Leveraging pre-trained conditional diffusion models for video editing without
further tuning has gained increasing attention due to its promise in film
production, advertising, etc. Yet, seminal works in this line fall short in
generation length, temporal coherence, or fidelity to the source video. This
paper aims to bridge the gap, establishing a simple and effective baseline for
training-free diffusion model-based long video editing. As suggested by prior
arts, we build the pipeline upon ControlNet, which excels at various image
editing tasks based on text prompts. To break down the length constraints
caused by limited computational memory, we split the long video into
consecutive windows and develop a novel cross-window attention mechanism to
ensure the consistency of global style and maximize the smoothness among
windows. To achieve more accurate control, we extract the information from the
source video via DDIM inversion and integrate the outcomes into the latent
states of the generations. We also incorporate a video frame interpolation
model to mitigate the frame-level flickering issue. Extensive empirical studies
verify the superior efficacy of our method over competing baselines across
scenarios, including the replacement of the attributes of foreground objects,
style transfer, and background replacement. In particular, our method manages
to edit videos with up to 128 frames according to user requirements. Code is
available at this https URL

本研究旨在建立一个简单而有效的基于训练无关扩散模型的长视频编辑基线，通过分割长视频并采用新颖的跨窗口注意机制，以确保全局风格的一致性和窗口之间的平滑性。通过 DDIM 反演从源视频中提取信息，并将结果整合到生成的潜在状态中，同时结合视频帧插值模型以减轻帧级闪烁问题。经过广泛的实证研究，验证了我们的方法在替换前景物体属性、风格转移和背景替换等场景下相对竞争基准的卓越有效性。