timoncool/YuE2-Studio

选用指南

AI 根据 README 整理 · 2026/10/21 05:21:12

使用方式

通过 Windows 安装程序或便携版运行，提供图形界面，支持 MCP 服务器供 AI 代理连接。

查看 README 依据

# YuE2 Studio **Full songs with an editable score, generated on your own GPU. One executable — no Python, no Node.js, no launcher.** [](https://timoncool.github.io/YuE2-Studio/) [](https://github.com/timoncool/YuE2-Studio/releases/latest) [](DONATE.md)

1. Install the studio from the [latest release](https://github.com/timoncool/YuE2-Studio/releases/latest) and start it. 2. Connect to its MCP server at `http://127.0.0.1:8791/mcp`: `claude mcp add --transport http yue2-studio http://127.0.0.1:8791/mcp` 3. Read the skill it serves (resource `studio://skill`, prompt `studio`), the same text as [docs/mcp-skill.md](docs/mcp-skill.md), and start with the tool `studio_status`. [llms.txt](llms.txt) says the same for tools that look for it. To keep the skill in Claude

部署要求

需要 Windows 10/11 x64，NVIDIA GPU 至少 6 GB 显存（GTX 900 系列起），或 AMD/Intel 显卡（实验性支持），无 GPU 时可用 CPU 但速度慢。

查看 README 依据

| [Ночной вор Барсик](docs/samples/barsik.mp3) | Style and lyrics written by the built-in assistant from a one-line idea | pop-punk, distorted guitars, 185 BPM | ## What it needs - Windows 10/11 x64. - A GPU with **6 GB of VRAM** or more: - **NVIDIA**, from the GTX 900 series on, runs on CUDA — the fastest path. The studio ships two CUDA builds of the engine and picks the one your card and driver run: CUDA 13 for Turing and newer (GTX 16, RTX 20–50, Tesla T4, A100, RTX A-series, L4/L40, H100) with driver 580 or newer, CUDA 12 for Maxwell, Pascal and Volta

(GTX 900/1000, Titan X/Xp/V, Tesla M40, P40, P100, V100) and for any card on a driver from 525 to 579. Both carry compiled code for every one of those architectures, so nothing is left for the driver to compile. - **AMD or Intel — experimental.** The engine runs on Vulkan through the card's own driver. The Vulkan path itself is verified on NVIDIA (the same words heard as on CUDA), but on AMD Radeon integrated graphics the song came out with unintelligible vocals, and discrete AMD and Intel cards are untested. Reports from owners are welcome.

- Without a GPU the engine falls back to the processor, which works but is many times slower. - 4–10 GB of disk for one model set. - Training a LoRA (optional): an NVIDIA RTX 30-series card or newer with 11 GB of VRAM and about 8 GB more disk for the trainer and its weights, downloaded only when you open training. Describing songs by ear needs about 12 GB of VRAM and 10 GB more disk for MOSS-Music; without it the styles are written by hand. ### Linux (experimental) The .deb and the AppImage for Linux x86-64 are **experimental**. They come from the same code with the

已知限制

AMD/Intel 显卡路径为实验性，部分功能（如训练 LoRA）仅支持 NVIDIA；Linux 版本为实验性，作者未在真实 Linux 桌面测试。

查看 README 依据

(GTX 900/1000, Titan X/Xp/V, Tesla M40, P40, P100, V100) and for any card on a driver from 525 to 579. Both carry compiled code for every one of those architectures, so nothing is left for the driver to compile. - **AMD or Intel — experimental.** The engine runs on Vulkan through the card's own driver. The Vulkan path itself is verified on NVIDIA (the same words heard as on CUDA), but on AMD Radeon integrated graphics the song came out with unintelligible vocals, and discrete AMD and Intel cards are untested. Reports from owners are welcome.

Linux build of the same engine (Vulkan, and the processor), but the author works on Windows and has not run them on a real Linux desktop. **If you live on Linux, it would be great if you polished them and sent the fixes back as a pull request.** The studio keeps its data in `~/.local/share/yue2-studio` (`$XDG_DATA_HOME`). ## What runs where | Part | NVIDIA | AMD, Intel | No graphics card | | --- | --- | --- | --- | | Making songs · yue2.cpp | CUDA | Vulkan | processor | | Training a LoRA · music-train | CUDA | not available | not available |

The main, tested path is an NVIDIA card. - Training needs an NVIDIA card with 11 GB of video memory or more. The trainer has no other path, and on the processor one run would take days, so on any other machine the studio does not offer the training files. - On AMD and Intel cards stems are separated on the processor: the HT-Demucs model does not run through DirectML (out of memory on a 2 GB integrated card, over 20 GB and minutes for 30 seconds of audio on a 24 GB one). - Karaoke's Parakeet and the tempo model reach any DirectX 12 card through DirectML. Its