#
Hybrid Inference API Reference

##
Remote Decode

####
diffusers.utils.remote_decode

< source >

( 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

- endpoint (`str`) — Endpoint for Remote Decode.
- tensor (`torch.Tensor`) — Tensor to be decoded.
- processor (`VaeImageProcessor` or `VideoProcessor`, optional) — Used with `return_type="pt"`, and `return_type="pil"` for Video models.
- do_scaling (`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.
- scaling_factor (`float`, optional) — Scaling is applied when passed e.g. `latents / self.vae.config.scaling_factor`.
  - SD v1: 0.18215
  - SD XL: 0.13025
  - Flux: 0.3611 If `None`, input must be passed with scaling applied.
- shift_factor (`float`, optional) — Shift is applied when passed e.g. `latents + self.vae.config.shift_factor`.
  - Flux: 0.1159 If `None`, input must be passed with scaling applied.
- output_type (`"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` bytes`of video.`“pil”`: Supported by image and video models. Image models: Endpoint returns` bytes`of an image in`image_format`. Video models: Endpoint returns` torch.Tensor`with partial`postprocessing`applied. Requires`processor`as a flag (any`None`value will work).`“pt”`: Support by image and video models. Endpoint returns` torch.Tensor`. With` partial_postprocess=True`the tensor is postprocessed`uint8` 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.

- return_type (`"mp4"` or `"pil"` or `"pt", default` “pil”) — Function return type.

  `"mp4": Function returns` bytes`of video.`“pil”`: Function returns` PIL.Image.Image`. With` output_type=“pil” no further processing is applied. With `output_type="pt" a` PIL.Image.Image`is created.`partial_postprocess=False`` processor`is required.`partial_postprocess=True`` processor`is **not** required.`“pt”`: Function returns` torch.Tensor`.` processor`is **not** required.`partial_postprocess=False`tensor is`float16`or`bfloat16`, without denormalization.` partial_postprocess=True`tensor is`uint8`, denormalized.

- image_format (`"png"` or `"jpg"`, default `jpg`) — Used with `output_type="pil"`. Endpoint returns `jpg` or `png`.
- partial_postprocess (`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.
- input_tensor_type (`"binary"`, default `"binary"`) — Tensor transfer type.
- output_tensor_type (`"binary"`, default `"binary"`) — Tensor transfer type.
- height (`int`, optional) — Required for `"packed"` latents.
- width (`int`, optional) — Required for `"packed"` latents.

Hugging Face Hybrid Inference that allow running VAE decode remotely.

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