  Diffusion models have proven to be highly effective in image and video
generation; however, they still face composition challenges when generating
images of varying sizes due to single-scale training data. Adapting large
pre-trained diffusion models for higher resolution demands substantial
computational and optimization resources, yet achieving a generation capability
comparable to low-resolution models remains elusive. This paper proposes a
novel self-cascade diffusion model that leverages the rich knowledge gained
from a well-trained low-resolution model for rapid adaptation to
higher-resolution image and video generation, employing either tuning-free or
cheap upsampler tuning paradigms. Integrating a sequence of multi-scale
upsampler modules, the self-cascade diffusion model can efficiently adapt to a
higher resolution, preserving the original composition and generation
capabilities. We further propose a pivot-guided noise re-schedule strategy to
speed up the inference process and improve local structural details. Compared
to full fine-tuning, our approach achieves a 5X training speed-up and requires
only an additional 0.002M tuning parameters. Extensive experiments demonstrate
that our approach can quickly adapt to higher resolution image and video
synthesis by fine-tuning for just 10k steps, with virtually no additional
inference time.
