  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.
