SpotEdit: Selective Region Editing in Diffusion Transformers
This work addresses efficiency issues in image editing for users of diffusion models, though it is incremental as it builds on existing methods.
The paper tackles the problem of redundant computation in diffusion transformer-based image editing by proposing SpotEdit, a training-free framework that selectively updates only modified regions, achieving efficient and precise editing.
Diffusion Transformer models have significantly advanced image editing by encoding conditional images and integrating them into transformer layers. However, most edits involve modifying only small regions, while current methods uniformly process and denoise all tokens at every timestep, causing redundant computation and potentially degrading unchanged areas. This raises a fundamental question: Is it truly necessary to regenerate every region during editing? To address this, we propose SpotEdit, a training-free diffusion editing framework that selectively updates only the modified regions. SpotEdit comprises two key components: SpotSelector identifies stable regions via perceptual similarity and skips their computation by reusing conditional image features; SpotFusion adaptively blends these features with edited tokens through a dynamic fusion mechanism, preserving contextual coherence and editing quality. By reducing unnecessary computation and maintaining high fidelity in unmodified areas, SpotEdit achieves efficient and precise image editing.