Return the noise prediction model.

Replace the keywords in text with placeholder tokens. This function will be called in `self.__call__` and `self.encode`. Args:

r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to comp

r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or o

slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC

Takes a prompt and converts it to a list of (token, weight, word id) elements. Tokens can both be integer tokens and pre computed CLI

Attempt to add tokens to the tokenizer. Args: tokens (Union[str, List[str]]): The tokens to be added.

The call function of the wrapper. Args: text (Union[str, List[str]]): The text to be tokenized. vector_shuffle (bool,

Apply the RMSNorm normalization to the input tensor. Args: x (torch.Tensor): The input tensor. Returns:

Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transf

Takes a potential embedding name and tries to retrieve it. Returns a Tuple consisting of the embedding and any leftover string, embed

Add placeholder tokens to the tokenizer. Args: placeholder_token (str): The placeholder token to be added. num_vec_pe

Add token for training. # TODO: support add tokens as dict, then we can load pretrained tokens.

Create a 1D, 2D, or 3D average pooling module.

Create a 1D, 2D, or 3D average pooling module.

Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [