Step-Video-T2V Technical Report: The Practice, Challenges, and Future of
  Video Foundation Model
We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model
with 30B parameters and the ability to generate videos up to 204 frames in
length. A deep compression Variational Autoencoder, Video-VAE, is designed for
video generation tasks, achieving 16x16 spatial and 8x temporal compression
ratios, while maintaining exceptional video reconstruction quality. User
prompts are encoded using two bilingual text encoders to handle both English
and Chinese. A DiT with 3D full attention is trained using Flow Matching and is
employed to denoise input noise into latent frames. A video-based DPO approach,
Video-DPO, is applied to reduce artifacts and improve the visual quality of the
generated videos. We also detail our training strategies and share key
observations and insights. Step-Video-T2V's performance is evaluated on a novel
video generation benchmark, Step-Video-T2V-Eval, demonstrating its
state-of-the-art text-to-video quality when compared with both open-source and
commercial engines. Additionally, we discuss the limitations of current
diffusion-based model paradigm and outline future directions for video
foundation models. We make both Step-Video-T2V and Step-Video-T2V-Eval
available at https://github.com/stepfun-ai/Step-Video-T2V. The online version
can be accessed from https://yuewen.cn/videos as well. Our goal is to
accelerate the innovation of video foundation models and empower video content
creators.
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Guoqing Ma
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Haoyang Huang
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Kun Yan
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Liangyu Chen
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Nan Duan
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Shengming Yin
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Changyi Wan
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Ranchen Ming
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Xiaoniu Song
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Xing Chen
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Yu Zhou
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Deshan Sun
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Deyu Zhou
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Jian Zhou
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Kaijun Tan
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Kang An
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Mei Chen
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Wei Ji
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Qiling Wu
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Wen Sun
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Xin Han
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Yanan Wei
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Zheng Ge
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Aojie Li
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Bin Wang
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Bizhu Huang
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Bo Wang
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Brian Li
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Changxing Miao
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Chen Xu
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Chenfei Wu
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Chenguang Yu
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Dapeng Shi
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Dingyuan Hu
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Enle Liu
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Gang Yu
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Ge Yang
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Guanzhe Huang
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Gulin Yan
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Haiyang Feng
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Hao Nie
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Haonan Jia
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Hanpeng Hu
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Hanqi Chen
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Haolong Yan
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Heng Wang
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Hongcheng Guo
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Huilin Xiong
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Huixin Xiong
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Jiahao Gong
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Jianchang Wu
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Jiaoren Wu
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Jie Wu
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Jie Yang
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Jiashuai Liu
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Jiashuo Li
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Jingyang Zhang
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Junjing Guo
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Junzhe Lin
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Kaixiang Li
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Lei Liu
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Lei Xia
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Liang Zhao
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Liguo Tan
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Liwen Huang
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Liying Shi
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Ming Li
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Mingliang Li
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Muhua Cheng
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Na Wang
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Qiaohui Chen
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Qinglin He
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Qiuyan Liang
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Quan Sun
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Ran Sun
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Rui Wang
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Shaoliang Pang
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Shiliang Yang
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Sitong Liu
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Siqi Liu
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Shuli Gao
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Tiancheng Cao
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Tianyu Wang
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Weipeng Ming
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Wenqing He
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Xu Zhao
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Xuelin Zhang
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Xianfang Zeng
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Xiaojia Liu
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Xuan Yang
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Yaqi Dai
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Yanbo Yu
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Yang Li
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Yineng Deng
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Yingming Wang
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Yilei Wang
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Yuanwei Lu
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Yu Chen
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Yu Luo
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Yuchu Luo
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Yuhe Yin
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Yuheng Feng
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Yuxiang Yang
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Zecheng Tang
claimed_verified
2025-08-06T19:40:24.743Z
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Zecheng Tang
user
ZetangForward
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Zekai Zhang
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Zidong Yang
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Binxing Jiao
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Jiansheng Chen
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Jing Li
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Shuchang Zhou
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Xiangyu Zhang
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Xinhao Zhang
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Yibo Zhu
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Heung-Yeung Shum
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Daxin Jiang
Step-Video-T2V Technical Report: The Practice, Challenges, and Future of
  Video Foundation Model
We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model
with 30B parameters and the ability to generate videos up to 204 frames in
length. A deep compression Variational Autoencoder, Video-VAE, is designed for
video generation tasks, achieving 16x16 spatial and 8x temporal compression
ratios, while maintaining exceptional video reconstruction quality. User
prompts are encoded using two bilingual text encoders to handle both English
and Chinese. A DiT with 3D full attention is trained using Flow Matching and is
employed to denoise input noise into latent frames. A video-based DPO approach,
Video-DPO, is applied to reduce artifacts and improve the visual quality of the
generated videos. We also detail our training strategies and share key
observations and insights. Step-Video-T2V's performance is evaluated on a novel
video generation benchmark, Step-Video-T2V-Eval, demonstrating its
state-of-the-art text-to-video quality when compared with both open-source and
commercial engines. Additionally, we discuss the limitations of current
diffusion-based model paradigm and outline future directions for video
foundation models. We make both Step-Video-T2V and Step-Video-T2V-Eval
available at https://github.com/stepfun-ai/Step-Video-T2V. The online version
can be accessed from https://yuewen.cn/videos as well. Our goal is to
accelerate the innovation of video foundation models and empower video content
creators.
Step-Video-T2V is a state-of-the-art text-to-video model with 30B parameters, utilizing a deep compression Variational Autoencoder and a DiT with 3D full attention for high-quality video generation.
Step-Video-T2V Variational Autoencoder Video-VAE DiT 3D full attention Flow Matching Video-DPO Step-Video-T2V-Eval diffusion-based model video foundation models