following adjusts keyframe end positions for small grid IC-LoRA.
+        # After dilation, the small grid has the same size and position as the large grid,
+        # but each token encodes a larger image patch. We adjust the end position (not start)
+        # so that RoPE represents the correct middle point of each token.
+        # keyframe_idxs dims: (batch, spatial_dim [t,h,w], token_id, [start, end])
+        # We only adjust h,w (not t) in dim 1, and only end (not start) in dim 3.
+        spatial_end_offset = (latent_downscale_factor - 1) * torch.tensor(
+            scale_factors[1:],
+            device=pixel_coords.device,
+        ).view(1, -1, 1, 1)
+        pixel_coords[:, 1:, :, 1:] += spatial_end_offset.to(pixel_coords.dtype)
+
+        if keyframe_idxs is None:
+            keyframe_idxs = pixel_coords
+        else:
+            keyframe_idxs = torch.cat([keyframe_idxs, pixel_coords], dim=2)
+        return node_helpers.conditioning_set_values(cond, {"keyframe_idxs": keyframe_idxs})
+
+    @classmethod
+    def append_keyframe(cls, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors, guide_mask=None, in_channels=128, latent_downscale_factor=1, causal_fix=None):
+        if latent_image.shape[1] != in_channels or guiding_latent.shape[1] != in_channels:
+            raise ValueError("Adding guide to a combined AV latent is not supported.")
+
+        positive = cls.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors, latent_downscale_factor, causal_fix=causal_fix)
+        negative = cls.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors, latent_downscale_factor, causal_fix=causal_fix)
+
+        if guide_mask is not None:
+            target_h = max(noise_mask.shape[3], guide_mask.shape[3])
+            target_w = max(noise_mask.shape[4], guide_mask.shape[4])
+
+            if noise_mask.shape[3] == 1 or noise_mask.shape[4] == 1:
+                noise_mask = noise_mask.expand(-1, -1, -1, target_h, target_w)
+
+            if guide_mask.shape[3] == 1 or guide_mask.shape[4] == 1:
+                guide_mask = guide_mask.expand(-1, -1, -1, target_h, target_w)
+            mask = guide_mask - strength
+        else:
+            mask = torch.full(
+                (noise_mask.shape[0], 1, guiding_latent.shape[2], noise_mask.shape[3], noise_mask.shape[4]),
+                max(0.0, 1.0 - strength), # clamp here to amplify only via the attention mask
+                dtype=noise_mask.dtype,
+                device=noise_mask.device,
+            )
+        # This solves audio video combined latent case where latent_image has audio latent concatenated
+        # in channel dimension with video latent. The solution is to pad guiding latent accordingly.
+        if latent_image.shape[1] > guiding_latent.shape[1]:
+            pad_len = latent_image.shape[1] - guiding_latent.shape[1]
+            guiding_latent = torch.nn.functional.pad(guiding_latent, pad=(0, 0, 0, 0, 0, 0, 0, pad_len), value=0)
+        latent_image = torch.cat([latent_image, guiding_latent], dim=2)
+        noise_mask = torch.cat([noise_mask, mask], dim=2)
+        return positive, negative, latent_image, noise_mask
+
+    @classmethod
+    def replace_latent_frames(cls, latent_image, noise_mask, guiding_latent, latent_idx, strength):
+        cond_length = guiding_latent.shape[2]
+        assert latent_image.shape[2] >= latent_idx + cond_length, "Conditioning frames exceed the length of the latent sequence."
+
+        mask = torch.full(
+            (noise_mask.shape[0], 1, cond_length, 1, 1),
+            max(0.0, 1.0 - strength), # clamp here to amplify only via the attention mask
+            dtype=noise_mask.dtype,
+            device=noise_mask.device,
+        )
+
+        latent_image = latent_image.clone()
+        noise_mask = noise_mask.clone()
+
+        latent_image[:, :, latent_idx : latent_idx + cond_length] = guiding_latent
+        noise_mask[:, :, latent_idx : latent_idx + cond_length] = mask
+
+        return latent_image, noise_mask
+
+    @classmethod
+    def execute(cls, positive, negative, vae, latent, image, frame_idx, strength, attention_mask=None, iclora_parameters=None) -> io.NodeOutput:
+        scale_factors = vae.downscale_index_formula
+        latent_image = latent["samples"]
+        noise_mask = get_noise_mask(latent)
+
+        _, _, latent_length, latent_height, latent_width = latent_image.shape
+
+        latent_downscale_factor = cls.get_reference_downscale_factor(iclora_parameters)
+        if latent_downscale_factor > 1:
+            if latent_width % latent_downscale_factor != 0 or latent_height % latent_downscale_factor != 0:
+                raise ValueError(
+                    f"Latent spatial size {latent_width}x{latent_height} must be divisible by "
+                    f"reference_downscale_factor {latent_downscale_factor} from the IC-LoRA parameters."
+                )
+
+        # For mid-video multi-frame guides, prepend+strip a throwaway first frame so the VAE's "first latent = 1 pixel frame" asymmetry lands on the discarded slot
+        time_scale_factor = scale_factors[0]
+        num_frames_to_keep = ((image.shape[0] - 1) // time_scale_factor) * time_scale_factor + 1
+        resolved_frame_idx = frame_idx
+        if frame_idx < 0:
+            _, num_keyframes = get_keyframe_idxs(positive, latent_image.shape)
+            resolved_frame_idx = max((latent_length - num_keyframes - 1) * time_scale_factor + 1 + frame_idx, 0)
+        causal_fix = resolved_frame_idx == 0 or num_frames_to_keep == 1
+
+        if not causal_fix:
+            image = torch.cat([image[:1], image], dim=0)
+
+        image, t = cls.encode(vae, latent_width, latent_height, image, scale_factors, latent_downscale_factor)
+
+        if not causal_fix:
+            t = t[:, :, 1:, :, :]
+            image = image[1:]
+
+        guide_latent_shape = list(t.shape[2:])  # pre-dilation [F, H, W] for spatial-mask downsampling
+        guide_mask = None
+        if latent_downscale_factor > 1:
+            t, guide_mask = cls.dilate_latent(t, latent_downscale_factor)
+
+        frame_idx, latent_idx = cls.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors, latent_shape=latent_image.shape)
+        assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence."
+
+        positive, negative, latent_image, noise_mask = cls.append_keyframe(
+            positive,
+            negative,
+            frame_idx,
+            latent_image,
+            noise_mask,
+            t,
+            strength,
+            scale_factors,
+            guide_mask=guide_mask,
+            latent_downscale_factor=latent_downscale_factor,
+            causal_fix=causal_fix,
+        )
+
+        # Track this guide for per-reference attention control.
+        pre_filter_count = t.shape[2] * t.shape[3] * t.shape[4]
+        positive, negative = _append_guide_attention_entry(
+            positive, negative, pre_filter_count, guide_latent_shape, strength=strength,
+            attention_mask=attention_mask,
+        )
+
+        return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
+
+    generate = execute  # TODO: remove
+
+
+class LTXVCropGuides(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVCropGuides",
+            category="model/conditioning/ltxv",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Latent.Input("latent"),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, latent) -> io.NodeOutput:
+        latent_image = latent["samples"].clone()
+        noise_mask = get_noise_mask(latent)
+
+        _, num_keyframes = get_keyframe_idxs(positive, latent_image.shape)
+        if num_keyframes == 0:
+            return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)
+
+        latent_image = latent_image[:, :, :-num_keyframes]
+        noise_mask = noise_mask[:, :, :-num_keyframes]
+
+        positive = node_helpers.conditioning_set_values(positive, {
+            "keyframe_idxs": None,
+            "guide_attention_entries": None,
+        })
+        negative = node_helpers.conditioning_set_values(negative, {
+            "keyframe_idxs": None,
+            "guide_attention_entries": None,
+        })
+
+        return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
+
+    crop = execute  # TODO: remove
+
+
+class LTXVConditioning(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVConditioning",
+            category="model/conditioning/ltxv",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Float.Input("frame_rate", default=25.0, min=0.0, max=1000.0, step=0.01),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, frame_rate) -> io.NodeOutput:
+        positive = node_helpers.conditioning_set_values(positive, {"frame_rate": frame_rate})
+        negative = node_helpers.conditioning_set_values(negative, {"frame_rate": frame_rate})
+        return io.NodeOutput(positive, negative)
+
+
+class ModelSamplingLTXV(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelSamplingLTXV",
+            category="model/patch/ltxv",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("base_shift", default=0.95, min=0.0, max=100.0, step=0.01),
+                io.Latent.Input("latent", optional=True),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, max_shift, base_shift, latent=None) -> io.NodeOutput:
+        m = model.clone()
+
+        if latent is None:
+            tokens = 4096
+        else:
+            tokens = math.prod(latent["samples"].shape[2:])
+
+        x1 = 1024
+        x2 = 4096
+        mm = (max_shift - base_shift) / (x2 - x1)
+        b = base_shift - mm * x1
+        shift = (tokens) * mm + b
+
+        sampling_base = comfy.model_sampling.ModelSamplingFlux
+        sampling_type = comfy.model_sampling.CONST
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(shift=shift)
+        m.add_object_patch("model_sampling", model_sampling)
+
+        return io.NodeOutput(m)
+
+
+class LTXVScheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVScheduler",
+            category="model/sampling/schedulers",
+            inputs=[
+                io.Int.Input("steps", default=20, min=1, max=10000),
+                io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("base_shift", default=0.95, min=0.0, max=100.0, step=0.01),
+                io.Boolean.Input(
+                    id="stretch",
+                    default=True,
+                    tooltip="Stretch the sigmas to be in the range [terminal, 1].",
+                    advanced=True,
+                ),
+                io.Float.Input(
+                    id="terminal",
+                    default=0.1,
+                    min=0.0,
+                    max=0.99,
+                    step=0.01,
+                    tooltip="The terminal value of the sigmas after stretching.",
+                    advanced=True,
+                ),
+                io.Latent.Input("latent", optional=True),
+            ],
+            outputs=[
+                io.Sigmas.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, steps, max_shift, base_shift, stretch, terminal, latent=None) -> io.NodeOutput:
+        if latent is None:
+            tokens = 4096
+        else:
+            tokens = math.prod(latent["samples"].shape[2:])
+
+        sigmas = torch.linspace(1.0, 0.0, steps + 1)
+
+        x1 = 1024
+        x2 = 4096
+        mm = (max_shift - base_shift) / (x2 - x1)
+        b = base_shift - mm * x1
+        sigma_shift = (tokens) * mm + b
+
+        power = 1
+        sigmas = torch.where(
+            sigmas != 0,
+            math.exp(sigma_shift) / (math.exp(sigma_shift) + (1 / sigmas - 1) ** power),
+            0,
+        )
+
+        # Stretch sigmas so that its final value matches the given terminal value.
+        if stretch:
+            non_zero_mask = sigmas != 0
+            non_zero_sigmas = sigmas[non_zero_mask]
+            one_minus_z = 1.0 - non_zero_sigmas
+            scale_factor = one_minus_z[-1] / (1.0 - terminal)
+            stretched = 1.0 - (one_minus_z / scale_factor)
+            sigmas[non_zero_mask] = stretched
+
+        return io.NodeOutput(sigmas)
+
+def encode_single_frame(output_file, image_array: np.ndarray, crf):
+    container = av.open(output_file, "w", format="mp4")
+    try:
+        stream = container.add_stream(
+            "libx264", rate=1, options={"crf": str(crf), "preset": "veryfast"}
+        )
+        stream.height = image_array.shape[0]
+        stream.width = image_array.shape[1]
+        av_frame = av.VideoFrame.from_ndarray(image_array, format="rgb24").reformat(
+            format="yuv420p"
+        )
+        container.mux(stream.encode(av_frame))
+        container.mux(stream.encode())
+    finally:
+        container.close()
+
+
+def decode_single_frame(video_file):
+    container = av.open(video_file)
+    try:
+        stream = next(s for s in container.streams if s.type == "video")
+        frame = next(container.decode(stream))
+    finally:
+        container.close()
+    return frame.to_ndarray(format="rgb24")
+
+
+def preprocess(image: torch.Tensor, crf=29):
+    if crf == 0:
+        return image
+
+    image_array = (image[:(image.shape[0] // 2) * 2, :(image.shape[1] // 2) * 2] * 255.0).byte().cpu().numpy()
+    with BytesIO() as output_file:
+        encode_single_frame(output_file, image_array, crf)
+        video_bytes = output_file.getvalue()
+    with BytesIO(video_bytes) as video_file:
+        image_array = decode_single_frame(video_file)
+    tensor = torch.tensor(image_array, dtype=image.dtype, device=image.device) / 255.0
+    return tensor
+
+
+class LTXVPreprocess(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVPreprocess",
+            display_name="LTXV Preprocess",
+            category="video/preprocessors",
+            inputs=[
+                io.Image.Input("image"),
+                io.Int.Input(
+                    id="img_compression", default=35, min=0, max=100, tooltip="Amount of compression to apply on image."
+                ),
+            ],
+            outputs=[
+                io.Image.Output(display_name="output_image"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image, img_compression) -> io.NodeOutput:
+        output_images = []
+        for i in range(image.shape[0]):
+            output_images.append(preprocess(image[i], img_compression))
+        return io.NodeOutput(torch.stack(output_images))
+
+    preprocess = execute  # TODO: remove
+
+
+import comfy.nested_tensor
+class LTXVConcatAVLatent(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVConcatAVLatent",
+            display_name="Concat AV Latent",
+            description="Merge a video latent and an audio latent into a joint AV latent (any AV model, e.g. LTXV or MiniMax H3).",
+            category="model/latent/ltxv",
+            inputs=[
+                io.Latent.Input("video_latent"),
+                io.Latent.Input("audio_latent"),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @staticmethod
+    def fit_audio(reference, audio, noise_mask):
+        """Trim or zero-pad the audio stream to the length of the one it replaces.
+
+        The padded tail is left unmasked so the model generates it, which is what a
+        clip shorter than the video should do.
+        """
+        dims = [i for i in range(reference.ndim) if reference.shape[i] != audio.shape[i]]
+        if len(dims) == 0:
+            return audio, noise_mask
+        if len(dims) > 1 or dims[0] < 2:
+            raise ValueError("audio latent {} cannot be fitted to {}".format(tuple(audio.shape), tuple(reference.shape)))
+
+        dim, length = dims[0], reference.shape[dims[0]]
+        if noise_mask is not None:  # masks carry their own shape until sampling resizes them
+            noise_mask = comfy.utils.reshape_mask(noise_mask, audio.shape)
+
+        if audio.shape[dim] > length:
+            audio = audio.narrow(dim, 0, length)
+            if noise_mask is not None:
+                noise_mask = noise_mask.narrow(dim, 0, length)
+        else:
+            pad = torch.zeros_like(audio.narrow(dim, 0, 1)).repeat(
+                [length - audio.shape[dim] if i == dim else 1 for i in range(audio.ndim)])
+            audio = torch.cat([audio, pad], dim=dim)
+            if noise_mask is not None:
+                noise_mask = torch.cat([noise_mask, torch.ones_like(pad)], dim=dim)
+        return audio, noise_mask
+
+    @classmethod
+    def execute(cls, video_latent, audio_latent) -> io.NodeOutput:
+        output = {}
+        output.update(video_latent)
+        output.update(audio_latent)
+        video_samples = video_latent["samples"]
+        audio_samples = audio_latent["samples"]
+        video_noise_mask = video_latent.get("noise_mask", None)
+        audio_noise_mask = audio_latent.get("noise_mask", None)
+
+        if video_samples.is_nested:  # already an AV latent: keep its video and swap the audio stream
+            streams = video_samples.unbind()
+            video_samples = streams[0]
+            if video_noise_mask is not None:
+                video_noise_mask = video_noise_mask.unbind()[0]
+            audio_samples, audio_noise_mask = cls.fit_audio(streams[1], audio_samples, audio_noise_mask)
+
+        if video_noise_mask is not None or audio_noise_mask is not None:
+            if video_noise_mask is None:
+                video_noise_mask = torch.ones_like(video_samples)
+            if audio_noise_mask is None:
+                audio_noise_mask = torch.ones_like(audio_samples)
+            output["noise_mask"] = comfy.nested_tensor.NestedTensor((video_noise_mask, audio_noise_mask))
+
+        output["samples"] = comfy.nested_tensor.NestedTensor((video_samples, audio_samples))
+
+        return io.NodeOutput(output)
+
+
+class LTXVSeparateAVLatent(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVSeparateAVLatent",
+            display_name="Separate AV Latent",
+            category="model/latent/ltxv",
+            description="Split a joint AV latent into its video and audio latents (any AV model, e.g. LTXV or MiniMax H3).",
+            inputs=[
+                io.Latent.Input("av_latent"),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="video_latent"),
+                io.Latent.Output(display_name="audio_latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, av_latent) -> io.NodeOutput:
+        latents = av_latent["samples"].unbind()
+        video_latent = av_latent.copy()
+        video_latent["samples"] = latents[0]
+        audio_latent = av_latent.copy()
+        audio_latent["samples"] = latents[1]
+        if "noise_mask" in av_latent:
+            masks = av_latent["noise_mask"]
+            if masks is not None:
+                masks = masks.unbind()
+                video_latent["noise_mask"] = masks[0]
+                audio_latent["noise_mask"] = masks[1]
+        return io.NodeOutput(video_latent, audio_latent)
+
+
+class LTXVReferenceAudio(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXVReferenceAudio",
+            display_name="LTXV Reference Audio (ID-LoRA)",
+            category="model/conditioning/ltxv",
+            description="Set reference audio for ID-LoRA speaker identity transfer. Encodes a reference audio clip into the conditioning and optionally patches the model with identity guidance (extra forward pass without reference, amplifying the speaker identity effect).",
+            inputs=[
+                io.Model.Input("model"),
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Audio.Input("reference_audio", tooltip="Reference audio clip whose speaker identity to transfer. ~5 seconds recommended (training duration). Shorter or longer clips may degrade voice identity transfer."),
+                io.Vae.Input(id="audio_vae", display_name="Audio VAE", tooltip="LTXV Audio VAE for encoding."),
+                io.Float.Input("identity_guidance_scale", default=3.0, min=0.0, max=100.0, step=0.01, round=0.01, tooltip="Strength of identity guidance. Runs an extra forward pass without reference each step to amplify speaker identity. Set to 0 to disable (no extra pass)."),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True, tooltip="Start of the sigma range where identity guidance is active."),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True, tooltip="End of the sigma range where identity guidance is active."),
+            ],
+            outputs=[
+                io.Model.Output(),
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, positive, negative, reference_audio, audio_vae, identity_guidance_scale, start_percent, end_percent) -> io.NodeOutput:
+        # Encode reference audio to latents and patchify
+        sample_rate = reference_audio["sample_rate"]
+        vae_sample_rate = getattr(audio_vae, "audio_sample_rate", 44100)
+        if vae_sample_rate != sample_rate:
+            waveform = torchaudio.functional.resample(reference_audio["waveform"], sample_rate, vae_sample_rate)
+        else:
+            waveform = reference_audio["waveform"]
+
+        audio_latents = audio_vae.encode(waveform.movedim(1, -1))
+        b, c, t, f = audio_latents.shape
+        ref_tokens = audio_latents.permute(0, 2, 1, 3).reshape(b, t, c * f)
+        ref_audio = {"tokens": ref_tokens}
+
+        positive = node_helpers.conditioning_set_values(positive, {"ref_audio": ref_audio})
+        negative = node_helpers.conditioning_set_values(negative, {"ref_audio": ref_audio})
+
+        # Patch model with identity guidance
+        m = model.clone()
+        scale = identity_guidance_scale
+        model_sampling = m.get_model_object("model_sampling")
+        sigma_start = model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model_sampling.percent_to_sigma(end_percent)
+
+        def post_cfg_function(args):
+            if scale == 0:
+                return args["denoised"]
+
+            sigma = args["sigma"]
+            sigma_ = sigma[0].item()
+            if sigma_ > sigma_start or sigma_ < sigma_end:
+                return args["denoised"]
+
+            cond_pred = args["cond_denoised"]
+            cond = args["cond"]
+            cfg_result = args["denoised"]
+            model_options = args["model_options"].copy()
+            x = args["input"]
+
+            # Strip ref_audio from conditioning for the no-reference pass
+            noref_cond = []
+            for entry in cond:
+                new_entry = entry.copy()
+                mc = new_entry.get("model_conds", {}).copy()
+                mc.pop("ref_audio", None)
+                new_entry["model_conds"] = mc
+                noref_cond.append(new_entry)
+
+            (pred_noref,) = comfy.samplers.calc_cond_batch(
+                args["model"], [noref_cond], x, sigma, model_options
+            )
+
+            return cfg_result + (cond_pred - pred_noref) * scale
+
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+
+        return io.NodeOutput(m, positive, negative)
+
+
+class LTXVSpatioTemporalGuidance(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVSpatioTemporalGuidance",
+            display_name="LTXV Spatio-Temporal Guidance (STG)",
+            category="advanced/guidance",
+            description="Runs one extra pass per step with the self-attention of the selected blocks degraded to a value-passthrough, "
+                        "then guides away from it - improving spatial detail and motion coherence.",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("scale", default=1.0, min=0.0, max=100.0, step=0.01, round=0.01),
+                io.String.Input("blocks", default="29", tooltip="Comma-separated transformer block indices to perturb."),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, scale, blocks, start_percent, end_percent) -> io.NodeOutput:
+        block_set = frozenset(int(b) for b in re.findall(r"\d+", blocks))
+
+        m = model.clone()
+        model_sampling = m.get_model_object("model_sampling")
+        sigma_start = model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model_sampling.percent_to_sigma(end_percent)
+
+        def post_cfg_function(args):
+            if scale == 0 or not block_set:
+                return args["denoised"]
+
+            sigma_ = args["sigma"][0].item()
+            if sigma_ > sigma_start or sigma_ < sigma_end:
+                return args["denoised"]
+
+            cond_pred = args["cond_denoised"]
+            cond = args["cond"]
+            cfg_result = args["denoised"]
+            x = args["input"]
+
+            model_options = args["model_options"].copy()
+            transformer_options = model_options.get("transformer_options", {}).copy()
+            transformer_options["stg_self_attn_blocks"] = block_set
+            model_options["transformer_options"] = transformer_options
+
+            (perturbed,) = comfy.samplers.calc_cond_batch(args["model"], [cond], x, args["sigma"], model_options)
+
+            return cfg_result + (cond_pred - perturbed) * scale
+
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+        return io.NodeOutput(m)
+
+
+class LTXVModalityGuidance(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVModalityGuidance",
+            display_name="LTXV Modality Guidance (A/V coupling)",
+            category="advanced/guidance",
+            description="Cross-modal (audio-video) guidance for LTXV-AV. Runs one extra forward "
+                        "pass per step with the a2v/v2a cross-attention severed, then pushes the "
+                        "result toward the coupled prediction - strengthening audio-visual sync "
+                        "(e.g. lip-sync). Reference default modality_scale is 3.0. Stacks with the "
+                        "dual-CFG guider and STG. Set to 1.0 to disable (no extra pass).",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("modality_scale", default=3.0, min=1.0, max=100.0, step=0.1, round=0.01),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, modality_scale, start_percent, end_percent) -> io.NodeOutput:
+        m = model.clone()
+        model_sampling = m.get_model_object("model_sampling")
+        sigma_start = model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model_sampling.percent_to_sigma(end_percent)
+
+        def post_cfg_function(args):
+            if math.isclose(modality_scale, 1.0):
+                return args["denoised"]
+
+            sigma_ = args["sigma"][0].item()
+            if sigma_ > sigma_start or sigma_ < sigma_end:
+                return args["denoised"]
+
+            cond_pred = args["cond_denoised"]
+            cond = args["cond"]
+            cfg_result = args["denoised"]
+            x = args["input"]
+
+            # Extra pass with audio-video cross-attention severed (both directions)
+            model_options = args["model_options"].copy()
+            transformer_options = model_options.get("transformer_options", {}).copy()
+            transformer_options["a2v_cross_attn"] = False
+            transformer_options["v2a_cross_attn"] = False
+            model_options["transformer_options"] = transformer_options
+
+            (mod_pred,) = comfy.samplers.calc_cond_batch(
+                args["model"], [cond], x, args["sigma"], model_options
+            )
+
+            # (modality_scale - 1) * (cond - uncond_modality), per the reference guider.
+            return cfg_result + (cond_pred - mod_pred) * (modality_scale - 1.0)
+
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+        return io.NodeOutput(m)
+
+
+class Guider_LTXAVDualCFG(comfy.samplers.CFGGuider):
+    """CFG guider that applies separate guidance scales to the video and audio
+    modalities of a packed LTXV-AV latent.
+    """
+
+    def set_conds(self, positive, negative):
+        self.inner_set_conds({"positive": positive, "negative": negative})
+
+    def set_cfg(self, video_cfg, audio_cfg):
+        self.video_cfg = video_cfg
+        self.audio_cfg = audio_cfg
+        self.cfg = max(video_cfg, audio_cfg)
+
+    def sample(self, noise, latent_image, *args, **kwargs):
+        # Capture the video/audio split from the nested latent before it is packed.
+        self._v_numel = None
+        if getattr(latent_image, "is_nested", False):
+            parts = latent_image.unbind()
+            if len(parts) >= 2:
+                self._v_numel = math.prod(parts[0].shape[1:])
+        return super().sample(noise, latent_image, *args, **kwargs)
+
+    def predict_noise(self, x, timestep, model_options={}, seed=None):
+        v = getattr(self, "_v_numel", None)
+        if v is None or math.isclose(self.video_cfg, self.audio_cfg):
+            # Not an AV latent, or equal scales: fall back to standard single-CFG.
+            self.cfg = self.video_cfg
+            return super().predict_noise(x, timestep, model_options, seed)
+
+        video_cfg, audio_cfg = self.video_cfg, self.audio_cfg
+
+        def dual_cfg(args):
+            # Noise-space: cond = x - cond_pred, uncond = x - uncond_pred; the
+            # returned tensor is subtracted from x by cfg_function.
+            cond, uncond = args["cond"], args["uncond"]
+            out = uncond + (cond - uncond) * video_cfg
+            out[..., v:] = uncond[..., v:] + (cond[..., v:] - uncond[..., v:]) * audio_cfg
+            return out
+
+        # disable_cfg1_optimization so the uncond pass always runs even if one of the two scales is 1.0.
+        model_options = {**model_options, "sampler_cfg_function": dual_cfg, "disable_cfg1_optimization": True}
+        return super().predict_noise(x, timestep, model_options, seed)
+
+
+class LTXVDualCFGGuider(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVDualCFGGuider",
+            display_name="LTXV Dual CFG Guider",
+            category="model/sampling/guiders",
+            description="Separate CFG scales for the video and audio modalities of a packed LTXV-AV latent.",
+            inputs=[
+                io.Model.Input("model"),
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Float.Input("video_cfg", default=3.0, min=0.0, max=100.0, step=0.1, round=0.01),
+                io.Float.Input("audio_cfg", default=7.0, min=0.0, max=100.0, step=0.1, round=0.01),
+            ],
+            outputs=[io.Guider.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, positive, negative, video_cfg, audio_cfg) -> io.NodeOutput:
+        guider = Guider_LTXAVDualCFG(model)
+        guider.set_conds(positive, negative)
+        guider.set_cfg(video_cfg, audio_cfg)
+        return io.NodeOutput(guider)
+
+
+class LTXVDurationPredictor(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVDurationPredictor",
+            display_name="LTXV Duration Predictor",
+            category="conditioning/video_models",
+            description="Predicts the natural shot duration for a prompt using the LTX 2.4 duration "
+                        "head (loaded with ModelPatchLoader), and snaps it to the VAE's 8k+1 frame grid.",
+            search_aliases=["auto duration", "duration head", "num_frames"],
+            inputs=[
+                io.Model.Input("model"),
+                io.Conditioning.Input("positive"),
+                io.Custom("MODEL_PATCH").Input("duration_head",
+                                               tooltip="LTX 2.4 duration head loaded with ModelPatchLoader."),
+                io.Float.Input("frame_rate", default=24.0, min=1.0, max=120.0, step=0.01),
+                io.Float.Input("min_seconds", default=1.0, min=0.5, max=120.0, step=0.1),
+                io.Float.Input("max_seconds", default=20.0, min=0.5, max=120.0, step=0.1),
+            ],
+            outputs=[
+                io.Int.Output(display_name="num_frames"),
+                io.Float.Output(display_name="seconds", tooltip="Raw (unclamped) predicted duration."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, positive, duration_head, frame_rate, min_seconds, max_seconds) -> io.NodeOutput:
+        dm = model.model.diffusion_model
+        head = duration_head.model
+        if not isinstance(head, comfy.ldm.lightricks.duration_head.DurationHead):
+            raise ValueError("The connected model_patch is not an LTX duration head.")
+
+        context = positive[0][0]
+        meta = positive[0][1]
+        if context.shape[0] != 1:
+            context = context[:1]
+
+        # Run the caption connectors exactly the way sampling does.
+        comfy.model_management.load_models_gpu([model, duration_head])
+        device = model.load_device
+        head = head.to(device)
+        with torch.no_grad():
+            context = context.to(device=device, dtype=model.model.get_dtype_inference())
+            processed = dm.preprocess_text_embeds(context, unprocessed=meta.get("unprocessed_ltxav_embeds", False))
+            video_tokens = processed[..., :dm.cross_attention_dim].float()
+            audio_tokens = processed[..., dm.cross_attention_dim:].float()
+            seconds = float(head(video_tokens, audio_tokens)[0])
+
+        num_frames = comfy.ldm.lightricks.duration_head.seconds_to_num_frames(
+            seconds, frame_rate, min_seconds, max_seconds)
+        logging.info("LTXV duration head predicted %.2fs -> %d frames @ %.2f fps", seconds, num_frames, frame_rate)
+        return io.NodeOutput(num_frames, seconds)
+
+
+class LtxvExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EmptyLTXVLatentVideo,
+            LTXVImgToVideo,
+            LTXVImgToVideoInplace,
+            ModelSamplingLTXV,
+            LTXVConditioning,
+            LTXVScheduler,
+            GetICLoRAParameters,
+            LTXVAddGuide,
+            LTXVPreprocess,
+            LTXVCropGuides,
+            LTXVConcatAVLatent,
+            LTXVSeparateAVLatent,
+            LTXVReferenceAudio,
+            LTXVDualCFGGuider,
+            LTXVModalityGuidance,
+            LTXVSpatioTemporalGuidance,
+            LTXVDurationPredictor,
+        ]
+
+
+async def comfy_entrypoint() -> LtxvExtension:
+    return LtxvExtension()
diff --git a/comfy_extras/nodes_lt_audio.py b/comfy_extras/nodes_lt_audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaa0c6cb91c5b24287ee9b4d4e739505b01a6a71
--- /dev/null
+++ b/comfy_extras/nodes_lt_audio.py
@@ -0,0 +1,222 @@
+import folder_paths
+import comfy.utils
+import comfy.model_management
+import torch
+
+from comfy_api.latest import ComfyExtension, io
+from comfy_extras.nodes_audio import VAEEncodeAudio
+
+class LTXVAudioVAELoader(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXVAudioVAELoader",
+            display_name="Load LTXV Audio VAE",
+            category="model/loaders",
+            inputs=[
+                io.Combo.Input(
+                    "ckpt_name",
+                    options=folder_paths.get_filename_list("checkpoints"),
+                    tooltip="Audio VAE checkpoint to load.",
+                )
+            ],
+            outputs=[io.Vae.Output(display_name="Audio VAE")],
+        )
+
+    @classmethod
+    def execute(cls, ckpt_name: str) -> io.NodeOutput:
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
+        sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True)
+        sd = comfy.utils.state_dict_prefix_replace(sd, {"audio_vae.": "autoencoder.", "vocoder.": "vocoder."}, filter_keys=True)
+        vae = comfy.sd.VAE(sd=sd, metadata=metadata)
+        vae.throw_exception_if_invalid()
+
+        return io.NodeOutput(vae)
+
+
+class LTXVAudioVAEEncode(VAEEncodeAudio):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXVAudioVAEEncode",
+            display_name="LTXV Audio VAE Encode",
+            category="model/latent/ltxv",
+            inputs=[
+                io.Audio.Input("audio", tooltip="The audio to be encoded."),
+                io.Vae.Input(
+                    id="audio_vae",
+                    display_name="Audio VAE",
+                    tooltip="The Audio VAE model to use for encoding.",
+                ),
+            ],
+            outputs=[io.Latent.Output(display_name="Audio Latent")],
+        )
+
+    @classmethod
+    def execute(cls, audio, audio_vae) -> io.NodeOutput:
+        return super().execute(audio_vae, audio)
+
+
+class LTXVAudioVAEDecode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXVAudioVAEDecode",
+            display_name="LTXV Audio VAE Decode",
+            category="model/latent/ltxv",
+            inputs=[
+                io.Latent.Input("samples", tooltip="The latent to be decoded."),
+                io.Vae.Input(
+                    id="audio_vae",
+                    display_name="Audio VAE",
+                    tooltip="The Audio VAE model used for decoding the latent.",
+                ),
+            ],
+            outputs=[io.Audio.Output(display_name="Audio")],
+        )
+
+    @classmethod
+    def execute(cls, samples, audio_vae) -> io.NodeOutput:
+        audio_latent = samples["samples"]
+        if audio_latent.is_nested:
+            audio_latent = audio_latent.unbind()[-1]
+        audio = audio_vae.decode(audio_latent).movedim(-1, 1).to(audio_latent.device)
+        output_audio_sample_rate = audio_vae.first_stage_model.output_sample_rate
+        return io.NodeOutput(
+            {
+                "waveform": audio,
+                "sample_rate": int(output_audio_sample_rate),
+            }
+        )
+
+
+class LTXVEmptyLatentAudio(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXVEmptyLatentAudio",
+            display_name="LTXV Empty Latent Audio",
+            category="model/latent/ltxv",
+            inputs=[
+                io.Int.Input(
+                    "frames_number",
+                    default=97,
+                    min=1,
+                    max=1000,
+                    step=1,
+                    display_mode=io.NumberDisplay.number,
+                    tooltip="Number of frames.",
+                ),
+                io.MultiType.Input(
+                    io.Float.Input(
+                        "frame_rate",
+                        default=25.0,
+                        min=1.0,
+                        max=1000.0,
+                        step=0.01,
+                        display_mode=io.NumberDisplay.number,
+                        tooltip="Number of frames per second.",
+                    ),
+                    [io.Int],
+                ),
+                io.Int.Input(
+                    "batch_size",
+                    default=1,
+                    min=1,
+                    max=4096,
+                    display_mode=io.NumberDisplay.number,
+                    tooltip="The number of latent audio samples in the batch.",
+                ),
+                io.Vae.Input(
+                    id="audio_vae",
+                    display_name="Audio VAE",
+                    tooltip="The Audio VAE model to get configuration from.",
+                ),
+            ],
+            outputs=[io.Latent.Output(display_name="Latent")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        frames_number: int,
+        frame_rate: float,
+        batch_size: int,
+        audio_vae,
+    ) -> io.NodeOutput:
+        """Generate empty audio latents matching the reference pipeline structure."""
+
+        assert audio_vae is not None, "Audio VAE model is required"
+
+        z_channels = audio_vae.latent_channels
+        audio_freq = audio_vae.first_stage_model.latent_frequency_bins
+
+        num_audio_latents = audio_vae.first_stage_model.num_of_latents_from_frames(frames_number, frame_rate)
+
+        audio_latents = torch.zeros(
+            (batch_size, z_channels, num_audio_latents, audio_freq),
+            device=comfy.model_management.intermediate_device(),
+        )
+
+        return io.NodeOutput(
+            {
+                "samples": audio_latents,
+                "type": "audio",
+            }
+        )
+
+
+class LTXAVTextEncoderLoader(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXAVTextEncoderLoader",
+            display_name="Load LTXV Audio Text Encoder",
+            category="model/loaders",
+            description="Recipes:\nltxav: gemma 3 12B or matching gemma 4 model",
+            inputs=[
+                io.Combo.Input(
+                    "text_encoder",
+                    options=folder_paths.get_filename_list("text_encoders"),
+                ),
+                io.Combo.Input(
+                    "ckpt_name",
+                    options=folder_paths.get_filename_list("checkpoints"),
+                ),
+                io.Combo.Input(
+                    "device",
+                    options=["default", "cpu"],
+                    advanced=True,
+                )
+            ],
+            outputs=[io.Clip.Output()],
+        )
+
+    @classmethod
+    def execute(cls, text_encoder, ckpt_name, device="default"):
+        clip_type = comfy.sd.CLIPType.LTXV
+
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", text_encoder)
+        clip_path2 = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
+
+        model_options = {}
+        if device == "cpu":
+            model_options["load_device"] = model_options["offload_device"] = torch.device("cpu")
+
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options)
+        return io.NodeOutput(clip)
+
+
+class LTXVAudioExtension(ComfyExtension):
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LTXVAudioVAELoader,
+            LTXVAudioVAEEncode,
+            LTXVAudioVAEDecode,
+            LTXVEmptyLatentAudio,
+            LTXAVTextEncoderLoader,
+        ]
+
+
+async def comfy_entrypoint() -> ComfyExtension:
+    return LTXVAudioExtension()
diff --git a/comfy_extras/nodes_lt_upsampler.py b/comfy_extras/nodes_lt_upsampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..617dde74f111ea966e710ea6a1279985dd57d0c3
--- /dev/null
+++ b/comfy_extras/nodes_lt_upsampler.py
@@ -0,0 +1,75 @@
+from comfy import model_management
+from comfy_api.latest import ComfyExtension, IO
+from typing_extensions import override
+import math
+
+
+class LTXVLatentUpsampler(IO.ComfyNode):
+    """
+    Upsamples a video latent by a factor of 2.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LTXVLatentUpsampler",
+            category="model/latent/ltxv",
+            is_experimental=True,
+            inputs=[
+                IO.Latent.Input("samples"),
+                IO.LatentUpscaleModel.Input("upscale_model"),
+                IO.Vae.Input("vae"),
+            ],
+            outputs=[
+                IO.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples, upscale_model, vae) -> IO.NodeOutput:
+        """
+        Upsample the input latent using the provided model.
+
+        Args:
+            samples (dict): Input latent samples
+            upscale_model (LatentUpsampler): Loaded upscale model
+            vae: VAE model for normalization
+
+        Returns:
+            tuple: Tuple containing the upsampled latent
+        """
+        device = upscale_model.load_device
+        model = upscale_model.model
+        model_dtype = upscale_model.model_dtype()
+        latents = samples["samples"]
+        input_dtype = latents.dtype
+
+        memory_required = math.prod(latents.shape) * 3000.0  # TODO: more accurate
+        model_management.load_models_gpu([upscale_model], memory_required=memory_required)
+
+        latents = latents.to(dtype=model_dtype, device=device)
+
+        """Upsample latents without tiling."""
+        latents = vae.first_stage_model.per_channel_statistics.un_normalize(latents)
+        upsampled_latents = model(latents)
+
+        upsampled_latents = vae.first_stage_model.per_channel_statistics.normalize(
+            upsampled_latents
+        )
+        upsampled_latents = upsampled_latents.to(dtype=input_dtype, device=model_management.intermediate_device())
+        return_dict = samples.copy()
+        return_dict["samples"] = upsampled_latents
+        return_dict.pop("noise_mask", None)
+        return IO.NodeOutput(return_dict)
+
+    upsample_latent = execute  # TODO: remove
+
+
+class LTXVLatentUpsamplerExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [LTXVLatentUpsampler]
+
+
+async def comfy_entrypoint() -> LTXVLatentUpsamplerExtension:
+    return LTXVLatentUpsamplerExtension()
diff --git a/comfy_extras/nodes_lumina2.py b/comfy_extras/nodes_lumina2.py
new file mode 100644
index 0000000000000000000000000000000000000000..00fb406dd56d18f8554a3bef3dc0fb47ed2d7cea
--- /dev/null
+++ b/comfy_extras/nodes_lumina2.py
@@ -0,0 +1,128 @@
+from typing_extensions import override
+import torch
+
+from comfy_api.latest import ComfyExtension, io
+
+
+class RenormCFG(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="RenormCFG",
+            category="model/patch",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01, advanced=True),
+                io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, cfg_trunc, renorm_cfg) -> io.NodeOutput:
+        def renorm_cfg_func(args):
+            cond_denoised = args["cond_denoised"]
+            uncond_denoised = args["uncond_denoised"]
+            cond_scale = args["cond_scale"]
+            timestep = args["timestep"]
+            x_orig = args["input"]
+            in_channels = model.model.diffusion_model.in_channels
+
+            if timestep[0] < cfg_trunc:
+                cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels]
+                cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:]
+                half_eps = uncond_eps + cond_scale * (cond_eps - uncond_eps)
+                half_rest = cond_rest
+
+                if float(renorm_cfg) > 0.0:
+                    ori_pos_norm = torch.linalg.vector_norm(cond_eps
+                            , dim=tuple(range(1, len(cond_eps.shape))), keepdim=True
+                    )
+                    max_new_norm = ori_pos_norm * float(renorm_cfg)
+                    new_pos_norm = torch.linalg.vector_norm(
+                            half_eps, dim=tuple(range(1, len(half_eps.shape))), keepdim=True
+                        )
+                    if new_pos_norm >= max_new_norm:
+                        half_eps = half_eps * (max_new_norm / new_pos_norm)
+            else:
+                cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels]
+                cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:]
+                half_eps = cond_eps
+                half_rest = cond_rest
+
+            cfg_result = torch.cat([half_eps, half_rest], dim=1)
+
+            # cfg_result = uncond_denoised + (cond_denoised - uncond_denoised) * cond_scale
+
+            return x_orig - cfg_result
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(renorm_cfg_func)
+        return io.NodeOutput(m)
+
+
+class CLIPTextEncodeLumina2(io.ComfyNode):
+    SYSTEM_PROMPT = {
+        "superior": "You are an assistant designed to generate superior images with the superior "\
+            "degree of image-text alignment based on textual prompts or user prompts.",
+        "alignment": "You are an assistant designed to generate high-quality images with the "\
+            "highest degree of image-text alignment based on textual prompts."
+    }
+    SYSTEM_PROMPT_TIP = "Lumina2 provide two types of system prompts:" \
+        "Superior: You are an assistant designed to generate superior images with the superior "\
+        "degree of image-text alignment based on textual prompts or user prompts. "\
+        "Alignment: You are an assistant designed to generate high-quality images with the highest "\
+        "degree of image-text alignment based on textual prompts."
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeLumina2",
+            search_aliases=["lumina prompt"],
+            display_name="CLIP Text Encode (Lumina 2)",
+            category="model/conditioning/lumina",
+            description="Encodes a system prompt and a user prompt using a CLIP model into an embedding "
+                        "that can be used to guide the diffusion model towards generating specific images.",
+            inputs=[
+                io.Combo.Input(
+                    "system_prompt",
+                    options=list(cls.SYSTEM_PROMPT.keys()),
+                    tooltip=cls.SYSTEM_PROMPT_TIP,
+                ),
+                io.String.Input(
+                    "user_prompt",
+                    multiline=True,
+                    dynamic_prompts=True,
+                    tooltip="The text to be encoded.",
+                ),
+                io.Clip.Input("clip", tooltip="The CLIP model used for encoding the text."),
+            ],
+            outputs=[
+                io.Conditioning.Output(
+                    tooltip="A conditioning containing the embedded text used to guide the diffusion model.",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, user_prompt, system_prompt) -> io.NodeOutput:
+        if clip is None:
+            raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
+        system_prompt = cls.SYSTEM_PROMPT[system_prompt]
+        prompt = f'{system_prompt} <Prompt Start> {user_prompt}'
+        tokens = clip.tokenize(prompt)
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
+
+
+class Lumina2Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeLumina2,
+            RenormCFG,
+        ]
+
+
+async def comfy_entrypoint() -> Lumina2Extension:
+    return Lumina2Extension()
diff --git a/comfy_extras/nodes_mage.py b/comfy_extras/nodes_mage.py
new file mode 100644
index 0000000000000000000000000000000000000000..e02e66c78f0e6ef77e71d952c0fa7f662577c3f4
--- /dev/null
+++ b/comfy_extras/nodes_mage.py
@@ -0,0 +1,103 @@
+from typing_extensions import override
+
+import comfy.utils
+import node_helpers
+import torch
+import comfy.model_management
+from comfy_api.latest import ComfyExtension, io
+
+
+class TextEncodeMageFlowEdit(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeMageFlowEdit",
+            category="model/conditioning/mage",
+            description="Encode an edit instruction with one or more reference images for Mage-Flow-Edit. Reference latents are resized to the output resolution (width/height, or the first image's size when 0). Use the latent output for sampling so the sizes always match.",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.String.Input("negative_prompt", multiline=True, dynamic_prompts=True, advanced=True),
+                io.Vae.Input("vae", optional=True),
+                io.Autogrow.Input(
+                    "images",
+                    template=io.Autogrow.TemplateNames(
+                        io.Image.Input("image"),
+                        names=[f"image_{i}" for i in range(1, 17)],
+                        min=0,
+                    ),
+                    tooltip="Reference image(s) to edit. All references are resized to the output resolution before encoding.",
+                ),
+                io.Int.Input("width", default=0, min=0, max=8192, step=16, tooltip="Output width. 0 = use the first reference image's size."),
+                io.Int.Input("height", default=0, min=0, max=8192, step=16, tooltip="Output height. 0 = use the first reference image's size."),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, negative_prompt="", vae=None, images: io.Autogrow.Type = None, width=0, height=0, batch_size=1) -> io.NodeOutput:
+        ref_latents = []
+        images = images or {}
+        images = [images[name] for name in sorted(images, key=lambda n: int(n.rsplit("_", 1)[-1])) if images[name] is not None]
+        images_vl = []
+
+        # Output resolution: explicit width/height, else the primary reference's own size, floored to /16.
+        # Each dimension falls back independently so a 0 on one axis keeps an explicit value on the other.
+        if width == 0 or height == 0:
+            if len(images) > 0:
+                ref_h, ref_w = images[0].shape[1], images[0].shape[2]
+            else:
+                ref_h, ref_w = 1024, 1024
+            height = height or ref_h
+            width = width or ref_w
+        width = max(16, (width // 16) * 16)
+        height = max(16, (height // 16) * 16)
+
+        for image in images:
+            samples = image.movedim(-1, 1)
+
+            # VL conditioning copy: cap the long edge at 384 (training preprocessing).
+            long_edge = max(samples.shape[3], samples.shape[2])
+            if long_edge > 384:
+                scale_by = 384 / long_edge
+                s = comfy.utils.common_upscale(samples, max(1, round(samples.shape[3] * scale_by)), max(1, round(samples.shape[2] * scale_by)), "bicubic", "disabled")
+                images_vl.append(s.movedim(1, -1))
+            else:
+                images_vl.append(image)
+
+            if vae is not None:
+                # All references are resized to the output resolution before encoding, because Mage's RoPE aligns reference and target content by position
+                if samples.shape[3] != width or samples.shape[2] != height:
+                    s = comfy.utils.common_upscale(samples, width, height, "bicubic", "disabled")
+                else:
+                    s = samples
+                ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3]))
+
+        # Negative branch keeps the same reference images (VL tokens + ref latents), only the instruction differs.
+        positive = clip.encode_from_tokens_scheduled(clip.tokenize(prompt, images=images_vl))
+        negative = clip.encode_from_tokens_scheduled(clip.tokenize(negative_prompt if negative_prompt else " ", images=images_vl))
+
+        if len(ref_latents) > 0:
+            positive = node_helpers.conditioning_set_values(positive, {"reference_latents": ref_latents}, append=True)
+            negative = node_helpers.conditioning_set_values(negative, {"reference_latents": ref_latents}, append=True)
+
+        latent = torch.zeros([batch_size, 128, height // 16, width // 16], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput(positive, negative, {"samples": latent})
+
+
+class MageExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TextEncodeMageFlowEdit,
+        ]
+
+
+async def comfy_entrypoint() -> MageExtension:
+    return MageExtension()
diff --git a/comfy_extras/nodes_mahiro.py b/comfy_extras/nodes_mahiro.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5556fee0dea34ca749735a7e6feb67ac3607285
--- /dev/null
+++ b/comfy_extras/nodes_mahiro.py
@@ -0,0 +1,65 @@
+from typing_extensions import override
+import torch
+import torch.nn.functional as F
+
+from comfy_api.latest import ComfyExtension, io
+
+
+class Mahiro(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Mahiro",
+            display_name="Positive-Biased Guidance",
+            category="experimental",
+            description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
+            inputs=[
+                io.Model.Input("model"),
+            ],
+            outputs=[
+                io.Model.Output(display_name="patched_model"),
+            ],
+            is_experimental=True,
+            search_aliases=[
+                "mahiro",
+                "mahiro cfg",
+                "similarity-adaptive guidance",
+                "positive-biased cfg",
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model) -> io.NodeOutput:
+        m = model.clone()
+
+        def mahiro_normd(args):
+            scale: float = args["cond_scale"]
+            cond_p: torch.Tensor = args["cond_denoised"]
+            uncond_p: torch.Tensor = args["uncond_denoised"]
+            # naive leap
+            leap = cond_p * scale
+            # sim with uncond leap
+            u_leap = uncond_p * scale
+            cfg = args["denoised"]
+            merge = (leap + cfg) / 2
+            normu = torch.sqrt(u_leap.abs()) * u_leap.sign()
+            normm = torch.sqrt(merge.abs()) * merge.sign()
+            sim = F.cosine_similarity(normu, normm).mean()
+            simsc = 2 * (sim + 1)
+            wm = (simsc * cfg + (4 - simsc) * leap) / 4
+            return wm
+
+        m.set_model_sampler_post_cfg_function(mahiro_normd)
+        return io.NodeOutput(m)
+
+
+class MahiroExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            Mahiro,
+        ]
+
+
+async def comfy_entrypoint() -> MahiroExtension:
+    return MahiroExtension()
diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..673e6cb53625c81cf77da1225a0bc19397fd29df
--- /dev/null
+++ b/comfy_extras/nodes_mask.py
@@ -0,0 +1,457 @@
+import numpy as np
+import scipy.ndimage
+import torch
+import comfy.utils
+import comfy.model_management
+import node_helpers
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO, UI
+
+import nodes
+
+def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
+    source = source.to(destination.device)
+    if resize_source:
+        source = torch.nn.functional.interpolate(source, size=(destination.shape[-2], destination.shape[-1]), mode="bilinear")
+
+    source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])
+
+    x = max(-source.shape[-1] * multiplier, min(x, destination.shape[-1] * multiplier))
+    y = max(-source.shape[-2] * multiplier, min(y, destination.shape[-2] * multiplier))
+
+    left, top = (x // multiplier, y // multiplier)
+    right, bottom = (left + source.shape[-1], top + source.shape[-2],)
+
+    if mask is None:
+        mask = torch.ones_like(source)
+    else:
+        mask = mask.to(destination.device, copy=True)
+        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[-2], source.shape[-1]), mode="bilinear")
+        mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])
+
+    # calculate the bounds of the source that will be overlapping the destination
+    # this prevents the source trying to overwrite latent pixels that are out of bounds
+    # of the destination
+    visible_width, visible_height = (destination.shape[-1] - left + min(0, x), destination.shape[-2] - top + min(0, y),)
+
+    mask = mask[:, :, :visible_height, :visible_width]
+    if mask.ndim < source.ndim:
+        mask = mask.unsqueeze(1)
+
+    inverse_mask = torch.ones_like(mask) - mask
+
+    source_portion = mask * source[..., :visible_height, :visible_width]
+    destination_portion = inverse_mask  * destination[..., top:bottom, left:right]
+
+    destination[..., top:bottom, left:right] = source_portion + destination_portion
+    return destination
+
+
+class LatentCompositeMasked(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LatentCompositeMasked",
+            search_aliases=["overlay latent", "layer latent", "paste latent", "inpaint latent"],
+            display_name="Latent Composite Masked",
+            category="model/latent",
+            inputs=[
+                IO.Latent.Input("destination"),
+                IO.Latent.Input("source"),
+                IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
+                IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
+                IO.Boolean.Input("resize_source", default=False),
+                IO.Mask.Input("mask", optional=True),
+            ],
+            outputs=[IO.Latent.Output()],
+        )
+
+    @classmethod
+    def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
+        output = destination.copy()
+        destination = destination["samples"].clone()
+        source = source["samples"]
+        output["samples"] = composite(destination, source, x, y, mask, 8, resize_source)
+        return IO.NodeOutput(output)
+
+    composite = execute  # TODO: remove
+
+
+class ImageCompositeMasked(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageCompositeMasked",
+            search_aliases=["overlay", "layer", "paste image", "images composition"],
+            display_name="Image Composite Masked",
+            category="image/compositing",
+            inputs=[
+                IO.Image.Input("destination"),
+                IO.Image.Input("source"),
+                IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Boolean.Input("resize_source", default=False),
+                IO.Mask.Input("mask", optional=True),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
+        destination, source = node_helpers.image_alpha_fix(destination, source)
+        destination = destination.clone().movedim(-1, 1)
+        output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
+        return IO.NodeOutput(output)
+
+    composite = execute  # TODO: remove
+
+
+class MaskToImage(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MaskToImage",
+            search_aliases=["convert mask"],
+            display_name="Convert Mask to Image",
+            category="image/mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+            ],
+            outputs=[IO.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, mask) -> IO.NodeOutput:
+        result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
+        return IO.NodeOutput(result)
+
+    mask_to_image = execute  # TODO: remove
+
+
+class ImageToMask(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageToMask",
+            search_aliases=["extract channel", "channel to mask"],
+            display_name="Convert Image to Mask",
+            category="image/mask",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Combo.Input("channel", options=["red", "green", "blue", "alpha"]),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, channel) -> IO.NodeOutput:
+        channels = ["red", "green", "blue", "alpha"]
+        mask = image[:, :, :, channels.index(channel)]
+        return IO.NodeOutput(mask)
+
+    image_to_mask = execute  # TODO: remove
+
+
+class ImageColorToMask(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageColorToMask",
+            search_aliases=["color keying", "chroma key"],
+            display_name="Convert Image Color to Mask",
+            category="image/mask",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Int.Input("color", default=0, min=0, max=0xFFFFFF, step=1, display_mode=IO.NumberDisplay.number),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image, color) -> IO.NodeOutput:
+        temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
+        temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2]
+        mask = torch.where(temp == color, 1.0, 0).float()
+        return IO.NodeOutput(mask)
+
+    image_to_mask = execute  # TODO: remove
+
+
+class SolidMask(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SolidMask",
+            display_name="Create Solid Mask",
+            category="image/mask",
+            inputs=[
+                IO.Float.Input("value", default=1.0, min=0.0, max=1.0, step=0.01),
+                IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, value, width, height) -> IO.NodeOutput:
+        out = torch.full((1, height, width), value, dtype=torch.float32, device=comfy.model_management.intermediate_device())
+        return IO.NodeOutput(out)
+
+    solid = execute  # TODO: remove
+
+
+class InvertMask(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="InvertMask",
+            search_aliases=["reverse mask", "flip mask"],
+            display_name="Invert Mask",
+            category="image/mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, mask) -> IO.NodeOutput:
+        out = 1.0 - mask
+        return IO.NodeOutput(out)
+
+    invert = execute  # TODO: remove
+
+
+class CropMask(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="CropMask",
+            search_aliases=["cut mask", "extract mask region", "mask slice"],
+            display_name="Crop Mask",
+            category="image/mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+                IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, mask, x, y, width, height) -> IO.NodeOutput:
+        mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
+        out = mask[:, y:y + height, x:x + width]
+        return IO.NodeOutput(out)
+
+    crop = execute  # TODO: remove
+
+
+class MaskComposite(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MaskComposite",
+            search_aliases=["combine masks", "blend masks", "layer masks", "masks composition"],
+            display_name="Combine Masks",
+            category="image/mask",
+            inputs=[
+                IO.Mask.Input("destination"),
+                IO.Mask.Input("source"),
+                IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Combo.Input("operation", options=["multiply", "add", "subtract", "and", "or", "xor"]),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, destination, source, x, y, operation) -> IO.NodeOutput:
+        output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
+        source = source.reshape((-1, source.shape[-2], source.shape[-1]))
+        source = source.to(output.device)
+
+        left, top = (x, y,)
+        right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
+        visible_width, visible_height = (right - left, bottom - top,)
+
+        source_portion = source[:, :visible_height, :visible_width]
+        destination_portion = output[:, top:bottom, left:right]
+
+        if operation == "multiply":
+            output[:, top:bottom, left:right] = destination_portion * source_portion
+        elif operation == "add":
+            output[:, top:bottom, left:right] = destination_portion + source_portion
+        elif operation == "subtract":
+            output[:, top:bottom, left:right] = destination_portion - source_portion
+        elif operation == "and":
+            output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float()
+        elif operation == "or":
+            output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float()
+        elif operation == "xor":
+            output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float()
+
+        output = torch.clamp(output, 0.0, 1.0)
+
+        return IO.NodeOutput(output)
+
+    combine = execute  # TODO: remove
+
+
+class FeatherMask(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="FeatherMask",
+            search_aliases=["soft edge mask", "blur mask edges", "gradient mask edge"],
+            display_name="Feather Mask",
+            category="image/mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+                IO.Int.Input("left", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("top", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("right", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("bottom", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, mask, left, top, right, bottom) -> IO.NodeOutput:
+        output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone()
+
+        left = min(left, output.shape[-1])
+        right = min(right, output.shape[-1])
+        top = min(top, output.shape[-2])
+        bottom = min(bottom, output.shape[-2])
+
+        for x in range(left):
+            feather_rate = (x + 1.0) / left
+            output[:, :, x] *= feather_rate
+
+        for x in range(right):
+            feather_rate = (x + 1) / right
+            output[:, :, -(x + 1)] *= feather_rate
+
+        for y in range(top):
+            feather_rate = (y + 1) / top
+            output[:, y, :] *= feather_rate
+
+        for y in range(bottom):
+            feather_rate = (y + 1) / bottom
+            output[:, -(y + 1), :] *= feather_rate
+
+        return IO.NodeOutput(output)
+
+    feather = execute  # TODO: remove
+
+
+class GrowMask(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrowMask",
+            search_aliases=["expand mask", "shrink mask"],
+            display_name="Grow Mask",
+            category="image/mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+                IO.Int.Input("expand", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Boolean.Input("tapered_corners", default=True, advanced=True),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, mask, expand, tapered_corners) -> IO.NodeOutput:
+        c = 0 if tapered_corners else 1
+        kernel = np.array([[c, 1, c],
+                           [1, 1, 1],
+                           [c, 1, c]])
+        mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
+        out = []
+        for m in mask:
+            output = m.numpy()
+            for _ in range(abs(expand)):
+                if expand < 0:
+                    output = scipy.ndimage.grey_erosion(output, footprint=kernel)
+                else:
+                    output = scipy.ndimage.grey_dilation(output, footprint=kernel)
+            output = torch.from_numpy(output)
+            out.append(output)
+        return IO.NodeOutput(torch.stack(out, dim=0))
+
+    expand_mask = execute  # TODO: remove
+
+class ThresholdMask(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ThresholdMask",
+            search_aliases=["binary mask"],
+            display_name="Threshold Mask",
+            category="image/mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+                IO.Float.Input("value", default=0.5, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, mask, value) -> IO.NodeOutput:
+        mask = (mask > value).float()
+        return IO.NodeOutput(mask)
+
+    image_to_mask = execute  # TODO: remove
+
+
+# Mask Preview - original implement from
+# https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
+# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes
+class MaskPreview(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MaskPreview",
+            search_aliases=["show mask", "view mask", "inspect mask", "debug mask"],
+            display_name="Preview Mask",
+            category="image/mask",
+            description="Preview the masks without saving them to the ComfyUI output directory.",
+            inputs=[
+                IO.Mask.Input("mask"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+            outputs=[IO.Mask.Output(display_name="mask")]
+        )
+
+    @classmethod
+    def execute(cls, mask, filename_prefix="ComfyUI") -> IO.NodeOutput:
+        return IO.NodeOutput(mask, ui=UI.PreviewMask(mask))
+
+
+class MaskExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            LatentCompositeMasked,
+            ImageCompositeMasked,
+            MaskToImage,
+            ImageToMask,
+            ImageColorToMask,
+            SolidMask,
+            InvertMask,
+            CropMask,
+            MaskComposite,
+            FeatherMask,
+            GrowMask,
+            ThresholdMask,
+            MaskPreview,
+        ]
+
+
+async def comfy_entrypoint() -> MaskExtension:
+    return MaskExtension()
diff --git a/comfy_extras/nodes_math.py b/comfy_extras/nodes_math.py
new file mode 100644
index 0000000000000000000000000000000000000000..32cfbda16421602f6c5754bde3ddbaf3b21af227
--- /dev/null
+++ b/comfy_extras/nodes_math.py
@@ -0,0 +1,126 @@
+"""Math expression node using simpleeval for safe evaluation.
+
+Provides a ComfyMathExpression node that evaluates math expressions
+against dynamically-grown numeric inputs.
+"""
+
+
+import math
+import string
+
+from simpleeval import simple_eval
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
+
+MAX_EXPONENT = 4000
+
+
+def _variadic_sum(*args):
+    """Support both sum(values) and sum(a, b, c)."""
+    if len(args) == 1 and hasattr(args[0], "__iter__"):
+        return sum(args[0])
+    return sum(args)
+
+
+def _safe_pow(base, exp):
+    """Wrap pow() with an exponent cap to prevent DoS via huge exponents.
+
+    The ** operator is already guarded by simpleeval's safe_power, but
+    pow() as a callable bypasses that guard.
+    """
+    if abs(exp) > MAX_EXPONENT:
+        raise ValueError(f"Exponent {exp} exceeds maximum allowed ({MAX_EXPONENT})")
+    return pow(base, exp)
+
+
+MATH_FUNCTIONS = {
+    "sum": _variadic_sum,
+    "min": min,
+    "max": max,
+    "abs": abs,
+    "round": round,
+    "pow": _safe_pow,
+    "sqrt": math.sqrt,
+    "ceil": math.ceil,
+    "floor": math.floor,
+    "log": math.log,
+    "log2": math.log2,
+    "log10": math.log10,
+    "sin": math.sin,
+    "cos": math.cos,
+    "tan": math.tan,
+    "int": int,
+    "float": float,
+}
+
+
+class MathExpressionNode(io.ComfyNode):
+    """Evaluates a math expression against dynamically-grown inputs."""
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        autogrow = io.Autogrow.TemplateNames(
+            input=io.MultiType.Input("value", [io.Float, io.Int, io.Boolean]),
+            names=list(string.ascii_lowercase),
+            min=1,
+        )
+        return io.Schema(
+            node_id="ComfyMathExpression",
+            display_name="Math Expression",
+            category="utilities",
+            search_aliases=[
+                "expression", "formula", "calculate", "calculator",
+                "eval", "math",
+            ],
+            inputs=[
+                io.String.Input("expression", default="a + b", multiline=True),
+                io.Autogrow.Input("values", template=autogrow),
+            ],
+            outputs=[
+                io.Float.Output(display_name="FLOAT"),
+                io.Int.Output(display_name="INT"),
+                io.Boolean.Output(display_name="BOOL"),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls, expression: str, values: io.Autogrow.Type
+    ) -> io.NodeOutput:
+        if not expression.strip():
+            raise ValueError("Expression cannot be empty.")
+
+        context: dict = dict(values)
+        context["values"] = list(values.values())
+
+        result = simple_eval(expression, names=context, functions=MATH_FUNCTIONS)
+        # bool check must come first because bool is a subclass of int in Python
+        if not isinstance(result, (int, float)):
+            raise ValueError(
+                f"Math Expression '{expression}' must evaluate to a numeric result, "
+                f"got {type(result).__name__}: {result!r}"
+            )
+        try:
+            float_result = float(result)
+        except OverflowError:
+            raise ValueError(
+                f"Math Expression '{expression}' produced a result too large to "
+                f"represent as a float: {result}"
+            ) from None
+        if not math.isfinite(float_result):
+            raise ValueError(
+                f"Math Expression '{expression}' produced a non-finite result: {result}"
+            )
+        return io.NodeOutput(float_result, int(result), bool(result))
+
+
+class MathExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [MathExpressionNode]
+
+
+async def comfy_entrypoint() -> MathExtension:
+    return MathExtension()
diff --git a/comfy_extras/nodes_mediapipe.py b/comfy_extras/nodes_mediapipe.py
new file mode 100644
index 0000000000000000000000000000000000000000..5dd28752901358a32740ae661155d821e45cda54
--- /dev/null
+++ b/comfy_extras/nodes_mediapipe.py
@@ -0,0 +1,508 @@
+"""ComfyUI nodes for the pure-PyTorch MediaPipe Face Landmarker port.
+
+Custom IO types:
+  FACE_LANDMARKER  — FaceLandmarkerModel wrapper (ModelPatcher inside)
+  FACE_LANDMARKS   — {"frames": List[List[face_dict]], "image_size": (H, W),
+                      "connection_sets": dict[str, frozenset[(int, int)]]}
+                     face_dict: bbox_xyxy, blendshapes, landmarks_xy,
+                                landmarks_3d, presence, score, transformation_matrix
+
+MediaPipeFaceLandmarker also emits the core BOUNDING_BOX type — pair with DrawBBoxes.
+"""
+
+
+import numpy as np
+import torch
+from PIL import Image, ImageColor, ImageDraw
+from tqdm.auto import tqdm
+from typing_extensions import override
+
+import comfy.model_management
+import comfy.model_patcher
+import comfy.utils
+import folder_paths
+from comfy_api.latest import ComfyExtension, io
+
+from comfy_extras.mediapipe.face_landmarker import FaceLandmarker
+from comfy_extras.mediapipe.face_geometry import transformation_matrix_from_detection
+
+
+FaceDetectionType = io.Custom("FACE_DETECTION_MODEL")
+FaceLandmarksType = io.Custom("FACE_LANDMARKS")
+
+_CANONICAL_KEYS = ("canonical_vertices", "procrustes_indices", "procrustes_weights")
+_CONTOUR_PARTS = ("face_oval", "left_eye", "right_eye", "left_eyebrow", "right_eyebrow", "lips")
+
+
+class FaceLandmarkerModel:
+    """Loaded FaceLandmarker variants + ModelPatcher per variant.
+
+    Safetensors layout: `detector_short.*` / `detector_full.*` plus shared
+    `mesh.*`, `blendshapes.*`, `canonical_*`, and `topology.*`.
+    PReLU forces plain-nn / fp32 (manual_cast strands buffers across devices).
+    """
+
+    def __init__(self, state_dict: dict):
+        self.load_device = comfy.model_management.text_encoder_device()
+        offload_device = comfy.model_management.text_encoder_offload_device()
+        self.dtype = torch.float32
+
+        # FACEMESH_* connection sets, embedded as int32 (N, 2) under topology.*.
+        base: dict[str, frozenset] = {}
+        for k in [k for k in state_dict if k.startswith("topology.")]:
+            base[k[len("topology."):]] = frozenset(map(tuple, state_dict.pop(k).tolist()))
+        base["contours"] = frozenset().union(*(base[p] for p in _CONTOUR_PARTS))
+        base["all"] = base["contours"] | base["irises"] | base["nose"]
+
+        self.connection_sets: dict[str, frozenset] = base
+        self.canonical_data: dict[str, np.ndarray] = {k: state_dict.pop(k).numpy() for k in _CANONICAL_KEYS}
+
+        shared = {k: v for k, v in state_dict.items() if k.startswith(("mesh.", "blendshapes."))}
+
+        self.models: dict[str, FaceLandmarker] = {}
+        self.patchers: dict[str, comfy.model_patcher.ModelPatcher] = {}
+        for variant in ("short", "full"):
+            prefix = f"detector_{variant}."
+            sub = dict(shared)
+            sub.update({f"detector.{k[len(prefix):]}": v for k, v in state_dict.items() if k.startswith(prefix)})
+            fl = FaceLandmarker(device=offload_device, dtype=self.dtype, operations=None, detector_variant=variant).eval()
+            fl.load_state_dict(sub, strict=False)
+
+            self.models[variant] = fl
+            self.patchers[variant] = comfy.model_patcher.CoreModelPatcher(
+                fl, load_device=self.load_device, offload_device=offload_device,
+                size=comfy.model_management.module_size(fl),
+            )
+
+    def detect_batch(self, images, num_faces: int, score_thresh: float, variant: str):
+        comfy.model_management.load_model_gpu(self.patchers[variant])
+        return self.models[variant].detect_batch(images, num_faces=num_faces, score_thresh=score_thresh)
+
+
+def _image_to_uint8(image: torch.Tensor) -> np.ndarray:
+    return image[..., :3].mul(255.0).add_(0.5).clamp_(0, 255).to(torch.uint8).cpu().numpy()
+
+
+def _parse_color(color: str) -> tuple[int, int, int]:
+    try:
+        return ImageColor.getrgb(color)[:3]
+    except ValueError:
+        return (0, 255, 0)
+
+
+def _copy_face(face: dict) -> dict:
+    """Shallow copy of a face_dict with array-fields cloned so callers can mutate."""
+    return {
+        "bbox_xyxy":    face["bbox_xyxy"].copy(),
+        "blendshapes":  dict(face["blendshapes"]),
+        "landmarks_xy": face["landmarks_xy"].copy(),
+        "landmarks_3d": face["landmarks_3d"].copy(),
+        "presence":     face["presence"],
+        "score":        face["score"],
+    }
+
+
+def _lerp_face(a: dict, b: dict, t: float) -> dict:
+    return {
+        "bbox_xyxy":    (1 - t) * a["bbox_xyxy"]    + t * b["bbox_xyxy"],
+        "blendshapes":  {k: (1 - t) * a["blendshapes"][k] + t * b["blendshapes"][k] for k in a["blendshapes"]},
+        "landmarks_xy": (1 - t) * a["landmarks_xy"] + t * b["landmarks_xy"],
+        "landmarks_3d": (1 - t) * a["landmarks_3d"] + t * b["landmarks_3d"],
+        "presence":     (1 - t) * a["presence"] + t * b["presence"],
+        "score":        (1 - t) * a["score"]    + t * b["score"],
+    }
+
+
+def _match_faces(a: list[dict], b: list[dict]) -> list[tuple[int, int]]:
+    """Greedy nearest-neighbour pairing of faces between two frames by bbox
+    centre distance. Unmatched (when counts differ) are dropped."""
+    if not a or not b:
+        return []
+    centers_a = np.array([(0.5 * (f["bbox_xyxy"][0] + f["bbox_xyxy"][2]),
+                           0.5 * (f["bbox_xyxy"][1] + f["bbox_xyxy"][3])) for f in a])
+    centers_b = np.array([(0.5 * (f["bbox_xyxy"][0] + f["bbox_xyxy"][2]),
+                           0.5 * (f["bbox_xyxy"][1] + f["bbox_xyxy"][3])) for f in b])
+    dists = np.linalg.norm(centers_a[:, None] - centers_b[None], axis=-1)
+    pairs: list[tuple[int, int]] = []
+    used_a: set[int] = set()
+    used_b: set[int] = set()
+    candidates = sorted((dists[ia, ib], ia, ib) for ia in range(len(a)) for ib in range(len(b)))
+    for _, ia, ib in candidates:
+        if ia in used_a or ib in used_b:
+            continue
+        pairs.append((ia, ib))
+        used_a.add(ia)
+        used_b.add(ib)
+    return pairs
+
+
+def _fill_missing_frames(frames: list[list[dict]], mode: str) -> None:
+    """In-place fill empty frame slots from neighbouring detections. Multi-face
+    aware: pairs faces across bracketing frames by greedy bbox-centre NN.
+    When counts differ, unmatched faces are dropped from the synthesised frame."""
+    if mode == "empty":
+        return
+    valid = [i for i, fr in enumerate(frames) if fr]
+    if not valid:
+        return  # nothing to fill from
+    if mode == "previous":
+        last: list[dict] = []
+        for i, fr in enumerate(frames):
+            if fr:
+                last = fr
+            elif last:
+                frames[i] = [_copy_face(f) for f in last]
+        return
+    # interpolate: lerp between bracketing valid frames; clamp at ends.
+    for i in range(len(frames)):
+        if frames[i]:
+            continue
+        prev_i = max((v for v in valid if v < i), default=None)
+        next_i = min((v for v in valid if v > i), default=None)
+        if prev_i is None:
+            frames[i] = [_copy_face(f) for f in frames[next_i]]
+        elif next_i is None:
+            frames[i] = [_copy_face(f) for f in frames[prev_i]]
+        else:
+            t = (i - prev_i) / (next_i - prev_i)
+            pairs = _match_faces(frames[prev_i], frames[next_i])
+            frames[i] = [_lerp_face(frames[prev_i][a], frames[next_i][b], t) for a, b in pairs]
+
+
+def _ordered_rings(edges: frozenset[tuple[int, int]]) -> list[list[int]]:
+    """Walk an unordered edge set into one or more closed-loop vertex rings
+    (handles multi-loop sets like FACEMESH_LIPS: outer + inner)."""
+    adj: dict[int, set[int]] = {}
+    for a, b in edges:
+        adj.setdefault(a, set()).add(b)
+        adj.setdefault(b, set()).add(a)
+    visited: set[int] = set()
+    rings: list[list[int]] = []
+    for start in adj:
+        if start in visited:
+            continue
+        ring = [start]
+        visited.add(start)
+        prev, cur = -1, start
+        while True:
+            nxt = next((v for v in adj[cur] if v != prev), None)
+            if nxt is None or nxt == start:
+                break
+            ring.append(nxt)
+            visited.add(nxt)
+            prev, cur = cur, nxt
+        rings.append(ring)
+    return rings
+
+
+class LoadMediaPipeFaceLandmarker(io.ComfyNode):
+    """Load MediaPipe Face Landmarker v2 weights. Contains both detector variants
+    (short / full), shared mesh, blendshapes, and canonical geometry."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadMediaPipeFaceLandmarker",
+            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection"],
+            display_name="Load Face Detection Model (MediaPipe)",
+            category="model/loaders",
+            inputs=[
+                io.Combo.Input("model_name", options=folder_paths.get_filename_list("detection"),
+                               tooltip="Face detection model from models/detection/."),
+            ],
+            outputs=[FaceDetectionType.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model_name) -> io.NodeOutput:
+        sd = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("detection", model_name), safe_load=True)
+        wrapper = FaceLandmarkerModel(sd)
+        return io.NodeOutput(wrapper)
+
+
+# Per-frame fallback modes for detection failures in a batch.
+_FALLBACK_MODES = ("empty", "previous", "interpolate")
+
+
+class MediaPipeFaceLandmarker(io.ComfyNode):
+    """BlazeFace → FaceMesh v2 → ARKit-52 blendshapes, batched across the
+    input. Also emits a BOUNDING_BOX list (landmark-extent bbox per face) —
+    pair with DrawBBoxes for detector-only viz or MediaPipeFaceMeshVisualize
+    for the mesh overlay."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="MediaPipeFaceLandmarker",
+            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection"],
+            display_name="Detect Face Landmarks (MediaPipe)",
+            category="image/detection",
+            description="Detects facial landmarks using MediaPipe model.",
+            inputs=[
+                FaceDetectionType.Input("face_detection_model"),
+                io.Image.Input("image"),
+                io.Combo.Input("detector_variant", options=["short", "full", "both"], default="short",
+                               tooltip="Face detector range. 'short' is tuned for close-up faces "
+                                       "(within ~2 m of the camera); 'full' covers farther / smaller "
+                                       "faces (up to ~5 m) but is slower. 'both' runs both detectors and "
+                                       "keeps whichever found more faces per frame (~2× detection cost)."),
+                io.Int.Input("num_faces", default=1, min=0, max=16, step=1,
+                             tooltip="Maximum faces to return per frame. 0 = no cap (return all detected)."),
+                io.Float.Input("min_confidence", default=0.5, min=0.0, max=1.0, step=0.01, advanced=True,
+                               tooltip="BlazeFace score threshold. Lower to catch small/occluded faces."),
+                io.Combo.Input("missing_frame_fallback", options=list(_FALLBACK_MODES), default="empty", advanced=True,
+                               tooltip="Per-frame behaviour when detection fails in a batch. "
+                                       "'empty' leaves the frame faceless. 'previous' copies the most recent successful "
+                                       "detection. 'interpolate' lerps landmarks/bbox/blendshapes between bracketing "
+                                       "successful frames. Multi-face: pairs faces across frames by greedy bbox-centre NN."),
+            ],
+            outputs=[
+                FaceLandmarksType.Output(display_name="face_landmarks"),
+                io.BoundingBox.Output("bboxes"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, face_detection_model, image, detector_variant, num_faces, min_confidence,
+                missing_frame_fallback) -> io.NodeOutput:
+        canonical = face_detection_model.canonical_data
+        img_np = _image_to_uint8(image)
+        B, H, W = img_np.shape[:3]
+        chunk = 16
+        is_both = detector_variant == "both"
+        total_work = 2 * B if is_both else B
+        pbar = comfy.utils.ProgressBar(total_work)
+
+        def _run(variant: str) -> list[list[dict]]:
+            res: list[list[dict]] = []
+            with tqdm(total=B, desc=f"MediaPipe Face Landmarker ({variant})") as tq:
+                for i in range(0, B, chunk):
+                    end = min(i + chunk, B)
+                    res.extend(face_detection_model.detect_batch(
+                        [img_np[bi] for bi in range(i, end)],
+                        num_faces=int(num_faces),
+                        score_thresh=float(min_confidence),
+                        variant=variant,
+                    ))
+                    pbar.update_absolute(min(pbar.current + (end - i), total_work))
+                    tq.update(end - i)
+            return res
+
+        if is_both:
+            short_res = _run("short")
+            full_res = _run("full")
+            # Per-frame keep whichever found more faces (tie → short).
+            frames: list[list[dict]] = [
+                short_res[bi] if len(short_res[bi]) >= len(full_res[bi]) else full_res[bi]
+                for bi in range(B)
+            ]
+        else:
+            frames = _run(detector_variant)
+        _fill_missing_frames(frames, missing_frame_fallback)
+        bboxes = []
+        for per_frame in frames:
+            per_bb = []
+            for f in per_frame:
+                f["transformation_matrix"] = transformation_matrix_from_detection(f, W, H, canonical)
+                x1, y1, x2, y2 = (float(v) for v in f["bbox_xyxy"])
+                per_bb.append({"x": x1, "y": y1, "width": x2 - x1, "height": y2 - y1, "label": "face", "score": float(f["score"])})
+            bboxes.append(per_bb)
+        return io.NodeOutput({"frames": frames, "image_size": (H, W),
+                              "connection_sets": face_detection_model.connection_sets}, bboxes)
+
+
+# Topology keys unioned by the 'all' connections preset (contour parts + irises + nose).
+_ALL_CONNECTION_PARTS: tuple[str, ...] = (*_CONTOUR_PARTS, "irises", "nose")
+_CUSTOM_FEATURES: tuple[tuple[str, bool], ...] = (
+    ("face_oval",     True),
+    ("lips",          True),
+    ("left_eye",      True),
+    ("right_eye",     True),
+    ("left_eyebrow",  True),
+    ("right_eyebrow", True),
+    ("irises",        True),
+    ("nose",          True),
+    ("tesselation",   False),
+)
+
+
+class MediaPipeFaceMeshVisualize(io.ComfyNode):
+    """Draw a FACEMESH_* subset over an image. Topology travels with the
+    FACE_LANDMARKS payload (set at detection time)."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="MediaPipeFaceMeshVisualize",
+            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection", "visualize"],
+            display_name="Visualize Face Landmarks (MediaPipe)",
+            category="image/detection",
+            description="Draws face landmarks mesh on the input image.",
+            inputs=[
+                FaceLandmarksType.Input("face_landmarks"),
+                io.Image.Input("image", optional=True, tooltip="If not connected, a black canvas will be used."),
+                io.DynamicCombo.Input(
+                    "connections",
+                    tooltip="'all' = oval+eyes+brows+lips+irises+nose. 'fill' = solid face_oval polygon (silhouette mask). 'custom' = toggle each feature individually (including 'tesselation', the full 2547-edge wireframe).",
+                    options=[
+                        io.DynamicCombo.Option("all", []),
+                        io.DynamicCombo.Option("fill", []),
+                        io.DynamicCombo.Option("custom", [
+                            io.Boolean.Input(feat, default=default,
+                                             tooltip=f"Draw the '{feat}' connection set.")
+                            for feat, default in _CUSTOM_FEATURES
+                        ]),
+                    ],
+                ),
+                io.Color.Input("color", default="#00ff00"),
+                io.Int.Input("thickness", default=1, min=0, max=8, step=1,
+                             tooltip="Edge line thickness in pixels. 0 disables edge drawing."),
+                io.Int.Input("point_size", default=2, min=0, max=16, step=1,
+                             tooltip="Landmark dot radius in pixels. 0 disables point drawing."),
+            ],
+            outputs=[io.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, face_landmarks, connections, color, thickness, point_size, image=None) -> io.NodeOutput:
+        sets = face_landmarks["connection_sets"]
+        sel = connections["connections"]
+        fill_rings: list[list[int]] | None = None
+        if sel == "fill":
+            fill_rings = _ordered_rings(sets["face_oval"])
+            edges = frozenset()
+        elif sel == "custom":
+            parts = [feat for feat, _ in _CUSTOM_FEATURES if connections.get(feat, False)]
+            edges = frozenset().union(*(sets[p] for p in parts))
+        else:  # "all"
+            edges = frozenset().union(*(sets[p] for p in _ALL_CONNECTION_PARTS))
+        rgb, thick, psize = _parse_color(color), int(thickness), int(point_size)
+        frames = face_landmarks["frames"]
+        if image is None:
+            H, W = face_landmarks["image_size"]
+            img_np = np.zeros((len(frames), H, W, 3), dtype=np.uint8)
+        else:
+            img_np = _image_to_uint8(image)
+        B = img_np.shape[0]
+        n_frames = len(frames)
+        pbar = comfy.utils.ProgressBar(B)
+        out = np.empty_like(img_np)
+        for bi in range(B):
+            faces = frames[bi] if bi < n_frames else []
+            out[bi] = _draw_mesh(img_np[bi], faces, edges, rgb, thick, psize, fill_rings)
+            pbar.update_absolute(bi + 1)
+        return io.NodeOutput(torch.from_numpy(out).to(
+            device=comfy.model_management.intermediate_device(),
+            dtype=comfy.model_management.intermediate_dtype(),
+        ).div_(255.0))
+
+
+def _draw_mesh(image_rgb: np.ndarray, faces: list, edges,
+               rgb: tuple[int, int, int], thickness: int,
+               point_size: int, fill_rings: list[list[int]] | None = None) -> np.ndarray:
+    draw_edges = thickness > 0 and edges
+    if not faces or (fill_rings is None and not draw_edges and point_size <= 0):
+        return image_rgb.copy()
+    pil = Image.fromarray(image_rgb)
+    draw = ImageDraw.Draw(pil)
+    r = point_size * 0.5
+    if fill_rings is not None:
+        for f in faces:
+            lmks = f["landmarks_xy"]
+            for ring in fill_rings:
+                draw.polygon([(float(lmks[i, 0]), float(lmks[i, 1])) for i in ring], fill=rgb)
+        return np.asarray(pil)
+    for f in faces:
+        lmks = f["landmarks_xy"]
+        n = lmks.shape[0]
+        if draw_edges:
+            for a, b in edges:
+                if a < n and b < n:
+                    draw.line([(float(lmks[a, 0]), float(lmks[a, 1])),
+                               (float(lmks[b, 0]), float(lmks[b, 1]))], fill=rgb, width=thickness)
+        if point_size == 1:
+            draw.point(lmks.flatten().tolist(), fill=rgb)
+        elif point_size > 1:
+            for x, y in lmks:
+                draw.ellipse((float(x) - r, float(y) - r, float(x) + r, float(y) + r), fill=rgb)
+    return np.asarray(pil)
+
+
+# Mask region presets — closed-loop topologies only.
+_MASK_REGIONS: tuple[str, ...] = ("face_oval", "lips", "left_eye", "right_eye", "irises")
+_MASK_CUSTOM_FEATURES: tuple[tuple[str, bool], ...] = (
+    ("face_oval",  True),
+    ("lips",       False),
+    ("left_eye",   False),
+    ("right_eye",  False),
+    ("irises",     False),
+)
+
+
+class MediaPipeFaceMask(io.ComfyNode):
+    """Binary mask from face landmarks, filled polygon per face. One mask per
+    frame in the batch; faces in the same frame composite (union)."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="MediaPipeFaceMask",
+            search_aliases=["face", "facial", "mediapipe", "face mask", "blazeface", "face detection", "visualize"],
+            display_name="Draw Face Mask (MediaPipe)",
+            category="image/detection",
+            description="Draws a mask from face landmarks.",
+            inputs=[
+                FaceLandmarksType.Input("face_landmarks"),
+                io.DynamicCombo.Input(
+                    "regions",
+                    tooltip="'all' = union of face_oval+lips+eyes+irises (which collapses to face_oval since it encloses the rest). 'custom' = toggle each region individually for combos like lips+eyes.",
+                    options=[
+                        io.DynamicCombo.Option("all", []),
+                        io.DynamicCombo.Option("custom", [
+                            io.Boolean.Input(reg, default=default,
+                                             tooltip=f"Include the '{reg}' region in the mask.")
+                            for reg, default in _MASK_CUSTOM_FEATURES
+                        ]),
+                    ],
+                ),
+            ],
+            outputs=[io.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, face_landmarks, regions) -> io.NodeOutput:
+        sets = face_landmarks["connection_sets"]
+        sel = regions["regions"]
+        if sel == "custom":
+            picked = [reg for reg, _ in _MASK_CUSTOM_FEATURES if regions.get(reg, False)]
+        else:
+            picked = list(_MASK_REGIONS)
+        rings = [r for reg in picked for r in _ordered_rings(sets[reg])]
+        frames = face_landmarks["frames"]
+        H, W = face_landmarks["image_size"]
+        masks = np.zeros((len(frames), H, W), dtype=np.uint8)
+        pbar = comfy.utils.ProgressBar(len(frames))
+        for bi, per_frame in enumerate(frames):
+            if per_frame:
+                pil = Image.new("L", (W, H), 0)
+                draw = ImageDraw.Draw(pil)
+                for f in per_frame:
+                    lmks = f["landmarks_xy"]
+                    for ring in rings:
+                        draw.polygon([(float(lmks[i, 0]), float(lmks[i, 1])) for i in ring], fill=255)
+                masks[bi] = np.asarray(pil)
+            pbar.update_absolute(bi + 1)
+        return io.NodeOutput(torch.from_numpy(masks).to(
+            device=comfy.model_management.intermediate_device(),
+            dtype=comfy.model_management.intermediate_dtype(),
+        ).div_(255.0))
+
+
+class MediaPipeFaceExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [LoadMediaPipeFaceLandmarker, MediaPipeFaceLandmarker, MediaPipeFaceMeshVisualize, MediaPipeFaceMask]
+
+
+async def comfy_entrypoint() -> MediaPipeFaceExtension:
+    return MediaPipeFaceExtension()
diff --git a/comfy_extras/nodes_mesh_io.py b/comfy_extras/nodes_mesh_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..d00a40d32997d0f450b33b90cceb38cad2a0bcae
--- /dev/null
+++ b/comfy_extras/nodes_mesh_io.py
@@ -0,0 +1,163 @@
+import logging
+import os
+
+import numpy as np
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, IO, Types
+from comfy_extras.mesh3d.fileio import gltf_read, mesh_file_read
+
+
+def _sniff_format(data: bytes) -> str:
+    if data[:4] == b"glTF":
+        return "glb"
+    head = data[:512].lstrip()
+    if head[:1] == b"{":
+        return "gltf"
+    if head[:5].lower() == b"solid":
+        return "stl"
+    return ""
+
+
+def _merge_primitives(prims: list[dict]) -> dict:
+    any_uv = any(p["uvs"] is not None for p in prims)
+    any_color = any(p["colors"] is not None for p in prims)
+    all_normals = all(p["normals"] is not None for p in prims)
+    all_tangents = all_normals and all(p["tangents"] is not None for p in prims)
+    color_channels = max((p["colors"].shape[1] for p in prims if p["colors"] is not None), default=3)
+    if not all_normals and any(p["normals"] is not None for p in prims):
+        logging.warning("Get3DComponents: some primitives lack normals; normals dropped "
+                        "(MeshSmoothNormals can regenerate them)")
+
+    verts, faces, uvs, colors, normals, tangents = [], [], [], [], [], []
+    offset = 0
+    for p in prims:
+        v = p["positions"]
+        n = v.shape[0]
+        verts.append(v)
+        faces.append(p["faces"] + offset)
+        offset += n
+        if any_uv:
+            uvs.append(p["uvs"] if p["uvs"] is not None else np.zeros((n, 2), np.float32))
+        if any_color:
+            c = p["colors"] if p["colors"] is not None else np.ones((n, color_channels), np.float32)
+            if c.shape[1] < color_channels:
+                c = np.concatenate([c, np.ones((n, color_channels - c.shape[1]), np.float32)], axis=1)
+            colors.append(c)
+        if all_normals:
+            normals.append(p["normals"])
+        if all_tangents:
+            tangents.append(p["tangents"])
+
+    return {
+        "vertices": np.concatenate(verts, axis=0),
+        "faces": np.concatenate(faces, axis=0),
+        "uvs": np.concatenate(uvs, axis=0) if any_uv else None,
+        "colors": np.concatenate(colors, axis=0) if any_color else None,
+        "normals": np.concatenate(normals, axis=0) if all_normals else None,
+        "tangents": np.concatenate(tangents, axis=0) if all_tangents else None,
+    }
+
+
+def _batch(arr):
+    return torch.from_numpy(arr)[None] if arr is not None else None
+
+
+class Get3DComponents(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Get3DComponents",
+            display_name="Get 3D Components",
+            category="3d",
+            description=(
+                "Parse a 3D model file (GLB, GLTF, OBJ, STL) into an editable MESH for the "
+                "mesh-processing nodes (decimate, remesh, UV unwrap, bake, ...). All scene "
+                "nodes/primitives are merged into one mesh with their transforms applied; "
+                "textures and material factors come from the first material. "
+                "Counterpart of MeshToFile3D."
+            ),
+            search_aliases=["file 3d to mesh", "extract mesh", "convert 3d", "parse glb", "import mesh",
+                            "load mesh from file", "file to mesh"],
+            is_experimental=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DGLB, IO.File3DGLTF, IO.File3DOBJ, IO.File3DSTL, IO.File3DAny],
+                    tooltip="3D model file from Load 3D or another 3D node. "
+                            "FBX/USDZ are not supported - convert to GLB first.",
+                ),
+            ],
+            outputs=[IO.Mesh.Output(display_name="mesh")],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D) -> IO.NodeOutput:
+        data = model_3d.get_bytes()
+        fmt = (model_3d.format or _sniff_format(data)).lower()
+        if fmt in ("fbx", "usdz"):
+            raise ValueError(f"Get3DComponents: .{fmt} parsing is not supported; convert the model to GLB/GLTF first")
+
+        warned = set()
+
+        def warn_once(key, message):
+            if key not in warned:
+                warned.add(key)
+                logging.warning("Get3DComponents: %s", message)
+
+        material_info = None
+        if fmt in ("glb", "gltf"):
+            base_dir = os.path.dirname(model_3d.get_source()) if model_3d.is_disk_backed else None
+            gltf, buffers, prims = gltf_read.load_gltf(data, base_dir, warn_once)
+            if not prims:
+                raise ValueError("Get3DComponents: no triangle geometry found in the glTF scene")
+            material_indices = [p["material"] for p in prims if p["material"] is not None]
+            if len(set(material_indices)) > 1:
+                warn_once("multimat", f"{len(set(material_indices))} materials found; "
+                                      "keeping textures/factors of the first only")
+            first_material = material_indices[0] if material_indices else None
+            material_info = gltf_read.extract_material(gltf, buffers, base_dir, first_material, warn_once)
+        elif fmt == "obj":
+            prims = [mesh_file_read.load_obj(data)]
+        elif fmt == "stl":
+            prims = [mesh_file_read.load_stl(data)]
+        else:
+            raise ValueError(f"Get3DComponents: unsupported or unrecognized format {fmt!r} "
+                             "(supported: glb, gltf, obj, stl)")
+
+        merged = _merge_primitives(prims)
+        n_verts = merged["vertices"].shape[0]
+        max_face = int(merged["faces"].max())
+        if max_face >= n_verts:
+            raise ValueError(f"Get3DComponents: face index {max_face} out of range for {n_verts} vertices (corrupt file?)")
+
+        material_info = material_info or {}
+        mesh = Types.MESH(
+            vertices=_batch(merged["vertices"]),
+            faces=_batch(merged["faces"]),
+            uvs=_batch(merged["uvs"]),
+            vertex_colors=_batch(merged["colors"]),
+            normals=_batch(merged["normals"]),
+            tangents=_batch(merged["tangents"]),
+            texture=_batch(material_info.get("texture")),
+            metallic_roughness=_batch(material_info.get("metallic_roughness")),
+            normal_map=_batch(material_info.get("normal_map")),
+            emissive=_batch(material_info.get("emissive")),
+            unlit=bool(material_info.get("unlit", False)),
+            occlusion_in_mr=bool(material_info.get("occlusion_in_mr", False)),
+            material=material_info.get("material") or None,
+        )
+        logging.info("Get3DComponents: %s -> %d vertices, %d faces (%d primitives)",
+                     fmt, n_verts, merged["faces"].shape[0], len(prims))
+        return IO.NodeOutput(mesh)
+
+
+class MeshIOExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [Get3DComponents]
+
+
+async def comfy_entrypoint() -> MeshIOExtension:
+    return MeshIOExtension()