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Usage: train.py [OPTIONS]
Train a GAN using the techniques described in the paper "Training
Generative Adversarial Networks with Limited Data".
Examples:
# Train with custom images using 1 GPU.
python train.py --outdir=~/training-runs --data=~/my-image-folder
# Train class-conditional CIFAR-10 using 2 GPUs.
python train.py --outdir=~/training-runs --data=~/datasets/cifar10.zip \
--gpus=2 --cfg=cifar --cond=1
# Transfer learn MetFaces from FFHQ using 4 GPUs.
python train.py --outdir=~/training-runs --data=~/datasets/metfaces.zip \
--gpus=4 --cfg=paper1024 --mirror=1 --resume=ffhq1024 --snap=10
# Reproduce original StyleGAN2 config F.
python train.py --outdir=~/training-runs --data=~/datasets/ffhq.zip \
--gpus=8 --cfg=stylegan2 --mirror=1 --aug=noaug
Base configs (--cfg):
auto Automatically select reasonable defaults based on resolution
and GPU count. Good starting point for new datasets.
stylegan2 Reproduce results for StyleGAN2 config F at 1024x1024.
paper256 Reproduce results for FFHQ and LSUN Cat at 256x256.
paper512 Reproduce results for BreCaHAD and AFHQ at 512x512.
paper1024 Reproduce results for MetFaces at 1024x1024.
cifar Reproduce results for CIFAR-10 at 32x32.
Transfer learning source networks (--resume):
ffhq256 FFHQ trained at 256x256 resolution.
ffhq512 FFHQ trained at 512x512 resolution.
ffhq1024 FFHQ trained at 1024x1024 resolution.
celebahq256 CelebA-HQ trained at 256x256 resolution.
lsundog256 LSUN Dog trained at 256x256 resolution.
<PATH or URL> Custom network pickle.
Options:
--outdir DIR Where to save the results [required]
--gpus INT Number of GPUs to use [default: 1]
--snap INT Snapshot interval [default: 50 ticks]
--metrics LIST Comma-separated list or "none" [default:
fid50k_full]
--seed INT Random seed [default: 0]
-n, --dry-run Print training options and exit
--data PATH Training data (directory or zip) [required]
--cond BOOL Train conditional model based on dataset
labels [default: false]
--subset INT Train with only N images [default: all]
--mirror BOOL Enable dataset x-flips [default: false]
--cfg [auto|stylegan2|paper256|paper512|paper1024|cifar]
Base config [default: auto]
--gamma FLOAT Override R1 gamma
--kimg INT Override training duration
--batch INT Override batch size
--aug [noaug|ada|fixed] Augmentation mode [default: ada]
--p FLOAT Augmentation probability for --aug=fixed
--target FLOAT ADA target value for --aug=ada
--augpipe [blit|geom|color|filter|noise|cutout|bg|bgc|bgcf|bgcfn|bgcfnc]
Augmentation pipeline [default: bgc]
--resume PKL Resume training [default: noresume]
--freezed INT Freeze-D [default: 0 layers]
--fp32 BOOL Disable mixed-precision training
--nhwc BOOL Use NHWC memory format with FP16
--nobench BOOL Disable cuDNN benchmarking
--allow-tf32 BOOL Allow PyTorch to use TF32 internally
--workers INT Override number of DataLoader workers
--help Show this message and exit.
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