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

Video Editing via Factorized Diffusion Distillation

The information retrieval community has made significant progress in
improving the efficiency of Dual Encoder (DE) dense passage retrieval systems,
making them suitable for latency-sensitive settings. However, many proposed
procedures are often too complex or resource-intensive, which makes it
difficult for practitioners to adopt them or identify sources of empirical
gains. Therefore, in this work, we propose a trivially simple recipe to serve
as a baseline method for boosting the efficiency of DE retrievers leveraging an
asymmetric architecture. Our results demonstrate that even a 2-layer,
BERT-based query encoder can still retain 92.5% of the full DE performance on
the BEIR benchmark via unsupervised distillation and proper student
initialization. We hope that our findings will encourage the community to
re-evaluate the trade-offs between method complexity and performance
improvements.

本文中，我们提出了一个简单的基线方法，使用不对称架构来提高 Dual Encoder 检索器的效率。我们发现即使使用只有两层的 BERT-based 查询编码器，通过无监督的蒸馏和适当的学生初始化，仍然可以在 BEIR 基准测试中保持 92.5％的 DE 性能。我们希望我们的研究可以鼓励社区重新评估方法复杂性和性能提升之间的权衡。