File size: 20,664 Bytes
158fb03
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
"""
LoRA module for Diffusers
==========================

This file works independently and is designed to operate with Diffusers.

Credits
-------
- Modified from: https://github.com/vladmandic/automatic/blob/master/modules/lora_diffusers.py
- Originally from: https://github.com/kohya-ss/sd-scripts/blob/sdxl/networks/lora_diffusers.py
"""

import bisect
import math
import random
from typing import Any, Dict, List, Mapping, Optional, Union
from diffusers import UNet2DConditionModel
import numpy as np
from tqdm import tqdm
import diffusers.models.lora as diffusers_lora
from transformers import CLIPTextModel
import torch


def make_unet_conversion_map() -> Dict[str, str]:
    unet_conversion_map_layer = []

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

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

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

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

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

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

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

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

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

    unet_conversion_map = []
    for sd, hf in unet_conversion_map_layer:
        if "resnets" in hf:
            for sd_res, hf_res in unet_conversion_map_resnet:
                unet_conversion_map.append((sd + sd_res, hf + hf_res))
        else:
            unet_conversion_map.append((sd, hf))

    for j in range(2):
        hf_time_embed_prefix = f"time_embedding.linear_{j+1}."
        sd_time_embed_prefix = f"time_embed.{j*2}."
        unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix))

    for j in range(2):
        hf_label_embed_prefix = f"add_embedding.linear_{j+1}."
        sd_label_embed_prefix = f"label_emb.0.{j*2}."
        unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix))

    unet_conversion_map.append(("input_blocks.0.0.", "conv_in."))
    unet_conversion_map.append(("out.0.", "conv_norm_out."))
    unet_conversion_map.append(("out.2.", "conv_out."))

    sd_hf_conversion_map = {
        sd.replace(".", "_")[:-1]: hf.replace(".", "_")[:-1]
        for sd, hf in unet_conversion_map
    }
    return sd_hf_conversion_map


UNET_CONVERSION_MAP = make_unet_conversion_map()


class LoRAModule(torch.nn.Module):
    """
    replaces forward method of the original Linear, instead of replacing the original Linear module.
    """

    def __init__(
        self,
        lora_name,
        org_module: torch.nn.Module,
        multiplier=1.0,
        lora_dim=4,
        alpha=1,
    ):
        """if alpha == 0 or None, alpha is rank (no scaling)."""
        super().__init__()
        self.lora_name = lora_name

        if isinstance(
            org_module, diffusers_lora.LoRACompatibleConv
        ):  # Modified to support Diffusers>=0.19.2
            in_dim = org_module.in_channels
            out_dim = org_module.out_channels
        else:
            in_dim = org_module.in_features
            out_dim = org_module.out_features

        self.lora_dim = lora_dim

        if isinstance(
            org_module, diffusers_lora.LoRACompatibleConv
        ):  # Modified to support Diffusers>=0.19.2
            kernel_size = org_module.kernel_size
            stride = org_module.stride
            padding = org_module.padding
            self.lora_down = torch.nn.Conv2d(
                in_dim, self.lora_dim, kernel_size, stride, padding, bias=False
            )
            self.lora_up = torch.nn.Conv2d(
                self.lora_dim, out_dim, (1, 1), (1, 1), bias=False
            )
        else:
            self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False)
            self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False)

        if isinstance(alpha, torch.Tensor):
            alpha = alpha.detach().float().numpy()  # without casting, bf16 causes error
        alpha = self.lora_dim if alpha is None or alpha == 0 else alpha
        self.scale = alpha / self.lora_dim
        self.register_buffer(
            "alpha", torch.tensor(alpha)
        )  # 勾配計算に含めない / not included in gradient calculation

        # same as microsoft's
        torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
        torch.nn.init.zeros_(self.lora_up.weight)

        self.multiplier = multiplier
        self.org_module = [org_module]
        self.enabled = True
        self.network: LoRANetwork = None
        self.org_forward = None

    # override org_module's forward method
    def apply_to(self, multiplier=None):
        if multiplier is not None:
            self.multiplier = multiplier
        if self.org_forward is None:
            self.org_forward = self.org_module[0].forward
            self.org_module[0].forward = self.forward

    # restore org_module's forward method
    def unapply_to(self):
        if self.org_forward is not None:
            self.org_module[0].forward = self.org_forward

    # forward with lora
    def forward(self, x):
        if not self.enabled:
            return self.org_forward(x)
        return (
            self.org_forward(x)
            + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
        )

    def set_network(self, network):
        self.network = network

    # merge lora weight to org weight
    def merge_to(self, multiplier=1.0):
        # get lora weight
        lora_weight = self.get_weight(multiplier)

        # get org weight
        org_sd = self.org_module[0].state_dict()
        org_weight = org_sd["weight"]
        weight = org_weight + lora_weight.to(org_weight.device, dtype=org_weight.dtype)

        # set weight to org_module
        org_sd["weight"] = weight
        self.org_module[0].load_state_dict(org_sd)

    # restore org weight from lora weight
    def restore_from(self, multiplier=1.0):
        # get lora weight
        lora_weight = self.get_weight(multiplier)

        # get org weight
        org_sd = self.org_module[0].state_dict()
        org_weight = org_sd["weight"]
        weight = org_weight - lora_weight.to(org_weight.device, dtype=org_weight.dtype)

        # set weight to org_module
        org_sd["weight"] = weight
        self.org_module[0].load_state_dict(org_sd)

    # return lora weight
    def get_weight(self, multiplier=None):
        if multiplier is None:
            multiplier = self.multiplier

        # get up/down weight from module
        up_weight = self.lora_up.weight.to(torch.float)
        down_weight = self.lora_down.weight.to(torch.float)

        # pre-calculated weight
        if len(down_weight.size()) == 2:
            # linear
            weight = self.multiplier * (up_weight @ down_weight) * self.scale
        elif down_weight.size()[2:4] == (1, 1):
            # conv2d 1x1
            weight = (
                self.multiplier
                * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2))
                .unsqueeze(2)
                .unsqueeze(3)
                * self.scale
            )
        else:
            # conv2d 3x3
            conved = torch.nn.functional.conv2d(
                down_weight.permute(1, 0, 2, 3), up_weight
            ).permute(1, 0, 2, 3)
            weight = self.multiplier * conved * self.scale

        return weight


# Create network from weights for inference, weights are not loaded here
def create_network_from_weights(
    text_encoder: Union[CLIPTextModel, List[CLIPTextModel]],
    unet: UNet2DConditionModel,
    weights_sd: Dict,
    multiplier: float = 1.0,
):
    # get dim/alpha mapping
    modules_dim = {}
    modules_alpha = {}
    for key, value in weights_sd.items():
        if "." not in key:
            continue

        lora_name = key.split(".")[0]
        if "alpha" in key:
            modules_alpha[lora_name] = value
        elif "lora_down" in key:
            dim = value.size()[0]
            modules_dim[lora_name] = dim
            # print(lora_name, value.size(), dim)

    # support old LoRA without alpha
    for key in modules_dim.keys():
        if key not in modules_alpha:
            modules_alpha[key] = modules_dim[key]

    return LoRANetwork(
        text_encoder,
        unet,
        multiplier=multiplier,
        modules_dim=modules_dim,
        modules_alpha=modules_alpha,
    )


def merge_lora_weights(pipe, weights_sd: Dict, multiplier: float = 1.0):
    text_encoders = (
        [pipe.text_encoder, pipe.text_encoder_2]
        if hasattr(pipe, "text_encoder_2")
        else [pipe.text_encoder]
    )
    unet = pipe.unet

    lora_network = create_network_from_weights(
        text_encoders, unet, weights_sd, multiplier=multiplier
    )
    lora_network.load_state_dict(weights_sd)
    lora_network.merge_to(multiplier=multiplier)


# block weightや学習に対応しない簡易版 / simple version without block weight and training
class LoRANetwork(torch.nn.Module):
    UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
    UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = [
        "ResnetBlock2D",
        "Downsample2D",
        "Upsample2D",
    ]
    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
    LORA_PREFIX_UNET = "lora_unet"
    LORA_PREFIX_TEXT_ENCODER = "lora_te"

    # SDXL: must starts with LORA_PREFIX_TEXT_ENCODER
    LORA_PREFIX_TEXT_ENCODER1 = "lora_te1"
    LORA_PREFIX_TEXT_ENCODER2 = "lora_te2"

    def __init__(
        self,
        text_encoder: Union[List[CLIPTextModel], CLIPTextModel],
        unet: UNet2DConditionModel,
        multiplier: float = 1.0,
        modules_dim: Optional[Dict[str, int]] = None,
        modules_alpha: Optional[Dict[str, int]] = None,
        varbose: Optional[bool] = False,
    ) -> None:
        super().__init__()
        self.multiplier = multiplier

        print(f"create LoRA network from weights")

        # convert SDXL Stability AI's U-Net modules to Diffusers
        converted = self.convert_unet_modules(modules_dim, modules_alpha)
        if converted:
            print(
                f"converted {converted} Stability AI's U-Net LoRA modules to Diffusers (SDXL)"
            )

        # create module instances
        def create_modules(
            is_unet: bool,
            text_encoder_idx: Optional[int],  # None, 1, 2
            root_module: torch.nn.Module,
            target_replace_modules: List[torch.nn.Module],
        ) -> List[LoRAModule]:
            prefix = (
                self.LORA_PREFIX_UNET
                if is_unet
                else (
                    self.LORA_PREFIX_TEXT_ENCODER
                    if text_encoder_idx is None
                    else (
                        self.LORA_PREFIX_TEXT_ENCODER1
                        if text_encoder_idx == 1
                        else self.LORA_PREFIX_TEXT_ENCODER2
                    )
                )
            )
            loras = []
            skipped = []
            for name, module in root_module.named_modules():
                if module.__class__.__name__ in target_replace_modules:
                    for child_name, child_module in module.named_modules():
                        is_linear = isinstance(
                            child_module,
                            (torch.nn.Linear, diffusers_lora.LoRACompatibleLinear),
                        )  # Modified to support Diffusers>=0.19.2
                        is_conv2d = isinstance(
                            child_module,
                            (torch.nn.Conv2d, diffusers_lora.LoRACompatibleConv),
                        )  # Modified to support Diffusers>=0.19.2

                        if is_linear or is_conv2d:
                            lora_name = prefix + "." + name + "." + child_name
                            lora_name = lora_name.replace(".", "_")

                            if lora_name not in modules_dim:
                                # print(f"skipped {lora_name} (not found in modules_dim)")
                                skipped.append(lora_name)
                                continue

                            dim = modules_dim[lora_name]
                            alpha = modules_alpha[lora_name]
                            lora = LoRAModule(
                                lora_name,
                                child_module,
                                self.multiplier,
                                dim,
                                alpha,
                            )
                            loras.append(lora)
            return loras, skipped

        text_encoders = text_encoder if type(text_encoder) == list else [text_encoder]

        # create LoRA for text encoder
        # 毎回すべてのモジュールを作るのは無駄なので要検討 / it is wasteful to create all modules every time, need to consider
        self.text_encoder_loras: List[LoRAModule] = []
        skipped_te = []
        for i, text_encoder in enumerate(text_encoders):
            if len(text_encoders) > 1:
                index = i + 1
            else:
                index = None

            text_encoder_loras, skipped = create_modules(
                False,
                index,
                text_encoder,
                LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE,
            )
            self.text_encoder_loras.extend(text_encoder_loras)
            skipped_te += skipped
        print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
        if len(skipped_te) > 0:
            print(f"skipped {len(skipped_te)} modules because of missing weight.")

        # extend U-Net target modules to include Conv2d 3x3
        target_modules = (
            LoRANetwork.UNET_TARGET_REPLACE_MODULE
            + LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
        )

        self.unet_loras: List[LoRAModule]
        self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules)
        print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
        if len(skipped_un) > 0:
            print(f"skipped {len(skipped_un)} modules because of missing weight.")

        # assertion
        names = set()
        for lora in self.text_encoder_loras + self.unet_loras:
            names.add(lora.lora_name)
        for lora_name in modules_dim.keys():
            assert (
                lora_name in names
            ), f"{lora_name} is not found in created LoRA modules."

        # make to work load_state_dict
        for lora in self.text_encoder_loras + self.unet_loras:
            self.add_module(lora.lora_name, lora)

    # SDXL: convert SDXL Stability AI's U-Net modules to Diffusers
    def convert_unet_modules(self, modules_dim, modules_alpha):
        converted_count = 0
        not_converted_count = 0

        map_keys = list(UNET_CONVERSION_MAP.keys())
        map_keys.sort()

        for key in list(modules_dim.keys()):
            if key.startswith(LoRANetwork.LORA_PREFIX_UNET + "_"):
                search_key = key.replace(LoRANetwork.LORA_PREFIX_UNET + "_", "")
                position = bisect.bisect_right(map_keys, search_key)
                map_key = map_keys[position - 1]
                if search_key.startswith(map_key):
                    new_key = key.replace(map_key, UNET_CONVERSION_MAP[map_key])
                    modules_dim[new_key] = modules_dim[key]
                    modules_alpha[new_key] = modules_alpha[key]
                    del modules_dim[key]
                    del modules_alpha[key]
                    converted_count += 1
                else:
                    not_converted_count += 1
        assert (
            converted_count == 0 or not_converted_count == 0
        ), f"some modules are not converted: {converted_count} converted, {not_converted_count} not converted"
        return converted_count

    def set_multiplier(self, multiplier):
        self.multiplier = multiplier
        for lora in self.text_encoder_loras + self.unet_loras:
            lora.multiplier = self.multiplier

    def apply_to(self, multiplier=1.0, apply_text_encoder=True, apply_unet=True):
        if apply_text_encoder:
            print("enable LoRA for text encoder")
            for lora in self.text_encoder_loras:
                lora.apply_to(multiplier)
        if apply_unet:
            print("enable LoRA for U-Net")
            for lora in self.unet_loras:
                lora.apply_to(multiplier)

    def unapply_to(self):
        for lora in self.text_encoder_loras + self.unet_loras:
            lora.unapply_to()

    def merge_to(self, multiplier=1.0):
        print("merge LoRA weights to original weights")
        for lora in tqdm(self.text_encoder_loras + self.unet_loras):
            lora.merge_to(multiplier)
        print(f"weights are merged")

    def restore_from(self, multiplier=1.0):
        print("restore LoRA weights from original weights")
        for lora in tqdm(self.text_encoder_loras + self.unet_loras):
            lora.restore_from(multiplier)
        print(f"weights are restored")

    def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
        # convert SDXL Stability AI's state dict to Diffusers' based state dict
        map_keys = list(UNET_CONVERSION_MAP.keys())  # prefix of U-Net modules
        map_keys.sort()
        for key in list(state_dict.keys()):
            if key.startswith(LoRANetwork.LORA_PREFIX_UNET + "_"):
                search_key = key.replace(LoRANetwork.LORA_PREFIX_UNET + "_", "")
                position = bisect.bisect_right(map_keys, search_key)
                map_key = map_keys[position - 1]
                if search_key.startswith(map_key):
                    new_key = key.replace(map_key, UNET_CONVERSION_MAP[map_key])
                    state_dict[new_key] = state_dict[key]
                    del state_dict[key]

        # in case of V2, some weights have different shape, so we need to convert them
        # because V2 LoRA is based on U-Net created by use_linear_projection=False
        my_state_dict = self.state_dict()
        for key in state_dict.keys():
            if state_dict[key].size() != my_state_dict[key].size():
                # print(f"convert {key} from {state_dict[key].size()} to {my_state_dict[key].size()}")
                state_dict[key] = state_dict[key].view(my_state_dict[key].size())

        return super().load_state_dict(state_dict, strict)