In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation. Most current work generates long videos segment by segment sequentially, which normally leads to the gap between training on short videos and inferring long videos, and the sequential generation is inefficient. Instead, our approach adopts a ``coarse-to-fine'' process, in which the video can be generated in parallel at the same granularity. A global diffusion model is applied to generate the keyframes across the entire time range, and then local diffusion models recursively fill in the content between nearby frames. This simple yet effective strategy allows us to directly train on long videos (3376 frames) to reduce the training-inference gap, and makes it possible to generate all segments in parallel. To evaluate our model, we build FlintstonesHD dataset, a new benchmark for long video generation. Experiments show that our model not only generates high-quality long videos with both global and local coherence, but also decreases the average inference time from 7.55min to 26s (by 94.26\%) at the same hardware setting when generating 1024 frames. The homepage link is \url{https://msra-nuwa.azurewebsites.net/}
@article{arxiv.2303.12346,
title = {NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation},
author = {Shengming Yin and Chenfei Wu and Huan Yang and Jianfeng Wang and Xiaodong Wang and Minheng Ni and Zhengyuan Yang and Linjie Li and Shuguang Liu and Fan Yang and Jianlong Fu and Gong Ming and Lijuan Wang and Zicheng Liu and Houqiang Li and Nan Duan},
journal= {arXiv preprint arXiv:2303.12346},
year = {2023}
}