Text-to-image (T2I) diffusion models achieve state-of-the-art results in image synthesis and editing. However, leveraging such pre-trained models for video editing is considered a major challenge. Many existing works attempt to enforce temporal consistency in the edited video through explicit correspondence mechanisms, either in pixel space or between deep features. These methods, however, struggle with strong nonrigid motion. In this paper, we introduce a fundamentally different approach, which is based on the observation that spatiotemporal slices of natural videos exhibit similar characteristics to natural images. Thus, the same T2I diffusion model that is normally used only as a prior on video frames, can also serve as a strong prior for enhancing temporal consistency by applying it on spatiotemporal slices. Based on this observation, we present Slicedit^#, a method for text-based video editing that utilizes a pre-trained T2I diffusion model to process both spatial and spatiotemporal slices. Our method generates videos that retain the structure and motion of the original video while adhering to the target text. Through extensive experiments, we demonstrate Slicedit's ability to edit a wide range of real-world videos, confirming its clear advantages compared to existing baselines.

^#Slicedit can be pronounced ``slice-edit'' or ``sliced-it''

To edit a video, I₀, we apply DDPM inversion using our video-denoising model, which is an inflated version of the T2I model. This process extracts noise volumes and attention maps for each diffusion timestep. Subsequently, we run DDPM sampling using the extracted noise space, while injecting the extended attention maps at specific timesteps, as can be seen on the left pane of the figure. The inversion and sampling are performed while conditioning the inflated denoiser on the source and target text prompts, respectively.
Our inflated denoiser can be seen on the right pane. It employs two versions of the pre-trained image denoiser. A version with extended attention is applied to x - y slices (blue), and the original denoiser is applied to y - t slices (red). The two predicted noise volumes are then combined into the final predicted noise volume (marked in green).

See our paper for more details.

Paper

Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices
Nathaniel Cohen^*, Vladimir Kulikov^*, Matan Kleiner^*, Inbar Huberman-Spiegelglas, Tomer Michaeli.

Arxiv Proceedings

Code

Bibtex

@InProceedings{cohen2024slicedit,
	title = 	 {Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices},
	author =       {Cohen, Nathaniel and Kulikov, Vladimir and Kleiner, Matan and Huberman-Spiegelglas, Inbar and Michaeli, Tomer},
	booktitle = 	 {Proceedings of the 41st International Conference on Machine Learning},
	pages = 	 {9109--9137},
	year = 	 {2024},
	editor = 	 {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
	volume = 	 {235},
	series = 	 {Proceedings of Machine Learning Research},
	month = 	 {21--27 Jul},
	publisher =    {PMLR},
	url = 	 {https://proceedings.mlr.press/v235/cohen24a.html},
	}	

References

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