In this repository, we provide a family of diffusion models to generate a video or an image (Coming Soon) given a textual prompt and distilled model for faster generation.

• 🔥 Source: 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. Released checkpoints: kandinsky5lite_t2v_pretrain_5s, kandinsky5lite_t2v_pretrain_10s, kandinsky5lite_t2v_sft_5s, kandinsky5lite_t2v_sft_10s, kandinsky5lite_t2v_nocfg_5s, kandinsky5lite_t2v_nocfg_10s, kandinsky5lite_t2v_distilled16steps_5s, kandinsky5lite_t2v_distilled16steps_10s contains weight from pretrain, supervised finetuning, cfg distillation and diffusion distillation into 16 steps. 5s checkpoints are capable of generating videos up to 5 seconds long. 10s checkpoints is faster models checkpoints trained with NABLA algorithm and capable to generate videos up to 10 seconds long.
• 🔥 Source: 2025/10/7: The ComfyUI README file has been updated. SDPA support has been added, allowing you to run our code without Flash attention. Magcache support for nocfg checkpoints has been added, allowing Magcache support for sft and nocfg checkpoints. Memory consumption in the VAE has been reduced, with the entire pipeline now running at 24 GB with offloading.

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.

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.

*Latency was measured after the second inference run. The first run of the model can be slower due to the compilation process. Inference was measured on an NVIDIA H100 GPU with 80 GB of memory, using CUDA 12.8.1 and PyTorch 2.8. For 5-second models Flash Attention 3 was used.

The evaluation is based on the expanded prompts from the Movie Gen benchmark, which are available in the expanded_prompt column of the benchmark/moviegen_bench.csv file.

Clone the repo:

git clone https://github.com/ai-forever/Kandinsky-5.git
cd Kandinsky-5

Install dependencies:

pip install -r requirements.txt

To improve inference performance on NVidia Hopper GPUs, we recommend installing Flash Attention 3.

python download_models.py

python test.py --prompt "A dog in red hat"

python test.py --config ./configs/config_10s_sft.yaml --prompt "A dog in red hat" --video_duration 10 

python test.py --config ./configs/config_5s_pretrain.yaml --prompt "A dog in red hat"

python test.py --config ./configs/config_10s_pretrain.yaml --prompt "A dog in red hat" --video_duration 10

python test.py --config ./configs/config_5s_nocfg.yaml --prompt "A dog in red hat" 

python test.py --config ./configs/config_10s_nocfg.yaml --prompt "A dog in red hat" --video_duration 10

python test.py --config ./configs/config_5s_distil.yaml --prompt "A dog in red hat"          

python test.py --config ./configs/config_10s_distil.yaml --prompt "A dog in red hat" --video_duration 10

import torch
from IPython.display import Video
from kandinsky import get_T2V_pipeline

device_map = {
    "dit": torch.device('cuda:0'), 
    "vae": torch.device('cuda:0'), 
    "text_embedder": torch.device('cuda:0')
}

pipe = get_T2V_pipeline(device_map, conf_path="configs/config_5s_sft.yaml")

images = pipe(
    seed=42,
    time_length=5,
    width=768,
    height=512,
    save_path="./test.mp4",
    text="A cat in a red hat",
)

Video("./test.mp4")

Please, refer to inference_example.ipynb notebook for more usage details.

For a faster inference, we also provide the capability to perform inference in a distributed way:

NUMBER_OF_NODES=1
NUMBER_OF_DEVICES_PER_NODE=1 / 2 / 4
python -m torch.distributed.launch --nnodes $NUMBER_OF_NODES --nproc-per-node $NUMBER_OF_DEVICES_PER_NODE test.py

For less memory consumption you can use offloading of the models.

python test.py --prompt "A dog in red hat" --offload

Also we provide Magcache inference for faster generations (now available for sft 5s and sft 10s checkpoints).

python test.py --prompt "A dog in red hat" --magcache

See the instruction here

You can apply to participate in the beta testing of the Kandinsky Video Lite via the telegram bot.

• Kandinsky 5.0 Lite Text-to-Video
  • Multi-GPU Inference code of the 2B models
  • Checkpoints 2B models
    • pretrain
    • sft
    • rl
    • cfg distil
    • distil 16 steps
    • autoregressive generation
  • ComfyUI integration
  • Diffusers integration
  • Caching acceleration support
• Kandinsky 5.0 Lite Image-to-Video
  • Multi-GPU Inference code of the 2B model
  • Checkpoints of the 2B model
  • ComfyUI integration
  • Diffusers integration
• Kandinsky 5.0 Pro Text-to-Video
  • Multi-GPU Inference code of the models
  • Checkpoints of the model
  • ComfyUI integration
  • Diffusers integration
• Kandinsky 5.0 Pro Image-to-Video
  • Multi-GPU Inference code of the model
  • Checkpoints of the model
  • ComfyUI integration
  • Diffusers integration
• Technical report

Project Leader: Denis Dimitrov

Team Leads: Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko, Denis Parkhomenko

Core Contributors: Alexey Letunovskiy, Maria Kovaleva, Ivan Kirillov, Lev Novitskiy, Denis Koposov, Dmitrii Mikhailov, Anna Averchenkova, Andrey Shutkin, Julia Agafonova, Olga Kim, Anastasiia Kargapoltseva, Nikita Kiselev

Contributors: Anna Dmitrienko, Anastasia Maltseva, Kirill Chernyshev, Ilia Vasiliev, Viacheslav Vasilev, Vladimir Polovnikov, Yury Kolabushin, Alexander Belykh, Mikhail Mamaev, Anastasia Aliaskina, Tatiana Nikulina, Polina Gavrilova

@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}, 
}

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.