@inproceedings{yin-etal-2023-nuwa,
title = "{NUWA}-{XL}: Diffusion over Diffusion for e{X}tremely Long Video Generation",
author = "Yin, Shengming and
Wu, Chenfei and
Yang, Huan and
Wang, Jianfeng and
Wang, Xiaodong and
Ni, Minheng and
Yang, Zhengyuan and
Li, Linjie and
Liu, Shuguang and
Yang, Fan and
Fu, Jianlong and
Gong, Ming and
Wang, Lijuan and
Liu, Zicheng and
Li, Houqiang and
Duan, Nan",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.73",
doi = "10.18653/v1/2023.acl-long.73",
pages = "1309--1320",
abstract = "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 [NUWA-XL](\url{https://msra-nuwa.azurewebsites.net})",
}
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<abstract>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 [NUWA-XL](https://msra-nuwa.azurewebsites.net)</abstract>
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%0 Conference Proceedings
%T NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation
%A Yin, Shengming
%A Wu, Chenfei
%A Yang, Huan
%A Wang, Jianfeng
%A Wang, Xiaodong
%A Ni, Minheng
%A Yang, Zhengyuan
%A Li, Linjie
%A Liu, Shuguang
%A Yang, Fan
%A Fu, Jianlong
%A Gong, Ming
%A Wang, Lijuan
%A Liu, Zicheng
%A Li, Houqiang
%A Duan, Nan
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yin-etal-2023-nuwa
%X 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 [NUWA-XL](https://msra-nuwa.azurewebsites.net)
%R 10.18653/v1/2023.acl-long.73
%U https://aclanthology.org/2023.acl-long.73
%U https://doi.org/10.18653/v1/2023.acl-long.73
%P 1309-1320
Markdown (Informal)
[NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation](https://aclanthology.org/2023.acl-long.73) (Yin et al., ACL 2023)
ACL
- Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Ming Gong, Lijuan Wang, Zicheng Liu, Houqiang Li, and Nan Duan. 2023. NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1309–1320, Toronto, Canada. Association for Computational Linguistics.