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),
flash_attn = True
)
diffusion = GaussianDiffusion(
model,
image_size = 128,
timesteps = 1000 # number of steps
)
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),
flash_attn = True
)
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])
)
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, Dataset1D
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
dataset = Dataset1D(training_seq) # this is just an example, but you can formulate your own Dataset and pass it into the `Trainer1D` below
loss = diffusion(training_seq)
loss.backward()
# Or using trainer
trainer = Trainer1D(
diffusion,
dataset = dataset,
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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booktitle = {Advances in Neural Information Processing Systems},
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pages = {6840--6851},
publisher = {Curran Associates, Inc.},
title = {Denoising Diffusion Probabilistic Models},
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volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
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title = {Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning},
author = {Ting Chen and Ruixiang Zhang and Geoffrey Hinton},
year = {2022},
eprint = {2208.04202},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}@article{Salimans2022ProgressiveDF,
title = {Progressive Distillation for Fast Sampling of Diffusion Models},
author = {Tim Salimans and Jonathan Ho},
journal = {ArXiv},
year = {2022},
volume = {abs/2202.00512}
}@article{Ho2022ClassifierFreeDG,
title = {Classifier-Free Diffusion Guidance},
author = {Jonathan Ho},
journal = {ArXiv},
year = {2022},
volume = {abs/2207.12598}
}@article{Sunkara2022NoMS,
title = {No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects},
author = {Raja Sunkara and Tie Luo},
journal = {ArXiv},
year = {2022},
volume = {abs/2208.03641}
}@inproceedings{Jabri2022ScalableAC,
title = {Scalable Adaptive Computation for Iterative Generation},
author = {A. Jabri and David J. Fleet and Ting Chen},
year = {2022}
}@article{Cheng2022DPMSolverPlusPlus,
title = {DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models},
author = {Cheng Lu and Yuhao Zhou and Fan Bao and Jianfei Chen and Chongxuan Li and Jun Zhu},
journal = {NeuRips 2022 Oral},
year = {2022},
volume = {abs/2211.01095}
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title = {simple diffusion: End-to-end diffusion for high resolution images},
author = {Emiel Hoogeboom and Jonathan Heek and Tim Salimans},
year = {2023}
}@misc{https://doi.org/10.48550/arxiv.2302.01327,
doi = {10.48550/ARXIV.2302.01327},
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author = {Kumar, Manoj and Dehghani, Mostafa and Houlsby, Neil},
title = {Dual PatchNorm},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution 4.0 International}
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title = {Efficient Diffusion Training via Min-SNR Weighting Strategy},
author = {Tiankai Hang and Shuyang Gu and Chen Li and Jianmin Bao and Dong Chen and Han Hu and Xin Geng and Baining Guo},
year = {2023}
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author = {Nicholas Guttenberg},
url = {https://www.crosslabs.org/blog/diffusion-with-offset-noise}
}@inproceedings{Lin2023CommonDN,
title = {Common Diffusion Noise Schedules and Sample Steps are Flawed},
author = {Shanchuan Lin and Bingchen Liu and Jiashi Li and Xiao Yang},
year = {2023}
}@inproceedings{dao2022flashattention,
title = {Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness},
author = {Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
booktitle = {Advances in Neural Information Processing Systems},
year = {2022}
}