#
LTXTransformer3DModel

A Diffusion Transformer model for 3D data from LTX was introduced by Lightricks.

The model can be loaded with the following code snippet.

    from diffusers import LTXTransformer3DModel

    transformer = LTXTransformer3DModel.from_pretrained("TODO/TODO", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")

##
LTXTransformer3DModel

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class diffusers.LTXTransformer3DModel

< source >

( in_channels: int = 128 out_channels: int = 128 patch_size: int = 1 patch_size_t: int = 1 num_attention_heads: int = 32 attention_head_dim: int = 64 cross_attention_dim: int = 2048 num_layers: int = 28 activation_fn: str = 'gelu-approximate' qk_norm: str = 'rms_norm_across_heads' norm_elementwise_affine: bool = False norm_eps: float = 1e-06 caption_channels: int = 4096 attention_bias: bool = True attention_out_bias: bool = True )

Parameters

- in_channels (`int`, defaults to `128`) — The number of channels in the input.
- out_channels (`int`, defaults to `128`) — The number of channels in the output.
- patch_size (`int`, defaults to `1`) — The size of the spatial patches to use in the patch embedding layer.
- patch_size_t (`int`, defaults to `1`) — The size of the tmeporal patches to use in the patch embedding layer.
- num_attention_heads (`int`, defaults to `32`) — The number of heads to use for multi-head attention.
- attention_head_dim (`int`, defaults to `64`) — The number of channels in each head.
- cross_attention_dim (`int`, defaults to `2048` ) — The number of channels for cross attention heads.
- num_layers (`int`, defaults to `28`) — The number of layers of Transformer blocks to use.
- activation_fn (`str`, defaults to `"gelu-approximate"`) — Activation function to use in feed-forward.
- qk_norm (`str`, defaults to `"rms_norm_across_heads"`) — The normalization layer to use.

A Transformer model for video-like data used in LTX.

##
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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