43 lines (26 loc) · 1.22 KB

Pytorch Conditional-Flow-Matching(CFM) Tutorial

A simple tutorial of Conditional Flow Matching Models (CFMs, Y. Lipman et. al., 2022) using MNIST dataset.

Implementations

Prerequisites

(1) Download Pytorch and etcs.

Anaconda
Python=3.11
Pytorch=2.5. with CUDA 12.4

(2) Install dependencies via following command

sh install.sh

Expremental Results

Used a RTX-3090 GPU for all implementations.
trained on MNIST dataset for 200 epochs
ground-truth samples

generated samples

References

[1] Neural Ordinary Differential Equations, R. T. Q. Chen et. al., 2018

[2] Denoising Diffusion Probabilistic Models, J. Ho et. al., 2020

[3] Flow Matching for Generative Modeling, Y. Lipman et. al., 2022

[4] Mean Flows for One-step Generative Modeling, Z. Geng et. al., 2025