WD14 Tagger — SmilingWolf WD14 models (ONNX)
Camie Tagger — Camais03/camie-tagger-v2 (ONNX)
PixAI Tagger — pixai-labs/pixai-tagger-v0.9 (PyTorch, default) or deepghs/pixai-tagger-v0.9-onnx (ONNX, no auth required)

Install in ComfyUI/custom_nodes/booru-helper-mini and run:

pip install -r requirements.txt

torch and torchvision are provided by ComfyUI itself; timm is only required for the PixAI PyTorch backend.

Features

Three independent nodes — pick the model you need
Real ONNX batching with user-defined batch_size
LRU session caching (switching between WD14 variants reuses loaded sessions)
Tuned ort.SessionOptions + CUDA provider options for fast inference
Vectorized threshold selection with numpy
PixAI: FP16 support on CUDA, optional IP (copyright/series) mapping
Output is a list of strings (one per image in the batch)

PixAI — HuggingFace token

The official PyTorch repo is gated. Set a token via env var before launching ComfyUI:

export HF_TOKEN="hf_..."

Or paste it into the hf_token field of the PixAI node. If you don't have access, switch the node's backend to onnx and it will use deepghs/pixai-tagger-v0.9-onnx instead (no auth needed).

Models directory

Models are stored under ./models/{wd14,camie,pixai}/ inside the node folder. If your ComfyUI has a wd14_tagger folder registered in folder_paths, that one is used as base instead.