Continuous-time generative models, such as diffusion models, flow matching, and rectified flow, learn time-dependent vector fields but are typically trained with objectives that treat timesteps independently, leading to high estimator variance and inefficient sampling. Prior approaches mitigate this via explicit smoothness penalties, trajectory regularization, or modified probability paths and solvers. We introduce Temporal Pair Consistency (TPC), a lightweight variance-reduction principle that couples velocity predictions at paired timesteps along the same probability path, operating entirely at the estimator level without modifying the model architecture, probability path, or solver. We provide a theoretical analysis showing that TPC induces a quadratic, trajectory-coupled regularization that provably reduces gradient variance while preserving the underlying flow-matching objective. Instantiated within flow matching, TPC improves sample quality and efficiency across CIFAR-10 and ImageNet at multiple resolutions, achieving lower FID at identical or lower computational cost than prior methods, and extends seamlessly to modern SOTA-style pipelines with noise-augmented training, score-based denoising, and rectified flow.

翻译：连续时间生成模型（如扩散模型、流匹配和整流流）学习时间依赖的向量场，但通常采用独立处理时间步的训练目标，导致估计器方差高且采样效率低。先前方法通过显式平滑惩罚、轨迹正则化或修改概率路径与求解器来缓解此问题。我们提出时序配对一致性（TPC），这是一种轻量级的降方差原理，它将同一概率路径上配对时间步的速度预测耦合起来，完全在估计器层面操作，无需修改模型架构、概率路径或求解器。我们提供了理论分析，表明TPC引入了一种二次的、轨迹耦合的正则化，可证明在保持底层流匹配目标的同时降低梯度方差。在流匹配框架中实例化后，TPC在CIFAR-10和ImageNet多个分辨率上提升了样本质量与效率，在相同或更低计算成本下实现了比先前方法更低的FID，并可无缝扩展到现代SOTA风格流程，包括噪声增强训练、基于分数的去噪和整流流。