Denoising Diffusion Probabilistic Model, in Pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution.
This implementation was transcribed from the official Tensorflow version here and Pytorch version here.
Modified here for use in COLAB environemnt - ET May 2022
-> added method to save checkpoints into Google Drive
-> implemented method to load model and run inference in colab
-> modified image resizing and data augmentation scheme
This is for educational purposes only. See below for the shared Colaboratory tutorial file.
Usage
Use the Trainer class to easily train a model.
from denoising_diffusion_pytorch import Unet, GaussianDiffusion, Trainer
model = Unet(
dim = 64,
dim_mults = (1, 2, 4, 8)
).cuda()
diffusion = GaussianDiffusion(
model,
image_size = 128,
timesteps = 1000, # number of steps
loss_type = 'l1' # L1 or L2
).cuda()
trainer = Trainer(
diffusion,
'path/to/your/images',
train_batch_size = 32,
train_lr = 1e-4,
train_num_steps = 700000, # total training steps
gradient_accumulate_every = 2, # gradient accumulation steps
ema_decay = 0.995, # exponential moving average decay
amp = True # turn on mixed precision
)
trainer.train()Samples and model checkpoints will be logged to ./results periodically
The colab notebook tutorial
->To be attached here soon - ET
Citations
@inproceedings{NEURIPS2020_4c5bcfec,
author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {6840--6851},
publisher = {Curran Associates, Inc.},
title = {Denoising Diffusion Probabilistic Models},
url = {https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
volume = {33},
year = {2020}
}@InProceedings{pmlr-v139-nichol21a,
title = {Improved Denoising Diffusion Probabilistic Models},
author = {Nichol, Alexander Quinn and Dhariwal, Prafulla},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {8162--8171},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf},
url = {https://proceedings.mlr.press/v139/nichol21a.html},
}