Linear layer with column parallelism. The linear layer is defined as Y = XA + b. A is parallelized along its second dimension as A = [A_1, ..

Linear layer with column parallelism. The linear layer is defined as Y = XA + b. A is parallelized along its second dimension as A = [A_1, ..

Linear layer with column parallelism. The linear layer is defined as Y = XA + b. A is parallelized along its second dimension as A = [A_1, ..

A class that keeps all conversation history.

Linear layer with column parallelism. The linear layer is defined as Y = XA + b. A is parallelized along its second dimension as A = [A_1, ..

r""" Norm layer modified to incorporate timestep embeddings. Parameters: embedding_dim (`int`): The size of each embedding vector.

Linear layer with column parallelism. The linear layer is defined as Y = XA + b. A is parallelized along its second dimension as A = [A_1, ..

r""" Default processor for performing attention-related computations.

Causal 3d convolusion.

Causal 3d convolusion.

Causal 3d convolusion.

Causal 3d convolusion.

Causal 3d convolusion.

Causal 3d convolusion.

A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: t

A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: t

A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: t

A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: t

A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: t

A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: t

r""" A cross attention layer. Parameters: query_dim (`int`): The number of channels in the query. cross_attention

Apply positional information to a sequence of embeddings. Takes in a sequence of embeddings with shape (batch_size, seq_length, embed_dim) and ad

A sequential module that passes timestep embeddings to the children that support it as an extra input.

A sequential module that passes timestep embeddings to the children that support it as an extra input.

A sequential module that passes timestep embeddings to the children that support it as an extra input.

A sequential module that passes timestep embeddings to the children that support it as an extra input.

A sequential module that passes timestep embeddings to the children that support it as an extra input.

A sequential module that passes timestep embeddings to the children that support it as an extra input.

Implements a causal 3D convolution layer where each position only depends on previous timesteps and current spatial locations. This maintai

Implements a causal 3D convolution layer where each position only depends on previous timesteps and current spatial locations. This maintai

r"""A 3D causal convolution layer that pads the input tensor to ensure causality in CogVideoX Model. Args: in_channels (`int`): Number of

Linear layer with row parallelism. The linear layer is defined as Y = XA + b. A is parallelized along its first dimension and X along its sec

Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002. Args: f_channels (`int`): The number

Computes and stores the average and current value

The rotary position embeddings from RoFormer_ (Su et. al). A crucial insight from the method is that the query and keys are transformed b

The rotary position embeddings from RoFormer_ (Su et. al). A crucial insight from the method is that the query and keys are transformed b

The rotary position embeddings from RoFormer_ (Su et. al). A crucial insight from the method is that the query and keys are transformed b

The rotary position embeddings from RoFormer_ (Su et. al). A crucial insight from the method is that the query and keys are transformed b