train_StyleGAN2_256_Cmul2_FFHQ.yml

# general settings
name: 501_StyleGAN2_256_Cmul2_FFHQ_800k_B24G8_scratch
model_type: StyleGAN2Model
num_gpu: 8  # set num_gpu: 0 for cpu mode
manual_seed: 0

# dataset and data loader settings
datasets:
  train:
    name: FFHQ
    type: FFHQDataset
    dataroot_gt: datasets/ffhq/ffhq_256.lmdb
    io_backend:
      type: lmdb

    use_hflip: true
    mean: [0.5, 0.5, 0.5]
    std: [0.5, 0.5, 0.5]

    # data loader
    num_worker_per_gpu: 6
    batch_size_per_gpu: 3
    dataset_enlarge_ratio: 100
    prefetch_mode: ~

# network structures
network_g:
  type: StyleGAN2Generator
  out_size: 256
  num_style_feat: 512
  num_mlp: 8
  channel_multiplier: 2
  resample_kernel: [1, 3, 3, 1]
  lr_mlp: 0.01

network_d:
  type: StyleGAN2Discriminator
  out_size: 256
  channel_multiplier: 2
  resample_kernel: [1, 3, 3, 1]

# path
path:
  pretrain_network_g: ~
  strict_load_g: true
  resume_state: ~

# training settings
train:
  optim_g:
    type: Adam
    lr: !!float 2e-3

  optim_d:
    type: Adam
    lr: !!float 2e-3

  scheduler:
    type: MultiStepLR
    milestones: [600000]
    gamma: 0.5

  total_iter: 800000
  warmup_iter: -1  # no warm up

  # losses
  gan_opt:
    type: GANLoss
    gan_type: wgan_softplus
    loss_weight: !!float 1
  # r1 regularization for discriminator
  r1_reg_weight: 10
  # path length regularization for generator
  path_batch_shrink: 2
  path_reg_weight: 2

  net_g_reg_every: 4
  net_d_reg_every: 16
  mixing_prob: 0.9

  net_d_iters: 1
  net_d_init_iters: 0

# validation settings
val:
  val_freq: !!float 5e3
  save_img: true

# logging settings
logger:
  print_freq: 100
  save_checkpoint_freq: !!float 5e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500