Datasets:

ArXiv:
File size: 62,006 Bytes
3d01aa6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import abc
import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import numpy as np
import torch
import torch.nn.functional as F
from packaging import version
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel
from diffusers.configuration_utils import FrozenDict, deprecate
from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import (
    FromSingleFileMixin,
    IPAdapterMixin,
    LoraLoaderMixin,
    TextualInversionLoaderMixin,
)
from diffusers.models.attention import Attention
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import (
    StableDiffusionSafetyChecker,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
    USE_PEFT_BACKEND,
    logging,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.utils.torch_utils import randn_tensor


logger = logging.get_logger(__name__)


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """
    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
    """
    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    # rescale the results from guidance (fixes overexposure)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
    return noise_cfg


class Prompt2PromptPipeline(
    DiffusionPipeline,
    TextualInversionLoaderMixin,
    LoraLoaderMixin,
    IPAdapterMixin,
    FromSingleFileMixin,
):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
    implemented for all pipelines (downloading, saving, running on a particular device, etc.).

    The pipeline also inherits the following loading methods:
        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
        - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
        text_encoder ([`~transformers.CLIPTextModel`]):
            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        tokenizer ([`~transformers.CLIPTokenizer`]):
            A `CLIPTokenizer` to tokenize text.
        unet ([`UNet2DConditionModel`]):
            A `UNet2DConditionModel` to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
    """

    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
    _exclude_from_cpu_offload = ["safety_checker"]
    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
    _optional_components = ["safety_checker", "feature_extractor"]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPImageProcessor,
        image_encoder: CLIPVisionModelWithProjection = None,
        requires_safety_checker: bool = True,
    ):
        super().__init__()

        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                " file"
            )
            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
            )
            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
            new_config = dict(scheduler.config)
            new_config["clip_sample"] = False
            scheduler._internal_dict = FrozenDict(new_config)

        if safety_checker is None and requires_safety_checker:
            logger.warning(
                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
            )

        if safety_checker is not None and feature_extractor is None:
            raise ValueError(
                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
            version.parse(unet.config._diffusers_version).base_version
        ) < version.parse("0.9.0.dev0")
        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
                " the `unet/config.json` file"
            )
            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
            new_config = dict(unet.config)
            new_config["sample_size"] = 64
            unet._internal_dict = FrozenDict(new_config)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
    def _encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        lora_scale: Optional[float] = None,
        **kwargs,
    ):
        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
        deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)

        prompt_embeds_tuple = self.encode_prompt(
            prompt=prompt,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            lora_scale=lora_scale,
            **kwargs,
        )

        # concatenate for backwards comp
        prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])

        return prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
    def encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        lora_scale: Optional[float] = None,
        clip_skip: Optional[int] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            device: (`torch.device`):
                torch device
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`):
                whether to use classifier free guidance or not
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            lora_scale (`float`, *optional*):
                A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
            clip_skip (`int`, *optional*):
                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                the output of the pre-final layer will be used for computing the prompt embeddings.
        """
        # set lora scale so that monkey patched LoRA
        # function of text encoder can correctly access it
        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
            self._lora_scale = lora_scale

            # dynamically adjust the LoRA scale
            if not USE_PEFT_BACKEND:
                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
            else:
                scale_lora_layers(self.text_encoder, lora_scale)

        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if prompt_embeds is None:
            # textual inversion: process multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(
                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
                )
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = text_inputs.attention_mask.to(device)
            else:
                attention_mask = None

            if clip_skip is None:
                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
                prompt_embeds = prompt_embeds[0]
            else:
                prompt_embeds = self.text_encoder(
                    text_input_ids.to(device),
                    attention_mask=attention_mask,
                    output_hidden_states=True,
                )
                # Access the `hidden_states` first, that contains a tuple of
                # all the hidden states from the encoder layers. Then index into
                # the tuple to access the hidden states from the desired layer.
                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
                # We also need to apply the final LayerNorm here to not mess with the
                # representations. The `last_hidden_states` that we typically use for
                # obtaining the final prompt representations passes through the LayerNorm
                # layer.
                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)

        if self.text_encoder is not None:
            prompt_embeds_dtype = self.text_encoder.dtype
        elif self.unet is not None:
            prompt_embeds_dtype = self.unet.dtype
        else:
            prompt_embeds_dtype = prompt_embeds.dtype

        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            # textual inversion: process multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = uncond_input.attention_mask.to(device)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
            # Retrieve the original scale by scaling back the LoRA layers
            unscale_lora_layers(self.text_encoder, lora_scale)

        return prompt_embeds, negative_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
    def run_safety_checker(self, image, device, dtype):
        if self.safety_checker is None:
            has_nsfw_concept = None
        else:
            if torch.is_tensor(image):
                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
            else:
                feature_extractor_input = self.image_processor.numpy_to_pil(image)
            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
            image, has_nsfw_concept = self.safety_checker(
                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
            )
        return image, has_nsfw_concept

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
    def check_inputs(
        self,
        prompt,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        ip_adapter_image=None,
        ip_adapter_image_embeds=None,
        callback_on_step_end_tensor_inputs=None,
    ):
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )
        if callback_on_step_end_tensor_inputs is not None and not all(
            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
        ):
            raise ValueError(
                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

        if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
            raise ValueError(
                "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
            )

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(
        self,
        batch_size,
        num_channels_latents,
        height,
        width,
        dtype,
        device,
        generator,
        latents=None,
    ):
        shape = (
            batch_size,
            num_channels_latents,
            height // self.vae_scale_factor,
            width // self.vae_scale_factor,
        )
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]],
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
        callback_steps: Optional[int] = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `guidance_scale` is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.Tensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).

                The keyword arguments to configure the edit are:
                - edit_type (`str`). The edit type to apply. Can be either of `replace`, `refine`, `reweight`.
                - n_cross_replace (`int`): Number of diffusion steps in which cross attention should be replaced
                - n_self_replace (`int`): Number of diffusion steps in which self attention should be replaced
                - local_blend_words(`List[str]`, *optional*, default to `None`): Determines which area should be
                  changed. If None, then the whole image can be changed.
                - equalizer_words(`List[str]`, *optional*, default to `None`): Required for edit type `reweight`.
                  Determines which words should be enhanced.
                - equalizer_strengths (`List[float]`, *optional*, default to `None`) Required for edit type `reweight`.
                  Determines which how much the words in `equalizer_words` should be enhanced.

            guidance_rescale (`float`, *optional*, defaults to 0.0):
                Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
                Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
                using zero terminal SNR.

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        self.controller = create_controller(
            prompt,
            cross_attention_kwargs,
            num_inference_steps,
            tokenizer=self.tokenizer,
            device=self.device,
        )
        self.register_attention_control(self.controller)  # add attention controller

        # 0. Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(prompt, height, width, callback_steps)

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
        )
        prompt_embeds = self._encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
        )

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                # predict the noise residual
                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                if do_classifier_free_guidance and guidance_rescale > 0.0:
                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

                # step callback
                latents = self.controller.step_callback(latents)

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

        # 8. Post-processing
        if not output_type == "latent":
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
        else:
            image = latents
            has_nsfw_concept = None

        # 9. Run safety checker
        if has_nsfw_concept is None:
            do_denormalize = [True] * image.shape[0]
        else:
            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

        # Offload last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

    def register_attention_control(self, controller):
        attn_procs = {}
        cross_att_count = 0
        for name in self.unet.attn_processors.keys():
            (None if name.endswith("attn1.processor") else self.unet.config.cross_attention_dim)
            if name.startswith("mid_block"):
                self.unet.config.block_out_channels[-1]
                place_in_unet = "mid"
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                list(reversed(self.unet.config.block_out_channels))[block_id]
                place_in_unet = "up"
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                self.unet.config.block_out_channels[block_id]
                place_in_unet = "down"
            else:
                continue
            cross_att_count += 1
            attn_procs[name] = P2PCrossAttnProcessor(controller=controller, place_in_unet=place_in_unet)

        self.unet.set_attn_processor(attn_procs)
        controller.num_att_layers = cross_att_count


class P2PCrossAttnProcessor:
    def __init__(self, controller, place_in_unet):
        super().__init__()
        self.controller = controller
        self.place_in_unet = place_in_unet

    def __call__(
        self,
        attn: Attention,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
    ):
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        query = attn.to_q(hidden_states)

        is_cross = encoder_hidden_states is not None
        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)

        # one line change
        self.controller(attention_probs, is_cross, self.place_in_unet)

        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


def create_controller(
    prompts: List[str],
    cross_attention_kwargs: Dict,
    num_inference_steps: int,
    tokenizer,
    device,
) -> AttentionControl:
    edit_type = cross_attention_kwargs.get("edit_type", None)
    local_blend_words = cross_attention_kwargs.get("local_blend_words", None)
    equalizer_words = cross_attention_kwargs.get("equalizer_words", None)
    equalizer_strengths = cross_attention_kwargs.get("equalizer_strengths", None)
    n_cross_replace = cross_attention_kwargs.get("n_cross_replace", 0.4)
    n_self_replace = cross_attention_kwargs.get("n_self_replace", 0.4)

    # only replace
    if edit_type == "replace" and local_blend_words is None:
        return AttentionReplace(
            prompts,
            num_inference_steps,
            n_cross_replace,
            n_self_replace,
            tokenizer=tokenizer,
            device=device,
        )

    # replace + localblend
    if edit_type == "replace" and local_blend_words is not None:
        lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device)
        return AttentionReplace(
            prompts,
            num_inference_steps,
            n_cross_replace,
            n_self_replace,
            lb,
            tokenizer=tokenizer,
            device=device,
        )

    # only refine
    if edit_type == "refine" and local_blend_words is None:
        return AttentionRefine(
            prompts,
            num_inference_steps,
            n_cross_replace,
            n_self_replace,
            tokenizer=tokenizer,
            device=device,
        )

    # refine + localblend
    if edit_type == "refine" and local_blend_words is not None:
        lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device)
        return AttentionRefine(
            prompts,
            num_inference_steps,
            n_cross_replace,
            n_self_replace,
            lb,
            tokenizer=tokenizer,
            device=device,
        )

    # reweight
    if edit_type == "reweight":
        assert (
            equalizer_words is not None and equalizer_strengths is not None
        ), "To use reweight edit, please specify equalizer_words and equalizer_strengths."
        assert len(equalizer_words) == len(
            equalizer_strengths
        ), "equalizer_words and equalizer_strengths must be of same length."
        equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)
        return AttentionReweight(
            prompts,
            num_inference_steps,
            n_cross_replace,
            n_self_replace,
            tokenizer=tokenizer,
            device=device,
            equalizer=equalizer,
        )

    raise ValueError(f"Edit type {edit_type} not recognized. Use one of: replace, refine, reweight.")


class AttentionControl(abc.ABC):
    def step_callback(self, x_t):
        return x_t

    def between_steps(self):
        return

    @property
    def num_uncond_att_layers(self):
        return 0

    @abc.abstractmethod
    def forward(self, attn, is_cross: bool, place_in_unet: str):
        raise NotImplementedError

    def __call__(self, attn, is_cross: bool, place_in_unet: str):
        if self.cur_att_layer >= self.num_uncond_att_layers:
            h = attn.shape[0]
            attn[h // 2 :] = self.forward(attn[h // 2 :], is_cross, place_in_unet)
        self.cur_att_layer += 1
        if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
            self.cur_att_layer = 0
            self.cur_step += 1
            self.between_steps()
        return attn

    def reset(self):
        self.cur_step = 0
        self.cur_att_layer = 0

    def __init__(self):
        self.cur_step = 0
        self.num_att_layers = -1
        self.cur_att_layer = 0


class EmptyControl(AttentionControl):
    def forward(self, attn, is_cross: bool, place_in_unet: str):
        return attn


class AttentionStore(AttentionControl):
    @staticmethod
    def get_empty_store():
        return {
            "down_cross": [],
            "mid_cross": [],
            "up_cross": [],
            "down_self": [],
            "mid_self": [],
            "up_self": [],
        }

    def forward(self, attn, is_cross: bool, place_in_unet: str):
        key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
        if attn.shape[1] <= 32**2:  # avoid memory overhead
            self.step_store[key].append(attn)
        return attn

    def between_steps(self):
        if len(self.attention_store) == 0:
            self.attention_store = self.step_store
        else:
            for key in self.attention_store:
                for i in range(len(self.attention_store[key])):
                    self.attention_store[key][i] += self.step_store[key][i]
        self.step_store = self.get_empty_store()

    def get_average_attention(self):
        average_attention = {
            key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store
        }
        return average_attention

    def reset(self):
        super(AttentionStore, self).reset()
        self.step_store = self.get_empty_store()
        self.attention_store = {}

    def __init__(self):
        super(AttentionStore, self).__init__()
        self.step_store = self.get_empty_store()
        self.attention_store = {}


class LocalBlend:
    def __call__(self, x_t, attention_store):
        k = 1
        maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
        maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, self.max_num_words) for item in maps]
        maps = torch.cat(maps, dim=1)
        maps = (maps * self.alpha_layers).sum(-1).mean(1)
        mask = F.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
        mask = F.interpolate(mask, size=(x_t.shape[2:]))
        mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
        mask = mask.gt(self.threshold)
        mask = (mask[:1] + mask[1:]).float()
        x_t = x_t[:1] + mask * (x_t - x_t[:1])
        return x_t

    def __init__(
        self,
        prompts: List[str],
        words: [List[List[str]]],
        tokenizer,
        device,
        threshold=0.3,
        max_num_words=77,
    ):
        self.max_num_words = 77

        alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words)
        for i, (prompt, words_) in enumerate(zip(prompts, words)):
            if isinstance(words_, str):
                words_ = [words_]
            for word in words_:
                ind = get_word_inds(prompt, word, tokenizer)
                alpha_layers[i, :, :, :, :, ind] = 1
        self.alpha_layers = alpha_layers.to(device)
        self.threshold = threshold


class AttentionControlEdit(AttentionStore, abc.ABC):
    def step_callback(self, x_t):
        if self.local_blend is not None:
            x_t = self.local_blend(x_t, self.attention_store)
        return x_t

    def replace_self_attention(self, attn_base, att_replace):
        if att_replace.shape[2] <= 16**2:
            return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
        else:
            return att_replace

    @abc.abstractmethod
    def replace_cross_attention(self, attn_base, att_replace):
        raise NotImplementedError

    def forward(self, attn, is_cross: bool, place_in_unet: str):
        super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
        # FIXME not replace correctly
        if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
            h = attn.shape[0] // (self.batch_size)
            attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
            attn_base, attn_repalce = attn[0], attn[1:]
            if is_cross:
                alpha_words = self.cross_replace_alpha[self.cur_step]
                attn_repalce_new = (
                    self.replace_cross_attention(attn_base, attn_repalce) * alpha_words
                    + (1 - alpha_words) * attn_repalce
                )
                attn[1:] = attn_repalce_new
            else:
                attn[1:] = self.replace_self_attention(attn_base, attn_repalce)
            attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
        return attn

    def __init__(
        self,
        prompts,
        num_steps: int,
        cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
        self_replace_steps: Union[float, Tuple[float, float]],
        local_blend: Optional[LocalBlend],
        tokenizer,
        device,
    ):
        super(AttentionControlEdit, self).__init__()
        # add tokenizer and device here

        self.tokenizer = tokenizer
        self.device = device

        self.batch_size = len(prompts)
        self.cross_replace_alpha = get_time_words_attention_alpha(
            prompts, num_steps, cross_replace_steps, self.tokenizer
        ).to(self.device)
        if isinstance(self_replace_steps, float):
            self_replace_steps = 0, self_replace_steps
        self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
        self.local_blend = local_blend  # 在外面定义后传进来


class AttentionReplace(AttentionControlEdit):
    def replace_cross_attention(self, attn_base, att_replace):
        return torch.einsum("hpw,bwn->bhpn", attn_base, self.mapper)

    def __init__(
        self,
        prompts,
        num_steps: int,
        cross_replace_steps: float,
        self_replace_steps: float,
        local_blend: Optional[LocalBlend] = None,
        tokenizer=None,
        device=None,
    ):
        super(AttentionReplace, self).__init__(
            prompts,
            num_steps,
            cross_replace_steps,
            self_replace_steps,
            local_blend,
            tokenizer,
            device,
        )
        self.mapper = get_replacement_mapper(prompts, self.tokenizer).to(self.device)


class AttentionRefine(AttentionControlEdit):
    def replace_cross_attention(self, attn_base, att_replace):
        attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
        attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
        return attn_replace

    def __init__(
        self,
        prompts,
        num_steps: int,
        cross_replace_steps: float,
        self_replace_steps: float,
        local_blend: Optional[LocalBlend] = None,
        tokenizer=None,
        device=None,
    ):
        super(AttentionRefine, self).__init__(
            prompts,
            num_steps,
            cross_replace_steps,
            self_replace_steps,
            local_blend,
            tokenizer,
            device,
        )
        self.mapper, alphas = get_refinement_mapper(prompts, self.tokenizer)
        self.mapper, alphas = self.mapper.to(self.device), alphas.to(self.device)
        self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])


class AttentionReweight(AttentionControlEdit):
    def replace_cross_attention(self, attn_base, att_replace):
        if self.prev_controller is not None:
            attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
        attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
        return attn_replace

    def __init__(
        self,
        prompts,
        num_steps: int,
        cross_replace_steps: float,
        self_replace_steps: float,
        equalizer,
        local_blend: Optional[LocalBlend] = None,
        controller: Optional[AttentionControlEdit] = None,
        tokenizer=None,
        device=None,
    ):
        super(AttentionReweight, self).__init__(
            prompts,
            num_steps,
            cross_replace_steps,
            self_replace_steps,
            local_blend,
            tokenizer,
            device,
        )
        self.equalizer = equalizer.to(self.device)
        self.prev_controller = controller


### util functions for all Edits
def update_alpha_time_word(
    alpha,
    bounds: Union[float, Tuple[float, float]],
    prompt_ind: int,
    word_inds: Optional[torch.Tensor] = None,
):
    if isinstance(bounds, float):
        bounds = 0, bounds
    start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
    if word_inds is None:
        word_inds = torch.arange(alpha.shape[2])
    alpha[:start, prompt_ind, word_inds] = 0
    alpha[start:end, prompt_ind, word_inds] = 1
    alpha[end:, prompt_ind, word_inds] = 0
    return alpha


def get_time_words_attention_alpha(
    prompts,
    num_steps,
    cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]],
    tokenizer,
    max_num_words=77,
):
    if not isinstance(cross_replace_steps, dict):
        cross_replace_steps = {"default_": cross_replace_steps}
    if "default_" not in cross_replace_steps:
        cross_replace_steps["default_"] = (0.0, 1.0)
    alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)
    for i in range(len(prompts) - 1):
        alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"], i)
    for key, item in cross_replace_steps.items():
        if key != "default_":
            inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]
            for i, ind in enumerate(inds):
                if len(ind) > 0:
                    alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)
    alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)
    return alpha_time_words


### util functions for LocalBlend and ReplacementEdit
def get_word_inds(text: str, word_place: int, tokenizer):
    split_text = text.split(" ")
    if isinstance(word_place, str):
        word_place = [i for i, word in enumerate(split_text) if word_place == word]
    elif isinstance(word_place, int):
        word_place = [word_place]
    out = []
    if len(word_place) > 0:
        words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
        cur_len, ptr = 0, 0

        for i in range(len(words_encode)):
            cur_len += len(words_encode[i])
            if ptr in word_place:
                out.append(i + 1)
            if cur_len >= len(split_text[ptr]):
                ptr += 1
                cur_len = 0
    return np.array(out)


### util functions for ReplacementEdit
def get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):
    words_x = x.split(" ")
    words_y = y.split(" ")
    if len(words_x) != len(words_y):
        raise ValueError(
            f"attention replacement edit can only be applied on prompts with the same length"
            f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words."
        )
    inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]
    inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]
    inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]
    mapper = np.zeros((max_len, max_len))
    i = j = 0
    cur_inds = 0
    while i < max_len and j < max_len:
        if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:
            inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]
            if len(inds_source_) == len(inds_target_):
                mapper[inds_source_, inds_target_] = 1
            else:
                ratio = 1 / len(inds_target_)
                for i_t in inds_target_:
                    mapper[inds_source_, i_t] = ratio
            cur_inds += 1
            i += len(inds_source_)
            j += len(inds_target_)
        elif cur_inds < len(inds_source):
            mapper[i, j] = 1
            i += 1
            j += 1
        else:
            mapper[j, j] = 1
            i += 1
            j += 1

    return torch.from_numpy(mapper).float()


def get_replacement_mapper(prompts, tokenizer, max_len=77):
    x_seq = prompts[0]
    mappers = []
    for i in range(1, len(prompts)):
        mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)
        mappers.append(mapper)
    return torch.stack(mappers)


### util functions for ReweightEdit
def get_equalizer(
    text: str,
    word_select: Union[int, Tuple[int, ...]],
    values: Union[List[float], Tuple[float, ...]],
    tokenizer,
):
    if isinstance(word_select, (int, str)):
        word_select = (word_select,)
    equalizer = torch.ones(len(values), 77)
    values = torch.tensor(values, dtype=torch.float32)
    for word in word_select:
        inds = get_word_inds(text, word, tokenizer)
        equalizer[:, inds] = values
    return equalizer


### util functions for RefinementEdit
class ScoreParams:
    def __init__(self, gap, match, mismatch):
        self.gap = gap
        self.match = match
        self.mismatch = mismatch

    def mis_match_char(self, x, y):
        if x != y:
            return self.mismatch
        else:
            return self.match


def get_matrix(size_x, size_y, gap):
    matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
    matrix[0, 1:] = (np.arange(size_y) + 1) * gap
    matrix[1:, 0] = (np.arange(size_x) + 1) * gap
    return matrix


def get_traceback_matrix(size_x, size_y):
    matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
    matrix[0, 1:] = 1
    matrix[1:, 0] = 2
    matrix[0, 0] = 4
    return matrix


def global_align(x, y, score):
    matrix = get_matrix(len(x), len(y), score.gap)
    trace_back = get_traceback_matrix(len(x), len(y))
    for i in range(1, len(x) + 1):
        for j in range(1, len(y) + 1):
            left = matrix[i, j - 1] + score.gap
            up = matrix[i - 1, j] + score.gap
            diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])
            matrix[i, j] = max(left, up, diag)
            if matrix[i, j] == left:
                trace_back[i, j] = 1
            elif matrix[i, j] == up:
                trace_back[i, j] = 2
            else:
                trace_back[i, j] = 3
    return matrix, trace_back


def get_aligned_sequences(x, y, trace_back):
    x_seq = []
    y_seq = []
    i = len(x)
    j = len(y)
    mapper_y_to_x = []
    while i > 0 or j > 0:
        if trace_back[i, j] == 3:
            x_seq.append(x[i - 1])
            y_seq.append(y[j - 1])
            i = i - 1
            j = j - 1
            mapper_y_to_x.append((j, i))
        elif trace_back[i][j] == 1:
            x_seq.append("-")
            y_seq.append(y[j - 1])
            j = j - 1
            mapper_y_to_x.append((j, -1))
        elif trace_back[i][j] == 2:
            x_seq.append(x[i - 1])
            y_seq.append("-")
            i = i - 1
        elif trace_back[i][j] == 4:
            break
    mapper_y_to_x.reverse()
    return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)


def get_mapper(x: str, y: str, tokenizer, max_len=77):
    x_seq = tokenizer.encode(x)
    y_seq = tokenizer.encode(y)
    score = ScoreParams(0, 1, -1)
    matrix, trace_back = global_align(x_seq, y_seq, score)
    mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]
    alphas = torch.ones(max_len)
    alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()
    mapper = torch.zeros(max_len, dtype=torch.int64)
    mapper[: mapper_base.shape[0]] = mapper_base[:, 1]
    mapper[mapper_base.shape[0] :] = len(y_seq) + torch.arange(max_len - len(y_seq))
    return mapper, alphas


def get_refinement_mapper(prompts, tokenizer, max_len=77):
    x_seq = prompts[0]
    mappers, alphas = [], []
    for i in range(1, len(prompts)):
        mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)
        mappers.append(mapper)
        alphas.append(alpha)
    return torch.stack(mappers), torch.stack(alphas)