



import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel

def      lowerCAmelCase_					(  )							->	Any:







			"""simple docstring"""
			a__       :						str							=  argparse.ArgumentParser()
			parser.add_argument(
			    """-m"""						,       """--pretrained_model_name_or_path"""						,       type=_lowercase						,       default=_lowercase						,       required=_lowercase						,       help="""Path to pretrained model or model identifier from huggingface.co/models."""						,       )
			parser.add_argument(
			    """-c"""						,       """--caption"""						,       type=_lowercase						,       default="""robotic cat with wings"""						,       help="""Text used to generate images."""						,       )
			parser.add_argument(
			    """-n"""						,       """--images_num"""						,       type=_lowercase						,       default=4						,       help="""How much images to generate."""						,       )
			parser.add_argument(
			    """-s"""						,       """--seed"""						,       type=_lowercase						,       default=42						,       help="""Seed for random process."""						,       )
			parser.add_argument(
			    """-ci"""						,       """--cuda_id"""						,       type=_lowercase						,       default=0						,       help="""cuda_id."""						,       )
			a__       :						str							=  parser.parse_args()
			return args

def      lowerCAmelCase_					(  _lowercase    :       Optional[int]						,       _lowercase    :       List[Any]						,       _lowercase    :       Tuple)							->	List[Any]:







			"""simple docstring"""
			if not len(_lowercase) == rows * cols:
						raise ValueError("""The specified number of rows and columns are not correct.""")

			a__	,						a__       :						int							=  imgs[0].size
			a__       :						List[Any]							=  Image.new("""RGB"""						,       size=(cols * w, rows * h))
			a__	,						a__       :						Optional[Any]							=  grid.size

			for i, img in enumerate(_lowercase):
						grid.paste(_lowercase						,       box=(i % cols * w, i // cols * h))
			return grid

def      lowerCAmelCase_					(  _lowercase    :       Optional[int]						,       _lowercase    :       Tuple="robotic cat with wings"						,       _lowercase    :       List[str]=7.5						,       _lowercase    :       str=50						,       _lowercase    :       Union[str, Any]=1						,       _lowercase    :       Union[str, Any]=42						,       )							->	str:







			"""simple docstring"""
			a__       :						str							=  torch.Generator(pipeline.device).manual_seed(_lowercase)
			a__       :						str							=  pipeline(
			    _lowercase						,       guidance_scale=_lowercase						,       num_inference_steps=_lowercase						,       generator=_lowercase						,       num_images_per_prompt=_lowercase						,       ).images
			a__       :						str							=  int(math.sqrt(_lowercase))
			a__       :						List[Any]							=  image_grid(_lowercase						,       rows=_rows						,       cols=num_images_per_prompt // _rows)
			return grid, images


_lowercase		:				Optional[Any]      		=parse_args()
# Load models and create wrapper for stable diffusion
_lowercase		:				int      		=CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
_lowercase		:				Tuple      		=CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
_lowercase		:				str      		=AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
_lowercase		:				Optional[Any]      		=UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

_lowercase		:				Optional[Any]      		=StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_lowercase		:				int      		=lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
		_lowercase		:				Tuple      		=load(args.pretrained_model_name_or_path, model=unet)
		unet.eval()
		setattr(pipeline, "unet", unet)
else:
		_lowercase		:				str      		=unet.to(torch.device("cuda", args.cuda_id))
_lowercase		:				int      		=pipeline.to(unet.device)
_lowercase		,					_lowercase		:				Any      		=generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
_lowercase		:				List[str]      		=os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
		image.save(os.path.join(dirname, "{}.png".format(idx + 1)))









import torch

from diffusers import StableDiffusionPipeline


_lowercase		:				Optional[int]      		="path-to-your-trained-model"
_lowercase		:				Union[str, Any]      		=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

_lowercase		:				Any      		="A photo of sks dog in a bucket"
_lowercase		:				Any      		=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")




