统一注意力图：提升图像重建与编辑中的图像保真度
Uniform Attention Maps: Boosting Image Fidelity in Reconstruction and Editing
AI总结：
针对免调优扩散模型图像编辑中保真度与精度难以平衡的问题，提出用统一注意力图替代传统交叉注意力以提升重建保真度，并结合自适应掩码引导编辑技术，有效减少失真且增强编辑一致性与稳健性。
摘要：
利用扩散模型进行文本引导的图像生成与编辑已取得显著进展。其中，免调优方法因无需大量模型调整即可执行编辑、兼具简便性与高效性而受到关注。然而，现有免调优方法常难以平衡保真度与编辑精度。DDIM逆过程中的重建误差部分源于U-Net中的交叉注意力机制，该机制在逆过程与重建过程中引入了错位。为解决此问题，我们从结构视角分析重建过程，提出一种新方法：用统一注意力图替代传统交叉注意力，显著提升图像重建保真度。我们的方法有效减少了噪声预测期间不同文本条件导致的失真。为补充这一改进，我们引入自适应掩码引导编辑技术，与重建方法无缝集成，确保编辑任务的一致性与准确性。实验结果表明，我们的方法不仅在高保真图像重建方面表现出色，在真实图像合成与编辑场景中也表现稳健。本研究强调了统一注意力图在提升基于扩散的图像处理方法保真度与多功能性方面的潜力。代码可在https://github.com/Mowenyii/Uniform-Attention-Maps获取。
英文摘要：
Text-guided image generation and editing using diffusion models have achieved remarkable advancements. Among these, tuning-free methods have gained attention for their ability to perform edits without extensive model adjustments, offering simplicity and efficiency. However, existing tuning-free approaches often struggle with balancing fidelity and editing precision. Reconstruction errors in DDIM Inversion are partly attributed to the cross-attention mechanism in U-Net, which introduces misalignments during the inversion and reconstruction process. To address this, we analyze reconstruction from a structural perspective and propose a novel approach that replaces traditional cross-attention with uniform attention maps, significantly enhancing image reconstruction fidelity. Our method effectively minimizes distortions caused by varying text conditions during noise prediction. To complement this improvement, we introduce an adaptive mask-guided editing technique that integrates seamlessly with our reconstruction approach, ensuring consistency and accuracy in editing tasks. Experimental results demonstrate that our approach not only excels in achieving high-fidelity image reconstruction but also performs robustly in real image composition and editing scenarios. This study underscores the potential of uniform attention maps to enhance the fidelity and versatility of diffusion-based image processing methods. Code is available at https://github.com/Mowenyii/Uniform-Attention-Maps.
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。
