Restart Sampling for Improving Generative Processes
This addresses a key bottleneck in generative modeling for researchers and practitioners, offering a more efficient and higher-quality sampling method, though it is incremental as it builds on existing diffusion model frameworks.
The paper tackles the problem of balancing speed and quality in generative processes like diffusion models by proposing a novel sampling algorithm called Restart, which alternates between adding noise and following a backward ODE, resulting in surpassing previous SDE and ODE samplers with a 10-fold/2-fold speedup on CIFAR-10/ImageNet and better sample quality.
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