On Accelerating Diffusion-Based Sampling Process via Improved Integration Approximation

• URL: http://arxiv.org/abs/2304.11328v4
• Date: Tue, 3 Oct 2023 10:35:05 GMT
• Title: On Accelerating Diffusion-Based Sampling Process via Improved Integration Approximation
• Authors: Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn
• Abstract summary: A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE) We consider accelerating several popular ODE-based sampling processes by optimizing certain coefficients via improved integration approximation (IIA) We show that considerably better FID scores can be achieved by using IIA-EDM, IIA-DDIM, and IIA-DPM-r than the original counterparts.
• Score: 12.882586878998579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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