Restart Sampling for Improving Generative Processes
Authors: name: Yilun Xu, name: Mingyang Deng, name: Xiang Cheng, name: Yonglong Tian, name: Ziming Liu, name: Tommi Jaakkola

Generative processes that involve solving differential equations, such as
diffusion models, frequently necessitate balancing speed and quality. ODE-based
samplers are fast but plateau in performance while SDE-based samplers deliver
higher sample quality at the cost of increased sampling time. We attribute this
difference to sampling errors: ODE-samplers involve smaller discretization
errors while stochasticity in SDE contracts accumulated errors. Based on these
findings, we propose a novel sampling algorithm called Restart in order to
better balance discretization errors and contraction. The sampling method
alternates between adding substantial noise in additional forward steps and
strictly following a backward ODE. Empirically, Restart sampler surpasses
previous SDE and ODE samplers in both speed and accuracy. Restart not only
outperforms the previous best SDE results, but also accelerates the sampling
speed by 10-fold / 2-fold on CIFAR-10 / ImageNet 64 times 64. In addition,
it attains significantly better sample quality than ODE samplers within
comparable sampling times. Moreover, Restart better balances text-image
alignment/visual quality versus diversity than previous samplers in the
large-scale text-to-image Stable Diffusion model pre-trained on LAION 512
times 512. Code is available at
https://github.com/Newbeeer/diffusion_restart_sampling