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11/24/2025 - We are not in diffusers yet please use diffusers fork. We are working on merging with diffusers.

Kandinsky 5.0 T2I Lite SFT – 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 (T2I) 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

Kandinsky5T2IPipeline Usage Example

import torch
from diffusers import Kandinsky5T2IPipeline # use duffusers fork pip install git+https://github.com/leffff/diffusers

# Load the pipeline
model_id = "kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers"
pipe = Kandinsky5T2IPipeline.from_pretrained(model_id)
_ = pipe.to(device="cuda", dtype=torch.bfloat16)

# Generate image
prompt = "A fluffy, expressive cat wearing a bright red hat with a soft, slightly textured fabric. The hat should look cozy and well-fitted on the cat’s head. On the front of the hat, add clean, bold white text that reads “SWEET”, clearly visible and neatly centered. Ensure the overall lighting highlights the hat’s color and the cat’s fur details."

output = pipe(
    prompt=prompt,
    negative_prompt="",
    height=1024,
    width=1024,
    num_inference_steps=50,
    guidance_scale=3.5,
).image[0]

Results


Side-by-side evaluation of T2I on PartiPrompts with extended prompts	Side-by-side evaluation of I2I on the Flux Kontext benchmark with extended prompts

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}, 
}