  Diffusion Probabilistic Models (DPMs) have achieved considerable success in
generation tasks. As sampling from DPMs is equivalent to solving diffusion SDE
or ODE which is time-consuming, numerous fast sampling methods built upon
improved differential equation solvers are proposed. The majority of such
techniques consider solving the diffusion ODE due to its superior efficiency.
However, stochastic sampling could offer additional advantages in generating
diverse and high-quality data. In this work, we engage in a comprehensive
analysis of stochastic sampling from two aspects: variance-controlled diffusion
SDE and linear multi-step SDE solver. Based on our analysis, we propose
SA-Solver, which is an improved efficient stochastic Adams method for solving
diffusion SDE to generate data with high quality. Our experiments show that
SA-Solver achieves: 1) improved or comparable performance compared with the
existing state-of-the-art sampling methods for few-step sampling; 2) SOTA FID
scores on substantial benchmark datasets under a suitable number of function
evaluations (NFEs).
