documentation.md

AI Image Generation System Documentation

Table of Contents

1. Environment Setup
2. Configuration Guide
3. Training Process
4. Image Generation
5. Troubleshooting
6. Best Practices

Environment Setup

Requirements

• Google Colab Pro+ (Recommended)
• NVIDIA GPU (T4 or better)
• Python 3.9+
• 15GB+ Disk Space

Installation Steps

# Install core dependencies
!pip install torch==2.0.1+cu118
!pip install diffusers==0.19.3 transformers==4.31.0 accelerate==0.21.0

# Install additional utilities
!pip install xformers wandb safetensors

Google Drive Mounting

from google.colab import drive
drive.mount('/content/drive')

Configuration Guide

config.yaml Structure

drive_mount_path: "/content/drive"
images_dir: "/content/drive/MyDrive/babanne-images"
lora_output_dir: "/content/drive/MyDrive/lora_output"
instance_prompt: "a photo of <myspecialstyle> lace fabric"
# ... other parameters

Required Modifications

1. Set images_dir to your Google Drive folder containing lace images
2. Customize instance_prompt with your unique token
3. Adjust training parameters based on GPU capacity:

   train_batch_size: 1  # Reduce if OOM errors occur
   resolution: 512      # 768 for higher quality (requires more VRAM)

Training Process

Starting Training

python main.py --mode train

Expected Output

Mounting Google Drive...
Starting LoRA training...
Loading base model: stabilityai/stable-diffusion-xl-base-1.0
Creating annotations for 250 images...
Step 100, Loss: 0.1245
Saved checkpoint at step 500
Training completed successfully!

Monitoring Training

1. Check loss values decreasing over time
2. Verify checkpoint saving
3. Monitor GPU memory usage (nvidia-smi)

Image Generation

Generating New Designs

python main.py --mode inference

Output Files

• refined_output.png in your lora_output_dir
• Multiple versions with timestamps if run repeatedly

Custom Prompts

Modify instance_prompt in config.yaml:

instance_prompt: "close-up of <myspecialstyle> lace pattern with gold threads"

Troubleshooting

Common Issues

1. CUDA Out of Memory

# Solutions:
- Reduce batch_size in config.yaml
- Lower resolution to 512
- Enable memory optimizations:
  ```python
  pipe.enable_xformers_memory_efficient_attention()
  pipe.enable_model_cpu_offload()

2. Missing Dependencies

# Fix missing packages
!pip install [missing-package]

3. Poor Generation Quality

• Increase training steps (2000-5000)
• Use higher quality source images
• Experiment with different learning rates (1e-5 to 1e-4)

Best Practices

Training Tips

• Use 200-300 high-quality JPEG images
• Maintain consistent image dimensions
• Use descriptive prompts with unique token
• Start with 1000 training steps, increase gradually

Generation Tips

• Try different refiner strengths (0.2-0.5)
• Experiment with guidance scales (5-15)
• Combine with negative prompts:

  negative_prompt: "blurry, low quality, duplicate"

Performance Optimization

# config.yaml optimizations
mixed_precision: "fp16"  # For modern GPUs
gradient_checkpointing: true
use_xformers: true

Support

• Diffusers Documentation
• LoRA Training Guide
• SDXL Technical Report