  Diffusion probabilistic models have generated high quality image synthesis
recently. However, one pain point is the notorious inference to gradually
obtain clear images with thousands of steps, which is time consuming compared
to other generative models. In this paper, we present a shortcut MCMC sampling
algorithm, which balances training and inference, while keeping the generated
data's quality. In particular, we add the global fidelity constraint with
shortcut MCMC sampling to combat the local fitting from diffusion models. We do
some initial experiments and show very promising results. Our implementation is
available at https://github.com//vividitytech/diffusion-mcmc.git.
