Photorealistic Video Generation with Diffusion Models
Authors: name: Agrim Gupta, name: Lijun Yu, name: Kihyuk Sohn, name: Xiuye Gu, name: Meera Hahn, name: Li Fei-Fei, name: Irfan Essa, name: Lu Jiang, name: José Lezama

We present W.A.L.T, a transformer-based approach for photorealistic video
generation via diffusion modeling. Our approach has two key design decisions.
First, we use a causal encoder to jointly compress images and videos within a
unified latent space, enabling training and generation across modalities.
Second, for memory and training efficiency, we use a window attention
architecture tailored for joint spatial and spatiotemporal generative modeling.
Taken together these design decisions enable us to achieve state-of-the-art
performance on established video (UCF-101 and Kinetics-600) and image
(ImageNet) generation benchmarks without using classifier free guidance.
Finally, we also train a cascade of three models for the task of text-to-video
generation consisting of a base latent video diffusion model, and two video
super-resolution diffusion models to generate videos of 512 times 896
resolution at 8 frames per second.