Large-scale text-to-video models have shown remarkable abilities, but their direct application in video editing remains challenging due to limited available datasets. Current video editing methods commonly require per-video fine-tuning of diffusion models or specific inversion optimization to ensure high-fidelity edits. In this paper, we introduce EffiVED, an efficient diffusion-based model that directly supports instruction-guided video editing. To achieve this, we present two efficient workflows to gather video editing pairs, utilizing augmentation and fundamental vision-language techniques. These workflows transform vast image editing datasets and open-world videos into a high-quality dataset for training EffiVED. Experimental results reveal that EffiVED not only generates high-quality editing videos but also executes rapidly. Finally, we demonstrate that our data collection method significantly improves editing performance and can potentially tackle the scarcity of video editing data. The datasets will be made publicly available upon publication.

翻译：大规模文本到视频模型已展现出卓越能力，但由于可用数据集有限，其在视频编辑中的直接应用仍具挑战性。当前视频编辑方法通常需要对扩散模型进行每视频微调或特定的逆优化处理，以确保高保真编辑效果。本文提出EffiVED，一种高效基于扩散的模型，可直接支持指令引导的视频编辑。为此，我们引入两种高效工作流，利用数据增强和基础视觉-语言技术收集视频编辑对。这些工作流将大规模图像编辑数据集与开放世界视频转化为高质量数据集，用于训练EffiVED。实验结果表明，EffiVED不仅能生成高质量编辑视频，且执行速度迅速。最后，我们证明提出的数据收集方法显著提升了编辑性能，并有望解决视频编辑数据稀缺问题。数据集将在论文发表后公开发布。