Most of the posts start with a pic or a qoute, I’m going to start with a lame joke.

A gaussian noise and MINST dataset fell in love with each other. How did they meet?
They used flow matching app.

What is the post about?

Diffusion models and flow matching have improved image generation(they both can be wriiten under the same formulation). In this blog post I will write my learnings about flow matching from the ground up, which was used to develop SD3, open ai SORA, Meta’s movie gen video, etc. The topis covered are:

  1. Flow matching theory with self contained proofs.
  2. Guidance.
  3. Training a model on MINST dataset.

I think the MIT’s course Introduction to Flow Matching and Diffusion Models (thanks a lot!!) is much better than what I have written, I learned from it, please take a look at it, they have notes, codes and video lectures. This post is inspired from it.

Introduction

We want to generate images belonging to $P_{data}$. How can we go about this?

To read more please click the link below. Since writing math using plain html is time consuming, I use jupyter-book for wrtiing my blogs. (To read comfortably please toggle the side bar)

https://yogheswaran-a.github.io/blogs/02_Flow_Matching_I.html