## 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
Youtube AI Educators - Yannic Kilcher | AI Coffeebreak with Letitia | Outlier
Annotated code by Research Scientists / Engineers from 🤗 Huggingface
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[](https://badge.fury.io/py/denoising-diffusion-pytorch)
## Install
```bash
$ pip install denoising_diffusion_pytorch
```
## Usage
```python
import torch
from denoising_diffusion_pytorch import Unet, GaussianDiffusion
model = Unet(
dim = 64,
dim_mults = (1, 2, 4, 8)
)
diffusion = GaussianDiffusion(
model,
image_size = 128,
timesteps = 1000, # number of steps
loss_type = 'l1' # L1 or L2
)
training_images = torch.randn(8, 3, 128, 128) # images are normalized from 0 to 1
loss = diffusion(training_images)
loss.backward()
# after a lot of training
sampled_images = diffusion.sample(batch_size = 4)
sampled_images.shape # (4, 3, 128, 128)
```
Or, if you simply want to pass in a folder name and the desired image dimensions, you can use the `Trainer` class to easily train a model.
```python
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
## Citations
```bibtex
@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}
}
```
```bibtex
@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},
}
```
```bibtex
@inproceedings{kingma2021on,
title = {On Density Estimation with Diffusion Models},
author = {Diederik P Kingma and Tim Salimans and Ben Poole and Jonathan Ho},
booktitle = {Advances in Neural Information Processing Systems},
editor = {A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year = {2021},
url = {https://openreview.net/forum?id=2LdBqxc1Yv}
}
```
```bibtex
@article{Choi2022PerceptionPT,
title = {Perception Prioritized Training of Diffusion Models},
author = {Jooyoung Choi and Jungbeom Lee and Chaehun Shin and Sungwon Kim and Hyunwoo J. Kim and Sung-Hoon Yoon},
journal = {ArXiv},
year = {2022},
volume = {abs/2204.00227}
}
```