PairEdit: Learning Semantic Variations for Exemplar-based Image Editing
Haoguang Lu, Jiacheng Chen, Zhenguo Yang, Aurele Gnanha, Fu Lee Wang, Qing Li, Xudong Mao
Recent advancements in text-guided image editing have achieved notable success by leveraging natural language prompts for fine-grained semantic control. However, certain editing semantics are challenging to specify precisely using textual descriptions alone. A practical alternative involves learning editing semantics from paired examples. Existing methods using paired images typically handle only simple semantic changes or require extensive training on large-scale datasets. In this paper, we introduce PairEdit, a novel visual editing method designed to effectively learn complex editing semantics from a limited number of image pairs or even a single image pair. We propose a guidance-based noise prediction that explicitly models semantic variations within paired images through the guidance direction term. Moreover, we introduce a content-preserving noise schedule to facilitate more effective semantic learning. We also propose optimizing distinct LoRAs to disentangle the learning of semantic variations and content. Extensive qualitative and quantitative evaluations demonstrate that PairEdit successfully learns intricate semantics while significantly improving content consistency compared to baseline methods. Code will be made publicly available.