Diffusion models deliver state-of-the-art generative performance across diverse modalities but remain computationally expensive due to their inherently iterative sampling process. Existing training-free acceleration methods typically improve numerical solvers for the reverse-time ODE, yet their effectiveness is fundamentally constrained by the inefficiency of the underlying sampling trajectories. We propose A-FloPS (Adaptive Flow Path Sampler), a principled, training-free framework that reparameterizes the sampling trajectory of any pre-trained diffusion model into a flow-matching form and augments it with an adaptive velocity decomposition. The reparameterization analytically maps diffusion scores to flow-compatible velocities, yielding integration-friendly trajectories without retraining. The adaptive mechanism further factorizes the velocity field into a linear drift term and a residual component whose temporal variation is actively suppressed, restoring the accuracy benefits of high-order integration even in extremely low-NFE regimes. Extensive experiments on conditional image generation and text-to-image synthesis show that A-FloPS consistently outperforms state-of-the-art training-free samplers in both sample quality and efficiency. Notably, with as few as $5$ function evaluations, A-FloPS achieves substantially lower FID and generates sharper, more coherent images. The adaptive mechanism also improves native flow-based generative models, underscoring its generality. These results position A-FloPS as a versatile and effective solution for high-quality, low-latency generative modeling.

翻译：扩散模型在多种模态上实现了最先进的生成性能，但由于其固有的迭代采样过程，计算成本仍然高昂。现有的免训练加速方法通常改进反向时间常微分方程的数值求解器，但其有效性从根本上受到底层采样轨迹低效性的制约。我们提出A-FloPS（自适应流路径采样器），这是一个原理性的免训练框架，它将任何预训练扩散模型的采样轨迹重新参数化为流匹配形式，并通过自适应速度分解进行增强。该重新参数化将扩散分数解析映射为与流兼容的速度，从而在不重新训练的情况下产生易于积分的轨迹。自适应机制进一步将速度场分解为线性漂移项和残差分量，并主动抑制其时间变化，即使在极低NFE条件下也能恢复高阶积分的精度优势。在条件图像生成和文本到图像合成上的大量实验表明，A-FloPS在样本质量和效率方面均持续优于最先进的免训练采样器。值得注意的是，仅需$5$次函数评估，A-FloPS即可实现显著更低的FID，并生成更清晰、更连贯的图像。该自适应机制还能改进基于流的原生生成模型，突显了其通用性。这些结果使A-FloPS成为高质量、低延迟生成建模的通用且有效的解决方案。