A Few-Step Generative Model on Cumulative Flow Maps

2026-05-05

AI Summary

This work addresses the challenge of achieving high-quality generation with drastically reduced inference steps, enabling efficient one-step or few-step synthesis. The authors propose a unified few-step generation framework based on cumulative flow matching, which introduces cumulative flow abstraction and cumulative parameterization to jointly model local instantaneous updates and global probability transport over finite time horizons. Notably, this approach requires no increase in model capacity or architectural modifications, making it readily applicable to a broad range of diffusion and flow-based models. Empirical evaluations across diverse tasks—including image generation, geometric distribution modeling, joint prediction, and signed distance field (SDF) synthesis—demonstrate that the framework achieves compelling generation quality at substantially lower inference costs, highlighting its strong versatility and computational efficiency.

Abstract

We propose a unified, few-step generative modeling framework based on \emph{cumulative flow maps} for long-range transport in probability space, inspired by flow-map techniques for physical transport and dynamics. At its core is a cumulative-flow abstraction that connects local, instantaneous updates with finite-time transport, enabling generative models to reason about global state transitions. This perspective yields a unified few-step framework built on cumulative transport and \revise{cumulative} parameterization that applies broadly to existing diffusion- and flow-based models without being tied to a specific prediction \revise{instantiation}. Our formulation supports few-step and even one-step generation while preserving synthesis quality, requiring only minimal changes to time embeddings and training objectives, and no increase in model capacity. We demonstrate its effectiveness across diverse tasks, including image generation, geometric distribution modeling, joint prediction, and SDF generation, with reduced inference cost.

Problem

• few-step generation
• generative modeling
• cumulative flow maps
• inference efficiency
• probability transport

Innovation

• cumulative flow maps
• few-step generation
• probability transport
• unified generative framework
• global state transitions