Constructor for a general sampler; supporting different sampling methods Args: - transport: an tranport object specify model predictio

Initialize the RMSNorm normalization layer. Args: dim (int): The dimension of the input tensor. eps (float,

_summary_ Args: data_jsons_path (str): input data json path sample_n_frames (int, optional): training video length. D

align hight, width according to alignment Args: value (int): height or width alignment (int): target alignment factor Return

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

The index for the first timestep. It should be set from pipeline with `set_begin_index` method.

Compute coefficient of x1

Compute coefficient of x1

Special purposed function for computing numerical stabled d_alpha_t / alpha_t

Special purposed function for computing numerical stabled d_alpha_t / alpha_t

Compute the drift term of the SDE

Compute coefficient of x0

Compute coefficient of x0

function for creating Transport object **Note**: model prediction defaults to velocity Args: - path_type: type of path to use; default to

r""" Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing decoding in one

r""" Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing decoding in one st

r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in s

r"""The forward method of the `EncoderCausal3D` class.

r"""The forward method of the `DecoderCausal3D` class.

r""" Args: sample (`torch.FloatTensor`): Input sample. sample_posterior (`bool`, *optional*, defaults to `False`):

Forward pass through the RMSNorm layer. Args: x (torch.Tensor): The input tensor. Returns: torch.Te