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

StableAudioDiTModel

A Transformer model for audio waveforms from Stable Audio Open.

StableAudioDiTModel[[diffusers.StableAudioDiTModel]]

• 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

• 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.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.

Disables custom attention processors and sets the default attention implementation.