
'''simple docstring'''







import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       (DPMSolverSDEScheduler,)
							__lowercase      		=       10


							def  lowerCamelCase				(       self    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		{
								    'num_train_timesteps': 11_00,
								    'beta_start': 0.0001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'noise_sampler_seed': 0,
								}

								config.update(**lowerCAmelCase_  )
								return config


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,   [0.0002, 0.002, 0.02]  ):
									self.check_over_configs(beta_start=lowerCAmelCase_    ,   beta_end=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config()
								_snake_case   						=		scheduler_class(**lowerCAmelCase_  )

								scheduler.set_timesteps(self.num_inference_steps  )

								_snake_case   						=		self.dummy_model()
								_snake_case   						=		self.dummy_sample_deter * scheduler.init_noise_sigma
								_snake_case   						=		sample.to(lowerCAmelCase_  )

								for i, t in enumerate(scheduler.timesteps  ):
									_snake_case   						=		scheduler.scale_model_input(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		scheduler.step(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
									_snake_case   						=		output.prev_sample

								_snake_case   						=		torch.sum(torch.abs(lowerCAmelCase_  )  )
								_snake_case   						=		torch.mean(torch.abs(lowerCAmelCase_  )  )

								if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.47821044921875  ) < 1E-2
									assert abs(result_mean.item() - 0.2178705964565277  ) < 1E-3
								elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59352111816406  ) < 1E-2
									assert abs(result_mean.item() - 0.22342906892299652  ) < 1E-3
								else:
									assert abs(result_sum.item() - 162.52383422851562  ) < 1E-2
									assert abs(result_mean.item() - 0.211619570851326  ) < 1E-3


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config(prediction_type='v_prediction'  )
								_snake_case   						=		scheduler_class(**lowerCAmelCase_  )

								scheduler.set_timesteps(self.num_inference_steps  )

								_snake_case   						=		self.dummy_model()
								_snake_case   						=		self.dummy_sample_deter * scheduler.init_noise_sigma
								_snake_case   						=		sample.to(lowerCAmelCase_  )

								for i, t in enumerate(scheduler.timesteps  ):
									_snake_case   						=		scheduler.scale_model_input(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		scheduler.step(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
									_snake_case   						=		output.prev_sample

								_snake_case   						=		torch.sum(torch.abs(lowerCAmelCase_  )  )
								_snake_case   						=		torch.mean(torch.abs(lowerCAmelCase_  )  )

								if torch_device in ["mps"]:
									assert abs(result_sum.item() - 124.77149200439453  ) < 1E-2
									assert abs(result_mean.item() - 0.16226289014816284  ) < 1E-3
								elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 128.1663360595703  ) < 1E-2
									assert abs(result_mean.item() - 0.16688326001167297  ) < 1E-3
								else:
									assert abs(result_sum.item() - 119.8487548828125  ) < 1E-2
									assert abs(result_mean.item() - 0.1560530662536621  ) < 1E-3


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config()
								_snake_case   						=		scheduler_class(**lowerCAmelCase_  )

								scheduler.set_timesteps(self.num_inference_steps    ,   device=lowerCAmelCase_  )

								_snake_case   						=		self.dummy_model()
								_snake_case   						=		self.dummy_sample_deter.to(lowerCAmelCase_  ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
									_snake_case   						=		scheduler.scale_model_input(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		scheduler.step(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
									_snake_case   						=		output.prev_sample

								_snake_case   						=		torch.sum(torch.abs(lowerCAmelCase_  )  )
								_snake_case   						=		torch.mean(torch.abs(lowerCAmelCase_  )  )

								if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.46957397460938  ) < 1E-2
									assert abs(result_mean.item() - 0.21805934607982635  ) < 1E-3
								elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59353637695312  ) < 1E-2
									assert abs(result_mean.item() - 0.22342908382415771  ) < 1E-3
								else:
									assert abs(result_sum.item() - 162.52383422851562  ) < 1E-2
									assert abs(result_mean.item() - 0.211619570851326  ) < 1E-3




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config()
								_snake_case   						=		scheduler_class(**lowerCAmelCase_    ,   use_karras_sigmas=lowerCAmelCase_  )

								scheduler.set_timesteps(self.num_inference_steps    ,   device=lowerCAmelCase_  )

								_snake_case   						=		self.dummy_model()
								_snake_case   						=		self.dummy_sample_deter.to(lowerCAmelCase_  ) * scheduler.init_noise_sigma
								_snake_case   						=		sample.to(lowerCAmelCase_  )

								for t in scheduler.timesteps:
									_snake_case   						=		scheduler.scale_model_input(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		scheduler.step(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
									_snake_case   						=		output.prev_sample

								_snake_case   						=		torch.sum(torch.abs(lowerCAmelCase_  )  )
								_snake_case   						=		torch.mean(torch.abs(lowerCAmelCase_  )  )

								if torch_device in ["mps"]:
									assert abs(result_sum.item() - 176.66974135742188  ) < 1E-2
									assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2
								elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 177.63653564453125  ) < 1E-2
									assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2
								else:
									assert abs(result_sum.item() - 170.3135223388672  ) < 1E-2
									assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2



'''simple docstring'''







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase    :							Optional[Any]      =					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")
lowercase    :							Tuple      =					parser.parse_args()


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

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

# to channels last
lowercase    :							Optional[Any]      =					pipe.unet.to(memory_format=torch.channels_last)
lowercase    :							int      =					pipe.vae.to(memory_format=torch.channels_last)
lowercase    :							Optional[Any]      =					pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					lowercase    :							Optional[int]      =					pipe.safety_checker.to(memory_format=torch.channels_last)

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

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

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

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

