Update README.md

README.md

@@ -1,62 +1,168 @@
 ---
-library_name: peft
-license: other
 base_model: Qwen/Qwen2.5-VL-72B-Instruct
+library_name: peft
+pipeline_tag: text-generation
 tags:
-- llama-factory
+- base_model:adapter:Qwen/Qwen2.5-VL-72B-Instruct
 - lora
-- generated_from_trainer
-model-index:
-- name: 72B
-  results: []
+- transformers
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+<p align="center">
+  <img src="assets/logo.png" width="65%">
+</p>
+
+<p align="center">
+  <a href="https://vectorspacelab.github.io/EditScore"><img src="https://img.shields.io/badge/Project%20Page-EditScore-yellow" alt="project page"></a>
+  <a href="https://arxiv.org/abs/2509.23909"><img src="https://img.shields.io/badge/arXiv%20paper-2509.23909-b31b1b.svg" alt="arxiv"></a>
+  <a href="https://huggingface.co/collections/EditScore/editscore-68d8e27ee676981221db3cfe"><img src="https://img.shields.io/badge/EditScore-🤗-yellow" alt="model"></a>
+  <a href="https://huggingface.co/datasets/EditScore/EditReward-Bench"><img src="https://img.shields.io/badge/EditReward--Bench-🤗-yellow" alt="dataset"></a>
+</p>
+
+<h4 align="center">
+    <p>
+        <a href=#-news>News</a> |
+        <a href=#-quick-start>Quick Start</a> |
+        <a href=#-benchmark-your-image-editing-reward-model usage>Benchmark Usage</a> |
+        <a href=#%EF%B8%8F-citing-us>Citation</a>
+    <p>
+</h4>
+
+**EditScore** is a series of state-of-the-art open-source reward models (7B–72B) designed to evaluate and enhance instruction-guided image editing.
+## ✨ Highlights
+- **State-of-the-Art Performance**: Effectively matches the performance of leading proprietary VLMs. With a self-ensembling strategy, **our largest model surpasses even GPT-5** on our comprehensive benchmark, **EditReward-Bench**.
+- **A Reliable Evaluation Standard**: We introduce **EditReward-Bench**, the first public benchmark specifically designed for evaluating reward models in image editing, featuring 13 subtasks, 11 state-of-the-art editing models (*including proprietary models*) and expert human annotations.
+- **Simple and Easy-to-Use**: Get an accurate quality score for your image edits with just a few lines of code.
+- **Versatile Applications**: Ready to use as a best-in-class reranker to improve editing outputs, or as a high-fidelity reward signal for **stable and effective Reinforcement Learning (RL) fine-tuning**.
+
+## 🔥 News
+- **2025-09-30**: We release **OmniGen2-EditScore7B**, unlocking online RL For Image Editing via high-fidelity EditScore. LoRA weights are available at [Hugging Face](https://huggingface.co/OmniGen2/OmniGen2-EditScore7B) and [ModelScope](https://www.modelscope.cn/models/OmniGen2/OmniGen2-EditScore7B).
+- **2025-09-30**: We are excited to release **EditScore** and **EditReward-Bench**! Model weights and the benchmark dataset are now publicly available. You can access them on Hugging Face: [Models Collection](https://huggingface.co/collections/EditScore/editscore-68d8e27ee676981221db3cfe) and [Benchmark Dataset](https://huggingface.co/datasets/EditScore/EditReward-Bench), and on ModelScope: [Models Collection](https://www.modelscope.cn/collections/EditScore-8b0d53aa945d4e) and [Benchmark Dataset](https://www.modelscope.cn/datasets/EditScore/EditReward-Bench).
+
+## 📖 Introduction
+While Reinforcement Learning (RL) holds immense potential for this domain, its progress has been severely hindered by the absence of a high-fidelity, efficient reward signal.
+
+To overcome this barrier, we provide a systematic, two-part solution:
+
+- **A Rigorous Evaluation Standard**: We first introduce **EditReward-Bench**, a new public benchmark for the direct and reliable evaluation of reward models. It features 13 diverse subtasks and expert human annotations, establishing a gold standard for measuring reward signal quality.
+
+- **A Powerful & Versatile Tool**: Guided by our benchmark, we developed the **EditScore** model series. Through meticulous data curation and an effective self-ensembling strategy, EditScore sets a new state of the art for open-source reward models, even surpassing the accuracy of leading proprietary VLMs.
 
-# v7-72B
+<p align="center">
+  <img src="assets/table_reward_model_results.png" width="95%">
+  <br>
+  <em>Benchmark results on EditReward-Bench.</em>
+</p>
 
-This model is a fine-tuned version of [/share/project/shared_models/Qwen2.5-VL-72B-Instruct](https://huggingface.co//share/project/shared_models/Qwen2.5-VL-72B-Instruct) on the v7-2_8models_omnigen2-4samples_gpt4-1_range_0to25 dataset.
+We demonstrate the practical utility of EditScore through two key applications:
 
-## Model description
+- **As a State-of-the-Art Reranker**: Use EditScore to perform Best-of-*N* selection and instantly improve the output quality of diverse editing models.
+- **As a High-Fidelity Reward for RL**: Use EditScore as a robust reward signal to fine-tune models via RL, enabling stable training and unlocking significant performance gains where general-purpose VLMs fail.
 
-More information needed
+This repository releases both the **EditScore** models and the **EditReward-Bench** dataset to facilitate future research in reward modeling, policy optimization, and AI-driven model improvement.
 
-## Intended uses & limitations
+<p align="center">
+  <img src="assets/figure_edit_results.png" width="95%">
+  <br>
+  <em>EditScore as a superior reward signal for image editing.</em>
+</p>
 
-More information needed
 
-## Training and evaluation data
+## 📌 TODO
+We are actively working on improving EditScore and expanding its capabilities. Here's what's next:
+- [ ] Release RL training code applying EditScore to OmniGen2.
+- [ ] Provide Best-of-N inference scripts for OmniGen2, Flux-dev-Kontext, and Qwen-Image-Edit.
 
-More information needed
+## 🚀 Quick Start
 
-## Training procedure
+### 🛠️ Environment Setup
 
-### Training hyperparameters
+#### ✅ Recommended Setup
 
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 1
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 16
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 128
-- total_eval_batch_size: 128
-- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3.0
+```bash
+# 1. Clone the repo
+git clone [email protected]:VectorSpaceLab/EditScore.git
+cd EditScore
 
-### Training results
+# 2. (Optional) Create a clean Python environment
+conda create -n editscore python=3.12
+conda activate editscore
 
+# 3. Install dependencies
+# 3.1 Install PyTorch (choose correct CUDA version)
+pip install torch==2.7.1 torchvision --extra-index-url https://download.pytorch.org/whl/cu126
 
+# 3.2 Install other required packages
+pip install -r requirements.txt
 
-### Framework versions
+# EditScore runs even without vllm, though we recommend install it for best performance.
+pip install vllm
+```
+
+#### 🌏 For users in Mainland China
+
+```bash
+# Install PyTorch from a domestic mirror
+pip install torch==2.7.1 torchvision --index-url https://mirror.sjtu.edu.cn/pytorch-wheels/cu126
+
+# Install other dependencies from Tsinghua mirror
+pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+# EditScore runs even without vllm, though we recommend install it for best performance.
+pip install vllm -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
+
+---
+
+### 🧪 Usage Example
+Using EditScore is straightforward. The model will be automatically downloaded from the Hugging Face Hub on its first run.
+```python
+from PIL import Image
+from editscore import EditScore
+
+# Load the EditScore model. It will be downloaded automatically.
+# Replace with the specific model version you want to use.
+model_path = "Qwen/Qwen2.5-VL-7B-Instruct"
+lora_path = "EditScore/EditScore-7B"
+
+scorer = EditScore(
+    backbone="qwen25vl", # set to "qwen25vl_vllm" for faster inference
+    model_name_or_path=model_path,
+    enable_lora=True,
+    lora_path=lora_path,
+    score_range=25,
+    num_pass=1, # Increase for better performance via self-ensembling
+)
+
+input_image = Image.open("example_images/input.png")
+output_image = Image.open("example_images/output.png")
+instruction = "Adjust the background to a glass wall."
+
+result = scorer.evaluate([input_image, output_image], instruction)
+print(f"Edit Score: {result['final_score']}")
+# Expected output: A dictionary containing the final score and other details.
+```
+
+---
 
-- PEFT 0.15.2
-- Transformers 4.55.0
-- Pytorch 2.8.0+cu128
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+## 📊 Benchmark Your Image-Editing Reward Model
+We provide an evaluation script to benchmark reward models on **EditReward-Bench**. To evaluate your own custom reward model, simply create a scorer class with a similar interface and update the script.
+```bash
+# This script will evaluate the default EditScore model on the benchmark
+bash evaluate.sh
+
+# Or speed up inference with VLLM
+bash evaluate_vllm.sh
+```
+
+## ❤️ Citing Us
+If you find this repository or our work useful, please consider giving a star ⭐ and citation 🦖, which would be greatly appreciated:
+
+```bibtex
+@article{luo2025editscore,
+  title={EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling},
+  author={Xin Luo and Jiahao Wang and Chenyuan Wu and Shitao Xiao and Xiyan Jiang and Defu Lian and Jiajun Zhang and Dong Liu and Zheng Liu},
+  journal={arXiv preprint arXiv:2509.23909},
+  year={2025}
+}
+```