data/lora/README.md

LoRA Configuration - Safetensors Only

Directory Structure

data/lora/
├── memo-scene-lora.safetensors    # Main LoRA weights
├── readme.md                      # This file
└── versions/                      # Versioned LoRA files
    ├── v1.0/
    └── v1.1/

LoRA File Requirements

Security Requirements

• ONLY .safetensors files - No .bin, .ckpt, or other formats allowed
• Model signatures required - All LoRA files must have proper signatures
• Version tracking - Each version must be clearly identified

Technical Requirements

• Format: PyTorch safetensors
• Precision: FP16 recommended for memory efficiency
• Compression: Quantized versions for faster loading
• Metadata: Include training information and compatibility notes

Loading LoRA Weights

Basic Loading

from models.image.sd_generator import get_generator

generator = get_generator(lora_path="data/lora")

Version-Specific Loading

generator = get_generator(lora_path="data/lora/versions/v1.1")

Multiple LoRA Support

# Load multiple LoRA files
lora_paths = [
    "data/lora/memo-scene-lora.safetensors",
    "data/lora/style-lora.safetensors"
]

for lora_path in lora_paths:
    generator.pipe.load_lora_weights(
        os.path.dirname(lora_path),
        weight_name=os.path.basename(lora_path)
    )

LoRA Training Configuration

Recommended Settings

• Base Model: stabilityai/stable-diffusion-xl-base-1.0
• LoRA Rank: 16-64 (higher rank = more capacity)
• Alpha: 32-128 (typically 2x the rank)
• Dropout: 0.1-0.2 for regularization
• Precision: FP16 for training, FP16 inference

Training Script Usage

python scripts/train_scene_lora.py \
    --base_model "stabilityai/stable-diffusion-xl-base-1.0" \
    --output_dir "data/lora/versions/v1.2" \
    --rank 32 \
    --alpha 64 \
    --epochs 5

Model Tier Configuration

Free Tier

• Base model only (no LoRA)
• Lower inference steps (15-20)
• Standard resolution (512x512)

Pro Tier

• Base + scene LoRA
• Higher inference steps (25-30)
• Higher resolution (768x768 or 1024x1024)
• LCM acceleration

Enterprise Tier

• Base + multiple LoRAs
• Highest quality settings
• Custom resolution
• Priority processing

Security Notes

1. Never load .bin files - Use only safetensors
2. Verify signatures - Check LoRA file integrity
3. Isolate environments - Separate model loading contexts
4. Audit logs - Track all LoRA loading operations
5. Version pinning - Lock specific LoRA versions for production

Performance Notes

1. Memory optimization - Use quantized LoRA when possible
2. Preloading - Load frequently used LoRA files at startup
3. Caching - Cache LoRA states for faster switching
4. Cold start - Minimize initial LoRA loading time
5. Dynamic loading - Load LoRA on-demand for different scenes