  We present Imagen Video, a text-conditional video generation system based on
a cascade of video diffusion models. Given a text prompt, Imagen Video
generates high definition videos using a base video generation model and a
sequence of interleaved spatial and temporal video super-resolution models. We
describe how we scale up the system as a high definition text-to-video model
including design decisions such as the choice of fully-convolutional temporal
and spatial super-resolution models at certain resolutions, and the choice of
the v-parameterization of diffusion models. In addition, we confirm and
transfer findings from previous work on diffusion-based image generation to the
video generation setting. Finally, we apply progressive distillation to our
video models with classifier-free guidance for fast, high quality sampling. We
find Imagen Video not only capable of generating videos of high fidelity, but
also having a high degree of controllability and world knowledge, including the
ability to generate diverse videos and text animations in various artistic
styles and with 3D object understanding. See
https://imagen.research.google/video/ for samples.
