Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video
  Generation
Authors: name: David Junhao Zhang, name: Jay Zhangjie Wu, name: Jia-Wei Liu, name: Rui Zhao, name: Lingmin Ran, name: Yuchao Gu, name: Difei Gao, name: Mike Zheng Shou

Significant advancements have been achieved in the realm of large-scale
pre-trained text-to-video Diffusion Models (VDMs). However, previous methods
either rely solely on pixel-based VDMs, which come with high computational
costs, or on latent-based VDMs, which often struggle with precise text-video
alignment. In this paper, we are the first to propose a hybrid model, dubbed as
Show-1, which marries pixel-based and latent-based VDMs for text-to-video
generation. Our model first uses pixel-based VDMs to produce a low-resolution
video of strong text-video correlation. After that, we propose a novel expert
translation method that employs the latent-based VDMs to further upsample the
low-resolution video to high resolution. Compared to latent VDMs, Show-1 can
produce high-quality videos of precise text-video alignment; Compared to pixel
VDMs, Show-1 is much more efficient (GPU memory usage during inference is 15G
vs 72G). We also validate our model on standard video generation benchmarks.
Our code and model weights are publicly available at
https://github.com/showlab/Show-1.