diffusers/pr_14234/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", dtype=torch.bfloat16).to("cuda")

LTX2VideoTransformer3DModel[[diffusers.LTX2VideoTransformer3DModel]]

diffusers.LTX2VideoTransformer3DModel[[diffusers.LTX2VideoTransformer3DModel]]

diffusers.LTX2VideoTransformer3DModel(in_channels: int = 128, out_channels: int | None = 128, patch_size: int = 1, patch_size_t: int = 1, num_attention_heads: int = 32, attention_head_dim: int = 128, cross_attention_dim: int = 4096, vae_scale_factors: tuple = (8, 32, 32), pos_embed_max_pos: int = 20, base_height: int = 2048, base_width: int = 2048, gated_attn: bool = False, cross_attn_mod: bool = False, audio_in_channels: int = 128, audio_out_channels: int | None = 128, audio_patch_size: int = 1, audio_patch_size_t: int = 1, audio_num_attention_heads: int = 32, audio_attention_head_dim: int = 64, audio_cross_attention_dim: int = 2048, audio_scale_factor: int = 4, audio_pos_embed_max_pos: int = 20, audio_sampling_rate: int = 16000, audio_hop_length: int = 160, audio_gated_attn: bool = False, audio_cross_attn_mod: bool = False, num_layers: int = 48, 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 = 3840, attention_bias: bool = True, attention_out_bias: bool = True, rope_theta: float = 10000.0, rope_double_precision: bool = True, causal_offset: int = 1, timestep_scale_multiplier: int = 1000, cross_attn_timestep_scale_multiplier: int = 1000, rope_type: str = 'interleaved', use_prompt_embeddings = True, perturbed_attn: bool = False)

Source

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[[diffusers.LTX2VideoTransformer3DModel.forward]]

forward(hidden_states: Tensor, audio_hidden_states: Tensor, encoder_hidden_states: Tensor, audio_encoder_hidden_states: Tensor, timestep: LongTensor, audio_timestep: typing.Optional[torch.LongTensor] = None, sigma: typing.Optional[torch.Tensor] = None, audio_sigma: typing.Optional[torch.Tensor] = None, encoder_attention_mask: typing.Optional[torch.Tensor] = None, audio_encoder_attention_mask: typing.Optional[torch.Tensor] = None, num_frames: int | None = None, height: int | None = None, width: int | None = None, fps: float = 24.0, audio_num_frames: int | None = None, video_coords: typing.Optional[torch.Tensor] = None, audio_coords: typing.Optional[torch.Tensor] = None, isolate_modalities: bool = False, spatio_temporal_guidance_blocks: list[int] | None = None, perturbation_mask: typing.Optional[torch.Tensor] = None, use_cross_timestep: bool = False, attention_kwargs: dict[str, typing.Any] | None = None, video_self_attention_mask: typing.Optional[torch.Tensor] = None, return_dict: bool = True)

Source

Parameters:

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.

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.

Forward pass for LTX-2.0 audiovisual video transformer.