  As the methods evolve, inversion is mainly divided into two steps. The first
step is Image Embedding, in which an encoder or optimization process embeds
images to get the corresponding latent codes. Afterward, the second step aims
to refine the inversion and editing results, which we named Result Refinement.
Although the second step significantly improves fidelity, perception and
editability are almost unchanged, deeply dependent on inverse latent codes
attained in the first step. Therefore, a crucial problem is gaining the latent
codes with better perception and editability while retaining the reconstruction
fidelity. In this work, we first point out that these two characteristics are
related to the degree of alignment (or disalignment) of the inverse codes with
the synthetic distribution. Then, we propose Latent Space Alignment Inversion
Paradigm (LSAP), which consists of evaluation metric and solution for this
problem. Specifically, we introduce Normalized Style Space ($\mathcal{S^N}$
space) and $\mathcal{S^N}$ Cosine Distance (SNCD) to measure disalignment of
inversion methods. Since our proposed SNCD is differentiable, it can be
optimized in both encoder-based and optimization-based embedding methods to
conduct a uniform solution. Extensive experiments in various domains
demonstrate that SNCD effectively reflects perception and editability, and our
alignment paradigm archives the state-of-the-art in both two steps. Code is
available on https://github.com/caopulan/GANInverter/tree/main/configs/lsap.
