File size: 55,734 Bytes
344b0bb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
# v1: split from train_db_fixed.py.
# v2: support safetensors

import math
import os

import torch
from library.device_utils import init_ipex
init_ipex()

import diffusers
from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig, logging
from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline  # , UNet2DConditionModel
from safetensors.torch import load_file, save_file
from library.original_unet import UNet2DConditionModel
from library.utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)

# DiffUsers版StableDiffusionのモデルパラメータ
NUM_TRAIN_TIMESTEPS = 1000
BETA_START = 0.00085
BETA_END = 0.0120

UNET_PARAMS_MODEL_CHANNELS = 320
UNET_PARAMS_CHANNEL_MULT = [1, 2, 4, 4]
UNET_PARAMS_ATTENTION_RESOLUTIONS = [4, 2, 1]
UNET_PARAMS_IMAGE_SIZE = 64  # fixed from old invalid value `32`
UNET_PARAMS_IN_CHANNELS = 4
UNET_PARAMS_OUT_CHANNELS = 4
UNET_PARAMS_NUM_RES_BLOCKS = 2
UNET_PARAMS_CONTEXT_DIM = 768
UNET_PARAMS_NUM_HEADS = 8
# UNET_PARAMS_USE_LINEAR_PROJECTION = False

VAE_PARAMS_Z_CHANNELS = 4
VAE_PARAMS_RESOLUTION = 256
VAE_PARAMS_IN_CHANNELS = 3
VAE_PARAMS_OUT_CH = 3
VAE_PARAMS_CH = 128
VAE_PARAMS_CH_MULT = [1, 2, 4, 4]
VAE_PARAMS_NUM_RES_BLOCKS = 2

# V2
V2_UNET_PARAMS_ATTENTION_HEAD_DIM = [5, 10, 20, 20]
V2_UNET_PARAMS_CONTEXT_DIM = 1024
# V2_UNET_PARAMS_USE_LINEAR_PROJECTION = True

# Diffusersの設定を読み込むための参照モデル
DIFFUSERS_REF_MODEL_ID_V1 = "runwayml/stable-diffusion-v1-5"
DIFFUSERS_REF_MODEL_ID_V2 = "stabilityai/stable-diffusion-2-1"


# region StableDiffusion->Diffusersの変換コード
# convert_original_stable_diffusion_to_diffusers をコピーして修正している(ASL 2.0)


def shave_segments(path, n_shave_prefix_segments=1):
    """
    Removes segments. Positive values shave the first segments, negative shave the last segments.
    """
    if n_shave_prefix_segments >= 0:
        return ".".join(path.split(".")[n_shave_prefix_segments:])
    else:
        return ".".join(path.split(".")[:n_shave_prefix_segments])


def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
    """
    Updates paths inside resnets to the new naming scheme (local renaming)
    """
    mapping = []
    for old_item in old_list:
        new_item = old_item.replace("in_layers.0", "norm1")
        new_item = new_item.replace("in_layers.2", "conv1")

        new_item = new_item.replace("out_layers.0", "norm2")
        new_item = new_item.replace("out_layers.3", "conv2")

        new_item = new_item.replace("emb_layers.1", "time_emb_proj")
        new_item = new_item.replace("skip_connection", "conv_shortcut")

        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

        mapping.append({"old": old_item, "new": new_item})

    return mapping


def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
    """
    Updates paths inside resnets to the new naming scheme (local renaming)
    """
    mapping = []
    for old_item in old_list:
        new_item = old_item

        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

        mapping.append({"old": old_item, "new": new_item})

    return mapping


def renew_attention_paths(old_list, n_shave_prefix_segments=0):
    """
    Updates paths inside attentions to the new naming scheme (local renaming)
    """
    mapping = []
    for old_item in old_list:
        new_item = old_item

        #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
        #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

        #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
        #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

        #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

        mapping.append({"old": old_item, "new": new_item})

    return mapping


def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
    """
    Updates paths inside attentions to the new naming scheme (local renaming)
    """
    mapping = []
    for old_item in old_list:
        new_item = old_item

        new_item = new_item.replace("norm.weight", "group_norm.weight")
        new_item = new_item.replace("norm.bias", "group_norm.bias")

        if diffusers.__version__ < "0.17.0":
            new_item = new_item.replace("q.weight", "query.weight")
            new_item = new_item.replace("q.bias", "query.bias")

            new_item = new_item.replace("k.weight", "key.weight")
            new_item = new_item.replace("k.bias", "key.bias")

            new_item = new_item.replace("v.weight", "value.weight")
            new_item = new_item.replace("v.bias", "value.bias")

            new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
            new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
        else:
            new_item = new_item.replace("q.weight", "to_q.weight")
            new_item = new_item.replace("q.bias", "to_q.bias")

            new_item = new_item.replace("k.weight", "to_k.weight")
            new_item = new_item.replace("k.bias", "to_k.bias")

            new_item = new_item.replace("v.weight", "to_v.weight")
            new_item = new_item.replace("v.bias", "to_v.bias")

            new_item = new_item.replace("proj_out.weight", "to_out.0.weight")
            new_item = new_item.replace("proj_out.bias", "to_out.0.bias")

        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

        mapping.append({"old": old_item, "new": new_item})

    return mapping


def assign_to_checkpoint(
    paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
):
    """
    This does the final conversion step: take locally converted weights and apply a global renaming
    to them. It splits attention layers, and takes into account additional replacements
    that may arise.

    Assigns the weights to the new checkpoint.
    """
    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."

    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
        for path, path_map in attention_paths_to_split.items():
            old_tensor = old_checkpoint[path]
            channels = old_tensor.shape[0] // 3

            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)

            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3

            old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
            query, key, value = old_tensor.split(channels // num_heads, dim=1)

            checkpoint[path_map["query"]] = query.reshape(target_shape)
            checkpoint[path_map["key"]] = key.reshape(target_shape)
            checkpoint[path_map["value"]] = value.reshape(target_shape)

    for path in paths:
        new_path = path["new"]

        # These have already been assigned
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        # Global renaming happens here
        new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
        new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
        new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")

        if additional_replacements is not None:
            for replacement in additional_replacements:
                new_path = new_path.replace(replacement["old"], replacement["new"])

        # proj_attn.weight has to be converted from conv 1D to linear
        reshaping = False
        if diffusers.__version__ < "0.17.0":
            if "proj_attn.weight" in new_path:
                reshaping = True
        else:
            if ".attentions." in new_path and ".0.to_" in new_path and old_checkpoint[path["old"]].ndim > 2:
                reshaping = True

        if reshaping:
            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0]
        else:
            checkpoint[new_path] = old_checkpoint[path["old"]]


def conv_attn_to_linear(checkpoint):
    keys = list(checkpoint.keys())
    attn_keys = ["query.weight", "key.weight", "value.weight"]
    for key in keys:
        if ".".join(key.split(".")[-2:]) in attn_keys:
            if checkpoint[key].ndim > 2:
                checkpoint[key] = checkpoint[key][:, :, 0, 0]
        elif "proj_attn.weight" in key:
            if checkpoint[key].ndim > 2:
                checkpoint[key] = checkpoint[key][:, :, 0]


def linear_transformer_to_conv(checkpoint):
    keys = list(checkpoint.keys())
    tf_keys = ["proj_in.weight", "proj_out.weight"]
    for key in keys:
        if ".".join(key.split(".")[-2:]) in tf_keys:
            if checkpoint[key].ndim == 2:
                checkpoint[key] = checkpoint[key].unsqueeze(2).unsqueeze(2)


def convert_ldm_unet_checkpoint(v2, checkpoint, config):
    """
    Takes a state dict and a config, and returns a converted checkpoint.
    """

    # extract state_dict for UNet
    unet_state_dict = {}
    unet_key = "model.diffusion_model."
    keys = list(checkpoint.keys())
    for key in keys:
        if key.startswith(unet_key):
            unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)

    new_checkpoint = {}

    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]

    new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
    new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]

    new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
    new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
    new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
    new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]

    # Retrieves the keys for the input blocks only
    num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
    input_blocks = {
        layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key] for layer_id in range(num_input_blocks)
    }

    # Retrieves the keys for the middle blocks only
    num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
    middle_blocks = {
        layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}." in key] for layer_id in range(num_middle_blocks)
    }

    # Retrieves the keys for the output blocks only
    num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
    output_blocks = {
        layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key] for layer_id in range(num_output_blocks)
    }

    for i in range(1, num_input_blocks):
        block_id = (i - 1) // (config["layers_per_block"] + 1)
        layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)

        resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key]
        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]

        if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
                f"input_blocks.{i}.0.op.weight"
            )
            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(f"input_blocks.{i}.0.op.bias")

        paths = renew_resnet_paths(resnets)
        meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
        assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)

        if len(attentions):
            paths = renew_attention_paths(attentions)
            meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
            assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)

    resnet_0 = middle_blocks[0]
    attentions = middle_blocks[1]
    resnet_1 = middle_blocks[2]

    resnet_0_paths = renew_resnet_paths(resnet_0)
    assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)

    resnet_1_paths = renew_resnet_paths(resnet_1)
    assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)

    attentions_paths = renew_attention_paths(attentions)
    meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)

    for i in range(num_output_blocks):
        block_id = i // (config["layers_per_block"] + 1)
        layer_in_block_id = i % (config["layers_per_block"] + 1)
        output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
        output_block_list = {}

        for layer in output_block_layers:
            layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(layer_name)
            else:
                output_block_list[layer_id] = [layer_name]

        if len(output_block_list) > 1:
            resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
            attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

            resnet_0_paths = renew_resnet_paths(resnets)
            paths = renew_resnet_paths(resnets)

            meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
            assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)

            # オリジナル:
            # if ["conv.weight", "conv.bias"] in output_block_list.values():
            #   index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])

            # biasとweightの順番に依存しないようにする:もっといいやり方がありそうだが
            for l in output_block_list.values():
                l.sort()

            if ["conv.bias", "conv.weight"] in output_block_list.values():
                index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
                    f"output_blocks.{i}.{index}.conv.bias"
                ]
                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
                    f"output_blocks.{i}.{index}.conv.weight"
                ]

                # Clear attentions as they have been attributed above.
                if len(attentions) == 2:
                    attentions = []

            if len(attentions):
                paths = renew_attention_paths(attentions)
                meta_path = {
                    "old": f"output_blocks.{i}.1",
                    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                }
                assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)
        else:
            resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
            for path in resnet_0_paths:
                old_path = ".".join(["output_blocks", str(i), path["old"]])
                new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])

                new_checkpoint[new_path] = unet_state_dict[old_path]

    # SDのv2では1*1のconv2dがlinearに変わっている
    # 誤って Diffusers 側を conv2d のままにしてしまったので、変換必要
    if v2 and not config.get("use_linear_projection", False):
        linear_transformer_to_conv(new_checkpoint)

    return new_checkpoint


def convert_ldm_vae_checkpoint(checkpoint, config):
    # extract state dict for VAE
    vae_state_dict = {}
    vae_key = "first_stage_model."
    keys = list(checkpoint.keys())
    for key in keys:
        if key.startswith(vae_key):
            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
    # if len(vae_state_dict) == 0:
    #   # 渡されたcheckpointは.ckptから読み込んだcheckpointではなくvaeのstate_dict
    #   vae_state_dict = checkpoint

    new_checkpoint = {}

    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]

    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]

    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]

    # Retrieves the keys for the encoder down blocks only
    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
    down_blocks = {layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)}

    # Retrieves the keys for the decoder up blocks only
    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
    up_blocks = {layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)}

    for i in range(num_down_blocks):
        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]

        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
                f"encoder.down.{i}.downsample.conv.weight"
            )
            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
                f"encoder.down.{i}.downsample.conv.bias"
            )

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)

    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
    num_mid_res_blocks = 2
    for i in range(1, num_mid_res_blocks + 1):
        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)

    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
    paths = renew_vae_attention_paths(mid_attentions)
    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
    conv_attn_to_linear(new_checkpoint)

    for i in range(num_up_blocks):
        block_id = num_up_blocks - 1 - i
        resnets = [key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key]

        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
                f"decoder.up.{block_id}.upsample.conv.weight"
            ]
            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
                f"decoder.up.{block_id}.upsample.conv.bias"
            ]

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)

    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
    num_mid_res_blocks = 2
    for i in range(1, num_mid_res_blocks + 1):
        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)

    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
    paths = renew_vae_attention_paths(mid_attentions)
    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
    conv_attn_to_linear(new_checkpoint)
    return new_checkpoint


def create_unet_diffusers_config(v2, use_linear_projection_in_v2=False):
    """
    Creates a config for the diffusers based on the config of the LDM model.
    """
    # unet_params = original_config.model.params.unet_config.params

    block_out_channels = [UNET_PARAMS_MODEL_CHANNELS * mult for mult in UNET_PARAMS_CHANNEL_MULT]

    down_block_types = []
    resolution = 1
    for i in range(len(block_out_channels)):
        block_type = "CrossAttnDownBlock2D" if resolution in UNET_PARAMS_ATTENTION_RESOLUTIONS else "DownBlock2D"
        down_block_types.append(block_type)
        if i != len(block_out_channels) - 1:
            resolution *= 2

    up_block_types = []
    for i in range(len(block_out_channels)):
        block_type = "CrossAttnUpBlock2D" if resolution in UNET_PARAMS_ATTENTION_RESOLUTIONS else "UpBlock2D"
        up_block_types.append(block_type)
        resolution //= 2

    config = dict(
        sample_size=UNET_PARAMS_IMAGE_SIZE,
        in_channels=UNET_PARAMS_IN_CHANNELS,
        out_channels=UNET_PARAMS_OUT_CHANNELS,
        down_block_types=tuple(down_block_types),
        up_block_types=tuple(up_block_types),
        block_out_channels=tuple(block_out_channels),
        layers_per_block=UNET_PARAMS_NUM_RES_BLOCKS,
        cross_attention_dim=UNET_PARAMS_CONTEXT_DIM if not v2 else V2_UNET_PARAMS_CONTEXT_DIM,
        attention_head_dim=UNET_PARAMS_NUM_HEADS if not v2 else V2_UNET_PARAMS_ATTENTION_HEAD_DIM,
        # use_linear_projection=UNET_PARAMS_USE_LINEAR_PROJECTION if not v2 else V2_UNET_PARAMS_USE_LINEAR_PROJECTION,
    )
    if v2 and use_linear_projection_in_v2:
        config["use_linear_projection"] = True

    return config


def create_vae_diffusers_config():
    """
    Creates a config for the diffusers based on the config of the LDM model.
    """
    # vae_params = original_config.model.params.first_stage_config.params.ddconfig
    # _ = original_config.model.params.first_stage_config.params.embed_dim
    block_out_channels = [VAE_PARAMS_CH * mult for mult in VAE_PARAMS_CH_MULT]
    down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
    up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)

    config = dict(
        sample_size=VAE_PARAMS_RESOLUTION,
        in_channels=VAE_PARAMS_IN_CHANNELS,
        out_channels=VAE_PARAMS_OUT_CH,
        down_block_types=tuple(down_block_types),
        up_block_types=tuple(up_block_types),
        block_out_channels=tuple(block_out_channels),
        latent_channels=VAE_PARAMS_Z_CHANNELS,
        layers_per_block=VAE_PARAMS_NUM_RES_BLOCKS,
    )
    return config


def convert_ldm_clip_checkpoint_v1(checkpoint):
    keys = list(checkpoint.keys())
    text_model_dict = {}
    for key in keys:
        if key.startswith("cond_stage_model.transformer"):
            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]

    # remove position_ids for newer transformer, which causes error :(
    if "text_model.embeddings.position_ids" in text_model_dict:
        text_model_dict.pop("text_model.embeddings.position_ids")

    return text_model_dict


def convert_ldm_clip_checkpoint_v2(checkpoint, max_length):
    # 嫌になるくらい違うぞ!
    def convert_key(key):
        if not key.startswith("cond_stage_model"):
            return None

        # common conversion
        key = key.replace("cond_stage_model.model.transformer.", "text_model.encoder.")
        key = key.replace("cond_stage_model.model.", "text_model.")

        if "resblocks" in key:
            # resblocks conversion
            key = key.replace(".resblocks.", ".layers.")
            if ".ln_" in key:
                key = key.replace(".ln_", ".layer_norm")
            elif ".mlp." in key:
                key = key.replace(".c_fc.", ".fc1.")
                key = key.replace(".c_proj.", ".fc2.")
            elif ".attn.out_proj" in key:
                key = key.replace(".attn.out_proj.", ".self_attn.out_proj.")
            elif ".attn.in_proj" in key:
                key = None  # 特殊なので後で処理する
            else:
                raise ValueError(f"unexpected key in SD: {key}")
        elif ".positional_embedding" in key:
            key = key.replace(".positional_embedding", ".embeddings.position_embedding.weight")
        elif ".text_projection" in key:
            key = None  # 使われない???
        elif ".logit_scale" in key:
            key = None  # 使われない???
        elif ".token_embedding" in key:
            key = key.replace(".token_embedding.weight", ".embeddings.token_embedding.weight")
        elif ".ln_final" in key:
            key = key.replace(".ln_final", ".final_layer_norm")
        return key

    keys = list(checkpoint.keys())
    new_sd = {}
    for key in keys:
        # remove resblocks 23
        if ".resblocks.23." in key:
            continue
        new_key = convert_key(key)
        if new_key is None:
            continue
        new_sd[new_key] = checkpoint[key]

    # attnの変換
    for key in keys:
        if ".resblocks.23." in key:
            continue
        if ".resblocks" in key and ".attn.in_proj_" in key:
            # 三つに分割
            values = torch.chunk(checkpoint[key], 3)

            key_suffix = ".weight" if "weight" in key else ".bias"
            key_pfx = key.replace("cond_stage_model.model.transformer.resblocks.", "text_model.encoder.layers.")
            key_pfx = key_pfx.replace("_weight", "")
            key_pfx = key_pfx.replace("_bias", "")
            key_pfx = key_pfx.replace(".attn.in_proj", ".self_attn.")
            new_sd[key_pfx + "q_proj" + key_suffix] = values[0]
            new_sd[key_pfx + "k_proj" + key_suffix] = values[1]
            new_sd[key_pfx + "v_proj" + key_suffix] = values[2]

    # rename or add position_ids
    ANOTHER_POSITION_IDS_KEY = "text_model.encoder.text_model.embeddings.position_ids"
    if ANOTHER_POSITION_IDS_KEY in new_sd:
        # waifu diffusion v1.4
        position_ids = new_sd[ANOTHER_POSITION_IDS_KEY]
        del new_sd[ANOTHER_POSITION_IDS_KEY]
    else:
        position_ids = torch.Tensor([list(range(max_length))]).to(torch.int64)

    new_sd["text_model.embeddings.position_ids"] = position_ids
    return new_sd


# endregion


# region Diffusers->StableDiffusion の変換コード
# convert_diffusers_to_original_stable_diffusion をコピーして修正している(ASL 2.0)


def conv_transformer_to_linear(checkpoint):
    keys = list(checkpoint.keys())
    tf_keys = ["proj_in.weight", "proj_out.weight"]
    for key in keys:
        if ".".join(key.split(".")[-2:]) in tf_keys:
            if checkpoint[key].ndim > 2:
                checkpoint[key] = checkpoint[key][:, :, 0, 0]


def convert_unet_state_dict_to_sd(v2, unet_state_dict):
    unet_conversion_map = [
        # (stable-diffusion, HF Diffusers)
        ("time_embed.0.weight", "time_embedding.linear_1.weight"),
        ("time_embed.0.bias", "time_embedding.linear_1.bias"),
        ("time_embed.2.weight", "time_embedding.linear_2.weight"),
        ("time_embed.2.bias", "time_embedding.linear_2.bias"),
        ("input_blocks.0.0.weight", "conv_in.weight"),
        ("input_blocks.0.0.bias", "conv_in.bias"),
        ("out.0.weight", "conv_norm_out.weight"),
        ("out.0.bias", "conv_norm_out.bias"),
        ("out.2.weight", "conv_out.weight"),
        ("out.2.bias", "conv_out.bias"),
    ]

    unet_conversion_map_resnet = [
        # (stable-diffusion, HF Diffusers)
        ("in_layers.0", "norm1"),
        ("in_layers.2", "conv1"),
        ("out_layers.0", "norm2"),
        ("out_layers.3", "conv2"),
        ("emb_layers.1", "time_emb_proj"),
        ("skip_connection", "conv_shortcut"),
    ]

    unet_conversion_map_layer = []
    for i in range(4):
        # loop over downblocks/upblocks

        for j in range(2):
            # loop over resnets/attentions for downblocks
            hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
            sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
            unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

            if i < 3:
                # no attention layers in down_blocks.3
                hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
                sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
                unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

        for j in range(3):
            # loop over resnets/attentions for upblocks
            hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
            sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
            unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

            if i > 0:
                # no attention layers in up_blocks.0
                hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
                sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
                unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

        if i < 3:
            # no downsample in down_blocks.3
            hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
            sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
            unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

            # no upsample in up_blocks.3
            hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
            sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
            unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

    hf_mid_atn_prefix = "mid_block.attentions.0."
    sd_mid_atn_prefix = "middle_block.1."
    unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

    for j in range(2):
        hf_mid_res_prefix = f"mid_block.resnets.{j}."
        sd_mid_res_prefix = f"middle_block.{2*j}."
        unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))

    # buyer beware: this is a *brittle* function,
    # and correct output requires that all of these pieces interact in
    # the exact order in which I have arranged them.
    mapping = {k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        mapping[hf_name] = sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                v = v.replace(hf_part, sd_part)
            mapping[k] = v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            v = v.replace(hf_part, sd_part)
        mapping[k] = v
    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}

    if v2:
        conv_transformer_to_linear(new_state_dict)

    return new_state_dict


def controlnet_conversion_map():
    unet_conversion_map = [
        ("time_embed.0.weight", "time_embedding.linear_1.weight"),
        ("time_embed.0.bias", "time_embedding.linear_1.bias"),
        ("time_embed.2.weight", "time_embedding.linear_2.weight"),
        ("time_embed.2.bias", "time_embedding.linear_2.bias"),
        ("input_blocks.0.0.weight", "conv_in.weight"),
        ("input_blocks.0.0.bias", "conv_in.bias"),
        ("middle_block_out.0.weight", "controlnet_mid_block.weight"),
        ("middle_block_out.0.bias", "controlnet_mid_block.bias"),
    ]

    unet_conversion_map_resnet = [
        ("in_layers.0", "norm1"),
        ("in_layers.2", "conv1"),
        ("out_layers.0", "norm2"),
        ("out_layers.3", "conv2"),
        ("emb_layers.1", "time_emb_proj"),
        ("skip_connection", "conv_shortcut"),
    ]

    unet_conversion_map_layer = []
    for i in range(4):
        for j in range(2):
            hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
            sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
            unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

            if i < 3:
                hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
                sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
                unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

        if i < 3:
            hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
            sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
            unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

    hf_mid_atn_prefix = "mid_block.attentions.0."
    sd_mid_atn_prefix = "middle_block.1."
    unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

    for j in range(2):
        hf_mid_res_prefix = f"mid_block.resnets.{j}."
        sd_mid_res_prefix = f"middle_block.{2*j}."
        unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))

    controlnet_cond_embedding_names = ["conv_in"] + [f"blocks.{i}" for i in range(6)] + ["conv_out"]
    for i, hf_prefix in enumerate(controlnet_cond_embedding_names):
        hf_prefix = f"controlnet_cond_embedding.{hf_prefix}."
        sd_prefix = f"input_hint_block.{i*2}."
        unet_conversion_map_layer.append((sd_prefix, hf_prefix))

    for i in range(12):
        hf_prefix = f"controlnet_down_blocks.{i}."
        sd_prefix = f"zero_convs.{i}.0."
        unet_conversion_map_layer.append((sd_prefix, hf_prefix))

    return unet_conversion_map, unet_conversion_map_resnet, unet_conversion_map_layer


def convert_controlnet_state_dict_to_sd(controlnet_state_dict):
    unet_conversion_map, unet_conversion_map_resnet, unet_conversion_map_layer = controlnet_conversion_map()

    mapping = {k: k for k in controlnet_state_dict.keys()}
    for sd_name, diffusers_name in unet_conversion_map:
        mapping[diffusers_name] = sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, diffusers_part in unet_conversion_map_resnet:
                v = v.replace(diffusers_part, sd_part)
            mapping[k] = v
    for k, v in mapping.items():
        for sd_part, diffusers_part in unet_conversion_map_layer:
            v = v.replace(diffusers_part, sd_part)
        mapping[k] = v
    new_state_dict = {v: controlnet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


def convert_controlnet_state_dict_to_diffusers(controlnet_state_dict):
    unet_conversion_map, unet_conversion_map_resnet, unet_conversion_map_layer = controlnet_conversion_map()

    mapping = {k: k for k in controlnet_state_dict.keys()}
    for sd_name, diffusers_name in unet_conversion_map:
        mapping[sd_name] = diffusers_name
    for k, v in mapping.items():
        for sd_part, diffusers_part in unet_conversion_map_layer:
            v = v.replace(sd_part, diffusers_part)
        mapping[k] = v
    for k, v in mapping.items():
        if "resnets" in v:
            for sd_part, diffusers_part in unet_conversion_map_resnet:
                v = v.replace(sd_part, diffusers_part)
            mapping[k] = v
    new_state_dict = {v: controlnet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


# ================#
# VAE Conversion #
# ================#


def reshape_weight_for_sd(w):
    # convert HF linear weights to SD conv2d weights
    return w.reshape(*w.shape, 1, 1)


def convert_vae_state_dict(vae_state_dict):
    vae_conversion_map = [
        # (stable-diffusion, HF Diffusers)
        ("nin_shortcut", "conv_shortcut"),
        ("norm_out", "conv_norm_out"),
        ("mid.attn_1.", "mid_block.attentions.0."),
    ]

    for i in range(4):
        # down_blocks have two resnets
        for j in range(2):
            hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
            sd_down_prefix = f"encoder.down.{i}.block.{j}."
            vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

        if i < 3:
            hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
            sd_downsample_prefix = f"down.{i}.downsample."
            vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

            hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
            sd_upsample_prefix = f"up.{3-i}.upsample."
            vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

        # up_blocks have three resnets
        # also, up blocks in hf are numbered in reverse from sd
        for j in range(3):
            hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
            sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
            vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

    # this part accounts for mid blocks in both the encoder and the decoder
    for i in range(2):
        hf_mid_res_prefix = f"mid_block.resnets.{i}."
        sd_mid_res_prefix = f"mid.block_{i+1}."
        vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))

    if diffusers.__version__ < "0.17.0":
        vae_conversion_map_attn = [
            # (stable-diffusion, HF Diffusers)
            ("norm.", "group_norm."),
            ("q.", "query."),
            ("k.", "key."),
            ("v.", "value."),
            ("proj_out.", "proj_attn."),
        ]
    else:
        vae_conversion_map_attn = [
            # (stable-diffusion, HF Diffusers)
            ("norm.", "group_norm."),
            ("q.", "to_q."),
            ("k.", "to_k."),
            ("v.", "to_v."),
            ("proj_out.", "to_out.0."),
        ]

    mapping = {k: k for k in vae_state_dict.keys()}
    for k, v in mapping.items():
        for sd_part, hf_part in vae_conversion_map:
            v = v.replace(hf_part, sd_part)
        mapping[k] = v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                v = v.replace(hf_part, sd_part)
            mapping[k] = v
    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
    weights_to_convert = ["q", "k", "v", "proj_out"]
    for k, v in new_state_dict.items():
        for weight_name in weights_to_convert:
            if f"mid.attn_1.{weight_name}.weight" in k:
                # logger.info(f"Reshaping {k} for SD format: shape {v.shape} -> {v.shape} x 1 x 1")
                new_state_dict[k] = reshape_weight_for_sd(v)

    return new_state_dict


# endregion

# region 自作のモデル読み書きなど


def is_safetensors(path):
    return os.path.splitext(path)[1].lower() == ".safetensors"


def load_checkpoint_with_text_encoder_conversion(ckpt_path, device="cpu"):
    # text encoderの格納形式が違うモデルに対応する ('text_model'がない)
    TEXT_ENCODER_KEY_REPLACEMENTS = [
        ("cond_stage_model.transformer.embeddings.", "cond_stage_model.transformer.text_model.embeddings."),
        ("cond_stage_model.transformer.encoder.", "cond_stage_model.transformer.text_model.encoder."),
        ("cond_stage_model.transformer.final_layer_norm.", "cond_stage_model.transformer.text_model.final_layer_norm."),
    ]

    if is_safetensors(ckpt_path):
        checkpoint = None
        state_dict = load_file(ckpt_path)  # , device) # may causes error
    else:
        checkpoint = torch.load(ckpt_path, map_location=device)
        if "state_dict" in checkpoint:
            state_dict = checkpoint["state_dict"]
        else:
            state_dict = checkpoint
            checkpoint = None

    key_reps = []
    for rep_from, rep_to in TEXT_ENCODER_KEY_REPLACEMENTS:
        for key in state_dict.keys():
            if key.startswith(rep_from):
                new_key = rep_to + key[len(rep_from) :]
                key_reps.append((key, new_key))

    for key, new_key in key_reps:
        state_dict[new_key] = state_dict[key]
        del state_dict[key]

    return checkpoint, state_dict


# TODO dtype指定の動作が怪しいので確認する text_encoderを指定形式で作れるか未確認
def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path, device="cpu", dtype=None, unet_use_linear_projection_in_v2=True):
    _, state_dict = load_checkpoint_with_text_encoder_conversion(ckpt_path, device)

    # Convert the UNet2DConditionModel model.
    unet_config = create_unet_diffusers_config(v2, unet_use_linear_projection_in_v2)
    converted_unet_checkpoint = convert_ldm_unet_checkpoint(v2, state_dict, unet_config)

    unet = UNet2DConditionModel(**unet_config).to(device)
    info = unet.load_state_dict(converted_unet_checkpoint)
    logger.info(f"loading u-net: {info}")

    # Convert the VAE model.
    vae_config = create_vae_diffusers_config()
    converted_vae_checkpoint = convert_ldm_vae_checkpoint(state_dict, vae_config)

    vae = AutoencoderKL(**vae_config).to(device)
    info = vae.load_state_dict(converted_vae_checkpoint)
    logger.info(f"loading vae: {info}")

    # convert text_model
    if v2:
        converted_text_encoder_checkpoint = convert_ldm_clip_checkpoint_v2(state_dict, 77)
        cfg = CLIPTextConfig(
            vocab_size=49408,
            hidden_size=1024,
            intermediate_size=4096,
            num_hidden_layers=23,
            num_attention_heads=16,
            max_position_embeddings=77,
            hidden_act="gelu",
            layer_norm_eps=1e-05,
            dropout=0.0,
            attention_dropout=0.0,
            initializer_range=0.02,
            initializer_factor=1.0,
            pad_token_id=1,
            bos_token_id=0,
            eos_token_id=2,
            model_type="clip_text_model",
            projection_dim=512,
            torch_dtype="float32",
            transformers_version="4.25.0.dev0",
        )
        text_model = CLIPTextModel._from_config(cfg)
        info = text_model.load_state_dict(converted_text_encoder_checkpoint)
    else:
        converted_text_encoder_checkpoint = convert_ldm_clip_checkpoint_v1(state_dict)

        # logging.set_verbosity_error()  # don't show annoying warning
        # text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14").to(device)
        # logging.set_verbosity_warning()
        # logger.info(f"config: {text_model.config}")
        cfg = CLIPTextConfig(
            vocab_size=49408,
            hidden_size=768,
            intermediate_size=3072,
            num_hidden_layers=12,
            num_attention_heads=12,
            max_position_embeddings=77,
            hidden_act="quick_gelu",
            layer_norm_eps=1e-05,
            dropout=0.0,
            attention_dropout=0.0,
            initializer_range=0.02,
            initializer_factor=1.0,
            pad_token_id=1,
            bos_token_id=0,
            eos_token_id=2,
            model_type="clip_text_model",
            projection_dim=768,
            torch_dtype="float32",
        )
        text_model = CLIPTextModel._from_config(cfg)
        info = text_model.load_state_dict(converted_text_encoder_checkpoint)
    logger.info(f"loading text encoder: {info}")

    return text_model, vae, unet


def get_model_version_str_for_sd1_sd2(v2, v_parameterization):
    # only for reference
    version_str = "sd"
    if v2:
        version_str += "_v2"
    else:
        version_str += "_v1"
    if v_parameterization:
        version_str += "_v"
    return version_str


def convert_text_encoder_state_dict_to_sd_v2(checkpoint, make_dummy_weights=False):
    def convert_key(key):
        # position_idsの除去
        if ".position_ids" in key:
            return None

        # common
        key = key.replace("text_model.encoder.", "transformer.")
        key = key.replace("text_model.", "")
        if "layers" in key:
            # resblocks conversion
            key = key.replace(".layers.", ".resblocks.")
            if ".layer_norm" in key:
                key = key.replace(".layer_norm", ".ln_")
            elif ".mlp." in key:
                key = key.replace(".fc1.", ".c_fc.")
                key = key.replace(".fc2.", ".c_proj.")
            elif ".self_attn.out_proj" in key:
                key = key.replace(".self_attn.out_proj.", ".attn.out_proj.")
            elif ".self_attn." in key:
                key = None  # 特殊なので後で処理する
            else:
                raise ValueError(f"unexpected key in DiffUsers model: {key}")
        elif ".position_embedding" in key:
            key = key.replace("embeddings.position_embedding.weight", "positional_embedding")
        elif ".token_embedding" in key:
            key = key.replace("embeddings.token_embedding.weight", "token_embedding.weight")
        elif "final_layer_norm" in key:
            key = key.replace("final_layer_norm", "ln_final")
        return key

    keys = list(checkpoint.keys())
    new_sd = {}
    for key in keys:
        new_key = convert_key(key)
        if new_key is None:
            continue
        new_sd[new_key] = checkpoint[key]

    # attnの変換
    for key in keys:
        if "layers" in key and "q_proj" in key:
            # 三つを結合
            key_q = key
            key_k = key.replace("q_proj", "k_proj")
            key_v = key.replace("q_proj", "v_proj")

            value_q = checkpoint[key_q]
            value_k = checkpoint[key_k]
            value_v = checkpoint[key_v]
            value = torch.cat([value_q, value_k, value_v])

            new_key = key.replace("text_model.encoder.layers.", "transformer.resblocks.")
            new_key = new_key.replace(".self_attn.q_proj.", ".attn.in_proj_")
            new_sd[new_key] = value

    # 最後の層などを捏造するか
    if make_dummy_weights:
        logger.info("make dummy weights for resblock.23, text_projection and logit scale.")
        keys = list(new_sd.keys())
        for key in keys:
            if key.startswith("transformer.resblocks.22."):
                new_sd[key.replace(".22.", ".23.")] = new_sd[key].clone()  # copyしないとsafetensorsの保存で落ちる

        # Diffusersに含まれない重みを作っておく
        new_sd["text_projection"] = torch.ones((1024, 1024), dtype=new_sd[keys[0]].dtype, device=new_sd[keys[0]].device)
        new_sd["logit_scale"] = torch.tensor(1)

    return new_sd


def save_stable_diffusion_checkpoint(
    v2, output_file, text_encoder, unet, ckpt_path, epochs, steps, metadata, save_dtype=None, vae=None
):
    if ckpt_path is not None:
        # epoch/stepを参照する。またVAEがメモリ上にないときなど、もう一度VAEを含めて読み込む
        checkpoint, state_dict = load_checkpoint_with_text_encoder_conversion(ckpt_path)
        if checkpoint is None:  # safetensors または state_dictのckpt
            checkpoint = {}
            strict = False
        else:
            strict = True
        if "state_dict" in state_dict:
            del state_dict["state_dict"]
    else:
        # 新しく作る
        assert vae is not None, "VAE is required to save a checkpoint without a given checkpoint"
        checkpoint = {}
        state_dict = {}
        strict = False

    def update_sd(prefix, sd):
        for k, v in sd.items():
            key = prefix + k
            assert not strict or key in state_dict, f"Illegal key in save SD: {key}"
            if save_dtype is not None:
                v = v.detach().clone().to("cpu").to(save_dtype)
            state_dict[key] = v

    # Convert the UNet model
    unet_state_dict = convert_unet_state_dict_to_sd(v2, unet.state_dict())
    update_sd("model.diffusion_model.", unet_state_dict)

    # Convert the text encoder model
    if v2:
        make_dummy = ckpt_path is None  # 参照元のcheckpointがない場合は最後の層を前の層から複製して作るなどダミーの重みを入れる
        text_enc_dict = convert_text_encoder_state_dict_to_sd_v2(text_encoder.state_dict(), make_dummy)
        update_sd("cond_stage_model.model.", text_enc_dict)
    else:
        text_enc_dict = text_encoder.state_dict()
        update_sd("cond_stage_model.transformer.", text_enc_dict)

    # Convert the VAE
    if vae is not None:
        vae_dict = convert_vae_state_dict(vae.state_dict())
        update_sd("first_stage_model.", vae_dict)

    # Put together new checkpoint
    key_count = len(state_dict.keys())
    new_ckpt = {"state_dict": state_dict}

    # epoch and global_step are sometimes not int
    try:
        if "epoch" in checkpoint:
            epochs += checkpoint["epoch"]
        if "global_step" in checkpoint:
            steps += checkpoint["global_step"]
    except:
        pass

    new_ckpt["epoch"] = epochs
    new_ckpt["global_step"] = steps

    if is_safetensors(output_file):
        # TODO Tensor以外のdictの値を削除したほうがいいか
        save_file(state_dict, output_file, metadata)
    else:
        torch.save(new_ckpt, output_file)

    return key_count


def save_diffusers_checkpoint(v2, output_dir, text_encoder, unet, pretrained_model_name_or_path, vae=None, use_safetensors=False):
    if pretrained_model_name_or_path is None:
        # load default settings for v1/v2
        if v2:
            pretrained_model_name_or_path = DIFFUSERS_REF_MODEL_ID_V2
        else:
            pretrained_model_name_or_path = DIFFUSERS_REF_MODEL_ID_V1

    scheduler = DDIMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler")
    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer")
    if vae is None:
        vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae")

    # original U-Net cannot be saved, so we need to convert it to the Diffusers version
    # TODO this consumes a lot of memory
    diffusers_unet = diffusers.UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder="unet")
    diffusers_unet.load_state_dict(unet.state_dict())

    pipeline = StableDiffusionPipeline(
        unet=diffusers_unet,
        text_encoder=text_encoder,
        vae=vae,
        scheduler=scheduler,
        tokenizer=tokenizer,
        safety_checker=None,
        feature_extractor=None,
        requires_safety_checker=None,
    )
    pipeline.save_pretrained(output_dir, safe_serialization=use_safetensors)


VAE_PREFIX = "first_stage_model."


def load_vae(vae_id, dtype):
    logger.info(f"load VAE: {vae_id}")
    if os.path.isdir(vae_id) or not os.path.isfile(vae_id):
        # Diffusers local/remote
        try:
            vae = AutoencoderKL.from_pretrained(vae_id, subfolder=None, torch_dtype=dtype)
        except EnvironmentError as e:
            logger.error(f"exception occurs in loading vae: {e}")
            logger.error("retry with subfolder='vae'")
            vae = AutoencoderKL.from_pretrained(vae_id, subfolder="vae", torch_dtype=dtype)
        return vae

    # local
    vae_config = create_vae_diffusers_config()

    if vae_id.endswith(".bin"):
        # SD 1.5 VAE on Huggingface
        converted_vae_checkpoint = torch.load(vae_id, map_location="cpu")
    else:
        # StableDiffusion
        vae_model = load_file(vae_id, "cpu") if is_safetensors(vae_id) else torch.load(vae_id, map_location="cpu")
        vae_sd = vae_model["state_dict"] if "state_dict" in vae_model else vae_model

        # vae only or full model
        full_model = False
        for vae_key in vae_sd:
            if vae_key.startswith(VAE_PREFIX):
                full_model = True
                break
        if not full_model:
            sd = {}
            for key, value in vae_sd.items():
                sd[VAE_PREFIX + key] = value
            vae_sd = sd
            del sd

        # Convert the VAE model.
        converted_vae_checkpoint = convert_ldm_vae_checkpoint(vae_sd, vae_config)

    vae = AutoencoderKL(**vae_config)
    vae.load_state_dict(converted_vae_checkpoint)
    return vae


# endregion


def make_bucket_resolutions(max_reso, min_size=256, max_size=1024, divisible=64):
    max_width, max_height = max_reso
    max_area = max_width * max_height

    resos = set()

    width = int(math.sqrt(max_area) // divisible) * divisible
    resos.add((width, width))

    width = min_size
    while width <= max_size:
        height = min(max_size, int((max_area // width) // divisible) * divisible)
        if height >= min_size:
            resos.add((width, height))
            resos.add((height, width))

        # # make additional resos
        # if width >= height and width - divisible >= min_size:
        #   resos.add((width - divisible, height))
        #   resos.add((height, width - divisible))
        # if height >= width and height - divisible >= min_size:
        #   resos.add((width, height - divisible))
        #   resos.add((height - divisible, width))

        width += divisible

    resos = list(resos)
    resos.sort()
    return resos


if __name__ == "__main__":
    resos = make_bucket_resolutions((512, 768))
    logger.info(f"{len(resos)}")
    logger.info(f"{resos}")
    aspect_ratios = [w / h for w, h in resos]
    logger.info(f"{aspect_ratios}")

    ars = set()
    for ar in aspect_ratios:
        if ar in ars:
            logger.error(f"error! duplicate ar: {ar}")
        ars.add(ar)