( endpoint: str tensor: torch.Tensor processor: typing.Union[ForwardRef('VaeImageProcessor'), ForwardRef('VideoProcessor'), NoneType] = None do_scaling: bool = True scaling_factor: typing.Optional[float] = None shift_factor: typing.Optional[float] = None output_type: typing.Literal['mp4', 'pil', 'pt'] = 'pil' return_type: typing.Literal['mp4', 'pil', 'pt'] = 'pil' image_format: typing.Literal['png', 'jpg'] = 'jpg' partial_postprocess: bool = False input_tensor_type: typing.Literal['binary'] = 'binary' output_tensor_type: typing.Literal['binary'] = 'binary' height: typing.Optional[int] = None width: typing.Optional[int] = None )
Parameters
str) —
Endpoint for Remote Decode. torch.Tensor) —
Tensor to be decoded. VaeImageProcessor or VideoProcessor, optional) —
Used with return_type="pt", and return_type="pil" for Video models. bool, default True, optional) —
DEPRECATED. pass scaling_factor/shift_factor instead. still set
do_scaling=None/do_scaling=False for no scaling until option is removed When True scaling e.g. latents / self.vae.config.scaling_factor is applied remotely. If False, input must be passed with scaling
applied. float, optional) —
Scaling is applied when passed e.g. latents / self.vae.config.scaling_factor.
None, input must be passed with scaling applied.float, optional) —
Shift is applied when passed e.g. latents + self.vae.config.shift_factor.
None, input must be passed with scaling applied."mp4" or "pil" or "pt", default “pil”) —
Endpoint output type. Subject to change. Report feedback on preferred type.
"mp4": Supported by video models. Endpoint returns bytesof video.“pil”: Supported by image and video models. Image models: Endpoint returns bytesof an image inimage_format. Video models: Endpoint returns torch.Tensorwith partialpostprocessingapplied. Requiresprocessoras a flag (anyNonevalue will work).“pt”: Support by image and video models. Endpoint returns torch.Tensor. With partial_postprocess=Truethe tensor is postprocesseduint8` image tensor.
Recommendations:
"pt" with partial_postprocess=True is the smallest transfer for full quality. "pt" with
partial_postprocess=False is the most compatible with third party code. "pil" with
image_format="jpg" is the smallest transfer overall.
"mp4" or "pil" or "pt", default “pil”) —
Function return type.
"mp4": Function returns bytesof video.“pil”: Function returns PIL.Image.Image. With output_type=“pil” no further processing is applied. With output_type="pt" a PIL.Image.Imageis created.partial_postprocess=False processoris required.partial_postprocess=True processoris **not** required.“pt”: Function returns torch.Tensor. processoris **not** required.partial_postprocess=Falsetensor isfloat16orbfloat16, without denormalization. partial_postprocess=Truetensor isuint8`, denormalized.
"png" or "jpg", default jpg) —
Used with output_type="pil". Endpoint returns jpg or png. bool, default False) —
Used with output_type="pt". partial_postprocess=False tensor is float16 or bfloat16, without
denormalization. partial_postprocess=True tensor is uint8, denormalized. "binary", default "binary") —
Tensor transfer type. "binary", default "binary") —
Tensor transfer type. int, optional) —
Required for "packed" latents. int, optional) —
Required for "packed" latents. Hugging Face Hybrid Inference that allow running VAE decode remotely.
( endpoint: str image: typing.Union[ForwardRef('torch.Tensor'), PIL.Image.Image] scaling_factor: typing.Optional[float] = None shift_factor: typing.Optional[float] = None )
Parameters
str) —
Endpoint for Remote Decode. torch.Tensor or PIL.Image.Image) —
Image to be encoded. float, optional) —
Scaling is applied when passed e.g. latents * self.vae.config.scaling_factor.None, input must be passed with scaling applied.float, optional) —
Shift is applied when passed e.g. latents - self.vae.config.shift_factor.None, input must be passed with scaling applied.Hugging Face Hybrid Inference that allow running VAE encode remotely.