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

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

r""" A 3D convolution layer that splits the input tensor into smaller parts to avoid OOM in CogVideoX Model.

r""" A 3D ResNet block used in the CogVideoX model. Args: in_channels (`int`): Number of input channels. out_chan

r""" Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002. This implementation is specific to 3D-video like data

r""" Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model. Parameters: dim (`int`): The number

r""" A middle block used in the CogVideoX model. Args: in_channels (`int`): Number of input channels. temb_channe

r""" Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on query and key vectors,

r""" Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on query and key vectors,

r""" Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on query and key vectors,

r""" Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on query and key vectors,

r""" Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model. Parameters: dim (`int`): The number

r""" Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model. Parameters: dim (`int`): The number

r""" Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model. Parameters: dim (`int`): The number

r""" The `CogVideoXDecoder3D` layer of a variational autoencoder that decodes its latent representation into an output sample. Args:

r""" A downsampling block used in the CogVideoX model. Args: in_channels (`int`): Number of input channels. out_c

r""" A 3D Downsampling layer using in [CogVideoX]() by Tsinghua University & ZhipuAI Args: in_channels (`int`): Number of

r""" The `CogVideoXEncoder3D` layer of a variational autoencoder that encodes its input into a latent representation. Args: in_channe

r""" Pipeline for text-to-video generation using CogVideoX. This model inherits from [`DiffusionPipeline`]. Check the superclass documentatio

r""" An upsampling block used in the CogVideoX model. Args: in_channels (`int`): Number of input channels. out_ch

r""" A 3D Upsample layer using in CogVideoX by Tsinghua University & ZhipuAI # Todo: Wait for paper relase. Args: in_channels (`int`)

r""" Pipeline for text-to-video generation using CogVideoX. This model inherits from [`DiffusionPipeline`]. Check the superclass documentatio

r""" Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on query and key vectors,

r""" Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on query and key vectors,

r""" Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on query and key vectors,

r""" A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in [CogVideoX](https://gi

A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo). Parameters: num_attention_heads (`int`, d

A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo). Parameters: num_attention_heads (`int`, d

A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo). Parameters: num_attention_heads (`int`, d

A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo). Parameters: num_attention_heads (`int`, d

r""" Output class for CogVideo pipelines. Args: video (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): Lis

r""" Output class for CogVideo pipelines. Args: video (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): Lis

r""" Pipeline for text-to-video generation using CogVideoX. This model inherits from [`DiffusionPipeline`]. Check the superclass documentatio

r""" Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on query and key vectors,

replaces forward method of the original Linear, instead of replacing the original Linear module.

Video to Patch Embedding. Args: patch_size (int): Patch token size. Default: (2,4,4). in_chans (int): Number of input video chann

Embeds scalar timesteps into vector representations.

Embeds scalar timesteps into vector representations.