无需训练即可精准编辑图像，自动识别修改区域并平滑边界。
FusionEdit: Semantic Fusion and Attention Modulation for Training-Free Image Editing
- 通过语义差异自动识别需编辑与保留区域
- 距离感知融合与总变差损失减少边界伪影
- 基于AdaIN的注意力调制提升编辑可控性
文本引导的图像编辑旨在根据目标提示修改特定区域，同时保持源图像的身份。现有方法使用显式的二值掩码约束编辑，但硬边界会引入伪影并降低可编辑性。为此，我们提出FusionEdit，一种无需训练的图像编辑框架，实现精确且可控的编辑。首先，通过测量源提示与目标提示之间的语义差异，自动识别编辑区域和保留区域。为缓解边界伪影，FusionEdit在区域边界处进行距离感知的潜在空间融合，生成软且准确的掩码，并采用总变差损失强制平滑过渡，获得自然的编辑结果。其次，FusionEdit在DiT注意力层中引入基于AdaIN的调制，实现编辑区域内的统计注意力融合，增强可编辑性的同时保持与源图像的全局一致性。大量实验表明，FusionEdit显著优于当前最优方法。代码已公开于https://github.com/Yvan1001/FusionEdit。
原文摘要 · Abstract (English)
Text-guided image editing aims to modify specific regions according to the target prompt while preserving the identity of the source image. Recent methods exploit explicit binary masks to constrain editing, but hard mask boundaries introduce artifacts and reduce editability. To address these issues, we propose FusionEdit, a training-free image editing framework that achieves precise and controllable edits. First, editing and preserved regions are automatically identified by measuring semantic discrepancies between source and target prompts. To mitigate boundary artifacts, FusionEdit performs distance-aware latent fusion along region boundaries to yield the soft and accurate mask, and employs a total variation loss to enforce smooth transitions, obtaining natural editing results. Second, FusionEdit leverages AdaIN-based modulation within DiT attention layers to perform a statistical attention fusion in the editing region, enhancing editability while preserving global consistency with the source image. Extensive experiments demonstrate that our FusionEdit significantly outperforms state-of-the-art methods. Code is available at \href{https://github.com/Yvan1001/FusionEdit}{https://github.com/Yvan1001/FusionEdit}.
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