README.md

---
pipeline_tag: any-to-any
library_name: transformers
tags:
- text-to-image
- image-editing
- image-understanding
- vision-language
- multimodal
- autoregressive
- unified-model
---

## 🌌 UniPic2-SD3.5M-Kontext-2B

<div align="center">   
  <img src="skywork-logo.png" alt="Skywork Logo" width="500"> 
</div>

<p align="center">
  <a href="https://github.com/SkyworkAI/UniPic">
    <img src="https://img.shields.io/badge/GitHub-UniPic-blue?logo=github" alt="GitHub Repo">
  </a>
  <a href="https://github.com/SkyworkAI/UniPic/stargazers">
    <img src="https://img.shields.io/github/stars/SkyworkAI/UniPic?style=social" alt="GitHub Stars">
  </a>
  <a href="https://github.com/SkyworkAI/UniPic/network/members">
    <img src="https://img.shields.io/github/forks/SkyworkAI/UniPic?style=social" alt="GitHub Forks">
  </a>
</p>



## 📖 Introduction

**UniPic2-SD3.5M-Kontext-2B** is a 2B-parameter post-trained model built on the SD3.5-Medium family. It focuses on text-to-image generation and image editing, delivering strong quality with a fast generation speed. It runs smoothly on a single 16 GB consumer GPU.

<div align="center"> <img src="teaser.png" alt="Model Teaser" width="720"> </div>

## 📊 Benchmarks

**UniPic2-SD3.5M-Kontext-2B** w/o GRPO achieves competitive results across a variety of vision-language tasks:

| Task               | Score  |
|--------------------|--------|
| 🧠 **GenEval**       | 0.83   |
| 🖼️ **DPG-Bench**     | 83.7   |
| ✂️ **GEditBench-EN** | 6.31   |
| 🧪 **ImgEdit-Bench** | 3.95   |



## 🧠 Usage

### 1. Clone the Repository

```bash
git clone https://github.com/SkyworkAI/UniPic
cd UniPic
```

### 2. Set Up the Environment
```bash
conda create -n unipic python=3.10.14
conda activate unipic
pip install -r requirements.txt
```


### 3.Text-to-Image Generation
```bash
import torch
from PIL import Image
from unipicv2.pipeline_stable_diffusion_3_kontext import StableDiffusion3KontextPipeline
from unipicv2.transformer_sd3_kontext import SD3Transformer2DKontextModel
from diffusers import FlowMatchEulerDiscreteScheduler, AutoencoderKL
from transformers import CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel, T5TokenizerFast

# Load model components
pretrained_model_name_or_path = "/path/to/unipicv2_sd_3_5m_kontext"

transformer = SD3Transformer2DKontextModel.from_pretrained(
    pretrained_model_name_or_path, subfolder="transformer", torch_dtype=torch.bfloat16).cuda()

vae = AutoencoderKL.from_pretrained(
    pretrained_model_name_or_path, subfolder="vae", torch_dtype=torch.bfloat16).cuda()

# Load text encoders
text_encoder = CLIPTextModelWithProjection.from_pretrained(
    pretrained_model_name_or_path, subfolder="text_encoder", torch_dtype=torch.bfloat16).cuda()
tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer")

text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(
    pretrained_model_name_or_path, subfolder="text_encoder_2", torch_dtype=torch.bfloat16).cuda()
tokenizer_2 = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer_2")

text_encoder_3 = T5EncoderModel.from_pretrained(
    pretrained_model_name_or_path, subfolder="text_encoder_3", torch_dtype=torch.bfloat16).cuda()
tokenizer_3 = T5TokenizerFast.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer_3")

scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler")

# Create pipeline
pipeline = StableDiffusion3KontextPipeline(
    transformer=transformer, vae=vae,
    text_encoder=text_encoder, tokenizer=tokenizer,
    text_encoder_2=text_encoder_2, tokenizer_2=tokenizer_2,
    text_encoder_3=text_encoder_3, tokenizer_3=tokenizer_3,
    scheduler=scheduler)

# Generate image
image = pipeline(
    prompt='a pig with wings and a top hat flying over a happy futuristic scifi city',
    negative_prompt='',
    height=512, width=384,
    num_inference_steps=50,
    guidance_scale=3.5,
    generator=torch.Generator(device=transformer.device).manual_seed(42)
).images[0]

image.save("text2image.png")
```


### 4.  Image Editing
The image editing feature within this unified model is an exploratory module at the forefront of research. And it is not yet production-ready.

```bash
# Load and preprocess image
def fix_longer_edge(x, image_size, factor=32):
    w, h = x.size
    if w >= h:
        target_w = image_size
        target_h = h * (target_w / w)
        target_h = round(target_h / factor) * factor
    else:
        target_h = image_size
        target_w = w * (target_h / h)
        target_w = round(target_w / factor) * factor
    x = x.resize(size=(target_w, target_h))
    return x

image = Image.open("text2image.png")
image = fix_longer_edge(image, image_size=512)

negative_prompt = "blurry, low quality, low resolution, distorted, deformed, broken content, missing parts, damaged details, artifacts, glitch, noise, pixelated, grainy, compression artifacts, bad composition, wrong proportion, incomplete editing, unfinished, unedited areas."

# Edit image
edited_image = pipeline(
    image=image,
    prompt="remove the pig's hat",
    negative_prompt=negative_prompt,
    height=image.height, width=image.width,
    num_inference_steps=50,
    guidance_scale=3.5,
    generator=torch.Generator(device=transformer.device).manual_seed(42)
).images[0]

edited_image.save("image_editing.png")
```

## 📄 License

This model is released under the MIT License.

## Citation
If you use Skywork-UniPic in your research, please cite:
```
@misc{wang2025skyworkunipicunifiedautoregressive,
      title={Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding and Generation}, 
      author={Peiyu Wang and Yi Peng and Yimeng Gan and Liang Hu and Tianyidan Xie and Xiaokun Wang and Yichen Wei and Chuanxin Tang and Bo Zhu and Changshi Li and Hongyang Wei and Eric Li and Xuchen Song and Yang Liu and Yahui Zhou},
      year={2025},
      eprint={2508.03320},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.03320}, 
}
```