README.md

---
library_name: diffusers
pipeline_tag: image-to-image
---

# EditHF-Reward

This repository contains the weights for **EditHF-Reward**, an advanced image editing model based on **Qwen-Image-Edit** that has been refined using reinforcement learning with human-aligned feedback. 

This model was introduced in the paper [EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing](https://huggingface.co/papers/2603.14916).

## Model Description

Recent text-guided image editing (TIE) models often suffer from issues such as artifacts, unexpected edits, or unaesthetic content. **EditHF-Reward** addresses these challenges by utilizing rewards from **EditHF**, a multimodal large language model (MLLM) trained on the **EditHF-1M** dataset.

**EditHF-1M** is a million-scale dataset featuring over 29 million human preference pairs and 148,000 human mean opinion ratings, covering three critical dimensions:
- **Visual Quality**: Reducing artifacts and improving aesthetic appeal.
- **Instruction Alignment**: Ensuring edited images accurately reflect the text instructions.
- **Attribute Preservation**: Maintaining the identity and characteristics of the original image.

By fine-tuning with reinforcement learning (RL) using these signals, this model achieves significantly improved performance and human alignment compared to its base version.

## Resources

- **Paper**: [EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing](https://huggingface.co/papers/2603.14916)
- **GitHub Repository**: [IntMeGroup/EditHF](https://github.com/IntMeGroup/EditHF)

## Citation

If you find this model useful in your research, please cite the following paper:

```bibtex
@article{edithf1m,
  title={EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing},
  author={Luo, Junyu and others},
  journal={arXiv preprint arXiv:2603.14916},
  year={2026}
}
```