

'''simple docstring'''

import torch

from diffusers import StableDiffusionPipeline


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

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

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








"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 1_00_00_00		)     ->					int:
  _snake_case					     =       limit + 1
  _snake_case					     =       [0] * limit
  for first_term in range(1 ,					__lowerCamelCase		):
    for n in range(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		):
      _snake_case					     =       first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  _snake_case					     =       sum(1 for x in frequency[1:limit] if x == 10		)

  return count


if __name__ == "__main__":
 print(F"{solution() = }")



