This article walks through the theory behind flow‑matching generative models, contrasting them with diffusion models, detailing the velocity‑field formulation, training objective, and sampling procedure, and includes visual illustrations of the core concepts.
The paper introduces SADA (Stability‑guided Adaptive Diffusion Acceleration), a novel paradigm that dynamically allocates sparsity per token using a unified stability criterion, enabling efficient ODE‑based sampling for diffusion and flow‑matching models, achieving up to 1.8× speedup with negligible fidelity loss across SD‑2, SDXL, Flux, ControlNet and MusicLDM.
This article explains how to obtain numerical solutions for ordinary differential equations in Python using SciPy’s odeint function, demonstrates several example problems including a simple ODE, a system converted from a second‑order equation, and the chaotic Lorenz model, and provides complete code snippets.
