Blended Latent Diffusion under Attention Control for Real-World Video Editing

TL;DR

This paper introduces a novel method for local real-world video editing by adapting a latent diffusion model with autonomous masking and enhanced temporal consistency, addressing limitations of existing approaches.

Contribution

It presents a blended latent diffusion framework with autonomous masking and temporal-spatial attention blocks for improved local video editing.

Findings

Effective in preserving background details.

Autonomous mask generation improves editing efficiency.

Enhanced temporal consistency across frames.

Abstract

Due to lack of fully publicly available text-to-video models, current video editing methods tend to build on pre-trained text-to-image generation models, however, they still face grand challenges in dealing with the local editing of video with temporal information. First, although existing methods attempt to focus on local area editing by a pre-defined mask, the preservation of the outside-area background is non-ideal due to the spatially entire generation of each frame. In addition, specially providing a mask by user is an additional costly undertaking, so an autonomous masking strategy integrated into the editing process is desirable. Last but not least, image-level pretrained model hasn't learned temporal information across frames of a video which is vital for expressing the motion and dynamics. In this paper, we propose to adapt a image-level blended latent diffusion model to perform local video editing tasks. Specifically, we leverage DDIM inversion to acquire the latents as background latents instead of the randomly noised ones to better preserve the background information of the input video. We further introduce an autonomous mask manufacture mechanism derived from cross-attention maps in diffusion steps. Finally, we enhance the temporal consistency across video frames by transforming the self-attention blocks of U-Net into temporal-spatial blocks. Through extensive experiments, our proposed approach demonstrates effectiveness in different real-world video editing tasks.