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kandinskylab/
Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers

Name: Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers
Availability: InStock

Kandinsky5T2VPipeline

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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()

python

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

# Load the pipeline
model_id = "kandinskylab/Kandinsky-5.0-T2V-Pro-sft-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=50,
    guidance_scale=5.0,
).frames[0]

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

Authors

Core Contributors:

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

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

Track Leaders: Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko, Denis Parkhomenko

Project Supervisor: Denis Dimitrov

Citation

@misc{kandinsky2025,
    author = {Alexander Belykh and Alexander Varlamov and Alexey Letunovskiy and Anastasia Aliaskina and Anastasia Maltseva and Anastasiia Kargapoltseva and Andrey Shutkin and Anna Averchenkova and Anna Dmitrienko and Bulat Akhmatov and Denis Dimitrov and Denis Koposov and Denis Parkhomenko and Dmitrii and Ilya Vasiliev and Ivan Kirillov and Julia Agafonova and Kirill Chernyshev and Kormilitsyn Semen and Lev Novitskiy and Maria Kovaleva and Mikhail Mamaev and Mikhailov and Nikita Kiselev and Nikita Osterov and Nikolai Gerasimenko and Nikolai Vaulin and Olga Kim and Olga Vdovchenko and Polina Gavrilova and Polina Mikhailova and Tatiana Nikulina and Viacheslav Vasilev and Vladimir Arkhipkin and Vladimir Korviakov and Vladimir Polovnikov and Yury Kolabushin},
    title = {Kandinsky 5.0: A family of diffusion models for Video & Image generation},
    howpublished = {\url{https://github.com/kandinskylab/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}, 
}

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.