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 LTXVideoTransformer3DModel
transformer = LTXVideoTransformer3DModel.from_pretrained("Lightricks/LTX-Video", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
( 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
int
, defaults to 128
) —
The number of channels in the input. int
, defaults to 128
) —
The number of channels in the output. int
, defaults to 1
) —
The size of the spatial patches to use in the patch embedding layer. int
, defaults to 1
) —
The size of the tmeporal patches to use in the patch embedding layer. int
, defaults to 32
) —
The number of heads to use for multi-head attention. int
, defaults to 64
) —
The number of channels in each head. int
, defaults to 2048
) —
The number of channels for cross attention heads. int
, defaults to 28
) —
The number of layers of Transformer blocks to use. str
, defaults to "gelu-approximate"
) —
Activation function to use in feed-forward. str
, defaults to "rms_norm_across_heads"
) —
The normalization layer to use. A Transformer model for video-like data used in LTX.
( sample: torch.Tensor )
Parameters
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.