Update README.md

README.md

@@ -1,16 +1,11 @@
 ---
 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 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;
@@ -18,6 +13,8 @@ Code Repository: [https://github.com/X-GenGroup/PaCo-RL](https://github.com/X-Ge
   <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.
@@ -27,60 +24,45 @@ Code Repository: [https://github.com/X-GenGroup/PaCo-RL](https://github.com/X-Ge
 - **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
-```
+## 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 os
+from PIL import Image
+import torch
+from diffusers import QwenImageEditPipeline
+from peft import PeftModel
+from diffusers.utils import load_image
 
-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
-```
+pipeline = QwenImageEditPipeline.from_pretrained(
+    "Qwen/Qwen-Image-Edit",
+    torch_dtype=torch.bfloat16,
+    device_map="balanced"
+)
 
-See 📖 [PaCo-GRPO Documentation](PaCo-GRPO/README.md) for detailed guide.
+pipeline.transformer = PeftModel.from_pretrained(
+    pipeline.transformer,
+    'X-GenGroup/PaCo-Qwen-Image-Edit-Lora'
+)
 
-## 📁 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
+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
@@ -93,9 +75,6 @@ PaCo-RL/
 | **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