Adaptive Stochastic Coefficients for Accelerating Diffusion Sampling
Ruoyu Wang, Beier Zhu, Junzhi Li, Liangyu Yuan, Chi Zhang
Diffusion-based generative processes, grounded in differential equation solving, frequently require striking a balance between computational speed and output quality. Our theoretical investigation of prevalent solving approaches - ordinary differential equations (ODE) and stochastic differential equations (SDE) solvers - uncovers distinct limitations: ODE solvers exhibit irreducible gradient error accumulation from deterministic path dependence, while SDE methods suffer amplified discretization errors when step counts are reduced. Building upon this insight, we introduce AdaSDE, a novel single-step SDE solver that aims to unify the efficiency of ODEs with the error resilience of SDEs. At the core of our design is a learnable parameter obtained through lightweight tuning, which dynamically regulates the error correction strength to accelerate diffusion sampling. Notably, our framework can be integrated with numerous solvers to enhance their capabilities through lightweight parameter tuning. Extensive experiments demonstrate state-of-the-art performance: At 5 NFE, AdaSDE achieves FID scores of 4.79 on CIFAR-10, 8.91 on FFHQ 64×64 and 6.96 on LSUN Bedroom.