



'''simple docstring'''

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin

@skip_mps
class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Optional[int]  							=   IFPipeline
							lowerCAmelCase			:       Optional[int]  							=   TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
							lowerCAmelCase			:       List[str]  							=   TEXT_TO_IMAGE_BATCH_PARAMS
							lowerCAmelCase			:       Dict  							=   PipelineTesterMixin.required_optional_params - {'latents'}







							def 				__lowercase     (				self   :			Optional[int]						):
									return self._get_dummy_components()







							def 				__lowercase     (				self   :			Optional[int]      ,_UpperCAmelCase   :			int      ,_UpperCAmelCase   :			int=0						):
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: List[Any]								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Optional[Any]								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)

									_a	: Union[str, Any]								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'output_type': 'numpy',
									}

									return inputs







							def 				__lowercase     (				self   :			str						):
									self._test_save_load_optional_components()







							@unittest.skipIf(torch_device != 'cuda'      ,reason='float16 requires CUDA'						)
							def 				__lowercase     (				self   :			List[str]						):
									# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
									super().test_save_load_floataa(expected_max_diff=1E-1						)







							def 				__lowercase     (				self   :			Dict						):
									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)







							def 				__lowercase     (				self   :			Dict						):
									self._test_save_load_local()







							def 				__lowercase     (				self   :			int						):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1E-2      ,)







							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()      ,reason='XFormers attention is only available with CUDA and `xformers` installed'      ,)
							def 				__lowercase     (				self   :			int						):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)

@slow
@require_torch_gpu
class       __magic_name__		(       unittest.TestCase  ):







							def 				__lowercase     (				self   :			Any						):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 				__lowercase     (				self   :			Dict						):
									# if

									_a	: Optional[Any]								=				IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'      ,variant='fp16'      ,torch_dtype=torch.floataa						)

									_a	: Tuple								=				IFSuperResolutionPipeline.from_pretrained(
									    'DeepFloyd/IF-II-L-v1.0'      ,variant='fp16'      ,torch_dtype=torch.floataa      ,text_encoder=_UpperCAmelCase      ,tokenizer=_UpperCAmelCase						)

									# pre compute text embeddings and remove T5 to save memory

									pipe_a.text_encoder.to('cuda'						)

									_a			,   _a	: Union[str, Any]								=				pipe_a.encode_prompt('anime turtle'      ,device='cuda'						)

									del pipe_a.tokenizer
									del pipe_a.text_encoder
									gc.collect()

									_a	: Optional[int]								=				None
									_a	: Optional[int]								=				None

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

									self._test_if(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# img2img

									_a	: List[str]								=				IFImgaImgPipeline(**pipe_a.components						)
									_a	: Optional[int]								=				IFImgaImgSuperResolutionPipeline(**pipe_a.components						)

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

									self._test_if_imgaimg(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# inpainting

									_a	: Union[str, Any]								=				IFInpaintingPipeline(**pipe_a.components						)
									_a	: Optional[Any]								=				IFInpaintingSuperResolutionPipeline(**pipe_a.components						)

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

									self._test_if_inpainting(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Tuple      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			Optional[int]						):
									# pipeline 1

									_start_torch_memory_measurement()

									_a	: Dict								=				torch.Generator(device='cpu'						).manual_seed(0						)
									_a	: List[Any]								=				pipe_a(
									    prompt_embeds=_UpperCAmelCase      ,negative_prompt_embeds=_UpperCAmelCase      ,num_inference_steps=2      ,generator=_UpperCAmelCase      ,output_type='np'      ,)

									_a	: int								=				output.images[0]

									assert image.shape == (64, 64, 3)

									_a	: List[str]								=				torch.cuda.max_memory_allocated()
									assert mem_bytes < 13 * 10**9

									_a	: Optional[Any]								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'						)
									assert_mean_pixel_difference(_UpperCAmelCase      ,_UpperCAmelCase						)

									# pipeline 2

									_start_torch_memory_measurement()

									_a	: Union[str, Any]								=				torch.Generator(device='cpu'						).manual_seed(0						)

									_a	: List[Any]								=				floats_tensor((1, 3, 64, 64)      ,rng=random.Random(0						)						).to(_UpperCAmelCase						)

									_a	: List[str]								=				pipe_a(
									    prompt_embeds=_UpperCAmelCase      ,negative_prompt_embeds=_UpperCAmelCase      ,image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: Any								=				output.images[0]

									assert image.shape == (256, 256, 3)

									_a	: Union[str, Any]								=				torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									_a	: List[str]								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'						)
									assert_mean_pixel_difference(_UpperCAmelCase      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			int      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			Any      ,_UpperCAmelCase   :			Optional[Any]						):
									# pipeline 1

									_start_torch_memory_measurement()

									_a	: Any								=				floats_tensor((1, 3, 64, 64)      ,rng=random.Random(0						)						).to(_UpperCAmelCase						)

									_a	: Any								=				torch.Generator(device='cpu'						).manual_seed(0						)

									_a	: int								=				pipe_a(
									    prompt_embeds=_UpperCAmelCase      ,negative_prompt_embeds=_UpperCAmelCase      ,image=_UpperCAmelCase      ,num_inference_steps=2      ,generator=_UpperCAmelCase      ,output_type='np'      ,)

									_a	: str								=				output.images[0]

									assert image.shape == (64, 64, 3)

									_a	: Optional[int]								=				torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									_a	: Optional[int]								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'						)
									assert_mean_pixel_difference(_UpperCAmelCase      ,_UpperCAmelCase						)

									# pipeline 2

									_start_torch_memory_measurement()

									_a	: List[Any]								=				torch.Generator(device='cpu'						).manual_seed(0						)

									_a	: Any								=				floats_tensor((1, 3, 256, 256)      ,rng=random.Random(0						)						).to(_UpperCAmelCase						)
									_a	: List[str]								=				floats_tensor((1, 3, 64, 64)      ,rng=random.Random(0						)						).to(_UpperCAmelCase						)

									_a	: Optional[int]								=				pipe_a(
									    prompt_embeds=_UpperCAmelCase      ,negative_prompt_embeds=_UpperCAmelCase      ,image=_UpperCAmelCase      ,original_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				output.images[0]

									assert image.shape == (256, 256, 3)

									_a	: Dict								=				torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									_a	: Union[str, Any]								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'						)
									assert_mean_pixel_difference(_UpperCAmelCase      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Dict      ,_UpperCAmelCase   :			Any      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			int      ,_UpperCAmelCase   :			Optional[int]						):
									# pipeline 1

									_start_torch_memory_measurement()

									_a	: int								=				floats_tensor((1, 3, 64, 64)      ,rng=random.Random(0						)						).to(_UpperCAmelCase						)
									_a	: Dict								=				floats_tensor((1, 3, 64, 64)      ,rng=random.Random(1						)						).to(_UpperCAmelCase						)

									_a	: Optional[int]								=				torch.Generator(device='cpu'						).manual_seed(0						)
									_a	: List[str]								=				pipe_a(
									    prompt_embeds=_UpperCAmelCase      ,negative_prompt_embeds=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,num_inference_steps=2      ,generator=_UpperCAmelCase      ,output_type='np'      ,)

									_a	: Optional[int]								=				output.images[0]

									assert image.shape == (64, 64, 3)

									_a	: Optional[Any]								=				torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									_a	: Any								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'						)
									assert_mean_pixel_difference(_UpperCAmelCase      ,_UpperCAmelCase						)

									# pipeline 2

									_start_torch_memory_measurement()

									_a	: Union[str, Any]								=				torch.Generator(device='cpu'						).manual_seed(0						)

									_a	: Tuple								=				floats_tensor((1, 3, 64, 64)      ,rng=random.Random(0						)						).to(_UpperCAmelCase						)
									_a	: Dict								=				floats_tensor((1, 3, 256, 256)      ,rng=random.Random(0						)						).to(_UpperCAmelCase						)
									_a	: Optional[int]								=				floats_tensor((1, 3, 256, 256)      ,rng=random.Random(1						)						).to(_UpperCAmelCase						)

									_a	: Any								=				pipe_a(
									    prompt_embeds=_UpperCAmelCase      ,negative_prompt_embeds=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,original_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: Any								=				output.images[0]

									assert image.shape == (256, 256, 3)

									_a	: List[str]								=				torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'						)
									assert_mean_pixel_difference(_UpperCAmelCase      ,_UpperCAmelCase						)



def 	__lowerCamelCase	( )     -> Optional[Any]:
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()









'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   StableDiffusionInpaintPipeline
							lowerCAmelCase			:       int  							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCAmelCase			:       Optional[Any]  							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCAmelCase			:       List[str]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       Optional[Any]  							=   frozenset([]  )







							def 				__lowercase     (				self   :			Dict						):
									torch.manual_seed(0						)
									_a	: Optional[int]								=				UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,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=_UpperCAmelCase      ,)
									_a	: Optional[int]								=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase						)
									torch.manual_seed(0						)
									_a	: Dict								=				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						)
									_a	: Union[str, Any]								=				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=1000      ,hidden_act='gelu'      ,projection_dim=512      ,)
									_a	: str								=				CLIPTextModel(_UpperCAmelCase						)
									_a	: Any								=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_a	: Optional[Any]								=				{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			int=0						):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_a	: Any								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
									_a	: Any								=				image.cpu().permute(0      ,2      ,3      ,1						)[0]
									_a	: Optional[int]								=				Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert('RGB'						).resize((64, 64)						)
									_a	: Tuple								=				Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: str								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Dict								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
									_a	: Any								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[int]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Dict								=				self.get_dummy_components()
									_a	: int								=				StableDiffusionInpaintPipeline(**_UpperCAmelCase						)
									_a	: Any								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				self.get_dummy_inputs(_UpperCAmelCase						)
									_a	: Tuple								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: Dict								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: List[Any]								=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2







							def 				__lowercase     (				self   :			List[Any]						):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

@slow
@require_torch_gpu
class       __magic_name__		(       unittest.TestCase  ):







							def 				__lowercase     (				self   :			List[str]						):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Union[str, Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'						)

									_a	: List[Any]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Tuple								=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase      ,safety_checker=_UpperCAmelCase						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: int								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: Any								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: List[str]								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 9E-3







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'						)

									_a	: int								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: List[str]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,torch_dtype=torch.floataa      ,safety_checker=_UpperCAmelCase      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: Dict								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: int								=				torch.manual_seed(0						)
									_a	: Dict								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: str								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 5E-1







							def 				__lowercase     (				self   :			Tuple						):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_a	: Dict								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[int]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

									_a	: List[str]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Any								=				PNDMScheduler.from_pretrained(_UpperCAmelCase      ,subfolder='scheduler'						)
									_a	: Optional[Any]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,safety_checker=_UpperCAmelCase      ,scheduler=_UpperCAmelCase      ,torch_dtype=torch.floataa      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									_a	: Any								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Optional[Any]								=				torch.manual_seed(0						)
									_a	: int								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9




