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.

翻译：DDIM反演已揭示了扩散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）尺度来增强编辑效果时，DDIM重建的准确性会下降。空文本反演（NTI）通过优化空嵌入，使重建与反演轨迹在更大的CFG尺度下对齐，从而实现基于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的无训练闭式解。但该方法可能引入伪影，且仍受限于DDIM的重建质量。为克服这些限制，我们提出近端引导，并将其与交叉注意力控制相结合以改进NPI。我们通过正则化项和重建引导增强NPI，在保持其无训练特性的同时减少伪影。此外，我们将这些概念扩展到互自注意力控制，以实现编辑过程中几何形状与布局的调整。我们的方法提供了一种高效且直接的方案，以最小计算开销有效解决真实图像编辑任务。