Towards Fast Stochastic Sampling in Diffusion Generative Models
CoRR(2024)
摘要
Diffusion models suffer from slow sample generation at inference time.
Despite recent efforts, improving the sampling efficiency of stochastic
samplers for diffusion models remains a promising direction. We propose
Splitting Integrators for fast stochastic sampling in pre-trained diffusion
models in augmented spaces. Commonly used in molecular dynamics,
splitting-based integrators attempt to improve sampling efficiency by cleverly
alternating between numerical updates involving the data, auxiliary, or noise
variables. However, we show that a naive application of splitting integrators
is sub-optimal for fast sampling. Consequently, we propose several principled
modifications to naive splitting samplers for improving sampling efficiency and
denote the resulting samplers as Reduced Splitting Integrators. In the context
of Phase Space Langevin Diffusion (PSLD) [Pandey & Mandt, 2023] on CIFAR-10,
our stochastic sampler achieves an FID score of 2.36 in only 100 network
function evaluations (NFE) as compared to 2.63 for the best baselines.
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