---
license: apache-2.0
language:
- en
task_categories:
- image-text-to-text
tags:
- image-editing
- reward-modeling
- reinforcement-learning
- benchmark
- evaluation
- multimodal
---

<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://github.com/VectorSpaceLab/EditScore"><img src="https://img.shields.io/badge/GitHub-Code-blue" alt="github code"></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.

## Paper Abstract

Instruction-guided image editing has achieved remarkable progress, yet current models still face challenges with complex instructions and often require multiple samples to produce a desired result. Reinforcement Learning (RL) offers a promising solution, but its adoption in image editing has been severely hindered by the lack of a high-fidelity, efficient reward signal. In this work, we present a comprehensive methodology to overcome this barrier, centered on the development of a state-of-the-art, specialized reward model. We first introduce EditReward-Bench, a comprehensive benchmark to systematically evaluate reward models on editing quality. Building on this benchmark, we develop EditScore, a series of reward models (7B-72B) for evaluating the quality of instruction-guided image editing. Through meticulous data curation and filtering, EditScore effectively matches the performance of learning proprietary VLMs. Furthermore, coupled with an effective self-ensemble strategy tailored for the generative nature of EditScore, our largest variant even surpasses GPT-5 in the benchmark. We then demonstrate that a high-fidelity reward model is the key to unlocking online RL for image editing. Our experiments show that, while even the largest open-source VLMs fail to provide an effective learning signal, EditScore enables efficient and robust policy optimization. Applying our framework to a strong base model, OmniGen2, results in a final model that shows a substantial and consistent performance uplift. Overall, this work provides the first systematic path from benchmarking to reward modeling to RL training in image editing, showing that a high-fidelity, domain-specialized reward model is the key to unlocking the full potential of RL in this domain.

## ✨ 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.

<p align="center">
  <img src="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="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

# 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.
```

---

## 📊 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}
}
```