diffusers/docs/source/en/api/pipelines/kandinsky5_video.md

Kandinsky 5.0 Video

Kandinsky 5.0 is a family of diffusion models for Video & Image generation.

Kandinsky 5.0 Lite line-up of lightweight video generation models (2B parameters) that ranks #1 among open-source models in its class. It outperforms larger models and offers the best understanding of Russian concepts in the open-source ecosystem.

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.

The model introduces several key innovations:

• Latent diffusion pipeline with Flow Matching for improved training stability
• Diffusion Transformer (DiT) as the main generative backbone with cross-attention to text embeddings
• Dual text encoding using Qwen2.5-VL and CLIP for comprehensive text understanding
• HunyuanVideo 3D VAE for efficient video encoding and decoding
• Sparse attention mechanisms (NABLA) for efficient long-sequence processing

The original codebase can be found at kandinskylab/Kandinsky-5.

Tip

Check out the Kandinsky Lab organization on the Hub for the official model checkpoints for text-to-video generation, including pretrained, SFT, no-CFG, and distilled variants.

Available Models

Kandinsky 5.0 T2V Pro:

model_id	Description	Use Cases
kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers	5 second Text-to-Video Pro model	High-quality text-to-video generation
kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers	5 second Image-to-Video Pro model	High-quality image-to-video generation

Kandinsky 5.0 T2V Lite:

model_id	Description	Use Cases
kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers	5 second Supervised Fine-Tuned model	Highest generation quality
kandinskylab/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers	10 second Supervised Fine-Tuned model	Highest generation quality
kandinskylab/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers	5 second Classifier-Free Guidance distilled	2× faster inference
kandinskylab/Kandinsky-5.0-T2V-Lite-nocfg-10s-Diffusers	10 second Classifier-Free Guidance distilled	2× faster inference
kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers	5 second Diffusion distilled to 16 steps	6× faster inference, minimal quality loss
kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-10s-Diffusers	10 second Diffusion distilled to 16 steps	6× faster inference, minimal quality loss
kandinskylab/Kandinsky-5.0-T2V-Lite-pretrain-5s-Diffusers	5 second Base pretrained model	Research and fine-tuning
kandinskylab/Kandinsky-5.0-T2V-Lite-pretrain-10s-Diffusers	10 second Base pretrained model	Research and fine-tuning

Usage Examples

Basic Text-to-Video Generation

Pro

⚠️ 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-sft-5s-Diffusers"
pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)

pipe = pipe.to("cuda")
pipeline.transformer.set_attention_backend("flex")                            # <--- Set attention bakend to Flex
pipeline.enable_model_cpu_offload()                                           # <--- Enable cpu offloading for single GPU inference
pipeline.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)

Lite

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

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

# 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=512,
    width=768,
    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)

10 second Models

⚠️ Warning! all 10 second models should be used with Flex attention and max-autotune-no-cudagraphs compilation:

pipe = Kandinsky5T2VPipeline.from_pretrained(
    "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers", 
    torch_dtype=torch.bfloat16
)
pipe = pipe.to("cuda")

pipe.transformer.set_attention_backend(
    "flex"
)                                       # <--- Set attention bakend to Flex
pipe.transformer.compile(
    mode="max-autotune-no-cudagraphs", 
    dynamic=True
)                                       # <--- Compile with max-autotune-no-cudagraphs

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=512,
    width=768,
    num_frames=241,
    num_inference_steps=50,
    guidance_scale=5.0,
).frames[0]

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

Diffusion Distilled model

⚠️ Warning! all nocfg and diffusion distilled models should be infered wothout CFG (guidance_scale=1.0):

model_id = "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers"
pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")

output = pipe(
    prompt="A beautiful sunset over mountains",
    num_inference_steps=16,  # <--- Model is distilled in 16 steps
    guidance_scale=1.0,      # <--- no CFG
).frames[0]

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

Basic Image-to-Video Generation

⚠️ 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-I2V-Pro-sft-5s-Diffusers"
pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)

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

# Generate video
image = load_image(
    "https://huggingface.co/kandinsky-community/kandinsky-3/resolve/main/assets/title.jpg?download=true"
)
height = 896
width = 896
image = image.resize((width, height))

prompt = "An funny furry creture smiles happily and holds a sign that says 'Kandinsky'"
negative_prompt = ""

output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=height,
    width=width,
    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)

Kandinsky 5.0 Pro Side-by-Side evaluation

Comparison with Veo 3	Comparison with Veo 3 fast
Comparison with Wan 2.2 A14B Text-to-Video mode	Comparison with Wan 2.2 A14B Image-to-Video mode

Kandinsky 5.0 Lite Side-by-Side evaluation

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.

Kandinsky 5.0 Lite Distill Side-by-Side evaluation

Kandinsky5T2VPipeline

autodoc Kandinsky5T2VPipeline - all - call

Kandinsky5I2VPipeline

autodoc Kandinsky5I2VPipeline - all - call

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
}