FusionEdit: 基于语义融合与注意力调节的无训练图像编辑
FusionEdit: Semantic Fusion and Attention Modulation for Training-Free Image Editing
AI总结：
FusionEdit通过语义融合与注意力调节实现无训练图像编辑，提升编辑精度与可控性。
摘要：
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。最近的方法利用显式的二进制掩码来约束编辑，但硬掩码边界会引入伪影并降低编辑性。为了解决这些问题，我们提出了FusionEdit，一种无需训练的图像编辑框架，实现了精确且可控的编辑。首先，编辑和保留区域通过测量源和目标提示之间的语义差异自动识别。为了缓解边界伪影，FusionEdit在区域边界上执行距离感知的潜在融合，以生成柔软且准确的掩码，并采用总变分损失以强制平滑过渡，从而获得自然的编辑结果。其次，FusionEdit利用基于AdaIN的调节在DiT注意力层内进行统计注意力融合，在编辑区域增强编辑性，同时保持与源图像的全局一致性。广泛的实验表明，我们的FusionEdit显著优于最先进的方法。代码可在https://github.com/Yvan1001/FusionEdit上获得。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
