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#!/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")
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