# LTXVideoTransformer3DModel

A Diffusion Transformer model for 3D data from [LTX](https://huggingface.co/Lightricks/LTX-Video) was introduced by Lightricks.

The model can be loaded with the following code snippet.

```python
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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_ltx.py#L385)

A Transformer model for video-like data used in [LTX](https://huggingface.co/Lightricks/LTX-Video).

forwarddiffusers.LTXVideoTransformer3DModel.forwardhttps://github.com/huggingface/diffusers/blob/main/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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **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](/docs/diffusers/main/en/api/models/ltx_video_transformer3d#diffusers.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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/modeling_outputs.py#L21)

The output of [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.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](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel) is discrete) : The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability distributions for the unnoised latent pixels.