scragnog/HOT-Step-CPP-StableStep

Model

1

stars

18

commits

1

linked in READMEs

Jul 22, 2026

updated

audio
gguf
hot-step-cpp
music
onnx
Browse cluster: Music Audio and Synthesis Tools

README

HOT-Step-CPP StableStep Models

Model files for the StableStep post-processing feature of HOT-Step-CPP — a local AI music generation app. StableStep re-renders the instrumental of a generated track through Stable Audio 3 (SDEdit-style partial re-noising) to replace autoencoder fizz with real spectral detail, while vocals are separated, cleaned, and remixed untouched.

These files are converted derivatives of stabilityai/stable-audio-3-medium (diffusion transformer + SAME-L autoencoder + T5Gemma text encoder), exported to ONNX and GGUF for native C++ inference. No weights were fine-tuned; only format conversion and precision changes (BF16/F16 storage, fp32-sensitive tensors kept at F32) were applied.

The easiest way to use these is HOT-Step-CPP's built-in Model Manager (StableStep tab), which downloads the set you need and places files automatically.

Backends

SetFilesSizeRuns on
GGML (recommended)sa3-*.gguf (4)~5.8 GBCUDA, Vulkan, CPU (Metal untested)
ONNXsa3-*.onnx + sa3-dit.onnx.data (6)~10.9 GBNVIDIA (ONNX Runtime CUDA / TensorRT)
Tokenizer (required by BOTH)tokenizer.json, tokenizer_config.json, special_tokens_map.json~35 MB

In current testing on an RTX 5090 the GGML backend is both smaller and substantially faster than the ONNX/TensorRT path for this workload.

Manual install layout

Relative to your HOT-Step-CPP models/ directory:

models/
├── sa3-dit-BF16.gguf              ← GGML set (repo root files)
├── sa3-same-enc-F16.gguf
├── sa3-same-dec-F16.gguf
├── sa3-text-enc-BF16.gguf
└── onnx/sa3/
    ├── sa3-dit.onnx               ← ONNX set
    ├── sa3-dit.onnx.data
    ├── sa3-same_encoder.onnx
    ├── sa3-same_decoder.onnx
    ├── sa3-text_encoder.onnx
    ├── sa3-seconds_embedder.onnx
    ├── tokenizer.json             ← tokenizer files: required for BOTH backends
    ├── tokenizer_config.json
    └── special_tokens_map.json

License

These weights are derived from Stable Audio 3 and are distributed under the Stability AI Community License (see LICENSE-STABILITY-AI-COMMUNITY.md / NOTICE in this repo). In short: free for research, non-commercial, and commercial use by individuals and organizations with less than $1M in annual revenue; larger organizations require an enterprise license from Stability AI. By downloading these files you agree to those terms. HOT-Step-CPP presents this license for acceptance before downloading in-app.

Powered by Stability AI.

Acknowledgements

  • Stability AI — Stable Audio 3 (DiT + SAME-L autoencoder)
  • Google — T5Gemma text encoder (via the Stable Audio 3 release)
  • Conversion + native inference: the HOT-Step-CPP project

Contributors

scragnog

18 commits

scragnog/HOT-Step-CPP-StableStep

Model

1

stars

18

commits

1

linked in READMEs

Jul 22, 2026

updated

audio
gguf
hot-step-cpp
music
onnx
Browse cluster: Music Audio and Synthesis Tools

README

HOT-Step-CPP StableStep Models

Model files for the StableStep post-processing feature of HOT-Step-CPP — a local AI music generation app. StableStep re-renders the instrumental of a generated track through Stable Audio 3 (SDEdit-style partial re-noising) to replace autoencoder fizz with real spectral detail, while vocals are separated, cleaned, and remixed untouched.

These files are converted derivatives of stabilityai/stable-audio-3-medium (diffusion transformer + SAME-L autoencoder + T5Gemma text encoder), exported to ONNX and GGUF for native C++ inference. No weights were fine-tuned; only format conversion and precision changes (BF16/F16 storage, fp32-sensitive tensors kept at F32) were applied.

The easiest way to use these is HOT-Step-CPP's built-in Model Manager (StableStep tab), which downloads the set you need and places files automatically.

Backends

SetFilesSizeRuns on
GGML (recommended)sa3-*.gguf (4)~5.8 GBCUDA, Vulkan, CPU (Metal untested)
ONNXsa3-*.onnx + sa3-dit.onnx.data (6)~10.9 GBNVIDIA (ONNX Runtime CUDA / TensorRT)
Tokenizer (required by BOTH)tokenizer.json, tokenizer_config.json, special_tokens_map.json~35 MB

In current testing on an RTX 5090 the GGML backend is both smaller and substantially faster than the ONNX/TensorRT path for this workload.

Manual install layout

Relative to your HOT-Step-CPP models/ directory:

models/
├── sa3-dit-BF16.gguf              ← GGML set (repo root files)
├── sa3-same-enc-F16.gguf
├── sa3-same-dec-F16.gguf
├── sa3-text-enc-BF16.gguf
└── onnx/sa3/
    ├── sa3-dit.onnx               ← ONNX set
    ├── sa3-dit.onnx.data
    ├── sa3-same_encoder.onnx
    ├── sa3-same_decoder.onnx
    ├── sa3-text_encoder.onnx
    ├── sa3-seconds_embedder.onnx
    ├── tokenizer.json             ← tokenizer files: required for BOTH backends
    ├── tokenizer_config.json
    └── special_tokens_map.json

License

These weights are derived from Stable Audio 3 and are distributed under the Stability AI Community License (see LICENSE-STABILITY-AI-COMMUNITY.md / NOTICE in this repo). In short: free for research, non-commercial, and commercial use by individuals and organizations with less than $1M in annual revenue; larger organizations require an enterprise license from Stability AI. By downloading these files you agree to those terms. HOT-Step-CPP presents this license for acceptance before downloading in-app.

Powered by Stability AI.

Acknowledgements

  • Stability AI — Stable Audio 3 (DiT + SAME-L autoencoder)
  • Google — T5Gemma text encoder (via the Stable Audio 3 release)
  • Conversion + native inference: the HOT-Step-CPP project

Contributors

scragnog

18 commits