Diffusion Model for Jupyter Notebook

Author: Elisa Warner
Email: elisawa@umich.edu
Date: 04/12/2023

Description:

This code is a written implementation of the Diffusion model for Jupyter Notebook.

Requirements:

1. Python 3.9 or higher
2. Package: torchvision
3. Package: torch (recommended 1.11 or higher)
4. Package: os
5. Package: matplotlib
6. Package: jupyter
7. For Preprocess.ipynb, Package: glob, shutil

Contents:

1. Preprocess.ipynb [Jupyter Notebook]: This notebook contains code for moving the images downloaded from Kaggle into a single folder.
2. DiffusionModel.ipynb [Jupyter Notebook] : This notebook contains the Diffusion Model code.
3. unet_mha.py [Executable Script]: This code contains the architecture for the U-Net with Multi-Head Attention. The advantage of this code is that the MHA layers ensure a greater probability that facial landmarks on the cat will be properly placed, but require many more parameters. Therefore, the recommended SQ_SIZE for this network is 32.
4. unet_stripped.py [Executable Script]: This code contains the architecture for the U-Net without Multi-Head Attention. The advantage of this code is that the stripped-down model contains less parameters, which means more data can be fit onto the GPU. Therefore, the recommend SQ_SIZE for this network is 64.
5. config.py [Executable Script]: This code contains the hyperparameter adjustments set by the user. Edit this code before running DiffusionModel.ipynb.
6. pre_train_example.pth : A pretrained 32x32 model example to load. This was trained for over 1200 epochs.
7. results_example.txt : An example output for the model.

Expected Outputs:

1. results.txt : Will contain the Epoch number as well as the loss.
2. model.pth : The most recently saved model from the latest epoch run on DiffusionModel.ipynb.