  Diffusion models are powerful generative models but suffer from slow
sampling, often taking 1000 sequential denoising steps for one sample. As a
result, considerable efforts have been directed toward reducing the number of
denoising steps, but these methods hurt sample quality. Instead of reducing the
number of denoising steps (trading quality for speed), in this paper we explore
an orthogonal approach: can we run the denoising steps in parallel (trading
compute for speed)? In spite of the sequential nature of the denoising steps,
we show that surprisingly it is possible to parallelize sampling via Picard
iterations, by guessing the solution of future denoising steps and iteratively
refining until convergence. With this insight, we present ParaDiGMS, a novel
method to accelerate the sampling of pretrained diffusion models by denoising
multiple steps in parallel. ParaDiGMS is the first diffusion sampling method
that enables trading compute for speed and is even compatible with existing
fast sampling techniques such as DDIM and DPMSolver. Using ParaDiGMS, we
improve sampling speed by 2-4x across a range of robotics and image generation
models, giving state-of-the-art sampling speeds of 0.2s on 100-step
DiffusionPolicy and 14.6s on 1000-step StableDiffusion-v2 with no measurable
degradation of task reward, FID score, or CLIP score.
