Instructed Editing

This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200K edits. Unlike prior approaches relying on human feedback, we devise a scalable data collection pipeline leveraging self-instruct with advanced foundation models, namely GPT-4V and DALL-E 3.