#
CogVideoXTransformer3DModel

A Diffusion Transformer model for 3D data from CogVideoX was introduced in CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer by Tsinghua University & ZhipuAI.

The model can be loaded with the following code snippet.

    from diffusers import CogVideoXTransformer3DModel

    vae = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")

##
CogVideoXTransformer3DModel

###
class diffusers.CogVideoXTransformer3DModel

< source >

( num_attention_heads: int = 30 attention_head_dim: int = 64 in_channels: Optional = 16 out_channels: Optional = 16 flip_sin_to_cos: bool = True freq_shift: int = 0 time_embed_dim: int = 512 text_embed_dim: int = 4096 num_layers: int = 30 dropout: float = 0.0 attention_bias: bool = True sample_width: int = 90 sample_height: int = 60 sample_frames: int = 49 patch_size: int = 2 temporal_compression_ratio: int = 4 max_text_seq_length: int = 226 activation_fn: str = 'gelu-approximate' timestep_activation_fn: str = 'silu' norm_elementwise_affine: bool = True norm_eps: float = 1e-05 spatial_interpolation_scale: float = 1.875 temporal_interpolation_scale: float = 1.0 )

Parameters

- num_attention_heads (`int`, optional, defaults to 16) — The number of heads to use for multi-head attention.
- attention_head_dim (`int`, optional, defaults to 88) — The number of channels in each head.
- in_channels (`int`, optional) — The number of channels in the input.
- out_channels (`int`, optional) — The number of channels in the output.
- num_layers (`int`, optional, defaults to 1) — The number of layers of Transformer blocks to use.
- dropout (`float`, optional, defaults to 0.0) — The dropout probability to use.
- cross_attention_dim (`int`, optional) — The number of `encoder_hidden_states` dimensions to use.
- attention_bias (`bool`, optional) — Configure if the `TransformerBlocks` attention should contain a bias parameter.
- sample_size (`int`, optional) — The width of the latent images (specify if the input is discrete). This is fixed during training since it is used to learn a number of position embeddings.
- patch_size (`int`, optional) — The size of the patches to use in the patch embedding layer.
- activation_fn (`str`, optional, defaults to `"geglu"`) — Activation function to use in feed-forward.
- num_embeds_ada_norm ( `int`, optional) — The number of diffusion steps used during training. Pass if at least one of the norm_layers is `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
- norm_type (`str`, optional, defaults to `"layer_norm"`) — The type of normalization to use. Options are `"layer_norm"` or `"ada_layer_norm"`.
- norm_elementwise_affine (`bool`, optional, defaults to `True`) — Whether or not to use elementwise affine in normalization layers.
- norm_eps (`float`, optional, defaults to 1e-5) — The epsilon value to use in normalization layers.
- caption_channels (`int`, optional) — The number of channels in the caption embeddings.
- video_length (`int`, optional) — The number of frames in the video-like data.

A Transformer model for video-like data in CogVideoX.

##
Transformer2DModelOutput

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class diffusers.models.modeling_outputs.Transformer2DModelOutput

< source >

( sample: torch.Tensor )

Parameters

- sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if Transformer2DModel is discrete) — The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability distributions for the unnoised latent pixels.

The output of Transformer2DModel.

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