In diffusion models, samples are generated through an iterative refinement process, requiring hundreds of sequential model evaluations. Several recent methods have introduced approximations (fewer discretization steps or distillation) to trade off speed at the cost of sample quality. In contrast, we introduce Self-Refining Diffusion Samplers (SRDS) that retain sample quality and can improve latency at the cost of additional parallel compute. We take inspiration from the Parareal algorithm, a popular numerical method for parallel-in-time integration of differential equations. In SRDS, a quick but rough estimate of a sample is first created and then iteratively refined in parallel through Parareal iterations. SRDS is not only guaranteed to accurately solve the ODE and converge to the serial solution but also benefits from parallelization across the diffusion trajectory, enabling batched inference and pipelining. As we demonstrate for pre-trained diffusion models, the early convergence of this refinement procedure drastically reduces the number of steps required to produce a sample, speeding up generation for instance by up to 1.7x on a 25-step StableDiffusion-v2 benchmark and up to 4.3x on longer trajectories.

翻译：在扩散模型中，样本通过迭代优化过程生成，需要数百次顺序模型评估。最近的一些方法引入了近似技术（减少离散化步骤或蒸馏）以牺牲样本质量为代价换取速度提升。相比之下，我们提出的自优化扩散采样器（SRDS）在保持样本质量的同时，能够以增加并行计算为代价改善延迟。我们的方法受到Parareal算法的启发，这是一种用于微分方程时间并行积分的常用数值方法。在SRDS中，首先快速生成样本的粗略估计，然后通过Parareal迭代进行并行优化。SRDS不仅保证精确求解ODE并收敛至串行解，还能受益于扩散轨迹的并行化，实现批量推理和流水线处理。正如我们在预训练扩散模型中所展示的，这种优化过程的早期收敛显著减少了生成样本所需的步骤数，例如在25步StableDiffusion-v2基准测试中生成速度提升高达1.7倍，在更长轨迹上提升高达4.3倍。