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LTXVideoTransformer3DModel

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LTXVideoTransformer3DModel

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")

LTXVideoTransformer3DModel

class diffusers.LTXVideoTransformer3DModel

< >

( 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.

forward

< >

( hidden_states: Tensor encoder_hidden_states: Tensor timestep: LongTensor encoder_attention_mask: Tensor num_frames: int | None = None height: int | None = None width: int | None = None rope_interpolation_scale: tuple[float, float, float] | torch.Tensor | None = None video_coords: torch.Tensor | None = None attention_kwargs: dict[str, typing.Any] | None = None return_dict: bool = True ) → torch.Tensor

Parameters

  • hidden_states (torch.Tensor of shape (batch_size, sequence_length, in_channels)) — Input hidden_states.
  • encoder_hidden_states (torch.Tensor of shape (batch_size, sequence_len, embed_dims)) — Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
  • timestep (torch.LongTensor) — Used to indicate denoising step.
  • encoder_attention_mask (torch.Tensor) — Mask applied to encoder_hidden_states during attention.
  • num_frames (int, optional) — Number of frames in the video used to compute the rotary positional embeddings.
  • height (int, optional) — Height of the latent used to compute the rotary positional embeddings.
  • width (int, optional) — Width of the latent used to compute the rotary positional embeddings.
  • rope_interpolation_scale (tuple of float or torch.Tensor, optional) — Interpolation scale used by the rotary positional embeddings.
  • video_coords (torch.Tensor, optional) — Pre-computed video coordinates used by the rotary positional embeddings.
  • attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.
  • return_dict (bool, optional, defaults to True) — Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.

Returns

torch.Tensor

The denoised output tensor of shape (batch_size, sequence_length, out_channels).

The LTXVideoTransformer3DModel forward method.

Transformer2DModelOutput

class diffusers.models.modeling_outputs.Transformer2DModelOutput

< >

( 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.

Update on GitHub

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