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model:
model_type: const_sde
model_name: cond_unet
image_size: [320, 320]
input_keys: ['image', 'cond']
ckpt_path:
ignore_keys: [ ]
only_model: False
timesteps: 1000
train_sample: -1
sampling_timesteps: 1
loss_type: l2
objective: pred_noise
start_dist: normal
perceptual_weight: 0
scale_factor: 0.3
scale_by_std: True
default_scale: True
scale_by_softsign: False
eps: !!float 1e-4
weighting_loss: False
first_stage:
embed_dim: 3
lossconfig:
disc_start: 50001
kl_weight: 0.000001
disc_weight: 0.5
disc_in_channels: 1
ddconfig:
double_z: True
z_channels: 3
resolution: [ 320, 320 ]
in_channels: 1
out_ch: 1
ch: 128
ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
ckpt_path:
unet:
dim: 128
cond_net: swin
without_pretrain: False
channels: 3
out_mul: 1
dim_mults: [ 1, 2, 4, 4, ] # num_down = len(dim_mults)
cond_in_dim: 3
cond_dim: 128
cond_dim_mults: [ 2, 4 ] # num_down = len(cond_dim_mults)
# window_sizes1: [ [4, 4], [2, 2], [1, 1], [1, 1] ]
# window_sizes2: [ [4, 4], [2, 2], [1, 1], [1, 1] ]
window_sizes1: [ [ 8, 8 ], [ 4, 4 ], [ 2, 2 ], [ 1, 1 ] ]
window_sizes2: [ [ 8, 8 ], [ 4, 4 ], [ 2, 2 ], [ 1, 1 ] ]
fourier_scale: 16
cond_pe: False
num_pos_feats: 128
cond_feature_size: [ 80, 80 ]
data:
name: edge
img_folder: '/data/yeyunfan/edge_detection_datasets/datasets/BSDS_test'
augment_horizontal_flip: True
batch_size: 8
num_workers: 4
sampler:
sample_type: "slide"
stride: [240, 240]
batch_size: 1
sample_num: 300
use_ema: True
save_folder:
ckpt_path: