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Copy pathfaceir_gfpgan512_lpips.yaml
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faceir_gfpgan512_lpips.yaml
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trainer:
target: trainer.TrainerDifIRLPIPS
model:
target: models.unet.UNetModelSwin
ckpt_path: ~
params:
image_size: 64
in_channels: 8
model_channels: 160
out_channels: 8
attention_resolutions: [64,32,16,8]
dropout: 0
channel_mult: [1, 2, 2, 4]
num_res_blocks: [2, 2, 2, 2]
conv_resample: True
dims: 2
use_fp16: False
num_head_channels: 32
use_scale_shift_norm: True
resblock_updown: False
swin_depth: 2
swin_embed_dim: 192
window_size: 8
mlp_ratio: 4
cond_lq: True
lq_size: 512
diffusion:
target: models.script_util.create_gaussian_diffusion
params:
sf: 1
schedule_name: exponential
schedule_kwargs:
power: 0.3
etas_end: 0.99
steps: 4
min_noise_level: 0.2
kappa: 2.0
weighted_mse: False
predict_type: xstart
timestep_respacing: ~
scale_factor: 1.0
normalize_input: True
latent_flag: True
autoencoder:
target: ldm.models.autoencoder.VQModelTorch
ckpt_path: weights/ffhq512_vq_f8_dim8_face.pth
use_fp16: True
params:
embed_dim: 8
n_embed: 4096
ddconfig:
double_z: False
z_channels: 8
resolution: 512
in_channels: 3
out_ch: 3
ch: 64
ch_mult:
- 1
- 2
- 4
- 8
num_res_blocks:
- 1
- 2
- 3
- 4
attn_resolutions: []
dropout: 0.0
padding_mode: zeros
data:
train:
type: gfpgan
params:
dir_path: /mnt/sfs-common/zsyue/database/FFHQ/images512x512
im_exts: png
io_backend:
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, 15]
downsample_range: [0.8, 32]
noise_range: [0, 20]
jpeg_range: [30, 100]
color_jitter_prob: ~
color_jitter_pt_prob: ~
gray_prob: 0.01
gt_gray: True
need_gt_path: False
val:
type: base
params:
dir_path: testdata/faceir/cropped_faces/lq
transform_type: default
transform_kwargs:
mean: 0.5
std: 0.5
im_exts: png
need_path: False
recursive: False
train:
# learning rate
lr: 5e-5 # learning rate
lr_min: 2e-5
lr_schedule: cosin
warmup_iterations: 5000
# dataloader
batch: [56, 8]
microbatch: 7
num_workers: 6
prefetch_factor: 2
# optimization settings
weight_decay: 0
ema_rate: 0.999
iterations: 400000 # total iterations
# save logging
save_freq: 10000
log_freq: [200, 2000, 1] # [training loss, training images, val images]
loss_coef: [1.0, 10.0] # [mse, lpips]
local_logging: True # manually save images
tf_logging: False # tensorboard logging
# validation settings
use_ema_val: True
val_freq: ${train.save_freq}
val_y_channel: True
val_resolution: ${model.params.lq_size}
val_padding_mode: reflect
# training setting
use_amp: True # amp training
seed: 123456 # random seed
global_seeding: False
# model compile
compile:
flag: False
mode: reduce-overhead # default, reduce-overhead