EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing

Recent text-guided image editing (TIE) models have achievedremarkable progress, while many edited images still suffer from issuessuch as artifacts, unexpected editings, unaesthetic contents. Althoughsome benchmarks and methods have been proposed for evaluating editedimages, scalable evaluation models are still lacking, which limits the de-velopment of human feedback reward models for image editing. To ad-dress the challenges, we first introduce EditHF-1M, a million-scale im-age editing dataset with over 29M human preference pairs and 148Khuman mean opinion ratings, both evaluated from three dimensions,i.e., visual quality, instruction alignment, and attribute preservation.Based on EditHF-1M, we propose EditHF, a multimodal large lan-guage model (MLLM) based evaluation model, to provide human-alignedfeedback from image editing. Finally, we introduce EditHF-Reward,which utilizes EditHF as a reward signal to optimize the text-guidedimage editing models through reinforcement learning. Extensive experi-ments show that EditHF achieves superior alignment with human prefer-ences and demonstrates strong generalization on other datasets. Further-more, we fine-tune the Qwen-Image-Edit using EditHF-Reward, achiev-ing significant performance improvements, which demonstrates the abil-ity of EditHF to serve as a reward model to scale-up the image editing.Both the dataset and code will be released in our GitHub repository:https://github.com/IntMeGroup/EditHF.