  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.

Computer Vision and Pattern Recognition