Cascaded Diffusion Models (Ho et al., 2022a) are an effective method for scaling diffusion models to
high resolution outputs, ﬁnding considerable success in both class-conditional ImageNet (Ho et al.,
2022a) and text-to-image generation (Ramesh et al., 2022; Saharia et al., 2022b). Cascaded diffusion
models generate an image or video at a low resolution, then sequentially increase the resolution
of the image or video through a series of super-resolution diffusion models. Cascaded Diffusion
Models can model very high dimensional problems while still keeping each sub-model relatively
simple. Imagen (Saharia et al., 2022b) also showed that by conditioning on text embeddings from a
large frozen language model in conjunction with cascaded diffusion models, one can generate high
quality 1024 × 1024 images from text descriptions. In this work we extend this approach to video
generation.
Figure 6 summarizes the entire cascading pipeline of Imagen Video. In total, we have 1 frozen text
IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS