code: 
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      EulerAncestralDiscreteScheduler,
      PNDMScheduler,
      StableDiffusionModelEditingPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  @skip_mps
  class    A					(     A_      ,       A_      ,       A_      ,       unittest.TestCase				):
  							UpperCamelCase_     :							List[Any] =StableDiffusionModelEditingPipeline
  							UpperCamelCase_     :							Union[str, Any] =TEXT_TO_IMAGE_PARAMS
  							UpperCamelCase_     :							Tuple =TEXT_TO_IMAGE_BATCH_PARAMS
  							UpperCamelCase_     :							List[Any] =TEXT_TO_IMAGE_IMAGE_PARAMS
  							UpperCamelCase_     :							Union[str, Any] =TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  							def     _A (self      ):
  									torch.manual_seed(0      )
  									__lowercase=    UNetaDConditionModel(
  									    block_out_channels=(3_2, 6_4)   ,       layers_per_block=2   ,       sample_size=3_2   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,       cross_attention_dim=3_2   ,       )
  									__lowercase=    DDIMScheduler()
  									torch.manual_seed(0      )
  									__lowercase=    AutoencoderKL(
  									    block_out_channels=[3_2, 6_4]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,       latent_channels=4   ,       )
  									torch.manual_seed(0      )
  									__lowercase=    CLIPTextConfig(
  									    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=3_2   ,       intermediate_size=3_7   ,       layer_norm_eps=1E-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1_0_0_0   ,       )
  									__lowercase=    CLIPTextModel(A_      )
  									__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     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase=0      ):
  									__lowercase=    torch.manual_seed(A_      )
  									__lowercase=    {
  									    'prompt': 'A field of roses',
  									    'generator': generator,
  									    # Setting height and width to None to prevent OOMs on CPU.
  									    'height': None,
  									    'width': None,
  									    'num_inference_steps': 2,
  									    'guidance_scale': 6.0,
  									    'output_type': 'numpy',
  									}
  									return inputs
  
  
  							def     _A (self      ):
  									__lowercase=    'cpu'  # ensure determinism for the device-dependent torch.Generator
  									__lowercase=    self.get_dummy_components()
  									__lowercase=    StableDiffusionModelEditingPipeline(**A_      )
  									__lowercase=    sd_pipe.to(A_      )
  									sd_pipe.set_progress_bar_config(disable=A_      )
  
  									__lowercase=    self.get_dummy_inputs(A_      )
  									__lowercase=    sd_pipe(**A_      ).images
  									__lowercase=    image[0, -3:, -3:, -1]
  									assert image.shape == (1, 6_4, 6_4, 3)
  
  									__lowercase=    np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]      )
  
  									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
  
  
  							def     _A (self      ):
  									__lowercase=    'cpu'  # ensure determinism for the device-dependent torch.Generator
  									__lowercase=    self.get_dummy_components()
  									__lowercase=    StableDiffusionModelEditingPipeline(**A_      )
  									__lowercase=    sd_pipe.to(A_      )
  									sd_pipe.set_progress_bar_config(disable=A_      )
  
  									__lowercase=    self.get_dummy_inputs(A_      )
  									__lowercase=    'french fries'
  									__lowercase=    sd_pipe(**A_   ,       negative_prompt=A_      )
  									__lowercase=    output.images
  									__lowercase=    image[0, -3:, -3:, -1]
  
  									assert image.shape == (1, 6_4, 6_4, 3)
  
  									__lowercase=    np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]      )
  
  									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
  
  
  							def     _A (self      ):
  									__lowercase=    'cpu'  # ensure determinism for the device-dependent torch.Generator
  									__lowercase=    self.get_dummy_components()
  									__lowercase=    EulerAncestralDiscreteScheduler(
  									    beta_start=0.0_00_85   ,       beta_end=0.0_12   ,       beta_schedule='scaled_linear'      )
  									__lowercase=    StableDiffusionModelEditingPipeline(**A_      )
  									__lowercase=    sd_pipe.to(A_      )
  									sd_pipe.set_progress_bar_config(disable=A_      )
  
  									__lowercase=    self.get_dummy_inputs(A_      )
  									__lowercase=    sd_pipe(**A_      ).images
  									__lowercase=    image[0, -3:, -3:, -1]
  
  									assert image.shape == (1, 6_4, 6_4, 3)
  
  									__lowercase=    np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]      )
  
  									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
  
  
  							def     _A (self      ):
  									__lowercase=    'cpu'  # ensure determinism for the device-dependent torch.Generator
  									__lowercase=    self.get_dummy_components()
  									__lowercase=    PNDMScheduler()
  									__lowercase=    StableDiffusionModelEditingPipeline(**A_      )
  									__lowercase=    sd_pipe.to(A_      )
  									sd_pipe.set_progress_bar_config(disable=A_      )
  
  									__lowercase=    self.get_dummy_inputs(A_      )
  									# the pipeline does not expect pndm so test if it raises error.
  									with self.assertRaises(A_      ):
  											__lowercase=    sd_pipe(**A_      ).images
  
  
  							def     _A (self      ):
  									super().test_inference_batch_single_identical(expected_max_diff=5E-3      )
  
  
  
  
  
  
  
  							def     _A (self      ):
  									super().test_attention_slicing_forward_pass(expected_max_diff=5E-3      )
  
  
  
  
  
  @slow
  @require_torch_gpu
  class    A					(     unittest.TestCase				):
  
  
  							def     _A (self      ):
  									super().tearDown()
  									gc.collect()
  									torch.cuda.empty_cache()
  
  
  							def     _A (self   ,       lowerCAmelCase=0      ):
  									__lowercase=    torch.manual_seed(A_      )
  									__lowercase=    {
  									    'prompt': 'A field of roses',
  									    'generator': generator,
  									    'num_inference_steps': 3,
  									    'guidance_scale': 7.5,
  									    'output_type': 'numpy',
  									}
  									return inputs
  
  
  							def     _A (self      ):
  									__lowercase=    'CompVis/stable-diffusion-v1-4'
  									__lowercase=    StableDiffusionModelEditingPipeline.from_pretrained(A_   ,       safety_checker=A_      )
  									pipe.to(A_      )
  									pipe.set_progress_bar_config(disable=A_      )
  									pipe.enable_attention_slicing()
  
  									__lowercase=    self.get_inputs()
  									__lowercase=    pipe(**A_      ).images
  									__lowercase=    image[0, -3:, -3:, -1].flatten()
  
  									assert image.shape == (1, 5_1_2, 5_1_2, 3)
  
  									__lowercase=    np.array(
  									    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]      )
  
  									assert np.abs(expected_slice - image_slice      ).max() < 1E-2
  
  									# make sure image changes after editing
  									pipe.edit_model('A pack of roses'   ,       'A pack of blue roses'      )
  
  									__lowercase=    pipe(**A_      ).images
  									__lowercase=    image[0, -3:, -3:, -1].flatten()
  
  									assert image.shape == (1, 5_1_2, 5_1_2, 3)
  
  									assert np.abs(expected_slice - image_slice      ).max() > 1E-1
  
  
  
  
  
  
  
  							def     _A (self      ):
  									torch.cuda.empty_cache()
  									torch.cuda.reset_max_memory_allocated()
  									torch.cuda.reset_peak_memory_stats()
  
  									__lowercase=    'CompVis/stable-diffusion-v1-4'
  									__lowercase=    DDIMScheduler.from_pretrained(A_   ,       subfolder='scheduler'      )
  									__lowercase=    StableDiffusionModelEditingPipeline.from_pretrained(
  									    A_   ,       scheduler=A_   ,       safety_checker=A_      )
  									__lowercase=    pipe.to(A_      )
  									pipe.set_progress_bar_config(disable=A_      )
  									pipe.enable_attention_slicing(1      )
  									pipe.enable_sequential_cpu_offload()
  
  									__lowercase=    self.get_inputs()
  									__lowercase=    pipe(**A_      )
  
  									__lowercase=    torch.cuda.max_memory_allocated()
  									# make sure that less than 4.4 GB is allocated
  									assert mem_bytes < 4.4 * 1_0**9
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Optional[int] ={
  							    '''sld_warmup_steps''': 15,
  							    '''sld_guidance_scale''': 20,
  							    '''sld_threshold''': 0.0,
  							    '''sld_momentum_scale''': 0.0,
  							    '''sld_mom_beta''': 0.0,
  							}
  							UpperCamelCase_     :							Tuple ={
  							    '''sld_warmup_steps''': 10,
  							    '''sld_guidance_scale''': 1_000,
  							    '''sld_threshold''': 0.01,
  							    '''sld_momentum_scale''': 0.3,
  							    '''sld_mom_beta''': 0.4,
  							}
  							UpperCamelCase_     :							Union[str, Any] ={
  							    '''sld_warmup_steps''': 7,
  							    '''sld_guidance_scale''': 2_000,
  							    '''sld_threshold''': 0.025,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  							UpperCamelCase_     :							List[Any] ={
  							    '''sld_warmup_steps''': 0,
  							    '''sld_guidance_scale''': 5_000,
  							    '''sld_threshold''': 1.0,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  							UpperCamelCase_     :							Optional[Union[List[PIL.Image.Image], np.ndarray]]
  							UpperCamelCase_     :							Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
  	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  	from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  
  