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1
+ """
2
+ wild mixture of
3
+ https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
4
+ https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
5
+ https://github.com/CompVis/taming-transformers
6
+ -- merci
7
+ """
8
+
9
+ import torch
10
+ import torch.nn as nn
11
+ import numpy as np
12
+ import pytorch_lightning as pl
13
+ from torch.optim.lr_scheduler import LambdaLR
14
+ from einops import rearrange, repeat
15
+ from contextlib import contextmanager
16
+ from functools import partial
17
+ from tqdm import tqdm
18
+ from torchvision.utils import make_grid
19
+ from pytorch_lightning.utilities.distributed import rank_zero_only
20
+
21
+ from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
22
+ from ldm.modules.ema import LitEma
23
+ from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
24
+ from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
25
+ from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
26
+ from ldm.models.diffusion.ddim import DDIMSampler
27
+
28
+
29
+ __conditioning_keys__ = {'concat': 'c_concat',
30
+ 'crossattn': 'c_crossattn',
31
+ 'adm': 'y'}
32
+
33
+
34
+ def disabled_train(self, mode=True):
35
+ """Overwrite model.train with this function to make sure train/eval mode
36
+ does not change anymore."""
37
+ return self
38
+
39
+
40
+ def uniform_on_device(r1, r2, shape, device):
41
+ return (r1 - r2) * torch.rand(*shape, device=device) + r2
42
+
43
+
44
+ class DDPM(pl.LightningModule):
45
+ # classic DDPM with Gaussian diffusion, in image space
46
+ def __init__(self,
47
+ unet_config,
48
+ timesteps=1000,
49
+ beta_schedule="linear",
50
+ loss_type="l2",
51
+ ckpt_path=None,
52
+ ignore_keys=[],
53
+ load_only_unet=False,
54
+ monitor="val/loss",
55
+ use_ema=True,
56
+ first_stage_key="image",
57
+ image_size=256,
58
+ channels=3,
59
+ log_every_t=100,
60
+ clip_denoised=True,
61
+ linear_start=1e-4,
62
+ linear_end=2e-2,
63
+ cosine_s=8e-3,
64
+ given_betas=None,
65
+ original_elbo_weight=0.,
66
+ v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
67
+ l_simple_weight=1.,
68
+ conditioning_key=None,
69
+ parameterization="eps", # all assuming fixed variance schedules
70
+ scheduler_config=None,
71
+ use_positional_encodings=False,
72
+ learn_logvar=False,
73
+ logvar_init=0.,
74
+ ):
75
+ super().__init__()
76
+ assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
77
+ self.parameterization = parameterization
78
+ print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
79
+ self.cond_stage_model = None
80
+ self.clip_denoised = clip_denoised
81
+ self.log_every_t = log_every_t
82
+ self.first_stage_key = first_stage_key
83
+ self.image_size = image_size # try conv?
84
+ self.channels = channels
85
+ self.use_positional_encodings = use_positional_encodings
86
+ self.model = DiffusionWrapper(unet_config, conditioning_key)
87
+ count_params(self.model, verbose=True)
88
+ self.use_ema = use_ema
89
+ if self.use_ema:
90
+ self.model_ema = LitEma(self.model)
91
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
92
+
93
+ self.use_scheduler = scheduler_config is not None
94
+ if self.use_scheduler:
95
+ self.scheduler_config = scheduler_config
96
+
97
+ self.v_posterior = v_posterior
98
+ self.original_elbo_weight = original_elbo_weight
99
+ self.l_simple_weight = l_simple_weight
100
+
101
+ if monitor is not None:
102
+ self.monitor = monitor
103
+ if ckpt_path is not None:
104
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
105
+
106
+ self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
107
+ linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
108
+
109
+ self.loss_type = loss_type
110
+
111
+ self.learn_logvar = learn_logvar
112
+ self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
113
+ if self.learn_logvar:
114
+ self.logvar = nn.Parameter(self.logvar, requires_grad=True)
115
+
116
+
117
+ def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
118
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
119
+ if exists(given_betas):
120
+ betas = given_betas
121
+ else:
122
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
123
+ cosine_s=cosine_s)
124
+ alphas = 1. - betas
125
+ alphas_cumprod = np.cumprod(alphas, axis=0)
126
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
127
+
128
+ timesteps, = betas.shape
129
+ self.num_timesteps = int(timesteps)
130
+ self.linear_start = linear_start
131
+ self.linear_end = linear_end
132
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
133
+
134
+ to_torch = partial(torch.tensor, dtype=torch.float32)
135
+
136
+ self.register_buffer('betas', to_torch(betas))
137
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
138
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
139
+
140
+ # calculations for diffusion q(x_t | x_{t-1}) and others
141
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
142
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
143
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
144
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
145
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
146
+
147
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
148
+ posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
149
+ 1. - alphas_cumprod) + self.v_posterior * betas
150
+ # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
151
+ self.register_buffer('posterior_variance', to_torch(posterior_variance))
152
+ # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
153
+ self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
154
+ self.register_buffer('posterior_mean_coef1', to_torch(
155
+ betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
156
+ self.register_buffer('posterior_mean_coef2', to_torch(
157
+ (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
158
+
159
+ if self.parameterization == "eps":
160
+ lvlb_weights = self.betas ** 2 / (
161
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
162
+ elif self.parameterization == "x0":
163
+ lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
164
+ else:
165
+ raise NotImplementedError("mu not supported")
166
+ # TODO how to choose this term
167
+ lvlb_weights[0] = lvlb_weights[1]
168
+ self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
169
+ assert not torch.isnan(self.lvlb_weights).all()
170
+
171
+ @contextmanager
172
+ def ema_scope(self, context=None):
173
+ if self.use_ema:
174
+ self.model_ema.store(self.model.parameters())
175
+ self.model_ema.copy_to(self.model)
176
+ if context is not None:
177
+ print(f"{context}: Switched to EMA weights")
178
+ try:
179
+ yield None
180
+ finally:
181
+ if self.use_ema:
182
+ self.model_ema.restore(self.model.parameters())
183
+ if context is not None:
184
+ print(f"{context}: Restored training weights")
185
+
186
+ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
187
+ sd = torch.load(path, map_location="cpu")
188
+ if "state_dict" in list(sd.keys()):
189
+ sd = sd["state_dict"]
190
+ keys = list(sd.keys())
191
+ for k in keys:
192
+ for ik in ignore_keys:
193
+ if k.startswith(ik):
194
+ print("Deleting key {} from state_dict.".format(k))
195
+ del sd[k]
196
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
197
+ sd, strict=False)
198
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
199
+ if len(missing) > 0:
200
+ print(f"Missing Keys: {missing}")
201
+ if len(unexpected) > 0:
202
+ print(f"Unexpected Keys: {unexpected}")
203
+
204
+ def q_mean_variance(self, x_start, t):
205
+ """
206
+ Get the distribution q(x_t | x_0).
207
+ :param x_start: the [N x C x ...] tensor of noiseless inputs.
208
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
209
+ :return: A tuple (mean, variance, log_variance), all of x_start's shape.
210
+ """
211
+ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
212
+ variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
213
+ log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
214
+ return mean, variance, log_variance
215
+
216
+ def predict_start_from_noise(self, x_t, t, noise):
217
+ return (
218
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
219
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
220
+ )
221
+
222
+ def q_posterior(self, x_start, x_t, t):
223
+ posterior_mean = (
224
+ extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
225
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
226
+ )
227
+ posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
228
+ posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
229
+ return posterior_mean, posterior_variance, posterior_log_variance_clipped
230
+
231
+ def p_mean_variance(self, x, t, clip_denoised: bool):
232
+ model_out = self.model(x, t)
233
+ if self.parameterization == "eps":
234
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
235
+ elif self.parameterization == "x0":
236
+ x_recon = model_out
237
+ if clip_denoised:
238
+ x_recon.clamp_(-1., 1.)
239
+
240
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
241
+ return model_mean, posterior_variance, posterior_log_variance
242
+
243
+ @torch.no_grad()
244
+ def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
245
+ b, *_, device = *x.shape, x.device
246
+ model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
247
+ noise = noise_like(x.shape, device, repeat_noise)
248
+ # no noise when t == 0
249
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
250
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
251
+
252
+ @torch.no_grad()
253
+ def p_sample_loop(self, shape, return_intermediates=False):
254
+ device = self.betas.device
255
+ b = shape[0]
256
+ img = torch.randn(shape, device=device)
257
+ intermediates = [img]
258
+ for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
259
+ img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
260
+ clip_denoised=self.clip_denoised)
261
+ if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
262
+ intermediates.append(img)
263
+ if return_intermediates:
264
+ return img, intermediates
265
+ return img
266
+
267
+ @torch.no_grad()
268
+ def sample(self, batch_size=16, return_intermediates=False):
269
+ image_size = self.image_size
270
+ channels = self.channels
271
+ return self.p_sample_loop((batch_size, channels, image_size, image_size),
272
+ return_intermediates=return_intermediates)
273
+
274
+ def q_sample(self, x_start, t, noise=None):
275
+ noise = default(noise, lambda: torch.randn_like(x_start))
276
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
277
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
278
+
279
+ def get_loss(self, pred, target, mean=True):
280
+ if self.loss_type == 'l1':
281
+ loss = (target - pred).abs()
282
+ if mean:
283
+ loss = loss.mean()
284
+ elif self.loss_type == 'l2':
285
+ if mean:
286
+ loss = torch.nn.functional.mse_loss(target, pred)
287
+ else:
288
+ loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
289
+ else:
290
+ raise NotImplementedError("unknown loss type '{loss_type}'")
291
+
292
+ return loss
293
+
294
+ def p_losses(self, x_start, t, noise=None):
295
+ noise = default(noise, lambda: torch.randn_like(x_start))
296
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
297
+ model_out = self.model(x_noisy, t)
298
+
299
+ loss_dict = {}
300
+ if self.parameterization == "eps":
301
+ target = noise
302
+ elif self.parameterization == "x0":
303
+ target = x_start
304
+ else:
305
+ raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
306
+
307
+ loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
308
+
309
+ log_prefix = 'train' if self.training else 'val'
310
+
311
+ loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
312
+ loss_simple = loss.mean() * self.l_simple_weight
313
+
314
+ loss_vlb = (self.lvlb_weights[t] * loss).mean()
315
+ loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
316
+
317
+ loss = loss_simple + self.original_elbo_weight * loss_vlb
318
+
319
+ loss_dict.update({f'{log_prefix}/loss': loss})
320
+
321
+ return loss, loss_dict
322
+
323
+ def forward(self, x, *args, **kwargs):
324
+ # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
325
+ # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
326
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
327
+ return self.p_losses(x, t, *args, **kwargs)
328
+
329
+ def get_input(self, batch, k):
330
+ x = batch[k]
331
+ if len(x.shape) == 3:
332
+ x = x[..., None]
333
+ x = rearrange(x, 'b h w c -> b c h w')
334
+ x = x.to(memory_format=torch.contiguous_format).float()
335
+ return x
336
+
337
+ def shared_step(self, batch):
338
+ x = self.get_input(batch, self.first_stage_key)
339
+ loss, loss_dict = self(x)
340
+ return loss, loss_dict
341
+
342
+ def training_step(self, batch, batch_idx):
343
+ loss, loss_dict = self.shared_step(batch)
344
+
345
+ self.log_dict(loss_dict, prog_bar=True,
346
+ logger=True, on_step=True, on_epoch=True)
347
+
348
+ self.log("global_step", self.global_step,
349
+ prog_bar=True, logger=True, on_step=True, on_epoch=False)
350
+
351
+ if self.use_scheduler:
352
+ lr = self.optimizers().param_groups[0]['lr']
353
+ self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
354
+
355
+ return loss
356
+
357
+ @torch.no_grad()
358
+ def validation_step(self, batch, batch_idx):
359
+ _, loss_dict_no_ema = self.shared_step(batch)
360
+ with self.ema_scope():
361
+ _, loss_dict_ema = self.shared_step(batch)
362
+ loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
363
+ self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
364
+ self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
365
+
366
+ def on_train_batch_end(self, *args, **kwargs):
367
+ if self.use_ema:
368
+ self.model_ema(self.model)
369
+
370
+ def _get_rows_from_list(self, samples):
371
+ n_imgs_per_row = len(samples)
372
+ denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
373
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
374
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
375
+ return denoise_grid
376
+
377
+ @torch.no_grad()
378
+ def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
379
+ log = dict()
380
+ x = self.get_input(batch, self.first_stage_key)
381
+ N = min(x.shape[0], N)
382
+ n_row = min(x.shape[0], n_row)
383
+ x = x.to(self.device)[:N]
384
+ log["inputs"] = x
385
+
386
+ # get diffusion row
387
+ diffusion_row = list()
388
+ x_start = x[:n_row]
389
+
390
+ for t in range(self.num_timesteps):
391
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
392
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
393
+ t = t.to(self.device).long()
394
+ noise = torch.randn_like(x_start)
395
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
396
+ diffusion_row.append(x_noisy)
397
+
398
+ log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
399
+
400
+ if sample:
401
+ # get denoise row
402
+ with self.ema_scope("Plotting"):
403
+ samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
404
+
405
+ log["samples"] = samples
406
+ log["denoise_row"] = self._get_rows_from_list(denoise_row)
407
+
408
+ if return_keys:
409
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
410
+ return log
411
+ else:
412
+ return {key: log[key] for key in return_keys}
413
+ return log
414
+
415
+ def configure_optimizers(self):
416
+ lr = self.learning_rate
417
+ params = list(self.model.parameters())
418
+ if self.learn_logvar:
419
+ params = params + [self.logvar]
420
+ opt = torch.optim.AdamW(params, lr=lr)
421
+ return opt
422
+
423
+
424
+ class LatentDiffusion(DDPM):
425
+ """main class"""
426
+ def __init__(self,
427
+ first_stage_config,
428
+ cond_stage_config,
429
+ num_timesteps_cond=None,
430
+ cond_stage_key="image",
431
+ cond_stage_trainable=False,
432
+ concat_mode=True,
433
+ cond_stage_forward=None,
434
+ conditioning_key=None,
435
+ scale_factor=1.0,
436
+ scale_by_std=False,
437
+ *args, **kwargs):
438
+ self.num_timesteps_cond = default(num_timesteps_cond, 1)
439
+ self.scale_by_std = scale_by_std
440
+ assert self.num_timesteps_cond <= kwargs['timesteps']
441
+ # for backwards compatibility after implementation of DiffusionWrapper
442
+ if conditioning_key is None:
443
+ conditioning_key = 'concat' if concat_mode else 'crossattn'
444
+ if cond_stage_config == '__is_unconditional__':
445
+ conditioning_key = None
446
+ ckpt_path = kwargs.pop("ckpt_path", None)
447
+ ignore_keys = kwargs.pop("ignore_keys", [])
448
+ super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
449
+ self.concat_mode = concat_mode
450
+ self.cond_stage_trainable = cond_stage_trainable
451
+ self.cond_stage_key = cond_stage_key
452
+ try:
453
+ self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
454
+ except:
455
+ self.num_downs = 0
456
+ if not scale_by_std:
457
+ self.scale_factor = scale_factor
458
+ else:
459
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
460
+ self.instantiate_first_stage(first_stage_config)
461
+ self.instantiate_cond_stage(cond_stage_config)
462
+ self.cond_stage_forward = cond_stage_forward
463
+ self.clip_denoised = False
464
+ self.bbox_tokenizer = None
465
+
466
+ self.restarted_from_ckpt = False
467
+ if ckpt_path is not None:
468
+ self.init_from_ckpt(ckpt_path, ignore_keys)
469
+ self.restarted_from_ckpt = True
470
+
471
+ def make_cond_schedule(self, ):
472
+ self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
473
+ ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
474
+ self.cond_ids[:self.num_timesteps_cond] = ids
475
+
476
+ @rank_zero_only
477
+ @torch.no_grad()
478
+ def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
479
+ # only for very first batch
480
+ if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
481
+ assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
482
+ # set rescale weight to 1./std of encodings
483
+ print("### USING STD-RESCALING ###")
484
+ x = super().get_input(batch, self.first_stage_key)
485
+ x = x.to(self.device)
486
+ encoder_posterior = self.encode_first_stage(x)
487
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
488
+ del self.scale_factor
489
+ self.register_buffer('scale_factor', 1. / z.flatten().std())
490
+ print(f"setting self.scale_factor to {self.scale_factor}")
491
+ print("### USING STD-RESCALING ###")
492
+
493
+ def register_schedule(self,
494
+ given_betas=None, beta_schedule="linear", timesteps=1000,
495
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
496
+ super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
497
+
498
+ self.shorten_cond_schedule = self.num_timesteps_cond > 1
499
+ if self.shorten_cond_schedule:
500
+ self.make_cond_schedule()
501
+
502
+ def instantiate_first_stage(self, config):
503
+ model = instantiate_from_config(config)
504
+ self.first_stage_model = model.eval()
505
+ self.first_stage_model.train = disabled_train
506
+ for param in self.first_stage_model.parameters():
507
+ param.requires_grad = False
508
+
509
+ def instantiate_cond_stage(self, config):
510
+ if not self.cond_stage_trainable:
511
+ if config == "__is_first_stage__":
512
+ print("Using first stage also as cond stage.")
513
+ self.cond_stage_model = self.first_stage_model
514
+ elif config == "__is_unconditional__":
515
+ print(f"Training {self.__class__.__name__} as an unconditional model.")
516
+ self.cond_stage_model = None
517
+ # self.be_unconditional = True
518
+ else:
519
+ model = instantiate_from_config(config)
520
+ self.cond_stage_model = model.eval()
521
+ self.cond_stage_model.train = disabled_train
522
+ for param in self.cond_stage_model.parameters():
523
+ param.requires_grad = False
524
+ else:
525
+ assert config != '__is_first_stage__'
526
+ assert config != '__is_unconditional__'
527
+ model = instantiate_from_config(config)
528
+ self.cond_stage_model = model
529
+
530
+ def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
531
+ denoise_row = []
532
+ for zd in tqdm(samples, desc=desc):
533
+ denoise_row.append(self.decode_first_stage(zd.to(self.device),
534
+ force_not_quantize=force_no_decoder_quantization))
535
+ n_imgs_per_row = len(denoise_row)
536
+ denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
537
+ denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
538
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
539
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
540
+ return denoise_grid
541
+
542
+ def get_first_stage_encoding(self, encoder_posterior):
543
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
544
+ z = encoder_posterior.sample()
545
+ elif isinstance(encoder_posterior, torch.Tensor):
546
+ z = encoder_posterior
547
+ else:
548
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
549
+ return self.scale_factor * z
550
+
551
+ def get_learned_conditioning(self, c):
552
+ if self.cond_stage_forward is None:
553
+ if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
554
+ c = self.cond_stage_model.encode(c)
555
+ if isinstance(c, DiagonalGaussianDistribution):
556
+ c = c.mode()
557
+ else:
558
+ c = self.cond_stage_model(c)
559
+ else:
560
+ assert hasattr(self.cond_stage_model, self.cond_stage_forward)
561
+ c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
562
+ return c
563
+
564
+ def meshgrid(self, h, w):
565
+ y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
566
+ x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
567
+
568
+ arr = torch.cat([y, x], dim=-1)
569
+ return arr
570
+
571
+ def delta_border(self, h, w):
572
+ """
573
+ :param h: height
574
+ :param w: width
575
+ :return: normalized distance to image border,
576
+ wtith min distance = 0 at border and max dist = 0.5 at image center
577
+ """
578
+ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
579
+ arr = self.meshgrid(h, w) / lower_right_corner
580
+ dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
581
+ dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
582
+ edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
583
+ return edge_dist
584
+
585
+ def get_weighting(self, h, w, Ly, Lx, device):
586
+ weighting = self.delta_border(h, w)
587
+ weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
588
+ self.split_input_params["clip_max_weight"], )
589
+ weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
590
+
591
+ if self.split_input_params["tie_braker"]:
592
+ L_weighting = self.delta_border(Ly, Lx)
593
+ L_weighting = torch.clip(L_weighting,
594
+ self.split_input_params["clip_min_tie_weight"],
595
+ self.split_input_params["clip_max_tie_weight"])
596
+
597
+ L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
598
+ weighting = weighting * L_weighting
599
+ return weighting
600
+
601
+ def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
602
+ """
603
+ :param x: img of size (bs, c, h, w)
604
+ :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
605
+ """
606
+ bs, nc, h, w = x.shape
607
+
608
+ # number of crops in image
609
+ Ly = (h - kernel_size[0]) // stride[0] + 1
610
+ Lx = (w - kernel_size[1]) // stride[1] + 1
611
+
612
+ if uf == 1 and df == 1:
613
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
614
+ unfold = torch.nn.Unfold(**fold_params)
615
+
616
+ fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
617
+
618
+ weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
619
+ normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
620
+ weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
621
+
622
+ elif uf > 1 and df == 1:
623
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
624
+ unfold = torch.nn.Unfold(**fold_params)
625
+
626
+ fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
627
+ dilation=1, padding=0,
628
+ stride=(stride[0] * uf, stride[1] * uf))
629
+ fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
630
+
631
+ weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
632
+ normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
633
+ weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
634
+
635
+ elif df > 1 and uf == 1:
636
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
637
+ unfold = torch.nn.Unfold(**fold_params)
638
+
639
+ fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
640
+ dilation=1, padding=0,
641
+ stride=(stride[0] // df, stride[1] // df))
642
+ fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
643
+
644
+ weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
645
+ normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
646
+ weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
647
+
648
+ else:
649
+ raise NotImplementedError
650
+
651
+ return fold, unfold, normalization, weighting
652
+
653
+ @torch.no_grad()
654
+ def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
655
+ cond_key=None, return_original_cond=False, bs=None):
656
+ x = super().get_input(batch, k)
657
+ if bs is not None:
658
+ x = x[:bs]
659
+ x = x.to(self.device)
660
+ encoder_posterior = self.encode_first_stage(x)
661
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
662
+
663
+ if self.model.conditioning_key is not None:
664
+ if cond_key is None:
665
+ cond_key = self.cond_stage_key
666
+ if cond_key != self.first_stage_key:
667
+ if cond_key in ['caption', 'coordinates_bbox']:
668
+ xc = batch[cond_key]
669
+ elif cond_key == 'class_label':
670
+ xc = batch
671
+ else:
672
+ xc = super().get_input(batch, cond_key).to(self.device)
673
+ else:
674
+ xc = x
675
+ if not self.cond_stage_trainable or force_c_encode:
676
+ if isinstance(xc, dict) or isinstance(xc, list):
677
+ # import pudb; pudb.set_trace()
678
+ c = self.get_learned_conditioning(xc)
679
+ else:
680
+ c = self.get_learned_conditioning(xc.to(self.device))
681
+ else:
682
+ c = xc
683
+ if bs is not None:
684
+ c = c[:bs]
685
+
686
+ if self.use_positional_encodings:
687
+ pos_x, pos_y = self.compute_latent_shifts(batch)
688
+ ckey = __conditioning_keys__[self.model.conditioning_key]
689
+ c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
690
+
691
+ else:
692
+ c = None
693
+ xc = None
694
+ if self.use_positional_encodings:
695
+ pos_x, pos_y = self.compute_latent_shifts(batch)
696
+ c = {'pos_x': pos_x, 'pos_y': pos_y}
697
+ out = [z, c]
698
+ if return_first_stage_outputs:
699
+ xrec = self.decode_first_stage(z)
700
+ out.extend([x, xrec])
701
+ if return_original_cond:
702
+ out.append(xc)
703
+ return out
704
+
705
+ @torch.no_grad()
706
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
707
+ if predict_cids:
708
+ if z.dim() == 4:
709
+ z = torch.argmax(z.exp(), dim=1).long()
710
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
711
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
712
+
713
+ z = 1. / self.scale_factor * z
714
+
715
+ if hasattr(self, "split_input_params"):
716
+ if self.split_input_params["patch_distributed_vq"]:
717
+ ks = self.split_input_params["ks"] # eg. (128, 128)
718
+ stride = self.split_input_params["stride"] # eg. (64, 64)
719
+ uf = self.split_input_params["vqf"]
720
+ bs, nc, h, w = z.shape
721
+ if ks[0] > h or ks[1] > w:
722
+ ks = (min(ks[0], h), min(ks[1], w))
723
+ print("reducing Kernel")
724
+
725
+ if stride[0] > h or stride[1] > w:
726
+ stride = (min(stride[0], h), min(stride[1], w))
727
+ print("reducing stride")
728
+
729
+ fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
730
+
731
+ z = unfold(z) # (bn, nc * prod(**ks), L)
732
+ # 1. Reshape to img shape
733
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
734
+
735
+ # 2. apply model loop over last dim
736
+ if isinstance(self.first_stage_model, VQModelInterface):
737
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
738
+ force_not_quantize=predict_cids or force_not_quantize)
739
+ for i in range(z.shape[-1])]
740
+ else:
741
+
742
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
743
+ for i in range(z.shape[-1])]
744
+
745
+ o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
746
+ o = o * weighting
747
+ # Reverse 1. reshape to img shape
748
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
749
+ # stitch crops together
750
+ decoded = fold(o)
751
+ decoded = decoded / normalization # norm is shape (1, 1, h, w)
752
+ return decoded
753
+ else:
754
+ if isinstance(self.first_stage_model, VQModelInterface):
755
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
756
+ else:
757
+ return self.first_stage_model.decode(z)
758
+
759
+ else:
760
+ if isinstance(self.first_stage_model, VQModelInterface):
761
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
762
+ else:
763
+ return self.first_stage_model.decode(z)
764
+
765
+ # same as above but without decorator
766
+ def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
767
+ if predict_cids:
768
+ if z.dim() == 4:
769
+ z = torch.argmax(z.exp(), dim=1).long()
770
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
771
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
772
+
773
+ z = 1. / self.scale_factor * z
774
+
775
+ if hasattr(self, "split_input_params"):
776
+ if self.split_input_params["patch_distributed_vq"]:
777
+ ks = self.split_input_params["ks"] # eg. (128, 128)
778
+ stride = self.split_input_params["stride"] # eg. (64, 64)
779
+ uf = self.split_input_params["vqf"]
780
+ bs, nc, h, w = z.shape
781
+ if ks[0] > h or ks[1] > w:
782
+ ks = (min(ks[0], h), min(ks[1], w))
783
+ print("reducing Kernel")
784
+
785
+ if stride[0] > h or stride[1] > w:
786
+ stride = (min(stride[0], h), min(stride[1], w))
787
+ print("reducing stride")
788
+
789
+ fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
790
+
791
+ z = unfold(z) # (bn, nc * prod(**ks), L)
792
+ # 1. Reshape to img shape
793
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
794
+
795
+ # 2. apply model loop over last dim
796
+ if isinstance(self.first_stage_model, VQModelInterface):
797
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
798
+ force_not_quantize=predict_cids or force_not_quantize)
799
+ for i in range(z.shape[-1])]
800
+ else:
801
+
802
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
803
+ for i in range(z.shape[-1])]
804
+
805
+ o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
806
+ o = o * weighting
807
+ # Reverse 1. reshape to img shape
808
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
809
+ # stitch crops together
810
+ decoded = fold(o)
811
+ decoded = decoded / normalization # norm is shape (1, 1, h, w)
812
+ return decoded
813
+ else:
814
+ if isinstance(self.first_stage_model, VQModelInterface):
815
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
816
+ else:
817
+ return self.first_stage_model.decode(z)
818
+
819
+ else:
820
+ if isinstance(self.first_stage_model, VQModelInterface):
821
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
822
+ else:
823
+ return self.first_stage_model.decode(z)
824
+
825
+ @torch.no_grad()
826
+ def encode_first_stage(self, x):
827
+ if hasattr(self, "split_input_params"):
828
+ if self.split_input_params["patch_distributed_vq"]:
829
+ ks = self.split_input_params["ks"] # eg. (128, 128)
830
+ stride = self.split_input_params["stride"] # eg. (64, 64)
831
+ df = self.split_input_params["vqf"]
832
+ self.split_input_params['original_image_size'] = x.shape[-2:]
833
+ bs, nc, h, w = x.shape
834
+ if ks[0] > h or ks[1] > w:
835
+ ks = (min(ks[0], h), min(ks[1], w))
836
+ print("reducing Kernel")
837
+
838
+ if stride[0] > h or stride[1] > w:
839
+ stride = (min(stride[0], h), min(stride[1], w))
840
+ print("reducing stride")
841
+
842
+ fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
843
+ z = unfold(x) # (bn, nc * prod(**ks), L)
844
+ # Reshape to img shape
845
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
846
+
847
+ output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
848
+ for i in range(z.shape[-1])]
849
+
850
+ o = torch.stack(output_list, axis=-1)
851
+ o = o * weighting
852
+
853
+ # Reverse reshape to img shape
854
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
855
+ # stitch crops together
856
+ decoded = fold(o)
857
+ decoded = decoded / normalization
858
+ return decoded
859
+
860
+ else:
861
+ return self.first_stage_model.encode(x)
862
+ else:
863
+ return self.first_stage_model.encode(x)
864
+
865
+ def shared_step(self, batch, **kwargs):
866
+ x, c = self.get_input(batch, self.first_stage_key)
867
+ loss = self(x, c)
868
+ return loss
869
+
870
+ def forward(self, x, c, *args, **kwargs):
871
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
872
+ if self.model.conditioning_key is not None:
873
+ assert c is not None
874
+ if self.cond_stage_trainable:
875
+ c = self.get_learned_conditioning(c)
876
+ if self.shorten_cond_schedule: # TODO: drop this option
877
+ tc = self.cond_ids[t].to(self.device)
878
+ c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
879
+ return self.p_losses(x, c, t, *args, **kwargs)
880
+
881
+ def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
882
+ def rescale_bbox(bbox):
883
+ x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
884
+ y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
885
+ w = min(bbox[2] / crop_coordinates[2], 1 - x0)
886
+ h = min(bbox[3] / crop_coordinates[3], 1 - y0)
887
+ return x0, y0, w, h
888
+
889
+ return [rescale_bbox(b) for b in bboxes]
890
+
891
+ def apply_model(self, x_noisy, t, cond, return_ids=False):
892
+
893
+ if isinstance(cond, dict):
894
+ # hybrid case, cond is exptected to be a dict
895
+ pass
896
+ else:
897
+ if not isinstance(cond, list):
898
+ cond = [cond]
899
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
900
+ cond = {key: cond}
901
+
902
+ if hasattr(self, "split_input_params"):
903
+ assert len(cond) == 1 # todo can only deal with one conditioning atm
904
+ assert not return_ids
905
+ ks = self.split_input_params["ks"] # eg. (128, 128)
906
+ stride = self.split_input_params["stride"] # eg. (64, 64)
907
+
908
+ h, w = x_noisy.shape[-2:]
909
+
910
+ fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
911
+
912
+ z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
913
+ # Reshape to img shape
914
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
915
+ z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
916
+
917
+ if self.cond_stage_key in ["image", "LR_image", "segmentation",
918
+ 'bbox_img'] and self.model.conditioning_key: # todo check for completeness
919
+ c_key = next(iter(cond.keys())) # get key
920
+ c = next(iter(cond.values())) # get value
921
+ assert (len(c) == 1) # todo extend to list with more than one elem
922
+ c = c[0] # get element
923
+
924
+ c = unfold(c)
925
+ c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
926
+
927
+ cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
928
+
929
+ elif self.cond_stage_key == 'coordinates_bbox':
930
+ assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
931
+
932
+ # assuming padding of unfold is always 0 and its dilation is always 1
933
+ n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
934
+ full_img_h, full_img_w = self.split_input_params['original_image_size']
935
+ # as we are operating on latents, we need the factor from the original image size to the
936
+ # spatial latent size to properly rescale the crops for regenerating the bbox annotations
937
+ num_downs = self.first_stage_model.encoder.num_resolutions - 1
938
+ rescale_latent = 2 ** (num_downs)
939
+
940
+ # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
941
+ # need to rescale the tl patch coordinates to be in between (0,1)
942
+ tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
943
+ rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
944
+ for patch_nr in range(z.shape[-1])]
945
+
946
+ # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
947
+ patch_limits = [(x_tl, y_tl,
948
+ rescale_latent * ks[0] / full_img_w,
949
+ rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
950
+ # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
951
+
952
+ # tokenize crop coordinates for the bounding boxes of the respective patches
953
+ patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
954
+ for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
955
+ print(patch_limits_tknzd[0].shape)
956
+ # cut tknzd crop position from conditioning
957
+ assert isinstance(cond, dict), 'cond must be dict to be fed into model'
958
+ cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
959
+ print(cut_cond.shape)
960
+
961
+ adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
962
+ adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
963
+ print(adapted_cond.shape)
964
+ adapted_cond = self.get_learned_conditioning(adapted_cond)
965
+ print(adapted_cond.shape)
966
+ adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
967
+ print(adapted_cond.shape)
968
+
969
+ cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
970
+
971
+ else:
972
+ cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
973
+
974
+ # apply model by loop over crops
975
+ output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
976
+ assert not isinstance(output_list[0],
977
+ tuple) # todo cant deal with multiple model outputs check this never happens
978
+
979
+ o = torch.stack(output_list, axis=-1)
980
+ o = o * weighting
981
+ # Reverse reshape to img shape
982
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
983
+ # stitch crops together
984
+ x_recon = fold(o) / normalization
985
+
986
+ else:
987
+ x_recon = self.model(x_noisy, t, **cond)
988
+
989
+ if isinstance(x_recon, tuple) and not return_ids:
990
+ return x_recon[0]
991
+ else:
992
+ return x_recon
993
+
994
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
995
+ return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
996
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
997
+
998
+ def _prior_bpd(self, x_start):
999
+ """
1000
+ Get the prior KL term for the variational lower-bound, measured in
1001
+ bits-per-dim.
1002
+ This term can't be optimized, as it only depends on the encoder.
1003
+ :param x_start: the [N x C x ...] tensor of inputs.
1004
+ :return: a batch of [N] KL values (in bits), one per batch element.
1005
+ """
1006
+ batch_size = x_start.shape[0]
1007
+ t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
1008
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
1009
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
1010
+ return mean_flat(kl_prior) / np.log(2.0)
1011
+
1012
+ def p_losses(self, x_start, cond, t, noise=None):
1013
+ noise = default(noise, lambda: torch.randn_like(x_start))
1014
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
1015
+ model_output = self.apply_model(x_noisy, t, cond)
1016
+
1017
+ loss_dict = {}
1018
+ prefix = 'train' if self.training else 'val'
1019
+
1020
+ if self.parameterization == "x0":
1021
+ target = x_start
1022
+ elif self.parameterization == "eps":
1023
+ target = noise
1024
+ else:
1025
+ raise NotImplementedError()
1026
+
1027
+ loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
1028
+ loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
1029
+
1030
+ logvar_t = self.logvar[t].to(self.device)
1031
+ loss = loss_simple / torch.exp(logvar_t) + logvar_t
1032
+ # loss = loss_simple / torch.exp(self.logvar) + self.logvar
1033
+ if self.learn_logvar:
1034
+ loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
1035
+ loss_dict.update({'logvar': self.logvar.data.mean()})
1036
+
1037
+ loss = self.l_simple_weight * loss.mean()
1038
+
1039
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
1040
+ loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
1041
+ loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
1042
+ loss += (self.original_elbo_weight * loss_vlb)
1043
+ loss_dict.update({f'{prefix}/loss': loss})
1044
+
1045
+ return loss, loss_dict
1046
+
1047
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
1048
+ return_x0=False, score_corrector=None, corrector_kwargs=None):
1049
+ t_in = t
1050
+ model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
1051
+
1052
+ if score_corrector is not None:
1053
+ assert self.parameterization == "eps"
1054
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
1055
+
1056
+ if return_codebook_ids:
1057
+ model_out, logits = model_out
1058
+
1059
+ if self.parameterization == "eps":
1060
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
1061
+ elif self.parameterization == "x0":
1062
+ x_recon = model_out
1063
+ else:
1064
+ raise NotImplementedError()
1065
+
1066
+ if clip_denoised:
1067
+ x_recon.clamp_(-1., 1.)
1068
+ if quantize_denoised:
1069
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
1070
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
1071
+ if return_codebook_ids:
1072
+ return model_mean, posterior_variance, posterior_log_variance, logits
1073
+ elif return_x0:
1074
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
1075
+ else:
1076
+ return model_mean, posterior_variance, posterior_log_variance
1077
+
1078
+ @torch.no_grad()
1079
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
1080
+ return_codebook_ids=False, quantize_denoised=False, return_x0=False,
1081
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
1082
+ b, *_, device = *x.shape, x.device
1083
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
1084
+ return_codebook_ids=return_codebook_ids,
1085
+ quantize_denoised=quantize_denoised,
1086
+ return_x0=return_x0,
1087
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
1088
+ if return_codebook_ids:
1089
+ raise DeprecationWarning("Support dropped.")
1090
+ model_mean, _, model_log_variance, logits = outputs
1091
+ elif return_x0:
1092
+ model_mean, _, model_log_variance, x0 = outputs
1093
+ else:
1094
+ model_mean, _, model_log_variance = outputs
1095
+
1096
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
1097
+ if noise_dropout > 0.:
1098
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
1099
+ # no noise when t == 0
1100
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
1101
+
1102
+ if return_codebook_ids:
1103
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
1104
+ if return_x0:
1105
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
1106
+ else:
1107
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
1108
+
1109
+ @torch.no_grad()
1110
+ def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
1111
+ img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
1112
+ score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
1113
+ log_every_t=None):
1114
+ if not log_every_t:
1115
+ log_every_t = self.log_every_t
1116
+ timesteps = self.num_timesteps
1117
+ if batch_size is not None:
1118
+ b = batch_size if batch_size is not None else shape[0]
1119
+ shape = [batch_size] + list(shape)
1120
+ else:
1121
+ b = batch_size = shape[0]
1122
+ if x_T is None:
1123
+ img = torch.randn(shape, device=self.device)
1124
+ else:
1125
+ img = x_T
1126
+ intermediates = []
1127
+ if cond is not None:
1128
+ if isinstance(cond, dict):
1129
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
1130
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
1131
+ else:
1132
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
1133
+
1134
+ if start_T is not None:
1135
+ timesteps = min(timesteps, start_T)
1136
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
1137
+ total=timesteps) if verbose else reversed(
1138
+ range(0, timesteps))
1139
+ if type(temperature) == float:
1140
+ temperature = [temperature] * timesteps
1141
+
1142
+ for i in iterator:
1143
+ ts = torch.full((b,), i, device=self.device, dtype=torch.long)
1144
+ if self.shorten_cond_schedule:
1145
+ assert self.model.conditioning_key != 'hybrid'
1146
+ tc = self.cond_ids[ts].to(cond.device)
1147
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
1148
+
1149
+ img, x0_partial = self.p_sample(img, cond, ts,
1150
+ clip_denoised=self.clip_denoised,
1151
+ quantize_denoised=quantize_denoised, return_x0=True,
1152
+ temperature=temperature[i], noise_dropout=noise_dropout,
1153
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
1154
+ if mask is not None:
1155
+ assert x0 is not None
1156
+ img_orig = self.q_sample(x0, ts)
1157
+ img = img_orig * mask + (1. - mask) * img
1158
+
1159
+ if i % log_every_t == 0 or i == timesteps - 1:
1160
+ intermediates.append(x0_partial)
1161
+ if callback: callback(i)
1162
+ if img_callback: img_callback(img, i)
1163
+ return img, intermediates
1164
+
1165
+ @torch.no_grad()
1166
+ def p_sample_loop(self, cond, shape, return_intermediates=False,
1167
+ x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
1168
+ mask=None, x0=None, img_callback=None, start_T=None,
1169
+ log_every_t=None):
1170
+
1171
+ if not log_every_t:
1172
+ log_every_t = self.log_every_t
1173
+ device = self.betas.device
1174
+ b = shape[0]
1175
+ if x_T is None:
1176
+ img = torch.randn(shape, device=device)
1177
+ else:
1178
+ img = x_T
1179
+
1180
+ intermediates = [img]
1181
+ if timesteps is None:
1182
+ timesteps = self.num_timesteps
1183
+
1184
+ if start_T is not None:
1185
+ timesteps = min(timesteps, start_T)
1186
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
1187
+ range(0, timesteps))
1188
+
1189
+ if mask is not None:
1190
+ assert x0 is not None
1191
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
1192
+
1193
+ for i in iterator:
1194
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
1195
+ if self.shorten_cond_schedule:
1196
+ assert self.model.conditioning_key != 'hybrid'
1197
+ tc = self.cond_ids[ts].to(cond.device)
1198
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
1199
+
1200
+ img = self.p_sample(img, cond, ts,
1201
+ clip_denoised=self.clip_denoised,
1202
+ quantize_denoised=quantize_denoised)
1203
+ if mask is not None:
1204
+ img_orig = self.q_sample(x0, ts)
1205
+ img = img_orig * mask + (1. - mask) * img
1206
+
1207
+ if i % log_every_t == 0 or i == timesteps - 1:
1208
+ intermediates.append(img)
1209
+ if callback: callback(i)
1210
+ if img_callback: img_callback(img, i)
1211
+
1212
+ if return_intermediates:
1213
+ return img, intermediates
1214
+ return img
1215
+
1216
+ @torch.no_grad()
1217
+ def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
1218
+ verbose=True, timesteps=None, quantize_denoised=False,
1219
+ mask=None, x0=None, shape=None,**kwargs):
1220
+ if shape is None:
1221
+ shape = (batch_size, self.channels, self.image_size, self.image_size)
1222
+ if cond is not None:
1223
+ if isinstance(cond, dict):
1224
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
1225
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
1226
+ else:
1227
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
1228
+ return self.p_sample_loop(cond,
1229
+ shape,
1230
+ return_intermediates=return_intermediates, x_T=x_T,
1231
+ verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
1232
+ mask=mask, x0=x0)
1233
+
1234
+ @torch.no_grad()
1235
+ def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
1236
+
1237
+ if ddim:
1238
+ ddim_sampler = DDIMSampler(self)
1239
+ shape = (self.channels, self.image_size, self.image_size)
1240
+ samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
1241
+ shape,cond,verbose=False,**kwargs)
1242
+
1243
+ else:
1244
+ samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
1245
+ return_intermediates=True,**kwargs)
1246
+
1247
+ return samples, intermediates
1248
+
1249
+
1250
+ @torch.no_grad()
1251
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
1252
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
1253
+ plot_diffusion_rows=True, **kwargs):
1254
+
1255
+ use_ddim = ddim_steps is not None
1256
+
1257
+ log = dict()
1258
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
1259
+ return_first_stage_outputs=True,
1260
+ force_c_encode=True,
1261
+ return_original_cond=True,
1262
+ bs=N)
1263
+ N = min(x.shape[0], N)
1264
+ n_row = min(x.shape[0], n_row)
1265
+ log["inputs"] = x
1266
+ log["reconstruction"] = xrec
1267
+ if self.model.conditioning_key is not None:
1268
+ if hasattr(self.cond_stage_model, "decode"):
1269
+ xc = self.cond_stage_model.decode(c)
1270
+ log["conditioning"] = xc
1271
+ elif self.cond_stage_key in ["caption"]:
1272
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
1273
+ log["conditioning"] = xc
1274
+ elif self.cond_stage_key == 'class_label':
1275
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
1276
+ log['conditioning'] = xc
1277
+ elif isimage(xc):
1278
+ log["conditioning"] = xc
1279
+ if ismap(xc):
1280
+ log["original_conditioning"] = self.to_rgb(xc)
1281
+
1282
+ if plot_diffusion_rows:
1283
+ # get diffusion row
1284
+ diffusion_row = list()
1285
+ z_start = z[:n_row]
1286
+ for t in range(self.num_timesteps):
1287
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
1288
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
1289
+ t = t.to(self.device).long()
1290
+ noise = torch.randn_like(z_start)
1291
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
1292
+ diffusion_row.append(self.decode_first_stage(z_noisy))
1293
+
1294
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
1295
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
1296
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
1297
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
1298
+ log["diffusion_row"] = diffusion_grid
1299
+
1300
+ if sample:
1301
+ # get denoise row
1302
+ with self.ema_scope("Plotting"):
1303
+ samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
1304
+ ddim_steps=ddim_steps,eta=ddim_eta)
1305
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
1306
+ x_samples = self.decode_first_stage(samples)
1307
+ log["samples"] = x_samples
1308
+ if plot_denoise_rows:
1309
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
1310
+ log["denoise_row"] = denoise_grid
1311
+
1312
+ if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
1313
+ self.first_stage_model, IdentityFirstStage):
1314
+ # also display when quantizing x0 while sampling
1315
+ with self.ema_scope("Plotting Quantized Denoised"):
1316
+ samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
1317
+ ddim_steps=ddim_steps,eta=ddim_eta,
1318
+ quantize_denoised=True)
1319
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
1320
+ # quantize_denoised=True)
1321
+ x_samples = self.decode_first_stage(samples.to(self.device))
1322
+ log["samples_x0_quantized"] = x_samples
1323
+
1324
+ if inpaint:
1325
+ # make a simple center square
1326
+ b, h, w = z.shape[0], z.shape[2], z.shape[3]
1327
+ mask = torch.ones(N, h, w).to(self.device)
1328
+ # zeros will be filled in
1329
+ mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
1330
+ mask = mask[:, None, ...]
1331
+ with self.ema_scope("Plotting Inpaint"):
1332
+
1333
+ samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
1334
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
1335
+ x_samples = self.decode_first_stage(samples.to(self.device))
1336
+ log["samples_inpainting"] = x_samples
1337
+ log["mask"] = mask
1338
+
1339
+ # outpaint
1340
+ with self.ema_scope("Plotting Outpaint"):
1341
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
1342
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
1343
+ x_samples = self.decode_first_stage(samples.to(self.device))
1344
+ log["samples_outpainting"] = x_samples
1345
+
1346
+ if plot_progressive_rows:
1347
+ with self.ema_scope("Plotting Progressives"):
1348
+ img, progressives = self.progressive_denoising(c,
1349
+ shape=(self.channels, self.image_size, self.image_size),
1350
+ batch_size=N)
1351
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
1352
+ log["progressive_row"] = prog_row
1353
+
1354
+ if return_keys:
1355
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
1356
+ return log
1357
+ else:
1358
+ return {key: log[key] for key in return_keys}
1359
+ return log
1360
+
1361
+ def configure_optimizers(self):
1362
+ lr = self.learning_rate
1363
+ params = list(self.model.parameters())
1364
+ if self.cond_stage_trainable:
1365
+ print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
1366
+ params = params + list(self.cond_stage_model.parameters())
1367
+ if self.learn_logvar:
1368
+ print('Diffusion model optimizing logvar')
1369
+ params.append(self.logvar)
1370
+ opt = torch.optim.AdamW(params, lr=lr)
1371
+ if self.use_scheduler:
1372
+ assert 'target' in self.scheduler_config
1373
+ scheduler = instantiate_from_config(self.scheduler_config)
1374
+
1375
+ print("Setting up LambdaLR scheduler...")
1376
+ scheduler = [
1377
+ {
1378
+ 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
1379
+ 'interval': 'step',
1380
+ 'frequency': 1
1381
+ }]
1382
+ return [opt], scheduler
1383
+ return opt
1384
+
1385
+ @torch.no_grad()
1386
+ def to_rgb(self, x):
1387
+ x = x.float()
1388
+ if not hasattr(self, "colorize"):
1389
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
1390
+ x = nn.functional.conv2d(x, weight=self.colorize)
1391
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
1392
+ return x
1393
+
1394
+
1395
+ class DiffusionWrapper(pl.LightningModule):
1396
+ def __init__(self, diff_model_config, conditioning_key):
1397
+ super().__init__()
1398
+ self.diffusion_model = instantiate_from_config(diff_model_config)
1399
+ self.conditioning_key = conditioning_key
1400
+ assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
1401
+
1402
+ def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
1403
+ if self.conditioning_key is None:
1404
+ out = self.diffusion_model(x, t)
1405
+ elif self.conditioning_key == 'concat':
1406
+ xc = torch.cat([x] + c_concat, dim=1)
1407
+ out = self.diffusion_model(xc, t)
1408
+ elif self.conditioning_key == 'crossattn':
1409
+ cc = torch.cat(c_crossattn, 1)
1410
+ out = self.diffusion_model(x, t, context=cc)
1411
+ elif self.conditioning_key == 'hybrid':
1412
+ xc = torch.cat([x] + c_concat, dim=1)
1413
+ cc = torch.cat(c_crossattn, 1)
1414
+ out = self.diffusion_model(xc, t, context=cc)
1415
+ elif self.conditioning_key == 'adm':
1416
+ cc = c_crossattn[0]
1417
+ out = self.diffusion_model(x, t, y=cc)
1418
+ else:
1419
+ raise NotImplementedError()
1420
+
1421
+ return out
1422
+
1423
+
1424
+ class Layout2ImgDiffusion(LatentDiffusion):
1425
+ # TODO: move all layout-specific hacks to this class
1426
+ def __init__(self, cond_stage_key, *args, **kwargs):
1427
+ assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
1428
+ super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
1429
+
1430
+ def log_images(self, batch, N=8, *args, **kwargs):
1431
+ logs = super().log_images(batch=batch, N=N, *args, **kwargs)
1432
+
1433
+ key = 'train' if self.training else 'validation'
1434
+ dset = self.trainer.datamodule.datasets[key]
1435
+ mapper = dset.conditional_builders[self.cond_stage_key]
1436
+
1437
+ bbox_imgs = []
1438
+ map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
1439
+ for tknzd_bbox in batch[self.cond_stage_key][:N]:
1440
+ bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
1441
+ bbox_imgs.append(bboximg)
1442
+
1443
+ cond_img = torch.stack(bbox_imgs, dim=0)
1444
+ logs['bbox_image'] = cond_img
1445
+ return logs