利用近端引导改进免调优的真实图像编辑
Improving Tuning-Free Real Image Editing with Proximal Guidance
AI总结：
本文提出近端引导并结合交叉注意力与互自注意力控制来增强负提示反演，在保持免训练特性的同时减少伪影并支持几何布局编辑，从而以最小计算开销改进免调优真实图像编辑。
摘要：
DDIM反演已经揭示了基于扩散的方法在真实图像编辑中的显著潜力。然而，当使用更大的无分类器引导（CFG）尺度以增强编辑效果时，DDIM重建的准确性会下降。空文本反演（NTI）通过优化空嵌入，使重建轨迹和反演轨迹在更大的CFG尺度下对齐，从而实现了具有交叉注意力控制的真实图像编辑。负提示反演（NPI）进一步提供了NTI的一种免训练闭式解。然而，它可能引入伪影，并且仍然受到DDIM重建质量的限制。为了克服这些局限性，我们提出了近端引导，并将其与交叉注意力控制结合到NPI中。我们通过一个正则化项和重建引导来增强NPI，在利用其免训练特性的同时减少伪影。此外，我们扩展了这些概念以纳入互自注意力控制，从而在编辑过程中实现几何和布局的改变。我们的方法提供了一种高效且直接的方式，以最小的计算开销有效处理真实图像编辑任务。
英文摘要：
DDIM inversion has revealed the remarkable potential of real image editing within diffusion-based methods. However, the accuracy of DDIM reconstruction degrades as larger classifier-free guidance (CFG) scales being used for enhanced editing. Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control. Negative-prompt inversion (NPI) further offers a training-free closed-form solution of NTI. However, it may introduce artifacts and is still constrained by DDIM reconstruction quality. To overcome these limitations, we propose proximal guidance and incorporate it to NPI with cross-attention control. We enhance NPI with a regularization term and reconstruction guidance, which reduces artifacts while capitalizing on its training-free nature. Additionally, we extend the concepts to incorporate mutual self-attention control, enabling geometry and layout alterations in the editing process. Our method provides an efficient and straightforward approach, effectively addressing real image editing tasks with minimal computational overhead.
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。
