  Existing works have advanced Text-to-Image (TTI) diffusion models for video
editing in a one-shot learning manner. Despite their low requirements of data
and computation, these methods might produce results of unsatisfied consistency
with text prompt as well as temporal sequence, limiting their applications in
the real world. In this paper, we propose to address the above issues with a
novel EI$^2$ model towards \textbf{E}nhancing v\textbf{I}deo \textbf{E}diting
cons\textbf{I}stency of TTI-based frameworks. Specifically, we analyze and find
that the inconsistent problem is caused by newly added modules into TTI models
for learning temporal information. These modules lead to covariate shift in the
feature space, which harms the editing capability. Thus, we design EI$^2$ to
tackle the above drawbacks with two classical modules: Shift-restricted
Temporal Attention Module (STAM) and Fine-coarse Frame Attention Module (FFAM).
First, through theoretical analysis, we demonstrate that covariate shift is
highly related to Layer Normalization, thus STAM employs a \textit{Instance
Centering} layer replacing it to preserve the distribution of temporal
features. In addition, {STAM} employs an attention layer with normalized
mapping to transform temporal features while constraining the variance shift.
As the second part, we incorporate {STAM} with a novel {FFAM}, which
efficiently leverages fine-coarse spatial information of overall frames to
further enhance temporal consistency. Extensive experiments demonstrate the
superiority of the proposed EI$^2$ model for text-driven video editing.
