T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with
  Trajectory Stitching
Zizheng Pan Bohan Zhuang De-An Huang Weili Nie Zhiding Yu Chaowei Xiao Jianfei Cai Anima Anandkumar
Sampling from diffusion probabilistic models (DPMs) is often expensive for
high-quality image generation and typically requires many steps with a large
model. In this paper, we introduce sampling Trajectory Stitching T-Stitch, a
simple yet efficient technique to improve the sampling efficiency with little
or no generation degradation. Instead of solely using a large DPM for the
entire sampling trajectory, T-Stitch first leverages a smaller DPM in the
initial steps as a cheap drop-in replacement of the larger DPM and switches to
the larger DPM at a later stage. Our key insight is that different diffusion
models learn similar encodings under the same training data distribution and
smaller models are capable of generating good global structures in the early
steps. Extensive experiments demonstrate that T-Stitch is training-free,
generally applicable for different architectures, and complements most existing
fast sampling techniques with flexible speed and quality trade-offs. On DiT-XL,
for example, 40% of the early timesteps can be safely replaced with a 10x
faster DiT-S without performance drop on class-conditional ImageNet generation.
We further show that our method can also be used as a drop-in technique to not
only accelerate the popular pretrained stable diffusion (SD) models but also
improve the prompt alignment of stylized SD models from the public model zoo.
Code is released at https://github.com/NVlabs/T-Stitch