Instructions to use AEmotionStudio/yue2-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AEmotionStudio/yue2-models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="AEmotionStudio/yue2-models")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AEmotionStudio/yue2-models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AEmotionStudio/yue2-models: direct link, hf CLI and curl.
- Browser
- Download file 2.77 kB
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https://huggingface.co/AEmotionStudio/yue2-models/resolve/main/README.md
- Command line
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hf download hf://AEmotionStudio/yue2-models/README.md
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curl -L -o README.md https://huggingface.co/AEmotionStudio/yue2-models/resolve/main/README.md
2.77 kB
| license: cc-by-nc-4.0 | |
| library_name: transformers | |
| pipeline_tag: text-to-audio | |
| tags: | |
| - music-generation | |
| - symbolic-music | |
| - abc-notation | |
| - mirror | |
| # YuE2 — Mæstræa mirror | |
| A byte-identical mirror of the YuE2 release, laid out in lanes so each component | |
| can be fetched on its own. **Nothing here is original work.** It exists so | |
| Mæstræa can pull the LM and a decoder in one request instead of stitching | |
| together several upstream repositories. | |
| If you are not using Mæstræa, prefer the originals — they are the canonical | |
| source and carry the authors' own documentation. | |
| ## Attribution | |
| YuE2 is by the **[M·A·P](https://huggingface.co/m-a-p)** team (Multimodal Art | |
| Projection) and its collaborators. Per the YuE2 weight licence, the attribution | |
| for these files is the model name and its source repository: | |
| | Lane | Model | Source | | |
| |---|---|---| | |
| | `lm/` | **YuE2-3B** | https://huggingface.co/m-a-p/YuE2-3B | | |
| | `vae/` | **YuE2-Vae** | https://huggingface.co/m-a-p/YuE2-Vae | | |
| | `vae_legacy/` | **YuE2-Vae-legacy** | https://huggingface.co/m-a-p/YuE2-Vae-legacy | | |
| | `sheetsage2/` | **SheetSage2** | https://huggingface.co/m-a-p/SheetSage2 | | |
| | `mert2/` | **MERT-v2-FullSong** | https://huggingface.co/m-a-p/MERT-v2-FullSong | | |
| Upstream code and documentation: https://github.com/multimodal-art-projection/YuE | |
| ## Licence — non-commercial | |
| All weights in this repository are licensed **CC BY-NC 4.0** | |
| (Creative Commons Attribution-NonCommercial 4.0 International): | |
| - **Non-commercial use only.** You may not use these weights, or music made with | |
| them, for commercial purposes. | |
| - **Attribution required.** If you share what you make, identify YuE2, the model | |
| name, and its source repository. | |
| The full licence text is in `MODEL_LICENSE`, unmodified from upstream. YuE2's | |
| *inference code* is separately Apache-2.0; third-party components inside the VAE | |
| (Oobleck / stable-audio-tools and SnakeBeta / BigVGAN, both MIT) retain their own | |
| licences — see `THIRD_PARTY_NOTICES.md` and `licenses/`. | |
| Mirroring is done under the CC BY-NC grant to reproduce and share the licensed | |
| material for non-commercial purposes. No file has been modified; integrity can be | |
| checked against each lane's `weights_manifest.json`, which is upstream's own. | |
| ## Verifying | |
| Each lane carries upstream's `weights_manifest.json` with SHA-256 digests: | |
| ```python | |
| import hashlib, json | |
| from pathlib import Path | |
| lane = Path("lm") | |
| manifest = json.loads((lane / "weights_manifest.json").read_text()) | |
| for name, meta in manifest["files"].items(): | |
| h = hashlib.sha256() | |
| with open(lane / name, "rb") as f: | |
| for block in iter(lambda: f.read(8 << 20), b""): | |
| h.update(block) | |
| assert h.hexdigest() == meta["sha256"], name | |
| print("all files match upstream") | |
| ``` | |