Latte: Latent Diffusion Transformer for Video Generation
Xin Ma Yaohui Wang Gengyun Jia Xinyuan Chen Ziwei Liu Yuan-Fang Li Cunjian Chen Yu Qiao
We propose a novel Latent Diffusion Transformer, namely Latte, for video
generation. Latte first extracts spatio-temporal tokens from input videos and
then adopts a series of Transformer blocks to model video distribution in the
latent space. In order to model a substantial number of tokens extracted from
videos, four efficient variants are introduced from the perspective of
decomposing the spatial and temporal dimensions of input videos. To improve the
quality of generated videos, we determine the best practices of Latte through
rigorous experimental analysis, including video clip patch embedding, model
variants, timestep-class information injection, temporal positional embedding,
and learning strategies. Our comprehensive evaluation demonstrates that Latte
achieves state-of-the-art performance across four standard video generation
datasets, i.e., FaceForensics, SkyTimelapse, UCF101, and Taichi-HD. In
addition, we extend Latte to text-to-video generation (T2V) task, where Latte
achieves comparable results compared to recent T2V models. We strongly believe
that Latte provides valuable insights for future research on incorporating
Transformers into diffusion models for video generation.