Make a Cheap Scaling: A Self-Cascade Diffusion Model for
  Higher-Resolution Adaptation
Lanqing Guo Yingqing He Haoxin Chen Menghan Xia Xiaodong Cun Yufei Wang Siyu Huang Yong Zhang Xintao Wang Qifeng Chen Ying Shan Bihan Wen
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.