3 EXPERIMENTS

We train our models on a combination of an internal dataset consisting of 14 million video-text pairs
and 60 million image-text pairs, and the publicly available LAION-400M image-text dataset. To
process the data into a form suitable for training our cascading pipeline, we spatially resize images
and videos using antialiased bilinear resizing, and we temporally resize videos by skipping frames.
Throughout our model development process, we evaluated Imagen Video on several different met-
rics, such as FID on individual frames (Heusel et al., 2017), FVD (Unterthiner et al., 2019) for
temporal consistency, and frame-wise CLIP scores (Hessel et al., 2021; Park et al., 2021) for video-
text alignment. Below, we explore the capabilities of our model and investigate its performance in
regards to 1) scaling up the number of parameters in our model, 2) changing the parameterization
of our model, and 3) distilling our models so that they are fast to sample from.
IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS