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0.5.3 fix MPS device compatibility; update readme

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SWivid
2025-02-18 18:42:03 +08:00
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README.md
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@@ -22,40 +22,75 @@
## Installation
### Create a separate environment if needed
```bash
# Create a python 3.10 conda env (you could also use virtualenv)
conda create -n f5-tts python=3.10
conda activate f5-tts
# NVIDIA GPU: install pytorch with your CUDA version, e.g.
pip install torch==2.3.0+cu118 torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
# AMD GPU: install pytorch with your ROCm version, e.g. (Linux only)
pip install torch==2.5.1+rocm6.2 torchaudio==2.5.1+rocm6.2 --extra-index-url https://download.pytorch.org/whl/rocm6.2
# Intel GPU: install pytorch with your XPU version, e.g.
# Intel® Deep Learning Essentials or Intel® oneAPI Base Toolkit must be installed
pip install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu
```
Then you can choose from a few options below:
### Install PyTorch with matched device
### 1. As a pip package (if just for inference)
<details>
<summary>NVIDIA GPU</summary>
```bash
pip install git+https://github.com/SWivid/F5-TTS.git
```
> ```bash
> # Install pytorch with your CUDA version, e.g.
> pip install torch==2.3.0+cu118 torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
> ```
### 2. Local editable (if also do training, finetuning)
</details>
```bash
git clone https://github.com/SWivid/F5-TTS.git
cd F5-TTS
# git submodule update --init --recursive # (optional, if need bigvgan)
pip install -e .
```
<details>
<summary>AMD GPU</summary>
### 3. Docker usage
> ```bash
> # Install pytorch with your ROCm version (Linux only), e.g.
> pip install torch==2.5.1+rocm6.2 torchaudio==2.5.1+rocm6.2 --extra-index-url https://download.pytorch.org/whl/rocm6.2
> ```
</details>
<details>
<summary>Intel GPU</summary>
> ```bash
> # Install pytorch with your XPU version, e.g.
> # Intel® Deep Learning Essentials or Intel® oneAPI Base Toolkit must be installed
> pip install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu
> ```
</details>
<details>
<summary>Apple Silicon</summary>
> ```bash
> # Install the stable pytorch, e.g.
> pip install torch torchaudio
> ```
</details>
### Then you can choose one from below:
> ### 1. As a pip package (if just for inference)
>
> ```bash
> pip install git+https://github.com/SWivid/F5-TTS.git
> ```
>
> ### 2. Local editable (if also do training, finetuning)
>
> ```bash
> git clone https://github.com/SWivid/F5-TTS.git
> cd F5-TTS
> # git submodule update --init --recursive # (optional, if need > bigvgan)
> pip install -e .
> ```
### Docker usage also available
```bash
# Build from Dockerfile
docker build -t f5tts:v1 .
@@ -90,7 +125,7 @@ f5-tts_infer-gradio --share
<details>
<summary>NVIDIA device docker compose file example</summary>
``` yaml
```yaml
services:
f5-tts:
image: ghcr.io/swivid/f5-tts:main