m_pretrained(
...     "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")

>>> prompt = "make the mountains snowy"
>>> image = pipe(prompt=prompt, image=image).images[0]

load_textual_inversion

<
source
>
(
pretrained_model_name_or_path: typing.Union[str, typing.Dict[str, torch.Tensor]]
token: typing.Optional[str] = None
**kwargs

)


Parameters 

pretrained_model_name_or_path (str or os.PathLike) —
Can be 