Conditioning Matters: Stabilizing Inversion and Attention in Diffusion Image Editing

The paper introduces SimEdit, a novel conditioning-aware framework aimed at enhancing inversion-based image editing in diffusion models. It features two main components: conditioning refinement for improved semantic precision and structural alignment, and token-wise cross-branch attention control to differentiate between edit-relevant and structure-preserving components. Empirical results on the PIE-Bench benchmark indicate that SimEdit significantly enhances inversion reconstruction quality and editing performance, addressing key challenges in maintaining background preservation and semantic fidelity during image editing tasks.