Utility and helper functions for working with 🤗 Diffusers.
Convert a numpy image or a batch of images to a PIL image.
Convert a torch image to a PIL image.
( image: typing.Union[str, PIL.Image.Image] convert_method: typing.Optional[typing.Callable[[PIL.Image.Image], PIL.Image.Image]] = None ) → PIL.Image.Image
Parameters
str
or PIL.Image.Image
) —
The image to convert to the PIL Image format. None
the image will be converted
“RGB”. Returns
PIL.Image.Image
A PIL Image.
Loads image
to a PIL Image.
( image: typing.List[PIL.Image.Image] output_gif_path: str = None fps: int = 10 )
( video_frames: typing.Union[typing.List[numpy.ndarray], typing.List[PIL.Image.Image]] output_video_path: str = None fps: int = 10 )
( images: typing.List[PIL.Image.Image] rows: int cols: int resize: int = None )
Prepares a single grid of images. Useful for visualization purposes.
( 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.
( module: Module storage_dtype: dtype compute_dtype: dtype skip_modules_pattern: typing.Union[str, typing.Tuple[str, ...]] = 'auto' skip_modules_classes: typing.Optional[typing.Tuple[typing.Type[torch.nn.modules.module.Module], ...]] = None non_blocking: bool = False )
Parameters
torch.nn.Module
) —
The module whose leaf modules will be cast to a high precision dtype for computation, and to a low
precision dtype for storage. torch.dtype
) —
The dtype to cast the module to before/after the forward pass for storage. torch.dtype
) —
The dtype to cast the module to during the forward pass for computation. Tuple[str, ...]
, defaults to "auto"
) —
A list of patterns to match the names of the modules to skip during the layerwise casting process. If set
to "auto"
, the default patterns are used. If set to None
, no modules are skipped. If set to None
alongside skip_modules_classes
being None
, the layerwise casting is applied directly to the module
instead of its internal submodules. Tuple[Type[torch.nn.Module], ...]
, defaults to None
) —
A list of module classes to skip during the layerwise casting process. bool
, defaults to False
) —
If True
, the weight casting operations are non-blocking. Applies layerwise casting to a given module. The module expected here is a Diffusers ModelMixin but it can be any nn.Module using diffusers layers or pytorch primitives.
Example:
>>> import torch
>>> from diffusers import CogVideoXTransformer3DModel
>>> transformer = CogVideoXTransformer3DModel.from_pretrained(
... model_id, subfolder="transformer", torch_dtype=torch.bfloat16
... )
>>> apply_layerwise_casting(
... transformer,
... storage_dtype=torch.float8_e4m3fn,
... compute_dtype=torch.bfloat16,
... skip_modules_pattern=["patch_embed", "norm", "proj_out"],
... non_blocking=True,
... )