YuE-extend-mp3 with Exllamav2 and UI

Now YuE can extend any uploaded mp3, with voice cloning and music contunuation.

It uses:

original Yue: official repository.
exllama module: YuE-Exllamav2.
web UI: YuE-exllamav2-UI.

Updates

2025.02.22 🔥: --extend_current_segment (use with --extend_mp3) to continue first(0) segment. Can also be used to generate TTS (use speech.mp3 + some_melody.mp3)
2025.02.16 🔥: Free Google colab with COT and mp3-extend: https://colab.research.google.com/github/Mozer/YuE-extend/blob/main/colab/Yue_extend_with_exllama.ipynb
2025.02.14 🔥: --extend_mp3 feature by Mozer. It will create new verses after segment 0, taken from mp3.
2025.02.14 🔥: Contunue previous generation feature (--resume_after_n) by Mozer
2025.02.03: Added ExLlamaV2 integration with up to 500% speedup by sgsdxzy (https://github.com/sgsdxzy/YuE-exllamav2)

2025.01.30: Initial release with BF16 model support.

Notes

--extend_mp3 works best with segments <= 30s. Long mp3s can cause OOM error. I recommend extending right after first verse end. Put needed seconds into Seconds to take from mp3
--extend_mp3 takes 2 separate tracks as input: vocal.mp3 + instrumental.mp3. To split your mp3 use: python-audio-separator or audiostrip.com or lalal.ai or vocalremover.org
seeding is currently not working with exllama
YuE-Exllamav2, the ultimate optimized interface for music generation using YuE models with ExLlamaV2 acceleration. This project delivers the best possible performance for YuE models, achieving exceptional speed and efficiency on modern NVIDIA GPUs like the RTX 4090 and RTX 3060.

Interface

Installation for Windows (without wsl)

Requirements

python 3.9 (3.10 will also work, but urls will be different)
torch 2.4.0 (exllama and flash_attn are compiled for several torch versions. 2.5.1 won't do)
cuda toolkit 12.4+
Windows. Linux is not tested (may work)

conda create -n yue python=3.9
conda activate yue

# torch, exllama и flash_attn-2
pip install torch==2.4.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
pip install https://github.com/turboderp-org/exllamav2/releases/download/v0.2.7/exllamav2-0.2.7+cu121.torch2.4.0-cp39-cp39-win_amd64.whl
pip install https://github.com/bdashore3/flash-attention/releases/download/v2.7.1.post1/flash_attn-2.7.1.post1+cu124torch2.4.0cxx11abiFALSE-cp39-cp39-win_amd64.whl

git lfs install
git clone https://github.com/Mozer/YuE-extend
cd YuE-extend
pip install -r requirements.txt
git clone https://huggingface.co/m-a-p/xcodec_mini_infer

# download 3 exl2 модели (5.7 + 5.7 + 1.8 GB) into workspace\models:
huggingface-cli download Alissonerdx/YuE-s1-7B-anneal-en-cot-exl2-8.0bpw --local-dir workspace\models\YuE-s1-7B-anneal-en-cot-exl2-8.0bpw
huggingface-cli download Ftfyhh/YuE-s1-7B-anneal-en-icl-8.0bpw-exl2 --local-dir YuE-s1-7B-anneal-en-icl-8.0bpw-exl2
huggingface-cli download Alissonerdx/YuE-s2-1B-general-exl2-8.0bpw --local-dir workspace\models\YuE-s2-1B-general-exl2-8.0bpw

Run (double click) start-gui.bat open (ctrl click) http://127.0.0.1:7860/

Inference (mp3-extend)

There are 3 ways: COT model; ICL model + 2 additional tracks; ICL model + 1 additional common track. The first one is the simplest and most proven. In the web UI:

Stage 1 model: YuE-s1-7B-anneal-en-cot-exl2-8.0bpw
Lyrics: the full text of the song. The first [verse] segment should include everything you skip, after which a continuation will be generated. It is recommended to limit the first segment to one quatrain.
Check the box "Extend mp3".
Split your mp3 into vocals.mp3 + instrumental.mp3. To split, use: python-audio-separator or audiostrip.com or [lalal.ai](https://www.lalal.ai /) or [vocalremover.org](https://vocalremover.org /) Upload vocal.mp3 + instrumental.mp3 in 2 fields of the file.
Find the exact time when the vocals in the first verse end in your mp3, for example, 15s, and put it into "Seconds to take from mp3".
Generate

Additional checkboxes "Use Dual Tracks Audio Prompt?" and "Use Audio Prompt? (both vocal and instrumental)" will give the model the full music of the entire song. But this makes it much more difficult to control the generation. The model will try to generate what it has already heard from mp3, and it will repeat the source one by one. In these modes, try setting the "Audio prompt End Time" to 1-3 seconds more than in the "Seconds to take from mp3" field. Experiment with different segments to find a balance of similarity and novelty of generation.

OLD readme

Key Features

ExLlamaV2 Optimization: Inference acceleration with Flash Attention 2 and BF16 support.
Exceptional Performance: Up to 500% speedup compared to the original implementation.
NVIDIA GPU Support: Compatible with CUDA 12.2 and optimized for modern GPUs.
User-Friendly Interface: Web-based Gradio interface for easy configuration and music generation.
Audio Playback and Download: Listen to generated audio and download it directly from the interface.
Pre-configured Docker: Easy deployment with Docker and support for persistent volumes.

Performance Benchmarks

RTX 4090 24GB

Stage	Original	ExLlamaV2	Speedup
Stage 1	282s	125s	2.25x
Stage 2	666s	49s	13.6x
Total	948s	174s	5.45x

Configuration: BF16 models, Flash Attention 2 enabled, CFG enabled, batch size 4, 64k cache.

RTX 3060 Mobile 6GB

Stage	ExLlamaV2
Stage 1	317s
Stage 2	350s
Total	667s

Configuration: Quantized models with Q4/Q8 cache, Flash Attention 2 enabled.

How to Use

Prerequisites

Docker

Ensure you have Docker installed on your system. Follow the official Docker installation guides for your platform:

NVIDIA GPU Support

This interface requires NVIDIA GPUs for acceleration. Ensure you have the necessary hardware and drivers set up.

Linux:
- Install the NVIDIA Container Toolkit.
- Ensure your NVIDIA drivers are properly installed and up to date.
Windows/macOS:
- Refer to the respective Docker and NVIDIA documentation for GPU passthrough (e.g., WSL2 on Windows).
- Note: GPU support is mandatory. Without compatible NVIDIA GPUs, the container will not function correctly.

Docker Compose

To simplify the setup and management of the YuE-Exllamav2-UI, you can use Docker Compose. Docker Compose allows you to define and run multi-container Docker applications with a single configuration file (docker-compose.yml). Below are the steps to get started.

Note: This docker-compose.yml file already exists in the root of the repository, you just need to download or copy the file and replace the directory mapping and run the command in the same directory as the file, see the explanation below.

Create a docker-compose.yml file:

version: '3.8'

services:
  yue-exllamav2:
    image: alissonpereiraanjos/yue-exllamav2-interface:latest
    container_name: yue-exllamav2
    restart: unless-stopped
    ports:
      - "7860:7860"
    environment:
      - DOWNLOAD_MODELS=all_bf16
    volumes:
      - /path/to/models:/workspace/models
      - /path/to/outputs:/workspace/outputs
    deploy:
      resources:
        reservations:
          devices:
            - capabilities: [gpu]

Run the container:

docker-compose up -d

Access the Interface: Once the container is running, access the Gradio UI at http://localhost:7860.

Explanation of the Configuration

image: Specifies the Docker image to use (alissonpereiraanjos/yue-exllamav2-interface:latest).
container_name: Sets a name for the container (yue-interface).
restart: unless-stopped: Ensures the container restarts automatically unless manually stopped.
ports: Maps container ports to the host:
- 7860:7860: Port for accessing the Gradio UI.
- 8888:8888: Optional additional port (JupyterLab).
environment: Defines environment variables:
- DOWNLOAD_MODELS=all: Downloads all available models. Replace all with specific model keys (e.g., YuE-s2-1B-general,YuE-s1-7B-anneal-en-cot) to download only selected models.
volumes: Maps host directories to the container:
- /path/to/models:/workspace/models: Directory where models will be stored.
- /path/to/outputs:/workspace/outputs: Directory where generated outputs will be saved.
- Replace /path/to/models and /path/to/outputs with the actual paths on your system.
deploy.resources.reservations.devices: Enables GPU support in the container (requires NVIDIA GPU and drivers).

Customization

Specific models: To download only specific models, modify the DOWNLOAD_MODELS environment variable in the docker-compose.yml file. For example:
```
environment:
  - DOWNLOAD_MODELS=YuE-s2-1B-general,YuE-s1-7B-anneal-en-cot,YuE-s1-7B-anneal-en-icl,YuE-upsampler
```
Different ports: If you need to use different ports, adjust the port mappings under the ports section.

Direct Docker Run

To run without Docker Compose:

docker run --gpus all -d \
  -p 7860:7860 \
  -e DOWNLOAD_MODELS=all_bf16 \
  -v /path/to/models:/workspace/models \
  -v /path/to/outputs:/workspace/outputs \
  alissonpereiraanjos/yue-exllamav2-interface:latest

--gpus all: Enables NVIDIA GPU support.
-d: Runs the container in detached mode (background).
-p 7860:7860: Exposes port 7860 for accessing the Gradio UI at http://localhost:7860.
-p 8888:8888: Exposes port 8888 for additional services if applicable.
-e DOWNLOAD_MODELS=all_bf16: Downloads all bf16 available models upon initialization.

Environment Variables

DOWNLOAD_MODELS: Determines which models to download.
- Set to all_bf16 to download all available models (BF16).
- Alternatively, specify a comma-separated list of model keys to download specific models (e.g., DOWNLOAD_MODELS=YuE-s2-1B-general,YuE-s1-7B-anneal-en-cot).

Supported Models

Model Key	Model HF Repository	Container Path	Quantization
`xcodec_mini_infer`	`m-a-p/xcodec_mini_infer`	`/workspace/YuE-Interface/inference/xcodec_mini_infer`	N/A
`YuE-s1-7B-anneal-en-cot`	`m-a-p/YuE-s1-7B-anneal-en-cot`	`/workspace/models/YuE-s1-7B-anneal-en-cot`	BF16
`YuE-s1-7B-anneal-en-icl`	`m-a-p/YuE-s1-7B-anneal-en-icl`	`/workspace/models/YuE-s1-7B-anneal-en-icl`	BF16
`YuE-s1-7B-anneal-jp-kr-cot`	`m-a-p/YuE-s1-7B-anneal-jp-kr-cot`	`/workspace/models/YuE-s1-7B-anneal-jp-kr-cot`	BF16
`YuE-s1-7B-anneal-jp-kr-icl`	`m-a-p/YuE-s1-7B-anneal-jp-kr-icl`	`/workspace/models/YuE-s1-7B-anneal-jp-kr-icl`	BF16
`YuE-s1-7B-anneal-zh-cot`	`m-a-p/YuE-s1-7B-anneal-zh-cot`	`/workspace/models/YuE-s1-7B-anneal-zh-cot`	BF16
`YuE-s1-7B-anneal-zh-icl`	`m-a-p/YuE-s1-7B-anneal-zh-icl`	`/workspace/models/YuE-s1-7B-anneal-zh-icl`	BF16
`YuE-s2-1B-general`	`m-a-p/YuE-s2-1B-general`	`/workspace/models/YuE-s2-1B-general`	BF16
`YuE-upsampler`	`m-a-p/YuE-upsampler`	`/workspace/models/YuE-upsampler`	BF16
`YuE-s1-7B-anneal-en-cot-exl2-3.0bpw`	`Alissonerdx/YuE-s1-7B-anneal-en-cot-exl2-3.0bpw`	`/workspace/models/YuE-s1-7B-anneal-en-cot-exl2-3.0bpw`	EXL2 (3.0bpw)
`YuE-s1-7B-anneal-en-cot-exl2-4.0bpw`	`Alissonerdx/YuE-s1-7B-anneal-en-cot-exl2-4.0bpw`	`/workspace/models/YuE-s1-7B-anneal-en-cot-exl2-4.0bpw`	EXL2 (4.0bpw)
`YuE-s1-7B-anneal-en-cot-exl2-5.0bpw`	`Alissonerdx/YuE-s1-7B-anneal-en-cot-exl2-5.0bpw`	`/workspace/models/YuE-s1-7B-anneal-en-cot-exl2-5.0bpw`	EXL2 (5.0bpw)
`YuE-s1-7B-anneal-en-cot-exl2-6.0bpw`	`Alissonerdx/YuE-s1-7B-anneal-en-cot-exl2-6.0bpw`	`/workspace/models/YuE-s1-7B-anneal-en-cot-exl2-6.0bpw`	EXL2 (6.0bpw)
`YuE-s1-7B-anneal-en-cot-exl2-8.0bpw`	`Alissonerdx/YuE-s1-7B-anneal-en-cot-exl2-8.0bpw`	`/workspace/models/YuE-s1-7B-anneal-en-cot-exl2-8.0bpw`	EXL2 (8.0bpw)
`YuE-s2-1B-general-exl2-3.0bpw`	`Alissonerdx/YuE-s2-1B-general-exl2-3.0bpw`	`/workspace/models/YuE-s2-1B-general-exl2-3.0bpw`	EXL2 (3.0bpw)
`YuE-s2-1B-general-exl2-4.0bpw`	`Alissonerdx/YuE-s2-1B-general-exl2-4.0bpw`	`/workspace/models/YuE-s2-1B-general-exl2-4.0bpw`	EXL2 (4.0bpw)
`YuE-s2-1B-general-exl2-5.0bpw`	`Alissonerdx/YuE-s2-1B-general-exl2-5.0bpw`	`/workspace/models/YuE-s2-1B-general-exl2-5.0bpw`	EXL2 (5.0bpw)
`YuE-s2-1B-general-exl2-6.0bpw`	`Alissonerdx/YuE-s2-1B-general-exl2-6.0bpw`	`/workspace/models/YuE-s2-1B-general-exl2-6.0bpw`	EXL2 (6.0bpw)
`YuE-s2-1B-general-exl2-8.0bpw`	`Alissonerdx/YuE-s2-1B-general-exl2-8.0bpw`	`/workspace/models/YuE-s2-1B-general-exl2-8.0bpw`	EXL2 (8.0bpw)

Mapping Directories for Models and Output

You can mount host directories to store models and outputs outside the container:

docker run --gpus all -it \
  -v /path/to/models:/workspace/models \
  -v /path/to/outputs:/workspace/outputs \
  -p 7860:7860 \
  -p 8888:8888 \
  -e DOWNLOAD_MODELS=false \
  alissonpereiraanjos/yue-exllamav2-interface:latest

-v /path/to/models:/workspace/models: Mounts the host's /path/to/models directory to /workspace/models inside the container.
-v /path/to/outputs:/workspace/outputs: Mounts the host's /path/to/outputs directory to /workspace/outputs inside the container.
-e DOWNLOAD_MODELS=false: Skips automatic model downloads (useful if models are already present in the mounted directories).

RunPod Deployment

If you prefer to use RunPod, you can quickly deploy an instance based on this image by using the following template link:

Deploy on RunPod

This link directs you to the RunPod console, allowing you to set up a machine directly with the YuE Interface image. Configure your GPU, volume mounts, and environment variables as needed.

Tip: If you generate music frequently, consider creating a Network Volume in RunPod. This allows you to store models and data persistently, avoiding repeated downloads and saving time.

Recommended Settings:

GPU: RTX 4090 or A100
Network Volume: Minimum 100GB for model storage
Environment Variables: DOWNLOAD_MODELS=all_bf16

Update Docker Image (Important)

To update the Docker image with the latest changes, run:

docker pull alissonpereiraanjos/yue-exllamav2-interface:latest

Note: Always update the image before running the container to ensure you have the latest features and fixes. This is especially important when deploying on RunPod, as it pulls the latest image upon creating a new pod.

Model Suffixes Explained

The suffixes in the model keys indicate specific training or optimization techniques applied to the models:

Suffix	Meaning	Description
`COT`	Chain-of-Thought	Models trained with Chain-of-Thought to enhance reasoning and logical generation capabilities.
`ICL`	In-Context Learning	Models optimized for In-Context Learning, allowing dynamic adaptation based on the provided context.

Examples:

YuE-s1-7B-anneal-en-cot: A model trained with Chain-of-Thought techniques.
YuE-s1-7B-anneal-en-icl: A model optimized for In-Context Learning.

Acknowledgements

A special thanks to the YuE-exllamav2 repository for their incredible optimization work, which made this project possible.

Support

For technical support or questions:

Citation

If you use this project in your research, please consider citing:

@misc{yuan2025yue,
  title={YuE: Open Music Foundation Models for Full-Song Generation},
  author={Ruibin Yuan et al.},
  year={2025},
  howpublished={\url{https://github.com/multimodal-art-projection/YuE}}
}

Experience music generation with accelerated performance! 🎵🚀

Name		Name	Last commit message	Last commit date
Latest commit History 172 Commits
assets/logo		assets/logo
benchmark		benchmark
colab		colab
docker		docker
prompt_egs		prompt_egs
segments		segments
src/yue		src/yue
workspace		workspace
.dockerignore		.dockerignore
.gitattributes		.gitattributes
.gitignore		.gitignore
Dockerfile		Dockerfile
LICENSE		LICENSE
NOTICE		NOTICE
README.md		README.md
common.py		common.py
docker-compose.yml		docker-compose.yml
infer_stage1.py		infer_stage1.py
interface.py		interface.py
preview.png		preview.png
requirements.txt		requirements.txt
start-gui.bat		start-gui.bat
top_200_tags.json		top_200_tags.json

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YuE-extend-mp3 with Exllamav2 and UI

Updates

Notes

Interface

Installation for Windows (without wsl)

Requirements

Inference (mp3-extend)

OLD readme

Key Features

Performance Benchmarks

RTX 4090 24GB

RTX 3060 Mobile 6GB

How to Use

Prerequisites

Docker

NVIDIA GPU Support

Docker Compose

Explanation of the Configuration

Customization

Direct Docker Run

Environment Variables

Supported Models

Mapping Directories for Models and Output

RunPod Deployment

Update Docker Image (Important)

Model Suffixes Explained

Acknowledgements

Support

Citation

About

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Mozer/YuE-extend

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Repository files navigation

YuE-extend-mp3 with Exllamav2 and UI

Updates

Notes

Interface

Installation for Windows (without wsl)

Requirements

Inference (mp3-extend)

OLD readme

Key Features

Performance Benchmarks

RTX 4090 24GB

RTX 3060 Mobile 6GB

How to Use

Prerequisites

Docker

NVIDIA GPU Support

Docker Compose

Explanation of the Configuration

Customization

Direct Docker Run

Environment Variables

Supported Models

Mapping Directories for Models and Output

RunPod Deployment

Update Docker Image (Important)

Model Suffixes Explained

Acknowledgements

Support

Citation

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