




import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCAmelCase_		:					Tuple          =  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")
UpperCAmelCase_		:					int          =  parser.parse_args()


UpperCAmelCase_		:					Union[str, Any]          =  "cpu"
UpperCAmelCase_		:					int          =  "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

UpperCAmelCase_		:					Optional[int]          =  "path-to-your-trained-model"
UpperCAmelCase_		:					Union[str, Any]          =  StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
							UpperCAmelCase_		:					Any          =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCAmelCase_		:					Union[str, Any]          =  pipe.to(device)

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

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

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

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

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










from typing import List

from .keymap import KEYMAP, get_character




def 		lowerCAmelCase_ (						lowerCamelCase ):

				def decorator(lowerCamelCase ):
								__magic_name__						:						str										=getattr(lowerCamelCase						,  """handle_key"""						,  [] )
								handle += [key]
								setattr(lowerCamelCase						,  """handle_key"""						,  lowerCamelCase )
								return func

				return decorator




def 		lowerCAmelCase_ (						*lowerCamelCase ):

				def decorator(lowerCamelCase ):
								__magic_name__						:						Dict										=getattr(lowerCamelCase						,  """handle_key"""						,  [] )
								handle += keys
								setattr(lowerCamelCase						,  """handle_key"""						,  lowerCamelCase )
								return func

				return decorator




class 		__A   (      UpperCamelCase__      ):

						def __new__(		cls :Dict     ,			__snake_case :Optional[Any]     ,			__snake_case :Union[str, Any]     ,			__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						int										=super().__new__(cls     ,			__snake_case     ,			__snake_case     ,			__snake_case			)
										if not hasattr(__snake_case     ,			"""key_handler"""			):
														setattr(__snake_case     ,			"""key_handler"""     ,			{}			)
										setattr(__snake_case     ,			"""handle_input"""     ,			KeyHandler.handle_input			)

										for value in attrs.values():
														__magic_name__						:						int										=getattr(__snake_case     ,			"""handle_key"""     ,			[]			)
														for key in handled_keys:
																		__magic_name__						:						List[str]										=value
										return new_cls


						@staticmethod
						def      A__    (		cls :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=get_character()
										if char != KEYMAP["undefined"]:
														__magic_name__						:						Optional[int]										=ord(__snake_case			)
										__magic_name__						:						int										=cls.key_handler.get(__snake_case			)
										if handler:
														__magic_name__						:						Dict										=char
														return handler(cls			)
										else:
														return None




def 		lowerCAmelCase_ (						cls ):
				return KeyHandler(cls.__name__						,  cls.__bases__						,  cls.__dict__.copy() )




