



"""simple docstring"""





import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			1.5
						snake_case_   : Any						=			int(factor * num_class_images    )
						snake_case_   : Any						=			ClipClient(
						    url="""https://knn.laion.ai/knn-service"""					, indice_name="""laion_400m"""					, num_images=__UpperCamelCase					, aesthetic_weight=0.1    )

						os.makedirs(F'{class_data_dir}/images'					, exist_ok=__UpperCamelCase    )
						if len(list(Path(F'{class_data_dir}/images'    ).iterdir()    )    ) >= num_class_images:
												return

						while True:
												snake_case_   : Dict						=			client.query(text=__UpperCamelCase    )
												if len(__UpperCamelCase    ) >= factor * num_class_images or num_images > 1E4:
																		break
												else:
																		snake_case_   : Union[str, Any]						=			int(factor * num_images    )
																		snake_case_   : str						=			ClipClient(
																		    url="""https://knn.laion.ai/knn-service"""					, indice_name="""laion_400m"""					, num_images=__UpperCamelCase					, aesthetic_weight=0.1					, )

						snake_case_   : Optional[Any]						=			0
						snake_case_   : int						=			0
						snake_case_   : str						=			tqdm(desc="""downloading real regularization images"""					, total=__UpperCamelCase    )

						with open(F'{class_data_dir}/caption.txt'					, """w"""    ) as fa, open(F'{class_data_dir}/urls.txt'					, """w"""    ) as fa, open(
						    F'{class_data_dir}/images.txt'					, """w"""    ) as fa:
												while total < num_class_images:
																		snake_case_   : int						=			class_images[count]
																		count += 1
																		try:
																								snake_case_   : List[Any]						=			requests.get(images["""url"""]    )
																								if img.status_code == 2_0_0:
																														snake_case_   : List[str]						=			Image.open(BytesIO(img.content    )    )
																														with open(F'{class_data_dir}/images/{total}.jpg'					, """wb"""    ) as f:
																																				f.write(img.content    )
																														fa.write(images["""caption"""] + """\n"""    )
																														fa.write(images["""url"""] + """\n"""    )
																														fa.write(F'{class_data_dir}/images/{total}.jpg' + """\n"""    )
																														total += 1
																														pbar.update(1    )
																								else:
																														continue
																		except Exception:
																								continue
						return
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			argparse.ArgumentParser(""""""					, add_help=__UpperCamelCase    )
						parser.add_argument("""--class_prompt"""					, help="""text prompt to retrieve images"""					, required=__UpperCamelCase					, type=__UpperCamelCase    )
						parser.add_argument("""--class_data_dir"""					, help="""path to save images"""					, required=__UpperCamelCase					, type=__UpperCamelCase    )
						parser.add_argument("""--num_class_images"""					, help="""number of images to download"""					, default=2_0_0					, type=__UpperCamelCase    )
						return parser.parse_args()


if __name__ == "__main__":
			__lowerCAmelCase       :					Optional[Any]      					=					parse_args()
			retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)






"""simple docstring"""





import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__lowerCAmelCase       :					Optional[int]      					=					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''')
__lowerCAmelCase       :					Optional[Any]      					=					parser.parse_args()


__lowerCAmelCase       :					Dict      					=					'''cpu'''
__lowerCAmelCase       :					Optional[Any]      					=					'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

__lowerCAmelCase       :					Tuple      					=					'''path-to-your-trained-model'''
__lowerCAmelCase       :					List[Any]      					=					StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					__lowerCAmelCase       :					Optional[int]      					=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__lowerCAmelCase       :					List[Any]      					=					pipe.to(device)

# to channels last
__lowerCAmelCase       :					Optional[Any]      					=					pipe.unet.to(memory_format=torch.channels_last)
__lowerCAmelCase       :					List[str]      					=					pipe.vae.to(memory_format=torch.channels_last)
__lowerCAmelCase       :					Optional[Any]      					=					pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					__lowerCAmelCase       :					Dict      					=					pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
__lowerCAmelCase       :					Tuple      					=					torch.randn(2, 4, 64, 64)
__lowerCAmelCase       :					Any      					=					torch.rand(1) * 999
__lowerCAmelCase       :					List[str]      					=					torch.randn(2, 77, 768)
__lowerCAmelCase       :					Optional[int]      					=					(sample, timestep, encoder_hidden_status)
try:
					__lowerCAmelCase       :					List[Any]      					=					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					__lowerCAmelCase       :					Optional[Any]      					=					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase       :					Any      					=					ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase       :					int      					=					ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					__lowerCAmelCase       :					Union[str, Any]      					=					ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
__lowerCAmelCase       :					List[str]      					=					666
__lowerCAmelCase       :					Optional[int]      					=					torch.Generator(device).manual_seed(seed)
__lowerCAmelCase       :					List[Any]      					=					{'''generator''': generator}
if args.steps is not None:
					__lowerCAmelCase       :					Any      					=					args.steps

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

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

