SR-Edit: Region-Aware Image Editing via Self-Refinement

With the recent rapid progress in generative models, im-age editing has made remarkable advances, yet achieving faithful editsthat precisely modify only the target regions while strictly preservingall other regions remains challenging. Since externally provided regionannotations are often difficult to obtain in practice, a growing body ofwork seeks to improve preservation by automatically inferring edit andnon-edit regions, and then enforcing consistency on the latter. However,these approaches still suffer from inaccurate region estimation and heuris-tic correction strategies that distort the native inference process, makingmethods designed for fidelity themselves a new source of artifacts. Wepropose SR-Edit, an image editing framework that overcomes these is-sues via iterative self-refinement. Specifically, at each iteration, SR-Editfirst (i) extracts progressively precise and self-consistent region separa-tion from the model’s own predictions by lightweight post-processing,and then (ii) enforces preservation in non-edit areas through correctionupdates that remain aligned with the original sampling dynamics. Ex-tensive experiments demonstrate that SR-Edit achieves superior preser-vation and overall image quality compared to existing editing techniques.