LOCATEdit：基于图拉普拉斯优化的交叉注意力实现局部文本引导图像编辑
LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized Text-Guided Image Editing
AI总结：
LOCATEdit通过图拉普拉斯优化交叉注意力，利用自注意力块关系增强空间一致性，实现局部文本引导图像编辑，在PIE-Bench上显著优于现有方法。
摘要：
文本引导图像编辑旨在根据自然语言指令修改图像的特定区域，同时保持整体结构和背景保真度。现有方法利用从扩散模型生成的交叉注意力图中获得的掩码来识别要修改的目标区域。然而，由于交叉注意力机制侧重于语义相关性，它们难以保持图像完整性。因此，这些方法往往缺乏空间一致性，导致编辑伪影和失真。在本工作中，我们解决了这些局限性，并引入了LOCATEdit，它通过基于图的方法利用自注意力导出的块关系来增强交叉注意力图，以在图像区域间维持平滑、连贯的注意力，确保修改仅限于指定对象，同时保留周围结构。LOCATEdit在PIE-Bench上持续且显著优于现有基线，展示了其在各种编辑任务上的最新性能和有效性。代码可在https://github.com/LOCATEdit/LOCATEdit/找到。
英文摘要：
Text-guided image editing aims to modify specific regions of an image according to natural language instructions while maintaining the general structure and the background fidelity. Existing methods utilize masks derived from cross-attention maps generated from diffusion models to identify the target regions for modification. However, since cross-attention mechanisms focus on semantic relevance, they struggle to maintain the image integrity. As a result, these methods often lack spatial consistency, leading to editing artifacts and distortions. In this work, we address these limitations and introduce LOCATEdit, which enhances cross-attention maps through a graph-based approach utilizing self-attention-derived patch relationships to maintain smooth, coherent attention across image regions, ensuring that alterations are limited to the designated items while retaining the surrounding structure. LOCATEdit consistently and substantially outperforms existing baselines on PIE-Bench, demonstrating its state-of-the-art performance and effectiveness on various editing tasks. Code can be found on https://github.com/LOCATEdit/LOCATEdit/
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。
