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Show HN: Flow Matching model inference in C
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.
- bialamusic 2mo agoWhat is "Flow Matching paradigm in C"? Your Read Me file is 1 line ! Please explain these fancy words to us the old school developers
- ecrasevvv 2mo agoHi, 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 https://arxiv.org/abs/2210.02747, MIT Course with practical examples: https://diffusion.csail.mit.edu/2026/index.html 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.).