#
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

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class diffusers.CogVideoXTransformer3DModel

< source >

( num_attention_heads: int = 30 attention_head_dim: int = 64 in_channels: int = 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`, defaults to `30`) — The number of heads to use for multi-head attention.
- attention_head_dim (`int`, defaults to `64`) — The number of channels in each head.
- in_channels (`int`, defaults to `16`) — The number of channels in the input.
- out_channels (`int`, optional, defaults to `16`) — The number of channels in the output.
- flip_sin_to_cos (`bool`, defaults to `True`) — Whether to flip the sin to cos in the time embedding.
- time_embed_dim (`int`, defaults to `512`) — Output dimension of timestep embeddings.
- text_embed_dim (`int`, defaults to `4096`) — Input dimension of text embeddings from the text encoder.
- num_layers (`int`, defaults to `30`) — The number of layers of Transformer blocks to use.
- dropout (`float`, defaults to `0.0`) — The dropout probability to use.
- attention_bias (`bool`, defaults to `True`) — Whether or not to use bias in the attention projection layers.
- sample_width (`int`, defaults to `90`) — The width of the input latents.
- sample_height (`int`, defaults to `60`) — The height of the input latents.
- sample_frames (`int`, defaults to `49`) — The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49 instead of 13 because CogVideoX processed 13 latent frames at once in its default and recommended settings, but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1).
- patch_size (`int`, defaults to `2`) — The size of the patches to use in the patch embedding layer.
- temporal_compression_ratio (`int`, defaults to `4`) — The compression ratio across the temporal dimension. See documentation for `sample_frames`.
- max_text_seq_length (`int`, defaults to `226`) — The maximum sequence length of the input text embeddings.
- activation_fn (`str`, defaults to `"gelu-approximate"`) — Activation function to use in feed-forward.
- timestep_activation_fn (`str`, defaults to `"silu"`) — Activation function to use when generating the timestep embeddings.
- norm_elementwise_affine (`bool`, defaults to `True`) — Whether or not to use elementwise affine in normalization layers.
- norm_eps (`float`, defaults to `1e-5`) — The epsilon value to use in normalization layers.
- spatial_interpolation_scale (`float`, defaults to `1.875`) — Scaling factor to apply in 3D positional embeddings across spatial dimensions.
- temporal_interpolation_scale (`float`, defaults to `1.0`) — Scaling factor to apply in 3D positional embeddings across temporal dimensions.

A Transformer model for video-like data in CogVideoX.

##
Transformer2DModelOutput

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class diffusers.models.modeling_outputs.Transformer2DModelOutput

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( 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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