Latest updates
11/24/2025 - We are not in diffusers yet please use diffusers fork. We are working on merging with diffusers.
Kandinsky 5.0 is a family of diffusion models for video and image generation.
Kandinsky 5.0 Image Lite is a lightweight text-to-image (I2I) generation model with 6B parameters.
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
- Flux VAE for efficient image encoding and decoding
The original codebase can be found at kandinskylab/Kandinsky-5.
Available Models
Kandinsky 5.0 Image Lite:
| model_id | Description | Use Cases |
|---|---|---|
| kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers | 6B supervised fine-tuned text-to-image model | Highest generation quality |
| kandinskylab/Kandinsky-5.0-I2I-Lite-sft-Diffusers | 6B supervised fine-tuned image-to-image editing model | Highest generation quality |
| kandinskylab/Kandinsky-5.0-T2I-Lite-pretrain-Diffusers | 6B base pretrained text-to-image model | Research and fine-tuning |
| kandinskylab/Kandinsky-5.0-I2I-Lite-pretrain-Diffusers | 6B base pretrained image-to-image editing model | Research and fine-tuning |
Examples
Kandinsky5I2IPipeline Usage Example
import torch
from diffusers import Kandinsky5I2IPipeline # use duffusers fork: pip install git+https://github.com/leffff/diffusers
from diffusers.utils import load_image
# Load the pipeline
model_id = "kandinskylab/Kandinsky-5.0-I2I-Lite-sft-Diffusers"
pipe = Kandinsky5I2IPipeline.from_pretrained(model_id)
_ = pipe.to(device='cuda',dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() # <--- Enable CPU offloading for single GPU inference
# Edit the input image
image = load_image(
"https://huggingface.co/kandinsky-community/kandinsky-3/resolve/main/assets/title.jpg?download=true"
)
prompt = "Change the background from a winter night scene to a bright summer day. Place the character on a sandy beach with clear blue sky, soft sunlight, and gentle waves in the distance. Replace the winter clothing with a light short-sleeved T-shirt (in soft pastel colors) and casual shorts. Ensure the character’s fur reflects warm daylight instead of cold winter tones. Add small beach details such as seashells, footprints in the sand, and a few scattered beach toys nearby. Keep the oranges in the scene, but place them naturally on the sand."
negative_prompt = ""
output = pipe(
image=image,
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=3.5,
).image[0]
Results
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},
}