ParaSolver: A Hierarchical Parallel Integral Solver for Diffusion Models
Jianrong Lu Zhiyu Zhu Junhui Hou
This paper explores the challenge of accelerating the sequential inference process of Diffusion Probabilistic Models (DPMs). We tackle this critical issue from a dynamic systems perspective, in which the inherent sequential nature is transformed into a parallel sampling process. Specifically, we propose a unified framework that generalizes the sequential sampling process of DPMs as solving a system of banded nonlinear equations. Under this generic framework, we reveal that the Jacobian of the banded nonlinear equations system possesses a unit-diagonal structure, enabling further approximation for acceleration. Moreover, we theoretically propose an effective initialization approach for parallel sampling methods. Finally, we construct \textit{ParaSolver}, a hierarchical parallel sampling technique that enhances sampling speed without compromising quality. Extensive experiments show that ParaSolver achieves up to \textbf{12.1× speedup} in terms of wall-clock time. The source code is publicly available at https://github.com/Jianrong-Lu/ParaSolver.git.
Diffusion Models;
A parallel sampling algorithm for accelerating inference of diffusion models