






'''simple docstring'''

def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      if not isinstance(a_    ,   a_	):
            raise ValueError("""Input must be an integer"""	)
      if input_num <= 0:
            raise ValueError("""Input must be positive"""	)
      return sum(
          divisor for divisor in range(1    ,   input_num // 2 + 1	) if input_num % divisor == 0	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()














'''simple docstring'''

import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase							="path-to-your-trained-model"
UpperCamelCase							=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

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

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






