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Unofficial PyTorch implementation of Denoising Diffusion Probabilistic Models. This implementation follows the most of details in official TensorFlow implementation.
pip3 install -U pip setuptools
pip3 install -r requirements.txt
pip3 install protobuf==3.20.3
yum install -y mesa-libGL
pip3 install urllib3==1.26.6
mkdir -p stats && cd stats
Download precalculated statistic for dataset:
the dataset structure sholud look like:
stats
└── cifar10.train.npz
cd ..
# 8 GPUs
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python3 main.py --train \
--flagfile ./config/CIFAR10.txt \
--parallel
# 1 GPU
export CUDA_VISIBLE_DEVICES=0
python3 main.py --train \
--flagfile ./config/CIFAR10.txt
# 8 GPUs
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python3 main.py \
--flagfile ./logs/DDPM_CIFAR10_EPS/flagfile.txt \
--notrain \
--eval \
--parallel
# 1 GPU
export CUDA_VISIBLE_DEVICES=0
python3 main.py \
--flagfile ./logs/DDPM_CIFAR10_EPS/flagfile.txt \
--notrain \
--eval
GPUs | FPS |
---|---|
BI-V100 x8 | 1.65 it/s |
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