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Utilities

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Utilities

Utility and helper functions for working with 🤗 Diffusers.

randn_tensor

diffusers.utils.randn_tensor

< >

( shape: typing.Union[typing.Tuple, typing.List] generator: typing.Union[typing.List[ForwardRef('torch.Generator')], ForwardRef('torch.Generator'), NoneType] = None device: typing.Optional[ForwardRef('torch.device')] = None dtype: typing.Optional[ForwardRef('torch.dtype')] = None layout: typing.Optional[ForwardRef('torch.layout')] = None )

A helper function to create random tensors on the desired device with the desired dtype. When passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor is always created on the CPU.

numpy_to_pil

diffusers.utils.numpy_to_pil

< >

( images )

Convert a numpy image or a batch of images to a PIL image.

pt_to_pil

diffusers.utils.pt_to_pil

< >

( images )

Convert a torch image to a PIL image.

load_image

diffusers.utils.load_image

< >

( image: typing.Union[str, PIL.Image.Image] ) → PIL.Image.Image

Parameters

  • image (str or PIL.Image.Image) — The image to convert to the PIL Image format.

Returns

PIL.Image.Image

A PIL Image.

Loads image to a PIL Image.

export_to_video

diffusers.utils.export_to_video

< >

( video_frames: typing.List[numpy.ndarray] output_video_path: str = None )

make_image_grid

diffusers.utils.make_image_grid

< >

( images: typing.List[PIL.Image.Image] rows: int cols: int resize: int = None )

Prepares a single grid of images. Useful for visualization purposes.

←Loaders VAE Image Processor→