# MotifVideoTransformer3DModel

A Diffusion Transformer model for 3D video-like data was introduced in Motif-Video by the Motif Technologies Team.

The model uses a three-stage architecture with 12 dual-stream + 16 single-stream + 8 DDT decoder layers and rotary positional embeddings (RoPE) for video generation.

The model can be loaded with the following code snippet.

```python
from diffusers import MotifVideoTransformer3DModel

transformer = MotifVideoTransformer3DModel.from_pretrained("Motif-Technologies/Motif-Video-2B", subfolder="transformer", dtype=torch.bfloat16)
```

## MotifVideoTransformer3DModel[[diffusers.MotifVideoTransformer3DModel]]

#### diffusers.MotifVideoTransformer3DModel[[diffusers.MotifVideoTransformer3DModel]]

```python
diffusers.MotifVideoTransformer3DModel(in_channels: int = 33, out_channels: int = 16, num_attention_heads: int = 24, attention_head_dim: int = 128, num_layers: int = 20, num_single_layers: int = 40, num_decoder_layers: int = 0, mlp_ratio: float = 4.0, patch_size: int = 2, patch_size_t: int = 1, qk_norm: str = 'rms_norm', norm_type: str = 'layer_norm', text_embed_dim: int = 4096, image_embed_dim: int | None = None, rope_theta: float = 256.0, rope_axes_dim: typing.Tuple[int, ...] = (16, 56, 56), enable_text_cross_attention_dual: bool = False, enable_text_cross_attention_single: bool = False)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_motif_video.py#L730)

**Parameters:**

in_channels (`int`, defaults to `33`) : The number of channels in the input.

out_channels (`int`, defaults to `16`) : The number of channels in the output.

num_attention_heads (`int`, defaults to `24`) : The number of heads to use for multi-head attention.

attention_head_dim (`int`, defaults to `128`) : The number of channels in each head.

num_layers (`int`, defaults to `20`) : The number of layers of dual-stream blocks to use.

num_single_layers (`int`, defaults to `40`) : The number of layers of single-stream blocks to use.

num_decoder_layers (`int`, defaults to `0`) : The number of decoder layers in single-stream blocks.

mlp_ratio (`float`, defaults to `4.0`) : The ratio of the hidden layer size to the input size in the feedforward network.

patch_size (`int`, defaults to `2`) : The size of the spatial patches to use in the patch embedding layer.

patch_size_t (`int`, defaults to `1`) : The size of the temporal patches to use in the patch embedding layer.

qk_norm (`str`, defaults to `rms_norm`) : The normalization to use for the query and key projections in the attention layers.

text_embed_dim (`int`, defaults to `4096`) : Input dimension of text embeddings from the text encoder.

image_embed_dim (`int`, *optional*) : Input dimension of image embeddings from a vision encoder. If provided, enables image conditioning.

rope_theta (`float`, defaults to `256.0`) : The value of theta to use in the RoPE layer.

rope_axes_dim (`Tuple[int]`, defaults to `(16, 56, 56)`) : The dimensions of the axes to use in the RoPE layer.

A Transformer model for video-like data used in the Motif-Video model.

#### forward[[diffusers.MotifVideoTransformer3DModel.forward]]

```python
forward(hidden_states: Tensor, timestep: LongTensor, encoder_hidden_states: Tensor, encoder_attention_mask: typing.Optional[torch.Tensor] = None, image_embeds: typing.Optional[torch.Tensor] = None, attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None, return_dict: bool = True)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_motif_video.py#L888)

**Parameters:**

hidden_states (`torch.Tensor`) : Input latent tensor of shape `(batch_size, channels, num_frames, height, width)`.

timestep (`torch.LongTensor`) : Diffusion timesteps of shape `(batch_size,)`.

encoder_hidden_states (`torch.Tensor`) : Text conditioning of shape `(batch_size, sequence_length, embed_dim)`.

encoder_attention_mask (`torch.Tensor`) : Mask for text conditioning of shape `(batch_size, sequence_length)`.

image_embeds (`torch.Tensor`, *optional*) : Image embeddings from vision encoder of shape `(batch_size, num_tokens, embed_dim)`.

attention_kwargs (`dict`, *optional*) : Additional arguments for attention processors.

return_dict (`bool`, defaults to `True`) : Whether to return a [Transformer2DModelOutput](/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.models.modeling_outputs.Transformer2DModelOutput).

**Returns:** [Transformer2DModelOutput](/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.models.modeling_outputs.Transformer2DModelOutput) or `tuple`

The predicted samples.

Forward pass of the MotifVideoTransformer3DModel.

## Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

#### diffusers.models.modeling_outputs.Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

```python
diffusers.models.modeling_outputs.Transformer2DModelOutput(sample: torch.Tensor)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/modeling_outputs.py#L21)

**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.

The output of [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).