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Existing text-to-image editing methods tend to excel either in rigid ornon-rigid editing but encounter challenges when combining both, resulting inmisaligned outputs with the provided text prompts. In addition, integratingreference images for control remains challenging. To address these issues, wepresent a versatile image editing framework capable of executing both rigid andnon-rigid edits, guided by either textual prompts or reference images. Weleverage a dual-path injection scheme to handle diverse editing scenarios andintroduce an integrated self-attention mechanism for fusion of appearance andstructural information. To mitigate potential visual artifacts, we furtheremploy latent fusion techniques to adjust intermediate latents. Compared toprevious work, our approach represents a significant advance in achievingprecise and versatile image editing. Comprehensive experiments validate theefficacy of our method, showcasing competitive or superior results intext-based editing and appearance transfer tasks, encompassing both rigid andnon-rigid settings.

Unified Diffusion-Based Rigid and Non-Rigid Editing with Text and Image Guidance