Kandinsky 5.0 Image Lite Diffusers
Collection
4 items • Updated • 11
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]
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 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
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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
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| 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},
}
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