forked from huggingface/diffusers
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathresnet.py
More file actions
829 lines (697 loc) 路 32.4 KB
/
Copy pathresnet.py
File metadata and controls
829 lines (697 loc) 路 32.4 KB
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
# Copyright 2024 The HuggingFace Team. All rights reserved.
# `TemporalConvLayer` Copyright 2024 Alibaba DAMO-VILAB, The ModelScope Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..utils import USE_PEFT_BACKEND
from .activations import get_activation
from .attention_processor import SpatialNorm
from .downsampling import ( # noqa
Downsample1D,
Downsample2D,
FirDownsample2D,
KDownsample2D,
downsample_2d,
)
from .lora import LoRACompatibleConv, LoRACompatibleLinear
from .normalization import AdaGroupNorm
from .upsampling import ( # noqa
FirUpsample2D,
KUpsample2D,
Upsample1D,
Upsample2D,
upfirdn2d_native,
upsample_2d,
)
class ResnetBlockCondNorm2D(nn.Module):
r"""
A Resnet block that use normalization layer that incorporate conditioning information.
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
groups_out (`int`, *optional*, default to None):
The number of groups to use for the second normalization layer. if set to None, same as `groups`.
eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
time_embedding_norm (`str`, *optional*, default to `"ada_group"` ):
The normalization layer for time embedding `temb`. Currently only support "ada_group" or "spatial".
kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
[`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
use_in_shortcut (`bool`, *optional*, default to `True`):
If `True`, add a 1x1 nn.conv2d layer for skip-connection.
up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the
`conv_shortcut` output.
conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
If None, same as `out_channels`.
"""
def __init__(
self,
*,
in_channels: int,
out_channels: Optional[int] = None,
conv_shortcut: bool = False,
dropout: float = 0.0,
temb_channels: int = 512,
groups: int = 32,
groups_out: Optional[int] = None,
eps: float = 1e-6,
non_linearity: str = "swish",
time_embedding_norm: str = "ada_group", # ada_group, spatial
output_scale_factor: float = 1.0,
use_in_shortcut: Optional[bool] = None,
up: bool = False,
down: bool = False,
conv_shortcut_bias: bool = True,
conv_2d_out_channels: Optional[int] = None,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.up = up
self.down = down
self.output_scale_factor = output_scale_factor
self.time_embedding_norm = time_embedding_norm
conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
if groups_out is None:
groups_out = groups
if self.time_embedding_norm == "ada_group": # ada_group
self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
elif self.time_embedding_norm == "spatial":
self.norm1 = SpatialNorm(in_channels, temb_channels)
else:
raise ValueError(f" unsupported time_embedding_norm: {self.time_embedding_norm}")
self.conv1 = conv_cls(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
if self.time_embedding_norm == "ada_group": # ada_group
self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
elif self.time_embedding_norm == "spatial": # spatial
self.norm2 = SpatialNorm(out_channels, temb_channels)
else:
raise ValueError(f" unsupported time_embedding_norm: {self.time_embedding_norm}")
self.dropout = torch.nn.Dropout(dropout)
conv_2d_out_channels = conv_2d_out_channels or out_channels
self.conv2 = conv_cls(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
self.nonlinearity = get_activation(non_linearity)
self.upsample = self.downsample = None
if self.up:
self.upsample = Upsample2D(in_channels, use_conv=False)
elif self.down:
self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = conv_cls(
in_channels,
conv_2d_out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=conv_shortcut_bias,
)
def forward(
self,
input_tensor: torch.FloatTensor,
temb: torch.FloatTensor,
scale: float = 1.0,
) -> torch.FloatTensor:
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states, temb)
hidden_states = self.nonlinearity(hidden_states)
if self.upsample is not None:
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
if hidden_states.shape[0] >= 64:
input_tensor = input_tensor.contiguous()
hidden_states = hidden_states.contiguous()
input_tensor = self.upsample(input_tensor, scale=scale)
hidden_states = self.upsample(hidden_states, scale=scale)
elif self.downsample is not None:
input_tensor = self.downsample(input_tensor, scale=scale)
hidden_states = self.downsample(hidden_states, scale=scale)
hidden_states = self.conv1(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv1(hidden_states)
hidden_states = self.norm2(hidden_states, temb)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv2(hidden_states)
if self.conv_shortcut is not None:
input_tensor = (
self.conv_shortcut(input_tensor, scale) if not USE_PEFT_BACKEND else self.conv_shortcut(input_tensor)
)
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
return output_tensor
class ResnetBlock2D(nn.Module):
r"""
A Resnet block.
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
groups_out (`int`, *optional*, default to None):
The number of groups to use for the second normalization layer. if set to None, same as `groups`.
eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config.
By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift"
for a stronger conditioning with scale and shift.
kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
[`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
use_in_shortcut (`bool`, *optional*, default to `True`):
If `True`, add a 1x1 nn.conv2d layer for skip-connection.
up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the
`conv_shortcut` output.
conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
If None, same as `out_channels`.
"""
def __init__(
self,
*,
in_channels: int,
out_channels: Optional[int] = None,
conv_shortcut: bool = False,
dropout: float = 0.0,
temb_channels: int = 512,
groups: int = 32,
groups_out: Optional[int] = None,
pre_norm: bool = True,
eps: float = 1e-6,
non_linearity: str = "swish",
skip_time_act: bool = False,
time_embedding_norm: str = "default", # default, scale_shift,
kernel: Optional[torch.FloatTensor] = None,
output_scale_factor: float = 1.0,
use_in_shortcut: Optional[bool] = None,
up: bool = False,
down: bool = False,
conv_shortcut_bias: bool = True,
conv_2d_out_channels: Optional[int] = None,
):
super().__init__()
if time_embedding_norm == "ada_group":
raise ValueError(
"This class cannot be used with `time_embedding_norm==ada_group`, please use `ResnetBlockCondNorm2D` instead",
)
if time_embedding_norm == "spatial":
raise ValueError(
"This class cannot be used with `time_embedding_norm==spatial`, please use `ResnetBlockCondNorm2D` instead",
)
self.pre_norm = True
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.up = up
self.down = down
self.output_scale_factor = output_scale_factor
self.time_embedding_norm = time_embedding_norm
self.skip_time_act = skip_time_act
linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
if groups_out is None:
groups_out = groups
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = conv_cls(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
if temb_channels is not None:
if self.time_embedding_norm == "default":
self.time_emb_proj = linear_cls(temb_channels, out_channels)
elif self.time_embedding_norm == "scale_shift":
self.time_emb_proj = linear_cls(temb_channels, 2 * out_channels)
else:
raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
else:
self.time_emb_proj = None
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
conv_2d_out_channels = conv_2d_out_channels or out_channels
self.conv2 = conv_cls(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
self.nonlinearity = get_activation(non_linearity)
self.upsample = self.downsample = None
if self.up:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel)
elif kernel == "sde_vp":
self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
else:
self.upsample = Upsample2D(in_channels, use_conv=False)
elif self.down:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel)
elif kernel == "sde_vp":
self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
else:
self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = conv_cls(
in_channels,
conv_2d_out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=conv_shortcut_bias,
)
def forward(
self,
input_tensor: torch.FloatTensor,
temb: torch.FloatTensor,
scale: float = 1.0,
) -> torch.FloatTensor:
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
if self.upsample is not None:
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
if hidden_states.shape[0] >= 64:
input_tensor = input_tensor.contiguous()
hidden_states = hidden_states.contiguous()
input_tensor = (
self.upsample(input_tensor, scale=scale)
if isinstance(self.upsample, Upsample2D)
else self.upsample(input_tensor)
)
hidden_states = (
self.upsample(hidden_states, scale=scale)
if isinstance(self.upsample, Upsample2D)
else self.upsample(hidden_states)
)
elif self.downsample is not None:
input_tensor = (
self.downsample(input_tensor, scale=scale)
if isinstance(self.downsample, Downsample2D)
else self.downsample(input_tensor)
)
hidden_states = (
self.downsample(hidden_states, scale=scale)
if isinstance(self.downsample, Downsample2D)
else self.downsample(hidden_states)
)
hidden_states = self.conv1(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv1(hidden_states)
if self.time_emb_proj is not None:
if not self.skip_time_act:
temb = self.nonlinearity(temb)
temb = (
self.time_emb_proj(temb, scale)[:, :, None, None]
if not USE_PEFT_BACKEND
else self.time_emb_proj(temb)[:, :, None, None]
)
if self.time_embedding_norm == "default":
if temb is not None:
hidden_states = hidden_states + temb
hidden_states = self.norm2(hidden_states)
elif self.time_embedding_norm == "scale_shift":
if temb is None:
raise ValueError(
f" `temb` should not be None when `time_embedding_norm` is {self.time_embedding_norm}"
)
time_scale, time_shift = torch.chunk(temb, 2, dim=1)
hidden_states = self.norm2(hidden_states)
hidden_states = hidden_states * (1 + time_scale) + time_shift
else:
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv2(hidden_states)
if self.conv_shortcut is not None:
input_tensor = (
self.conv_shortcut(input_tensor, scale) if not USE_PEFT_BACKEND else self.conv_shortcut(input_tensor)
)
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
return output_tensor
# unet_rl.py
def rearrange_dims(tensor: torch.Tensor) -> torch.Tensor:
if len(tensor.shape) == 2:
return tensor[:, :, None]
if len(tensor.shape) == 3:
return tensor[:, :, None, :]
elif len(tensor.shape) == 4:
return tensor[:, :, 0, :]
else:
raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.")
class Conv1dBlock(nn.Module):
"""
Conv1d --> GroupNorm --> Mish
Parameters:
inp_channels (`int`): Number of input channels.
out_channels (`int`): Number of output channels.
kernel_size (`int` or `tuple`): Size of the convolving kernel.
n_groups (`int`, default `8`): Number of groups to separate the channels into.
activation (`str`, defaults to `mish`): Name of the activation function.
"""
def __init__(
self,
inp_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
n_groups: int = 8,
activation: str = "mish",
):
super().__init__()
self.conv1d = nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2)
self.group_norm = nn.GroupNorm(n_groups, out_channels)
self.mish = get_activation(activation)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
intermediate_repr = self.conv1d(inputs)
intermediate_repr = rearrange_dims(intermediate_repr)
intermediate_repr = self.group_norm(intermediate_repr)
intermediate_repr = rearrange_dims(intermediate_repr)
output = self.mish(intermediate_repr)
return output
# unet_rl.py
class ResidualTemporalBlock1D(nn.Module):
"""
Residual 1D block with temporal convolutions.
Parameters:
inp_channels (`int`): Number of input channels.
out_channels (`int`): Number of output channels.
embed_dim (`int`): Embedding dimension.
kernel_size (`int` or `tuple`): Size of the convolving kernel.
activation (`str`, defaults `mish`): It is possible to choose the right activation function.
"""
def __init__(
self,
inp_channels: int,
out_channels: int,
embed_dim: int,
kernel_size: Union[int, Tuple[int, int]] = 5,
activation: str = "mish",
):
super().__init__()
self.conv_in = Conv1dBlock(inp_channels, out_channels, kernel_size)
self.conv_out = Conv1dBlock(out_channels, out_channels, kernel_size)
self.time_emb_act = get_activation(activation)
self.time_emb = nn.Linear(embed_dim, out_channels)
self.residual_conv = (
nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
)
def forward(self, inputs: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
"""
Args:
inputs : [ batch_size x inp_channels x horizon ]
t : [ batch_size x embed_dim ]
returns:
out : [ batch_size x out_channels x horizon ]
"""
t = self.time_emb_act(t)
t = self.time_emb(t)
out = self.conv_in(inputs) + rearrange_dims(t)
out = self.conv_out(out)
return out + self.residual_conv(inputs)
class TemporalConvLayer(nn.Module):
"""
Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from:
https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016
Parameters:
in_dim (`int`): Number of input channels.
out_dim (`int`): Number of output channels.
dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
"""
def __init__(
self,
in_dim: int,
out_dim: Optional[int] = None,
dropout: float = 0.0,
norm_num_groups: int = 32,
):
super().__init__()
out_dim = out_dim or in_dim
self.in_dim = in_dim
self.out_dim = out_dim
# conv layers
self.conv1 = nn.Sequential(
nn.GroupNorm(norm_num_groups, in_dim),
nn.SiLU(),
nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0)),
)
self.conv2 = nn.Sequential(
nn.GroupNorm(norm_num_groups, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
)
self.conv3 = nn.Sequential(
nn.GroupNorm(norm_num_groups, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
)
self.conv4 = nn.Sequential(
nn.GroupNorm(norm_num_groups, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
)
# zero out the last layer params,so the conv block is identity
nn.init.zeros_(self.conv4[-1].weight)
nn.init.zeros_(self.conv4[-1].bias)
def forward(self, hidden_states: torch.Tensor, num_frames: int = 1) -> torch.Tensor:
hidden_states = (
hidden_states[None, :].reshape((-1, num_frames) + hidden_states.shape[1:]).permute(0, 2, 1, 3, 4)
)
identity = hidden_states
hidden_states = self.conv1(hidden_states)
hidden_states = self.conv2(hidden_states)
hidden_states = self.conv3(hidden_states)
hidden_states = self.conv4(hidden_states)
hidden_states = identity + hidden_states
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(
(hidden_states.shape[0] * hidden_states.shape[2], -1) + hidden_states.shape[3:]
)
return hidden_states
class TemporalResnetBlock(nn.Module):
r"""
A Resnet block.
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
"""
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
temb_channels: int = 512,
eps: float = 1e-6,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
kernel_size = (3, 1, 1)
padding = [k // 2 for k in kernel_size]
self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = nn.Conv3d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=1,
padding=padding,
)
if temb_channels is not None:
self.time_emb_proj = nn.Linear(temb_channels, out_channels)
else:
self.time_emb_proj = None
self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(0.0)
self.conv2 = nn.Conv3d(
out_channels,
out_channels,
kernel_size=kernel_size,
stride=1,
padding=padding,
)
self.nonlinearity = get_activation("silu")
self.use_in_shortcut = self.in_channels != out_channels
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = nn.Conv3d(
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
)
def forward(self, input_tensor: torch.FloatTensor, temb: torch.FloatTensor) -> torch.FloatTensor:
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv1(hidden_states)
if self.time_emb_proj is not None:
temb = self.nonlinearity(temb)
temb = self.time_emb_proj(temb)[:, :, :, None, None]
temb = temb.permute(0, 2, 1, 3, 4)
hidden_states = hidden_states + temb
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.conv_shortcut is not None:
input_tensor = self.conv_shortcut(input_tensor)
output_tensor = input_tensor + hidden_states
return output_tensor
# VideoResBlock
class SpatioTemporalResBlock(nn.Module):
r"""
A SpatioTemporal Resnet block.
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the spatial resenet.
temporal_eps (`float`, *optional*, defaults to `eps`): The epsilon to use for the temporal resnet.
merge_factor (`float`, *optional*, defaults to `0.5`): The merge factor to use for the temporal mixing.
merge_strategy (`str`, *optional*, defaults to `learned_with_images`):
The merge strategy to use for the temporal mixing.
switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):
If `True`, switch the spatial and temporal mixing.
"""
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
temb_channels: int = 512,
eps: float = 1e-6,
temporal_eps: Optional[float] = None,
merge_factor: float = 0.5,
merge_strategy="learned_with_images",
switch_spatial_to_temporal_mix: bool = False,
):
super().__init__()
self.spatial_res_block = ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=eps,
)
self.temporal_res_block = TemporalResnetBlock(
in_channels=out_channels if out_channels is not None else in_channels,
out_channels=out_channels if out_channels is not None else in_channels,
temb_channels=temb_channels,
eps=temporal_eps if temporal_eps is not None else eps,
)
self.time_mixer = AlphaBlender(
alpha=merge_factor,
merge_strategy=merge_strategy,
switch_spatial_to_temporal_mix=switch_spatial_to_temporal_mix,
)
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
image_only_indicator: Optional[torch.Tensor] = None,
):
num_frames = image_only_indicator.shape[-1]
hidden_states = self.spatial_res_block(hidden_states, temb)
batch_frames, channels, height, width = hidden_states.shape
batch_size = batch_frames // num_frames
hidden_states_mix = (
hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
)
hidden_states = (
hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
)
if temb is not None:
temb = temb.reshape(batch_size, num_frames, -1)
hidden_states = self.temporal_res_block(hidden_states, temb)
hidden_states = self.time_mixer(
x_spatial=hidden_states_mix,
x_temporal=hidden_states,
image_only_indicator=image_only_indicator,
)
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)
return hidden_states
class AlphaBlender(nn.Module):
r"""
A module to blend spatial and temporal features.
Parameters:
alpha (`float`): The initial value of the blending factor.
merge_strategy (`str`, *optional*, defaults to `learned_with_images`):
The merge strategy to use for the temporal mixing.
switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):
If `True`, switch the spatial and temporal mixing.
"""
strategies = ["learned", "fixed", "learned_with_images"]
def __init__(
self,
alpha: float,
merge_strategy: str = "learned_with_images",
switch_spatial_to_temporal_mix: bool = False,
):
super().__init__()
self.merge_strategy = merge_strategy
self.switch_spatial_to_temporal_mix = switch_spatial_to_temporal_mix # For TemporalVAE
if merge_strategy not in self.strategies:
raise ValueError(f"merge_strategy needs to be in {self.strategies}")
if self.merge_strategy == "fixed":
self.register_buffer("mix_factor", torch.Tensor([alpha]))
elif self.merge_strategy == "learned" or self.merge_strategy == "learned_with_images":
self.register_parameter("mix_factor", torch.nn.Parameter(torch.Tensor([alpha])))
else:
raise ValueError(f"Unknown merge strategy {self.merge_strategy}")
def get_alpha(self, image_only_indicator: torch.Tensor, ndims: int) -> torch.Tensor:
if self.merge_strategy == "fixed":
alpha = self.mix_factor
elif self.merge_strategy == "learned":
alpha = torch.sigmoid(self.mix_factor)
elif self.merge_strategy == "learned_with_images":
if image_only_indicator is None:
raise ValueError("Please provide image_only_indicator to use learned_with_images merge strategy")
alpha = torch.where(
image_only_indicator.bool(),
torch.ones(1, 1, device=image_only_indicator.device),
torch.sigmoid(self.mix_factor)[..., None],
)
# (batch, channel, frames, height, width)
if ndims == 5:
alpha = alpha[:, None, :, None, None]
# (batch*frames, height*width, channels)
elif ndims == 3:
alpha = alpha.reshape(-1)[:, None, None]
else:
raise ValueError(f"Unexpected ndims {ndims}. Dimensions should be 3 or 5")
else:
raise NotImplementedError
return alpha
def forward(
self,
x_spatial: torch.Tensor,
x_temporal: torch.Tensor,
image_only_indicator: Optional[torch.Tensor] = None,
) -> torch.Tensor:
alpha = self.get_alpha(image_only_indicator, x_spatial.ndim)
alpha = alpha.to(x_spatial.dtype)
if self.switch_spatial_to_temporal_mix:
alpha = 1.0 - alpha
x = alpha * x_spatial + (1.0 - alpha) * x_temporal
return x