Large-scale text-to-image diffusion models achieve unprecedented success in image generation and editing. However, how to extend such success to video editing is unclear. Recent initial attempts at video editing require significant text-to-video data and computation resources for training, which is often not accessible. In this work, we propose vid2vid-zero, a simple yet effective method for zero-shot video editing. Our vid2vid-zero leverages off-the-shelf image diffusion models, and doesn't require training on any video. At the core of our method is a null-text inversion module for text-to-video alignment, a cross-frame modeling module for temporal consistency, and a spatial regularization module for fidelity to the original video. Without any training, we leverage the dynamic nature of the attention mechanism to enable bi-directional temporal modeling at test time. Experiments and analyses show promising results in editing attributes, subjects, places, etc., in real-world videos. Code is made available at \url{https://github.com/baaivision/vid2vid-zero}.
翻译:大规模的文本到图像扩散模型在图像生成和编辑方面取得了前所未有的成功。但如何将这样的成功延伸到视频编辑领域还不清楚。最近的初步尝试需要大量的文本到视频数据和计算资源进行训练,这通常是不可行的。在这项工作中,我们提出了 vid2vid-zero,一种简单而有效的零样本视频编辑方法。我们的 vid2vid-zero 利用现成的图像扩散模型,不需要在任何视频上进行训练。我们方法的核心是一个空文本反转模块用于文本和视频的对齐,一个交叉帧建模模块用于时域一致性,并且一个空间正则化模块用于保持与原始视频的保真度。我们利用注意力机制的动态特性,在测试时实现了双向时域建模。实验证明,我们的方法在编辑实际视频中的属性、主体、场所等方面具有良好的效果。代码可以在 \url{https://github.com/baaivision/vid2vid-zero} 找到。