code: 
  
  
  
  
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMParallelScheduler,
      DDPMParallelScheduler,
      StableDiffusionParadigmsPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import (
      enable_full_determinism,
      require_torch_gpu,
  )
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  class 					snake_case_							(       lowercase_      ,			lowercase_      ,			unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						StableDiffusionParadigmsPipeline
     __lowerCAmelCase      =						TEXT_TO_IMAGE_PARAMS
     __lowerCAmelCase      =						TEXT_TO_IMAGE_BATCH_PARAMS
     __lowerCAmelCase      =						TEXT_TO_IMAGE_IMAGE_PARAMS
     __lowerCAmelCase      =						TEXT_TO_IMAGE_IMAGE_PARAMS
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         torch.manual_seed(0	)
         _lowercase     =	UNetaDConditionModel(
             block_out_channels=(32, 64)   ,       layers_per_block=2   ,       sample_size=32   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,       cross_attention_dim=32   ,       attention_head_dim=(2, 4)   ,       use_linear_projection=snake_case__   ,       )
         _lowercase     =	DDIMParallelScheduler(
             beta_start=0.0_00_85   ,       beta_end=0.0_12   ,       beta_schedule='scaled_linear'   ,       clip_sample=snake_case__   ,       set_alpha_to_one=snake_case__   ,       )
         torch.manual_seed(0	)
         _lowercase     =	AutoencoderKL(
             block_out_channels=[32, 64]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,       latent_channels=4   ,       sample_size=128   ,       )
         torch.manual_seed(0	)
         _lowercase     =	CLIPTextConfig(
             bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       intermediate_size=37   ,       layer_norm_eps=1E-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1_000   ,       hidden_act='gelu'   ,       projection_dim=512   ,       )
         _lowercase     =	CLIPTextModel(snake_case__	)
         _lowercase     =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
  
         _lowercase     =	{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Any=0	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         if str(snake_case__	).startswith('mps'	):
             _lowercase     =	torch.manual_seed(snake_case__	)
         else:
             _lowercase     =	torch.Generator(device=snake_case__	).manual_seed(snake_case__	)
         _lowercase     =	{
             'prompt': 'a photograph of an astronaut riding a horse',
             'generator': generator,
             'num_inference_steps': 10,
             'guidance_scale': 6.0,
             'output_type': 'numpy',
             'parallel': 3,
             'debug': True,
         }
         return inputs
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	'cpu'  # ensure determinism for the device-dependent torch.Generator
         _lowercase     =	self.get_dummy_components()
         _lowercase     =	StableDiffusionParadigmsPipeline(**snake_case__	)
         _lowercase     =	sd_pipe.to(snake_case__	)
         sd_pipe.set_progress_bar_config(disable=snake_case__	)
  
         _lowercase     =	self.get_dummy_inputs(snake_case__	)
         _lowercase     =	sd_pipe(**snake_case__	).images
         _lowercase     =	image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)
  
         _lowercase     =	np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]	)
  
         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	'cpu'  # ensure determinism for the device-dependent torch.Generator
         _lowercase     =	self.get_dummy_components()
         torch.manual_seed(0	)
         _lowercase     =	DDPMParallelScheduler()
         torch.manual_seed(0	)
         _lowercase     =	StableDiffusionParadigmsPipeline(**snake_case__	)
         _lowercase     =	sd_pipe.to(snake_case__	)
         sd_pipe.set_progress_bar_config(disable=snake_case__	)
  
         _lowercase     =	self.get_dummy_inputs(snake_case__	)
         _lowercase     =	sd_pipe(**snake_case__	).images
         _lowercase     =	image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)
  
         _lowercase     =	np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]	)
  
         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         super().test_inference_batch_consistent(batch_sizes=[1, 2]	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         super().test_inference_batch_single_identical(batch_size=2   ,       expected_max_diff=3E-3	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	'cpu'  # ensure determinism for the device-dependent torch.Generator
         _lowercase     =	self.get_dummy_components()
         _lowercase     =	StableDiffusionParadigmsPipeline(**snake_case__	)
         _lowercase     =	sd_pipe.to(snake_case__	)
         sd_pipe.set_progress_bar_config(disable=snake_case__	)
  
         _lowercase     =	self.get_dummy_inputs(snake_case__	)
         _lowercase     =	'french fries'
         _lowercase     =	sd_pipe(**snake_case__   ,       negative_prompt=snake_case__	)
         _lowercase     =	output.images
         _lowercase     =	image[0, -3:, -3:, -1]
  
         assert image.shape == (1, 64, 64, 3)
  
         _lowercase     =	np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]	)
  
         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 					snake_case_							(       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	Tuple=0	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	torch.Generator(device=snake_case__	).manual_seed(snake_case__	)
         _lowercase     =	{
             'prompt': 'a photograph of an astronaut riding a horse',
             'generator': generator,
             'num_inference_steps': 10,
             'guidance_scale': 7.5,
             'output_type': 'numpy',
             'parallel': 3,
             'debug': True,
         }
         return inputs
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	'stabilityai/stable-diffusion-2-base'
         _lowercase     =	DDIMParallelScheduler.from_pretrained(snake_case__   ,       subfolder='scheduler'	)
         _lowercase     =	StableDiffusionParadigmsPipeline.from_pretrained(snake_case__   ,       scheduler=snake_case__   ,       safety_checker=snake_case__	)
         pipe.to(snake_case__	)
         pipe.set_progress_bar_config(disable=snake_case__	)
         pipe.enable_attention_slicing()
  
         _lowercase     =	self.get_inputs()
         _lowercase     =	pipe(**snake_case__	).images
         _lowercase     =	image[0, -3:, -3:, -1].flatten()
  
         assert image.shape == (1, 512, 512, 3)
  
         _lowercase     =	np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]	)
  
         assert np.abs(expected_slice - image_slice	).max() < 1E-2
  
style_context: 
  
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
     import PIL
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						["""pixel_values"""]
  
     def __init__(    self:	int   ,       snake_case__:	bool = True   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	PILImageResampling = PIL.Image.NEAREST   ,       snake_case__:	bool = False   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	Union[int, float] = 1 / 255   ,       snake_case__:	bool = False   ,       snake_case__:	bool = True   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	bool = True   ,       **snake_case__:	Optional[Any]   ,       )  ->		None:
  
  
         """simple docstring"""
  
  
  
         super().__init__(**snake_case__	)
         _lowercase     =	size if size is not None else {'height': 346, 'width': 346}
         _lowercase     =	get_size_dict(snake_case__	)
         _lowercase     =	crop_size if crop_size is not None else {'height': 289, 'width': 289}
         _lowercase     =	get_size_dict(snake_case__   ,       param_name='crop_size'	)
  
         _lowercase     =	do_resize
         _lowercase     =	size
         _lowercase     =	resample
         _lowercase     =	do_center_crop
         _lowercase     =	crop_size
         _lowercase     =	do_rescale
         _lowercase     =	rescale_factor
         _lowercase     =	rescale_offset
         _lowercase     =	do_normalize
         _lowercase     =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _lowercase     =	image_std if image_std is not None else IMAGENET_STANDARD_STD
         _lowercase     =	include_top
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	np.ndarray   ,       snake_case__:	Dict[str, int]   ,       snake_case__:	PILImageResampling = PIL.Image.NEAREST   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	Optional[int]   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	get_size_dict(snake_case__	)
         if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
         return resize(
             snake_case__   ,       size=(size['height'], size['width'])   ,       resample=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	np.ndarray   ,       snake_case__:	Dict[str, int]   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	str   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	get_size_dict(snake_case__	)
         if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
         return center_crop(snake_case__   ,       size=(size['height'], size['width'])   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	np.ndarray   ,       snake_case__:	Union[int, float]   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	List[str]   ,       )  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         if offset:
             _lowercase     =	(image - 1_27.5) * scale
             if data_format is not None:
                 _lowercase     =	to_channel_dimension_format(snake_case__   ,       snake_case__	)
             _lowercase     =	rescaled_image.astype(np.floataa	)
         else:
             _lowercase     =	rescale(snake_case__   ,       scale=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
         return rescaled_image
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	np.ndarray   ,       snake_case__:	Union[float, List[float]]   ,       snake_case__:	Union[float, List[float]]   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	Any   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         return normalize(snake_case__   ,       mean=snake_case__   ,       std=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	ImageInput   ,       snake_case__:	bool = None   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	Union[str, Any]=None   ,       snake_case__:	bool = None   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	bool = None   ,       snake_case__:	float = None   ,       snake_case__:	bool = None   ,       snake_case__:	bool = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	bool = None   ,       snake_case__:	Optional[Union[str, TensorType]] = None   ,       snake_case__:	ChannelDimension = ChannelDimension.FIRST   ,       **snake_case__:	int   ,       )  ->		PIL.Image.Image:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	do_resize if do_resize is not None else self.do_resize
         _lowercase     =	resample if resample is not None else self.resample
         _lowercase     =	do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowercase     =	do_rescale if do_rescale is not None else self.do_rescale
         _lowercase     =	rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase     =	rescale_offset if rescale_offset is not None else self.rescale_offset
         _lowercase     =	do_normalize if do_normalize is not None else self.do_normalize
         _lowercase     =	image_mean if image_mean is not None else self.image_mean
         _lowercase     =	image_std if image_std is not None else self.image_std
         _lowercase     =	include_top if include_top is not None else self.include_top
  
         _lowercase     =	size if size is not None else self.size
         _lowercase     =	get_size_dict(snake_case__	)
         _lowercase     =	crop_size if crop_size is not None else self.crop_size
         _lowercase     =	get_size_dict(snake_case__   ,       param_name='crop_size'	)
  
         _lowercase     =	make_list_of_images(snake_case__	)
  
         if not valid_images(snake_case__	):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'	)
  
         if do_resize and size is None or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'	)
  
         if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'	)
  
         if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'	)
  
         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'	)
  
         # All transformations expect numpy arrays.
         _lowercase     =	[to_numpy_array(snake_case__	) for image in images]
  
         if do_resize:
             _lowercase     =	[self.resize(image=snake_case__   ,       size=snake_case__   ,       resample=snake_case__	) for image in images]
  
         if do_center_crop:
             _lowercase     =	[self.center_crop(image=snake_case__   ,       size=snake_case__	) for image in images]
  
         if do_rescale:
             _lowercase     =	[self.rescale(image=snake_case__   ,       scale=snake_case__   ,       offset=snake_case__	) for image in images]
  
         if do_normalize:
             _lowercase     =	[self.normalize(image=snake_case__   ,       mean=snake_case__   ,       std=snake_case__	) for image in images]
  
         if include_top:
             _lowercase     =	[self.normalize(image=snake_case__   ,       mean=[0, 0, 0]   ,       std=snake_case__	) for image in images]
  
         _lowercase     =	[to_channel_dimension_format(snake_case__   ,       snake_case__	) for image in images]
  
         _lowercase     =	{'pixel_values': images}
         return BatchFeature(data=snake_case__   ,       tensor_type=snake_case__	)
  