Add comprehensive model card for PaCo-RL

README.md

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+---
+license: apache-2.0
+pipeline_tag: image-to-image
+library_name: diffusers
+---
+
+# PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling
+
+This repository presents **PaCo-RL**, a comprehensive framework for consistent image generation, as described in the paper [PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling](https://huggingface.co/papers/2512.04784).
+
+Project Page: [https://x-gengroup.github.io/HomePage_PaCo-RL/](https://x-gengroup.github.io/HomePage_PaCo-RL/)
+Code Repository: [https://github.com/X-GenGroup/PaCo-RL](https://github.com/X-GenGroup/PaCo-RL)
+
+<div align="center">
+  <a href='https://arxiv.org/abs/2512.04784'><img src='https://img.shields.io/badge/ArXiv-red?logo=arxiv'></a>  &nbsp;
+  <a href='https://x-gengroup.github.io/HomePage_PaCo-RL/'><img src='https://img.shields.io/badge/ProjectPage-purple?logo=github'></a> &nbsp;
+  <a href="https://github.com/X-GenGroup/PaCo-RL"><img src="https://img.shields.io/badge/Code-9E95B7?logo=github"></a> &nbsp; 
+  <a href='https://huggingface.co/collections/X-GenGroup/paco-rl'><img src='https://img.shields.io/badge/Data & Model-green?logo=huggingface'></a> &nbsp;
+</div>
+
+## 🌟 Overview
+
+**PaCo-RL** is a comprehensive framework for consistent image generation through reinforcement learning, addressing challenges in preserving identities, styles, and logical coherence across multiple images for storytelling and character design applications.
+
+### Key Components
+
+- **PaCo-Reward**: A pairwise consistency evaluator with task-aware instruction and CoT reasoning.
+- **PaCo-GRPO**: Efficient RL optimization with resolution-decoupled training and log-tamed multi-reward aggregation
+
+## 🚀 Quick Start
+
+### Installation
+```bash
+git clone https://github.com/X-GenGroup/PaCo-RL.git
+cd PaCo-RL
+```
+
+### Train Reward Model
+```bash
+cd PaCo-Reward
+conda create -n paco-reward python=3.12 -y
+conda activate paco-reward
+cd LLaMA-Factory && pip install -e ".[torch,metrics]" --no-build-isolation
+cd .. && bash train/paco_reward.sh
+```
+
+See 📖 [PaCo-Reward Documentation](PaCo-Reward/README.md) for detailed guide.
+
+### Run RL Training
+```bash
+cd PaCo-GRPO
+conda create -n paco-grpo python=3.12 -y
+conda activate paco-grpo
+pip install -e .
+
+# Setup vLLM reward server
+conda create -n vllm python=3.12 -y
+conda activate vllm && pip install vllm
+export CUDA_VISIBLE_DEVICES=0
+export VLLM_MODEL_PATHS='X-GenGroup/PaCo-Reward-7B'
+export VLLM_MODEL_NAMES='Paco-Reward-7B'
+bash vllm_server/launch.sh
+
+# Start training
+export CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7
+conda activate paco-grpo
+bash scripts/single_node/train_flux.sh t2is
+```
+
+See 📖 [PaCo-GRPO Documentation](PaCo-GRPO/README.md) for detailed guide.
+
+## 📁 Repository Structure
+```
+PaCo-RL/
+├── PaCo-GRPO/              # RL training framework
+│   ├── config/             # RL configurations
+│   ├── scripts/            # Training scripts
+│   └── README.md
+├── PaCo-Reward/            # Reward model training
+│   ├── LLaMA-Factory/      # Training framework
+│   ├── config/             # Training configurations
+│   └── README.md
+└── README.md
+```
+
+## 🎁 Model Zoo
+
+| Model | Type | HuggingFace |
+|-------|------|-------------|
+| **PaCo-Reward-7B** | Reward Model | [🤗 Link](https://huggingface.co/X-GenGroup/PaCo-Reward-7B) |
+| **PaCo-Reward-7B-Lora** | Reward Model (LoRA) | [🤗 Link](https://huggingface.co/X-GenGroup/PaCo-Reward-7B-Lora) |
+| **PaCo-FLUX.1-dev** | T2I Model (LoRA) | [🤗 Link](https://huggingface.co/X-GenGroup/PaCo-FLUX.1-dev-Lora) |
+| **PaCo-FLUX.1-Kontext-dev** | Image Editing Model (LoRA) | [🤗 Link](https://huggingface.co/X-GenGroup/PaCo-FLUX.1-Kontext-Lora) |
+| **PaCo-QwenImage-Edit** | Image Editing Model (LoRA) | [🤗 Link](https://huggingface.co/X-GenGroup/PaCo-Qwen-Image-Edit-Lora) |
+
+## 🤗 Acknowledgement
+
+Our work is built upon [Flow-GRPO](https://github.com/yifan123/flow_grpo), [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory), [vLLM](https://github.com/vllm-project/vllm), and [Qwen2.5-VL](https://github.com/QwenLM/Qwen3-VL). We sincerely thank the authors for their valuable contributions to the community.
+
+## ⭐ Citation
+```bibtex
+@misc{ping2025pacorladvancingreinforcementlearning,
+      title={PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling}, 
+      author={Bowen Ping and Chengyou Jia and Minnan Luo and Changliang Xia and Xin Shen and Zhuohang Dang and Hangwei Qian},
+      year={2025},
+      eprint={2512.04784},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2512.04784}, 
+}
+```
+
+<div align="center">
+  <sub>⭐ Star us on GitHub if you find PaCo-RL helpful!</sub>
+</div>