On Accelerating Diffusion-Based Sampling Processes via Improved Integration Approximation

Published: 16 Jan 2024, Last Modified: 14 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: diffusion, EDM, DDIM, DPM-Solver, optimal stepsize
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TL;DR: A new technique for accelerating diffusion-based sampling processes such as EDM, DDIM, and DPM-Solver by optimizing the stepsizes in front of the gradients
Abstract: A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE). In existing samplers, the coefficients of the ODE solvers are pre-determined by the ODE formulation, the reverse discrete timesteps, and the employed ODE methods. In this paper, we consider accelerating several popular ODE-based sampling processes (including EDM, DDIM, and DPM-Solver) by optimizing certain coefficients via improved integration approximation (IIA). We propose to minimize, for each time step, a mean squared error (MSE) function with respect to the selected coefficients. The MSE is constructed by applying the original ODE solver for a set of fine-grained timesteps, which in principle provides a more accurate integration approximation in predicting the next diffusion state. The proposed IIA technique does not require any change of a pre-trained model, and only introduces a very small computational overhead for solving a number of quadratic optimization problems. Extensive experiments show that considerably better FID scores can be achieved by using IIA-EDM, IIA-DDIM, and IIA-DPM-Solver than the original counterparts when the neural function evaluation (NFE) is small (i.e., less than 25).
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Primary Area: generative models
Submission Number: 5121