# StableAudioDiTModel

A Transformer model for audio waveforms from [Stable Audio Open](https://huggingface.co/papers/2407.14358).

## StableAudioDiTModel[[diffusers.StableAudioDiTModel]]

#### diffusers.StableAudioDiTModel[[diffusers.StableAudioDiTModel]]

```python
diffusers.StableAudioDiTModel(sample_size: int = 1024, in_channels: int = 64, num_layers: int = 24, attention_head_dim: int = 64, num_attention_heads: int = 24, num_key_value_attention_heads: int = 12, out_channels: int = 64, cross_attention_dim: int = 768, time_proj_dim: int = 256, global_states_input_dim: int = 1536, cross_attention_input_dim: int = 768)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/stable_audio_transformer.py#L183)

**Parameters:**

sample_size ( `int`, *optional*, defaults to 1024) : The size of the input sample.

in_channels (`int`, *optional*, defaults to 64) : The number of channels in the input.

num_layers (`int`, *optional*, defaults to 24) : The number of layers of Transformer blocks to use.

attention_head_dim (`int`, *optional*, defaults to 64) : The number of channels in each head.

num_attention_heads (`int`, *optional*, defaults to 24) : The number of heads to use for the query states.

num_key_value_attention_heads (`int`, *optional*, defaults to 12) : The number of heads to use for the key and value states.

out_channels (`int`, defaults to 64) : Number of output channels.

cross_attention_dim ( `int`, *optional*, defaults to 768) : Dimension of the cross-attention projection.

time_proj_dim ( `int`, *optional*, defaults to 256) : Dimension of the timestep inner projection.

global_states_input_dim ( `int`, *optional*, defaults to 1536) : Input dimension of the global hidden states projection.

cross_attention_input_dim ( `int`, *optional*, defaults to 768) : Input dimension of the cross-attention projection

The Diffusion Transformer model introduced in Stable Audio.

Reference: https://github.com/Stability-AI/stable-audio-tools

#### forward[[diffusers.StableAudioDiTModel.forward]]

```python
forward(hidden_states: FloatTensor, timestep: LongTensor = None, encoder_hidden_states: FloatTensor = None, global_hidden_states: FloatTensor = None, rotary_embedding: FloatTensor = None, return_dict: bool = True, attention_mask: typing.Optional[torch.LongTensor] = None, encoder_attention_mask: typing.Optional[torch.LongTensor] = None)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/stable_audio_transformer.py#L282)

**Parameters:**

hidden_states (`torch.FloatTensor` of shape `(batch size, in_channels, sequence_len)`) : Input `hidden_states`.

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

encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, encoder_sequence_len, cross_attention_input_dim)`) : Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.

global_hidden_states (`torch.FloatTensor` of shape `(batch size, global_sequence_len, global_states_input_dim)`) : Global embeddings that will be prepended to the hidden states.

rotary_embedding (`torch.Tensor`) : The rotary embeddings to apply on query and key tensors during attention calculation.

return_dict (`bool`, *optional*, defaults to `True`) : Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain tuple.

attention_mask (`torch.Tensor` of shape `(batch_size, sequence_len)`, *optional*) : Mask to avoid performing attention on padding token indices, formed by concatenating the attention masks for the two text encoders together. Mask values selected in `[0, 1]`:  - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**.

encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_len)`, *optional*) : Mask to avoid performing attention on padding token cross-attention indices, formed by concatenating the attention masks for the two text encoders together. Mask values selected in `[0, 1]`:  - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**.

**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 [StableAudioDiTModel](/docs/diffusers/main/en/api/models/stable_audio_transformer#diffusers.StableAudioDiTModel) forward method.

#### set_default_attn_processor[[diffusers.StableAudioDiTModel.set_default_attn_processor]]

```python
set_default_attn_processor()
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/stable_audio_transformer.py#L276)

Disables custom attention processors and sets the default attention implementation.