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StableAudioDiTModel

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StableAudioDiTModel

A Transformer model for audio waveforms from Stable Audio Open.

StableAudioDiTModel

class 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 )

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

< >

( 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 )

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.

The StableAudioDiTModel forward method.

set_default_attn_processor

< >

( )

Disables custom attention processors and sets the default attention implementation.

Update on GitHub

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