Instructions to use maxgraf/sa3-variations-torch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Stable Audio 3
How to use maxgraf/sa3-variations-torch 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
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: other
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license_name: stability-ai-community
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license_link: https://huggingface.co/stabilityai/stable-audio-3-medium/blob/main/LICENSE.md
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base_model:
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- stabilityai/stable-audio-3-medium
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- stabilityai/stable-audio-3-small-music
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tags:
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- audio
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- text-to-audio
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- stable-audio
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library_name: stable-audio-3
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---
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# SA3 Variations — fp16 weights
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Float16 conversions of Stability AI's [Stable Audio 3](https://huggingface.co/collections/stabilityai/stable-audio-3)
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checkpoints, repackaged for the Windows build of **SA3 Variations**.
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**These are Stability AI's models. This repo is a redistribution, not a new
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model, and is not affiliated with or endorsed by Stability AI.**
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## Why this exists
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The SA3 Variations Windows app runs inference through PyTorch and needs its
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weights on disk at install time. The upstream repos are gated, which would mean
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every user creating a Hugging Face account and a token just to install a
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desktop app. This repo removes that step.
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## What was changed
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Converted with [`scripts/convert_weights_fp16.py`](https://github.com/maxgraf96/stable-audio-3):
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every float32/float64 tensor cast to float16. Integer indices, masks and bools
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are untouched, and the bundled T5Gemma text encoder is left in **bfloat16** —
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bf16 to fp16 narrows exponent range and risks overflow.
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This is a storage change, not a quality one: the app loads with
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`model_half=True` on CUDA, so these weights were being cast to fp16 at load
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anyway. Verified by generating identical seeds from the original fp32 model and
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this conversion:
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corr(fp32, fp16) = +1.000000 (3 seeds)
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| | upstream fp32 | here |
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|---|---|---|
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| `medium/model.safetensors` | 9.22 GB | 4.3 GB |
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| `medium/t5gemma-b-b-ul2/` | 1.18 GB | unchanged (bf16) |
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## Layout
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Each directory is self-contained — checkpoint, config, and its own copy of the
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text encoder, so the app can load it with a local path and no network access:
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```
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medium/
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model_config.json
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model.safetensors
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t5gemma-b-b-ul2/
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small-music/
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model_config.json
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model.safetensors
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t5gemma-b-b-ul2/
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```
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## Licence
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Inherits the upstream terms, which you should read before using these files:
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- `medium/` — [stabilityai/stable-audio-3-medium](https://huggingface.co/stabilityai/stable-audio-3-medium)
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- `small-music/` — [stabilityai/stable-audio-3-small-music](https://huggingface.co/stabilityai/stable-audio-3-small-music)
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The bundled T5Gemma encoder additionally carries Google's
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[Gemma Terms of Use](https://ai.google.dev/gemma/terms).
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## Source
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App and conversion script: <https://github.com/maxgraf96/stable-audio-3>
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