EditScore
/

EditScore-7B

@@ -8,200 +8,156 @@ tags:
 - transformers
 ---
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-### Framework versions
-- PEFT 0.17.1

 - transformers
 ---
+<p align="center">
+  <img src="https://github.com/VectorSpaceLab/EditScore/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/2506.18871"><img src="https://img.shields.io/badge/arXiv%20paper-2506.18871-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=#-usage-tips>Usage Tips</a> |
+        <a href=#-limitations-and-suggestions>Limitations</a> |
+        <a href=#-gradio-demo>Online Demos</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
+- **2025-09-29**: 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.
+<p align="center">
+  <img src="https://github.com/VectorSpaceLab/EditScore/assets/table_reward_model_results.png" width="95%">
+  <br>
+  <em>Benchmark results on EditReward-Bench.</em>
+</p>
+We demonstrate the practical utility of EditScore through two key applications:
+- **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.
+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.
+<p align="center">
+  <img src="https://github.com/VectorSpaceLab/EditScore/assets/figure_edit_results.png" width="95%">
+  <br>
+  <em>EditScore as a superior reward signal for image editing.</em>
+</p>
+## 📌 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.
+## 🚀 Quick Start
+### 🛠️ Environment Setup
+#### ✅ Recommended Setup
+```bash
+# 1. Clone the repo
+git clone git@github.com:VectorSpaceLab/EditScore.git
+cd EditScore
+# 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
+```
+#### 🌏 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
+```
+---
+### 🧪 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.
+```
+---
+## 📊 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:},
+  year={2025}
+}
+```