BSS WD14 Batch Tagger
Automatic image tagging using WD14 models with batch processing for ComfyUI.
Features
- 4 WD14 v3 Models: ViT, SwinV2, EVA02, ConvNeXT
- Auto Download: Models download automatically from Hugging Face
- GPU Support: CUDA acceleration for faster processing
- Batch Processing: Process multiple images from folders
- Format Support: JPG, JPEG, PNG, WEBP
- Custom Tags: Add/remove tags as needed
Installation
Via ComfyUI Manager
- Open ComfyUI Manager
- Go to Registry tab
- Search for "BSS WD14 Batch Tagger"
- Click Install
- Restart ComfyUI
Manual Installation
cd ComfyUI/custom_nodes/
git clone https://github.com/BlackSnowSkill/wd14_batch_tagger.git
cd wd14_batch_tagger
pip install -r requirements.txt
Usage
Nodes
BSS Load Images from Folder 📂
- Loads images from a folder for batch processing
BSS WD14 Batch Tagger 🌿
- Tags images using WD14 models
- Saves tags to .txt files
Basic Workflow
- Use BSS Load Images from Folder to load your images
- Connect to BSS WD14 Batch Tagger for each image
- Set output folder for tag files
- Run the workflow
Settings
- Model: Choose WD14 model (auto-downloads if needed)
- Threshold: Tag confidence (0.35 default)
- GPU: Enable for faster processing
- Prepend/Exclude: Add custom tags or remove unwanted ones
Models
- WD ViT Tagger v3: Fast, good quality (default)
- WD SwinV2 Tagger v3: Balanced speed/quality
- WD EVA02 Large Tagger v3: Best accuracy
- WD ConvNeXT Tagger v3: Modern architecture
Models download automatically on first use.
Requirements
- Python 3.8+
- ComfyUI
- CUDA GPU (optional)
Support
License
MIT License - see LICENSE file for details.
Author: Blacksnowskill