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Open-Sora Plan: Open-Source Large Video Generation Model

About

We introduce Open-Sora Plan, an open-source project that aims to contribute a large generation model for generating desired high-resolution videos with long durations based on various user inputs. Our project comprises multiple components for the entire video generation process, including a Wavelet-Flow Variational Autoencoder, a Joint Image-Video Skiparse Denoiser, and various condition controllers. Moreover, many assistant strategies for efficient training and inference are designed, and a multi-dimensional data curation pipeline is proposed for obtaining desired high-quality data. Benefiting from efficient thoughts, our Open-Sora Plan achieves impressive video generation results in both qualitative and quantitative evaluations. We hope our careful design and practical experience can inspire the video generation research community. All our codes and model weights are publicly available at \url{https://github.com/PKU-YuanGroup/Open-Sora-Plan}.

Bin Lin, Yunyang Ge, Xinhua Cheng, Zongjian Li, Bin Zhu, Shaodong Wang, Xianyi He, Yang Ye, Shenghai Yuan, Liuhan Chen, Tanghui Jia, Junwu Zhang, Zhenyu Tang, Yatian Pang, Bin She, Cen Yan, Zhiheng Hu, Xiaoyi Dong, Lin Chen, Zhang Pan, Xing Zhou, Shaoling Dong, Yonghong Tian, Li Yuan• 2024

Related benchmarks

TaskDatasetResultRank
Video GenerationVBench
Quality Score80.14
126
Text-to-Video GenerationT2V-CompBench
Consistency Attribute Score0.6076
63
Video GenerationPhysics-IQ
Phys. IQ Score19.42
45
Video GenerationVBench 2.0 (test)
Total Score77.23
44
Video GenerationKinetics-600
FVD384.3
22
Text-to-Video GenerationVBench T2V
Overall Score77.23
13
Video GenerationVBench Leaderboard Comparison 1.0
Total Score79.76
12
Text-to-Video GenerationVBench--
10
Text-to-Video GenerationVBench
Total Score78
7
Text-to-Video GenerationVBench general prompts 22
Aesthetic Quality56.85
7
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Other info

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