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	"""
	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
	"""

	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 util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
	from ema import LitEma
	from distributions import normal_kl, DiagonalGaussianDistribution
	from autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
	from util2 import make_beta_schedule, extract_into_tensor, noise_like
	from ddim import DDIMSampler


	__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=[],
	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.,
	):
	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
	if self.use_ema:
	self.model_ema = LitEma(self.model)
	print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")

	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 ckpt_path is not None:
	self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)

	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=list(), only_model=False):
	sd = torch.load(path, map_location="cpu")
	if "state_dict" in list(sd.keys()):
	sd = sd["state_dict"]
	keys = list(sd.keys())
	for k in keys:
	for ik in ignore_keys:
	if k.startswith(ik):
	print("Deleting key {} from state_dict.".format(k))
	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 len(missing) > 0:
	print(f"Missing Keys: {missing}")
	if len(unexpected) > 0:
	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"Paramterization {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):
	x = batch[k]
	if len(x.shape) == 3:
	x = x[..., None]
	x = rearrange(x, 'b h w c -> b c h w')
	x = x.to(memory_format=torch.contiguous_format).float()
	return x

	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 = {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 = dict()
	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 = list()
	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,
	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__(conditioning_key=conditioning_key, args, *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:
	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

	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):
	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()

	if self.model.conditioning_key is not None:
	if cond_key is None:
	cond_key = self.cond_stage_key
	if cond_key != self.first_stage_key:
	if cond_key in ['caption', 'coordinates_bbox']:
	xc = batch[cond_key]
	elif cond_key == 'class_label':
	xc = batch
	else:
	xc = super().get_input(batch, cond_key).to(self.device)
	else:
	xc = x
	if not self.cond_stage_trainable or force_c_encode:
	if isinstance(xc, dict) or isinstance(xc, list):
	# import pudb; pudb.set_trace()
	c = self.get_learned_conditioning(xc)
	else:
	c = self.get_learned_conditioning(xc.to(self.device))
	else:
	c = xc
	if bs is not None:
	c = c[:bs]

	if self.use_positional_encodings:
	pos_x, pos_y = self.compute_latent_shifts(batch)
	ckey = __conditioning_keys__[self.model.conditioning_key]
	c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}

	else:
	c = None
	xc = None
	if self.use_positional_encodings:
	pos_x, pos_y = self.compute_latent_shifts(batch)
	c = {'pos_x': pos_x, 'pos_y': pos_y}
	out = [z, c]
	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 _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
	def rescale_bbox(bbox):
	x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
	y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
	w = min(bbox[2] / crop_coordinates[2], 1 - x0)
	h = min(bbox[3] / crop_coordinates[3], 1 - y0)
	return x0, y0, w, h

	return [rescale_bbox(b) for b in bboxes]

	def apply_model(self, x_noisy, t, cond, return_ids=False):

	if isinstance(cond, dict):
	# hybrid case, cond is exptected 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, 'BoudingBoxRescaling 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 postions 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
	list(map(lambda x: x[:batch_size], 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
	list(map(lambda x: x[:batch_size], 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=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
	quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
	plot_diffusion_rows=True, **kwargs):

	use_ddim = ddim_steps is not None

	log = dict()
	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)
	N = min(x.shape[0], N)
	n_row = min(x.shape[0], n_row)
	log["inputs"] = x
	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 = list()
	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
	b, h, w = z.shape[0], 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__(cond_stage_key=cond_stage_key, args, *kwargs)

	def log_images(self, batch, N=8, args, *kwargs):
	logs = super().log_images(batch=batch, N=N, args, *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