You can deploy this model and start earning money today!

kandinskylab/
Kandinsky-5.0-T2I-Lite-pretrain-Diffusers

Name: Kandinsky-5.0-T2I-Lite-pretrain-Diffusers
Availability: InStock

Kandinsky5T2IPipeline

Views

No views yet

Habr | Project Page | Technical Report | Original GitHub | 🤗 Diffusers

Kandinsky 5.0 T2I Lite Pretrain – 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

python

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


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

Citation

bibtex

@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
}