Diffusion-based methods can generate realistic images and videos, but they struggle to edit existing objects in a video while preserving their appearance over time. This prevents diffusion models from being applied to natural video editing in practical scenarios. In this paper, we tackle this problem by introducing temporal dependency to existing text-driven diffusion models, which allows them to generate consistent appearance for the edited objects. Specifically, we develop a novel inter-frame propagation mechanism for diffusion video editing, which leverages the concept of layered representations to propagate the appearance information from one frame to the next. We then build up a text-driven video editing framework based on this mechanism, namely StableVideo, which can achieve consistency-aware video editing. Extensive experiments demonstrate the strong editing capability of our approach. Compared with state-of-the-art video editing methods, our approach shows superior qualitative and quantitative results. Our code is available at \href{https://github.com/rese1f/StableVideo}{this https URL}.

翻译：基于扩散的方法可以生成逼真的图像和视频，但在编辑视频中现有对象的同时保持其外观随时间一致性方面仍存在挑战。这使得扩散模型难以应用于实际场景中的自然视频编辑。本文通过向现有文本驱动扩散模型引入时间依赖关系来解决该问题，使其能够为编辑对象生成一致的外观。具体而言，我们针对扩散视频编辑提出了一种新颖的帧间传播机制，该机制利用分层表示的概念将外观信息从一帧传播到下一帧。基于此机制，我们构建了名为StableVideo的文本驱动视频编辑框架，能够实现感知一致性的视频编辑。大量实验证明了我们方法强大的编辑能力。与最先进的视频编辑方法相比，我们的方法在定性和定量结果上均展现出优越性。我们的代码开源在\href{https://github.com/rese1f/StableVideo}{此https链接}。