






"""simple docstring"""





import os

try:
							from .build_directory_md import good_file_paths
except ImportError:
							from build_directory_md import good_file_paths  # type: ignore

__lowerCamelCase							=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__lowerCamelCase							=  [file for file in filepaths if file != file.lower()]
if upper_files:
							print(f'''{len(upper_files)} files contain uppercase characters:''')
							print('\n'.join(upper_files) + '\n')

__lowerCamelCase							=  [file for file in filepaths if ' ' in file]
if space_files:
							print(f'''{len(space_files)} files contain space characters:''')
							print('\n'.join(space_files) + '\n')

__lowerCamelCase							=  [file for file in filepaths if '-' in file]
if hyphen_files:
							print(f'''{len(hyphen_files)} files contain hyphen characters:''')
							print('\n'.join(hyphen_files) + '\n')

__lowerCamelCase							=  [file for file in filepaths if os.sep not in file]
if nodir_files:
							print(f'''{len(nodir_files)} files are not in a directory:''')
							print('\n'.join(nodir_files) + '\n')

__lowerCamelCase							=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
							import sys

							sys.exit(bad_files)





"""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')

