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

class diffusers.AutoencoderSAME

< source >

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

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

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( sample: Tensorreturn_dict: bool = True )

Parameters

sample — (B, audio_channels, T) waveform tensor.
return_dict — If True return an AutoencoderSAMEOutput.

Encode stereo audio to latents.

decode

< source >

( latents: Tensorreturn_dict: bool = True )

Parameters

latents — (B, latent_dim, T_latent) latent tensor.
return_dict — If True return an AutoencoderSAMEDecoderOutput.

Decode latents back to stereo audio.

forward

< source >

( sample: Tensorreturn_dict: bool = True )

Parameters

sample — (B, audio_channels, T) waveform tensor.
return_dict — If True return an AutoencoderSAMEDecoderOutput.

Encode and immediately decode sample (reconstruction).

SAMETransformerResamplingBlock

class diffusers.models.autoencoders.autoencoder_same.SAMETransformerResamplingBlock

< source >

( in_channels: intout_channels: intstride: intmode: str = 'encoder'transformer_depth: int = 3dim_heads: int = 128use_differential: bool = Trueff_mult: int = 3sliding_window: int = 1sinusoidal_blocks: int = 0 )

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).