diffusers/pr_14409/en/api/models/stable_audio_transformer.md

StableAudioDiTModel

A Transformer model for audio waveforms from Stable Audio Open.

StableAudioDiTModel[[diffusers.StableAudioDiTModel]]

diffusers.StableAudioDiTModel[[diffusers.StableAudioDiTModel]]

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)

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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]]

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)

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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 forward method.

set_default_attn_processor[[diffusers.StableAudioDiTModel.set_default_attn_processor]]

set_default_attn_processor()

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Disables custom attention processors and sets the default attention implementation.