Efficient Integrators for Diffusion Generative Models
Kushagra Pandey Maja Rudolph Stephan Mandt
Diffusion models suffer from slow sample generation at inference time.
Therefore, developing a principled framework for fast deterministic/stochastic
sampling for a broader class of diffusion models is a promising direction. We
propose two complementary frameworks for accelerating sample generation in
pre-trained models: Conjugate Integrators and Splitting Integrators. Conjugate
integrators generalize DDIM, mapping the reverse diffusion dynamics to a more
amenable space for sampling. In contrast, splitting-based integrators, commonly
used in molecular dynamics, reduce the numerical simulation error by cleverly
alternating between numerical updates involving the data and auxiliary
variables. After extensively studying these methods empirically and
theoretically, we present a hybrid method that leads to the best-reported
performance for diffusion models in augmented spaces. Applied to Phase Space
Langevin Diffusion [Pandey & Mandt, 2023] on CIFAR-10, our deterministic and
stochastic samplers achieve FID scores of 2.11 and 2.36 in only 100 network
function evaluations (NFE) as compared to 2.57 and 2.63 for the best-performing
baselines, respectively. Our code and model checkpoints will be made publicly
available at \url{https://github.com/mandt-lab/PSLD}.