diffusers/pr_13745/en/api/models/ltx_video_transformer3d.md

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[[diffusers.LTXVideoTransformer3DModel]]

diffusers.LTXVideoTransformer3DModel[[diffusers.LTXVideoTransformer3DModel]]

Source

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

forwarddiffusers.LTXVideoTransformer3DModel.forwardhttps://github.com/huggingface/diffusers/blob/vr_13745/src/diffusers/models/transformers/transformer_ltx.py#L494[{"name": "hidden_states", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": LongTensor"}, {"name": "encoder_attention_mask", "val": ": Tensor"}, {"name": "num_frames", "val": ": int | None = None"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "rope_interpolation_scale", "val": ": tuple[float, float, float] | torch.Tensor | None = None"}, {"name": "video_coords", "val": ": torch.Tensor | None = None"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "return_dict", "val": ": bool = True"}]- 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.0

The LTXVideoTransformer3DModel forward method.

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.

Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

diffusers.models.modeling_outputs.Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

Source

The output of Transformer2DModel.

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.