File size: 35,574 Bytes
ba1bf39
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import math
from contextlib import nullcontext
from functools import partial
from typing import Dict, List, Optional, Tuple, Union

import kornia
import numpy as np
import open_clip
import torch
import torch.nn as nn
from einops import rearrange, repeat
from omegaconf import ListConfig
from torch.utils.checkpoint import checkpoint
from transformers import (ByT5Tokenizer, CLIPTextModel, CLIPTokenizer,
                          T5EncoderModel, T5Tokenizer)

from ...modules.autoencoding.regularizers import DiagonalGaussianRegularizer
from ...modules.diffusionmodules.model import Encoder
from ...modules.diffusionmodules.openaimodel import Timestep
from ...modules.diffusionmodules.util import (extract_into_tensor,
                                              make_beta_schedule)
from ...modules.distributions.distributions import DiagonalGaussianDistribution
from ...util import (append_dims, autocast, count_params, default,
                     disabled_train, expand_dims_like, instantiate_from_config)


class AbstractEmbModel(nn.Module):
    def __init__(self):
        super().__init__()
        self._is_trainable = None
        self._ucg_rate = None
        self._input_key = None

    @property
    def is_trainable(self) -> bool:
        return self._is_trainable

    @property
    def ucg_rate(self) -> Union[float, torch.Tensor]:
        return self._ucg_rate

    @property
    def input_key(self) -> str:
        return self._input_key

    @is_trainable.setter
    def is_trainable(self, value: bool):
        self._is_trainable = value

    @ucg_rate.setter
    def ucg_rate(self, value: Union[float, torch.Tensor]):
        self._ucg_rate = value

    @input_key.setter
    def input_key(self, value: str):
        self._input_key = value

    @is_trainable.deleter
    def is_trainable(self):
        del self._is_trainable

    @ucg_rate.deleter
    def ucg_rate(self):
        del self._ucg_rate

    @input_key.deleter
    def input_key(self):
        del self._input_key


class GeneralConditioner(nn.Module):
    OUTPUT_DIM2KEYS = {2: "vector", 3: "crossattn", 4: "concat", 5: "concat"}
    KEY2CATDIM = {"vector": 1, "crossattn": 2, "concat": 1}

    def __init__(self, emb_models: Union[List, ListConfig]):
        super().__init__()
        embedders = []
        for n, embconfig in enumerate(emb_models):
            embedder = instantiate_from_config(embconfig)
            assert isinstance(
                embedder, AbstractEmbModel
            ), f"embedder model {embedder.__class__.__name__} has to inherit from AbstractEmbModel"
            embedder.is_trainable = embconfig.get("is_trainable", False)
            embedder.ucg_rate = embconfig.get("ucg_rate", 0.0)
            if not embedder.is_trainable:
                embedder.train = disabled_train
                for param in embedder.parameters():
                    param.requires_grad = False
                embedder.eval()
            print(
                f"Initialized embedder #{n}: {embedder.__class__.__name__} "
                f"with {count_params(embedder, False)} params. Trainable: {embedder.is_trainable}"
            )

            if "input_key" in embconfig:
                embedder.input_key = embconfig["input_key"]
            elif "input_keys" in embconfig:
                embedder.input_keys = embconfig["input_keys"]
            else:
                raise KeyError(
                    f"need either 'input_key' or 'input_keys' for embedder {embedder.__class__.__name__}"
                )

            embedder.legacy_ucg_val = embconfig.get("legacy_ucg_value", None)
            if embedder.legacy_ucg_val is not None:
                embedder.ucg_prng = np.random.RandomState()

            embedders.append(embedder)
        self.embedders = nn.ModuleList(embedders)

    def possibly_get_ucg_val(self, embedder: AbstractEmbModel, batch: Dict) -> Dict:
        assert embedder.legacy_ucg_val is not None
        p = embedder.ucg_rate
        val = embedder.legacy_ucg_val
        for i in range(len(batch[embedder.input_key])):
            if embedder.ucg_prng.choice(2, p=[1 - p, p]):
                batch[embedder.input_key][i] = val
        return batch

    def forward(
        self, batch: Dict, force_zero_embeddings: Optional[List] = None
    ) -> Dict:
        output = dict()
        if force_zero_embeddings is None:
            force_zero_embeddings = []
        for embedder in self.embedders:
            embedding_context = nullcontext if embedder.is_trainable else torch.no_grad
            with embedding_context():
                if hasattr(embedder, "input_key") and (embedder.input_key is not None):
                    if embedder.legacy_ucg_val is not None:
                        batch = self.possibly_get_ucg_val(embedder, batch)
                    emb_out = embedder(batch[embedder.input_key])
                elif hasattr(embedder, "input_keys"):
                    emb_out = embedder(*[batch[k] for k in embedder.input_keys])
            assert isinstance(
                emb_out, (torch.Tensor, list, tuple)
            ), f"encoder outputs must be tensors or a sequence, but got {type(emb_out)}"
            if not isinstance(emb_out, (list, tuple)):
                emb_out = [emb_out]
            for emb in emb_out:
                out_key = self.OUTPUT_DIM2KEYS[emb.dim()]
                if embedder.ucg_rate > 0.0 and embedder.legacy_ucg_val is None:
                    emb = (
                        expand_dims_like(
                            torch.bernoulli(
                                (1.0 - embedder.ucg_rate)
                                * torch.ones(emb.shape[0], device=emb.device)
                            ),
                            emb,
                        )
                        * emb
                    )
                if (
                    hasattr(embedder, "input_key")
                    and embedder.input_key in force_zero_embeddings
                ):
                    emb = torch.zeros_like(emb)
                if out_key in output:
                    output[out_key] = torch.cat(
                        (output[out_key], emb), self.KEY2CATDIM[out_key]
                    )
                else:
                    output[out_key] = emb
        return output

    def get_unconditional_conditioning(
        self,
        batch_c: Dict,
        batch_uc: Optional[Dict] = None,
        force_uc_zero_embeddings: Optional[List[str]] = None,
        force_cond_zero_embeddings: Optional[List[str]] = None,
    ):
        if force_uc_zero_embeddings is None:
            force_uc_zero_embeddings = []
        ucg_rates = list()
        for embedder in self.embedders:
            ucg_rates.append(embedder.ucg_rate)
            embedder.ucg_rate = 0.0
        c = self(batch_c, force_cond_zero_embeddings)
        uc = self(batch_c if batch_uc is None else batch_uc, force_uc_zero_embeddings)

        for embedder, rate in zip(self.embedders, ucg_rates):
            embedder.ucg_rate = rate
        return c, uc


class InceptionV3(nn.Module):
    """Wrapper around the https://github.com/mseitzer/pytorch-fid inception
    port with an additional squeeze at the end"""

    def __init__(self, normalize_input=False, **kwargs):
        super().__init__()
        from pytorch_fid import inception

        kwargs["resize_input"] = True
        self.model = inception.InceptionV3(normalize_input=normalize_input, **kwargs)

    def forward(self, inp):
        outp = self.model(inp)

        if len(outp) == 1:
            return outp[0].squeeze()

        return outp


class IdentityEncoder(AbstractEmbModel):
    def encode(self, x):
        return x

    def forward(self, x):
        return x


class ClassEmbedder(AbstractEmbModel):
    def __init__(self, embed_dim, n_classes=1000, add_sequence_dim=False):
        super().__init__()
        self.embedding = nn.Embedding(n_classes, embed_dim)
        self.n_classes = n_classes
        self.add_sequence_dim = add_sequence_dim

    def forward(self, c):
        c = self.embedding(c)
        if self.add_sequence_dim:
            c = c[:, None, :]
        return c

    def get_unconditional_conditioning(self, bs, device="cuda"):
        uc_class = (
            self.n_classes - 1
        )  # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
        uc = torch.ones((bs,), device=device) * uc_class
        uc = {self.key: uc.long()}
        return uc


class ClassEmbedderForMultiCond(ClassEmbedder):
    def forward(self, batch, key=None, disable_dropout=False):
        out = batch
        key = default(key, self.key)
        islist = isinstance(batch[key], list)
        if islist:
            batch[key] = batch[key][0]
        c_out = super().forward(batch, key, disable_dropout)
        out[key] = [c_out] if islist else c_out
        return out


class FrozenT5Embedder(AbstractEmbModel):
    """Uses the T5 transformer encoder for text"""

    def __init__(
        self, version="google/t5-v1_1-xxl", device="cuda", max_length=77, freeze=True
    ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
        super().__init__()
        self.tokenizer = T5Tokenizer.from_pretrained(version)
        self.transformer = T5EncoderModel.from_pretrained(version)
        self.device = device
        self.max_length = max_length
        if freeze:
            self.freeze()

    def freeze(self):
        self.transformer = self.transformer.eval()

        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=True,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )
        tokens = batch_encoding["input_ids"].to(self.device)
        with torch.autocast("cuda", enabled=False):
            outputs = self.transformer(input_ids=tokens)
        z = outputs.last_hidden_state
        return z

    def encode(self, text):
        return self(text)


class FrozenByT5Embedder(AbstractEmbModel):
    """
    Uses the ByT5 transformer encoder for text. Is character-aware.
    """

    def __init__(
        self, version="google/byt5-base", device="cuda", max_length=77, freeze=True
    ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
        super().__init__()
        self.tokenizer = ByT5Tokenizer.from_pretrained(version)
        self.transformer = T5EncoderModel.from_pretrained(version)
        self.device = device
        self.max_length = max_length
        if freeze:
            self.freeze()

    def freeze(self):
        self.transformer = self.transformer.eval()

        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=True,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )
        tokens = batch_encoding["input_ids"].to(self.device)
        with torch.autocast("cuda", enabled=False):
            outputs = self.transformer(input_ids=tokens)
        z = outputs.last_hidden_state
        return z

    def encode(self, text):
        return self(text)


class FrozenCLIPEmbedder(AbstractEmbModel):
    """Uses the CLIP transformer encoder for text (from huggingface)"""

    LAYERS = ["last", "pooled", "hidden"]

    def __init__(
        self,
        version="openai/clip-vit-large-patch14",
        device="cuda",
        max_length=77,
        freeze=True,
        layer="last",
        layer_idx=None,
        always_return_pooled=False,
    ):  # clip-vit-base-patch32
        super().__init__()
        assert layer in self.LAYERS
        self.tokenizer = CLIPTokenizer.from_pretrained(version)
        self.transformer = CLIPTextModel.from_pretrained(version)
        self.device = device
        self.max_length = max_length
        if freeze:
            self.freeze()
        self.layer = layer
        self.layer_idx = layer_idx
        self.return_pooled = always_return_pooled
        if layer == "hidden":
            assert layer_idx is not None
            assert 0 <= abs(layer_idx) <= 12

    def freeze(self):
        self.transformer = self.transformer.eval()

        for param in self.parameters():
            param.requires_grad = False

    @autocast
    def forward(self, text):
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=True,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )
        tokens = batch_encoding["input_ids"].to(self.device)
        outputs = self.transformer(
            input_ids=tokens, output_hidden_states=self.layer == "hidden"
        )
        if self.layer == "last":
            z = outputs.last_hidden_state
        elif self.layer == "pooled":
            z = outputs.pooler_output[:, None, :]
        else:
            z = outputs.hidden_states[self.layer_idx]
        if self.return_pooled:
            return z, outputs.pooler_output
        return z

    def encode(self, text):
        return self(text)


class FrozenOpenCLIPEmbedder2(AbstractEmbModel):
    """
    Uses the OpenCLIP transformer encoder for text
    """

    LAYERS = ["pooled", "last", "penultimate"]

    def __init__(
        self,
        arch="ViT-H-14",
        version="laion2b_s32b_b79k",
        device="cuda",
        max_length=77,
        freeze=True,
        layer="last",
        always_return_pooled=False,
        legacy=True,
    ):
        super().__init__()
        assert layer in self.LAYERS
        model, _, _ = open_clip.create_model_and_transforms(
            arch,
            device=torch.device("cpu"),
            pretrained=version,
        )
        del model.visual
        self.model = model

        self.device = device
        self.max_length = max_length
        self.return_pooled = always_return_pooled
        if freeze:
            self.freeze()
        self.layer = layer
        if self.layer == "last":
            self.layer_idx = 0
        elif self.layer == "penultimate":
            self.layer_idx = 1
        else:
            raise NotImplementedError()
        self.legacy = legacy

    def freeze(self):
        self.model = self.model.eval()
        for param in self.parameters():
            param.requires_grad = False

    @autocast
    def forward(self, text):
        tokens = open_clip.tokenize(text)
        z = self.encode_with_transformer(tokens.to(self.device))
        if not self.return_pooled and self.legacy:
            return z
        if self.return_pooled:
            assert not self.legacy
            return z[self.layer], z["pooled"]
        return z[self.layer]

    def encode_with_transformer(self, text):
        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
        x = x + self.model.positional_embedding
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
        if self.legacy:
            x = x[self.layer]
            x = self.model.ln_final(x)
            return x
        else:
            # x is a dict and will stay a dict
            o = x["last"]
            o = self.model.ln_final(o)
            pooled = self.pool(o, text)
            x["pooled"] = pooled
            return x

    def pool(self, x, text):
        # take features from the eot embedding (eot_token is the highest number in each sequence)
        x = (
            x[torch.arange(x.shape[0]), text.argmax(dim=-1)]
            @ self.model.text_projection
        )
        return x

    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
        outputs = {}
        for i, r in enumerate(self.model.transformer.resblocks):
            if i == len(self.model.transformer.resblocks) - 1:
                outputs["penultimate"] = x.permute(1, 0, 2)  # LND -> NLD
            if (
                self.model.transformer.grad_checkpointing
                and not torch.jit.is_scripting()
            ):
                x = checkpoint(r, x, attn_mask)
            else:
                x = r(x, attn_mask=attn_mask)
        outputs["last"] = x.permute(1, 0, 2)  # LND -> NLD
        return outputs

    def encode(self, text):
        return self(text)


class FrozenOpenCLIPEmbedder(AbstractEmbModel):
    LAYERS = [
        # "pooled",
        "last",
        "penultimate",
    ]

    def __init__(
        self,
        arch="ViT-H-14",
        version="laion2b_s32b_b79k",
        device="cuda",
        max_length=77,
        freeze=True,
        layer="last",
    ):
        super().__init__()
        assert layer in self.LAYERS
        model, _, _ = open_clip.create_model_and_transforms(
            arch, device=torch.device("cpu"), pretrained=version
        )
        del model.visual
        self.model = model

        self.device = device
        self.max_length = max_length
        if freeze:
            self.freeze()
        self.layer = layer
        if self.layer == "last":
            self.layer_idx = 0
        elif self.layer == "penultimate":
            self.layer_idx = 1
        else:
            raise NotImplementedError()

    def freeze(self):
        self.model = self.model.eval()
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        tokens = open_clip.tokenize(text)
        z = self.encode_with_transformer(tokens.to(self.device))
        return z

    def encode_with_transformer(self, text):
        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
        x = x + self.model.positional_embedding
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
        x = x.permute(1, 0, 2)  # LND -> NLD
        x = self.model.ln_final(x)
        return x

    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
        for i, r in enumerate(self.model.transformer.resblocks):
            if i == len(self.model.transformer.resblocks) - self.layer_idx:
                break
            if (
                self.model.transformer.grad_checkpointing
                and not torch.jit.is_scripting()
            ):
                x = checkpoint(r, x, attn_mask)
            else:
                x = r(x, attn_mask=attn_mask)
        return x

    def encode(self, text):
        return self(text)


class FrozenOpenCLIPImageEmbedder(AbstractEmbModel):
    """
    Uses the OpenCLIP vision transformer encoder for images
    """

    def __init__(
        self,
        arch="ViT-H-14",
        version="laion2b_s32b_b79k",
        device="cuda",
        init_device="cpu",
        max_length=77,
        freeze=True,
        antialias=True,
        ucg_rate=0.0,
        unsqueeze_dim=False,
        repeat_to_max_len=False,
        num_image_crops=0,
        output_tokens=False,
        l2_norm_tokens=False,
        only_tokens=False,
        cache_dir: Optional[str] = None,
    ):
        super().__init__()
        model, _, _ = open_clip.create_model_and_transforms(
            arch,
            device=torch.device(init_device),
            pretrained=version,
            cache_dir=cache_dir,
        )
        del model.transformer
        self.model = model
        self.max_crops = num_image_crops
        self.pad_to_max_len = self.max_crops > 0
        self.repeat_to_max_len = repeat_to_max_len and (not self.pad_to_max_len)
        self.device = device
        self.max_length = max_length
        if freeze:
            self.freeze()

        self.antialias = antialias

        self.register_buffer(
            "mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
        )
        self.register_buffer(
            "std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
        )
        self.ucg_rate = ucg_rate
        self.unsqueeze_dim = unsqueeze_dim
        self.stored_batch = None
        self.model.visual.output_tokens = output_tokens
        self.output_tokens = output_tokens
        if only_tokens:
            assert output_tokens
        self.only_tokens = only_tokens
        self.l2_norm_tokens = l2_norm_tokens
        if l2_norm_tokens:
            assert output_tokens

    def preprocess(self, x):
        # normalize to [0,1]
        x = kornia.geometry.resize(
            x,
            (224, 224),
            interpolation="bicubic",
            align_corners=True,
            antialias=self.antialias,
        )
        x = (x + 1.0) / 2.0
        # renormalize according to clip
        x = kornia.enhance.normalize(x, self.mean, self.std)
        return x

    def freeze(self):
        self.model = self.model.eval()
        for param in self.parameters():
            param.requires_grad = False

    @autocast
    def forward(self, image, no_dropout=False):
        z = self.encode_with_vision_transformer(image)
        tokens = None
        if self.output_tokens:
            z, tokens = z[0], z[1]
        z = z.to(image.dtype)
        if self.ucg_rate > 0.0 and not no_dropout and not (self.max_crops > 0):
            z = (
                torch.bernoulli(
                    (1.0 - self.ucg_rate) * torch.ones(z.shape[0], device=z.device)
                )[:, None]
                * z
            )
            if tokens is not None:
                tokens = (
                    expand_dims_like(
                        torch.bernoulli(
                            (1.0 - self.ucg_rate)
                            * torch.ones(tokens.shape[0], device=tokens.device)
                        ),
                        tokens,
                    )
                    * tokens
                )
        if self.unsqueeze_dim:
            z = z[:, None, :]
        if self.output_tokens:
            assert not self.repeat_to_max_len
            assert not self.pad_to_max_len
            if self.only_tokens:
                return tokens
            return tokens, z
        if self.repeat_to_max_len:
            if z.dim() == 2:
                z_ = z[:, None, :]
            else:
                z_ = z
            return repeat(z_, "b 1 d -> b n d", n=self.max_length), z
        elif self.pad_to_max_len:
            assert z.dim() == 3
            z_pad = torch.cat(
                (
                    z,
                    torch.zeros(
                        z.shape[0],
                        self.max_length - z.shape[1],
                        z.shape[2],
                        device=z.device,
                    ),
                ),
                1,
            )
            return z_pad, z_pad[:, 0, ...]
        return z

    def encode_with_vision_transformer(self, img):
        # if self.max_crops > 0:
        #    img = self.preprocess_by_cropping(img)
        if img.dim() == 5:
            assert self.max_crops == img.shape[1]
            img = rearrange(img, "b n c h w -> (b n) c h w")
        img = self.preprocess(img)
        if not self.output_tokens:
            assert not self.model.visual.output_tokens
            x = self.model.visual(img)
            tokens = None
        else:
            assert self.model.visual.output_tokens
            x, tokens = self.model.visual(img)
            if self.l2_norm_tokens:
                token_shape = tokens.shape
                tokens = tokens.flatten(1)
                tokens = torch.nn.functional.normalize(tokens, dim=-1)
                tokens = (tokens - .0002) / .0015
                tokens = tokens.view(token_shape)
                tokens = (tokens * 1.0957) + .1598
        if self.max_crops > 0:
            x = rearrange(x, "(b n) d -> b n d", n=self.max_crops)
            # drop out between 0 and all along the sequence axis
            x = (
                torch.bernoulli(
                    (1.0 - self.ucg_rate)
                    * torch.ones(x.shape[0], x.shape[1], 1, device=x.device)
                )
                * x
            )
            if tokens is not None:
                tokens = rearrange(tokens, "(b n) t d -> b t (n d)", n=self.max_crops)
                logpy.warning(
                    f"You are running very experimental token-concat in {self.__class__.__name__}. "
                    f"Check what you are doing, and then remove this message."
                )
        if self.output_tokens:
            return x, tokens
        return x

    def encode(self, text):
        return self(text)
    

class FrozenCLIPT5Encoder(AbstractEmbModel):
    def __init__(
        self,
        clip_version="openai/clip-vit-large-patch14",
        t5_version="google/t5-v1_1-xl",
        device="cuda",
        clip_max_length=77,
        t5_max_length=77,
    ):
        super().__init__()
        self.clip_encoder = FrozenCLIPEmbedder(
            clip_version, device, max_length=clip_max_length
        )
        self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
        print(
            f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
            f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params."
        )

    def encode(self, text):
        return self(text)

    def forward(self, text):
        clip_z = self.clip_encoder.encode(text)
        t5_z = self.t5_encoder.encode(text)
        return [clip_z, t5_z]


class SpatialRescaler(nn.Module):
    def __init__(
        self,
        n_stages=1,
        method="bilinear",
        multiplier=0.5,
        in_channels=3,
        out_channels=None,
        bias=False,
        wrap_video=False,
        kernel_size=1,
        remap_output=False,
    ):
        super().__init__()
        self.n_stages = n_stages
        assert self.n_stages >= 0
        assert method in [
            "nearest",
            "linear",
            "bilinear",
            "trilinear",
            "bicubic",
            "area",
        ]
        self.multiplier = multiplier
        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
        self.remap_output = out_channels is not None or remap_output
        if self.remap_output:
            print(
                f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing."
            )
            self.channel_mapper = nn.Conv2d(
                in_channels,
                out_channels,
                kernel_size=kernel_size,
                bias=bias,
                padding=kernel_size // 2,
            )
        self.wrap_video = wrap_video

    def forward(self, x):
        if self.wrap_video and x.ndim == 5:
            B, C, T, H, W = x.shape
            x = rearrange(x, "b c t h w -> b t c h w")
            x = rearrange(x, "b t c h w -> (b t) c h w")

        for stage in range(self.n_stages):
            x = self.interpolator(x, scale_factor=self.multiplier)

        if self.wrap_video:
            x = rearrange(x, "(b t) c h w -> b t c h w", b=B, t=T, c=C)
            x = rearrange(x, "b t c h w -> b c t h w")
        if self.remap_output:
            x = self.channel_mapper(x)
        return x

    def encode(self, x):
        return self(x)


class LowScaleEncoder(nn.Module):
    def __init__(
        self,
        model_config,
        linear_start,
        linear_end,
        timesteps=1000,
        max_noise_level=250,
        output_size=64,
        scale_factor=1.0,
    ):
        super().__init__()
        self.max_noise_level = max_noise_level
        self.model = instantiate_from_config(model_config)
        self.augmentation_schedule = self.register_schedule(
            timesteps=timesteps, linear_start=linear_start, linear_end=linear_end
        )
        self.out_size = output_size
        self.scale_factor = scale_factor

    def register_schedule(
        self,
        beta_schedule="linear",
        timesteps=1000,
        linear_start=1e-4,
        linear_end=2e-2,
        cosine_s=8e-3,
    ):
        betas = make_beta_schedule(
            beta_schedule,
            timesteps,
            linear_start=linear_start,
            linear_end=linear_end,
            cosine_s=cosine_s,
        )
        alphas = 1.0 - betas
        alphas_cumprod = np.cumprod(alphas, axis=0)
        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])

        (timesteps,) = betas.shape
        self.num_timesteps = int(timesteps)
        self.linear_start = linear_start
        self.linear_end = linear_end
        assert (
            alphas_cumprod.shape[0] == self.num_timesteps
        ), "alphas have to be defined for each timestep"

        to_torch = partial(torch.tensor, dtype=torch.float32)

        self.register_buffer("betas", to_torch(betas))
        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
        self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
        self.register_buffer(
            "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
        )
        self.register_buffer(
            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
        )
        self.register_buffer(
            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
        )
        self.register_buffer(
            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
        )

    def q_sample(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        return (
            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
            + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
            * noise
        )

    def forward(self, x):
        z = self.model.encode(x)
        if isinstance(z, DiagonalGaussianDistribution):
            z = z.sample()
        z = z * self.scale_factor
        noise_level = torch.randint(
            0, self.max_noise_level, (x.shape[0],), device=x.device
        ).long()
        z = self.q_sample(z, noise_level)
        if self.out_size is not None:
            z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest")
        return z, noise_level

    def decode(self, z):
        z = z / self.scale_factor
        return self.model.decode(z)


class ConcatTimestepEmbedderND(AbstractEmbModel):
    """embeds each dimension independently and concatenates them"""

    def __init__(self, outdim):
        super().__init__()
        self.timestep = Timestep(outdim)
        self.outdim = outdim

    def forward(self, x):
        if x.ndim == 1:
            x = x[:, None]
        assert len(x.shape) == 2
        b, dims = x.shape[0], x.shape[1]
        x = rearrange(x, "b d -> (b d)")
        emb = self.timestep(x)
        emb = rearrange(emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim)
        return emb


class GaussianEncoder(Encoder, AbstractEmbModel):
    def __init__(
        self, weight: float = 1.0, flatten_output: bool = True, *args, **kwargs
    ):
        super().__init__(*args, **kwargs)
        self.posterior = DiagonalGaussianRegularizer()
        self.weight = weight
        self.flatten_output = flatten_output

    def forward(self, x) -> Tuple[Dict, torch.Tensor]:
        z = super().forward(x)
        z, log = self.posterior(z)
        log["loss"] = log["kl_loss"]
        log["weight"] = self.weight
        if self.flatten_output:
            z = rearrange(z, "b c h w -> b (h w ) c")
        return log, z


class VideoPredictionEmbedderWithEncoder(AbstractEmbModel):
    def __init__(
        self,
        n_cond_frames: int,
        n_copies: int,
        encoder_config: dict,
        sigma_sampler_config: Optional[dict] = None,
        sigma_cond_config: Optional[dict] = None,
        is_ae: bool = False,
        scale_factor: float = 1.0,
        disable_encoder_autocast: bool = False,
        en_and_decode_n_samples_a_time: Optional[int] = None,
    ):
        super().__init__()

        self.n_cond_frames = n_cond_frames
        self.n_copies = n_copies
        self.encoder = instantiate_from_config(encoder_config)
        self.sigma_sampler = (
            instantiate_from_config(sigma_sampler_config)
            if sigma_sampler_config is not None
            else None
        )
        self.sigma_cond = (
            instantiate_from_config(sigma_cond_config)
            if sigma_cond_config is not None
            else None
        )
        self.is_ae = is_ae
        self.scale_factor = scale_factor
        self.disable_encoder_autocast = disable_encoder_autocast
        self.en_and_decode_n_samples_a_time = en_and_decode_n_samples_a_time

    def forward(
        self, vid: torch.Tensor
    ) -> Union[
        torch.Tensor,
        Tuple[torch.Tensor, torch.Tensor],
        Tuple[torch.Tensor, dict],
        Tuple[Tuple[torch.Tensor, torch.Tensor], dict],
    ]:
        if self.sigma_sampler is not None:
            b = vid.shape[0] // self.n_cond_frames
            sigmas = self.sigma_sampler(b).to(vid.device)
            if self.sigma_cond is not None:
                sigma_cond = self.sigma_cond(sigmas)
                sigma_cond = repeat(sigma_cond, "b d -> (b t) d", t=self.n_copies)
            sigmas = repeat(sigmas, "b -> (b t)", t=self.n_cond_frames)
            noise = torch.randn_like(vid)
            vid = vid + noise * append_dims(sigmas, vid.ndim)

        with torch.autocast("cuda", enabled=not self.disable_encoder_autocast):
            n_samples = (
                self.en_and_decode_n_samples_a_time
                if self.en_and_decode_n_samples_a_time is not None
                else vid.shape[0]
            )
            n_rounds = math.ceil(vid.shape[0] / n_samples)
            all_out = []
            for n in range(n_rounds):
                if self.is_ae:
                    out = self.encoder.encode(vid[n * n_samples : (n + 1) * n_samples])
                else:
                    out = self.encoder(vid[n * n_samples : (n + 1) * n_samples])
                all_out.append(out)

        vid = torch.cat(all_out, dim=0)
        vid *= self.scale_factor

        vid = rearrange(vid, "(b t) c h w -> b () (t c) h w", t=self.n_cond_frames)
        vid = repeat(vid, "b 1 c h w -> (b t) c h w", t=self.n_copies)

        return_val = (vid, sigma_cond) if self.sigma_cond is not None else vid

        return return_val


class FrozenOpenCLIPImagePredictionEmbedder(AbstractEmbModel):
    def __init__(
        self,
        open_clip_embedding_config: Dict,
        n_cond_frames: int,
        n_copies: int,
    ):
        super().__init__()

        self.n_cond_frames = n_cond_frames
        self.n_copies = n_copies
        self.open_clip = instantiate_from_config(open_clip_embedding_config)

    def forward(self, vid):
        vid = self.open_clip(vid)
        vid = rearrange(vid, "(b t) d -> b t d", t=self.n_cond_frames)
        vid = repeat(vid, "b t d -> (b s) t d", s=self.n_copies)

        return vid