Diffusers documentation

Utilities

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Utilities

Utility and helper functions for working with 🤗 Diffusers.

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: Union convert_method: Optional = None ) PIL.Image.Image

Parameters

  • image (str or PIL.Image.Image) — The image to convert to the PIL Image format.
  • convert_method (Callable[[PIL.Image.Image], PIL.Image.Image], optional) — A conversion method to apply to the image after loading it. When set to None the image will be converted “RGB”.

Returns

PIL.Image.Image

A PIL Image.

Loads image to a PIL Image.

export_to_gif

diffusers.utils.export_to_gif

< >

( image: List output_gif_path: str = None fps: int = 10 )

export_to_video

diffusers.utils.export_to_video

< >

( video_frames: Union output_video_path: str = None fps: int = 10 )

make_image_grid

diffusers.utils.make_image_grid

< >

( images: List rows: int cols: int resize: int = None )

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

randn_tensor

diffusers.utils.torch_utils.randn_tensor

< >

( shape: Union generator: Union = None device: Optional = None dtype: Optional = None layout: Optional = 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.

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