

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,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 	A_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Dict										=				StableDiffusionInstructPixaPixPipeline
							_UpperCAmelCase   :  Optional[Any]										=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
							_UpperCAmelCase   :  str										=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							_UpperCAmelCase   :  List[Any]										=				IMAGE_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase   :  Union[str, Any]										=				IMAGE_TO_IMAGE_IMAGE_PARAMS
							def    lowerCAmelCase					(		self		:      Dict):
								torch.manual_seed(0)
								__lowerCamelCase    :    str 	=			UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)       ,layers_per_block=2       ,sample_size=3_2       ,in_channels=8       ,out_channels=4       ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,cross_attention_dim=3_2       ,)
								__lowerCamelCase    :    Optional[Any] 	=			PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__)
								torch.manual_seed(0)
								__lowerCamelCase    :    List[str] 	=			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)
								__lowerCamelCase    :    List[str] 	=			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       ,)
								__lowerCamelCase    :    Optional[int] 	=			CLIPTextModel(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

								__lowerCamelCase    :    Optional[int] 	=			{
								    'unet': unet,
								    'scheduler': scheduler,
								    'vae': vae,
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'safety_checker': None,
								    'feature_extractor': None,
								}
								return components
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=0):
								__lowerCamelCase    :    Optional[int] 	=			floats_tensor((1, 3, 3_2, 3_2)       ,rng=random.Random(SCREAMING_SNAKE_CASE__)).to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			image.cpu().permute(0       ,2       ,3       ,1)[0]
								__lowerCamelCase    :    Any 	=			Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__)).convert('RGB')
								if str(SCREAMING_SNAKE_CASE__).startswith('mps'):
									__lowerCamelCase    :    str 	=			torch.manual_seed(SCREAMING_SNAKE_CASE__)
								else:
									__lowerCamelCase    :    Any 	=			torch.Generator(device=SCREAMING_SNAKE_CASE__).manual_seed(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Tuple 	=			{
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': image,
								    'generator': generator,
								    'num_inference_steps': 2,
								    'guidance_scale': 6.0,
								    'image_guidance_scale': 1,
								    'output_type': 'numpy',
								}
								return inputs
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Dict 	=			'cpu'  # ensure determinism for the device-dependent torch.Generator
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_dummy_components()
								__lowerCamelCase    :    Union[str, Any] 	=			StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Tuple 	=			sd_pipe.to(SCREAMING_SNAKE_CASE__)
								sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Union[str, Any] 	=			self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			sd_pipe(**SCREAMING_SNAKE_CASE__).images
								__lowerCamelCase    :    List[str] 	=			image[0, -3:, -3:, -1]
								assert image.shape == (1, 3_2, 3_2, 3)
								__lowerCamelCase    :    Dict 	=			np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815])

								assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    int 	=			'cpu'  # ensure determinism for the device-dependent torch.Generator
								__lowerCamelCase    :    int 	=			self.get_dummy_components()
								__lowerCamelCase    :    Optional[Any] 	=			StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Dict 	=			sd_pipe.to(SCREAMING_SNAKE_CASE__)
								sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Dict 	=			self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Dict 	=			'french fries'
								__lowerCamelCase    :    str 	=			sd_pipe(**SCREAMING_SNAKE_CASE__       ,negative_prompt=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			output.images
								__lowerCamelCase    :    Optional[Any] 	=			image[0, -3:, -3:, -1]

								assert image.shape == (1, 3_2, 3_2, 3)
								__lowerCamelCase    :    List[str] 	=			np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831])

								assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Dict 	=			'cpu'  # ensure determinism for the device-dependent torch.Generator
								__lowerCamelCase    :    int 	=			self.get_dummy_components()
								__lowerCamelCase    :    Tuple 	=			StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			sd_pipe.to(SCREAMING_SNAKE_CASE__)
								sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[int] 	=			self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[Any] 	=			[inputs['prompt']] * 2

								__lowerCamelCase    :    int 	=			np.array(inputs['image']).astype(np.floataa) / 255.0
								__lowerCamelCase    :    Any 	=			torch.from_numpy(SCREAMING_SNAKE_CASE__).unsqueeze(0).to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			image / 2 + 0.5
								__lowerCamelCase    :    List[Any] 	=			image.permute(0       ,3       ,1       ,2)
								__lowerCamelCase    :    List[str] 	=			image.repeat(2       ,1       ,1       ,1)

								__lowerCamelCase    :    List[Any] 	=			sd_pipe(**SCREAMING_SNAKE_CASE__).images
								__lowerCamelCase    :    Optional[int] 	=			image[-1, -3:, -3:, -1]

								assert image.shape == (2, 3_2, 3_2, 3)
								__lowerCamelCase    :    int 	=			np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579])

								assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Tuple 	=			'cpu'  # ensure determinism for the device-dependent torch.Generator
								__lowerCamelCase    :    Dict 	=			self.get_dummy_components()
								__lowerCamelCase    :    int 	=			EulerAncestralDiscreteScheduler(
								    beta_start=0.00085       ,beta_end=0.012       ,beta_schedule='scaled_linear')
								__lowerCamelCase    :    Optional[Any] 	=			StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Dict 	=			sd_pipe.to(SCREAMING_SNAKE_CASE__)
								sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    str 	=			self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			sd_pipe(**SCREAMING_SNAKE_CASE__).images
								__lowerCamelCase    :    Union[str, Any] 	=			image[0, -3:, -3:, -1]

								__lowerCamelCase    :    List[str] 	=			[round(SCREAMING_SNAKE_CASE__       ,4) for x in image_slice.flatten().tolist()]
								print(','.join([str(SCREAMING_SNAKE_CASE__) for x in slice]))

								assert image.shape == (1, 3_2, 3_2, 3)
								__lowerCamelCase    :    int 	=			np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986])

								assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
							def    lowerCAmelCase					(		self		:      Optional[int]):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3)


							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_dummy_components()
								__lowerCamelCase    :    Any 	=			StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE__       ,do_normalize=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[int] 	=			pipe(**self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE__       ,input_image_type='pt'))[0]

								__lowerCamelCase    :    Any 	=			components['vae']
								__lowerCamelCase    :    Optional[Any] 	=			self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE__       ,input_image_type='pt')

								for image_param in self.image_latents_params:
									if image_param in inputs.keys():
										__lowerCamelCase    :    Any 	=			vae.encode(inputs[image_param]).latent_dist.mode()

								__lowerCamelCase    :    List[Any] 	=			pipe(**SCREAMING_SNAKE_CASE__)[0]

								__lowerCamelCase    :    List[Any] 	=			np.abs(out - out_latents_inputs).max()
								self.assertLess(SCREAMING_SNAKE_CASE__       ,1E-4       ,'passing latents as image input generate different result from passing image')






@slow
@require_torch_gpu
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      Dict):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      int=0):
								__lowerCamelCase    :    List[str] 	=			torch.manual_seed(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Any 	=			load_image(
								    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg')
								__lowerCamelCase    :    List[str] 	=			{
								    'prompt': 'turn him into a cyborg',
								    'image': image,
								    'generator': generator,
								    'num_inference_steps': 3,
								    'guidance_scale': 7.5,
								    'image_guidance_scale': 1.0,
								    'output_type': 'numpy',
								}
								return inputs
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Tuple 	=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'       ,safety_checker=SCREAMING_SNAKE_CASE__)
								pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
								pipe.enable_attention_slicing()

								__lowerCamelCase    :    Dict 	=			self.get_inputs()
								__lowerCamelCase    :    Tuple 	=			pipe(**SCREAMING_SNAKE_CASE__).images
								__lowerCamelCase    :    List[str] 	=			image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__lowerCamelCase    :    Tuple 	=			np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555])

								assert np.abs(expected_slice - image_slice).max() < 1E-3
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Union[str, Any] 	=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'       ,safety_checker=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			LMSDiscreteScheduler.from_config(pipe.scheduler.config)
								pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
								pipe.enable_attention_slicing()

								__lowerCamelCase    :    Any 	=			self.get_inputs()
								__lowerCamelCase    :    str 	=			pipe(**SCREAMING_SNAKE_CASE__).images
								__lowerCamelCase    :    List[Any] 	=			image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__lowerCamelCase    :    Optional[Any] 	=			np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301])

								assert np.abs(expected_slice - image_slice).max() < 1E-3
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    List[str] 	=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'       ,safety_checker=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			DDIMScheduler.from_config(pipe.scheduler.config)
								pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
								pipe.enable_attention_slicing()

								__lowerCamelCase    :    Dict 	=			self.get_inputs()
								__lowerCamelCase    :    Dict 	=			pipe(**SCREAMING_SNAKE_CASE__).images
								__lowerCamelCase    :    Any 	=			image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__lowerCamelCase    :    Optional[int] 	=			np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753])

								assert np.abs(expected_slice - image_slice).max() < 1E-3
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Dict 	=			0

								def callback_fn(SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      torch.FloatTensor) -> None:
									__lowerCamelCase    :    Dict 	=			True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
										__lowerCamelCase    :    Dict 	=			latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 6_4, 6_4)
										__lowerCamelCase    :    Any 	=			latents[0, -3:, -3:, -1]
										__lowerCamelCase    :    Any 	=			np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983])

										assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2
									elif step == 2:
										__lowerCamelCase    :    Tuple 	=			latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 6_4, 6_4)
										__lowerCamelCase    :    int 	=			latents[0, -3:, -3:, -1]
										__lowerCamelCase    :    int 	=			np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115])

										assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2

								__lowerCamelCase    :    Optional[int] 	=			False

								__lowerCamelCase    :    str 	=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'       ,safety_checker=SCREAMING_SNAKE_CASE__       ,torch_dtype=torch.floataa)
								__lowerCamelCase    :    Optional[int] 	=			pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
								pipe.enable_attention_slicing()

								__lowerCamelCase    :    Optional[Any] 	=			self.get_inputs()
								pipe(**SCREAMING_SNAKE_CASE__       ,callback=SCREAMING_SNAKE_CASE__       ,callback_steps=1)
								assert callback_fn.has_been_called
								assert number_of_steps == 3
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								__lowerCamelCase    :    Optional[int] 	=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'       ,safety_checker=SCREAMING_SNAKE_CASE__       ,torch_dtype=torch.floataa)
								__lowerCamelCase    :    Any 	=			pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
								pipe.enable_attention_slicing(1)
								pipe.enable_sequential_cpu_offload()

								__lowerCamelCase    :    Any 	=			self.get_inputs()
								__lowerCamelCase    :    Dict 	=			pipe(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 1_0**9


							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								__lowerCamelCase    :    Any 	=			inputs['image'].resize((5_0_4, 5_0_4))

								__lowerCamelCase    :    Dict 	=			'timbrooks/instruct-pix2pix'
								__lowerCamelCase    :    int 	=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    SCREAMING_SNAKE_CASE__       ,safety_checker=SCREAMING_SNAKE_CASE__       ,)
								pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
								pipe.enable_attention_slicing()

								__lowerCamelCase    :    Dict 	=			pipe(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Dict 	=			output.images[0]

								__lowerCamelCase    :    Dict 	=			image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

								assert image.shape == (5_0_4, 5_0_4, 3)
								__lowerCamelCase    :    Any 	=			np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590])

								assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a		=argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
a		=parser.parse_args()


a		="""cpu"""
a		="""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

a		="""path-to-your-trained-model"""
a		=StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					a		=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
a		=pipe.to(device)

# to channels last
a		=pipe.unet.to(memory_format=torch.channels_last)
a		=pipe.vae.to(memory_format=torch.channels_last)
a		=pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					a		=pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
a		=torch.randn(2, 4, 64, 64)
a		=torch.rand(1) * 999
a		=torch.randn(2, 77, 768)
a		=(sample, timestep, encoder_hidden_status)
try:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					a		=ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
a		=666
a		=torch.Generator(device).manual_seed(seed)
a		={"""generator""": generator}
if args.steps is not None:
					a		=args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					a		=pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")




