DCEdit: Dual-Level Controlled Image Editing via Precisely Localized Semantics

Understand

This paper presents a novel approach to improving text-guided image editing using diffusion-based models.

• Text-guided image editing task poses key challenge of precisly locate and edit the target semantic, and previous methods fall shorts in this aspect.
• Our method introduces a Precise Semantic Localization strategy that leverages visual and textual self-attention to enhance the cross-attention map, which can serve as a regional cues to improve editing performance.
• Then we propose a Dual-Level Control mechanism for incorporating regional cues at both feature and latent levels, offering fine-grained control for more precise edits.