EditScore-7B

<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 and ModelScope.
• 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 and Benchmark Dataset, and on ModelScope: Models Collection and Benchmark Dataset.

📖 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

# 1. Clone the repo
git clone [email protected]: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

# 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.

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.

# 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:

@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}
}