This repository contains the code for the seminar paper "Denoising Diffusion Probabilistic Models" that was implemented as part of the course "Project Machine Learning" in WS23/24. The reports for the three milestones can be found in the docs/reports directory.

pip install . -r requirements.txt

This will install the dependencies listed in requirements.txt.

python3 train_denoiser.py <dataset_name> [options]

Train the denoising module on one of the provided datasets, i.e. CIFAR-10, MNIST, FGVC-Aircraft, Flowers102 or CelebA. The dataset is split into a training set, which is used to train the denoiser, and a test set, which is used to validate the model at end of each epoch. After the training is complete, the training results (metadata, losses, etc.) are stored together with the model weights in a directory named after the experiment.

python3 generate_images.py <run_dir> <image_count> [options]

Generate images using a trained denoiser. The routine takes the path of a run created by train_denoiser.py and the number of images to generate. The resulting images are saved to the specified output directory.

python3 evaluate_images.py <image_dir> [options]

Calculate the Inception Score to evaluate the quality of generated images using the InceptionV3 model. The routine takes the path of a directory containing the generated images and saves their resulting score to inception_score.txt in the same directory.

python3 apply_noise.py <image_path> [options]

This routine visualizes the forward diffusion process. It takes an image as parameter (e.g. one of the example images in data/images) and applies gradual noise according to the pre-configured schedule.

• Saif Daknou - Github
• Patrick Lindemann - Github
• Nguyen Pham - Github