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
Flax implementation from YiYi Xu
Annotated code by Research Scientists / Engineers from 🤗 Huggingface
Update: Turns out none of the technicalities really matters at all | "Cold Diffusion" paper | Muse
$ pip install denoising_diffusion_pytorchimport 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.rand(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.
from denoising_diffusion_pytorch import Unet, GaussianDiffusion, Trainer
model = Unet(
dim = 64,
dim_mults = (1, 2, 4, 8)
)
diffusion = GaussianDiffusion(
model,
image_size = 128,
timesteps = 1000, # number of steps
sampling_timesteps = 250, # number of sampling timesteps (using ddim for faster inference [see citation for ddim paper])
loss_type = 'l1' # L1 or L2
)
trainer = Trainer(
diffusion,
'path/to/your/images',
train_batch_size = 32,
train_lr = 8e-5,
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
calculate_fid = True # whether to calculate fid during training
)
trainer.train()Samples and model checkpoints will be logged to ./results periodically
The Trainer class is now equipped with 🤗 Accelerator. You can easily do multi-gpu training in two steps using their accelerate CLI
At the project root directory, where the training script is, run
$ accelerate configThen, in the same directory
$ accelerate launch train.pyBy popular request, a 1D Unet + Gaussian Diffusion implementation.
import torch
from denoising_diffusion_pytorch import Unet1D, GaussianDiffusion1D, Trainer1D
model = Unet1D(
dim = 64,
dim_mults = (1, 2, 4, 8),
channels = 32
)
diffusion = GaussianDiffusion1D(
model,
seq_length = 128,
timesteps = 1000,
objective = 'pred_v'
)
training_seq = torch.rand(64, 32, 128) # features are normalized from 0 to 1
loss = diffusion(training_seq)
loss.backward()
# Or using trainer
trainer = Trainer1D(
diffusion,
dataset = training_seq,
train_batch_size = 32,
train_lr = 8e-5,
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()
# after a lot of training
sampled_seq = diffusion.sample(batch_size = 4)
sampled_seq.shape # (4, 32, 128)Trainer1D does not evaluate the generated samples in any way since the type of data is not known.
You could consider adding a suitable metric to the training loop yourself after doing an editable install of this package
pip install -e ..
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pages = {6840--6851},
publisher = {Curran Associates, Inc.},
title = {Denoising Diffusion Probabilistic Models},
url = {https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
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}