Based on the work of https://github.com/Newbeeer/pfgmpp.
Create conda environment with provided environment.yml file. We recommend using mamba to speed up the process.
conda env create -f environment.ymlIn downloads:
wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gzIn root to make it zip and get data for FID score computation:
python dataset_tool.py --source=downloads/cifar-10-python.tar.gz --dest=datasets/cifar-10-python.zip
python fid.py ref --data=datasets/cifar-10-python.zip --dest=fid-refs/cifar-10-python.npzIn downloads:
wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz In root to make it zip and get data for FID score computation:
python dataset_tool.py --source=downloads/train-images-idx3-ubyte.gz --dest=datasets/mnist_train.ziptorchrun --standalone --nproc_per_node=4 train.py --outdir=training-runs --name pfgm_baseline_128 --data=datasets/cifar10-32x32.zip --cond=0 --arch=ddpmpp --pfgmpp=1 --batch 512 --aug_dim 128 --duration 20Adapt --name to the desired name of the training run.
You can set the flags --l1prior to enable l1 prior sampling and --dct to enable DCT based training/sampling.
torchrun --standalone --nproc_per_node=4 generate.py --seeds=0-49999 --outdir=training-runs/pfgm_baseline_128 --pfgmpp=1 --aug_dim=128torchrun --standalone --nproc_per_node=4 fid.py calc --images=training-runs/pfgm_baseline_128 --ref=fid-refs/cifar10-32x32.npz --num 50000
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