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Kandinsky 5.0: A family of diffusion models for Video & Image generation

In this repository, we provide a family of diffusion models to generate a video or an image given a textual prompt and/or image.

https://github.com/user-attachments/assets/b06f56de-1b05-4def-a611-1a3159ed71b0

Kandinsky 5.0 Image Lite

Kandinsky 5.0 Image Lite is a line-up of 6B image generation models with the following capabilities:

• 1K resulution (1280x768, 1024x1024 and others).

• High visual quality

• Strong text-writing

• Russian concepts understanding

Model Zoo

*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.

Examples:

Results:

Side-by-Side evaluation

Kandinsky 5.0 Image Editing

Kandinsky 5.0 Image Editing is a line-up of 6B image editing models with the following capabilities:

• 1K resulution (1280x768, 1024x1024 and others).

• High visual quality

• Strong text-writing

• Russian concepts understanding

Model Zoo

*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.

Examples:

Results:

Side-by-Side evaluation

Quickstart

Installation

Clone the repo:

git clone https://github.com/kandinskylab/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.

Model Download

python download_models.py

use models argument to download some specific models, otherwise all models will be downloaded

example to download only kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s and kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s:

python download_models.py --models kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s,kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s

Run Kandinsky 5.0 T2I Lite

python test.py --config ./configs/k5_lite_t2i_sft_hd.yaml --prompt "A dog in a red hat" --width=1280 --height=768

T2I Inference

import torch
from kandinsky import get_T2I_pipeline

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

pipe = get_T2I_pipeline(device_map, conf_path="configs/k5_lite_t2i_sft_hd.yaml")

images = pipe(
    seed=42,
    save_path='./test.png',
    text="A cat in a red hat with a label 'HELLO'"
)

I2I Inference

import torch
from kandinsky import get_I2I_pipeline

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

pipe = get_I2I_pipeline(
    resolution=1024, offload=True,
    device_map=device_map,
)
out = pipe(
    "Replace the cat with a husky, leave the rest unchanged",
    image='./assets/cat_in_hat.png'
)

Please, refer to examples folder for more examples in various notebooks.

Distributed Inference

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

Optimized Inference

Offloading

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

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

Magcache

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

Qwen encoder quantization

To reduce GPU memory needed for Qwen encoder we provide option to use NF4-quantized version from bitsandbytes.

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

Attention engine selection

Depending on your hardware you can use the follwing full attention algorithm implementation:

• PyTorch SDPA
• Flash Attention 2
• Flash Attention 3
• Sage Attention

The attention algorithm can be selected using an option "--attention_engine" of test.py script for 5 second (and less) video generation. For 10-second generation we use sparse attention algorithm NABLA.

Note that currently (19 Oct. 2025) version build from source contains a bug and produces noisy output. A temporary workaround to fix it is decribed here.

python test.py --prompt "A dog in red hat" --attention_engine=flash_attention_3

python test.py --prompt "A dog in red hat" --attention_engine=flash_attention_2

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

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

By default we use option --attention_engine=auto which enables automatic selection of the most optimal algorithm installed in your system.

ComfyUI

See the instruction here

CacheDiT

cache-dit offers Fully Cache Acceleration support for Kandinsky-5 with DBCache, TaylorSeer and Cache CFG. Visit their example for more details.

Beta testing

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

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{arkhipkin2025kandinsky50familyfoundation,
      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={2025},
      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.