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# general settings
name: train_GFPGANv1_512
model_type: GFPGANModel
num_gpu: auto # officially, we use 4 GPUs
manual_seed: 0
# dataset and data loader settings
datasets:
train:
name: FFHQ
type: FFHQDegradationDataset
# dataroot_gt: datasets/ffhq/ffhq_512.lmdb
dataroot_gt: datasets/ffhq/ffhq_512
io_backend:
# type: lmdb
type: disk
use_hflip: true
mean: [0.5, 0.5, 0.5]
std: [0.5, 0.5, 0.5]
out_size: 512
blur_kernel_size: 41
kernel_list: ['iso', 'aniso']
kernel_prob: [0.5, 0.5]
blur_sigma: [0.1, 10]
downsample_range: [0.8, 8]
noise_range: [0, 20]
jpeg_range: [60, 100]
# color jitter and gray
color_jitter_prob: 0.3
color_jitter_shift: 20
color_jitter_pt_prob: 0.3
gray_prob: 0.01
# If you do not want colorization, please set
# color_jitter_prob: ~
# color_jitter_pt_prob: ~
# gray_prob: 0.01
# gt_gray: True
crop_components: true
component_path: experiments/pretrained_models/FFHQ_eye_mouth_landmarks_512.pth
eye_enlarge_ratio: 1.4
# data loader
use_shuffle: true
num_worker_per_gpu: 6
batch_size_per_gpu: 3
dataset_enlarge_ratio: 1
prefetch_mode: ~
val:
# Please modify accordingly to use your own validation
# Or comment the val block if do not need validation during training
name: validation
type: PairedImageDataset
dataroot_lq: datasets/faces/validation/input
dataroot_gt: datasets/faces/validation/reference
io_backend:
type: disk
mean: [0.5, 0.5, 0.5]
std: [0.5, 0.5, 0.5]
scale: 1
# network structures
network_g:
type: GFPGANv1
out_size: 512
num_style_feat: 512
channel_multiplier: 1
resample_kernel: [1, 3, 3, 1]
decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth
fix_decoder: true
num_mlp: 8
lr_mlp: 0.01
input_is_latent: true
different_w: true
narrow: 1
sft_half: true
network_d:
type: StyleGAN2Discriminator
out_size: 512
channel_multiplier: 1
resample_kernel: [1, 3, 3, 1]
network_d_left_eye:
type: FacialComponentDiscriminator
network_d_right_eye:
type: FacialComponentDiscriminator
network_d_mouth:
type: FacialComponentDiscriminator
network_identity:
type: ResNetArcFace
block: IRBlock
layers: [2, 2, 2, 2]
use_se: False
# path
path:
pretrain_network_g: ~
param_key_g: params_ema
strict_load_g: ~
pretrain_network_d: ~
pretrain_network_d_left_eye: ~
pretrain_network_d_right_eye: ~
pretrain_network_d_mouth: ~
pretrain_network_identity: experiments/pretrained_models/arcface_resnet18.pth
# resume
resume_state: ~
ignore_resume_networks: ['network_identity']
# training settings
train:
optim_g:
type: Adam
lr: !!float 2e-3
optim_d:
type: Adam
lr: !!float 2e-3
optim_component:
type: Adam
lr: !!float 2e-3
scheduler:
type: MultiStepLR
milestones: [600000, 700000]
gamma: 0.5
total_iter: 800000
warmup_iter: -1 # no warm up
# losses
# pixel loss
pixel_opt:
type: L1Loss
loss_weight: !!float 1e-1
reduction: mean
# L1 loss used in pyramid loss, component style loss and identity loss
L1_opt:
type: L1Loss
loss_weight: 1
reduction: mean
# image pyramid loss
pyramid_loss_weight: 1
remove_pyramid_loss: 50000
# perceptual loss (content and style losses)
perceptual_opt:
type: PerceptualLoss
layer_weights:
# before relu
'conv1_2': 0.1
'conv2_2': 0.1
'conv3_4': 1
'conv4_4': 1
'conv5_4': 1
vgg_type: vgg19
use_input_norm: true
perceptual_weight: !!float 1
style_weight: 50
range_norm: true
criterion: l1
# gan loss
gan_opt:
type: GANLoss
gan_type: wgan_softplus
loss_weight: !!float 1e-1
# r1 regularization for discriminator
r1_reg_weight: 10
# facial component loss
gan_component_opt:
type: GANLoss
gan_type: vanilla
real_label_val: 1.0
fake_label_val: 0.0
loss_weight: !!float 1
comp_style_weight: 200
# identity loss
identity_weight: 10
net_d_iters: 1
net_d_init_iters: 0
net_d_reg_every: 16
# validation settings
val:
val_freq: !!float 5e3
save_img: true
metrics:
psnr: # metric name
type: calculate_psnr
crop_border: 0
test_y_channel: false
# 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
find_unused_parameters: true
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