luxdelux7
/

ForbiddenVision_Models

@@ -24,126 +24,6 @@ Made for the **Forbidden Vision** ComfyUI custom nodes
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-## 🎯 Why These Models Exist
-Standard face detection models are optimized for narrow use cases and struggle in generative AI workflows. These models address four specific failure modes:
-| **Problem** | **Why It Matters** |
-|-------------|-------------------|
-| 🎨 **Domain-locked** | Existing models excel at *either* anime *or* realistic — never both |
-| 🖼️ **Distribution mismatch** | Models trained on clean photography break on AI-generated imagery |
-| 👁️‍🗨️ **Detail blindness** | Most models miss stylized features like anime eyebrows, realistic eyelashes, etc. |
-| 🎲 **Generation artifacts** | Standard datasets don't include diffusion model quirks and failure modes |
-**These models solve all 4.**
-<div align="center">
-<img src="./images/masks.webp" alt="Mask Example" style="border-radius: 6px; box-shadow: 0 0 12px rgba(0,0,0,0.1);">
-<p><em>The segmentation model predicts face masks including stylistic features like eyebrows and eyelashes.</em></p>
-</div>
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-## 📊 Training Foundation
-### The Dataset Difference
-Built from **14,000+ manually annotated images** spanning the full range of domains encountered in real generative AI workflows:
-<table>
-<tr>
-<td width="50%">
-**🎨 Multi-Domain Coverage**
-- SDXL, SD1.5, Pony, Illustrious outputs
-- Curated Danbooru (anime styles)
-- Real photography
-- Unfiltered image distributions across all content ratings
-</td>
-<td width="50%">
-**💎 Edge Case Priority**
-- ✓ Extreme angles & occlusions
-- ✓ Failed/broken generations
-- ✓ Low-quality artifacts
-- ✓ Unusual expressions & poses
-- ✓ Edge cases other models ignore
-</td>
-</tr>
-</table>
-### 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.**
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-## Model Details
-### Face Detection (YOLOv11-Small)
-**Purpose:** Primary face detection with high recall across mixed domains
-**Training Approach:**
-- Iterative hard-mining pipeline: after each training run, the model was evaluated on a new mixed dataset; failures were collected, corrected, and folded back into training until acceptable performance was reached
-- Trained at 640px resolution — inference should use the same resolution
-**Why YOLOv11-Small instead of nano?**
-More reliable detection across mixed realistic/anime domains with an acceptable speed tradeoff.
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-### Segmentation (EfficientNetV2-S)
-**Purpose:** Precise face mask generation
-**Training Approach:**
-- Initial dataset prepared using the Forbidden Vision YOLO model at 512px resolution
-- Multi-phase iterative hard-mining:
-  1. Train on initial 700 samples
-  2. Evaluate on held-out images to surface failure cases
-  3. Correct failed masks and expand the dataset
-  4. Retrain on expanded dataset
-  5. Repeat until failure rate approaches zero
-  - Final dataset: 4,000+ images
-**Features:**
-- Captures stylized facial features often missed by standard models: protruding anime eyebrows, realistic eyelashes extending beyond the face boundary, etc.
-- Treats accessories like glasses as part of the face region, even when they extend outside the face shape
-- Robust across anime, realistic, and 3D rendering styles — including content ratings that cause other models to fail
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-## Usage
-These models are automatically downloaded and used by the **Fixer** node in ComfyUI Forbidden Vision. No manual setup required.
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-## Intended Use
-These models are designed for use in generative AI post-processing pipelines — specifically face detection and masking within ComfyUI workflows. They are not intended for surveillance, biometric identification, or any application involving real individuals without consent.
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-## License
-Apache 2.0
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 ## Contact

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 ## Contact