Minimal diffusion models - Minimal code and simple experiments to play with Denoising Diffusion Probabilistic Models (DDPMs)

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Deep Learningminimal_diffusion_models
Overview

Minimal code and simple experiments to play with Denoising Diffusion Probabilistic Models (DDPMs)

All experiments have tensorboard visualizations for samples / train curves etc.

  1. To run the toy data experiments:
python scripts/train_toy.py --dataset swissroll --save_path logs/swissroll
  1. To run the discrete mode collapse experiment:
python scripts/train_mnist.py --save_path logs/mnist_3 --n_stack 3

This requires the pretrained mnist classifier:

python scripts/train/mnist_classifier.py
  1. To run the CIFAR image generation experiment:
python scripts/train_cifar.py --save_path logs/cifar
  1. To run the CelebA image generation experiments:
python scripts/train_celeba.py --save_path logs/celeba
Owner
Rithesh Kumar
AI Researcher @ Descript. Formerly Mila | MSR Montreal | Lyrebird
Rithesh Kumar
GitHub Repository
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