Fast Sampling for Flows and Diffusions with Lazy and Point Mass Stochastic Interpolants

2026-02-03

AI Summary

This work proposes a method to significantly improve sampling efficiency in generative models without requiring retraining, thereby drastically reducing the number of steps needed for generation. By introducing point-mass interpolation scheduling and a family of lazy schedulers, the approach unifies the sampling trajectories of flow models and diffusion models. Leveraging stochastic interpolation theory, SDE path transformations, and Gaussian–point-mass measure bridging, it enables seamless conversion of sample paths across different schedulers and diffusion coefficients. Notably, this is the first successful application of accelerated sampling from pretrained flow models to real-world non-Gaussian data, achieving substantial reductions in image generation steps while preserving high sample quality. The results demonstrate the theoretical soundness and practical efficacy of the proposed framework on complex datasets.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Stochastic interpolants unify flows and diffusions, popular generative modeling frameworks. A primary hyperparameter in these methods is the interpolation schedule that determines how to bridge a standard Gaussian base measure to an arbitrary target measure. We prove how to convert a sample path of a stochastic differential equation (SDE) with arbitrary diffusion coefficient under any schedule into the unique sample path under another arbitrary schedule and diffusion coefficient. We then extend the stochastic interpolant framework to admit a larger class of point mass schedules in which the Gaussian base measure collapses to a point mass measure. Under the assumption of Gaussian data, we identify lazy schedule families that make the drift identically zero and show that with deterministic sampling one gets a variance-preserving schedule commonly used in diffusion models, whereas with statistically optimal SDE sampling one gets our point mass schedule. Finally, to demonstrate the usefulness of our theoretical results on realistic highly non-Gaussian data, we apply our lazy schedule conversion to a state-of-the-art pretrained flow model and show that this allows for generating images in fewer steps without retraining the model.

Problem

• stochastic interpolants
• sampling acceleration
• interpolation schedule
• generative modeling
• diffusion models

Innovation

• stochastic interpolants
• schedule conversion
• point mass schedules
• lazy schedules
• fast sampling