| 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. | |