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

Last update: Oct 06, 2022

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Overview

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

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

To run the toy data experiments:

python scripts/train_toy.py --dataset swissroll --save_path logs/swissroll

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

To run the CIFAR image generation experiment:

python scripts/train_cifar.py --save_path logs/cifar

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

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

Related tags

Overview

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

Owner

Rithesh Kumar

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

Related tags

Overview

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

Owner

Rithesh Kumar

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Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

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(ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning"

Meta-TTS: Meta-Learning for Few-shot SpeakerAdaptive Text-to-Speech

PyTorch implementation of neural style randomization for data augmentation

Official implementation of Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models at NeurIPS 2021

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Official implementation of the paper "Steganographer Detection via a Similarity Accumulation Graph Convolutional Network"

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