File size: 49,243 Bytes
d504563
ebaff66
 
 
 
 
 
cef1afc
ebaff66
 
 
1940326
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c811a04
 
325137b
 
 
 
 
c811a04
 
86b1a7e
ebaff66
 
 
 
 
 
 
 
 
86b1a7e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
d504563
ebaff66
d504563
ebaff66
 
 
 
 
 
 
 
d504563
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d504563
ebaff66
 
 
 
 
 
 
 
 
 
d504563
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
 
 
 
 
 
 
 
ebaff66
 
 
 
 
 
 
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
ebaff66
325137b
 
 
ebaff66
 
 
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
ebaff66
 
 
 
 
 
 
325137b
 
 
ebaff66
325137b
 
 
 
 
 
ebaff66
325137b
 
 
 
 
 
ebaff66
 
 
 
325137b
 
 
 
 
 
ebaff66
 
 
 
 
 
 
 
325137b
 
 
ebaff66
 
 
 
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
325137b
 
 
 
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
ebaff66
325137b
 
 
 
 
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
ebaff66
 
 
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
ebaff66
 
 
 
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
ebaff66
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
 
 
 
 
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
86b1a7e
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
325137b
 
 
ebaff66
 
 
 
 
 
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
325137b
 
 
 
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
86b1a7e
1940326
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d504563
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
ebaff66
86b1a7e
ebaff66
86b1a7e
 
 
325137b
 
 
 
 
 
86b1a7e
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
 
86b1a7e
 
ebaff66
86b1a7e
 
325137b
 
 
 
 
ebaff66
 
 
 
 
 
325137b
 
 
 
 
ebaff66
 
 
 
 
 
325137b
 
 
ebaff66
 
 
325137b
 
 
 
 
 
ebaff66
 
325137b
 
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
325137b
 
 
 
 
ebaff66
325137b
 
 
ebaff66
325137b
 
 
ebaff66
325137b
 
 
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
86b1a7e
 
1940326
 
 
 
 
86b1a7e
ebaff66
1940326
 
 
 
 
 
 
 
 
ebaff66
 
 
 
325137b
 
 
86b1a7e
ebaff66
 
325137b
 
 
 
ebaff66
 
 
86b1a7e
 
 
 
 
 
 
ebaff66
 
325137b
 
 
ebaff66
 
 
 
 
 
 
 
 
 
325137b
 
ebaff66
 
 
325137b
 
 
 
ebaff66
 
 
 
 
 
 
 
 
 
 
 
 
86b1a7e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
325137b
 
 
 
86b1a7e
 
 
 
 
325137b
 
 
86b1a7e
 
 
 
325137b
 
 
 
86b1a7e
325137b
 
 
 
86b1a7e
 
 
 
 
 
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
# Adapted from: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py
import html
import inspect
import math
import re
import urllib.parse as ul
from typing import Callable, Dict, List, Optional, Tuple, Union


import torch
import torch.nn.functional as F
from contextlib import nullcontext
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DPMSolverMultistepScheduler
from diffusers.utils import (
    BACKENDS_MAPPING,
    deprecate,
    is_bs4_available,
    is_ftfy_available,
    logging,
)
from diffusers.utils.torch_utils import randn_tensor
from einops import rearrange
from transformers import T5EncoderModel, T5Tokenizer

from xora.models.transformers.transformer3d import Transformer3DModel
from xora.models.transformers.symmetric_patchifier import Patchifier
from xora.models.autoencoders.vae_encode import (
    get_vae_size_scale_factor,
    vae_decode,
    vae_encode,
)
from xora.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
from xora.schedulers.rf import TimestepShifter
from xora.utils.conditioning_method import ConditioningMethod

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

if is_bs4_available():
    from bs4 import BeautifulSoup

if is_ftfy_available():
    import ftfy

ASPECT_RATIO_1024_BIN = {
    "0.25": [512.0, 2048.0],
    "0.28": [512.0, 1856.0],
    "0.32": [576.0, 1792.0],
    "0.33": [576.0, 1728.0],
    "0.35": [576.0, 1664.0],
    "0.4": [640.0, 1600.0],
    "0.42": [640.0, 1536.0],
    "0.48": [704.0, 1472.0],
    "0.5": [704.0, 1408.0],
    "0.52": [704.0, 1344.0],
    "0.57": [768.0, 1344.0],
    "0.6": [768.0, 1280.0],
    "0.68": [832.0, 1216.0],
    "0.72": [832.0, 1152.0],
    "0.78": [896.0, 1152.0],
    "0.82": [896.0, 1088.0],
    "0.88": [960.0, 1088.0],
    "0.94": [960.0, 1024.0],
    "1.0": [1024.0, 1024.0],
    "1.07": [1024.0, 960.0],
    "1.13": [1088.0, 960.0],
    "1.21": [1088.0, 896.0],
    "1.29": [1152.0, 896.0],
    "1.38": [1152.0, 832.0],
    "1.46": [1216.0, 832.0],
    "1.67": [1280.0, 768.0],
    "1.75": [1344.0, 768.0],
    "2.0": [1408.0, 704.0],
    "2.09": [1472.0, 704.0],
    "2.4": [1536.0, 640.0],
    "2.5": [1600.0, 640.0],
    "3.0": [1728.0, 576.0],
    "4.0": [2048.0, 512.0],
}

ASPECT_RATIO_512_BIN = {
    "0.25": [256.0, 1024.0],
    "0.28": [256.0, 928.0],
    "0.32": [288.0, 896.0],
    "0.33": [288.0, 864.0],
    "0.35": [288.0, 832.0],
    "0.4": [320.0, 800.0],
    "0.42": [320.0, 768.0],
    "0.48": [352.0, 736.0],
    "0.5": [352.0, 704.0],
    "0.52": [352.0, 672.0],
    "0.57": [384.0, 672.0],
    "0.6": [384.0, 640.0],
    "0.68": [416.0, 608.0],
    "0.72": [416.0, 576.0],
    "0.78": [448.0, 576.0],
    "0.82": [448.0, 544.0],
    "0.88": [480.0, 544.0],
    "0.94": [480.0, 512.0],
    "1.0": [512.0, 512.0],
    "1.07": [512.0, 480.0],
    "1.13": [544.0, 480.0],
    "1.21": [544.0, 448.0],
    "1.29": [576.0, 448.0],
    "1.38": [576.0, 416.0],
    "1.46": [608.0, 416.0],
    "1.67": [640.0, 384.0],
    "1.75": [672.0, 384.0],
    "2.0": [704.0, 352.0],
    "2.09": [736.0, 352.0],
    "2.4": [768.0, 320.0],
    "2.5": [800.0, 320.0],
    "3.0": [864.0, 288.0],
    "4.0": [1024.0, 256.0],
}


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
    scheduler,
    num_inference_steps: Optional[int] = None,
    device: Optional[Union[str, torch.device]] = None,
    timesteps: Optional[List[int]] = None,
    **kwargs,
):
    """
    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.

    Args:
        scheduler (`SchedulerMixin`):
            The scheduler to get timesteps from.
        num_inference_steps (`int`):
            The number of diffusion steps used when generating samples with a pre-trained model. If used,
            `timesteps` must be `None`.
        device (`str` or `torch.device`, *optional*):
            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
        timesteps (`List[int]`, *optional*):
                Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
                must be `None`.

    Returns:
        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
        second element is the number of inference steps.
    """
    if timesteps is not None:
        accepts_timesteps = "timesteps" in set(
            inspect.signature(scheduler.set_timesteps).parameters.keys()
        )
        if not accepts_timesteps:
            raise ValueError(
                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
                f" timestep schedules. Please check whether you are using the correct scheduler."
            )
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps


class XoraVideoPipeline(DiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using Xora.

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

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`T5EncoderModel`]):
            Frozen text-encoder. This uses
            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
        tokenizer (`T5Tokenizer`):
            Tokenizer of class
            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
        transformer ([`Transformer2DModel`]):
            A text conditioned `Transformer2DModel` to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
    """

    bad_punct_regex = re.compile(
        r"["
        + "#®•©™&@·º½¾¿¡§~"
        + r"\)"
        + r"\("
        + r"\]"
        + r"\["
        + r"\}"
        + r"\{"
        + r"\|"
        + "\\"
        + r"\/"
        + r"\*"
        + r"]{1,}"
    )  # noqa

    _optional_components = ["tokenizer", "text_encoder"]
    model_cpu_offload_seq = "text_encoder->transformer->vae"

    def __init__(
        self,
        tokenizer: T5Tokenizer,
        text_encoder: T5EncoderModel,
        vae: AutoencoderKL,
        transformer: Transformer3DModel,
        scheduler: DPMSolverMultistepScheduler,
        patchifier: Patchifier,
    ):
        super().__init__()

        self.register_modules(
            tokenizer=tokenizer,
            text_encoder=text_encoder,
            vae=vae,
            transformer=transformer,
            scheduler=scheduler,
            patchifier=patchifier,
        )

        self.video_scale_factor, self.vae_scale_factor, _ = get_vae_size_scale_factor(
            self.vae
        )
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

    def mask_text_embeddings(self, emb, mask):
        if emb.shape[0] == 1:
            keep_index = mask.sum().item()
            return emb[:, :, :keep_index, :], keep_index
        else:
            masked_feature = emb * mask[:, None, :, None]
            return masked_feature, emb.shape[2]

    # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt
    def encode_prompt(
        self,
        prompt: Union[str, List[str]],
        do_classifier_free_guidance: bool = True,
        negative_prompt: str = "",
        num_images_per_prompt: int = 1,
        device: Optional[torch.device] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        prompt_attention_mask: Optional[torch.FloatTensor] = None,
        negative_prompt_attention_mask: Optional[torch.FloatTensor] = None,
        clean_caption: bool = False,
        **kwargs,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt 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`). For
                This should be "".
            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
                whether to use classifier free guidance or not
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                number of images that should be generated per prompt
            device: (`torch.device`, *optional*):
                torch device to place the resulting embeddings on
            prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *optional*):
                Pre-generated negative text embeddings.
            clean_caption (bool, defaults to `False`):
                If `True`, the function will preprocess and clean the provided caption before encoding.
        """

        if "mask_feature" in kwargs:
            deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version."
            deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False)

        if device is None:
            device = self._execution_device

        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]

        # See Section 3.1. of the paper.
        # FIXME: to be configured in config not hardecoded. Fix in separate PR with rest of config
        max_length = 128  # TPU supports only lengths multiple of 128

        if prompt_embeds is None:
            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                add_special_tokens=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[:, max_length - 1 : -1]
                )
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {max_length} tokens: {removed_text}"
                )

            prompt_attention_mask = text_inputs.attention_mask
            prompt_attention_mask = prompt_attention_mask.to(device)

            prompt_embeds = self.text_encoder(
                text_input_ids.to(device), attention_mask=prompt_attention_mask
            )
            prompt_embeds = prompt_embeds[0]

        if self.text_encoder is not None:
            dtype = self.text_encoder.dtype
        elif self.transformer is not None:
            dtype = self.transformer.dtype
        else:
            dtype = None

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

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings and attention mask 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
        )
        prompt_attention_mask = prompt_attention_mask.repeat(1, num_images_per_prompt)
        prompt_attention_mask = prompt_attention_mask.view(
            bs_embed * num_images_per_prompt, -1
        )

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens = [negative_prompt] * batch_size
            uncond_tokens = self._text_preprocessing(
                uncond_tokens, clean_caption=clean_caption
            )
            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_attention_mask=True,
                add_special_tokens=True,
                return_tensors="pt",
            )
            negative_prompt_attention_mask = uncond_input.attention_mask
            negative_prompt_attention_mask = negative_prompt_attention_mask.to(device)

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=negative_prompt_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=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
            )

            negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(
                1, num_images_per_prompt
            )
            negative_prompt_attention_mask = negative_prompt_attention_mask.view(
                bs_embed * num_images_per_prompt, -1
            )
        else:
            negative_prompt_embeds = None
            negative_prompt_attention_mask = None

        return (
            prompt_embeds,
            prompt_attention_mask,
            negative_prompt_embeds,
            negative_prompt_attention_mask,
        )

    # 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

    def check_inputs(
        self,
        prompt,
        height,
        width,
        negative_prompt,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        prompt_attention_mask=None,
        negative_prompt_attention_mask=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 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 prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        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 prompt_attention_mask is None:
            raise ValueError(
                "Must provide `prompt_attention_mask` when specifying `prompt_embeds`."
            )

        if (
            negative_prompt_embeds is not None
            and negative_prompt_attention_mask is None
        ):
            raise ValueError(
                "Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`."
            )

        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 prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
                raise ValueError(
                    "`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but"
                    f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
                    f" {negative_prompt_attention_mask.shape}."
                )

    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
    def _text_preprocessing(self, text, clean_caption=False):
        if clean_caption and not is_bs4_available():
            logger.warn(
                BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")
            )
            logger.warn("Setting `clean_caption` to False...")
            clean_caption = False

        if clean_caption and not is_ftfy_available():
            logger.warn(
                BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")
            )
            logger.warn("Setting `clean_caption` to False...")
            clean_caption = False

        if not isinstance(text, (tuple, list)):
            text = [text]

        def process(text: str):
            if clean_caption:
                text = self._clean_caption(text)
                text = self._clean_caption(text)
            else:
                text = text.lower().strip()
            return text

        return [process(t) for t in text]

    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
    def _clean_caption(self, caption):
        caption = str(caption)
        caption = ul.unquote_plus(caption)
        caption = caption.strip().lower()
        caption = re.sub("<person>", "person", caption)
        # urls:
        caption = re.sub(
            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
            "",
            caption,
        )  # regex for urls
        caption = re.sub(
            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
            "",
            caption,
        )  # regex for urls
        # html:
        caption = BeautifulSoup(caption, features="html.parser").text

        # @<nickname>
        caption = re.sub(r"@[\w\d]+\b", "", caption)

        # 31C0—31EF CJK Strokes
        # 31F0—31FF Katakana Phonetic Extensions
        # 3200—32FF Enclosed CJK Letters and Months
        # 3300—33FF CJK Compatibility
        # 3400—4DBF CJK Unified Ideographs Extension A
        # 4DC0—4DFF Yijing Hexagram Symbols
        # 4E00—9FFF CJK Unified Ideographs
        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
        #######################################################

        # все виды тире / all types of dash --> "-"
        caption = re.sub(
            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
            "-",
            caption,
        )

        # кавычки к одному стандарту
        caption = re.sub(r"[`´«»“”¨]", '"', caption)
        caption = re.sub(r"[‘’]", "'", caption)

        # &quot;
        caption = re.sub(r"&quot;?", "", caption)
        # &amp
        caption = re.sub(r"&amp", "", caption)

        # ip adresses:
        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)

        # article ids:
        caption = re.sub(r"\d:\d\d\s+$", "", caption)

        # \n
        caption = re.sub(r"\\n", " ", caption)

        # "#123"
        caption = re.sub(r"#\d{1,3}\b", "", caption)
        # "#12345.."
        caption = re.sub(r"#\d{5,}\b", "", caption)
        # "123456.."
        caption = re.sub(r"\b\d{6,}\b", "", caption)
        # filenames:
        caption = re.sub(
            r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption
        )

        #
        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""

        caption = re.sub(
            self.bad_punct_regex, r" ", caption
        )  # ***AUSVERKAUFT***, #AUSVERKAUFT
        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "

        # this-is-my-cute-cat / this_is_my_cute_cat
        regex2 = re.compile(r"(?:\-|\_)")
        if len(re.findall(regex2, caption)) > 3:
            caption = re.sub(regex2, " ", caption)

        caption = ftfy.fix_text(caption)
        caption = html.unescape(html.unescape(caption))

        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231

        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
        caption = re.sub(
            r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption
        )
        caption = re.sub(r"\bpage\s+\d+\b", "", caption)

        caption = re.sub(
            r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption
        )  # j2d1a2a...

        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)

        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
        caption = re.sub(r"\s+", " ", caption)

        caption.strip()

        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
        caption = re.sub(r"^\.\S+$", "", caption)

        return caption.strip()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(
        self,
        batch_size,
        num_latent_channels,
        num_patches,
        dtype,
        device,
        generator,
        latents=None,
        latents_mask=None,
    ):
        shape = (
            batch_size,
            num_patches // math.prod(self.patchifier.patch_size),
            num_latent_channels,
        )

        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
            )
        elif latents_mask is not None:
            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
            latents = latents * latents_mask[..., None] + noise * (
                1 - latents_mask[..., None]
            )
        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

    @staticmethod
    def classify_height_width_bin(
        height: int, width: int, ratios: dict
    ) -> Tuple[int, int]:
        """Returns binned height and width."""
        ar = float(height / width)
        closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - ar))
        default_hw = ratios[closest_ratio]
        return int(default_hw[0]), int(default_hw[1])

    @staticmethod
    def resize_and_crop_tensor(
        samples: torch.Tensor, new_width: int, new_height: int
    ) -> torch.Tensor:
        n_frames, orig_height, orig_width = samples.shape[-3:]

        # Check if resizing is needed
        if orig_height != new_height or orig_width != new_width:
            ratio = max(new_height / orig_height, new_width / orig_width)
            resized_width = int(orig_width * ratio)
            resized_height = int(orig_height * ratio)

            # Resize
            samples = rearrange(samples, "b c n h w -> (b n) c h w")
            samples = F.interpolate(
                samples,
                size=(resized_height, resized_width),
                mode="bilinear",
                align_corners=False,
            )
            samples = rearrange(samples, "(b n) c h w -> b c n h w", n=n_frames)

            # Center Crop
            start_x = (resized_width - new_width) // 2
            end_x = start_x + new_width
            start_y = (resized_height - new_height) // 2
            end_y = start_y + new_height
            samples = samples[..., start_y:end_y, start_x:end_x]

        return samples

    @torch.no_grad()
    def __call__(
        self,
        height: int,
        width: int,
        num_frames: int,
        frame_rate: float,
        prompt: Union[str, List[str]] = None,
        negative_prompt: str = "",
        num_inference_steps: int = 20,
        timesteps: List[int] = None,
        guidance_scale: float = 4.5,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        prompt_attention_mask: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_attention_mask: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        clean_caption: bool = True,
        media_items: Optional[torch.FloatTensor] = None,
        mixed_precision: bool = False,
        **kwargs,
    ) -> Union[ImagePipelineOutput, Tuple]:
        """
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            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`).
            num_inference_steps (`int`, *optional*, defaults to 100):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            timesteps (`List[int]`, *optional*):
                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
                timesteps are used. Must be in descending order.
            guidance_scale (`float`, *optional*, defaults to 4.5):
                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.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            height (`int`, *optional*, defaults to self.unet.config.sample_size):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size):
                The width in pixels of the generated image.
            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` or `List[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.FloatTensor`, *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`.
            prompt_embeds (`torch.FloatTensor`, *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.
            prompt_attention_mask (`torch.FloatTensor`, *optional*): Pre-generated attention mask for text embeddings.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. This negative prompt should be "". If not
                provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
            negative_prompt_attention_mask (`torch.FloatTensor`, *optional*):
                Pre-generated attention mask for negative text embeddings.
            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.IFPipelineOutput`] instead of a plain tuple.
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            clean_caption (`bool`, *optional*, defaults to `True`):
                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
                be installed. If the dependencies are not installed, the embeddings will be created from the raw
                prompt.
            use_resolution_binning (`bool` defaults to `True`):
                If set to `True`, the requested height and width are first mapped to the closest resolutions using
                `ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to
                the requested resolution. Useful for generating non-square images.

        Examples:

        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
                returned where the first element is a list with the generated images
        """
        if "mask_feature" in kwargs:
            deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version."
            deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False)

        is_video = kwargs.get("is_video", False)
        self.check_inputs(
            prompt,
            height,
            width,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
            prompt_attention_mask,
            negative_prompt_attention_mask,
        )

        # 2. Default height and width to transformer
        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
        (
            prompt_embeds,
            prompt_attention_mask,
            negative_prompt_embeds,
            negative_prompt_attention_mask,
        ) = self.encode_prompt(
            prompt,
            do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            device=device,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            prompt_attention_mask=prompt_attention_mask,
            negative_prompt_attention_mask=negative_prompt_attention_mask,
            clean_caption=clean_caption,
        )
        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
            prompt_attention_mask = torch.cat(
                [negative_prompt_attention_mask, prompt_attention_mask], dim=0
            )

        # 3b. Encode and prepare conditioning data
        self.video_scale_factor = self.video_scale_factor if is_video else 1
        conditioning_method = kwargs.get("conditioning_method", None)
        vae_per_channel_normalize = kwargs.get("vae_per_channel_normalize", False)
        init_latents, conditioning_mask = self.prepare_conditioning(
            media_items,
            num_frames,
            height,
            width,
            conditioning_method,
            vae_per_channel_normalize,
        )

        # 4. Prepare latents.
        latent_height = height // self.vae_scale_factor
        latent_width = width // self.vae_scale_factor
        latent_num_frames = num_frames // self.video_scale_factor
        if isinstance(self.vae, CausalVideoAutoencoder) and is_video:
            latent_num_frames += 1
        latent_frame_rate = frame_rate / self.video_scale_factor
        num_latent_patches = latent_height * latent_width * latent_num_frames
        latents = self.prepare_latents(
            batch_size=batch_size * num_images_per_prompt,
            num_latent_channels=self.transformer.config.in_channels,
            num_patches=num_latent_patches,
            dtype=prompt_embeds.dtype,
            device=device,
            generator=generator,
            latents=init_latents,
            latents_mask=conditioning_mask,
        )
        if conditioning_mask is not None and is_video:
            assert num_images_per_prompt == 1
            conditioning_mask = (
                torch.cat([conditioning_mask] * 2)
                if do_classifier_free_guidance
                else conditioning_mask
            )

        # 5. Prepare timesteps
        retrieve_timesteps_kwargs = {}
        if isinstance(self.scheduler, TimestepShifter):
            retrieve_timesteps_kwargs["samples"] = latents
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler,
            num_inference_steps,
            device,
            timesteps,
            **retrieve_timesteps_kwargs,
        )

        # 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 = max(
            len(timesteps) - num_inference_steps * self.scheduler.order, 0
        )

        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                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
                )

                latent_frame_rates = (
                    torch.ones(
                        latent_model_input.shape[0], 1, device=latent_model_input.device
                    )
                    * latent_frame_rate
                )

                current_timestep = t
                if not torch.is_tensor(current_timestep):
                    # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                    # This would be a good case for the `match` statement (Python 3.10+)
                    is_mps = latent_model_input.device.type == "mps"
                    if isinstance(current_timestep, float):
                        dtype = torch.float32 if is_mps else torch.float64
                    else:
                        dtype = torch.int32 if is_mps else torch.int64
                    current_timestep = torch.tensor(
                        [current_timestep],
                        dtype=dtype,
                        device=latent_model_input.device,
                    )
                elif len(current_timestep.shape) == 0:
                    current_timestep = current_timestep[None].to(
                        latent_model_input.device
                    )
                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                current_timestep = current_timestep.expand(
                    latent_model_input.shape[0]
                ).unsqueeze(-1)
                scale_grid = (
                    (
                        1 / latent_frame_rates,
                        self.vae_scale_factor,
                        self.vae_scale_factor,
                    )
                    if self.transformer.use_rope
                    else None
                )
                indices_grid = self.patchifier.get_grid(
                    orig_num_frames=latent_num_frames,
                    orig_height=latent_height,
                    orig_width=latent_width,
                    batch_size=latent_model_input.shape[0],
                    scale_grid=scale_grid,
                    device=latents.device,
                )

                if conditioning_mask is not None:
                    current_timestep = current_timestep * (1 - conditioning_mask)
                # Choose the appropriate context manager based on `mixed_precision`
                if mixed_precision:
                    context_manager = torch.autocast("cuda", dtype=torch.bfloat16)
                else:
                    context_manager = nullcontext()  # Dummy context manager

                # predict noise model_output
                with context_manager:
                    noise_pred = self.transformer(
                        latent_model_input.to(self.transformer.dtype),
                        indices_grid,
                        encoder_hidden_states=prompt_embeds.to(self.transformer.dtype),
                        encoder_attention_mask=prompt_attention_mask,
                        timestep=current_timestep,
                        return_dict=False,
                    )[0]

                # 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
                    )
                    current_timestep, _ = current_timestep.chunk(2)

                # learned sigma
                if (
                    self.transformer.config.out_channels // 2
                    == self.transformer.config.in_channels
                ):
                    noise_pred = noise_pred.chunk(2, dim=1)[0]

                # compute previous image: x_t -> x_t-1
                latents = self.scheduler.step(
                    noise_pred,
                    t if current_timestep is None else current_timestep,
                    latents,
                    **extra_step_kwargs,
                    return_dict=False,
                )[0]

                # 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_on_step_end is not None:
                    callback_on_step_end(self, i, t, {})

        latents = self.patchifier.unpatchify(
            latents=latents,
            output_height=latent_height,
            output_width=latent_width,
            output_num_frames=latent_num_frames,
            out_channels=self.transformer.in_channels
            // math.prod(self.patchifier.patch_size),
        )
        if output_type != "latent":
            image = vae_decode(
                latents,
                self.vae,
                is_video,
                vae_per_channel_normalize=kwargs["vae_per_channel_normalize"],
            )
            image = self.image_processor.postprocess(image, output_type=output_type)

        else:
            image = latents

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)

    def prepare_conditioning(
        self,
        media_items: torch.Tensor,
        num_frames: int,
        height: int,
        width: int,
        method: ConditioningMethod = ConditioningMethod.UNCONDITIONAL,
        vae_per_channel_normalize: bool = False,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Prepare the conditioning data for the video generation. If an input media item is provided, encode it
        and set the conditioning_mask to indicate which tokens to condition on. Input media item should have
        the same height and width as the generated video.

        Args:
            media_items (torch.Tensor): media items to condition on (images or videos)
            num_frames (int): number of frames to generate
            height (int): height of the generated video
            width (int): width of the generated video
            method (ConditioningMethod, optional): conditioning method to use. Defaults to ConditioningMethod.UNCONDITIONAL.
            vae_per_channel_normalize (bool, optional): whether to normalize the input to the VAE per channel. Defaults to False.

        Returns:
            Tuple[torch.Tensor, torch.Tensor]: the conditioning latents and the conditioning mask
        """
        if media_items is None or method == ConditioningMethod.UNCONDITIONAL:
            return None, None

        assert media_items.ndim == 5
        assert height == media_items.shape[-2] and width == media_items.shape[-1]

        # Encode the input video and repeat to the required number of frame-tokens
        init_latents = vae_encode(
            media_items.to(dtype=self.vae.dtype, device=self.vae.device),
            self.vae,
            vae_per_channel_normalize=vae_per_channel_normalize,
        ).float()

        init_len, target_len = (
            init_latents.shape[2],
            num_frames // self.video_scale_factor,
        )
        if isinstance(self.vae, CausalVideoAutoencoder):
            target_len += 1
        init_latents = init_latents[:, :, :target_len]
        if target_len > init_len:
            repeat_factor = (target_len + init_len - 1) // init_len  # Ceiling division
            init_latents = init_latents.repeat(1, 1, repeat_factor, 1, 1)[
                :, :, :target_len
            ]

        # Prepare the conditioning mask (1.0 = condition on this token)
        b, n, f, h, w = init_latents.shape
        conditioning_mask = torch.zeros([b, 1, f, h, w], device=init_latents.device)
        if method in [
            ConditioningMethod.FIRST_FRAME,
            ConditioningMethod.FIRST_AND_LAST_FRAME,
        ]:
            conditioning_mask[:, :, 0] = 1.0
        if method in [
            ConditioningMethod.LAST_FRAME,
            ConditioningMethod.FIRST_AND_LAST_FRAME,
        ]:
            conditioning_mask[:, :, -1] = 1.0

        # Patchify the init latents and the mask
        conditioning_mask = self.patchifier.patchify(conditioning_mask).squeeze(-1)
        init_latents = self.patchifier.patchify(latents=init_latents)
        return init_latents, conditioning_mask