| library_name: peft | |
| pipeline_tag: image-text-to-text | |
| # EditHF | |
| EditHF is an MLLM-based evaluation model introduced in the paper [EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing](https://huggingface.co/papers/2603.14916). | |
| It is designed to provide fine-grained, human-aligned scores for text-guided image editing across three dimensions: **visual quality**, **editing alignment**, and **attribute preservation**. The model was trained on the **EditHF-1M** dataset, which contains over 29M human preference pairs. | |
| ## 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) | |
| ## Sample Usage | |
| To use EditHF for evaluating image editing results, you can use the inference script provided in the official repository: | |
| ```bash | |
| python inference.py \ | |
| --source_image "/path/to/source.jpg" \ | |
| --edited_image "/path/to/edited.jpg" \ | |
| --instruction "Editing instruction" \ | |
| --peft_dir "lora_checkpoints_visual" \ | |
| --mode visual | |
| ``` | |
| The `--mode` parameter can be set to: | |
| - `visual`: Evaluates visual quality. | |
| - `alignment`: Evaluates alignment with the editing instruction. | |
| - `preservation`: Evaluates the preservation of source image attributes. | |
| ## Citation | |
| ```bibtex | |
| @article{edithf1m, | |
| title={EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing}, | |
| author={...}, | |
| journal={arXiv preprint arXiv:2603.14916}, | |
| year={2026} | |
| } | |
| ``` |