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arXiv · 2503.16795

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

Yihan Hu·Jianing Peng·Yiheng Lin·Ting Liu·Xiaochao Qu·Luoqi Liu·Yao Zhao·Yunchao Wei
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Abstract

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. To fully compare our methods with other DiT-based approaches, we construct the RW-800 benchmark, featuring high resolution images, long descriptive texts, real-world images, and a new text editing task. Experimental results on the popular PIE-Bench and RW-800 benchmarks demonstrate the superior performance of our approach in preserving background and providing accurate edits.

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Yihan Hu, Jianing Peng, Yiheng Lin, Ting Liu, Xiaochao Qu, Luoqi Liu, Yao Zhao, Yunchao Wei. 2025-03-21. DCEdit: Dual-Level Controlled Image Editing via Precisely Localized Semantics. https://arxiv.org/abs/2503.16795

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