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https://github.com/p1atdev/wd-tagger-rs
https://github.com/p1atdev/wd-tagger-rs
Last synced: 6 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/p1atdev/wd-tagger-rs
- Owner: p1atdev
- License: apache-2.0
- Created: 2024-08-15T12:19:34.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-30T21:35:48.000Z (4 months ago)
- Last Synced: 2024-11-22T00:47:50.408Z (about 2 months ago)
- Language: Rust
- Size: 254 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE-APACHE
Awesome Lists containing this project
README
# wd-tagger-rs
An inference tool of [WaifuDiffusion Tagger](https://huggingface.co/spaces/SmilingWolf/wd-tagger).
> [!IMPORTANT]
> WIP. Not ready for use.
## Usage (Experimental)
Get Rust toolchain:
See https://www.rust-lang.org/tools/install
### With CPU
(Only tested on Ubuntu 24.04)
To build:
```bash
cargo install --git https://github.com/p1atdev/wd-tagger-rs
```
To run:
```bash
tagger ./assets/sample1_3x1024x1024.webp
```
Output:
```
[src/main.rs:183:13] result = TaggingResult {
rating: {
"general": 0.91256857,
},
character: {},
general: {
"1girl": 0.996445,
"solo": 0.977317,
"double_bun": 0.94901526,
"hair_bun": 0.94456,
"twintails": 0.9389738,
"pink_hair": 0.93058735,
"fang": 0.8859673,
"smile": 0.88062656,
"pink_eyes": 0.8463925,
"looking_at_viewer": 0.83266306,
...
```
### With CUDA
Very experimental.
#### Prerequisites
##### cuDNN
cuDDN 9.x **MUST** be installed. You can get it from here:
https://developer.nvidia.com/cudnn-downloads
##### onnxruntime
Downlaod prebuilt onnxruntime from ONNX Runtime's releases. (e.g. `onnxruntime-linux-x64-gpu-1.19.0.tgz`):
https://github.com/microsoft/onnxruntime/releases/tag/v1.19.0
Then extract it and place files to `~/.local/share`, and set `LD_LIBRARY_PATH`.
For example:
```bash
wget https://github.com/microsoft/onnxruntime/releases/download/v1.19.0/onnxruntime-linux-x64-gpu-1.19.0.tgz
tar -xvf onnxruntime-linux-x64-gpu-1.19.0.tgz
mkdir -p ~/.local/share/wdtagger/onnxruntime
mv onnxruntime-linux-x64-gpu-1.19.0 ~/.local/share/wdtagger/onnxruntime/1.19.0
rm onnxruntime-linux-x64-gpu-1.19.0.tgz
```
Add the following to your `.bashrc` or `.zshrc`:
```bash
# wdtagger
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.local/share/wdtagger/onnxruntime/1.19.0/lib
```
> [!NOTE]
> Please check that you are specifying the `lib` directory, not the root directory of the extracted onnxruntime.
To apply:
```bash
source ~/.bashrc
```
#### Build
To build:
```bash
cargo install --path . --features cuda
```
To run:
```bash
tagger ./assets/sample1_3x1024x1024.webp \
--devices 0 \
--v3 vit-large # vit, swin-v2, convnext, vit-large, eva02-large
```
#### Docker
This is just PoC.
Using docker:
```yml
services:
cuda:
build:
context: .
dockerfile: ./docker/Dockerfile.cuda
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
volumes:
- type: bind
source: ./assets
target: /workspace/assets
- type: bind # to use huggingface cache
source: ~/.cache/huggingface
target: /root/.cache/huggingface
command: ["./tagger"]
```
To run:
```bash
docker compose run cuda ./tagger ./assets/sample1_3x1024x1024.webp
```
To down:
```bash
docker compose down --remove-orphans
```
### With TensorRT
Very experimental.
#### Prerequisites
##### TensorRT
You need at least `libnvinfer`. You can get it from here:
https://developer.nvidia.com/tensorrt/download/10x
#### Build
```bash
cargo install --path . --features tensorrt
```
```bash
tagger ./assets/sample1_3x1024x1024.webp \
--devices 0 \
--v3 vit-large
```
> [!NOTE]
> Currently TensorRT mode is not so fast as CUDA mode.