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

## 🌌 Unipic3-Consistency-Model
<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
<div align="center"> <img src="unipic3.png" alt="Model Teaser" width="720"> </div>

**UniPic3-Consistency-Model** is a few-step image editing and multi-image composition model based on **Consistency Flow Matching (CM)**.
The model learns a *trajectory-consistent* mapping from noisy latent states to clean images, enabling stable generation with strong structural consistency.  
It is distilled from **UniPic-3** to support **fast inference (≤8 steps)** while preserving composition correctness.The model is especially suitable for scenarios requiring **geometric alignment** and **semantic coherence**, such as multi-image composition and human–object interaction (HOI).

## 📊 Benchmarks
<div align="center"> <img src="unipic3_eval.png" alt="Model Teaser" width="720"> </div>


## 🧠 Usage

### 1. Clone the Repository
```bash
git clone https://github.com/SkyworkAI/UniPic
cd UniPic-3
```

### 2. Set Up the Environment
```bash
conda create -n unipic python=3.10
conda activate unipic3
pip install -r requirements.txt
```


### 3.Batch Inference
```bash
transformer_path = "Skywork/Unipic3-Consistency-Model/ema_transformer"

python -m torch.distributed.launch --nproc_per_node=1 --master_port 29501 --use_env \
    qwen_image_edit_fast/batch_inference.py \
    --jsonl_path data/val.jsonl \
    --output_dir work_dirs/output \
    --distributed \
    --num_inference_steps 4 \
    --true_cfg_scale 4.0 \
    --transformer transformer_path \
    --skip_existing
```

## 📄 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}, 
}
```

```
@misc{wei2025skyworkunipic20building,
      title={Skywork UniPic 2.0: Building Kontext Model with Online RL for Unified Multimodal Model}, 
      author={Hongyang Wei and Baixin Xu and Hongbo Liu and Cyrus Wu and Jie Liu and Yi Peng and Peiyu Wang and Zexiang Liu and Jingwen He and Yidan Xietian and Chuanxin Tang and Zidong Wang and Yichen Wei and Liang Hu and Boyi Jiang and William Li and Ying He and Yang Liu and Xuchen Song and Eric Li and Yahui Zhou},
      year={2025},
      eprint={2509.04548},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.04548}, 
}
```