Kandinsky-5.0-T2I-Lite-pretrain-Diffusers

<div align="center"> <picture> <img src="assets/KANDINSKY_LOGO_1_BLACK.png"> </picture> </div> <div align="center"> <a href="https://habr.com/ru/companies/sberbank/articles/951800/">Habr</a> | <a href="https://kandinskylab.ai/">Project Page</a> | <a href="https://github.com/kandinskylab/kandinsky-5/blob/main/paper.pdf">Technical Report</a> | <a href="https://github.com/ai-forever/Kandinsky-5">Original GitHub</a> | <a href="https://huggingface.co/collections/kandinskylab/kandinsky-50-image-lite-diffusers">🤗 Diffusers</a> </div>

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<h1>Kandinsky 5.0 T2I Lite Pretrain – Diffusers</h1>

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:

Examples

<table border="0" style="width: 90%; text-align: left; margin-top: 20px;"> <tr> <td> <img src="assets/generation_examples/images/1.jpg" width=90% > </td> <td> <img src="assets/generation_examples/images/2.jpg" width=90% > </td> <td> <img src="assets/generation_examples/images/9.jpg" width=90% > </td> <tr> </table> <table border="0" style="width: 90%; text-align: left; margin-top: 10px;"> <td> <img src="assets/generation_examples/images/4.jpg" width=90% > </td> <td> <img src="assets/generation_examples/images/5.jpg" width=90% > </td> <td> <img src="assets/generation_examples/images/3.jpg" width=90% > </td> </table> <table border="0" style="width: 90%; text-align: left; margin-top: 10px;"> <td> <img src="assets/generation_examples/images/7.jpg" width=90% > </td> <td> <img src="assets/generation_examples/images/8.jpg" width=90% > </td> <td> <img src="assets/generation_examples/images/6.jpg" width=90% > </td> </table>

Kandinsky5T2IPipeline Usage Example

import torch
from diffusers import Kandinsky5T2IPipeline

# Load the pipeline
model_id = "kandinskylab/Kandinsky-5.0-T2I-Lite-pretrain-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

<table style="width:100%; text-align:center; margin-top:20px;"> <tr> <td> <img src="assets/sbs_image.png" width="100%"> </td> <td> <img src="assets/sbs_edit.png" width="100%"> </td> </tr> <tr> <td style="font-size: 1.1em; font-weight: 500; padding-top: 6px;"> Side-by-side evaluation of T2I SFT on PartiPrompts with extended prompts </td> <td style="font-size: 1.1em; font-weight: 500; padding-top: 6px;"> Side-by-side evaluation of I2I SFT on the Flux Kontext benchmark with extended prompts </td> </tr> </table>

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}, 
}