# 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).

**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.