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.

翻译：我们提出一个基于\textit{累积流图}的统一少步生成建模框架，用于概率空间中的长程传输。该框架灵感源于物理传输与动力学中的流图技术，其核心在于构建连接局部瞬时更新与有限时间传输的累积流抽象，使生成模型能够推理全局状态变迁。该视角产生了基于累积传输与累积参数化的统一少步框架，可广泛适用于现有基于扩散和流的生成模型，且不受特定预测实例化的约束。本公式在保持合成质量的前提下支持少步甚至单步生成，仅需对时间嵌入和训练目标进行极简改动，无需增加模型容量。我们通过图像生成、几何分布建模、联合预测及有符号距离场生成等多样化任务验证其有效性，并显著降低了推理成本。