# 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]] - **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](https://huggingface.co/Lightricks/LTX-Video). - **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.`torch.Tensor`The denoised output tensor of shape `(batch_size, sequence_length, out_channels)`. The [LTXVideoTransformer3DModel](/docs/diffusers/main/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel) forward method. ## Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]] - **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. The output of [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).