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Download optimization.py from black-forest-labs/FLUX.1-Kontext-Dev: direct link, hf CLI and curl.
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https://huggingface.co/spaces/black-forest-labs/FLUX.1-Kontext-Dev/resolve/ae8c337b5d504ba80c0ccd942eb6ec8278b8868d/optimization.py
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hf download hf://spaces/black-forest-labs/FLUX.1-Kontext-Dev@ae8c337b5d504ba80c0ccd942eb6ec8278b8868d/optimization.py
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curl -L -o optimization.py https://huggingface.co/spaces/black-forest-labs/FLUX.1-Kontext-Dev/resolve/ae8c337b5d504ba80c0ccd942eb6ec8278b8868d/optimization.py
1.63 kB
| """ | |
| """ | |
| from typing import Any | |
| from typing import Callable | |
| from typing import ParamSpec | |
| import spaces | |
| import torch | |
| from torch.utils._pytree import tree_map_only | |
| from optimization_utils import capture_component_call | |
| from optimization_utils import aoti_compile | |
| P = ParamSpec('P') | |
| TRANSFORMER_HIDDEN_DIM = torch.export.Dim('hidden', min=4096, max=8212) | |
| TRANSFORMER_DYNAMIC_SHAPES = { | |
| 'hidden_states': {1: TRANSFORMER_HIDDEN_DIM}, | |
| 'img_ids': {0: TRANSFORMER_HIDDEN_DIM}, | |
| } | |
| INDUCTOR_CONFIGS = { | |
| 'conv_1x1_as_mm': True, | |
| 'epilogue_fusion': False, | |
| 'coordinate_descent_tuning': True, | |
| 'coordinate_descent_check_all_directions': True, | |
| 'max_autotune': True, | |
| 'triton.cudagraphs': True, | |
| } | |
| def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kwargs): | |
| def compile_transformer(): | |
| with capture_component_call(pipeline, 'transformer') as call: | |
| pipeline(*args, **kwargs) | |
| dynamic_shapes = tree_map_only((torch.Tensor, bool), lambda t: None, call.kwargs) | |
| dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES | |
| pipeline.transformer.fuse_qkv_projections() | |
| exported = torch.export.export( | |
| mod=pipeline.transformer, | |
| args=call.args, | |
| kwargs=call.kwargs, | |
| dynamic_shapes=dynamic_shapes, | |
| ) | |
| return aoti_compile(exported, INDUCTOR_CONFIGS) | |
| transformer_config = pipeline.transformer.config | |
| pipeline.transformer = compile_transformer() | |
| pipeline.transformer.config = transformer_config # pyright: ignore[reportAttributeAccessIssue] | |