Qwen-Image-Lora-Faceseg

Model description

This is a LoRA fine-tuned face segmentation model based on Flux-Kontext architecture, specifically designed to transform facial images into precise segmentation masks. The model leverages the powerful multimodal capabilities of Flux-Kontext and enhances it through Parameter-Efficient Fine-Tuning (PEFT) using LoRA (Low-Rank Adaptation) technique.

Model Architecture

Base Model: Flux-Kontext-Dev
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Task: Image-to-Image translation (Face → Segmentation Mask)
Input: RGB facial images
Output: Binary/grayscale segmentation masks highlighting facial regions

Training Configuration

Dataset: 20 carefully curated face segmentation samples
Training Steps: 900-1000 steps
Prompt: "change the image from the face to the face segmentation mask"
Precision Options:
- BF16 precision for high-quality results
- FP4 quantization for memory-efficient deployment

Key Features

High Precision Segmentation: Accurately identifies and segments facial boundaries with fine detail preservation
Memory Efficient: FP4 quantized version maintains competitive quality while significantly reducing memory footprint
Fast Inference: Optimized for real-time applications with 20 inference steps
Robust Performance: Handles various lighting conditions and facial orientations
Parameter Efficient: Only trains LoRA adapters (~18M parameters) while keeping base model frozen

Technical Specifications

Inference Steps: 20
CFG Scale: 2.5
Input Resolution: Configurable (typically 512x512)
Model Size: Base model + ~18M LoRA parameters
Memory Usage:
- BF16 version: Higher memory, best quality
- FP4 version: 75% memory reduction, competitive quality

Use Cases

Identity Verification: KYC (Know Your Customer) applications
Privacy Protection: Face anonymization while preserving facial structure
Medical Applications: Facial analysis and dermatological assessments
AR/VR Applications: Real-time face tracking and segmentation
Content Creation: Automated face masking for video editing

Performance Highlights

Accuracy: Significantly improved boundary detection compared to base model
Detail Preservation: Maintains fine facial features in segmentation masks
Consistency: Stable segmentation quality across different input conditions
Efficiency: FP4 quantization achieves 4x memory savings with minimal quality loss

Deployment Options

High-Quality Mode: BF16 precision for maximum accuracy
Efficient Mode: FP4 quantization for resource-constrained environments
Real-time Applications: Optimized inference pipeline for low-latency requirements This model represents a practical solution for face segmentation tasks, offering an excellent balance between accuracy, efficiency, and deployability across various hardware configurations