  Diffusion models are the de facto approach for generating high-quality images
and videos, but learning high-dimensional models remains a formidable task due
to computational and optimization challenges. Existing methods often resort to
training cascaded models in pixel space or using a downsampled latent space of
a separately trained auto-encoder. In this paper, we introduce Matryoshka
Diffusion Models(MDM), an end-to-end framework for high-resolution image and
video synthesis. We propose a diffusion process that denoises inputs at
multiple resolutions jointly and uses a NestedUNet architecture where features
and parameters for small-scale inputs are nested within those of large scales.
In addition, MDM enables a progressive training schedule from lower to higher
resolutions, which leads to significant improvements in optimization for
high-resolution generation. We demonstrate the effectiveness of our approach on
various benchmarks, including class-conditioned image generation,
high-resolution text-to-image, and text-to-video applications. Remarkably, we
can train a single pixel-space model at resolutions of up to 1024x1024 pixels,
demonstrating strong zero-shot generalization using the CC12M dataset, which
contains only 12 million images.

Computer Vision and Pattern Recognition;Machine Learning