# HunyuanVideo15Transformer3DModel

A Diffusion Transformer model for 3D video-like data used in [HunyuanVideo1.5](https://github.com/Tencent/HunyuanVideo1-1.5).

The model can be loaded with the following code snippet.

```python
from diffusers import HunyuanVideo15Transformer3DModel

transformer = HunyuanVideo15Transformer3DModel.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v" subfolder="transformer", dtype=torch.bfloat16)
```

## HunyuanVideo15Transformer3DModel[[diffusers.HunyuanVideo15Transformer3DModel]]

#### diffusers.HunyuanVideo15Transformer3DModel[[diffusers.HunyuanVideo15Transformer3DModel]]

```python
diffusers.HunyuanVideo15Transformer3DModel(in_channels: int = 65, out_channels: int = 32, num_attention_heads: int = 16, attention_head_dim: int = 128, num_layers: int = 54, num_refiner_layers: int = 2, mlp_ratio: float = 4.0, patch_size: int = 1, patch_size_t: int = 1, qk_norm: str = 'rms_norm', text_embed_dim: int = 3584, text_embed_2_dim: int = 1472, image_embed_dim: int = 1152, rope_theta: float = 256.0, rope_axes_dim: tuple = (16, 56, 56), target_size: int = 640, task_type: str = 'i2v', use_meanflow: bool = False)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_hunyuan_video15.py#L510)

**Parameters:**

in_channels (`int`, defaults to `16`) : 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_refiner_layers (`int`, defaults to `2`) : The number of layers of refiner blocks to use.

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 tmeporal 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.

guidance_embeds (`bool`, defaults to `True`) : Whether to use guidance embeddings in the model.

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

pooled_projection_dim (`int`, defaults to `768`) : The dimension of the pooled projection of the text embeddings.

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 [HunyuanVideo1.5](https://huggingface.co/tencent/HunyuanVideo1.5).

#### forward[[diffusers.HunyuanVideo15Transformer3DModel.forward]]

```python
forward(hidden_states: Tensor, timestep: LongTensor, encoder_hidden_states: Tensor, encoder_attention_mask: Tensor, timestep_r: typing.Optional[torch.LongTensor] = None, encoder_hidden_states_2: typing.Optional[torch.Tensor] = None, encoder_attention_mask_2: typing.Optional[torch.Tensor] = None, image_embeds: typing.Optional[torch.Tensor] = None, attention_kwargs: dict[str, typing.Any] | None = None, return_dict: bool = True)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_hunyuan_video15.py#L623)

**Parameters:**

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

timestep (`torch.LongTensor`) : Used to indicate denoising step.

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.

encoder_attention_mask (`torch.Tensor`) : Mask applied to `encoder_hidden_states` during attention.

timestep_r (`torch.LongTensor`, *optional*) : Refiner timestep conditioning.

encoder_hidden_states_2 (`torch.Tensor`, *optional*) : Additional conditional embeddings computed from a second text encoder (ByT5).

encoder_attention_mask_2 (`torch.Tensor`, *optional*) : Mask applied to `encoder_hidden_states_2` during attention.

image_embeds (`torch.Tensor`, *optional*) : Image embeddings for image-conditioned generation.

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.

**Returns:**

If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.

The [HunyuanVideo15Transformer3DModel](/docs/diffusers/main/en/api/models/hunyuan_video15_transformer_3d#diffusers.HunyuanVideo15Transformer3DModel) forward method.

## 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).