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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}
}
``` |