modules/models/diffusion/ddpm_edit.py

# """
# wild mixture of
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
# https://github.com/CompVis/taming-transformers
# -- merci
# """
#
# # File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# # See more details in LICENSE.
# 
# import torch
# import torch.nn as nn
# import numpy as np
# import pytorch_lightning as pl
# from torch.optim.lr_scheduler import LambdaLR
# from einops import rearrange, repeat
# from contextlib import contextmanager
# from functools import partial
# from tqdm import tqdm
# from torchvision.utils import make_grid
# from pytorch_lightning.utilities.distributed import rank_zero_only
#
# from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
# from ldm.modules.ema import LitEma
# from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
# from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
# from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
# from ldm.models.diffusion.ddim import DDIMSampler
#
# try:
#     from ldm.models.autoencoder import VQModelInterface
# except Exception:
#     class VQModelInterface:
#         pass
#
# __conditioning_keys__ = {'concat': 'c_concat',
#                          'crossattn': 'c_crossattn',
#                          'adm': 'y'}
#
#
# def disabled_train(self, mode=True):
#     """Overwrite model.train with this function to make sure train/eval mode
#     does not change anymore."""
#     return self
#
#
# def uniform_on_device(r1, r2, shape, device):
#     return (r1 - r2) * torch.rand(*shape, device=device) + r2
#
#
# class DDPM(pl.LightningModule):
#     # classic DDPM with Gaussian diffusion, in image space
#     def __init__(self,
#                  unet_config,
#                  timesteps=1000,
#                  beta_schedule="linear",
#                  loss_type="l2",
#                  ckpt_path=None,
#                  ignore_keys=None,
#                  load_only_unet=False,
#                  monitor="val/loss",
#                  use_ema=True,
#                  first_stage_key="image",
#                  image_size=256,
#                  channels=3,
#                  log_every_t=100,
#                  clip_denoised=True,
#                  linear_start=1e-4,
#                  linear_end=2e-2,
#                  cosine_s=8e-3,
#                  given_betas=None,
#                  original_elbo_weight=0.,
#                  v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
#                  l_simple_weight=1.,
#                  conditioning_key=None,
#                  parameterization="eps",  # all assuming fixed variance schedules
#                  scheduler_config=None,
#                  use_positional_encodings=False,
#                  learn_logvar=False,
#                  logvar_init=0.,
#                  load_ema=True,
#                  ):
#         super().__init__()
#         assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
#         self.parameterization = parameterization
#         print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
#         self.cond_stage_model = None
#         self.clip_denoised = clip_denoised
#         self.log_every_t = log_every_t
#         self.first_stage_key = first_stage_key
#         self.image_size = image_size  # try conv?
#         self.channels = channels
#         self.use_positional_encodings = use_positional_encodings
#         self.model = DiffusionWrapper(unet_config, conditioning_key)
#         count_params(self.model, verbose=True)
#         self.use_ema = use_ema
#
#         self.use_scheduler = scheduler_config is not None
#         if self.use_scheduler:
#             self.scheduler_config = scheduler_config
#
#         self.v_posterior = v_posterior
#         self.original_elbo_weight = original_elbo_weight
#         self.l_simple_weight = l_simple_weight
#
#         if monitor is not None:
#             self.monitor = monitor
#
#         if self.use_ema and load_ema:
#             self.model_ema = LitEma(self.model)
#             print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
#
#         if ckpt_path is not None:
#             self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
#
#             # If initialing from EMA-only checkpoint, create EMA model after loading.
#             if self.use_ema and not load_ema:
#                 self.model_ema = LitEma(self.model)
#                 print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
#
#         self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
#                                linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
#
#         self.loss_type = loss_type
#
#         self.learn_logvar = learn_logvar
#         self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
#         if self.learn_logvar:
#             self.logvar = nn.Parameter(self.logvar, requires_grad=True)
#
#
#     def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
#                           linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
#         if exists(given_betas):
#             betas = given_betas
#         else:
#             betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
#                                        cosine_s=cosine_s)
#         alphas = 1. - betas
#         alphas_cumprod = np.cumprod(alphas, axis=0)
#         alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
#
#         timesteps, = betas.shape
#         self.num_timesteps = int(timesteps)
#         self.linear_start = linear_start
#         self.linear_end = linear_end
#         assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
#
#         to_torch = partial(torch.tensor, dtype=torch.float32)
#
#         self.register_buffer('betas', to_torch(betas))
#         self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
#         self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
#
#         # calculations for diffusion q(x_t | x_{t-1}) and others
#         self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
#         self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
#         self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
#         self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
#         self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
#
#         # calculations for posterior q(x_{t-1} | x_t, x_0)
#         posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
#                     1. - alphas_cumprod) + self.v_posterior * betas
#         # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
#         self.register_buffer('posterior_variance', to_torch(posterior_variance))
#         # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
#         self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
#         self.register_buffer('posterior_mean_coef1', to_torch(
#             betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
#         self.register_buffer('posterior_mean_coef2', to_torch(
#             (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
#
#         if self.parameterization == "eps":
#             lvlb_weights = self.betas ** 2 / (
#                         2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
#         elif self.parameterization == "x0":
#             lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
#         else:
#             raise NotImplementedError("mu not supported")
#         # TODO how to choose this term
#         lvlb_weights[0] = lvlb_weights[1]
#         self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
#         assert not torch.isnan(self.lvlb_weights).all()
#
#     @contextmanager
#     def ema_scope(self, context=None):
#         if self.use_ema:
#             self.model_ema.store(self.model.parameters())
#             self.model_ema.copy_to(self.model)
#             if context is not None:
#                 print(f"{context}: Switched to EMA weights")
#         try:
#             yield None
#         finally:
#             if self.use_ema:
#                 self.model_ema.restore(self.model.parameters())
#                 if context is not None:
#                     print(f"{context}: Restored training weights")
#
#     def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
#         ignore_keys = ignore_keys or []
#
#         sd = torch.load(path, map_location="cpu")
#         if "state_dict" in list(sd.keys()):
#             sd = sd["state_dict"]
#         keys = list(sd.keys())
#
#         # Our model adds additional channels to the first layer to condition on an input image.
#         # For the first layer, copy existing channel weights and initialize new channel weights to zero.
#         input_keys = [
#             "model.diffusion_model.input_blocks.0.0.weight",
#             "model_ema.diffusion_modelinput_blocks00weight",
#         ]
#
#         self_sd = self.state_dict()
#         for input_key in input_keys:
#             if input_key not in sd or input_key not in self_sd:
#                 continue
#
#             input_weight = self_sd[input_key]
#
#             if input_weight.size() != sd[input_key].size():
#                 print(f"Manual init: {input_key}")
#                 input_weight.zero_()
#                 input_weight[:, :4, :, :].copy_(sd[input_key])
#                 ignore_keys.append(input_key)
#
#         for k in keys:
#             for ik in ignore_keys:
#                 if k.startswith(ik):
#                     print(f"Deleting key {k} from state_dict.")
#                     del sd[k]
#         missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
#             sd, strict=False)
#         print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
#         if missing:
#             print(f"Missing Keys: {missing}")
#         if unexpected:
#             print(f"Unexpected Keys: {unexpected}")
#
#     def q_mean_variance(self, x_start, t):
#         """
#         Get the distribution q(x_t | x_0).
#         :param x_start: the [N x C x ...] tensor of noiseless inputs.
#         :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
#         :return: A tuple (mean, variance, log_variance), all of x_start's shape.
#         """
#         mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
#         variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
#         log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
#         return mean, variance, log_variance
#
#     def predict_start_from_noise(self, x_t, t, noise):
#         return (
#                 extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
#                 extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
#         )
#
#     def q_posterior(self, x_start, x_t, t):
#         posterior_mean = (
#                 extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
#                 extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
#         )
#         posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
#         posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
#         return posterior_mean, posterior_variance, posterior_log_variance_clipped
#
#     def p_mean_variance(self, x, t, clip_denoised: bool):
#         model_out = self.model(x, t)
#         if self.parameterization == "eps":
#             x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
#         elif self.parameterization == "x0":
#             x_recon = model_out
#         if clip_denoised:
#             x_recon.clamp_(-1., 1.)
#
#         model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
#         return model_mean, posterior_variance, posterior_log_variance
#
#     @torch.no_grad()
#     def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
#         b, *_, device = *x.shape, x.device
#         model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
#         noise = noise_like(x.shape, device, repeat_noise)
#         # no noise when t == 0
#         nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
#         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
#
#     @torch.no_grad()
#     def p_sample_loop(self, shape, return_intermediates=False):
#         device = self.betas.device
#         b = shape[0]
#         img = torch.randn(shape, device=device)
#         intermediates = [img]
#         for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
#             img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
#                                 clip_denoised=self.clip_denoised)
#             if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
#                 intermediates.append(img)
#         if return_intermediates:
#             return img, intermediates
#         return img
#
#     @torch.no_grad()
#     def sample(self, batch_size=16, return_intermediates=False):
#         image_size = self.image_size
#         channels = self.channels
#         return self.p_sample_loop((batch_size, channels, image_size, image_size),
#                                   return_intermediates=return_intermediates)
#
#     def q_sample(self, x_start, t, noise=None):
#         noise = default(noise, lambda: torch.randn_like(x_start))
#         return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
#                 extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
#
#     def get_loss(self, pred, target, mean=True):
#         if self.loss_type == 'l1':
#             loss = (target - pred).abs()
#             if mean:
#                 loss = loss.mean()
#         elif self.loss_type == 'l2':
#             if mean:
#                 loss = torch.nn.functional.mse_loss(target, pred)
#             else:
#                 loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
#         else:
#             raise NotImplementedError("unknown loss type '{loss_type}'")
#
#         return loss
#
#     def p_losses(self, x_start, t, noise=None):
#         noise = default(noise, lambda: torch.randn_like(x_start))
#         x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
#         model_out = self.model(x_noisy, t)
#
#         loss_dict = {}
#         if self.parameterization == "eps":
#             target = noise
#         elif self.parameterization == "x0":
#             target = x_start
#         else:
#             raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
#
#         loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
#
#         log_prefix = 'train' if self.training else 'val'
#
#         loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
#         loss_simple = loss.mean() * self.l_simple_weight
#
#         loss_vlb = (self.lvlb_weights[t] * loss).mean()
#         loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
#
#         loss = loss_simple + self.original_elbo_weight * loss_vlb
#
#         loss_dict.update({f'{log_prefix}/loss': loss})
#
#         return loss, loss_dict
#
#     def forward(self, x, *args, **kwargs):
#         # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
#         # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
#         t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
#         return self.p_losses(x, t, *args, **kwargs)
#
#     def get_input(self, batch, k):
#         return batch[k]
#
#     def shared_step(self, batch):
#         x = self.get_input(batch, self.first_stage_key)
#         loss, loss_dict = self(x)
#         return loss, loss_dict
#
#     def training_step(self, batch, batch_idx):
#         loss, loss_dict = self.shared_step(batch)
#
#         self.log_dict(loss_dict, prog_bar=True,
#                       logger=True, on_step=True, on_epoch=True)
#
#         self.log("global_step", self.global_step,
#                  prog_bar=True, logger=True, on_step=True, on_epoch=False)
#
#         if self.use_scheduler:
#             lr = self.optimizers().param_groups[0]['lr']
#             self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
#
#         return loss
#
#     @torch.no_grad()
#     def validation_step(self, batch, batch_idx):
#         _, loss_dict_no_ema = self.shared_step(batch)
#         with self.ema_scope():
#             _, loss_dict_ema = self.shared_step(batch)
#             loss_dict_ema = {f"{key}_ema": loss_dict_ema[key] for key in loss_dict_ema}
#         self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
#         self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
#
#     def on_train_batch_end(self, *args, **kwargs):
#         if self.use_ema:
#             self.model_ema(self.model)
#
#     def _get_rows_from_list(self, samples):
#         n_imgs_per_row = len(samples)
#         denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
#         denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
#         denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
#         return denoise_grid
#
#     @torch.no_grad()
#     def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
#         log = {}
#         x = self.get_input(batch, self.first_stage_key)
#         N = min(x.shape[0], N)
#         n_row = min(x.shape[0], n_row)
#         x = x.to(self.device)[:N]
#         log["inputs"] = x
#
#         # get diffusion row
#         diffusion_row = []
#         x_start = x[:n_row]
#
#         for t in range(self.num_timesteps):
#             if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
#                 t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
#                 t = t.to(self.device).long()
#                 noise = torch.randn_like(x_start)
#                 x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
#                 diffusion_row.append(x_noisy)
#
#         log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
#
#         if sample:
#             # get denoise row
#             with self.ema_scope("Plotting"):
#                 samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
#
#             log["samples"] = samples
#             log["denoise_row"] = self._get_rows_from_list(denoise_row)
#
#         if return_keys:
#             if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
#                 return log
#             else:
#                 return {key: log[key] for key in return_keys}
#         return log
#
#     def configure_optimizers(self):
#         lr = self.learning_rate
#         params = list(self.model.parameters())
#         if self.learn_logvar:
#             params = params + [self.logvar]
#         opt = torch.optim.AdamW(params, lr=lr)
#         return opt
#
#
# class LatentDiffusion(DDPM):
#     """main class"""
#     def __init__(self,
#                  first_stage_config,
#                  cond_stage_config,
#                  num_timesteps_cond=None,
#                  cond_stage_key="image",
#                  cond_stage_trainable=False,
#                  concat_mode=True,
#                  cond_stage_forward=None,
#                  conditioning_key=None,
#                  scale_factor=1.0,
#                  scale_by_std=False,
#                  load_ema=True,
#                  *args, **kwargs):
#         self.num_timesteps_cond = default(num_timesteps_cond, 1)
#         self.scale_by_std = scale_by_std
#         assert self.num_timesteps_cond <= kwargs['timesteps']
#         # for backwards compatibility after implementation of DiffusionWrapper
#         if conditioning_key is None:
#             conditioning_key = 'concat' if concat_mode else 'crossattn'
#         if cond_stage_config == '__is_unconditional__':
#             conditioning_key = None
#         ckpt_path = kwargs.pop("ckpt_path", None)
#         ignore_keys = kwargs.pop("ignore_keys", [])
#         super().__init__(*args, conditioning_key=conditioning_key, load_ema=load_ema, **kwargs)
#         self.concat_mode = concat_mode
#         self.cond_stage_trainable = cond_stage_trainable
#         self.cond_stage_key = cond_stage_key
#         try:
#             self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
#         except Exception:
#             self.num_downs = 0
#         if not scale_by_std:
#             self.scale_factor = scale_factor
#         else:
#             self.register_buffer('scale_factor', torch.tensor(scale_factor))
#         self.instantiate_first_stage(first_stage_config)
#         self.instantiate_cond_stage(cond_stage_config)
#         self.cond_stage_forward = cond_stage_forward
#         self.clip_denoised = False
#         self.bbox_tokenizer = None
#
#         self.restarted_from_ckpt = False
#         if ckpt_path is not None:
#             self.init_from_ckpt(ckpt_path, ignore_keys)
#             self.restarted_from_ckpt = True
#
#             if self.use_ema and not load_ema:
#                 self.model_ema = LitEma(self.model)
#                 print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
#
#     def make_cond_schedule(self, ):
#         self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
#         ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
#         self.cond_ids[:self.num_timesteps_cond] = ids
#
#     @rank_zero_only
#     @torch.no_grad()
#     def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
#         # only for very first batch
#         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:
#             assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
#             # set rescale weight to 1./std of encodings
#             print("### USING STD-RESCALING ###")
#             x = super().get_input(batch, self.first_stage_key)
#             x = x.to(self.device)
#             encoder_posterior = self.encode_first_stage(x)
#             z = self.get_first_stage_encoding(encoder_posterior).detach()
#             del self.scale_factor
#             self.register_buffer('scale_factor', 1. / z.flatten().std())
#             print(f"setting self.scale_factor to {self.scale_factor}")
#             print("### USING STD-RESCALING ###")
#
#     def register_schedule(self,
#                           given_betas=None, beta_schedule="linear", timesteps=1000,
#                           linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
#         super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
#
#         self.shorten_cond_schedule = self.num_timesteps_cond > 1
#         if self.shorten_cond_schedule:
#             self.make_cond_schedule()
#
#     def instantiate_first_stage(self, config):
#         model = instantiate_from_config(config)
#         self.first_stage_model = model.eval()
#         self.first_stage_model.train = disabled_train
#         for param in self.first_stage_model.parameters():
#             param.requires_grad = False
#
#     def instantiate_cond_stage(self, config):
#         if not self.cond_stage_trainable:
#             if config == "__is_first_stage__":
#                 print("Using first stage also as cond stage.")
#                 self.cond_stage_model = self.first_stage_model
#             elif config == "__is_unconditional__":
#                 print(f"Training {self.__class__.__name__} as an unconditional model.")
#                 self.cond_stage_model = None
#                 # self.be_unconditional = True
#             else:
#                 model = instantiate_from_config(config)
#                 self.cond_stage_model = model.eval()
#                 self.cond_stage_model.train = disabled_train
#                 for param in self.cond_stage_model.parameters():
#                     param.requires_grad = False
#         else:
#             assert config != '__is_first_stage__'
#             assert config != '__is_unconditional__'
#             model = instantiate_from_config(config)
#             self.cond_stage_model = model
#
#     def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
#         denoise_row = []
#         for zd in tqdm(samples, desc=desc):
#             denoise_row.append(self.decode_first_stage(zd.to(self.device),
#                                                             force_not_quantize=force_no_decoder_quantization))
#         n_imgs_per_row = len(denoise_row)
#         denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
#         denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
#         denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
#         denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
#         return denoise_grid
#
#     def get_first_stage_encoding(self, encoder_posterior):
#         if isinstance(encoder_posterior, DiagonalGaussianDistribution):
#             z = encoder_posterior.sample()
#         elif isinstance(encoder_posterior, torch.Tensor):
#             z = encoder_posterior
#         else:
#             raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
#         return self.scale_factor * z
#
#     def get_learned_conditioning(self, c):
#         if self.cond_stage_forward is None:
#             if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
#                 c = self.cond_stage_model.encode(c)
#                 if isinstance(c, DiagonalGaussianDistribution):
#                     c = c.mode()
#             else:
#                 c = self.cond_stage_model(c)
#         else:
#             assert hasattr(self.cond_stage_model, self.cond_stage_forward)
#             c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
#         return c
#
#     def meshgrid(self, h, w):
#         y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
#         x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
#
#         arr = torch.cat([y, x], dim=-1)
#         return arr
#
#     def delta_border(self, h, w):
#         """
#         :param h: height
#         :param w: width
#         :return: normalized distance to image border,
#          wtith min distance = 0 at border and max dist = 0.5 at image center
#         """
#         lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
#         arr = self.meshgrid(h, w) / lower_right_corner
#         dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
#         dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
#         edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
#         return edge_dist
#
#     def get_weighting(self, h, w, Ly, Lx, device):
#         weighting = self.delta_border(h, w)
#         weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
#                                self.split_input_params["clip_max_weight"], )
#         weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
#
#         if self.split_input_params["tie_braker"]:
#             L_weighting = self.delta_border(Ly, Lx)
#             L_weighting = torch.clip(L_weighting,
#                                      self.split_input_params["clip_min_tie_weight"],
#                                      self.split_input_params["clip_max_tie_weight"])
#
#             L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
#             weighting = weighting * L_weighting
#         return weighting
#
#     def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
#         """
#         :param x: img of size (bs, c, h, w)
#         :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
#         """
#         bs, nc, h, w = x.shape
#
#         # number of crops in image
#         Ly = (h - kernel_size[0]) // stride[0] + 1
#         Lx = (w - kernel_size[1]) // stride[1] + 1
#
#         if uf == 1 and df == 1:
#             fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
#             unfold = torch.nn.Unfold(**fold_params)
#
#             fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
#
#             weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
#             normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
#             weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
#
#         elif uf > 1 and df == 1:
#             fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
#             unfold = torch.nn.Unfold(**fold_params)
#
#             fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
#                                 dilation=1, padding=0,
#                                 stride=(stride[0] * uf, stride[1] * uf))
#             fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
#
#             weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
#             normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
#             weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
#
#         elif df > 1 and uf == 1:
#             fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
#             unfold = torch.nn.Unfold(**fold_params)
#
#             fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
#                                 dilation=1, padding=0,
#                                 stride=(stride[0] // df, stride[1] // df))
#             fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
#
#             weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
#             normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
#             weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
#
#         else:
#             raise NotImplementedError
#
#         return fold, unfold, normalization, weighting
#
#     @torch.no_grad()
#     def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
#                   cond_key=None, return_original_cond=False, bs=None, uncond=0.05):
#         x = super().get_input(batch, k)
#         if bs is not None:
#             x = x[:bs]
#         x = x.to(self.device)
#         encoder_posterior = self.encode_first_stage(x)
#         z = self.get_first_stage_encoding(encoder_posterior).detach()
#         cond_key = cond_key or self.cond_stage_key
#         xc = super().get_input(batch, cond_key)
#         if bs is not None:
#             xc["c_crossattn"] = xc["c_crossattn"][:bs]
#             xc["c_concat"] = xc["c_concat"][:bs]
#         cond = {}
#
#         # To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
#         random = torch.rand(x.size(0), device=x.device)
#         prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
#         input_mask = 1 - rearrange((random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1")
#
#         null_prompt = self.get_learned_conditioning([""])
#         cond["c_crossattn"] = [torch.where(prompt_mask, null_prompt, self.get_learned_conditioning(xc["c_crossattn"]).detach())]
#         cond["c_concat"] = [input_mask * self.encode_first_stage((xc["c_concat"].to(self.device))).mode().detach()]
#
#         out = [z, cond]
#         if return_first_stage_outputs:
#             xrec = self.decode_first_stage(z)
#             out.extend([x, xrec])
#         if return_original_cond:
#             out.append(xc)
#         return out
#
#     @torch.no_grad()
#     def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
#         if predict_cids:
#             if z.dim() == 4:
#                 z = torch.argmax(z.exp(), dim=1).long()
#             z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
#             z = rearrange(z, 'b h w c -> b c h w').contiguous()
#
#         z = 1. / self.scale_factor * z
#
#         if hasattr(self, "split_input_params"):
#             if self.split_input_params["patch_distributed_vq"]:
#                 ks = self.split_input_params["ks"]  # eg. (128, 128)
#                 stride = self.split_input_params["stride"]  # eg. (64, 64)
#                 uf = self.split_input_params["vqf"]
#                 bs, nc, h, w = z.shape
#                 if ks[0] > h or ks[1] > w:
#                     ks = (min(ks[0], h), min(ks[1], w))
#                     print("reducing Kernel")
#
#                 if stride[0] > h or stride[1] > w:
#                     stride = (min(stride[0], h), min(stride[1], w))
#                     print("reducing stride")
#
#                 fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
#
#                 z = unfold(z)  # (bn, nc * prod(**ks), L)
#                 # 1. Reshape to img shape
#                 z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
#
#                 # 2. apply model loop over last dim
#                 if isinstance(self.first_stage_model, VQModelInterface):
#                     output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
#                                                                  force_not_quantize=predict_cids or force_not_quantize)
#                                    for i in range(z.shape[-1])]
#                 else:
#
#                     output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
#                                    for i in range(z.shape[-1])]
#
#                 o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
#                 o = o * weighting
#                 # Reverse 1. reshape to img shape
#                 o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
#                 # stitch crops together
#                 decoded = fold(o)
#                 decoded = decoded / normalization  # norm is shape (1, 1, h, w)
#                 return decoded
#             else:
#                 if isinstance(self.first_stage_model, VQModelInterface):
#                     return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
#                 else:
#                     return self.first_stage_model.decode(z)
#
#         else:
#             if isinstance(self.first_stage_model, VQModelInterface):
#                 return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
#             else:
#                 return self.first_stage_model.decode(z)
#
#     # same as above but without decorator
#     def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
#         if predict_cids:
#             if z.dim() == 4:
#                 z = torch.argmax(z.exp(), dim=1).long()
#             z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
#             z = rearrange(z, 'b h w c -> b c h w').contiguous()
#
#         z = 1. / self.scale_factor * z
#
#         if hasattr(self, "split_input_params"):
#             if self.split_input_params["patch_distributed_vq"]:
#                 ks = self.split_input_params["ks"]  # eg. (128, 128)
#                 stride = self.split_input_params["stride"]  # eg. (64, 64)
#                 uf = self.split_input_params["vqf"]
#                 bs, nc, h, w = z.shape
#                 if ks[0] > h or ks[1] > w:
#                     ks = (min(ks[0], h), min(ks[1], w))
#                     print("reducing Kernel")
#
#                 if stride[0] > h or stride[1] > w:
#                     stride = (min(stride[0], h), min(stride[1], w))
#                     print("reducing stride")
#
#                 fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
#
#                 z = unfold(z)  # (bn, nc * prod(**ks), L)
#                 # 1. Reshape to img shape
#                 z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
#
#                 # 2. apply model loop over last dim
#                 if isinstance(self.first_stage_model, VQModelInterface):
#                     output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
#                                                                  force_not_quantize=predict_cids or force_not_quantize)
#                                    for i in range(z.shape[-1])]
#                 else:
#
#                     output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
#                                    for i in range(z.shape[-1])]
#
#                 o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
#                 o = o * weighting
#                 # Reverse 1. reshape to img shape
#                 o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
#                 # stitch crops together
#                 decoded = fold(o)
#                 decoded = decoded / normalization  # norm is shape (1, 1, h, w)
#                 return decoded
#             else:
#                 if isinstance(self.first_stage_model, VQModelInterface):
#                     return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
#                 else:
#                     return self.first_stage_model.decode(z)
#
#         else:
#             if isinstance(self.first_stage_model, VQModelInterface):
#                 return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
#             else:
#                 return self.first_stage_model.decode(z)
#
#     @torch.no_grad()
#     def encode_first_stage(self, x):
#         if hasattr(self, "split_input_params"):
#             if self.split_input_params["patch_distributed_vq"]:
#                 ks = self.split_input_params["ks"]  # eg. (128, 128)
#                 stride = self.split_input_params["stride"]  # eg. (64, 64)
#                 df = self.split_input_params["vqf"]
#                 self.split_input_params['original_image_size'] = x.shape[-2:]
#                 bs, nc, h, w = x.shape
#                 if ks[0] > h or ks[1] > w:
#                     ks = (min(ks[0], h), min(ks[1], w))
#                     print("reducing Kernel")
#
#                 if stride[0] > h or stride[1] > w:
#                     stride = (min(stride[0], h), min(stride[1], w))
#                     print("reducing stride")
#
#                 fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
#                 z = unfold(x)  # (bn, nc * prod(**ks), L)
#                 # Reshape to img shape
#                 z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
#
#                 output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
#                                for i in range(z.shape[-1])]
#
#                 o = torch.stack(output_list, axis=-1)
#                 o = o * weighting
#
#                 # Reverse reshape to img shape
#                 o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
#                 # stitch crops together
#                 decoded = fold(o)
#                 decoded = decoded / normalization
#                 return decoded
#
#             else:
#                 return self.first_stage_model.encode(x)
#         else:
#             return self.first_stage_model.encode(x)
#
#     def shared_step(self, batch, **kwargs):
#         x, c = self.get_input(batch, self.first_stage_key)
#         loss = self(x, c)
#         return loss
#
#     def forward(self, x, c, *args, **kwargs):
#         t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
#         if self.model.conditioning_key is not None:
#             assert c is not None
#             if self.cond_stage_trainable:
#                 c = self.get_learned_conditioning(c)
#             if self.shorten_cond_schedule:  # TODO: drop this option
#                 tc = self.cond_ids[t].to(self.device)
#                 c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
#         return self.p_losses(x, c, t, *args, **kwargs)
#
#     def apply_model(self, x_noisy, t, cond, return_ids=False):
#
#         if isinstance(cond, dict):
#             # hybrid case, cond is expected to be a dict
#             pass
#         else:
#             if not isinstance(cond, list):
#                 cond = [cond]
#             key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
#             cond = {key: cond}
#
#         if hasattr(self, "split_input_params"):
#             assert len(cond) == 1  # todo can only deal with one conditioning atm
#             assert not return_ids
#             ks = self.split_input_params["ks"]  # eg. (128, 128)
#             stride = self.split_input_params["stride"]  # eg. (64, 64)
#
#             h, w = x_noisy.shape[-2:]
#
#             fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
#
#             z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
#             # Reshape to img shape
#             z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
#             z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
#
#             if self.cond_stage_key in ["image", "LR_image", "segmentation",
#                                        'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
#                 c_key = next(iter(cond.keys()))  # get key
#                 c = next(iter(cond.values()))  # get value
#                 assert (len(c) == 1)  # todo extend to list with more than one elem
#                 c = c[0]  # get element
#
#                 c = unfold(c)
#                 c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
#
#                 cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
#
#             elif self.cond_stage_key == 'coordinates_bbox':
#                 assert 'original_image_size' in self.split_input_params, 'BoundingBoxRescaling is missing original_image_size'
#
#                 # assuming padding of unfold is always 0 and its dilation is always 1
#                 n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
#                 full_img_h, full_img_w = self.split_input_params['original_image_size']
#                 # as we are operating on latents, we need the factor from the original image size to the
#                 # spatial latent size to properly rescale the crops for regenerating the bbox annotations
#                 num_downs = self.first_stage_model.encoder.num_resolutions - 1
#                 rescale_latent = 2 ** (num_downs)
#
#                 # get top left positions of patches as conforming for the bbbox tokenizer, therefore we
#                 # need to rescale the tl patch coordinates to be in between (0,1)
#                 tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
#                                          rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
#                                         for patch_nr in range(z.shape[-1])]
#
#                 # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
#                 patch_limits = [(x_tl, y_tl,
#                                  rescale_latent * ks[0] / full_img_w,
#                                  rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
#                 # 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]
#
#                 # tokenize crop coordinates for the bounding boxes of the respective patches
#                 patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
#                                       for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
#                 print(patch_limits_tknzd[0].shape)
#                 # cut tknzd crop position from conditioning
#                 assert isinstance(cond, dict), 'cond must be dict to be fed into model'
#                 cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
#                 print(cut_cond.shape)
#
#                 adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
#                 adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
#                 print(adapted_cond.shape)
#                 adapted_cond = self.get_learned_conditioning(adapted_cond)
#                 print(adapted_cond.shape)
#                 adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
#                 print(adapted_cond.shape)
#
#                 cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
#
#             else:
#                 cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
#
#             # apply model by loop over crops
#             output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
#             assert not isinstance(output_list[0],
#                                   tuple)  # todo cant deal with multiple model outputs check this never happens
#
#             o = torch.stack(output_list, axis=-1)
#             o = o * weighting
#             # Reverse reshape to img shape
#             o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
#             # stitch crops together
#             x_recon = fold(o) / normalization
#
#         else:
#             x_recon = self.model(x_noisy, t, **cond)
#
#         if isinstance(x_recon, tuple) and not return_ids:
#             return x_recon[0]
#         else:
#             return x_recon
#
#     def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
#         return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
#                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
#
#     def _prior_bpd(self, x_start):
#         """
#         Get the prior KL term for the variational lower-bound, measured in
#         bits-per-dim.
#         This term can't be optimized, as it only depends on the encoder.
#         :param x_start: the [N x C x ...] tensor of inputs.
#         :return: a batch of [N] KL values (in bits), one per batch element.
#         """
#         batch_size = x_start.shape[0]
#         t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
#         qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
#         kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
#         return mean_flat(kl_prior) / np.log(2.0)
#
#     def p_losses(self, x_start, cond, t, noise=None):
#         noise = default(noise, lambda: torch.randn_like(x_start))
#         x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
#         model_output = self.apply_model(x_noisy, t, cond)
#
#         loss_dict = {}
#         prefix = 'train' if self.training else 'val'
#
#         if self.parameterization == "x0":
#             target = x_start
#         elif self.parameterization == "eps":
#             target = noise
#         else:
#             raise NotImplementedError()
#
#         loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
#         loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
#
#         logvar_t = self.logvar[t].to(self.device)
#         loss = loss_simple / torch.exp(logvar_t) + logvar_t
#         # loss = loss_simple / torch.exp(self.logvar) + self.logvar
#         if self.learn_logvar:
#             loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
#             loss_dict.update({'logvar': self.logvar.data.mean()})
#
#         loss = self.l_simple_weight * loss.mean()
#
#         loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
#         loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
#         loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
#         loss += (self.original_elbo_weight * loss_vlb)
#         loss_dict.update({f'{prefix}/loss': loss})
#
#         return loss, loss_dict
#
#     def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
#                         return_x0=False, score_corrector=None, corrector_kwargs=None):
#         t_in = t
#         model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
#
#         if score_corrector is not None:
#             assert self.parameterization == "eps"
#             model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
#
#         if return_codebook_ids:
#             model_out, logits = model_out
#
#         if self.parameterization == "eps":
#             x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
#         elif self.parameterization == "x0":
#             x_recon = model_out
#         else:
#             raise NotImplementedError()
#
#         if clip_denoised:
#             x_recon.clamp_(-1., 1.)
#         if quantize_denoised:
#             x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
#         model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
#         if return_codebook_ids:
#             return model_mean, posterior_variance, posterior_log_variance, logits
#         elif return_x0:
#             return model_mean, posterior_variance, posterior_log_variance, x_recon
#         else:
#             return model_mean, posterior_variance, posterior_log_variance
#
#     @torch.no_grad()
#     def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
#                  return_codebook_ids=False, quantize_denoised=False, return_x0=False,
#                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
#         b, *_, device = *x.shape, x.device
#         outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
#                                        return_codebook_ids=return_codebook_ids,
#                                        quantize_denoised=quantize_denoised,
#                                        return_x0=return_x0,
#                                        score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
#         if return_codebook_ids:
#             raise DeprecationWarning("Support dropped.")
#             model_mean, _, model_log_variance, logits = outputs
#         elif return_x0:
#             model_mean, _, model_log_variance, x0 = outputs
#         else:
#             model_mean, _, model_log_variance = outputs
#
#         noise = noise_like(x.shape, device, repeat_noise) * temperature
#         if noise_dropout > 0.:
#             noise = torch.nn.functional.dropout(noise, p=noise_dropout)
#         # no noise when t == 0
#         nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
#
#         if return_codebook_ids:
#             return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
#         if return_x0:
#             return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
#         else:
#             return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
#
#     @torch.no_grad()
#     def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
#                               img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
#                               score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
#                               log_every_t=None):
#         if not log_every_t:
#             log_every_t = self.log_every_t
#         timesteps = self.num_timesteps
#         if batch_size is not None:
#             b = batch_size if batch_size is not None else shape[0]
#             shape = [batch_size] + list(shape)
#         else:
#             b = batch_size = shape[0]
#         if x_T is None:
#             img = torch.randn(shape, device=self.device)
#         else:
#             img = x_T
#         intermediates = []
#         if cond is not None:
#             if isinstance(cond, dict):
#                 cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
#                 [x[:batch_size] for x in cond[key]] for key in cond}
#             else:
#                 cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
#
#         if start_T is not None:
#             timesteps = min(timesteps, start_T)
#         iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
#                         total=timesteps) if verbose else reversed(
#             range(0, timesteps))
#         if type(temperature) == float:
#             temperature = [temperature] * timesteps
#
#         for i in iterator:
#             ts = torch.full((b,), i, device=self.device, dtype=torch.long)
#             if self.shorten_cond_schedule:
#                 assert self.model.conditioning_key != 'hybrid'
#                 tc = self.cond_ids[ts].to(cond.device)
#                 cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
#
#             img, x0_partial = self.p_sample(img, cond, ts,
#                                             clip_denoised=self.clip_denoised,
#                                             quantize_denoised=quantize_denoised, return_x0=True,
#                                             temperature=temperature[i], noise_dropout=noise_dropout,
#                                             score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
#             if mask is not None:
#                 assert x0 is not None
#                 img_orig = self.q_sample(x0, ts)
#                 img = img_orig * mask + (1. - mask) * img
#
#             if i % log_every_t == 0 or i == timesteps - 1:
#                 intermediates.append(x0_partial)
#             if callback:
#                 callback(i)
#             if img_callback:
#                 img_callback(img, i)
#         return img, intermediates
#
#     @torch.no_grad()
#     def p_sample_loop(self, cond, shape, return_intermediates=False,
#                       x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
#                       mask=None, x0=None, img_callback=None, start_T=None,
#                       log_every_t=None):
#
#         if not log_every_t:
#             log_every_t = self.log_every_t
#         device = self.betas.device
#         b = shape[0]
#         if x_T is None:
#             img = torch.randn(shape, device=device)
#         else:
#             img = x_T
#
#         intermediates = [img]
#         if timesteps is None:
#             timesteps = self.num_timesteps
#
#         if start_T is not None:
#             timesteps = min(timesteps, start_T)
#         iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
#             range(0, timesteps))
#
#         if mask is not None:
#             assert x0 is not None
#             assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
#
#         for i in iterator:
#             ts = torch.full((b,), i, device=device, dtype=torch.long)
#             if self.shorten_cond_schedule:
#                 assert self.model.conditioning_key != 'hybrid'
#                 tc = self.cond_ids[ts].to(cond.device)
#                 cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
#
#             img = self.p_sample(img, cond, ts,
#                                 clip_denoised=self.clip_denoised,
#                                 quantize_denoised=quantize_denoised)
#             if mask is not None:
#                 img_orig = self.q_sample(x0, ts)
#                 img = img_orig * mask + (1. - mask) * img
#
#             if i % log_every_t == 0 or i == timesteps - 1:
#                 intermediates.append(img)
#             if callback:
#                 callback(i)
#             if img_callback:
#                 img_callback(img, i)
#
#         if return_intermediates:
#             return img, intermediates
#         return img
#
#     @torch.no_grad()
#     def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
#                verbose=True, timesteps=None, quantize_denoised=False,
#                mask=None, x0=None, shape=None,**kwargs):
#         if shape is None:
#             shape = (batch_size, self.channels, self.image_size, self.image_size)
#         if cond is not None:
#             if isinstance(cond, dict):
#                 cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
#                 [x[:batch_size] for x in cond[key]] for key in cond}
#             else:
#                 cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
#         return self.p_sample_loop(cond,
#                                   shape,
#                                   return_intermediates=return_intermediates, x_T=x_T,
#                                   verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
#                                   mask=mask, x0=x0)
#
#     @torch.no_grad()
#     def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
#
#         if ddim:
#             ddim_sampler = DDIMSampler(self)
#             shape = (self.channels, self.image_size, self.image_size)
#             samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
#                                                         shape,cond,verbose=False,**kwargs)
#
#         else:
#             samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
#                                                  return_intermediates=True,**kwargs)
#
#         return samples, intermediates
#
#
#     @torch.no_grad()
#     def log_images(self, batch, N=4, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
#                    quantize_denoised=True, inpaint=False, plot_denoise_rows=False, plot_progressive_rows=False,
#                    plot_diffusion_rows=False, **kwargs):
#
#         use_ddim = False
#
#         log = {}
#         z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
#                                            return_first_stage_outputs=True,
#                                            force_c_encode=True,
#                                            return_original_cond=True,
#                                            bs=N, uncond=0)
#         N = min(x.shape[0], N)
#         n_row = min(x.shape[0], n_row)
#         log["inputs"] = x
#         log["reals"] = xc["c_concat"]
#         log["reconstruction"] = xrec
#         if self.model.conditioning_key is not None:
#             if hasattr(self.cond_stage_model, "decode"):
#                 xc = self.cond_stage_model.decode(c)
#                 log["conditioning"] = xc
#             elif self.cond_stage_key in ["caption"]:
#                 xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
#                 log["conditioning"] = xc
#             elif self.cond_stage_key == 'class_label':
#                 xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
#                 log['conditioning'] = xc
#             elif isimage(xc):
#                 log["conditioning"] = xc
#             if ismap(xc):
#                 log["original_conditioning"] = self.to_rgb(xc)
#
#         if plot_diffusion_rows:
#             # get diffusion row
#             diffusion_row = []
#             z_start = z[:n_row]
#             for t in range(self.num_timesteps):
#                 if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
#                     t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
#                     t = t.to(self.device).long()
#                     noise = torch.randn_like(z_start)
#                     z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
#                     diffusion_row.append(self.decode_first_stage(z_noisy))
#
#             diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
#             diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
#             diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
#             diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
#             log["diffusion_row"] = diffusion_grid
#
#         if sample:
#             # get denoise row
#             with self.ema_scope("Plotting"):
#                 samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
#                                                          ddim_steps=ddim_steps,eta=ddim_eta)
#                 # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
#             x_samples = self.decode_first_stage(samples)
#             log["samples"] = x_samples
#             if plot_denoise_rows:
#                 denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
#                 log["denoise_row"] = denoise_grid
#
#             if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
#                     self.first_stage_model, IdentityFirstStage):
#                 # also display when quantizing x0 while sampling
#                 with self.ema_scope("Plotting Quantized Denoised"):
#                     samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
#                                                              ddim_steps=ddim_steps,eta=ddim_eta,
#                                                              quantize_denoised=True)
#                     # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
#                     #                                      quantize_denoised=True)
#                 x_samples = self.decode_first_stage(samples.to(self.device))
#                 log["samples_x0_quantized"] = x_samples
#
#             if inpaint:
#                 # make a simple center square
#                 h, w = z.shape[2], z.shape[3]
#                 mask = torch.ones(N, h, w).to(self.device)
#                 # zeros will be filled in
#                 mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
#                 mask = mask[:, None, ...]
#                 with self.ema_scope("Plotting Inpaint"):
#
#                     samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
#                                                 ddim_steps=ddim_steps, x0=z[:N], mask=mask)
#                 x_samples = self.decode_first_stage(samples.to(self.device))
#                 log["samples_inpainting"] = x_samples
#                 log["mask"] = mask
#
#                 # outpaint
#                 with self.ema_scope("Plotting Outpaint"):
#                     samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
#                                                 ddim_steps=ddim_steps, x0=z[:N], mask=mask)
#                 x_samples = self.decode_first_stage(samples.to(self.device))
#                 log["samples_outpainting"] = x_samples
#
#         if plot_progressive_rows:
#             with self.ema_scope("Plotting Progressives"):
#                 img, progressives = self.progressive_denoising(c,
#                                                                shape=(self.channels, self.image_size, self.image_size),
#                                                                batch_size=N)
#             prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
#             log["progressive_row"] = prog_row
#
#         if return_keys:
#             if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
#                 return log
#             else:
#                 return {key: log[key] for key in return_keys}
#         return log
#
#     def configure_optimizers(self):
#         lr = self.learning_rate
#         params = list(self.model.parameters())
#         if self.cond_stage_trainable:
#             print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
#             params = params + list(self.cond_stage_model.parameters())
#         if self.learn_logvar:
#             print('Diffusion model optimizing logvar')
#             params.append(self.logvar)
#         opt = torch.optim.AdamW(params, lr=lr)
#         if self.use_scheduler:
#             assert 'target' in self.scheduler_config
#             scheduler = instantiate_from_config(self.scheduler_config)
#
#             print("Setting up LambdaLR scheduler...")
#             scheduler = [
#                 {
#                     'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
#                     'interval': 'step',
#                     'frequency': 1
#                 }]
#             return [opt], scheduler
#         return opt
#
#     @torch.no_grad()
#     def to_rgb(self, x):
#         x = x.float()
#         if not hasattr(self, "colorize"):
#             self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
#         x = nn.functional.conv2d(x, weight=self.colorize)
#         x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
#         return x
#
#
# class DiffusionWrapper(pl.LightningModule):
#     def __init__(self, diff_model_config, conditioning_key):
#         super().__init__()
#         self.diffusion_model = instantiate_from_config(diff_model_config)
#         self.conditioning_key = conditioning_key
#         assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
#
#     def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
#         if self.conditioning_key is None:
#             out = self.diffusion_model(x, t)
#         elif self.conditioning_key == 'concat':
#             xc = torch.cat([x] + c_concat, dim=1)
#             out = self.diffusion_model(xc, t)
#         elif self.conditioning_key == 'crossattn':
#             cc = torch.cat(c_crossattn, 1)
#             out = self.diffusion_model(x, t, context=cc)
#         elif self.conditioning_key == 'hybrid':
#             xc = torch.cat([x] + c_concat, dim=1)
#             cc = torch.cat(c_crossattn, 1)
#             out = self.diffusion_model(xc, t, context=cc)
#         elif self.conditioning_key == 'adm':
#             cc = c_crossattn[0]
#             out = self.diffusion_model(x, t, y=cc)
#         else:
#             raise NotImplementedError()
#
#         return out
#
#
# class Layout2ImgDiffusion(LatentDiffusion):
#     # TODO: move all layout-specific hacks to this class
#     def __init__(self, cond_stage_key, *args, **kwargs):
#         assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
#         super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
#
#     def log_images(self, batch, N=8, *args, **kwargs):
#         logs = super().log_images(*args, batch=batch, N=N, **kwargs)
#
#         key = 'train' if self.training else 'validation'
#         dset = self.trainer.datamodule.datasets[key]
#         mapper = dset.conditional_builders[self.cond_stage_key]
#
#         bbox_imgs = []
#         map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
#         for tknzd_bbox in batch[self.cond_stage_key][:N]:
#             bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
#             bbox_imgs.append(bboximg)
#
#         cond_img = torch.stack(bbox_imgs, dim=0)
#         logs['bbox_image'] = cond_img
#         return logs