Denoising MCMC for Accelerating Diffusion-Based Generative Models. (arXiv:2209.14593v1 [cs.LG])

Diffusion models are powerful generative models that simulate the reverse of
diffusion processes using score functions to synthesize data from noise. The
sampling process of diffusion models can be interpreted as solving the reverse
stochastic differential equation (SDE) or the ordinary differential equation
(ODE) of the diffusion process, which often requires up to thousands of
discretization steps to generate a single image. This has sparked a great
interest in developing efficient integration techniques for reverse-S/ODEs.
Here, we propose an orthogonal …

arxiv denoising diffusion generative models mcmc