diffusers/pr_13745/en/api/models/ltx2_video_transformer3d.md

LTX2VideoTransformer3DModel

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 LTX2VideoTransformer3DModel

transformer = LTX2VideoTransformer3DModel.from_pretrained("Lightricks/LTX-2", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")

LTX2VideoTransformer3DModel[[diffusers.LTX2VideoTransformer3DModel]]

diffusers.LTX2VideoTransformer3DModel[[diffusers.LTX2VideoTransformer3DModel]]

Source

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

forwarddiffusers.LTX2VideoTransformer3DModel.forwardhttps://github.com/huggingface/diffusers/blob/vr_13745/src/diffusers/models/transformers/transformer_ltx2.py#L1321[{"name": "hidden_states", "val": ": Tensor"}, {"name": "audio_hidden_states", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": Tensor"}, {"name": "audio_encoder_hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": LongTensor"}, {"name": "audio_timestep", "val": ": torch.LongTensor | None = None"}, {"name": "sigma", "val": ": torch.Tensor | None = None"}, {"name": "audio_sigma", "val": ": torch.Tensor | None = None"}, {"name": "encoder_attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "audio_encoder_attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "num_frames", "val": ": int | None = None"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "fps", "val": ": float = 24.0"}, {"name": "audio_num_frames", "val": ": int | None = None"}, {"name": "video_coords", "val": ": torch.Tensor | None = None"}, {"name": "audio_coords", "val": ": torch.Tensor | None = None"}, {"name": "isolate_modalities", "val": ": bool = False"}, {"name": "spatio_temporal_guidance_blocks", "val": ": list[int] | None = None"}, {"name": "perturbation_mask", "val": ": torch.Tensor | None = None"}, {"name": "use_cross_timestep", "val": ": bool = False"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "video_self_attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "return_dict", "val": ": bool = True"}]- hidden_states (torch.Tensor) -- Input patchified video latents of shape (batch_size, num_video_tokens, in_channels).

• audio_hidden_states (torch.Tensor) -- Input patchified audio latents of shape (batch_size, num_audio_tokens, audio_in_channels).
• encoder_hidden_states (torch.Tensor) -- Input video text embeddings of shape (batch_size, text_seq_len, self.config.caption_channels).
• audio_encoder_hidden_states (torch.Tensor) -- Input audio text embeddings of shape (batch_size, text_seq_len, self.config.caption_channels).
• timestep (torch.Tensor) -- Input timestep of shape (batch_size, num_video_tokens). These should already be scaled by self.config.timestep_scale_multiplier.
• audio_timestep (torch.Tensor, optional) -- Input timestep of shape (batch_size,) or (batch_size, num_audio_tokens) for audio modulation params. This is only used by certain pipelines such as the I2V pipeline.
• sigma (torch.Tensor, optional) -- Input scaled timestep of shape (batch_size,). Used for video prompt cross attention modulation in models such as LTX-2.3.
• audio_sigma (torch.Tensor, optional) -- Input scaled timestep of shape (batch_size,). Used for audio prompt cross attention modulation in models such as LTX-2.3. If sigma is supplied but audio_sigma is not, audio_sigma will be set to the provided sigma value.
• encoder_attention_mask (torch.Tensor, optional) -- Optional multiplicative text attention mask of shape (batch_size, text_seq_len).
• audio_encoder_attention_mask (torch.Tensor, optional) -- Optional multiplicative text attention mask of shape (batch_size, text_seq_len) for audio modeling.
• num_frames (int, optional) -- The number of latent video frames. Used if calculating the video coordinates for RoPE.
• height (int, optional) -- The latent video height. Used if calculating the video coordinates for RoPE.
• width (int, optional) -- The latent video width. Used if calculating the video coordinates for RoPE.
• fps -- (float, optional, defaults to 24.0): The desired frames per second of the generated video. Used if calculating the video coordinates for RoPE.
• audio_num_frames -- (int, optional): The number of latent audio frames. Used if calculating the audio coordinates for RoPE.
• video_coords (torch.Tensor, optional) -- The video coordinates to be used when calculating the rotary positional embeddings (RoPE) of shape (batch_size, 3, num_video_tokens, 2). If not supplied, this will be calculated inside forward.
• audio_coords (torch.Tensor, optional) -- The audio coordinates to be used when calculating the rotary positional embeddings (RoPE) of shape (batch_size, 1, num_audio_tokens, 2). If not supplied, this will be calculated inside forward.
• isolate_modalities (bool, optional, defaults to False) -- Whether to isolate each modality by turning off cross-modality (audio-to-video and video-to-audio) cross attention (for all blocks). Use for modality guidance in LTX-2.3.
• spatio_temporal_guidance_blocks (list[int], optional, defaults to None) -- The transformer block indices at which to apply spatio-temporal guidance (STG), which shortcuts the self-attention operations by simply using the values rather than the full scaled dot-product attention (SDPA) operation. If None or empty, STG will not be applied to any block.
• perturbation_mask (torch.Tensor, optional) -- Perturbation mask for STG of shape (batch_size,) or (batch_size, 1, 1). Should be 0 at batch elements where STG should be applied and 1 elsewhere. If STG is being used but peturbation_mask is not supplied, will default to applying STG (perturbing) all batch elements.
• use_cross_timestep (bool optional, defaults to False) -- Whether to use the cross modality (audio is the cross modality of video, and vice versa) sigma when calculating the cross attention modulation parameters. True is the newer (e.g. LTX-2.3) behavior; False is the legacy LTX-2.0 behavior.
• attention_kwargs (dict[str, Any], optional) -- Optional dict of keyword args to be passed to the attention processor.
• video_self_attention_mask (torch.Tensor, optional) -- Optional multiplicative self-attention mask of shape (batch_size, num_video_tokens, num_video_tokens) applied to the video self-attention in each transformer block. Values in [0, 1] where 1 means full attention and 0 means masked. Used e.g. by the IC-LoRA pipeline to control attention strength between noisy tokens and appended reference tokens. Audio self-attention is not affected.
• return_dict (bool, optional, defaults to True) -- Whether to return a dict-like structured output of type AudioVisualModelOutput or a tuple.0AudioVisualModelOutput or tupleIf return_dict is True, returns a structured output of type AudioVisualModelOutput, otherwise a tuple is returned where the first element is the denoised video latent patch sequence and the second element is the denoised audio latent patch sequence.

Forward pass for LTX-2.0 audiovisual video transformer.

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.

Returns:

AudioVisualModelOutput` or `tuple

If return_dict is True, returns a structured output of type AudioVisualModelOutput, otherwise a tuple is returned where the first element is the denoised video latent patch sequence and the second element is the denoised audio latent patch sequence.