




from statistics import mean, stdev

def      lowerCAmelCase_					(  _lowercase    :       list						,       _lowercase    :       int = 3)							->	list:







			"""simple docstring"""
			a__       :						Tuple							=  min(_lowercase)
			a__       :						Union[str, Any]							=  max(_lowercase)
			# normalize data
			return [round((x - x_min) / (x_max - x_min)						,       _lowercase) for x in data]





def      lowerCAmelCase_					(  _lowercase    :       list						,       _lowercase    :       int = 3)							->	list:







			"""simple docstring"""
			a__       :						Optional[int]							=  mean(_lowercase)
			a__       :						Any							=  stdev(_lowercase)
			# standardize data
			return [round((x - mu) / (sigma)						,       _lowercase) for x in data]









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")




