通过精准定位语义实现更精细的图像编辑，背景保留更好。
DCEdit: Dual-Level Controlled Image Editing via Precisely Localized Semantics
- 用视觉与文本自注意力增强跨注意力图，精确定位需编辑区域。
- 在特征和潜在空间双层级控制，实现更细粒度编辑效果。
- 适用于需要高精度修改且保持背景一致的图像编辑场景。
本文提出一种基于扩散模型的新型文本引导图像编辑方法。文本引导图像编辑的核心挑战在于精确识别并修改目标语义区域，而现有方法在此方面表现不足。我们的方法引入精确语义定位策略，利用视觉与文本自注意力机制增强跨注意力图，作为区域提示以提升编辑性能。进一步提出双层级控制机制，在特征与潜在表示层面同时融入区域提示，实现更精细的编辑控制。为全面评估本方法与其它基于DiT的方案，我们构建了RW-800基准数据集，包含高分辨率图像、长描述性文本、真实世界图像及新的文本编辑任务。在主流PIE-Bench和新提出的RW-800基准上的实验结果表明，该方法在保留背景和实现精准编辑方面均表现出优越性能。
原文摘要 · Abstract (English)
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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