Distilling ODE Solvers of Diffusion Models into Smaller Steps

arXiv:2309.16421v2 Announce Type: replace
Abstract: Abstract Diffusion models have recently gained prominence as a novel category of generative models. Despite their success, these models face a notable drawback in terms of slow sampling speeds, requiring a high number of function evaluations (NFE) in the order of hundreds or thousands. In response, both learning-free and learning-based sampling strategies have been explored to expedite the sampling process. Learning-free sampling employs various ordinary differential equation (ODE) solvers based on the formulation of diffusion …

abstract arxiv cs.cv diffusion diffusion models face function generative generative models novel sampling success terms type