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Diffusion Model for Jupyter Notebook
Author: Elisa Warner
Email: [email protected]
Date: 04/12/2023
Description:
This code is a written implementation of the Diffusion model for Jupyter Notebook.
Requirements:
Python 3.9 or higher
Package: torchvision
Package: torch (recommended 1.11 or higher)
Package: os
Package: matplotlib
Package: jupyter
For Preprocess.ipynb, Package: glob, shutil
Contents:
Preprocess.ipynb [Jupyter Notebook]: This notebook contains code for moving the images downloaded from Kaggle into a single folder.
DiffusionModel.ipynb [Jupyter Notebook] : This notebook contains the Diffusion Model code.
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.
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.
config.py [Executable Script]: This code contains the hyperparameter adjustments set by the user. Edit this code before running DiffusionModel.ipynb.
pre_train_example.pth : A pretrained 32x32 model example to load. This was trained for over 1200 epochs.
results_example.txt : An example output for the model.
Expected Outputs:
results.txt : Will contain the Epoch number as well as the loss.
model.pth : The most recently saved model from the latest epoch run on DiffusionModel.ipynb.
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