AdaFlow: Efficient Long Video Editing via Adaptive Attention Slimming And Keyframe Selection
Keywords: video editing, diffusion model, keyframe selection, token slimming
TL;DR: AdaFlow enables efficient, high-quality editing of minute-long videos by adaptively selecting keyframes and pruning redundant tokens, achieving state-of-the-art results on a single GPU.
Abstract: Text-driven video editing is an emerging research hot spot in deep learning. Despite great progress, long video editing is still notoriously challenging mainly due to excessive memory overhead. To tackle this problem, recent efforts have simplified this task into a two-step process of keyframe translation and interpolation generation, enabling the editing of more frames. However, the token-wise keyframe translation still plagues the upper limit of video length. In this paper, we propose a novel and training-free approach towards efficient and effective long video editing, termed AdaFlow. We first reveal that not all tokens of video frames hold equal importance for keyframe-consistency editing, based on which we propose an Adaptive Attention Slimming scheme for AdaFlow to squeeze the $KV$ sequence of extended self-attention. This enhancement allows AdaFlow to increase the number of keyframes for translations by an order of magnitude. In addition, an Adaptive Keyframe Selection scheme is also equipped to select the representative frames for joint editing, further improving generation quality. With these innovative designs, AdaFlow achieves high-quality long video editing of minutes in one inference, i.e., more than 1$k$ frames on one A800 GPU, which is about ten times longer than the compared methods. To validate AdaFlow, we also build a new benchmark for long video editing with high-quality annotations, termed LongV-EVAL. The experimental results show that our AdaFlow can achieve obvious advantages in both the efficiency and quality of long video editing. Our code is anonymously released at https://anonymous.4open.science/r/AdaFlow-C28F.
Supplementary Material: zip
Primary Area: generative models
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Submission Number: 5625
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