train-help.txt

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.