COMPSCI 280 Project2: Flow Matching

Overview

You will train your own flow matching model on MNIST.

Part 1: Training a Single-Step Denoising UNet

Unconditioned UNet architecture

Visualization of the noising process

Training loss curve

Sample results on the test set after the first and the 5-th epoch

Sample results on the test set with out-of-distribution noise levels after the model is trained.

Sample results on the test set with pure noise

Average image of the training set

The average MNIST image is blurry and lacks clear digits, showing general intensity distributions. The denoised results, however, retain distinct shapes, indicating that it is doing denoising.

Part 2: Training a Flow Matching Model

Training loss curve plot for the time-conditioned UNet over the whole training process

Sampling results for the time-conditioned UNet for 5 and 10 epochs

Training loss curve plot for the class-conditioned UNet over the whole training process

Sampling results for the class-conditioned UNet for 5 and 10 epochs