Update README.md

README.md

@@ -1,77 +1,69 @@
 ---
 license: apache-2.0
-pipeline_tag: image-to-image
+pipeline_tag: text-to-image
 library_name: diffusers
 ---
 
 # PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling
 
-This repository contains the official implementation of **PaCo-RL**, a comprehensive framework for consistent image generation.
+<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>
 
-[\ud83d\udcda Paper](https://huggingface.co/papers/2512.04784) | [\ud83c\udf10 Project Page](https://x-gengroup.github.io/HomePage_PaCo-RL/) | [\ud83d\udcbb Code](https://github.com/X-GenGroup/PaCo-RL) | [\ud83e\udd17 Models & Data](https://huggingface.co/collections/X-GenGroup/paco-rl)
+The model presented in [PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling](https://huggingface.co/papers/2512.04784).
 
-PaCo-RL aims to preserve identities, styles, and logical coherence across multiple images, which is essential for applications such as storytelling and character design. It leverages reinforcement learning to learn complex and subjective visual criteria without large-scale datasets, by combining a specialized consistency reward model (PaCo-Reward) with an efficient RL algorithm (PaCo-GRPO).
+## 🌟 Overview
 
-## Key Components
+**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.
 
-- **PaCo-Reward**: A pairwise consistency evaluator trained on a large-scale dataset constructed via automated sub-figure pairing. It evaluates consistency through a generative, autoregressive scoring mechanism enhanced by task-aware instructions and CoT reasons.
-- **PaCo-GRPO**: An efficient RL optimization strategy that leverages a novel resolution-decoupled optimization to substantially reduce RL cost, alongside a log-tamed multi-reward aggregation mechanism that ensures balanced and stable reward optimization.
+### Key Components
 
-## \ud83d\ude80 Quick Start
+- **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
 
-For detailed instructions on installation, training the reward model, and running RL training, please refer to the [GitHub repository](https://github.com/X-GenGroup/PaCo-RL).
 
-### Installation
-```bash
-git clone https://github.com/X-GenGroup/PaCo-RL.git
-cd PaCo-RL
-```
+## Example Usage
 
-### 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
-```
+```python
+import torch
+from diffusers import FluxKontextPipeline
+from peft import PeftModel
+from diffusers.utils import load_image
+
+pipe = FluxKontextPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev",
+    torch_dtype=torch.bfloat16,
+    device_map="cuda"
+)
 
-### 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
+pipe.transformer = PeftModel.from_pretrained(
+    pipe.transformer,
+    'X-GenGroup/PaCo-FLUX.1-Kontext-Lora'
+)
+input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
+
+image = pipe(
+  image=input_image,
+  prompt="Add a blue hat to the cat",
+  guidance_scale=2.5
+).images[0]
 ```
 
-## \ud83c\udf81 Model Zoo
+## 🎁 Model Zoo
 
 | Model | Type | HuggingFace |
 |-------|------|-------------|
-| **PaCo-Reward-7B** | Reward Model | [\ud83e\udd17 Link](https://huggingface.co/X-GenGroup/PaCo-Reward-7B) |
-| **PaCo-Reward-7B-Lora** | Reward Model (LoRA) | [\ud83e\udd17 Link](https://huggingface.co/X-GenGroup/PaCo-Reward-7B-Lora) |
-| **PaCo-FLUX.1-dev** | T2I Model (LoRA) | [\ud83e\udd17 Link](https://huggingface.co/X-GenGroup/PaCo-FLUX.1-dev-Lora) |
-| **PaCo-FLUX.1-Kontext-dev** | Image Editing Model (LoRA) | [\ud83e\udd17 Link](https://huggingface.co/X-GenGroup/PaCo-FLUX.1-Kontext-Lora) |
-| **PaCo-QwenImage-Edit** | Image Editing Model (LoRA) | [\ud83e\udd17 Link](https://huggingface.co/X-GenGroup/PaCo-Qwen-Image-Edit-Lora) |
+| **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
-If you find our work helpful or inspiring, please feel free to cite it:
+## ⭐ Citation
 ```bibtex
 @misc{ping2025pacorladvancingreinforcementlearning,
       title={PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling}, 
@@ -82,4 +74,8 @@ If you find our work helpful or inspiring, please feel free to cite it:
       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>