Add model card for EditHF-Reward

README.md

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+---
+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}
+}
+```