  In recent years, diffusion models have become the most popular and powerful
methods in the field of image synthesis, even rivaling human artists in
artistic creativity. However, the key issue currently limiting the application
of diffusion models is its extremely slow generation process. Although several
methods were proposed to speed up the generation process, there still exists a
trade-off between efficiency and quality. In this paper, we first provide a
detailed theoretical and empirical analysis of the generation process of the
diffusion models based on schedulers. We transform the designing problem of
schedulers into the determination of several parameters, and further transform
the accelerated generation process into an expansion process of the linear
subspace. Based on these analyses, we consequently propose a novel method
called Optimal Linear Subspace Search (OLSS), which accelerates the generation
process by searching for the optimal approximation process of the complete
generation process in the linear subspaces spanned by latent variables. OLSS is
able to generate high-quality images with a very small number of steps. To
demonstrate the effectiveness of our method, we conduct extensive comparative
experiments on open-source diffusion models. Experimental results show that
with a given number of steps, OLSS can significantly improve the quality of
generated images. Using an NVIDIA A100 GPU, we make it possible to generate a
high-quality image by Stable Diffusion within only one second without other
optimization techniques.
