r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with

r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with

r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with

Creates an `FaceLandmarker` object from a TensorFlow Lite model and the default `FaceLandmarkerOptions`. Note that the created `FaceLandmarker` i

Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). Parameters ---------- model: Whisper the Wh

Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. This method decodes given tokens with timestam

Sends live image data to perform face landmarks detection. The results will be available via the "result_callback" provided in the FaceLandma

Detect the spoken language in the audio, and return them as list of strings, along with the ids of the most probable language tokens and the

Disables the FreeU mechanism.

r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. The suffixes after the scaling factors represent the stage blocks wher

The [`Transformer2DModel`] forward method. Args: hidden_states (`torch.LongTensor` of shape `(batch size, num latent pix

r""" Args: sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor timestep (`torch.FloatTen

r""" The [`UNet2DConditionModel`] forward method. Args: sample (`torch.FloatTensor`): The noisy input ten

r""" Args: sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor timestep (`torch.FloatTen

x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) the mel spectrogram of the audio include_embeddings: bool

x : torch.LongTensor, shape = (batch_size, <= n_ctx) the text tokens xa : torch.Tensor, shape = (batch_size, n_mels, n_au