Title: ParaSolver-Turbo: Accelerating Parallel Diffusion Integrator via Intrinsic Partially Linear Structure
Primary area: generative models
Keywords: Diffusion Models

Abstract: This paper explores the challenge of accelerating the sequential inference process of Diffusion Probabilistic Models (DPMs). We tackle this critical issue from a dynamic system perspective, in which the inherent sequential nature is transformed into a parallel sampling process. 
Specifically, we first reveal that the sequential integral solver of the diffusion model can be approximated by a full linear solver, enabling efficient computation for parallel integral solvers of DPMs. Based on such a linear formulation, we then introduce a unified framework that reformulates the original nonlinear sequential integral process of diffusion model as a system of partial linear equations. Moreover, we further develop an immediate update strategy and an adaptive stopping criterion to solve the system. In addition, we prove that (1) the system admits a unique root corresponding precisely to the trajectory of the sequential integral solver; (2) solving the system guarantees convergence to the trajectory of sequential integral solvers in equal or fewer iterations. 
Building on these insights, we present \textit{ParaSolver-Turbo}, a partial linear parallel integral solver to accelerate a broad class of sequential and parallel sampling methods such as DDPM and ParaSolver.
Extensive experiments validate that ParaSolver-Turbo achieves up to \textbf{71.4$\times$ speedup} in terms of wall-clock time without measurable quality degradation. The source code will be released publicly.

TL;DR: A parallel sampling algorithm for accelerating inference of diffusion models