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README.md

@@ -1,230 +1,3 @@
----
-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>
-</div>
-
-# Kandinsky 5.0 T2V Lite - Diffusers
-
-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
-
-All models are available in two versions: for generating 5-second and 10-second videos.
-
-## Pipeline
-
-**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>
-
-## 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=6)
-```
-
-## 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 (largest diversity, not aligned)
-pipe_pretrain = Kandinsky5T2VPipeline.from_pretrained(
-    "ai-forever/Kandinsky-5.0-T2V-Lite-pretrain-5s",
-    torch_dtype=torch.float16
-)
-```
-
-### 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](https://github.com/facebookresearch/MovieGenBench), which are available in the expanded_prompt column of the benchmark/moviegen_bench.csv file.
-<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>
-
-
-# Citation
-```
-@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}, 
-}
-```
+---
+license: apache-2.0
+---