'''simple docstring'''






import torch

from diffusers import StableDiffusionPipeline


_UpperCAmelCase    :     Any         =       '''path-to-your-trained-model'''
_UpperCAmelCase    :     Optional[int]         =       StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

_UpperCAmelCase    :     Dict         =       '''A photo of sks dog in a bucket'''
_UpperCAmelCase    :     Optional[int]         =       pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

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




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase    :     Any         =       {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''MaskFormerFeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''MaskFormerImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Tuple         =       [
       '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''MaskFormerForInstanceSegmentation''',
       '''MaskFormerModel''',
       '''MaskFormerPreTrainedModel''',
   ]
   _UpperCAmelCase    :     int         =       [
       '''MaskFormerSwinBackbone''',
       '''MaskFormerSwinModel''',
       '''MaskFormerSwinPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
   from .configuration_maskformer_swin import MaskFormerSwinConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_maskformer import MaskFormerFeatureExtractor
      from .image_processing_maskformer import MaskFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskformer import (
          MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskFormerForInstanceSegmentation,
          MaskFormerModel,
          MaskFormerPreTrainedModel,
      )
      from .modeling_maskformer_swin import (
          MaskFormerSwinBackbone,
          MaskFormerSwinModel,
          MaskFormerSwinPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase    :     str         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)



