  The diffusion-based generative models have achieved remarkable success in
text-based image generation. However, since it contains enormous randomness in
generation progress, it is still challenging to apply such models for
real-world visual content editing, especially in videos. In this paper, we
propose FateZero, a zero-shot text-based editing method on real-world videos
without per-prompt training or use-specific mask. To edit videos consistently,
we propose several techniques based on the pre-trained models. Firstly, in
contrast to the straightforward DDIM inversion technique, our approach captures
intermediate attention maps during inversion, which effectively retain both
structural and motion information. These maps are directly fused in the editing
process rather than generated during denoising. To further minimize semantic
leakage of the source video, we then fuse self-attentions with a blending mask
obtained by cross-attention features from the source prompt. Furthermore, we
have implemented a reform of the self-attention mechanism in denoising UNet by
introducing spatial-temporal attention to ensure frame consistency. Yet
succinct, our method is the first one to show the ability of zero-shot
text-driven video style and local attribute editing from the trained
text-to-image model. We also have a better zero-shot shape-aware editing
ability based on the text-to-video model. Extensive experiments demonstrate our
superior temporal consistency and editing capability than previous works.
