WhereEdit: Mask-aware Local Latent Editing for One-Step Image Editing

2026-07-22

AI Summary

Existing one-step image editing methods lack explicit spatial control, making it challenging to achieve strong semantic and structurally consistent modifications within user-specified regions. This work proposes a locally adaptive editing framework that leverages a mask-aware mechanism to automatically identify semantically relevant areas and applies adaptive modulation in the latent space to precisely edit only the target region while preserving the rest of the image unchanged. By integrating internal feature-driven editable region discovery, localized latent modulation, and spatial constraints, the method significantly outperforms current one-step approaches on PIE-Bench, striking an effective balance between editing fidelity and computational efficiency. These results underscore the critical role of explicit spatial reasoning in enabling high-quality image editing.

Abstract

Recent one-step text-to-image (T2I) models enable efficient image synthesis and provide new opportunities for real-time image editing. However, existing one-step editing methods primarily rely on text conditioning for semantic transformation, lacking explicit spatial control over \textit{where} to edit. More importantly, even when spatial constraints are introduced, these methods often struggle to achieve strong and stable semantic modifications within the target regions. In this work, we revisit one-step image editing from a spatially controlled perspective and identify two key challenges: discovering editable regions and achieving effective localized semantic transformation. We reveal that existing methods perform global semantic transport, which limits high-intensity local editing under the one-step setting. To address this issue, we propose \textbf{WhereEdit}, a framework that reformulates one-step editing as localized adaptive editing. WhereEdit automatically identifies semantically relevant regions from internal model features and applies adaptive local modulation to enhance target-region editing while preserving non-target areas and structural consistency. Experiments on the PIE-Bench benchmark demonstrate that WhereEdit consistently outperforms existing one-step image editing methods, achieving superior editing quality while maintaining the efficiency of one-step generation. Additional experiments with region-level supervision further highlight the importance of explicit spatial reasoning for high-quality one-step image editing.

Problem

• one-step image editing
• spatial control
• localized semantic transformation
• mask-aware editing
• text-to-image

Innovation

• mask-aware editing
• local latent modulation
• one-step image editing
• spatially controlled editing
• semantic transport