usage_example.py

#!/usr/bin/env python3
"""
Chain-of-Zoom 8-bit Complete Pipeline Usage Example
"""

from transformers import AutoModel, BitsAndBytesConfig
from PIL import Image
import torch

def load_chain_of_zoom_pipeline():
    """Load the complete Chain-of-Zoom pipeline"""
    
    # Configure quantization
    vlm_config = BitsAndBytesConfig(load_in_8bit=True)
    diffusion_config = BitsAndBytesConfig(load_in_8bit=True)
    ram_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")
    lora_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")
    
    print("🔄 Loading Chain-of-Zoom components...")
    
    # Load models (replace with actual repo names)
    vlm = AutoModel.from_pretrained("./vlm", quantization_config=vlm_config)
    diffusion = AutoModel.from_pretrained("./diffusion", quantization_config=diffusion_config)
    ram = AutoModel.from_pretrained("./ram", quantization_config=ram_config)
    lora = AutoModel.from_pretrained("./lora", quantization_config=lora_config)
    
    print("✅ All components loaded successfully!")
    
    return {
        'vlm': vlm,
        'diffusion': diffusion,
        'ram': ram,
        'lora': lora
    }

def super_resolve_image(image_path, target_scale=8):
    """Super-resolve an image using Chain-of-Zoom"""
    
    # Load pipeline
    pipeline = load_chain_of_zoom_pipeline()
    
    # Load image
    image = Image.open(image_path)
    print(f"📸 Input image: {image.size}")
    
    # Run Chain-of-Zoom (simplified example)
    current_scale = 1
    current_image = image
    
    while current_scale < target_scale:
        next_scale = min(current_scale * 2, target_scale)
        print(f"🔍 Scaling {current_scale}x → {next_scale}x")
        
        # VLM analysis (mock)
        # Enhanced prompt generation would go here
        
        # Diffusion super-resolution (mock)
        # Actual super-resolution would go here
        
        current_scale = next_scale
    
    print(f"✅ Super-resolution complete: {target_scale}x")
    return current_image

if __name__ == "__main__":
    # Example usage
    result = super_resolve_image("input.jpg", target_scale=8)
    result.save("output_8x.jpg")