Consistent editing of real images is a challenging task, as it requires
performing non-rigid edits (e.g., changing postures) to the main objects in the
input image without changing their identity or attributes. To guarantee
consistent attributes, some existing methods fine-tune the entire model or the
textual embedding for structural consistency, but they are time-consuming and
fail to perform non-rigid edits. Other works are tuning-free, but their
performances are weakened by the quality of Denoising Diffusion Implicit Model
(DDIM) reconstruction, which often fails in real-world scenarios. In this
paper, we present a novel approach called Tuning-free Inversion-enhanced
Control (TIC), which directly correlates features from the inversion process
with those from the sampling process to mitigate the inconsistency in DDIM
reconstruction. Specifically, our method effectively obtains inversion features
from the key and value features in the self-attention layers, and enhances the
sampling process by these inversion features, thus achieving accurate
reconstruction and content-consistent editing. To extend the applicability of
our method to general editing scenarios, we also propose a mask-guided
attention concatenation strategy that combines contents from both the inversion
and the naive DDIM editing processes. Experiments show that the proposed method
outperforms previous works in reconstruction and consistent editing, and
produces impressive results in various settings.

提出了一种名为 Tuning-free Inversion-enhanced Control (TIC) 的新方法，通过直接关联反演过程和采样过程的特征来缓解 DDIM 重建中的不一致性，实现准确的重建和内容一致性编辑。通过实验证明，该方法在重建和一致性编辑方面优于以前的工作，在各种设置中产生了令人印象深刻的结果。