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Xsmos
/
ml21cm

TensorBoard
generate 21cm lightcones
denoising diffusion probabilistic model
Model card Files
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1 Parent(s): efee093

0702-1611

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  1. diffusion.ipynb +66 -2265
diffusion.ipynb CHANGED
@@ -310,8 +310,8 @@
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  "source": [
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  "# @dataclass\n",
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  "class DDPM21CM:\n",
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- " def __init__(self):\n",
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- " config = TrainConfig()\n",
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  " # date = datetime.datetime.now().strftime(\"%m%d-%H%M\")\n",
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  " config.run_name = datetime.datetime.now().strftime(\"%m%d-%H%M\") # the unique name of each experiment\n",
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  " self.config = config\n",
@@ -537,7 +537,7 @@
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  "version_minor": 0
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  },
@@ -559,7 +559,7 @@
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  "metadata": {},
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  "outputs": [],
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  "source": [
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- "num_image_list = [1600,3200,6400,12800,25600]"
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  {
@@ -571,9 +571,9 @@
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  "name": "stdout",
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  "output_type": "stream",
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  "text": [
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- "-------------------- round 0 ---------------------\n",
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  "Number of parameters for nn_model: 111048705\n",
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- "run_name = 0702-1406\n",
 
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  "Launching training on one GPU.\n",
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  "dataset content: <KeysViewHDF5 ['brightness_temp', 'density', 'kwargs', 'params', 'redshifts_distances', 'seeds', 'xH_box']>\n",
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  "51200 images can be loaded\n",
@@ -595,14 +595,14 @@
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  "output_type": "stream",
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  "params loaded: (1600, 2)\n",
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- "images rescaled to [-1.0, 1.2026770114898682]\n",
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- "params rescaled to [0.00030335716583840266, 0.9999527071628225]\n"
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- "saved model at ./outputs/model_state-N1600\n",
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- "-------------------- round 1 ---------------------\n",
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- "Number of parameters for nn_model: 111048705\n",
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- "run_name = 0702-1412\n",
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- "Launching training on one GPU.\n",
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2040
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2773
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3498
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3499
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3500
  }
3501
  ],
3502
  "source": [
3503
  "if __name__ == \"__main__\":\n",
3504
  " # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)\n",
 
3505
  " for i, num_image in enumerate(num_image_list):\n",
3506
- " print(f\" round {i} \".center(50, '-'))\n",
3507
- " ddpm21cm = DDPM21CM()\n",
3508
- " ddpm21cm.config.num_image = num_image\n",
3509
  " print(f\"run_name = {ddpm21cm.config.run_name}\")\n",
3510
  " notebook_launcher(ddpm21cm.train, num_processes=1)"
3511
  ]
@@ -3627,20 +1429,19 @@
3627
  "if __name__ == \"__main__\":\n",
3628
  " # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)\n",
3629
  " repeat = 800\n",
 
3630
  " for i, num_image in enumerate(num_image_list):\n",
3631
- " ddpm21cm = DDPM21CM()\n",
 
 
3632
  " ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor([4.4, 131.341]), repeat=repeat)\n",
3633
  "\n",
3634
- " ddpm21cm = DDPM21CM()\n",
3635
  " ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((5.6, 19.037)), repeat=repeat)\n",
3636
  "\n",
3637
- " ddpm21cm = DDPM21CM()\n",
3638
  " ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((4.699, 30)), repeat=repeat)\n",
3639
  "\n",
3640
- " ddpm21cm = DDPM21CM()\n",
3641
  " ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((5.477, 200)), repeat=repeat)\n",
3642
  "\n",
3643
- " ddpm21cm = DDPM21CM()\n",
3644
  " ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((4.8, 131.341)), repeat=repeat)"
3645
  ]
3646
  },
 
310
  "source": [
311
  "# @dataclass\n",
312
  "class DDPM21CM:\n",
313
+ " def __init__(self, config):\n",
314
+ " # config = TrainConfig()\n",
315
  " # date = datetime.datetime.now().strftime(\"%m%d-%H%M\")\n",
316
  " config.run_name = datetime.datetime.now().strftime(\"%m%d-%H%M\") # the unique name of each experiment\n",
317
  " self.config = config\n",
 
537
  {
538
  "data": {
539
  "application/vnd.jupyter.widget-view+json": {
540
+ "model_id": "b70c4903cbb44ad69ad1e0a39af784d6",
541
  "version_major": 2,
542
  "version_minor": 0
543
  },
 
559
  "metadata": {},
560
  "outputs": [],
561
  "source": [
562
+ "num_image_list = [1600,3200]#,6400,12800,25600]"
563
  ]
564
  },
565
  {
 
571
  "name": "stdout",
572
  "output_type": "stream",
573
  "text": [
 
574
  "Number of parameters for nn_model: 111048705\n",
575
+ "---------------- num_image = 1600 ----------------\n",
576
+ "run_name = 0702-1611\n",
577
  "Launching training on one GPU.\n",
578
  "dataset content: <KeysViewHDF5 ['brightness_temp', 'density', 'kwargs', 'params', 'redshifts_distances', 'seeds', 'xH_box']>\n",
579
  "51200 images can be loaded\n",
 
595
  "output_type": "stream",
596
  "text": [
597
  "params loaded: (1600, 2)\n",
598
+ "images rescaled to [-1.0, 1.1719820499420166]\n",
599
+ "params rescaled to [0.0001702067256199591, 0.9995047166378379]\n"
600
  ]
601
  },
602
  {
603
  "data": {
604
  "application/vnd.jupyter.widget-view+json": {
605
+ "model_id": "8221e97102cf427a854982f512e208d4",
606
  "version_major": 2,
607
  "version_minor": 0
608
  },
 
616
  {
617
  "data": {
618
  "application/vnd.jupyter.widget-view+json": {
619
+ "model_id": "c929b4d92b2743dabee5a602c3708701",
620
  "version_major": 2,
621
  "version_minor": 0
622
  },
 
630
  {
631
  "data": {
632
  "application/vnd.jupyter.widget-view+json": {
633
+ "model_id": "c815ddfc4f1e4be698e824862db025ca",
634
  "version_major": 2,
635
  "version_minor": 0
636
  },
 
644
  {
645
  "data": {
646
  "application/vnd.jupyter.widget-view+json": {
647
+ "model_id": "1c37d32e1e1a4fb5b19af3adf7a2270b",
648
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649
  "version_minor": 0
650
  },
 
658
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659
  "data": {
660
  "application/vnd.jupyter.widget-view+json": {
661
+ "model_id": "22bca2108e174c1b8d9275a8ef94fe51",
662
  "version_major": 2,
663
  "version_minor": 0
664
  },
 
672
  {
673
  "data": {
674
  "application/vnd.jupyter.widget-view+json": {
675
+ "model_id": "3a66c3e5c2604585b2c93655f348485e",
676
  "version_major": 2,
677
  "version_minor": 0
678
  },
 
686
  {
687
  "data": {
688
  "application/vnd.jupyter.widget-view+json": {
689
+ "model_id": "78af1fe81f434067a501f743ce29a4c7",
690
  "version_major": 2,
691
  "version_minor": 0
692
  },
 
700
  {
701
  "data": {
702
  "application/vnd.jupyter.widget-view+json": {
703
+ "model_id": "801178edae5d4433a7d322bdb20a5f39",
704
  "version_major": 2,
705
  "version_minor": 0
706
  },
 
714
  {
715
  "data": {
716
  "application/vnd.jupyter.widget-view+json": {
717
+ "model_id": "366420e4fdb142a4b408146b38e6c75a",
718
  "version_major": 2,
719
  "version_minor": 0
720
  },
 
728
  {
729
  "data": {
730
  "application/vnd.jupyter.widget-view+json": {
731
+ "model_id": "7ff43a36ea264c4aaf40213feff4b900",
732
  "version_major": 2,
733
  "version_minor": 0
734
  },
 
742
  {
743
  "data": {
744
  "application/vnd.jupyter.widget-view+json": {
745
+ "model_id": "58de9ed1876f462cac5f239e6a636129",
746
  "version_major": 2,
747
  "version_minor": 0
748
  },
 
756
  {
757
  "data": {
758
  "application/vnd.jupyter.widget-view+json": {
759
+ "model_id": "6d290215b0094d83a8f4e52f34e26ea5",
760
  "version_major": 2,
761
  "version_minor": 0
762
  },
 
770
  {
771
  "data": {
772
  "application/vnd.jupyter.widget-view+json": {
773
+ "model_id": "c1bf94c5b65249d7b60cc413de17ea85",
774
  "version_major": 2,
775
  "version_minor": 0
776
  },
 
784
  {
785
  "data": {
786
  "application/vnd.jupyter.widget-view+json": {
787
+ "model_id": "a76207ecd8164d509cbc444d663a6713",
788
  "version_major": 2,
789
  "version_minor": 0
790
  },
 
798
  {
799
  "data": {
800
  "application/vnd.jupyter.widget-view+json": {
801
+ "model_id": "0a1bba004a0c43408d6cd43f01abb9da",
802
  "version_major": 2,
803
  "version_minor": 0
804
  },
 
812
  {
813
  "data": {
814
  "application/vnd.jupyter.widget-view+json": {
815
+ "model_id": "f88333941bd54d88b84908b862f6ce9d",
816
  "version_major": 2,
817
  "version_minor": 0
818
  },
 
826
  {
827
  "data": {
828
  "application/vnd.jupyter.widget-view+json": {
829
+ "model_id": "06d738e03de6455d93dfe1b9c86cd35f",
830
  "version_major": 2,
831
  "version_minor": 0
832
  },
 
840
  {
841
  "data": {
842
  "application/vnd.jupyter.widget-view+json": {
843
+ "model_id": "2b0e93a5b14743028cdc71daa0a2993b",
844
  "version_major": 2,
845
  "version_minor": 0
846
  },
 
854
  {
855
  "data": {
856
  "application/vnd.jupyter.widget-view+json": {
857
+ "model_id": "cd90f5c365a04e91b93c009ad7d6c1be",
858
  "version_major": 2,
859
  "version_minor": 0
860
  },
 
868
  {
869
  "data": {
870
  "application/vnd.jupyter.widget-view+json": {
871
+ "model_id": "0b2df44268574e499c735c23b632eef3",
872
  "version_major": 2,
873
  "version_minor": 0
874
  },
 
882
  {
883
  "data": {
884
  "application/vnd.jupyter.widget-view+json": {
885
+ "model_id": "d171a0e73a2c4652a9a91d822691a3ef",
886
  "version_major": 2,
887
  "version_minor": 0
888
  },
 
896
  {
897
  "data": {
898
  "application/vnd.jupyter.widget-view+json": {
899
+ "model_id": "4a03b39819294ccfb5567e89877ab371",
900
  "version_major": 2,
901
  "version_minor": 0
902
  },
 
910
  {
911
  "data": {
912
  "application/vnd.jupyter.widget-view+json": {
913
+ "model_id": "b554c5f005fa446eb764abb15c6b7771",
914
  "version_major": 2,
915
  "version_minor": 0
916
  },
 
924
  {
925
  "data": {
926
  "application/vnd.jupyter.widget-view+json": {
927
+ "model_id": "dc75e32e10ef44359aac93d0f895ebc9",
928
  "version_major": 2,
929
  "version_minor": 0
930
  },
 
938
  {
939
  "data": {
940
  "application/vnd.jupyter.widget-view+json": {
941
+ "model_id": "9a2c2a603c5941b582e8a6a2418ee040",
942
  "version_major": 2,
943
  "version_minor": 0
944
  },
 
952
  {
953
  "data": {
954
  "application/vnd.jupyter.widget-view+json": {
955
+ "model_id": "3b79ff4e2eee4bfeaddf5fde502f9e19",
956
  "version_major": 2,
957
  "version_minor": 0
958
  },
 
966
  {
967
  "data": {
968
  "application/vnd.jupyter.widget-view+json": {
969
+ "model_id": "4fdeb5a2cd5e4b0f8b46547c396ba95f",
970
  "version_major": 2,
971
  "version_minor": 0
972
  },
 
980
  {
981
  "data": {
982
  "application/vnd.jupyter.widget-view+json": {
983
+ "model_id": "b51164d8191c4568953a0ad6879824ef",
984
  "version_major": 2,
985
  "version_minor": 0
986
  },
 
994
  {
995
  "data": {
996
  "application/vnd.jupyter.widget-view+json": {
997
+ "model_id": "b7f35532c6eb441aa83be325d90ce9c2",
998
  "version_major": 2,
999
  "version_minor": 0
1000
  },
 
1008
  {
1009
  "data": {
1010
  "application/vnd.jupyter.widget-view+json": {
1011
+ "model_id": "efb4014a9ac34305b739133f69b9a0dd",
1012
  "version_major": 2,
1013
  "version_minor": 0
1014
  },
 
1022
  {
1023
  "data": {
1024
  "application/vnd.jupyter.widget-view+json": {
1025
+ "model_id": "5b16d28c24194525a0a5f86bfa2578c8",
1026
  "version_major": 2,
1027
  "version_minor": 0
1028
  },
 
1036
  {
1037
  "data": {
1038
  "application/vnd.jupyter.widget-view+json": {
1039
+ "model_id": "d400c07a2045464aab5d4800747253c8",
1040
  "version_major": 2,
1041
  "version_minor": 0
1042
  },
 
1050
  {
1051
  "data": {
1052
  "application/vnd.jupyter.widget-view+json": {
1053
+ "model_id": "c682e31c95a94427b5a21ce12db9c72a",
1054
  "version_major": 2,
1055
  "version_minor": 0
1056
  },
 
1064
  {
1065
  "data": {
1066
  "application/vnd.jupyter.widget-view+json": {
1067
+ "model_id": "d8253aed29f64d31b649546a74b81924",
1068
  "version_major": 2,
1069
  "version_minor": 0
1070
  },
 
1078
  {
1079
  "data": {
1080
  "application/vnd.jupyter.widget-view+json": {
1081
+ "model_id": "10ab6cdc70bf4d68864ade452fe949d2",
1082
  "version_major": 2,
1083
  "version_minor": 0
1084
  },
 
1092
  {
1093
  "data": {
1094
  "application/vnd.jupyter.widget-view+json": {
1095
+ "model_id": "5e96dcc2700447e08822dc7eb89f94e0",
1096
  "version_major": 2,
1097
  "version_minor": 0
1098
  },
 
1106
  {
1107
  "data": {
1108
  "application/vnd.jupyter.widget-view+json": {
1109
+ "model_id": "07b0e308a2314611873dfe22c257b6fe",
1110
  "version_major": 2,
1111
  "version_minor": 0
1112
  },
 
1120
  {
1121
  "data": {
1122
  "application/vnd.jupyter.widget-view+json": {
1123
+ "model_id": "75e09ae60eed4157b963c158aefbf1d1",
1124
  "version_major": 2,
1125
  "version_minor": 0
1126
  },
 
1134
  {
1135
  "data": {
1136
  "application/vnd.jupyter.widget-view+json": {
1137
+ "model_id": "8a4a9128ad7146918393d54de284ac62",
1138
  "version_major": 2,
1139
  "version_minor": 0
1140
  },
 
1148
  {
1149
  "data": {
1150
  "application/vnd.jupyter.widget-view+json": {
1151
+ "model_id": "c1bee14cfa6c4108b7791939edc19f4b",
1152
  "version_major": 2,
1153
  "version_minor": 0
1154
  },
 
1162
  {
1163
  "data": {
1164
  "application/vnd.jupyter.widget-view+json": {
1165
+ "model_id": "41b0cdd867434a9192a871eff9866c6c",
1166
  "version_major": 2,
1167
  "version_minor": 0
1168
  },
 
1176
  {
1177
  "data": {
1178
  "application/vnd.jupyter.widget-view+json": {
1179
+ "model_id": "140e00a6678e4fdd9d48b6a41472afda",
1180
  "version_major": 2,
1181
  "version_minor": 0
1182
  },
 
1190
  {
1191
  "data": {
1192
  "application/vnd.jupyter.widget-view+json": {
1193
+ "model_id": "f1674187645c4c6b916997fed2e6a2e6",
1194
  "version_major": 2,
1195
  "version_minor": 0
1196
  },
 
1204
  {
1205
  "data": {
1206
  "application/vnd.jupyter.widget-view+json": {
1207
+ "model_id": "834901365708495d9eccd1464947e3e9",
1208
  "version_major": 2,
1209
  "version_minor": 0
1210
  },
 
1218
  {
1219
  "data": {
1220
  "application/vnd.jupyter.widget-view+json": {
1221
+ "model_id": "0ec6f2f7d911415a8db6146c45f411e0",
1222
  "version_major": 2,
1223
  "version_minor": 0
1224
  },
 
1232
  {
1233
  "data": {
1234
  "application/vnd.jupyter.widget-view+json": {
1235
+ "model_id": "5cdbc436a4cf4bd69dce025bca6226c9",
1236
  "version_major": 2,
1237
  "version_minor": 0
1238
  },
 
1246
  {
1247
  "data": {
1248
  "application/vnd.jupyter.widget-view+json": {
1249
+ "model_id": "43edd2e511244951b4c4ca6a580aeb20",
1250
  "version_major": 2,
1251
  "version_minor": 0
1252
  },
 
1260
  {
1261
  "data": {
1262
  "application/vnd.jupyter.widget-view+json": {
1263
+ "model_id": "a761ab9692ef4bc89d68851b890c2a1b",
1264
  "version_major": 2,
1265
  "version_minor": 0
1266
  },
 
1274
  {
1275
  "data": {
1276
  "application/vnd.jupyter.widget-view+json": {
1277
+ "model_id": "9985062740dc4f218e4b29a40c013ad5",
1278
  "version_major": 2,
1279
  "version_minor": 0
1280
  },
 
1288
  {
1289
  "data": {
1290
  "application/vnd.jupyter.widget-view+json": {
1291
+ "model_id": "24f4dcd1c41b4bf58d479b467acb8646",
1292
  "version_major": 2,
1293
  "version_minor": 0
1294
  },
 
1298
  },
1299
  "metadata": {},
1300
  "output_type": "display_data"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1301
  }
1302
  ],
1303
  "source": [
1304
  "if __name__ == \"__main__\":\n",
1305
  " # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)\n",
1306
+ " config = TrainConfig()\n",
1307
  " for i, num_image in enumerate(num_image_list):\n",
1308
+ " config.num_image = num_image\n",
1309
+ " ddpm21cm = DDPM21CM(config)\n",
1310
+ " print(f\" num_image = {ddpm21cm.config.num_image} \".center(50, '-'))\n",
1311
  " print(f\"run_name = {ddpm21cm.config.run_name}\")\n",
1312
  " notebook_launcher(ddpm21cm.train, num_processes=1)"
1313
  ]
 
1429
  "if __name__ == \"__main__\":\n",
1430
  " # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)\n",
1431
  " repeat = 800\n",
1432
+ " config = TrainConfig()\n",
1433
  " for i, num_image in enumerate(num_image_list):\n",
1434
+ " config.num_image = num_image\n",
1435
+ " ddpm21cm = DDPM21CM(config)\n",
1436
+ "\n",
1437
  " ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor([4.4, 131.341]), repeat=repeat)\n",
1438
  "\n",
 
1439
  " ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((5.6, 19.037)), repeat=repeat)\n",
1440
  "\n",
 
1441
  " ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((4.699, 30)), repeat=repeat)\n",
1442
  "\n",
 
1443
  " ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((5.477, 200)), repeat=repeat)\n",
1444
  "\n",
 
1445
  " ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((4.8, 131.341)), repeat=repeat)"
1446
  ]
1447
  },