GFPGAN

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# general settings
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name: train_GFPGANv1_512_simple
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model_type: GFPGANModel
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num_gpu: auto  # officially, we use 4 GPUs
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manual_seed: 0
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# dataset and data loader settings
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datasets:
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  train:
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    name: FFHQ
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    type: FFHQDegradationDataset
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    # dataroot_gt: datasets/ffhq/ffhq_512.lmdb
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    dataroot_gt: datasets/ffhq/ffhq_512
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    io_backend:
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      # type: lmdb
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      type: disk
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    use_hflip: true
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    mean: [0.5, 0.5, 0.5]
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    std: [0.5, 0.5, 0.5]
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    out_size: 512
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    blur_kernel_size: 41
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    kernel_list: ['iso', 'aniso']
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    kernel_prob: [0.5, 0.5]
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    blur_sigma: [0.1, 10]
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    downsample_range: [0.8, 8]
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    noise_range: [0, 20]
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    jpeg_range: [60, 100]
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    # color jitter and gray
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    color_jitter_prob: 0.3
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    color_jitter_shift: 20
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    color_jitter_pt_prob: 0.3
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    gray_prob: 0.01
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    # If you do not want colorization, please set
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    # color_jitter_prob: ~
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    # color_jitter_pt_prob: ~
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    # gray_prob: 0.01
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    # gt_gray: True
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    # data loader
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    use_shuffle: true
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    num_worker_per_gpu: 6
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    batch_size_per_gpu: 3
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    dataset_enlarge_ratio: 1
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    prefetch_mode: ~
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  val:
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    # Please modify accordingly to use your own validation
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    # Or comment the val block if do not need validation during training
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    name: validation
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    type: PairedImageDataset
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    dataroot_lq: datasets/faces/validation/input
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    dataroot_gt: datasets/faces/validation/reference
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    io_backend:
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      type: disk
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    mean: [0.5, 0.5, 0.5]
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    std: [0.5, 0.5, 0.5]
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    scale: 1
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# network structures
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network_g:
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  type: GFPGANv1
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  out_size: 512
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  num_style_feat: 512
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  channel_multiplier: 1
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  resample_kernel: [1, 3, 3, 1]
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  decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth
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  fix_decoder: true
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  num_mlp: 8
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  lr_mlp: 0.01
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  input_is_latent: true
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  different_w: true
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  narrow: 1
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  sft_half: true
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network_d:
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  type: StyleGAN2Discriminator
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  out_size: 512
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  channel_multiplier: 1
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  resample_kernel: [1, 3, 3, 1]
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# path
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path:
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  pretrain_network_g: ~
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  param_key_g: params_ema
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  strict_load_g: ~
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  pretrain_network_d: ~
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  resume_state: ~
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# training settings
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train:
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  optim_g:
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    type: Adam
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    lr: !!float 2e-3
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  optim_d:
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    type: Adam
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    lr: !!float 2e-3
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  optim_component:
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    type: Adam
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    lr: !!float 2e-3
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  scheduler:
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    type: MultiStepLR
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    milestones: [600000, 700000]
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    gamma: 0.5
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  total_iter: 800000
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  warmup_iter: -1  # no warm up
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  # losses
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  # pixel loss
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  pixel_opt:
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    type: L1Loss
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    loss_weight: !!float 1e-1
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    reduction: mean
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  # L1 loss used in pyramid loss, component style loss and identity loss
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  L1_opt:
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    type: L1Loss
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    loss_weight: 1
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    reduction: mean
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  # image pyramid loss
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  pyramid_loss_weight: 1
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  remove_pyramid_loss: 50000
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  # perceptual loss (content and style losses)
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  perceptual_opt:
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    type: PerceptualLoss
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    layer_weights:
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      # before relu
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      'conv1_2': 0.1
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      'conv2_2': 0.1
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      'conv3_4': 1
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      'conv4_4': 1
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      'conv5_4': 1
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    vgg_type: vgg19
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    use_input_norm: true
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    perceptual_weight: !!float 1
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    style_weight: 50
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    range_norm: true
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    criterion: l1
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  # gan loss
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  gan_opt:
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    type: GANLoss
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    gan_type: wgan_softplus
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    loss_weight: !!float 1e-1
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  # r1 regularization for discriminator
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  r1_reg_weight: 10
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  net_d_iters: 1
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  net_d_init_iters: 0
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  net_d_reg_every: 16
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# validation settings
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val:
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  val_freq: !!float 5e3
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  save_img: true
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  metrics:
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    psnr: # metric name
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      type: calculate_psnr
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      crop_border: 0
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      test_y_channel: false
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# logging settings
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logger:
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  print_freq: 100
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  save_checkpoint_freq: !!float 5e3
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  use_tb_logger: true
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  wandb:
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    project: ~
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    resume_id: ~
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# dist training settings
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dist_params:
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  backend: nccl
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  port: 29500
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find_unused_parameters: true
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