README.md

---
license: apache-2.0
pipeline_tag: image-text-to-text
---

<p align="center">
  <img src="https://github.com/VectorSpaceLab/EditScore/raw/main/assets/logo.png" width="65%">
</p>

This repository contains **EditScore**, a series of state-of-the-art open-source reward models (7B–72B) designed to evaluate and enhance instruction-guided image editing.

The model was presented in the paper [EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling](https://huggingface.co/papers/2509.23909).

- 📚 [Paper](https://huggingface.co/papers/2509.23909)
- 🌐 [Project Page](https://vectorspacelab.github.io/EditScore)
- 💻 [Code Repository](https://github.com/VectorSpaceLab/EditScore)

## ✨ 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**.

## 📖 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/raw/main/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/raw/main/assets/figure_edit_results.png" width="95%">
  <br>
  <em>EditScore as a superior reward signal for image editing.</em>
</p>

## 🚀 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.
```

---

## ❤️ 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}
}
```