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.

在增广空间中的预训练扩散模型中，我们提出拆分积分器来改善随机抽样的速度，通过对常用于分子动力学中的拆分积分器进行改进，我们提出了减少拆分积分器的几种原则性修改来提高抽样效率，我们的随机抽样算法在CIFAR-10上实现了FID得分2.36，仅需要100次网络函数评估，相较于最佳基准值的2.63有了显著提升。

基于扩散生成模型的快速随机采样探索