## 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.
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## 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,
beta_start = 0.0001,
beta_end = 0.02,
num_diffusion_timesteps = 1000, # number of steps
loss_type = 'l1' # L1 or L2 (wavegrad paper claims l1 is better?)
)
training_images = torch.randn(8, 3, 128, 128)
loss = diffusion(training_images)
loss.backward()
# after a lot of training
sampled_images = diffusion.sample(128, 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,
beta_start = 0.0001,
beta_end = 0.02,
num_diffusion_timesteps = 1000, # number of steps
loss_type = 'l1' # L1 or L2
).cuda()
trainer = Trainer(
diffusion,
'path/to/your/images',
image_size = 128,
train_batch_size = 32,
train_lr = 2e-5,
train_num_steps = 100000, # total training steps
gradient_accumulate_every = 2, # gradient accumulation steps
ema_decay = 0.995 # exponential moving average decay
)
trainer.train()
```
Todo: Command line tool for one-line training
## Citations
```bibtex
@misc{ho2020denoising,
title={Denoising Diffusion Probabilistic Models},
author={Jonathan Ho and Ajay Jain and Pieter Abbeel},
year={2020},
eprint={2006.11239},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```