




from __future__ import annotations



def       UpperCAmelCase       (     _snake_case						, _snake_case = None						, _snake_case = None							):
							if start is None:
														lowerCAmelCase   =		0

							if end is None:
														lowerCAmelCase   =		len(_snake_case							) - 1

							if start >= end:
														return

							lowerCAmelCase   =		(start + end) // 2

							slowsort(_snake_case						, _snake_case						, _snake_case							)
							slowsort(_snake_case						, mid + 1						, _snake_case							)

							if sequence[end] < sequence[mid]:
														lowerCAmelCase   ,     lowerCAmelCase   =		sequence[mid], sequence[end]

							slowsort(_snake_case						, _snake_case						, end - 1							)


if __name__ == "__main__":
		from doctest import testmod

		testmod()









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""")



