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Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a powerfulfamily of generative models that can yield high-fidelity samples andcompetitive log-likelihoods across a range of domains, including image andspeech synthesis. Key advantages of DDPMs include ease of training, in contrastto generative adversarial networks, and speed of generation, in contrast toautoregressive models. However, DDPMs typically require hundreds-to-thousandsof steps to generate a high fidelity sample, making them prohibitivelyexpensive for high dimensional problems. Fortunately, DDPMs allow tradinggeneration speed for sample quality through adjusting the number of refinementsteps as a post process. Prior work has been successful in improving generationspeed through handcrafting the time schedule by trial and error. We insteadview the selection of the inference time schedules as an optimization problem,and introduce an exact dynamic programming algorithm that finds the optimaldiscrete time schedules for any pre-trained DDPM. Our method exploits the factthat ELBO can be decomposed into separate KL terms, and given any computationbudget, discovers the time schedule that maximizes the training ELBO exactly.Our method is efficient, has no hyper-parameters of its own, and can be appliedto any pre-trained DDPM with no retraining. We discover inference timeschedules requiring as few as 32 refinement steps, while sacrificing less than0.1 bits per dimension compared to the default 4,000 steps used on ImageNet64x64 [Ho et al., 2020; Nichol and Dhariwal, 2021].

Learning to Efficiently Sample from Diffusion Probabilistic Models