import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase_	       =			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_	       =			parser.parse_args()


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

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

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

# optimize with ipex
lowercase_	       =			torch.randn(2, 4, 64, 64)
lowercase_	       =			torch.rand(1) * 9_99
lowercase_	       =			torch.randn(2, 77, 7_68)
lowercase_	       =			(sample, timestep, encoder_hidden_status)
try:
				lowercase_	       =			ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
				lowercase_	       =			ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase_	       =			ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase_	       =			ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
				lowercase_	       =			ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase_	       =			6_66
lowercase_	       =			torch.Generator(device).manual_seed(seed)
lowercase_	       =			{'''generator''': generator}
if args.steps is not None:
				lowercase_	       =			args.steps

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

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









import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
				lowercase_	       =			{
				    '''linear''': PIL.Image.Resampling.BILINEAR,
				    '''bilinear''': PIL.Image.Resampling.BILINEAR,
				    '''bicubic''': PIL.Image.Resampling.BICUBIC,
				    '''lanczos''': PIL.Image.Resampling.LANCZOS,
				    '''nearest''': PIL.Image.Resampling.NEAREST,
				}
else:
				lowercase_	       =			{
				    '''linear''': PIL.Image.LINEAR,
				    '''bilinear''': PIL.Image.BILINEAR,
				    '''bicubic''': PIL.Image.BICUBIC,
				    '''lanczos''': PIL.Image.LANCZOS,
				    '''nearest''': PIL.Image.NEAREST,
				}






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]	)  ->						int:
					__lowerCAmelCase	   =(images / 2 + 0.5).clamp(0				,   1	)
					__lowerCAmelCase	   =images.cpu().permute(0				,   2				,   3				,   1	).float().numpy()
					__lowerCAmelCase	   =numpy_to_pil(__lowerCamelCase	)
					return images






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str	)  ->						str:
					if images.ndim == 3:
										__lowerCAmelCase	   =images[None, ...]
					__lowerCAmelCase	   =(images * 255).round().astype("""uint8"""	)
					if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										__lowerCAmelCase	   =[Image.fromarray(image.squeeze()				,   mode="""L"""	) for image in images]
					else:
										__lowerCAmelCase	   =[Image.fromarray(__lowerCamelCase	) for image in images]

					return pil_images




