train-help.txt

usage: train.py [-h] --outdir DIR [--gpus INT] [--snap INT] [--seed INT] [-n]
                --data PATH [--res INT] [--mirror BOOL] [--metrics LIST]
                [--metricdata PATH]
                [--cfg {auto,stylegan2,paper256,paper512,paper1024,cifar,cifarbaseline}]
                [--gamma FLOAT] [--kimg INT] [--aug {noaug,ada,fixed,adarv}]
                [--p FLOAT] [--target TARGET]
                [--augpipe {blit,geom,color,filter,noise,cutout,bg,bgc,bgcf,bgcfn,bgcfnc}]
                [--cmethod {nocmethod,bcr,zcr,pagan,wgangp,auxrot,spectralnorm,shallowmap,adropout}]
                [--dcap FLOAT] [--resume RESUME] [--freezed INT]

Train a GAN using the techniques described in the paper
"Training Generative Adversarial Networks with Limited Data".

optional arguments:
  -h, --help            show this help message and exit

general options:
  --outdir DIR          Where to save the results (required)
  --gpus INT            Number of GPUs to use (default: 1 gpu)
  --snap INT            Snapshot interval (default: 50 ticks)
  --seed INT            Random seed (default: 1000)
  -n, --dry-run         Print training options and exit

training dataset:
  --data PATH           Training dataset path (required)
  --res INT             Dataset resolution (default: highest available)
  --mirror BOOL         Augment dataset with x-flips (default: false)

metrics:
  --metrics LIST        Comma-separated list or "none" (default: fid50k_full)
  --metricdata PATH     Dataset to evaluate metrics against (optional)

base config:
  --cfg {auto,stylegan2,paper256,paper512,paper1024,cifar,cifarbaseline}
                        Base config (default: auto)
  --gamma FLOAT         Override R1 gamma
  --kimg INT            Override training duration

discriminator augmentation:
  --aug {noaug,ada,fixed,adarv}
                        Augmentation mode (default: ada)
  --p FLOAT             Specify augmentation probability for --aug=fixed
  --target TARGET       Override ADA target for --aug=ada and --aug=adarv
  --augpipe {blit,geom,color,filter,noise,cutout,bg,bgc,bgcf,bgcfn,bgcfnc}
                        Augmentation pipeline (default: bgc)

comparison methods:
  --cmethod {nocmethod,bcr,zcr,pagan,wgangp,auxrot,spectralnorm,shallowmap,adropout}
                        Comparison method (default: nocmethod)
  --dcap FLOAT          Multiplier for discriminator capacity

transfer learning:
  --resume RESUME       Resume from network pickle (default: noresume)
  --freezed INT         Freeze-D (default: 0 discriminator layers)

examples:

  # Train custom dataset using 1 GPU.
  python train.py --outdir=~/training-runs --gpus=1 --data=~/datasets/custom

  # Train class-conditional CIFAR-10 using 2 GPUs.
  python train.py --outdir=~/training-runs --gpus=2 --data=~/datasets/cifar10c \
      --cfg=cifar

  # Transfer learn MetFaces from FFHQ using 4 GPUs.
  python train.py --outdir=~/training-runs --gpus=4 --data=~/datasets/metfaces \
      --cfg=paper1024 --mirror=1 --resume=ffhq1024 --snap=10

  # Reproduce original StyleGAN2 config F.
  python train.py --outdir=~/training-runs --gpus=8 --data=~/datasets/ffhq \
      --cfg=stylegan2 --res=1024 --mirror=1 --aug=noaug

available 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 (tuned configuration).
  cifarbaseline  Reproduce results for CIFAR-10 (baseline configuration).

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.