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

• URL: http://arxiv.org/abs/2607.20883v1
• Date: Thu, 23 Jul 2026 03:11:25 GMT
• Title: WhereEdit: Mask-aware Local Latent Editing for One-Step Image Editing
• Abstract summary: One-step text-to-image (T2I) models enable efficient image synthesis and provide new opportunities for real-time image editing.<n>Existing one-step editing methods rely on text conditioning for semantic transformation, lacking explicit spatial control over textitwhere to edit.<n>We propose textbfWhereEdit, a framework that reformulates one-step editing as localized adaptive editing.
• Score: 16.758428220880734
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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