COMPUTER VISION

CORE MACHINE LEARNING

Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models

June 17, 2024

Abstract

This paper introduces Bespoke Non-Stationary (BNS) Solvers, a solver distillation approach to improve sample efficiency of Diffusion and Flow models. BNS solvers are based on a family of non-stationary solvers that provably subsumes existing numerical ODE solvers and consequently demonstrate considerable improvement in sample approximation (PSNR) over these baselines. Compared to model distillation, BNS solvers benefit from a tiny parameter space (<200 parameters), fast optimization (two orders of magnitude faster), maintain diversity of samples, and in contrast to previous solver distillation approaches nearly close the gap from standard distillation methods such as Progressive Distillation in the low-medium NFE regime. For example, BNS solver achieves 45 PSNR / 1.76 FID using 16 NFE in class-conditional ImageNet-64. We experimented with BNS solvers for conditional image generation, text-to-image generation, and text-2-audio generation showing significant improvement in sample approximation (PSNR) in all.

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AUTHORS

Written by

Neta Shaul

Uriel Singer

Ricky Chen

Matt Le

Ali Thabet

Albert Pumarola

Yaron Lipman

Publisher

ICML

Research Topics

Computer Vision

Core Machine Learning

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