File size: 22,677 Bytes
7e93a0e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import logging
import math
import re
from abc import abstractmethod
from contextlib import contextmanager
from typing import Any, Dict, List, Optional, Tuple, Union

import pytorch_lightning as pl
import torch
import torch.nn as nn
from einops import rearrange
from packaging import version

from ..modules.autoencoding.regularizers import AbstractRegularizer
from ..modules.ema import LitEma
from ..util import (
    default,
    get_nested_attribute,
    get_obj_from_str,
    instantiate_from_config,
)

logpy = logging.getLogger(__name__)


class AbstractAutoencoder(pl.LightningModule):
    """
    This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators,
    unCLIP models, etc. Hence, it is fairly general, and specific features
    (e.g. discriminator training, encoding, decoding) must be implemented in subclasses.
    """

    def __init__(
        self,
        ema_decay: Union[None, float] = None,
        monitor: Union[None, str] = None,
        input_key: str = "jpg",
    ):
        super().__init__()

        self.input_key = input_key
        self.use_ema = ema_decay is not None
        if monitor is not None:
            self.monitor = monitor

        if self.use_ema:
            self.model_ema = LitEma(self, decay=ema_decay)
            logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")

        if version.parse(torch.__version__) >= version.parse("2.0.0"):
            self.automatic_optimization = False

    def apply_ckpt(self, ckpt: Union[None, str, dict]):
        if ckpt is None:
            return
        if isinstance(ckpt, str):
            ckpt = {
                "target": "sgm.modules.checkpoint.CheckpointEngine",
                "params": {"ckpt_path": ckpt},
            }
        engine = instantiate_from_config(ckpt)
        engine(self)

    @abstractmethod
    def get_input(self, batch) -> Any:
        raise NotImplementedError()

    def on_train_batch_end(self, *args, **kwargs):
        # for EMA computation
        if self.use_ema:
            self.model_ema(self)

    @contextmanager
    def ema_scope(self, context=None):
        if self.use_ema:
            self.model_ema.store(self.parameters())
            self.model_ema.copy_to(self)
            if context is not None:
                logpy.info(f"{context}: Switched to EMA weights")
        try:
            yield None
        finally:
            if self.use_ema:
                self.model_ema.restore(self.parameters())
                if context is not None:
                    logpy.info(f"{context}: Restored training weights")

    @abstractmethod
    def encode(self, *args, **kwargs) -> torch.Tensor:
        raise NotImplementedError("encode()-method of abstract base class called")

    @abstractmethod
    def decode(self, *args, **kwargs) -> torch.Tensor:
        raise NotImplementedError("decode()-method of abstract base class called")

    def instantiate_optimizer_from_config(self, params, lr, cfg):
        logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config")
        return get_obj_from_str(cfg["target"])(
            params, lr=lr, **cfg.get("params", dict())
        )

    def configure_optimizers(self) -> Any:
        raise NotImplementedError()


class AutoencodingEngine(AbstractAutoencoder):
    """
    Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL
    (we also restore them explicitly as special cases for legacy reasons).
    Regularizations such as KL or VQ are moved to the regularizer class.
    """

    def __init__(
        self,
        *args,
        encoder_config: Dict,
        decoder_config: Dict,
        loss_config: Dict,
        regularizer_config: Dict,
        optimizer_config: Union[Dict, None] = None,
        lr_g_factor: float = 1.0,
        trainable_ae_params: Optional[List[List[str]]] = None,
        ae_optimizer_args: Optional[List[dict]] = None,
        trainable_disc_params: Optional[List[List[str]]] = None,
        disc_optimizer_args: Optional[List[dict]] = None,
        disc_start_iter: int = 0,
        diff_boost_factor: float = 3.0,
        ckpt_engine: Union[None, str, dict] = None,
        ckpt_path: Optional[str] = None,
        additional_decode_keys: Optional[List[str]] = None,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        self.automatic_optimization = False  # pytorch lightning

        self.encoder: torch.nn.Module = instantiate_from_config(encoder_config)
        self.decoder: torch.nn.Module = instantiate_from_config(decoder_config)
        self.loss: torch.nn.Module = instantiate_from_config(loss_config)
        self.regularization: AbstractRegularizer = instantiate_from_config(
            regularizer_config
        )
        self.optimizer_config = default(
            optimizer_config, {"target": "torch.optim.Adam"}
        )
        self.diff_boost_factor = diff_boost_factor
        self.disc_start_iter = disc_start_iter
        self.lr_g_factor = lr_g_factor
        self.trainable_ae_params = trainable_ae_params
        if self.trainable_ae_params is not None:
            self.ae_optimizer_args = default(
                ae_optimizer_args,
                [{} for _ in range(len(self.trainable_ae_params))],
            )
            assert len(self.ae_optimizer_args) == len(self.trainable_ae_params)
        else:
            self.ae_optimizer_args = [{}]  # makes type consitent

        self.trainable_disc_params = trainable_disc_params
        if self.trainable_disc_params is not None:
            self.disc_optimizer_args = default(
                disc_optimizer_args,
                [{} for _ in range(len(self.trainable_disc_params))],
            )
            assert len(self.disc_optimizer_args) == len(self.trainable_disc_params)
        else:
            self.disc_optimizer_args = [{}]  # makes type consitent

        if ckpt_path is not None:
            assert ckpt_engine is None, "Can't set ckpt_engine and ckpt_path"
            logpy.warn("Checkpoint path is deprecated, use `checkpoint_egnine` instead")
        self.apply_ckpt(default(ckpt_path, ckpt_engine))
        self.additional_decode_keys = set(default(additional_decode_keys, []))

    def get_input(self, batch: Dict) -> torch.Tensor:
        # assuming unified data format, dataloader returns a dict.
        # image tensors should be scaled to -1 ... 1 and in channels-first
        # format (e.g., bchw instead if bhwc)
        return batch[self.input_key]

    def get_autoencoder_params(self) -> list:
        params = []
        if hasattr(self.loss, "get_trainable_autoencoder_parameters"):
            params += list(self.loss.get_trainable_autoencoder_parameters())
        if hasattr(self.regularization, "get_trainable_parameters"):
            params += list(self.regularization.get_trainable_parameters())
        params = params + list(self.encoder.parameters())
        params = params + list(self.decoder.parameters())
        return params

    def get_discriminator_params(self) -> list:
        if hasattr(self.loss, "get_trainable_parameters"):
            params = list(self.loss.get_trainable_parameters())  # e.g., discriminator
        else:
            params = []
        return params

    def get_last_layer(self):
        return self.decoder.get_last_layer()

    def encode(
        self,
        x: torch.Tensor,
        return_reg_log: bool = False,
        unregularized: bool = False,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
        z = self.encoder(x)
        if unregularized:
            return z, dict()
        z, reg_log = self.regularization(z)
        if return_reg_log:
            return z, reg_log
        return z

    def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
        x = self.decoder(z, **kwargs)
        return x

    def forward(
        self, x: torch.Tensor, **additional_decode_kwargs
    ) -> Tuple[torch.Tensor, torch.Tensor, dict]:
        z, reg_log = self.encode(x, return_reg_log=True)
        dec = self.decode(z, **additional_decode_kwargs)
        return z, dec, reg_log

    def inner_training_step(
        self, batch: dict, batch_idx: int, optimizer_idx: int = 0
    ) -> torch.Tensor:
        x = self.get_input(batch)
        additional_decode_kwargs = {
            key: batch[key] for key in self.additional_decode_keys.intersection(batch)
        }
        z, xrec, regularization_log = self(x, **additional_decode_kwargs)
        if hasattr(self.loss, "forward_keys"):
            extra_info = {
                "z": z,
                "optimizer_idx": optimizer_idx,
                "global_step": self.global_step,
                "last_layer": self.get_last_layer(),
                "split": "train",
                "regularization_log": regularization_log,
                "autoencoder": self,
            }
            extra_info = {k: extra_info[k] for k in self.loss.forward_keys}
        else:
            extra_info = dict()

        if optimizer_idx == 0:
            # autoencode
            out_loss = self.loss(x, xrec, **extra_info)
            if isinstance(out_loss, tuple):
                aeloss, log_dict_ae = out_loss
            else:
                # simple loss function
                aeloss = out_loss
                log_dict_ae = {"train/loss/rec": aeloss.detach()}

            self.log_dict(
                log_dict_ae,
                prog_bar=False,
                logger=True,
                on_step=True,
                on_epoch=True,
                sync_dist=False,
            )
            self.log(
                "loss",
                aeloss.mean().detach(),
                prog_bar=True,
                logger=False,
                on_epoch=False,
                on_step=True,
            )
            return aeloss
        elif optimizer_idx == 1:
            # discriminator
            discloss, log_dict_disc = self.loss(x, xrec, **extra_info)
            # -> discriminator always needs to return a tuple
            self.log_dict(
                log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True
            )
            return discloss
        else:
            raise NotImplementedError(f"Unknown optimizer {optimizer_idx}")

    def training_step(self, batch: dict, batch_idx: int):
        opts = self.optimizers()
        if not isinstance(opts, list):
            # Non-adversarial case
            opts = [opts]
        optimizer_idx = batch_idx % len(opts)
        if self.global_step < self.disc_start_iter:
            optimizer_idx = 0
        opt = opts[optimizer_idx]
        opt.zero_grad()
        with opt.toggle_model():
            loss = self.inner_training_step(
                batch, batch_idx, optimizer_idx=optimizer_idx
            )
            self.manual_backward(loss)
        opt.step()

    def validation_step(self, batch: dict, batch_idx: int) -> Dict:
        log_dict = self._validation_step(batch, batch_idx)
        with self.ema_scope():
            log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
            log_dict.update(log_dict_ema)
        return log_dict

    def _validation_step(self, batch: dict, batch_idx: int, postfix: str = "") -> Dict:
        x = self.get_input(batch)

        z, xrec, regularization_log = self(x)
        if hasattr(self.loss, "forward_keys"):
            extra_info = {
                "z": z,
                "optimizer_idx": 0,
                "global_step": self.global_step,
                "last_layer": self.get_last_layer(),
                "split": "val" + postfix,
                "regularization_log": regularization_log,
                "autoencoder": self,
            }
            extra_info = {k: extra_info[k] for k in self.loss.forward_keys}
        else:
            extra_info = dict()
        out_loss = self.loss(x, xrec, **extra_info)
        if isinstance(out_loss, tuple):
            aeloss, log_dict_ae = out_loss
        else:
            # simple loss function
            aeloss = out_loss
            log_dict_ae = {f"val{postfix}/loss/rec": aeloss.detach()}
        full_log_dict = log_dict_ae

        if "optimizer_idx" in extra_info:
            extra_info["optimizer_idx"] = 1
            discloss, log_dict_disc = self.loss(x, xrec, **extra_info)
            full_log_dict.update(log_dict_disc)
        self.log(
            f"val{postfix}/loss/rec",
            log_dict_ae[f"val{postfix}/loss/rec"],
            sync_dist=True,
        )
        self.log_dict(full_log_dict, sync_dist=True)
        return full_log_dict

    def get_param_groups(
        self, parameter_names: List[List[str]], optimizer_args: List[dict]
    ) -> Tuple[List[Dict[str, Any]], int]:
        groups = []
        num_params = 0
        for names, args in zip(parameter_names, optimizer_args):
            params = []
            for pattern_ in names:
                pattern_params = []
                pattern = re.compile(pattern_)
                for p_name, param in self.named_parameters():
                    if re.match(pattern, p_name):
                        pattern_params.append(param)
                        num_params += param.numel()
                if len(pattern_params) == 0:
                    logpy.warn(f"Did not find parameters for pattern {pattern_}")
                params.extend(pattern_params)
            groups.append({"params": params, **args})
        return groups, num_params

    def configure_optimizers(self) -> List[torch.optim.Optimizer]:
        if self.trainable_ae_params is None:
            ae_params = self.get_autoencoder_params()
        else:
            ae_params, num_ae_params = self.get_param_groups(
                self.trainable_ae_params, self.ae_optimizer_args
            )
            logpy.info(f"Number of trainable autoencoder parameters: {num_ae_params:,}")
        if self.trainable_disc_params is None:
            disc_params = self.get_discriminator_params()
        else:
            disc_params, num_disc_params = self.get_param_groups(
                self.trainable_disc_params, self.disc_optimizer_args
            )
            logpy.info(
                f"Number of trainable discriminator parameters: {num_disc_params:,}"
            )
        opt_ae = self.instantiate_optimizer_from_config(
            ae_params,
            default(self.lr_g_factor, 1.0) * self.learning_rate,
            self.optimizer_config,
        )
        opts = [opt_ae]
        if len(disc_params) > 0:
            opt_disc = self.instantiate_optimizer_from_config(
                disc_params, self.learning_rate, self.optimizer_config
            )
            opts.append(opt_disc)

        return opts

    @torch.no_grad()
    def log_images(
        self, batch: dict, additional_log_kwargs: Optional[Dict] = None, **kwargs
    ) -> dict:
        log = dict()
        additional_decode_kwargs = {}
        x = self.get_input(batch)
        additional_decode_kwargs.update(
            {key: batch[key] for key in self.additional_decode_keys.intersection(batch)}
        )

        _, xrec, _ = self(x, **additional_decode_kwargs)
        log["inputs"] = x
        log["reconstructions"] = xrec
        diff = 0.5 * torch.abs(torch.clamp(xrec, -1.0, 1.0) - x)
        diff.clamp_(0, 1.0)
        log["diff"] = 2.0 * diff - 1.0
        # diff_boost shows location of small errors, by boosting their
        # brightness.
        log["diff_boost"] = (
            2.0 * torch.clamp(self.diff_boost_factor * diff, 0.0, 1.0) - 1
        )
        if hasattr(self.loss, "log_images"):
            log.update(self.loss.log_images(x, xrec))
        with self.ema_scope():
            _, xrec_ema, _ = self(x, **additional_decode_kwargs)
            log["reconstructions_ema"] = xrec_ema
            diff_ema = 0.5 * torch.abs(torch.clamp(xrec_ema, -1.0, 1.0) - x)
            diff_ema.clamp_(0, 1.0)
            log["diff_ema"] = 2.0 * diff_ema - 1.0
            log["diff_boost_ema"] = (
                2.0 * torch.clamp(self.diff_boost_factor * diff_ema, 0.0, 1.0) - 1
            )
        if additional_log_kwargs:
            additional_decode_kwargs.update(additional_log_kwargs)
            _, xrec_add, _ = self(x, **additional_decode_kwargs)
            log_str = "reconstructions-" + "-".join(
                [f"{key}={additional_log_kwargs[key]}" for key in additional_log_kwargs]
            )
            log[log_str] = xrec_add
        return log


class AutoencodingEngineLegacy(AutoencodingEngine):
    def __init__(self, embed_dim: int, **kwargs):
        self.max_batch_size = kwargs.pop("max_batch_size", None)
        ddconfig = kwargs.pop("ddconfig")
        ckpt_path = kwargs.pop("ckpt_path", None)
        ckpt_engine = kwargs.pop("ckpt_engine", None)
        super().__init__(
            encoder_config={
                "target": "sgm.modules.diffusionmodules.model.Encoder",
                "params": ddconfig,
            },
            decoder_config={
                "target": "sgm.modules.diffusionmodules.model.Decoder",
                "params": ddconfig,
            },
            **kwargs,
        )
        self.quant_conv = torch.nn.Conv2d(
            (1 + ddconfig["double_z"]) * ddconfig["z_channels"],
            (1 + ddconfig["double_z"]) * embed_dim,
            1,
        )
        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
        self.embed_dim = embed_dim

        self.apply_ckpt(default(ckpt_path, ckpt_engine))

    def get_autoencoder_params(self) -> list:
        params = super().get_autoencoder_params()
        return params

    def encode(
        self, x: torch.Tensor, return_reg_log: bool = False
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
        if self.max_batch_size is None:
            z = self.encoder(x)
            z = self.quant_conv(z)
        else:
            N = x.shape[0]
            bs = self.max_batch_size
            n_batches = int(math.ceil(N / bs))
            z = list()
            for i_batch in range(n_batches):
                z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs])
                z_batch = self.quant_conv(z_batch)
                z.append(z_batch)
            z = torch.cat(z, 0)

        z, reg_log = self.regularization(z)
        if return_reg_log:
            return z, reg_log
        return z

    def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
        if self.max_batch_size is None:
            dec = self.post_quant_conv(z)
            dec = self.decoder(dec, **decoder_kwargs)
        else:
            N = z.shape[0]
            bs = self.max_batch_size
            n_batches = int(math.ceil(N / bs))
            dec = list()
            for i_batch in range(n_batches):
                dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs])
                dec_batch = self.decoder(dec_batch, **decoder_kwargs)
                dec.append(dec_batch)
            dec = torch.cat(dec, 0)

        return dec


class AutoencoderKL(AutoencodingEngineLegacy):
    def __init__(self, **kwargs):
        if "lossconfig" in kwargs:
            kwargs["loss_config"] = kwargs.pop("lossconfig")
        super().__init__(
            regularizer_config={
                "target": (
                    "sgm.modules.autoencoding.regularizers"
                    ".DiagonalGaussianRegularizer"
                )
            },
            **kwargs,
        )


class AutoencoderLegacyVQ(AutoencodingEngineLegacy):
    def __init__(
        self,
        embed_dim: int,
        n_embed: int,
        sane_index_shape: bool = False,
        **kwargs,
    ):
        if "lossconfig" in kwargs:
            logpy.warn(f"Parameter `lossconfig` is deprecated, use `loss_config`.")
            kwargs["loss_config"] = kwargs.pop("lossconfig")
        super().__init__(
            regularizer_config={
                "target": (
                    "sgm.modules.autoencoding.regularizers.quantize" ".VectorQuantizer"
                ),
                "params": {
                    "n_e": n_embed,
                    "e_dim": embed_dim,
                    "sane_index_shape": sane_index_shape,
                },
            },
            **kwargs,
        )


class IdentityFirstStage(AbstractAutoencoder):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def get_input(self, x: Any) -> Any:
        return x

    def encode(self, x: Any, *args, **kwargs) -> Any:
        return x

    def decode(self, x: Any, *args, **kwargs) -> Any:
        return x


class AEIntegerWrapper(nn.Module):
    def __init__(
        self,
        model: nn.Module,
        shape: Union[None, Tuple[int, int], List[int]] = (16, 16),
        regularization_key: str = "regularization",
        encoder_kwargs: Optional[Dict[str, Any]] = None,
    ):
        super().__init__()
        self.model = model
        assert hasattr(model, "encode") and hasattr(
            model, "decode"
        ), "Need AE interface"
        self.regularization = get_nested_attribute(model, regularization_key)
        self.shape = shape
        self.encoder_kwargs = default(encoder_kwargs, {"return_reg_log": True})

    def encode(self, x) -> torch.Tensor:
        assert (
            not self.training
        ), f"{self.__class__.__name__} only supports inference currently"
        _, log = self.model.encode(x, **self.encoder_kwargs)
        assert isinstance(log, dict)
        inds = log["min_encoding_indices"]
        return rearrange(inds, "b ... -> b (...)")

    def decode(
        self, inds: torch.Tensor, shape: Union[None, tuple, list] = None
    ) -> torch.Tensor:
        # expect inds shape (b, s) with s = h*w
        shape = default(shape, self.shape)  # Optional[(h, w)]
        if shape is not None:
            assert len(shape) == 2, f"Unhandeled shape {shape}"
            inds = rearrange(inds, "b (h w) -> b h w", h=shape[0], w=shape[1])
        h = self.regularization.get_codebook_entry(inds)  # (b, h, w, c)
        h = rearrange(h, "b h w c -> b c h w")
        return self.model.decode(h)


class AutoencoderKLModeOnly(AutoencodingEngineLegacy):
    def __init__(self, **kwargs):
        if "lossconfig" in kwargs:
            kwargs["loss_config"] = kwargs.pop("lossconfig")
        super().__init__(
            regularizer_config={
                "target": (
                    "sgm.modules.autoencoding.regularizers"
                    ".DiagonalGaussianRegularizer"
                ),
                "params": {"sample": False},
            },
            **kwargs,
        )