We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data. To develop EVE we separately train an image editing adapter and a video generation adapter, and attach both to the same text-to-image model. Then, to align the adapters towards video editing we introduce a new unsupervised distillation procedure, Factorized Diffusion Distillation. This procedure distills knowledge from one or more teachers simultaneously, without any supervised data. We utilize this procedure to teach EVE to edit videos by jointly distilling knowledge to (i) precisely edit each individual frame from the image editing adapter, and (ii) ensure temporal consistency among the edited frames using the video generation adapter. Finally, to demonstrate the potential of our approach in unlocking other capabilities, we align additional combinations of adapters

Emu Video Edit (EVE) 是一种不依赖于任何有监督视频编辑数据的视频编辑模型，通过分别训练图像编辑适配器和视频生成适配器，并将两者连接到同一个文本到图像模型上，然后引入一种新的无监督蒸馏过程Factorized Diffusion Distillation来使适配器在视频编辑方面达到一致，通过联合蒸馏的方式精确地编辑每一帧，同时通过视频生成适配器确保帧与帧之间的时序一致性，并展示了该方法在解锁其他能力方面的潜力。

基于分解扩散蒸馏的视频编辑