Flow Matching Beats Diffusion in Efficiency on Low‑Resource Hardware
A new arXiv study directly compares diffusion probabilistic models and flow‑matching (rectified flow) using the same U‑Net backbone on MNIST. It finds that flow‑matching achieves comparable image fidelity with far fewer function evaluations, making it far more efficient for low‑resource hardware. The analysis highlights a smoother transport path and an efficiency frontier at just ten steps.