license: apache-2.0
base_model:
- timm/tf_efficientnetv2_s.in21k_ft_in1k
- Ultralytics/YOLO11
tags:
- comfyui
- object-detection
- face-detection
- face-segmentation
- pytorch
- image-segmentation
Custom-trained models for face detection and segmentation across realistic, anime, and NSFW content.
Made for the Forbidden Vision ComfyUI custom nodes
🎯 Why These Models Exist
Traditional face models fail where it matters most for AI art workflows:
| Problem | Why It Matters |
|---|---|
| 🎨 Domain-locked | Existing models excel at either anime or realistic—never both |
| 🔞 NSFW blindness | Most models trained only on SFW data break on adult content |
| 👁️🗨️ Detail blindness | Most models miss anime eyebrows, real eyelashes etc. |
| 🎲 Generation artifacts | Standard datasets don't include diffusion model quirks and failures |
These models solve all 4.
The segmentation model predicts face masks, stylistic eyebrows, eyelashes etc.
📊 Training Foundation
The Dataset Difference
Built from 14,000+ manually annotated images across the domains that actually matter for AI generation:
|
🎨 Multi-Domain Coverage
|
💎 Edge Case Priority
|
What This Means For You
Traditional models: Trained on clean celebrity faces
↓
Fail on real workflows
These models: Trained on what you actually generate
↓
Work when you need them
One model family. Every domain. Zero compromises.
Model Details
Face Detection (YOLOv11-Small)
Purpose: Primary face detection with high recall
Training Approach:
- After every training run, I ran the model on a new mixed dataset, hardmining failures and improving the dataset until an acceptable performance was reached
- Trained at 640px resolution (inference should use same resolution)
Why YOLOv11-Small instead of nano?
More reliable detection across mixed realistic/anime domains with acceptable speed tradeoff.
Segmentation (EfficientNet-v2)
Purpose: Precise face mask generation
Training Approach:
- Dataset prepared using the Forbidden Vision YOLO model at 512px resolution
- Iterative hardmine training in multiple phases:
- Train on the initial 700 samples
- Evaluate on remaining images to find failure cases
- Correct failed masks and add them to the dataset
- Retrain with the expanded dataset
- Repeat until failure cases drop to near-zero
(final dataset: 4k+ images)
Features:
- Detects and includes facial features other models ignore, like protruding anime eybrows, realistic eyelashes sticking out of the face etc.
- Glasses and similar are treated as part of the face, even if sticking outside the face shape
- NSFW friendly across both anime, realistic and 3d domains
Usage
These models are automatically downloaded and used by the Fixer node in ComfyUI Forbidden Vision.
License
Apache 2.0
Contact
- GitHub: ComfyUI-Forbidden-Vision
- Issues: GitHub Issues
- Support: Ko-fi