diffusers/pr_14550/en/api/models/autoencoder_same.md

AutoencoderSAME

The SAME (Semantically-Aligned Music Encoder) autoencoder is used by Stable Audio 3 to compress stereo audio waveforms into a compact latent sequence and reconstruct them.

The encoder stacks SAMETransformerResamplingBlock modules, each of which groups a fixed number of audio patch frames and produces one learnable output token via a differential transformer. The decoder inverts this process, expanding each latent token back to a patch of audio frames.

A soft-norm bottleneck (SoftNormBottleneck) normalises latents before and after the diffusion model, providing stable training dynamics.

AutoencoderSAME[[diffusers.AutoencoderSAME]]

diffusers.AutoencoderSAME[[diffusers.AutoencoderSAME]]

diffusers.AutoencoderSAME(audio_channels: int = 2, patch_size: int = 256, encoder_channels: int = 128, encoder_c_mults: typing.List[int] = (6,), encoder_strides: typing.List[int] = (16,), encoder_transformer_depths: typing.List[int] = (6,), latent_dim: int = 256, use_differential_attention: bool = True, dim_heads: int = 64, ff_mult: int = 3, sliding_window: int = 1, encoder_sinusoidal_blocks: typing.List[int] = (0,), decoder_sinusoidal_blocks: typing.List[int] = (0,), sampling_rate: int = 44100)

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Parameters:

audio_channels : Number of audio channels (2 for stereo).

patch_size : Non-overlapping patch size applied before the TRB encoder (and reversed after the TRB decoder). Contributes patch_size × to the total downsampling ratio. Production value: 256.

encoder_channels : Base channel count for the TRB. 128 for SAME-S, 256 for SAME-L.

encoder_c_mults : Channel multiplier for each TRB level (one entry per TRB). Both SAME-S and SAME-L use (6,) — a single TRB whose hidden dimension is encoder_channels × 6.

encoder_strides : Down-/up-sampling stride for each TRB level. Both SAME-S and SAME-L use (16,) — one TRB with stride 16.

encoder_transformer_depths : Transformer layers per TRB level. 6 for SAME-S, 12 for SAME-L.

latent_dim : Dimensionality of the latent space. 256 for both variants.

use_differential_attention : If True, use differential attention inside each TRB transformer block (default on for SAME-S/L).

dim_heads : Attention head dimension. 64 for production SAME-S/L.

ff_mult : SwiGLU feed-forward expansion factor.

sliding_window : Sliding-window half-width (in latents) for the band-mask attention. Production SAME-S/L use 1.

encoder_sinusoidal_blocks : Per-TRB count of trailing transformer layers that use sin FFN gating in the encoder (SAME-L: (0,)).

decoder_sinusoidal_blocks : Per-TRB count of trailing transformer layers that use sin FFN gating in the decoder (SAME-L: (8,)).

sampling_rate : Audio sample rate in Hz (e.g. 44100).

Semantically-Aligned Music Encoder (SAME) autoencoder from Stable Audio 3 (arXiv 2605.17991).

The model consists of:

• Patch embedding — reshapes stereo audio into non-overlapping patches, trading time for channels (patch_size × downsample, no learned params).
• Encoder TRB stack — SAMETransformerResamplingBlock blocks that further downsample by a factor of ∏(encoder_strides).
• Soft-norm bottleneck — learnable affine normalisation with running std.
• Decoder TRB stack — mirrors the encoder in reverse.
• Unpatch — reshapes channels back into the time dimension.

Total downsampling ratio: patch_size × ∏(encoder_strides).

The default hyperparameters match the SAME-S checkpoint (stabilityai/SAME-S). To load SAME-L (stabilityai/SAME-L, used by SA3 Medium) pass encoder_channels=256, encoder_transformer_depths=(12,).

# SAME-S (108 M params, used by SA3 small models) model = AutoencoderSAME() # default values

# SAME-L (852 M params, used by SA3 Medium) model = AutoencoderSAME(encoder_channels=256,
encoder_transformer_depths=(12,))

encode[[diffusers.AutoencoderSAME.encode]]

encode(sample: Tensor, return_dict: bool = True)

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Parameters:

sample : (B, audio_channels, T) waveform tensor.

return_dict : If True return an AutoencoderSAMEOutput.

Returns:

Latent tensor of shape (B, latent_dim, T // downsampling_ratio) (wrapped in AutoencoderSAMEOutput when return_dict is True).

Encode stereo audio to latents.

decode[[diffusers.AutoencoderSAME.decode]]

decode(latents: Tensor, return_dict: bool = True)

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Parameters:

latents : (B, latent_dim, T_latent) latent tensor.

return_dict : If True return an AutoencoderSAMEDecoderOutput.

Returns:

Waveform tensor of shape (B, audio_channels, T_latent × downsampling_ratio) (wrapped in AutoencoderSAMEDecoderOutput when return_dict is True).

Decode latents back to stereo audio.

forward[[diffusers.AutoencoderSAME.forward]]

forward(sample: Tensor, return_dict: bool = True)

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Parameters:

sample : (B, audio_channels, T) waveform tensor.

return_dict : If True return an AutoencoderSAMEDecoderOutput.

Returns:

Reconstructed waveform (same shape as sample, possibly longer due to padding), wrapped in AutoencoderSAMEDecoderOutput when return_dict is True.

Encode and immediately decode sample (reconstruction).

SAMETransformerResamplingBlock[[diffusers.models.autoencoders.autoencoder_same.SAMETransformerResamplingBlock]]

diffusers.models.autoencoders.autoencoder_same.SAMETransformerResamplingBlock[[diffusers.models.autoencoders.autoencoder_same.SAMETransformerResamplingBlock]]

diffusers.models.autoencoders.autoencoder_same.SAMETransformerResamplingBlock(in_channels: int, out_channels: int, stride: int, mode: str = 'encoder', transformer_depth: int = 3, dim_heads: int = 128, use_differential: bool = True, ff_mult: int = 3, sliding_window: int = 1, sinusoidal_blocks: int = 0)

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Parameters:

in_channels : Number of input channels.

out_channels : Number of output channels.

stride : Down-/up-sampling factor.

mode : "encoder" or "decoder".

transformer_depth : Number of TransformerBlock layers.

dim_heads : Attention head dimension.

use_differential : Whether to use differential attention.

ff_mult : Feed-forward expansion factor.

sliding_window : Sliding-window half-width in latents (band half-width is sliding_window * (stride + 1)).

sinusoidal_blocks : Number of trailing transformer layers that use sin FFN gating instead of SiLU.

Core building block of SAME.

Encoder mode (stride S): Groups S consecutive input frames into one segment, appends a single learnable output embedding, then runs D transformer layers over the full flattened segment sequence and keeps only the output embedding → downsample by S.

Decoder mode (stride S): Groups 1 input frame with S learnable output embeddings, runs D transformer layers over the full flattened sequence, then keeps the S output embeddings → upsample by S.

Attention uses an overlapping sliding-window band mask over the flattened segment sequence: each token attends to sliding_window * (stride + 1) neighbours on each side. RoPE is computed over the full sequence length. This matches the reference implementation exactly (a single non-overlapping chunk would only match for one segment).

AutoencoderSAMEOutput[[diffusers.models.autoencoders.autoencoder_same.AutoencoderSAMEOutput]]

diffusers.models.autoencoders.autoencoder_same.AutoencoderSAMEOutput[[diffusers.models.autoencoders.autoencoder_same.AutoencoderSAMEOutput]]

diffusers.models.autoencoders.autoencoder_same.AutoencoderSAMEOutput(latents: Tensor)

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Output of AutoencoderSAME.encode().

AutoencoderSAMEDecoderOutput[[diffusers.models.autoencoders.autoencoder_same.AutoencoderSAMEDecoderOutput]]

diffusers.models.autoencoders.autoencoder_same.AutoencoderSAMEDecoderOutput[[diffusers.models.autoencoders.autoencoder_same.AutoencoderSAMEDecoderOutput]]

diffusers.models.autoencoders.autoencoder_same.AutoencoderSAMEDecoderOutput(sample: Tensor)

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Output of AutoencoderSAME.decode().