Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+<div align="center">
+  <picture>
+    <img src="assets/KANDINSKY_LOGO_1_BLACK.png">
+  </picture>
+</div>
+
+<div align="center">
+  <a href="https://habr.com/ru/companies/sberbank/articles/951800/">Habr</a> | <a href="https://ai-forever.github.io/Kandinsky-5/">Project Page</a> | Technical Report (soon) | <a href="https://github.com/ai-forever/Kandinsky-5">Original Github</a> | <a href="https://huggingface.co/collections/ai-forever/kandinsky-50-t2v-lite-diffusers-68dd73ebac816748ed79d6cb"> 🤗 Diffusers</a>
+</div>
+
+<h1>Kandinsky 5.0 T2V Lite - Diffusers</h1>
+
+This repository provides the 🤗 Diffusers integration for Kandinsky 5.0 T2V Lite - a lightweight video generation model (2B parameters) that ranks #1 among open-source models in its class.
+
+## Project Updates
+
+- 🔥 **2025/09/29**: We have open-sourced `Kandinsky 5.0 T2V Lite` a lite (2B parameters) version of `Kandinsky 5.0 Video` text-to-video generation model. 
+- 🚀 **Diffusers Integration**: Now available with easy-to-use 🤗 Diffusers pipeline!
+
+## Kandinsky 5.0 T2V Lite
+
+Kandinsky 5.0 T2V Lite is a lightweight video generation model (2B parameters) that ranks #1 among open-source models in its class. It outperforms larger Wan models (5B and 14B) and offers the best understanding of Russian concepts in the open-source ecosystem.
+
+We provide 8 model variants, each optimized for different use cases:
+
+* **SFT model** — delivers the highest generation quality
+* **CFG-distilled** — runs 2× faster
+* **Diffusion-distilled** — enables low-latency generation with minimal quality loss (6× faster)
+* **Pretrain model** — designed for fine-tuning by researchers and enthusiasts
+
+
+## Basic Usage
+```python
+import torch
+from diffusers import Kandinsky5T2VPipeline
+from diffusers.utils import export_to_video
+
+# Load the pipeline
+pipe = Kandinsky5T2VPipeline.from_pretrained(
+    "ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s", 
+    torch_dtype=torch.float16
+)
+pipe = pipe.to("cuda")
+
+# Generate video
+prompt = "A cat and a dog baking a cake together in a kitchen."
+negative_prompt = "Bright tones, overexposed, static, blurred details"
+
+output = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    height=512,
+    width=768,
+    num_frames=25,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+).frames[0]
+
+## Save the video
+export_to_video(output, "output.mp4", fps=24)
+```
+
+## Using Different Model Variants
+```python
+import torch
+from diffusers import Kandinsky5T2VPipeline
+
+# SFT 5s model (highest quality)
+pipe_sft = Kandinsky5T2VPipeline.from_pretrained(
+    "ai-forever/Kandinsky-5.0-T2V-Lite-sft-5s",
+    torch_dtype=torch.float16
+)
+
+# Distilled 16-step model (fastest)
+pipe_distill = Kandinsky5T2VPipeline.from_pretrained(
+    "ai-forever/Kandinsky-5.0-T2V-Lite-distilled16steps-5s", 
+    torch_dtype=torch.float16
+)
+
+# No-CFG model (balanced speed/quality)
+pipe_nocfg = Kandinsky5T2VPipeline.from_pretrained(
+    "ai-forever/Kandinsky-5.0-T2V-Lite-nocfg-5s",
+    torch_dtype=torch.float16
+)
+
+# Pretrain model (most diverse)
+pipe_pretrain = Kandinsky5T2VPipeline.from_pretrained(
+    "ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-5s",
+    torch_dtype=torch.float16
+)
+```
+
+## Architecture
+Latent diffusion pipeline with Flow Matching.
+
+Diffusion Transformer (DiT) as the main generative backbone with cross-attention to text embeddings.
+
+Qwen2.5-VL and CLIP provides text embeddings
+
+HunyuanVideo 3D VAE encodes/decodes video into a latent space
+
+DiT is the main generative module using cross-attention to condition on text
+
+<div align="center">
+  <img width="1600" height="477" alt="Pipeline Architecture" src="https://github.com/user-attachments/assets/17fc2eb5-05e3-4591-9ec6-0f6e1ca397b3" />
+</div>
+
+<div align="center">
+  <img width="800" height="406" alt="Model Architecture" src="https://github.com/user-attachments/assets/f3006742-e261-4c39-b7dc-e39330be9a09" />
+</div>
+
+## Examples
+
+Kandinsky 5.0 T2V Lite SFT
+<table border="0" style="width: 200; text-align: left; margin-top: 20px;"> <tr> <td> <video src="https://github.com/user-attachments/assets/bc38821b-f9f1-46db-885f-1f70464669eb" width=200 controls autoplay loop></video> </td> <td> <video src="https://github.com/user-attachments/assets/9f64c940-4df8-4c51-bd81-a05de8e70fc3" width=200 controls autoplay loop></video> </td> <tr> <td> <video src="https://github.com/user-attachments/assets/77dd417f-e0bf-42bd-8d80-daffcd054add" width=200 controls autoplay loop></video> </td> <td> <video src="https://github.com/user-attachments/assets/385a0076-f01c-4663-aa46-6ce50352b9ed" width=200 controls autoplay loop></video> </td> <tr> <td> <video src="https://github.com/user-attachments/assets/7c1bcb31-cc7d-4385-9a33-2b0cc28393dd" width=200 controls autoplay loop></video> </td> <td> <video src="https://github.com/user-attachments/assets/990a8a0b-2df1-4bbc-b2e3-2859b6f1eea6" width=200 controls autoplay loop></video> </td> </tr> </table>
+Kandinsky 5.0 T2V Lite Distill
+<table border="0" style="width: 200; text-align: left; margin-top: 20px;"> <tr> <td> <video src="https://github.com/user-attachments/assets/861342f9-f576-4083-8a3b-94570a970d58" width=200 controls autoplay loop></video> </td> <td> <video src="https://github.com/user-attachments/assets/302e4e7d-781d-4a58-9b10-8c473d469c4b" width=200 controls autoplay loop></video> </td> <tr> <td> <video src="https://github.com/user-attachments/assets/3e70175c-40e5-4aec-b506-38006fe91a76" width=200 controls autoplay loop></video> </td> <td> <video src="https://github.com/user-attachments/assets/b7da85f7-8b62-4d46-9460-7f0e505de810" width=200 controls autoplay loop></video> </td> </table>
+Results
+Side-by-Side Evaluation
+The evaluation is based on the expanded prompts from the Movie Gen benchmark.
+
+<table border="0" style="width: 400; text-align: left; margin-top: 20px;"> <tr> <td> <img src="assets/sbs/kandinsky_5_video_lite_vs_sora.jpg" width=400 ></img> </td> <td> <img src="assets/sbs/kandinsky_5_video_lite_vs_wan_2.1_14B.jpg" width=400 ></img> </td> <tr> <td> <img src="assets/sbs/kandinsky_5_video_lite_vs_wan_2.2_5B.jpg" width=400 ></img> </td> <td> <img src="assets/sbs/kandinsky_5_video_lite_vs_wan_2.2_A14B.jpg" width=400 ></img> </td> <tr> <td> <img src="assets/sbs/kandinsky_5_video_lite_vs_wan_2.1_1.3B.jpg" width=400 ></img> </td> </table>
+Distill Side-by-Side Evaluation
+<table border="0" style="width: 400; text-align: left; margin-top: 20px;"> <tr> <td> <img src="assets/sbs/kandinsky_5_video_lite_5s_vs_kandinsky_5_video_lite_distill_5s.jpg" width=400 ></img> </td> <td> <img src="assets/sbs/kandinsky_5_video_lite_10s_vs_kandinsky_5_video_lite_distill_10s.jpg" width=400 ></img> </td> </table>
+VBench Results
+<div align="center"> <picture> <img src="assets/vbench.png"> </picture> </div>
+Beta Testing
+You can apply to participate in the beta testing of the Kandinsky Video Lite via the telegram bot.
+
+```bibtex
+@misc{kandinsky2025,
+    author = {Alexey Letunovskiy, Maria Kovaleva, Ivan Kirillov, Lev Novitskiy, Denis Koposov,
+              Dmitrii Mikhailov, Anna Averchenkova, Andrey Shutkin, Julia Agafonova, Olga Kim,
+              Anastasiia Kargapoltseva, Nikita Kiselev, Vladimir Arkhipkin, Vladimir Korviakov,
+              Nikolai Gerasimenko, Denis Parkhomenko, Anna Dmitrienko, Anastasia Maltseva,
+              Kirill Chernyshev, Ilia Vasiliev, Viacheslav Vasilev, Vladimir Polovnikov,
+              Yury Kolabushin, Alexander Belykh, Mikhail Mamaev, Anastasia Aliaskina,
+              Tatiana Nikulina, Polina Gavrilova, Denis Dimitrov},
+    title = {Kandinsky 5.0: A family of diffusion models for Video & Image generation},
+    howpublished = {\url{https://github.com/ai-forever/Kandinsky-5}},
+    year = 2025
+}
+
+@misc{mikhailov2025nablanablaneighborhoodadaptiveblocklevel,
+      title={$\nabla$NABLA: Neighborhood Adaptive Block-Level Attention}, 
+      author={Dmitrii Mikhailov and Aleksey Letunovskiy and Maria Kovaleva and Vladimir Arkhipkin
+              and Vladimir Korviakov and Vladimir Polovnikov and Viacheslav Vasilev
+              and Evelina Sidorova and Denis Dimitrov},
+      year={2025},
+      eprint={2507.13546},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2507.13546}, 
+}
+```