  Diffusion models (DMs) have established themselves as the state-of-the-art
generative modeling approach in the visual domain and beyond. A crucial
drawback of DMs is their slow sampling speed, relying on many sequential
function evaluations through large neural networks. Sampling from DMs can be
seen as solving a differential equation through a discretized set of noise
levels known as the sampling schedule. While past works primarily focused on
deriving efficient solvers, little attention has been given to finding optimal
sampling schedules, and the entire literature relies on hand-crafted
heuristics. In this work, for the first time, we propose a general and
principled approach to optimizing the sampling schedules of DMs for
high-quality outputs, called $\textit{Align Your Steps}$. We leverage methods
from stochastic calculus and find optimal schedules specific to different
solvers, trained DMs and datasets. We evaluate our novel approach on several
image, video as well as 2D toy data synthesis benchmarks, using a variety of
different samplers, and observe that our optimized schedules outperform
previous hand-crafted schedules in almost all experiments. Our method
demonstrates the untapped potential of sampling schedule optimization,
especially in the few-step synthesis regime.
