Instructions to use stabilityai/stable-audio-3-optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Stable Audio 3
How to use stabilityai/stable-audio-3-optimized with Stable Audio 3:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
cpu-amx README: consistent <model>_<precision> filenames
Browse files- cpu-amx/README.md +5 -5
cpu-amx/README.md
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@@ -34,12 +34,12 @@ than fp32** — rebuild from source (`optimized/cpu-amx/build/`) for other ISAs.
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| file(s) | model | precision / method | notes |
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| `same_{s,l}_decoder_bf16.*` | SAME-S (50M) / SAME-L (426M) decoder | **bf16** AMX GEMMs, fp32 differential-attention islands | highest-fidelity CPU decoders |
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| `same_{s,l}_decoder_int8.*` | SAME-S / SAME-L decoder | **int8 (w8a8, fused)** via **SmoothQuant α0.9 → GPTQ** ("improved" grid) | ½ size; SAME-S +1.2 dB over naive int8 on real music |
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| `same_{s,l}
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**Quantization findings baked into these choices** (full write-up in the repo's `LESSONS.md`):
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- The medium **DiT stays naive-int8** — GPTQ/SmoothQuant *lower* its accuracy (its adaLN-modulated qkv +
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Files are flat and self-describing: each engine is `<name>.so` + `<name>_weights.bin` (+ a
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`_manifest.txt` giving the mmap layout the loader reads). The DiT additionally needs
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| file(s) | model | precision / method | notes |
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| `t5gemma_bf16.*` | T5Gemma-b-b-ul2 encoder (text conditioner) | **bf16** AMX GEMMs, fp32 islands (RMSNorm/softmax/softcap/RoPE) | standard softmax attention → bf16-safe |
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| `dit_medium_int8.*` + `dit_medium_int8_kernels.tar.gz` | medium DiT (24-block rectified-flow) | **int8** — fully-fused all-integer, AOT-compiled Triton kernels + oneDNN | naive RTN (weight-PTQ *hurts* the DiT — see below) |
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| `same_{s,l}_decoder_bf16.*` | SAME-S (50M) / SAME-L (426M) decoder | **bf16** AMX GEMMs, fp32 differential-attention islands | highest-fidelity CPU decoders |
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| `same_{s,l}_decoder_int8.*` | SAME-S / SAME-L decoder | **int8 (w8a8, fused)** via **SmoothQuant α0.9 → GPTQ** ("improved" grid) | ½ size; SAME-S +1.2 dB over naive int8 on real music |
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| `same_{s,l}_encoder_bf16.*` | SAME-S / SAME-L encoder (audio→latent) | **bf16** AMX GEMMs, fp32 differential-attention islands | for audio-to-audio / inpainting |
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| `same_l_encoder_bf16_weights_f32.bin` | SAME-L encoder (optional) | **fp32** refinement mode | max-fidelity; bf16 is the default |
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**Quantization findings baked into these choices** (full write-up in the repo's `LESSONS.md`):
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- The medium **DiT stays naive-int8** — GPTQ/SmoothQuant *lower* its accuracy (its adaLN-modulated qkv +
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Files are flat and self-describing: each engine is `<name>.so` + `<name>_weights.bin` (+ a
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`_manifest.txt` giving the mmap layout the loader reads). The DiT additionally needs
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`dit_medium_int8_core.bin` (int8 block weights) and `dit_medium_int8_kernels.tar.gz` (its AOT Triton kernels).
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