
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")





import math






def    lowerCamelCase__				(				__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	0
			lowerCAmelCase_						=	0
			while num > 0:
						lowerCAmelCase_						=	num % 8
						lowerCAmelCase_						=	octal + (remainder * math.floor(math.pow(10    ,							__lowerCAmelCase				)				))
						counter += 1
						lowerCAmelCase_						=	math.floor(num / 8				)  # basically /= 8 without remainder if any
						# This formatting removes trailing '.0' from `octal`.
			return F"""0o{int(__lowerCAmelCase				)}"""






def    lowerCamelCase__				(				):




			"""simple docstring"""


			print("\n2 in octal is:"				)
			print(decimal_to_octal(2				)				)  # = 2
			print("\n8 in octal is:"				)
			print(decimal_to_octal(8				)				)  # = 10
			print("\n65 in octal is:"				)
			print(decimal_to_octal(65				)				)  # = 101
			print("\n216 in octal is:"				)
			print(decimal_to_octal(216				)				)  # = 330
			print("\n512 in octal is:"				)
			print(decimal_to_octal(512				)				)  # = 1000
			print("\n"				)


if __name__ == "__main__":
			main()



