注意力共享中的魔鬼：通过注意力协同提升复杂非刚性图像编辑的忠实性
The Devil is in Attention Sharing: Improving Complex Non-rigid Image Editing Faithfulness via Attention Synergy
AI总结：
SynPS通过协同利用位置嵌入和语义信息，解决现有注意力共享机制中的注意力崩溃问题，提升复杂非刚性图像编辑的忠实性。
摘要：
基于大型扩散模型的无训练图像编辑已成为现实，但忠实执行复杂非刚性编辑（例如姿态或形状变化）仍然极具挑战性。我们识别出一个关键的潜在原因：现有注意力共享机制中的注意力崩溃，其中要么位置嵌入要么语义特征主导了视觉内容检索，导致过度编辑或不足编辑。为了解决这个问题，我们引入了SynPS，一种协同利用位置嵌入和语义信息进行忠实非刚性图像编辑的方法。我们首先提出了一种编辑测量，用于量化每个去噪步骤所需的编辑幅度。基于此测量，我们设计了注意力协同流水线，动态调节位置嵌入的影响，使SynPS能够平衡语义修改和保真度保持。通过自适应整合位置和语义线索，SynPS有效避免了过度和不足的编辑。在公共和新整理的基准测试中，广泛实验展示了我们方法的优越性能和保真度。
英文摘要：
Training-free image editing with large diffusion models has become practical, yet faithfully performing complex non-rigid edits (e.g., pose or shape changes) remains highly challenging. We identify a key underlying cause: attention collapse in existing attention sharing mechanisms, where either positional embeddings or semantic features dominate visual content retrieval, leading to over-editing or under-editing. To address this issue, we introduce SynPS, a method that Synergistically leverages Positional embeddings and Semantic information for faithful non-rigid image editing. We first propose an editing measurement that quantifies the required editing magnitude at each denoising step. Based on this measurement, we design an attention synergy pipeline that dynamically modulates the influence of positional embeddings, enabling SynPS to balance semantic modifications and fidelity preservation. By adaptively integrating positional and semantic cues, SynPS effectively avoids both over- and under-editing. Extensive experiments on public and newly curated benchmarks demonstrate the superior performance and faithfulness of our approach.
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。
