Denoising Diffusion Probabilistic Model, in Pytorch (wip)

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.

Install

$ pip install denoising_diffusion_pytorch

Usage

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.p_sample_loop((1, 3, 128, 128))
sampled_images.shape # (1, 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.

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 = 3e-4,
    train_num_steps = 100000,
    gradient_accumulate_every = 1
)

trainer.train()

Todo: Command line tool for one-line training

Citations

@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}
}