| --- |
| 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. |
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| 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 |
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| 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. |
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| **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. |
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| 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 |
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| - **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 |
|
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| 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} |
| } |
| ``` |
