ODE Sampler

ODE samplers are algorithms used to generate samples from complex probability distributions, often arising in generative modeling, by efficiently solving ordinary differential equations (ODEs) that describe a reverse diffusion process. Current research focuses on improving the convergence speed and sample quality of these samplers, exploring various ODE solvers and hybrid approaches combining ODE and stochastic differential equation (SDE) methods to optimize the balance between speed and accuracy. These advancements have significant implications for generative modeling, enabling faster and higher-fidelity generation of synthetic data across diverse applications, including image synthesis and molecular simulations.