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

Rithesh Kumar

Last update: Oct 6, 2022

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Deep Learning minimal_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.

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

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Implementation of Common Image Evaluation Metrics by Sayed Nadim (sayednadim.github.io). The repo is built based on full reference image quality metrics such as L1, L2, PSNR, SSIM, LPIPS. and feature-level quality metrics such as FID, IS. It can be used for evaluating image denoising, colorization, inpainting, deraining, dehazing etc. where we have access to ground truth.

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Owner

Rithesh Kumar

AI Researcher @ Descript. Formerly Mila | MSR Montreal | Lyrebird

GitHub

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)

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The source code of the paper "Understanding Graph Neural Networks from Graph Signal Denoising Perspectives"

Source code and notebooks to reproduce experiments and benchmarks on Bias Faces in the Wild (BFW).

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Simple reimplemetation experiments about FcaNet

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)

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