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PyTorch implementation of 'PGGAN' (Karras et al., 2018) from scratch and training it on CelebA-HQ at 512 × 512

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Name		Name	Last commit message	Last commit date
Latest commit History 286 Commits
checkpoints		checkpoints
papers		papers
pretrained		pretrained
samples		samples
.gitignore		.gitignore
README.md		README.md
celebahq.py		celebahq.py
config.py		config.py
eval.py		eval.py
loss.py		loss.py
model.py		model.py
sample.py		sample.py
set_pythonpath.sh		set_pythonpath.sh
train.py		train.py
utils.py		utils.py

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1. Pre-trained Model

pggan_512.pth:
- Average SWD: 1559.362

2. Samples

1) Drawn During Training


4 × 4


8 × 8


16 × 16


32 × 32


64 × 64


128 × 128


256 × 256


512 × 512


1,024 × 1,024

2) Drawn From the Trained Model

3. Implementation Details

1) Normalization

이전까지는 CelebA-HQ의 Training set에 대해 Mean과 Std를 계산해서 이걸 가지고 Normalize했습니다.
- T.Normalize(mean=(0.517, 0.416, 0.363), std=(0.303, 0.275, 0.269))
논문에서는 "We represent training and generated images in $[-1, 1]$."라는 표현이 있는 것을 알고 있었으나 이를 간과하고 있었습니다.
다음과 같이 코드를 수정했습니다.
- T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))

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PyTorch implementation of 'PGGAN' (Karras et al., 2018) from scratch and training it on CelebA-HQ at 512 × 512

pytorch generative-adversarial-networks wgan-gp gradient-penalty pggan celeba-hq

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