stable-diffusion-webui
677 строк · 35.5 Кб
1import torch2import torch.nn as nn3import torch.nn.functional as F4
5from modules import devices6
7# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
8
9
10class DeepDanbooruModel(nn.Module):11def __init__(self):12super(DeepDanbooruModel, self).__init__()13
14self.tags = []15
16self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))17self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))18self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)19self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)20self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)21self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)22self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)23self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)24self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)25self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)26self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)27self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)28self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))29self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)30self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))31self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)32self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)33self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)34self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)35self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)36self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)37self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)38self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)39self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)40self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)41self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)42self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)43self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)44self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)45self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)46self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)47self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)48self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)49self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)50self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)51self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)52self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)53self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))54self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)55self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))56self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)57self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)58self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)59self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)60self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)61self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)62self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)63self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)64self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)65self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)66self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)67self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)68self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)69self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)70self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)71self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)72self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)73self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)74self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)75self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)76self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)77self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)78self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)79self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)80self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)81self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)82self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)83self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)84self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)85self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)86self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)87self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)88self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)89self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)90self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)91self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)92self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)93self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)94self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)95self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)96self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)97self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)98self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)99self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)100self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)101self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)102self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)103self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)104self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)105self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)106self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)107self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)108self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)109self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)110self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)111self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)112self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)113self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)114self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)115self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))116self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)117self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))118self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)119self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)120self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)121self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)122self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)123self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)124self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)125self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)126self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)127self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)128self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)129self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)130self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)131self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)132self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)133self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)134self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)135self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)136self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)137self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)138self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)139self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)140self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)141self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)142self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)143self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)144self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)145self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)146self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)147self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)148self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)149self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)150self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)151self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)152self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)153self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)154self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)155self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)156self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)157self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)158self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)159self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)160self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)161self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)162self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)163self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)164self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)165self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)166self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)167self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)168self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)169self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)170self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)171self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)172self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)173self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)174self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)175self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))176self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)177self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))178self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)179self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)180self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)181self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)182self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)183self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)184self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)185self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))186self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)187self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))188self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)189self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)190self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)191self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)192self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)193self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)194self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)195self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)196
197def forward(self, *inputs):198t_358, = inputs199t_359 = t_358.permute(*[0, 3, 1, 2])200t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)201t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded)202t_361 = F.relu(t_360)203t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))204t_362 = self.n_MaxPool_0(t_361)205t_363 = self.n_Conv_1(t_362)206t_364 = self.n_Conv_2(t_362)207t_365 = F.relu(t_364)208t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)209t_366 = self.n_Conv_3(t_365_padded)210t_367 = F.relu(t_366)211t_368 = self.n_Conv_4(t_367)212t_369 = torch.add(t_368, t_363)213t_370 = F.relu(t_369)214t_371 = self.n_Conv_5(t_370)215t_372 = F.relu(t_371)216t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)217t_373 = self.n_Conv_6(t_372_padded)218t_374 = F.relu(t_373)219t_375 = self.n_Conv_7(t_374)220t_376 = torch.add(t_375, t_370)221t_377 = F.relu(t_376)222t_378 = self.n_Conv_8(t_377)223t_379 = F.relu(t_378)224t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)225t_380 = self.n_Conv_9(t_379_padded)226t_381 = F.relu(t_380)227t_382 = self.n_Conv_10(t_381)228t_383 = torch.add(t_382, t_377)229t_384 = F.relu(t_383)230t_385 = self.n_Conv_11(t_384)231t_386 = self.n_Conv_12(t_384)232t_387 = F.relu(t_386)233t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)234t_388 = self.n_Conv_13(t_387_padded)235t_389 = F.relu(t_388)236t_390 = self.n_Conv_14(t_389)237t_391 = torch.add(t_390, t_385)238t_392 = F.relu(t_391)239t_393 = self.n_Conv_15(t_392)240t_394 = F.relu(t_393)241t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)242t_395 = self.n_Conv_16(t_394_padded)243t_396 = F.relu(t_395)244t_397 = self.n_Conv_17(t_396)245t_398 = torch.add(t_397, t_392)246t_399 = F.relu(t_398)247t_400 = self.n_Conv_18(t_399)248t_401 = F.relu(t_400)249t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)250t_402 = self.n_Conv_19(t_401_padded)251t_403 = F.relu(t_402)252t_404 = self.n_Conv_20(t_403)253t_405 = torch.add(t_404, t_399)254t_406 = F.relu(t_405)255t_407 = self.n_Conv_21(t_406)256t_408 = F.relu(t_407)257t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)258t_409 = self.n_Conv_22(t_408_padded)259t_410 = F.relu(t_409)260t_411 = self.n_Conv_23(t_410)261t_412 = torch.add(t_411, t_406)262t_413 = F.relu(t_412)263t_414 = self.n_Conv_24(t_413)264t_415 = F.relu(t_414)265t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)266t_416 = self.n_Conv_25(t_415_padded)267t_417 = F.relu(t_416)268t_418 = self.n_Conv_26(t_417)269t_419 = torch.add(t_418, t_413)270t_420 = F.relu(t_419)271t_421 = self.n_Conv_27(t_420)272t_422 = F.relu(t_421)273t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)274t_423 = self.n_Conv_28(t_422_padded)275t_424 = F.relu(t_423)276t_425 = self.n_Conv_29(t_424)277t_426 = torch.add(t_425, t_420)278t_427 = F.relu(t_426)279t_428 = self.n_Conv_30(t_427)280t_429 = F.relu(t_428)281t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)282t_430 = self.n_Conv_31(t_429_padded)283t_431 = F.relu(t_430)284t_432 = self.n_Conv_32(t_431)285t_433 = torch.add(t_432, t_427)286t_434 = F.relu(t_433)287t_435 = self.n_Conv_33(t_434)288t_436 = F.relu(t_435)289t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)290t_437 = self.n_Conv_34(t_436_padded)291t_438 = F.relu(t_437)292t_439 = self.n_Conv_35(t_438)293t_440 = torch.add(t_439, t_434)294t_441 = F.relu(t_440)295t_442 = self.n_Conv_36(t_441)296t_443 = self.n_Conv_37(t_441)297t_444 = F.relu(t_443)298t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)299t_445 = self.n_Conv_38(t_444_padded)300t_446 = F.relu(t_445)301t_447 = self.n_Conv_39(t_446)302t_448 = torch.add(t_447, t_442)303t_449 = F.relu(t_448)304t_450 = self.n_Conv_40(t_449)305t_451 = F.relu(t_450)306t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)307t_452 = self.n_Conv_41(t_451_padded)308t_453 = F.relu(t_452)309t_454 = self.n_Conv_42(t_453)310t_455 = torch.add(t_454, t_449)311t_456 = F.relu(t_455)312t_457 = self.n_Conv_43(t_456)313t_458 = F.relu(t_457)314t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)315t_459 = self.n_Conv_44(t_458_padded)316t_460 = F.relu(t_459)317t_461 = self.n_Conv_45(t_460)318t_462 = torch.add(t_461, t_456)319t_463 = F.relu(t_462)320t_464 = self.n_Conv_46(t_463)321t_465 = F.relu(t_464)322t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)323t_466 = self.n_Conv_47(t_465_padded)324t_467 = F.relu(t_466)325t_468 = self.n_Conv_48(t_467)326t_469 = torch.add(t_468, t_463)327t_470 = F.relu(t_469)328t_471 = self.n_Conv_49(t_470)329t_472 = F.relu(t_471)330t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)331t_473 = self.n_Conv_50(t_472_padded)332t_474 = F.relu(t_473)333t_475 = self.n_Conv_51(t_474)334t_476 = torch.add(t_475, t_470)335t_477 = F.relu(t_476)336t_478 = self.n_Conv_52(t_477)337t_479 = F.relu(t_478)338t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)339t_480 = self.n_Conv_53(t_479_padded)340t_481 = F.relu(t_480)341t_482 = self.n_Conv_54(t_481)342t_483 = torch.add(t_482, t_477)343t_484 = F.relu(t_483)344t_485 = self.n_Conv_55(t_484)345t_486 = F.relu(t_485)346t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)347t_487 = self.n_Conv_56(t_486_padded)348t_488 = F.relu(t_487)349t_489 = self.n_Conv_57(t_488)350t_490 = torch.add(t_489, t_484)351t_491 = F.relu(t_490)352t_492 = self.n_Conv_58(t_491)353t_493 = F.relu(t_492)354t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)355t_494 = self.n_Conv_59(t_493_padded)356t_495 = F.relu(t_494)357t_496 = self.n_Conv_60(t_495)358t_497 = torch.add(t_496, t_491)359t_498 = F.relu(t_497)360t_499 = self.n_Conv_61(t_498)361t_500 = F.relu(t_499)362t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)363t_501 = self.n_Conv_62(t_500_padded)364t_502 = F.relu(t_501)365t_503 = self.n_Conv_63(t_502)366t_504 = torch.add(t_503, t_498)367t_505 = F.relu(t_504)368t_506 = self.n_Conv_64(t_505)369t_507 = F.relu(t_506)370t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)371t_508 = self.n_Conv_65(t_507_padded)372t_509 = F.relu(t_508)373t_510 = self.n_Conv_66(t_509)374t_511 = torch.add(t_510, t_505)375t_512 = F.relu(t_511)376t_513 = self.n_Conv_67(t_512)377t_514 = F.relu(t_513)378t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)379t_515 = self.n_Conv_68(t_514_padded)380t_516 = F.relu(t_515)381t_517 = self.n_Conv_69(t_516)382t_518 = torch.add(t_517, t_512)383t_519 = F.relu(t_518)384t_520 = self.n_Conv_70(t_519)385t_521 = F.relu(t_520)386t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)387t_522 = self.n_Conv_71(t_521_padded)388t_523 = F.relu(t_522)389t_524 = self.n_Conv_72(t_523)390t_525 = torch.add(t_524, t_519)391t_526 = F.relu(t_525)392t_527 = self.n_Conv_73(t_526)393t_528 = F.relu(t_527)394t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)395t_529 = self.n_Conv_74(t_528_padded)396t_530 = F.relu(t_529)397t_531 = self.n_Conv_75(t_530)398t_532 = torch.add(t_531, t_526)399t_533 = F.relu(t_532)400t_534 = self.n_Conv_76(t_533)401t_535 = F.relu(t_534)402t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)403t_536 = self.n_Conv_77(t_535_padded)404t_537 = F.relu(t_536)405t_538 = self.n_Conv_78(t_537)406t_539 = torch.add(t_538, t_533)407t_540 = F.relu(t_539)408t_541 = self.n_Conv_79(t_540)409t_542 = F.relu(t_541)410t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)411t_543 = self.n_Conv_80(t_542_padded)412t_544 = F.relu(t_543)413t_545 = self.n_Conv_81(t_544)414t_546 = torch.add(t_545, t_540)415t_547 = F.relu(t_546)416t_548 = self.n_Conv_82(t_547)417t_549 = F.relu(t_548)418t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)419t_550 = self.n_Conv_83(t_549_padded)420t_551 = F.relu(t_550)421t_552 = self.n_Conv_84(t_551)422t_553 = torch.add(t_552, t_547)423t_554 = F.relu(t_553)424t_555 = self.n_Conv_85(t_554)425t_556 = F.relu(t_555)426t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)427t_557 = self.n_Conv_86(t_556_padded)428t_558 = F.relu(t_557)429t_559 = self.n_Conv_87(t_558)430t_560 = torch.add(t_559, t_554)431t_561 = F.relu(t_560)432t_562 = self.n_Conv_88(t_561)433t_563 = F.relu(t_562)434t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)435t_564 = self.n_Conv_89(t_563_padded)436t_565 = F.relu(t_564)437t_566 = self.n_Conv_90(t_565)438t_567 = torch.add(t_566, t_561)439t_568 = F.relu(t_567)440t_569 = self.n_Conv_91(t_568)441t_570 = F.relu(t_569)442t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)443t_571 = self.n_Conv_92(t_570_padded)444t_572 = F.relu(t_571)445t_573 = self.n_Conv_93(t_572)446t_574 = torch.add(t_573, t_568)447t_575 = F.relu(t_574)448t_576 = self.n_Conv_94(t_575)449t_577 = F.relu(t_576)450t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)451t_578 = self.n_Conv_95(t_577_padded)452t_579 = F.relu(t_578)453t_580 = self.n_Conv_96(t_579)454t_581 = torch.add(t_580, t_575)455t_582 = F.relu(t_581)456t_583 = self.n_Conv_97(t_582)457t_584 = F.relu(t_583)458t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)459t_585 = self.n_Conv_98(t_584_padded)460t_586 = F.relu(t_585)461t_587 = self.n_Conv_99(t_586)462t_588 = self.n_Conv_100(t_582)463t_589 = torch.add(t_587, t_588)464t_590 = F.relu(t_589)465t_591 = self.n_Conv_101(t_590)466t_592 = F.relu(t_591)467t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)468t_593 = self.n_Conv_102(t_592_padded)469t_594 = F.relu(t_593)470t_595 = self.n_Conv_103(t_594)471t_596 = torch.add(t_595, t_590)472t_597 = F.relu(t_596)473t_598 = self.n_Conv_104(t_597)474t_599 = F.relu(t_598)475t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)476t_600 = self.n_Conv_105(t_599_padded)477t_601 = F.relu(t_600)478t_602 = self.n_Conv_106(t_601)479t_603 = torch.add(t_602, t_597)480t_604 = F.relu(t_603)481t_605 = self.n_Conv_107(t_604)482t_606 = F.relu(t_605)483t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)484t_607 = self.n_Conv_108(t_606_padded)485t_608 = F.relu(t_607)486t_609 = self.n_Conv_109(t_608)487t_610 = torch.add(t_609, t_604)488t_611 = F.relu(t_610)489t_612 = self.n_Conv_110(t_611)490t_613 = F.relu(t_612)491t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)492t_614 = self.n_Conv_111(t_613_padded)493t_615 = F.relu(t_614)494t_616 = self.n_Conv_112(t_615)495t_617 = torch.add(t_616, t_611)496t_618 = F.relu(t_617)497t_619 = self.n_Conv_113(t_618)498t_620 = F.relu(t_619)499t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)500t_621 = self.n_Conv_114(t_620_padded)501t_622 = F.relu(t_621)502t_623 = self.n_Conv_115(t_622)503t_624 = torch.add(t_623, t_618)504t_625 = F.relu(t_624)505t_626 = self.n_Conv_116(t_625)506t_627 = F.relu(t_626)507t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)508t_628 = self.n_Conv_117(t_627_padded)509t_629 = F.relu(t_628)510t_630 = self.n_Conv_118(t_629)511t_631 = torch.add(t_630, t_625)512t_632 = F.relu(t_631)513t_633 = self.n_Conv_119(t_632)514t_634 = F.relu(t_633)515t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)516t_635 = self.n_Conv_120(t_634_padded)517t_636 = F.relu(t_635)518t_637 = self.n_Conv_121(t_636)519t_638 = torch.add(t_637, t_632)520t_639 = F.relu(t_638)521t_640 = self.n_Conv_122(t_639)522t_641 = F.relu(t_640)523t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)524t_642 = self.n_Conv_123(t_641_padded)525t_643 = F.relu(t_642)526t_644 = self.n_Conv_124(t_643)527t_645 = torch.add(t_644, t_639)528t_646 = F.relu(t_645)529t_647 = self.n_Conv_125(t_646)530t_648 = F.relu(t_647)531t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)532t_649 = self.n_Conv_126(t_648_padded)533t_650 = F.relu(t_649)534t_651 = self.n_Conv_127(t_650)535t_652 = torch.add(t_651, t_646)536t_653 = F.relu(t_652)537t_654 = self.n_Conv_128(t_653)538t_655 = F.relu(t_654)539t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)540t_656 = self.n_Conv_129(t_655_padded)541t_657 = F.relu(t_656)542t_658 = self.n_Conv_130(t_657)543t_659 = torch.add(t_658, t_653)544t_660 = F.relu(t_659)545t_661 = self.n_Conv_131(t_660)546t_662 = F.relu(t_661)547t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)548t_663 = self.n_Conv_132(t_662_padded)549t_664 = F.relu(t_663)550t_665 = self.n_Conv_133(t_664)551t_666 = torch.add(t_665, t_660)552t_667 = F.relu(t_666)553t_668 = self.n_Conv_134(t_667)554t_669 = F.relu(t_668)555t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)556t_670 = self.n_Conv_135(t_669_padded)557t_671 = F.relu(t_670)558t_672 = self.n_Conv_136(t_671)559t_673 = torch.add(t_672, t_667)560t_674 = F.relu(t_673)561t_675 = self.n_Conv_137(t_674)562t_676 = F.relu(t_675)563t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)564t_677 = self.n_Conv_138(t_676_padded)565t_678 = F.relu(t_677)566t_679 = self.n_Conv_139(t_678)567t_680 = torch.add(t_679, t_674)568t_681 = F.relu(t_680)569t_682 = self.n_Conv_140(t_681)570t_683 = F.relu(t_682)571t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)572t_684 = self.n_Conv_141(t_683_padded)573t_685 = F.relu(t_684)574t_686 = self.n_Conv_142(t_685)575t_687 = torch.add(t_686, t_681)576t_688 = F.relu(t_687)577t_689 = self.n_Conv_143(t_688)578t_690 = F.relu(t_689)579t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)580t_691 = self.n_Conv_144(t_690_padded)581t_692 = F.relu(t_691)582t_693 = self.n_Conv_145(t_692)583t_694 = torch.add(t_693, t_688)584t_695 = F.relu(t_694)585t_696 = self.n_Conv_146(t_695)586t_697 = F.relu(t_696)587t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)588t_698 = self.n_Conv_147(t_697_padded)589t_699 = F.relu(t_698)590t_700 = self.n_Conv_148(t_699)591t_701 = torch.add(t_700, t_695)592t_702 = F.relu(t_701)593t_703 = self.n_Conv_149(t_702)594t_704 = F.relu(t_703)595t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)596t_705 = self.n_Conv_150(t_704_padded)597t_706 = F.relu(t_705)598t_707 = self.n_Conv_151(t_706)599t_708 = torch.add(t_707, t_702)600t_709 = F.relu(t_708)601t_710 = self.n_Conv_152(t_709)602t_711 = F.relu(t_710)603t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)604t_712 = self.n_Conv_153(t_711_padded)605t_713 = F.relu(t_712)606t_714 = self.n_Conv_154(t_713)607t_715 = torch.add(t_714, t_709)608t_716 = F.relu(t_715)609t_717 = self.n_Conv_155(t_716)610t_718 = F.relu(t_717)611t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)612t_719 = self.n_Conv_156(t_718_padded)613t_720 = F.relu(t_719)614t_721 = self.n_Conv_157(t_720)615t_722 = torch.add(t_721, t_716)616t_723 = F.relu(t_722)617t_724 = self.n_Conv_158(t_723)618t_725 = self.n_Conv_159(t_723)619t_726 = F.relu(t_725)620t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)621t_727 = self.n_Conv_160(t_726_padded)622t_728 = F.relu(t_727)623t_729 = self.n_Conv_161(t_728)624t_730 = torch.add(t_729, t_724)625t_731 = F.relu(t_730)626t_732 = self.n_Conv_162(t_731)627t_733 = F.relu(t_732)628t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)629t_734 = self.n_Conv_163(t_733_padded)630t_735 = F.relu(t_734)631t_736 = self.n_Conv_164(t_735)632t_737 = torch.add(t_736, t_731)633t_738 = F.relu(t_737)634t_739 = self.n_Conv_165(t_738)635t_740 = F.relu(t_739)636t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)637t_741 = self.n_Conv_166(t_740_padded)638t_742 = F.relu(t_741)639t_743 = self.n_Conv_167(t_742)640t_744 = torch.add(t_743, t_738)641t_745 = F.relu(t_744)642t_746 = self.n_Conv_168(t_745)643t_747 = self.n_Conv_169(t_745)644t_748 = F.relu(t_747)645t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)646t_749 = self.n_Conv_170(t_748_padded)647t_750 = F.relu(t_749)648t_751 = self.n_Conv_171(t_750)649t_752 = torch.add(t_751, t_746)650t_753 = F.relu(t_752)651t_754 = self.n_Conv_172(t_753)652t_755 = F.relu(t_754)653t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)654t_756 = self.n_Conv_173(t_755_padded)655t_757 = F.relu(t_756)656t_758 = self.n_Conv_174(t_757)657t_759 = torch.add(t_758, t_753)658t_760 = F.relu(t_759)659t_761 = self.n_Conv_175(t_760)660t_762 = F.relu(t_761)661t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)662t_763 = self.n_Conv_176(t_762_padded)663t_764 = F.relu(t_763)664t_765 = self.n_Conv_177(t_764)665t_766 = torch.add(t_765, t_760)666t_767 = F.relu(t_766)667t_768 = self.n_Conv_178(t_767)668t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])669t_770 = torch.squeeze(t_769, 3)670t_770 = torch.squeeze(t_770, 2)671t_771 = torch.sigmoid(t_770)672return t_771673
674def load_state_dict(self, state_dict, **kwargs):675self.tags = state_dict.get('tags', [])676
677super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'})678
679