  This paper introduces Bespoke Non-Stationary (BNS) Solvers, a solver
distillation approach to improve sample efficiency of Diffusion and Flow
models. BNS solvers are based on a family of non-stationary solvers that
provably subsumes existing numerical ODE solvers and consequently demonstrate
considerable improvement in sample approximation (PSNR) over these baselines.
Compared to model distillation, BNS solvers benefit from a tiny parameter space
($<$200 parameters), fast optimization (two orders of magnitude faster),
maintain diversity of samples, and in contrast to previous solver distillation
approaches nearly close the gap from standard distillation methods such as
Progressive Distillation in the low-medium NFE regime. For example, BNS solver
achieves 45 PSNR / 1.76 FID using 16 NFE in class-conditional ImageNet-64. We
experimented with BNS solvers for conditional image generation, text-to-image
generation, and text-2-audio generation showing significant improvement in
sample approximation (PSNR) in all.
