LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion
  Models
This work aims to learn a high-quality text-to-video (T2V) generative model
by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a
highly desirable yet challenging task to simultaneously a) accomplish the
synthesis of visually realistic and temporally coherent videos while b)
preserving the strong creative generation nature of the pre-trained T2I model.
To this end, we propose LaVie, an integrated video generation framework that
operates on cascaded video latent diffusion models, comprising a base T2V
model, a temporal interpolation model, and a video super-resolution model. Our
key insights are two-fold: 1) We reveal that the incorporation of simple
temporal self-attentions, coupled with rotary positional encoding, adequately
captures the temporal correlations inherent in video data. 2) Additionally, we
validate that the process of joint image-video fine-tuning plays a pivotal role
in producing high-quality and creative outcomes. To enhance the performance of
LaVie, we contribute a comprehensive and diverse video dataset named Vimeo25M,
consisting of 25 million text-video pairs that prioritize quality, diversity,
and aesthetic appeal. Extensive experiments demonstrate that LaVie achieves
state-of-the-art performance both quantitatively and qualitatively.
Furthermore, we showcase the versatility of pre-trained LaVie models in various
long video generation and personalized video synthesis applications.
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Yaohui Wang
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Xinyuan Chen
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Xin Ma
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Xin Ma
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Shangchen Zhou
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Shangchen Zhou
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Ziqi Huang
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Ziqi Huang
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Yi Wang
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Ceyuan Yang
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Ceyuan
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Yinan He
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Jiashuo Yu
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Peiqing Yang
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Yuwei Guo
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Yuwei Guo
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Tianxing Wu
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Tianxing Wu
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Chenyang Si
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Yuming Jiang
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Yuming Jiang
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Cunjian Chen
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Bo Dai
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Intelligent Digital Creation
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Dahua Lin
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Dahua Lin
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Yu Qiao
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Ziwei Liu
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Ziwei Liu
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liuziwei7
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion
  Models
This work aims to learn a high-quality text-to-video (T2V) generative model
by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a
highly desirable yet challenging task to simultaneously a) accomplish the
synthesis of visually realistic and temporally coherent videos while b)
preserving the strong creative generation nature of the pre-trained T2I model.
To this end, we propose LaVie, an integrated video generation framework that
operates on cascaded video latent diffusion models, comprising a base T2V
model, a temporal interpolation model, and a video super-resolution model. Our
key insights are two-fold: 1) We reveal that the incorporation of simple
temporal self-attentions, coupled with rotary positional encoding, adequately
captures the temporal correlations inherent in video data. 2) Additionally, we
validate that the process of joint image-video fine-tuning plays a pivotal role
in producing high-quality and creative outcomes. To enhance the performance of
LaVie, we contribute a comprehensive and diverse video dataset named Vimeo25M,
consisting of 25 million text-video pairs that prioritize quality, diversity,
and aesthetic appeal. Extensive experiments demonstrate that LaVie achieves
state-of-the-art performance both quantitatively and qualitatively.
Furthermore, we showcase the versatility of pre-trained LaVie models in various
long video generation and personalized video synthesis applications.
LaVie, a text-to-video framework integrating cascaded video latent diffusion models, achieves state-of-the-art video generation by leveraging temporal self-attentions and joint image-video fine-tuning.
text-to-video text-to-image latent diffusion models temporal self-attentions rotary positional encoding video super-resolution joint image-video fine-tuning Vimeo25M