#
Utilities

Utility and helper functions for working with 🤗 Diffusers.

##
numpy_to_pil

####
diffusers.utils.numpy_to_pil

< source >

( images )

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

##
pt_to_pil

####
diffusers.utils.pt_to_pil

< source >

( images )

Convert a torch image to a PIL image.

##
load_image

####
diffusers.utils.load_image

< source >

( image: Union convert_method: Callable = 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

< source >

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

##
export_to_video

####
diffusers.utils.export_to_video

< source >

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

##
make_image_grid

####
diffusers.utils.make_image_grid

< source >

( 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

< source >

( 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.

< > Update on GitHub