用累积流图实现少步生成，提升长程概率传输效率
A Few-Step Generative Model on Cumulative Flow Maps
- 基于累积流图抽象，将局部更新与全局传输统一建模
- 支持少步甚至单步生成，合成质量不变，推理成本降低
- 适用于图像、几何分布等多任务，无需增加模型容量
我们提出一种基于累积流图的统一少步生成框架，用于概率空间中的长程传输，受物理传输中流图技术启发。核心是累积流抽象，将局部瞬时更新与有限时间传输相连接，使生成模型能够推理全局状态转移。该视角带来一个统一的少步框架，基于累积传输与参数化，可广泛应用于现有扩散和流模型，不依赖特定预测实例。该方法支持少步甚至单步生成，保持合成质量，仅需微调时间嵌入与训练目标，无需增加模型容量。我们在图像生成、几何分布建模、联合预测及SDF生成等多种任务上验证其有效性，显著降低推理开销。
原文摘要 · Abstract (English)
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.
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