Create README.md

README.md

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+---
+pipeline_tag: any-to-any
+library_name: transformers
+tags:
+- text-to-image
+- image-editing
+- image-understanding
+- vision-language
+- multimodal
+- unified-model
+- teacher-model
+- diffusion
+license: mit
+---
+
+## 🌌 UniPic3-Teacher-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-Teacher-Model** is the **high-quality teacher diffusion model** used in the UniPic 3.0 framework.
+It is trained with **full multi-step diffusion sampling** and optimized for **maximum perceptual quality, semantic consistency, and realism**.
+
+This model serves as the **teacher backbone** for:
+- **Distribution Matching Distillation (DMD)**
+- **Consistency / trajectory distillation**
+- **Few-step student model training**
+
+Rather than being optimized for fast inference, the teacher model prioritizes **generation fidelity and stability**, providing a strong and reliable supervision signal for downstream distilled models.
+
+---
+
+## 🧠 Model Characteristics
+
+- **Role**: Teacher model (not a distilled student)
+- **Sampling**: Multi-step diffusion (high-fidelity)
+- **Architecture**: Unified UniPic3 Transformer
+- **Tasks Supported**:
+  - Single-image editing
+  - Multi-image composition (2–6 images)
+  - Human–Object Interaction (HOI)
+- **Resolution**: Flexible, within pixel budget constraints
+- **Training Objective**:
+  - Flow Matching / Diffusion loss
+  - Used as teacher for DMD & consistency training
+
+---
+
+## 📊 Benchmarks
+<div align="center"> <img src="unipic3_eval.png" alt="Model Teaser" width="720"> </div>
+
+This teacher model achieves **state-of-the-art performance** on:
+- Image editing benchmarks
+- Multi-image composition benchmarks
+
+It provides **high-quality supervision targets** for distilled UniPic3 student models.
+
+---
+
+## ⚠️ Important Note
+
+> **This repository hosts the teacher model.**  
+> It is **not optimized for few-step inference**.
+
+If you are looking for:
+- ⚡ **4–8 step fast inference**
+- 🚀 **Deployment-friendly distilled models**
+
+please refer to the **UniPic3-DMD / distilled checkpoints** instead.
+
+---
+
+## 🧠 Usage (Teacher Model)
+
+### 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-DMD/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}, 
+}
+```
+
+
+