PyTorch pretrained Diffusion Models

A PyTorch reimplementation of Denoising Diffusion Probabilistic Models with checkpoints converted from the author's TensorFlow implementation.

Quickstart

Running

pip install -e git+https://github.com/pesser/pytorch_diffusion.git#egg=pytorch_diffusion
pytorch_diffusion_demo

will start a Streamlit demo. It is recommended to run the demo with a GPU available.

Usage

Diffusion models with pretrained weights for cifar10, lsun-bedroom, lsun_cat or lsun_church can be loaded as follows:

from pytorch_diffusion import Diffusion

diffusion = Diffusion.from_pretrained("lsun_church")
samples = diffusion.denoise(4)
diffusion.save(samples, "lsun_church_sample_{:02}.png")

The U-Net model used for denoising is available via diffusion.model and can also be instantiated on its own:

from pytorch_diffusion import Model

model = Model(resolution=32,
              in_channels=3,
              out_ch=3,
              ch=128,
              ch_mult=(1,2,2,2),
              num_res_blocks=2,
              attn_resolutions=(16,),
              dropout=0.1)

This configuration example corresponds to the model used on CIFAR-10.

Producing samples

If you installed directly from github, you can find the cloned repository in <venv path>/src/pytorch_diffusion for virtual environments, and <cwd>/src/pytorch_diffusion for global installs. There, you can run

python pytorch_diffusion/diffusion.py <name> <bs> <nb>

where <name> is one of cifar10, lsun-bedroom, lsun_cat, lsun_church, <bs> is the batch size and <nb> the number of batches. This will produce samples from the PyTorch models and save them to results/<name>/.

Running conversions

The converted pytorch checkpoints are provided for download. If you want to convert them on your own, you can follow the steps described here.

Setup

This section assumes your working directory is the root of this repository. Download the pretrained TensorFlow checkpoints. It should follow the original structure,

diffusion_models_release/
  diffusion_cifar10_model/
    model.ckpt-790000.data-00000-of-00001
    model.ckpt-790000.index
    model.ckpt-790000.meta
  diffusion_lsun_bedroom_model/
    ...
  ...

Set the environment variable TFROOT to the directory where you want to store the author's repository, e.g.

export TFROOT=".."

Clone the diffusion repository,

git clone https://github.com/hojonathanho/diffusion.git ${TFROOT}/diffusion

and install their required dependencies (pip install ${TFROOT}/requirements.txt). Then add the following to your PYTHONPATH:

export PYTHONPATH=".:./scripts:${TFROOT}/diffusion:${TFROOT}/diffusion/scripts:${PYTHONPATH}"

Testing operations

To test the pytorch implementations of the required operations against their TensorFlow counterparts under random initialization and random inputs, run

python -c "import convert as m; m.test_ops()"

Converting checkpoints

To load the pretrained TensorFlow models, copy the weights into the pytorch models, check for equality on random inputs and finally save the corresponding pytorch checkpoints, run

python -c "import convert as m; m.transplant_cifar10()"
python -c "import convert as m; m.transplant_lsun_bedroom()"
python -c "import convert as m; m.transplant_lsun_cat()"
python -c "import convert as m; m.transplant_lsun_church()"

Pytorch checkpoints will be saved in

diffusion_models_converted/
  diffusion_cifar10_model/
    model-790000.ckpt
  diffusion_lsun_bedroom_model/
    model-2388000.ckpt
  diffusion_lsun_cat_model/
    model-1761000.ckpt
  diffusion_lsun_church_model/
    model-4432000.ckpt

Sample TensorFlow models

To produce N samples from each of the pretrained TensorFlow models, run

python -c "import convert as m; m.sample_tf(N)"

Samples will be saved in results/.

rromb / pytorch_diffusion