  Diffusion modeling (DM) has high-quality generative performance, and the
sampling problem is an important part of the DM performance. Thanks to
efficient differential equation solvers, the sampling speed can be reduced
while higher sampling quality is guaranteed. However, currently, there is a
contradiction in samplers for diffusion-based generative models: the mainstream
sampler choices are diverse, each with its own characteristics in terms of
performance. However, only a single sampler algorithm can be specified on all
sampling steps in the generative process. This often makes one torn between
sampler choices; in other words, it makes it difficult to fully utilize the
advantages of each sampler. In this paper, we propose the feasibility of using
different samplers (ODE/SDE) on different sampling steps of the same sampling
process based on analyzing and generalizing the updating formulas of each
mainstream sampler, and experimentally demonstrate that such a multi-sampler
scheduling improves the sampling results to some extent. In particular, we also
verify that the combination of using SDE in the early sampling steps and ODE in
the later sampling steps solves the inherent problems previously caused by
using both singly. We show that our design changes improve the sampling
efficiency and quality in previous work. For instance, when Number of Function
Evaluations (NFE) = 24, the ODE Sampler Scheduler achieves a FID score of 1.91
on the CIFAR-10 dataset, compared to 2.02 for DPM++ 2M, 1.97 for DPM2, and
11.90 for Heun for the same NFE. Meanwhile the Sampler Scheduler with the
combined scheduling of SDE and ODE reaches 1.899, compared to 18.63 for Euler
a, 3.14 for DPM2 a and 23.14 for DPM++ SDE.
