metadata
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.
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
- GitHub Repository: IntMeGroup/EditHF
Sample Usage
To use EditHF for evaluating image editing results, you can use the inference script provided in the official repository:
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
@article{edithf1m,
title={EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing},
author={...},
journal={arXiv preprint arXiv:2603.14916},
year={2026}
}