BSS WD14 Batch Tagger

By BlacksnowskillView on GitHub →

Automatic image tagging using WD14 models with batch processing and GPU acceleration for ComfyUI

How much VRAM does BSS WD14 Batch Tagger require?

Direct Answer: The ComfyUI node BSS WD14 Batch Tagger requires a minimum base VRAM of 128MB and is optimized for GPUs with at least 4GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.

High (2-4GB)
Base VRAM:
128MB (0.1GB)
Recommended GPU:
4GB+ VRAM
Low VRAM Mode:
✓ Supported
Estimation Confidence:
MEDIUM

Interactive VRAM Compatibility Estimator

Estimated Total VRAM: 3.00 GBTarget: 8 GB
✅ Comfortable Fit

Your GPU has plenty of headroom. You can run this node safely with your active configurations!

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Deploy on Cloud GPUs

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What Python packages are required for BSS WD14 Batch Tagger?

Direct Answer: Running BSS WD14 Batch Tagger requires installing the following Python package dependencies: huggingface-hub>=0.16.0, numpy>=1.24.0, onnxruntime>=1.18.0,<2.0.0, pillow>=9.0.0. Ensure your ComfyUI environment has these packages active before launching.

requirements.txt
huggingface-hub>=0.16.0
numpy>=1.24.0
onnxruntime>=1.18.0,<2.0.0
pillow>=9.0.0

Interactive Setup & Dependency Resolver

Operating System:
Environment Type:
Run this terminal command in your ComfyUI root folder:
# Loading command...

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Frequently Asked Questions

How much VRAM does BSS WD14 Batch Tagger require?

BSS WD14 Batch Tagger requires a minimum of 128MB (0.1GB) of VRAM for base operation. For optimal performance, a GPU with at least 4GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.

Can I run BSS WD14 Batch Tagger on an RTX 3060, RTX 4070, or RTX 4090?

✅ RTX 3060 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 14.3GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 21.5GB headroom

What Python packages are required for BSS WD14 Batch Tagger?

To run BSS WD14 Batch Tagger, you need to install: huggingface-hub>=0.16.0, numpy>=1.24.0, onnxruntime>=1.18.0,<2.0.0, pillow>=9.0.0. You can install these using pip or add them to your requirements.txt file.

How can I reduce VRAM usage when running BSS WD14 Batch Tagger?

BSS WD14 Batch Tagger supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.

How do I install BSS WD14 Batch Tagger in ComfyUI?

To install BSS WD14 Batch Tagger: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/BlackSnowSkill/wd14_batch_tagger, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.