code: 
  
  
  '''simple docstring'''
  
  
  
  import math
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  
  __lowercase			:		Tuple =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	A_			    =					42
  	A_			    =					None
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Tuple=0.9_9_9		,   _SCREAMING_SNAKE_CASE   :     Optional[int]="cosine"		,   ):
  					if alpha_transform_type == "cosine":
  
  										def alpha_bar_fn(_SCREAMING_SNAKE_CASE   :     List[str]     ):
  															return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2     ) ** 2
  
  					elif alpha_transform_type == "exp":
  
  										def alpha_bar_fn(_SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
  															return math.exp(t * -12.0     )
  
  					else:
  										raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )
  
  					__a    : Any      					=						[]
  					for i in range(_SCREAMING_SNAKE_CASE     ):
  										__a    : Optional[int]      					=						i / num_diffusion_timesteps
  										__a    : List[str]      					=						(i + 1) / num_diffusion_timesteps
  										betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE     ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE     )		,   _SCREAMING_SNAKE_CASE     )     )
  					return torch.tensor(_SCREAMING_SNAKE_CASE		,   dtype=torch.floataa     )
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_      ):
  	A_			    =					[e.name for e in KarrasDiffusionSchedulers]
  	A_			    =					1
  
  
  
  
  	@register_to_config
  	def __init__(     self		,       __a = 1000		,       __a = 0.0001		,       __a = 0.02		,       __a = "linear"		,       __a = None		,       __a = "epsilon"		,       __a = "linear"		,       __a = False		,       __a = "linspace"		,       __a = 0		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						if trained_betas is not None:
  											__a    : Any      					=						torch.tensor(__a		,       dtype=torch.floataa							)
  						elif beta_schedule == "linear":
  											__a    : Dict      					=						torch.linspace(__a		,       __a		,       __a		,       dtype=torch.floataa							)
  						elif beta_schedule == "scaled_linear":
  											# this schedule is very specific to the latent diffusion model.
  											__a    : Any      					=						(
  											    torch.linspace(beta_start**0.5		,       beta_end**0.5		,       __a		,       dtype=torch.floataa							) ** 2
  											)
  						elif beta_schedule == "squaredcos_cap_v2":
  											# Glide cosine schedule
  											__a    : int      					=						betas_for_alpha_bar(__a							)
  						else:
  											raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)
  
  						__a    : List[Any]      					=						1.0 - self.betas
  						__a    : int      					=						torch.cumprod(self.alphas		,       dim=0							)
  
  						__a    : List[Any]      					=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
  						__a    : List[Any]      					=						np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
  						__a    : Union[str, Any]      					=						torch.from_numpy(__a							)
  
  						# setable values
  						__a    : List[str]      					=						None
  						__a    : List[Any]      					=						np.linspace(0		,       num_train_timesteps - 1		,       __a		,       dtype=__a							)[::-1].copy()
  						__a    : Optional[Any]      					=						torch.from_numpy(__a							)
  						__a    : Any      					=						False
  						__a    : str      					=						use_karras_sigmas
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if self.config.timestep_spacing in ["linspace", "trailing"]:
  											return self.sigmas.max()
  
  						return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if isinstance(__a		,       torch.Tensor							):
  											__a    : Optional[int]      					=						timestep.to(self.timesteps.device							)
  						__a    : Optional[Any]      					=						(self.timesteps == timestep).nonzero().item()
  						__a    : List[str]      					=						self.sigmas[step_index]
  
  						__a    : int      					=						sample / ((sigma**2 + 1) ** 0.5)
  
  						__a    : Union[str, Any]      					=						True
  						return sample
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a = None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						num_inference_steps
  
  						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
  						if self.config.timestep_spacing == "linspace":
  											__a    : Optional[Any]      					=						np.linspace(0		,       self.config.num_train_timesteps - 1		,       __a		,       dtype=__a							)[
  											    ::-1
  											].copy()
  						elif self.config.timestep_spacing == "leading":
  											__a    : str      					=						self.config.num_train_timesteps // self.num_inference_steps
  											# creates integer timesteps by multiplying by ratio
  											# casting to int to avoid issues when num_inference_step is power of 3
  											__a    : int      					=						(np.arange(0		,       __a							) * step_ratio).round()[::-1].copy().astype(__a							)
  											timesteps += self.config.steps_offset
  						elif self.config.timestep_spacing == "trailing":
  											__a    : Optional[int]      					=						self.config.num_train_timesteps / self.num_inference_steps
  											# creates integer timesteps by multiplying by ratio
  											# casting to int to avoid issues when num_inference_step is power of 3
  											__a    : str      					=						(np.arange(self.config.num_train_timesteps		,       0		,       -step_ratio							)).round().copy().astype(__a							)
  											timesteps -= 1
  						else:
  											raise ValueError(
  											    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""							)
  
  						__a    : Tuple      					=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
  						__a    : Tuple      					=						np.log(__a							)
  
  						if self.config.interpolation_type == "linear":
  											__a    : Optional[Any]      					=						np.interp(__a		,       np.arange(0		,       len(__a							)							)		,       __a							)
  						elif self.config.interpolation_type == "log_linear":
  											__a    : Optional[Any]      					=						torch.linspace(np.log(sigmas[-1]							)		,       np.log(sigmas[0]							)		,       num_inference_steps + 1							).exp()
  						else:
  											raise ValueError(
  											    f"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
  											    ' \'linear\' or \'log_linear\''							)
  
  						if self.use_karras_sigmas:
  											__a    : Any      					=						self._convert_to_karras(in_sigmas=__a		,       num_inference_steps=self.num_inference_steps							)
  											__a    : Tuple      					=						np.array([self._sigma_to_t(__a		,       __a							) for sigma in sigmas]							)
  
  						__a    : str      					=						np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
  						__a    : List[Any]      					=						torch.from_numpy(__a							).to(device=__a							)
  						if str(__a							).startswith('mps'							):
  											# mps does not support float64
  											__a    : int      					=						torch.from_numpy(__a							).to(__a		,       dtype=torch.floataa							)
  						else:
  											__a    : int      					=						torch.from_numpy(__a							).to(device=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						np.log(__a							)
  
  						# get distribution
  						__a    : Dict      					=						log_sigma - log_sigmas[:, np.newaxis]
  
  						# get sigmas range
  						__a    : Optional[Any]      					=						np.cumsum((dists >= 0)		,       axis=0							).argmax(axis=0							).clip(max=log_sigmas.shape[0] - 2							)
  						__a    : int      					=						low_idx + 1
  
  						__a    : Tuple      					=						log_sigmas[low_idx]
  						__a    : Any      					=						log_sigmas[high_idx]
  
  						# interpolate sigmas
  						__a    : Dict      					=						(low - log_sigma) / (low - high)
  						__a    : List[Any]      					=						np.clip(__a		,       0		,       1							)
  
  						# transform interpolation to time range
  						__a    : Union[str, Any]      					=						(1 - w) * low_idx + w * high_idx
  						__a    : Optional[Any]      					=						t.reshape(sigma.shape							)
  						return t
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : float      					=						in_sigmas[-1].item()
  						__a    : float      					=						in_sigmas[0].item()
  
  						__a    : Union[str, Any]      					=						7.0  # 7.0 is the value used in the paper
  						__a    : int      					=						np.linspace(0		,       1		,       __a							)
  						__a    : int      					=						sigma_min ** (1 / rho)
  						__a    : Optional[Any]      					=						sigma_max ** (1 / rho)
  						__a    : Optional[Any]      					=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
  						return sigmas
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a = 0.0		,       __a = 0.0		,       __a = float('inf'							)		,       __a = 1.0		,       __a = None		,       __a = True		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						if (
  						    isinstance(__a		,       __a							)
  						    or isinstance(__a		,       torch.IntTensor							)
  						    or isinstance(__a		,       torch.LongTensor							)
  						):
  											raise ValueError(
  											    (
  											        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
  											        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
  											        ' one of the `scheduler.timesteps` as a timestep.'
  											    )		,       )
  
  						if not self.is_scale_input_called:
  											logger.warning(
  											    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
  											    'See `StableDiffusionPipeline` for a usage example.'							)
  
  						if isinstance(__a		,       torch.Tensor							):
  											__a    : Optional[int]      					=						timestep.to(self.timesteps.device							)
  
  						__a    : str      					=						(self.timesteps == timestep).nonzero().item()
  						__a    : List[Any]      					=						self.sigmas[step_index]
  
  						__a    : List[Any]      					=						min(s_churn / (len(self.sigmas							) - 1)		,       2**0.5 - 1							) if s_tmin <= sigma <= s_tmax else 0.0
  
  						__a    : Optional[Any]      					=						randn_tensor(
  						    model_output.shape		,       dtype=model_output.dtype		,       device=model_output.device		,       generator=__a							)
  
  						__a    : Optional[Any]      					=						noise * s_noise
  						__a    : int      					=						sigma * (gamma + 1)
  
  						if gamma > 0:
  											__a    : Optional[Any]      					=						sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
  
  						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  						# NOTE: "original_sample" should not be an expected prediction_type but is left in for
  						# backwards compatibility
  						if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
  											__a    : Optional[Any]      					=						model_output
  						elif self.config.prediction_type == "epsilon":
  											__a    : Tuple      					=						sample - sigma_hat * model_output
  						elif self.config.prediction_type == "v_prediction":
  											# * c_out + input * c_skip
  											__a    : List[Any]      					=						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
  						else:
  											raise ValueError(
  											    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""							)
  
  						# 2. Convert to an ODE derivative
  						__a    : List[Any]      					=						(sample - pred_original_sample) / sigma_hat
  
  						__a    : Union[str, Any]      					=						self.sigmas[step_index + 1] - sigma_hat
  
  						__a    : Tuple      					=						sample + derivative * dt
  
  						if not return_dict:
  											return (prev_sample,)
  
  						return EulerDiscreteSchedulerOutput(prev_sample=__a		,       pred_original_sample=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[int]      					=						self.sigmas.to(device=original_samples.device		,       dtype=original_samples.dtype							)
  						if original_samples.device.type == "mps" and torch.is_floating_point(__a							):
  											# mps does not support float64
  											__a    : int      					=						self.timesteps.to(original_samples.device		,       dtype=torch.floataa							)
  											__a    : int      					=						timesteps.to(original_samples.device		,       dtype=torch.floataa							)
  						else:
  											__a    : Optional[Any]      					=						self.timesteps.to(original_samples.device							)
  											__a    : Any      					=						timesteps.to(original_samples.device							)
  
  						__a    : Tuple      					=						[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
  						__a    : str      					=						sigmas[step_indices].flatten()
  						while len(sigma.shape							) < len(original_samples.shape							):
  											__a    : str      					=						sigma.unsqueeze(-1							)
  
  						__a    : Optional[Any]      					=						original_samples + noise * sigma
  						return noisy_samples
  
  
  
  
  
  	def __len__(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return self.config.num_train_timesteps
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Callable, List, Optional, Union
  
  import torch
  
  from ...models import UNetaDModel
  from ...schedulers import CMStochasticIterativeScheduler
  from ...utils import (
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  __lowercase			:		List[str] =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  __lowercase			:		Optional[int] =			'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def __init__(     self		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().__init__()
  
  						self.register_modules(
  						    unet=__a		,       scheduler=__a		,       )
  
  						__a    : List[str]      					=						None
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a=0							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if is_accelerate_available() and is_accelerate_version('>='		,       '0.14.0'							):
  											from accelerate import cpu_offload
  						else:
  											raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'							)
  
  						__a    : str      					=						torch.device(f"""cuda:{gpu_id}"""							)
  
  						if self.device.type != "cpu":
  											self.to('cpu'		,       silence_dtype_warnings=__a							)
  											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  						for cpu_offloaded_model in [self.unet]:
  											cpu_offload(__a		,       __a							)
  
  						if self.safety_checker is not None:
  											cpu_offload(self.safety_checker		,       execution_device=__a		,       offload_buffers=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a=0							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if is_accelerate_available() and is_accelerate_version('>='		,       '0.17.0.dev0'							):
  											from accelerate import cpu_offload_with_hook
  						else:
  											raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'							)
  
  						__a    : Tuple      					=						torch.device(f"""cuda:{gpu_id}"""							)
  
  						if self.device.type != "cpu":
  											self.to('cpu'		,       silence_dtype_warnings=__a							)
  											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  						__a    : List[Any]      					=						None
  						for cpu_offloaded_model in [self.unet]:
  											__a					,       __a    : List[str]      					=						cpu_offload_with_hook(__a		,       __a		,       prev_module_hook=__a							)
  
  						if self.safety_checker is not None:
  											__a					,       __a    : str      					=						cpu_offload_with_hook(self.safety_checker		,       __a		,       prev_module_hook=__a							)
  
  						# We'll offload the last model manually.
  						__a    : List[Any]      					=						hook
  
  
  
  
  	@property
  	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if not hasattr(self.unet		,       '_hf_hook'							):
  											return self.device
  						for module in self.unet.modules():
  											if (
  											    hasattr(__a		,       '_hf_hook'							)
  											    and hasattr(module._hf_hook		,       'execution_device'							)
  											    and module._hf_hook.execution_device is not None
  											):
  																return torch.device(module._hf_hook.execution_device							)
  						return self.device
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a=None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						(batch_size, num_channels, height, width)
  						if isinstance(__a		,       __a							) and len(__a							) != batch_size:
  											raise ValueError(
  											    f"""You have passed a list of generators of length {len(__a							)}, but requested an effective batch"""
  											    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)
  
  						if latents is None:
  											__a    : Optional[Any]      					=						randn_tensor(__a		,       generator=__a		,       device=__a		,       dtype=__a							)
  						else:
  											__a    : Dict      					=						latents.to(device=__a		,       dtype=__a							)
  
  						# scale the initial noise by the standard deviation required by the scheduler
  						__a    : Union[str, Any]      					=						latents * self.scheduler.init_noise_sigma
  						return latents
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a = "pil"							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if output_type not in ["pt", "np", "pil"]:
  											raise ValueError(
  											    f"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""							)
  
  						# Equivalent to diffusers.VaeImageProcessor.denormalize
  						__a    : List[Any]      					=						(sample / 2 + 0.5).clamp(0		,       1							)
  						if output_type == "pt":
  											return sample
  
  						# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
  						__a    : str      					=						sample.cpu().permute(0		,       2		,       3		,       1							).numpy()
  						if output_type == "np":
  											return sample
  
  						# Output_type must be 'pil'
  						__a    : Union[str, Any]      					=						self.numpy_to_pil(__a							)
  						return sample
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a=None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if self.unet.config.num_class_embeds is not None:
  											if isinstance(__a		,       __a							):
  																__a    : Tuple      					=						torch.tensor(__a		,       dtype=torch.int							)
  											elif isinstance(__a		,       __a							):
  																assert batch_size == 1, "Batch size must be 1 if classes is an int"
  																__a    : int      					=						torch.tensor([class_labels]		,       dtype=torch.int							)
  											elif class_labels is None:
  																# Randomly generate batch_size class labels
  																# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
  																__a    : str      					=						torch.randint(0		,       self.unet.config.num_class_embeds		,       size=(batch_size,)							)
  											__a    : str      					=						class_labels.to(__a							)
  						else:
  											__a    : Optional[int]      					=						None
  						return class_labels
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if num_inference_steps is None and timesteps is None:
  											raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'							)
  
  						if num_inference_steps is not None and timesteps is not None:
  											logger.warning(
  											    f"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
  											    ' `timesteps` will be used over `num_inference_steps`.'							)
  
  						if latents is not None:
  											__a    : Optional[Any]      					=						(batch_size, 3, img_size, img_size)
  											if latents.shape != expected_shape:
  																raise ValueError(f"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""							)
  
  						if (callback_steps is None) or (
  						    callback_steps is not None and (not isinstance(__a		,       __a							) or callback_steps <= 0)
  						):
  											raise ValueError(
  											    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
  											    f""" {type(__a							)}."""							)
  
  
  
  
  
  	@torch.no_grad()
  	@replace_example_docstring(__a							)
  	def __call__(     self		,       __a = 1		,       __a = None		,       __a = 1		,       __a = None		,       __a = None		,       __a = None		,       __a = "pil"		,       __a = True		,       __a = None		,       __a = 1		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						self.unet.config.sample_size
  						__a    : Union[str, Any]      					=						self._execution_device
  
  						# 1. Check inputs
  						self.check_inputs(__a		,       __a		,       __a		,       __a		,       __a		,       __a							)
  
  						# 2. Prepare image latents
  						# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
  						__a    : Union[str, Any]      					=						self.prepare_latents(
  						    batch_size=__a		,       num_channels=self.unet.config.in_channels		,       height=__a		,       width=__a		,       dtype=self.unet.dtype		,       device=__a		,       generator=__a		,       latents=__a		,       )
  
  						# 3. Handle class_labels for class-conditional models
  						__a    : Optional[Any]      					=						self.prepare_class_labels(__a		,       __a		,       class_labels=__a							)
  
  						# 4. Prepare timesteps
  						if timesteps is not None:
  											self.scheduler.set_timesteps(timesteps=__a		,       device=__a							)
  											__a    : Union[str, Any]      					=						self.scheduler.timesteps
  											__a    : Tuple      					=						len(__a							)
  						else:
  											self.scheduler.set_timesteps(__a							)
  											__a    : Tuple      					=						self.scheduler.timesteps
  
  						# 5. Denoising loop
  						# Multistep sampling: implements Algorithm 1 in the paper
  						with self.progress_bar(total=__a							) as progress_bar:
  											for i, t in enumerate(__a							):
  																__a    : Optional[int]      					=						self.scheduler.scale_model_input(__a		,       __a							)
  																__a    : List[str]      					=						self.unet(__a		,       __a		,       class_labels=__a		,       return_dict=__a							)[0]
  
  																__a    : int      					=						self.scheduler.step(__a		,       __a		,       __a		,       generator=__a							)[0]
  
  																# call the callback, if provided
  																progress_bar.update()
  																if callback is not None and i % callback_steps == 0:
  																					callback(__a		,       __a		,       __a							)
  
          # 6. Post-process image sample
  						__a    : Tuple      					=						self.postprocess_image(__a		,       output_type=__a							)
  
  						# Offload last model to CPU
  						if hasattr(self		,       'final_offload_hook'							) and self.final_offload_hook is not None:
  											self.final_offload_hook.offload()
  
  						if not return_dict:
  											return (image,)
  
  						return ImagePipelineOutput(images=__a							)
  
  
  
  