Diffusion models have demonstrated remarkable capabilities in text-to-image
and text-to-video generation, opening up possibilities for video editing based
on textual input. However, the computational cost associated with sequential
sampling in diffusion models poses challenges for efficient video editing.
Existing approaches relying on image generation models for video editing suffer
from time-consuming one-shot fine-tuning, additional condition extraction, or
DDIM inversion, making real-time applications impractical. In this work, we
propose FastVideoEdit, an efficient zero-shot video editing approach inspired
by Consistency Models (CMs). By leveraging the self-consistency property of
CMs, we eliminate the need for time-consuming inversion or additional condition
extraction, reducing editing time. Our method enables direct mapping from
source video to target video with strong preservation ability utilizing a
special variance schedule. This results in improved speed advantages, as fewer
sampling steps can be used while maintaining comparable generation quality.
Experimental results validate the state-of-the-art performance and speed
advantages of FastVideoEdit across evaluation metrics encompassing editing
speed, temporal consistency, and text-video alignment.

借鉴一致性模型 (CM) 的自洽性特性，我们提出了一种高效的零样本视频编辑方法 FastVideoEdit，能够减少编辑时间并提高编辑速度，同时保持可比较的生成质量。