Kandinsky-5.0-T2V-Pro-distilled-5s-Diffusers

<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://kandinskylab.ai/">Project Page</a> | <a href="https://arxiv.org/abs/2511.14993">Technical Report</a> | 🤗 <a href=https://huggingface.co/collections/kandinskylab/kandinsky-50-video-lite> Video Lite </a> / <a href=https://huggingface.co/collections/kandinskylab/kandinsky-50-video-pro> Video Pro </a> / <a href=https://huggingface.co/collections/kandinskylab/kandinsky-50-image-lite> Image Lite </a> | <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/kandinsky5"> 🤗 Diffusers </a> | <a href="https://github.com/kandinskylab/kandinsky-5/blob/main/comfyui/README.md">ComfyUI</a> </div>

Kandinsky 5.0 T2V Pro Diffusers

Kandinsky 5.0 Pro line-up of large high quality video generation models (19B parameters). It offers high qualty generation in HD and more generation formats like I2V.

⚠️ Warning! all Pro models should be infered with pipeline.enable_model_cpu_offload()

import torch
from diffusers import Kandinsky5T2VPipeline
from diffusers.utils import export_to_video

# Load the pipeline
model_id = "kandinskylab/Kandinsky-5.0-T2V-Pro-distilled-5s-Diffusers"
pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)

pipe = pipe.to("cuda")
pipe.transformer.set_attention_backend("flex")                            # <--- Set attention bakend to Flex
pipe.enable_model_cpu_offload()                                           # <--- Enable cpu offloading for single GPU inference
pipe.transformer.compile(mode="max-autotune-no-cudagraphs", dynamic=True) # <--- Compile with max-autotune-no-cudagraphs

# Generate video
prompt = "A cat and a dog baking a cake together in a kitchen."
negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"

output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=768,
    width=1024,
    num_frames=121,  # ~5 seconds at 24fps
    num_inference_steps=16,
    guidance_scale=1.0,
).frames[0]

export_to_video(output, "output.mp4", fps=24, quality=9)

Authors

<B>Core Contributors</B>:

• <B>Video</B>: Alexey Letunovskiy, Maria Kovaleva, Lev Novitskiy, Denis Koposov, Dmitrii Mikhailov, Anastasiia Kargapoltseva, Anna Dmitrienko, Anastasia Maltseva
• <B>Image & Editing</B>: Nikolai Vaulin, Nikita Kiselev, Alexander Varlamov
• <B>Pre-training Data</B>: Ivan Kirillov, Andrey Shutkin, Nikolai Vaulin, Ilya Vasiliev
• <B>Post-training Data</B>: Julia Agafonova, Anna Averchenkova, Olga Kim
• <B>Research Consolidation & Paper</B>: Viacheslav Vasilev, Vladimir Polovnikov

<B>Contributors</B>: Yury Kolabushin, Kirill Chernyshev, Alexander Belykh, Mikhail Mamaev, Anastasia Aliaskina, Kormilitsyn Semen, Tatiana Nikulina, Olga Vdovchenko, Polina Mikhailova, Polina Gavrilova, Nikita Osterov, Bulat Akhmatov

<B>Track Leaders</B>: Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko, Denis Parkhomenko

<B>Project Supervisor</B>: Denis Dimitrov

Citation

@misc{arkhipkin2026kandinsky50familyfoundation,
      title={Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation}, 
      author={Vladimir Arkhipkin and Vladimir Korviakov and Nikolai Gerasimenko and Denis Parkhomenko and Viacheslav Vasilev and Alexey Letunovskiy and Nikolai Vaulin and Maria Kovaleva and Ivan Kirillov and Lev Novitskiy and Denis Koposov and Nikita Kiselev and Alexander Varlamov and Dmitrii Mikhailov and Vladimir Polovnikov and Andrey Shutkin and Julia Agafonova and Ilya Vasiliev and Anastasiia Kargapoltseva and Anna Dmitrienko and Anastasia Maltseva and Anna Averchenkova and Olga Kim and Tatiana Nikulina and Denis Dimitrov},
      year={2026},
      eprint={2511.14993},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.14993}, 
}

@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}, 
}

Acknowledgements

We gratefully acknowledge the open-source projects and research that made Kandinsky 5.0 possible:

• PyTorch — for model training and inference.
• FlashAttention 3 — for efficient attention and faster inference.
• Qwen2.5-VL — for providing high-quality text embeddings.
• CLIP — for robust text–image alignment.
• HunyuanVideo — for video latent encoding and decoding.
• MagCache — for accelerated inference.
• ComfyUI — for integration into node-based workflows.

We deeply appreciate the contributions of these communities and researchers to the open-source ecosystem.