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gpu_id: ""
seed: 10000
display: True
im_size: 512
aligned: True
background_enhance: True
face_upsample: True
diffusion:
target: models.script_util.create_gaussian_diffusion
params:
steps: 1000
learn_sigma: True
sigma_small: False
noise_schedule: linear
use_kl: False
predict_xstart: False
rescale_timesteps: False
rescale_learned_sigmas: True
timestep_respacing: "250"
model:
target: models.unet.UNetModel
ckpt_path: ./weights/diffusion/iddpm_ffhq512_ema500000.pth
params:
image_size: 512
in_channels: 3
model_channels: 32
out_channels: 6
attention_resolutions: [32, 16, 8]
dropout: 0
channel_mult: [1, 2, 4, 8, 8, 16, 16]
num_res_blocks: [1, 2, 2, 2, 2, 3, 4]
conv_resample: True
dims: 2
use_fp16: False
num_head_channels: 64
use_scale_shift_norm: True
resblock_updown: False
use_new_attention_order: False
model_ir:
target: models.swinir.SwinIR
ckpt_path: ./weights/SwinIR/General_Face_ffhq512.pth
params:
img_size: 64
patch_size: 1
in_chans: 3
embed_dim: 180
depths: [6, 6, 6, 6, 6, 6, 6, 6]
num_heads: [6, 6, 6, 6, 6, 6, 6, 6]
window_size: 8
mlp_ratio: 2
sf: 8
img_range: 1.0
upsampler: "nearest+conv"
resi_connection: "1conv"
unshuffle: True
unshuffle_scale: 8
# face detection model for unaligned face
detection:
det_model: "YOLOv5l" # large model: 'YOLOv5l', 'retinaface_resnet50'; small model: 'YOLOv5n', 'retinaface_mobile0.25'
upscale: 2 # The final upscaling factor for the whole image
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