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GFPGANv1

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GFPGANv1 / train_gfpgan_v1.yml

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	# general settings
	name: train_GFPGANv1_512
	model_type: GFPGANModel
	num_gpu: 4
	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

	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: 100
	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, can be arbitrary
	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