---
license: apache-2.0
pipeline_tag: text-to-image
library_name: diffusers
---

# PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling

<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>

The model presented in [PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling](https://huggingface.co/papers/2512.04784).

## 🌟 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


## Example Usage

```python
import os
from PIL import Image
import torch
from diffusers import QwenImageEditPipeline
from peft import PeftModel
from diffusers.utils import load_image

pipeline = QwenImageEditPipeline.from_pretrained(
    "Qwen/Qwen-Image-Edit",
    torch_dtype=torch.bfloat16,
    device_map="balanced"
)

pipeline.transformer = PeftModel.from_pretrained(
    pipeline.transformer,
    'X-GenGroup/PaCo-Qwen-Image-Edit-Lora'
)

pipeline.set_progress_bar_config(disable=None)

input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")

prompt = "Add a blue hat to the cat."
inputs = {
    "image": input_image,
    "prompt": prompt,
    "generator": torch.manual_seed(0),
    "true_cfg_scale": 4.0,
    "negative_prompt": " ",
    "num_inference_steps": 50,
}

with torch.inference_mode():
    output = pipeline(**inputs)
    output_image = output.images[0]
```

## 🎁 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) |


## ⭐ 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>