




'''simple docstring'''




import fcntl
import os
import socket

import torch
import torch.distributed as dist




def 							lowerCamelCase			(   *UpperCAmelCase__ :      Any				)    ->   Tuple:
							with open(UpperCamelCase__   ,   """r"""				) as fh:
														fcntl.flock(UpperCamelCase__   ,   fcntl.LOCK_EX				)
														try:
																					print(*UpperCamelCase__				)
														finally:
																					fcntl.flock(UpperCamelCase__   ,   fcntl.LOCK_UN				)


_lowercase		:				Union[str, Any]	       =		int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
_lowercase		:				Tuple	       =		torch.device("cuda", local_rank)
_lowercase		:				int	       =		socket.gethostname()

_lowercase		:				Tuple	       =		f"""[{hostname}-{local_rank}]"""

try:
						# test distributed
						dist.init_process_group("nccl")
						dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
						dist.barrier()

						# test cuda is available and can allocate memory
						torch.cuda.is_available()
						torch.ones(1).cuda(local_rank)

						# global rank
						_lowercase		:				Union[str, Any]	       =		dist.get_rank()
						_lowercase		:				str	       =		dist.get_world_size()

						printflock(f"""{gpu} is OK (global rank: {rank}/{world_size})""")

						dist.barrier()
						if rank == 0:
												printflock(f"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
						printflock(f"""{gpu} is broken""")
						raise






'''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		:				Dict	       =		parser.parse_args()


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

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

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

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

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

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

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





