RealCraft: Attention Control as A Tool for Zero-Shot Consistent Video Editing
arxiv(2023)
摘要
Even though large-scale text-to-image generative models show promising
performance in synthesizing high-quality images, applying these models directly
to image editing remains a significant challenge. This challenge is further
amplified in video editing due to the additional dimension of time. This is
especially the case for editing real-world videos as it necessitates
maintaining a stable structural layout across frames while executing localized
edits without disrupting the existing content. In this paper, we propose
RealCraft, an attention-control-based method for zero-shot real-world video
editing. By swapping cross-attention for new feature injection and relaxing
spatial-temporal attention of the editing object, we achieve localized
shape-wise edit along with enhanced temporal consistency. Our model directly
uses Stable Diffusion and operates without the need for additional information.
We showcase the proposed zero-shot attention-control-based method across a
range of videos, demonstrating shape-wise, time-consistent and parameter-free
editing in videos of up to 64 frames.
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