  Transforming large pre-trained low-resolution diffusion models to cater to
higher-resolution demands, i.e., diffusion extrapolation, significantly
improves diffusion adaptability. We propose tuning-free CutDiffusion, aimed at
simplifying and accelerating the diffusion extrapolation process, making it
more affordable and improving performance. CutDiffusion abides by the existing
patch-wise extrapolation but cuts a standard patch diffusion process into an
initial phase focused on comprehensive structure denoising and a subsequent
phase dedicated to specific detail refinement. Comprehensive experiments
highlight the numerous almighty advantages of CutDiffusion: (1) simple method
construction that enables a concise higher-resolution diffusion process without
third-party engagement; (2) fast inference speed achieved through a single-step
higher-resolution diffusion process, and fewer inference patches required; (3)
cheap GPU cost resulting from patch-wise inference and fewer patches during the
comprehensive structure denoising; (4) strong generation performance, stemming
from the emphasis on specific detail refinement.
