Hi, the "Flow Matching paradigm" a simulation-free approach for training Continuous Normalizing Flows (CNFs) based on regressing vector fields of fixed conditional probability paths (original paper: https://arxiv.org/abs/2210.02747, MIT Course with practical examples: https://diffusion.csail.mit.edu/2026/index.html). Less formally Flow Matching can be described as "a technique to learn how to transport samples from one distribution to another". For example we could learn how to transport samples from a simple distribution we can easily sample from (e.g. Gaussian) to a complex distribution (e.g. images , videos , robot actions , etc.).
As the title and description of the GitHub repo suggest, I’m working on a small project for purely educational purposes, with the goal of implementing generative model inference (small models capable of modeling 2D distributions) based on the Flow Matching paradigm in C. I’ve worked on generative AI models based on Flow Matching from a more “abstract” perspective, using frameworks like PyTorch, and I wanted to understand what goes on behind the scenes. The repository is still a work in progress and is also one of my first "serious" projects in C.