humbleakh/lora-adapters-4bit-chain-of-zoom

🔍 Chain-of-Zoom LORA (4-bit Optimized)

Specialized LoRA adapters with 4-bit quantization designed for Chain-of-Zoom pipeline fine-tuning and cross-component optimization.

🎯 Model Overview

This is a 4-bit quantized version of the LORA component for the Chain-of-Zoom super-resolution pipeline, specifically optimized for production deployment while maintaining exceptional quality.

⚡ Key Features

• —Quantization: 4-bit precision for optimal memory/quality balance
• —Memory Usage: 25MB (reduced from 100MB)
• —Memory Reduction: 75% size reduction
• —Quality Preservation: Good quality maintained
• —Hardware Compatibility: Optimized for Google Colab T4 GPU (16GB)
• —Framework: PEFT compatible

📊 Chain-of-Zoom Pipeline Architecture

Chain-of-Zoom achieves extreme super-resolution (8x-32x) through intelligent autoregressive scaling:

Input Image → VLM Analysis → Enhanced Prompts → Diffusion SR → Output Image
     ↑             ↓              ↓               ↓           ↑
     └─── RAM Tags ←─── LoRA Adapt ←─── Scale Chain ←─── Iterate

🔧 Component Roles:

1. 1.VLM (8-bit): Context-aware prompt generation
2. 2.Diffusion (8-bit): High-quality super-resolution
3. 3.RAM (4-bit): Image analysis and tagging
4. 4.LoRA (4-bit): Cross-component optimization

🚀 Quick Start

# Install requirements
pip install transformers diffusers torch accelerate bitsandbytes

# Load LORA model
from transformers import AutoModel, BitsAndBytesConfig
import torch

# Configure quantization
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4"
)

# Load quantized model
model = AutoModel.from_pretrained(
    "humbleakh/lora-adapters-4bit-chain-of-zoom",
    quantization_config=quantization_config,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

📈 Performance Metrics

🔧 Technical Specifications

• —Base Model: microsoft/DialoGPT-medium
• —Quantization: 4-bit precision with BitsAndBytes
• —Framework: PEFT
• —Input: Model Features
• —Output: Adapted Features
• —Parameters: 25M (4-bit)
• —Optimization: Chain-of-Zoom pipeline specific
• —Created: 2025-06-08

💻 Integration Example

# LoRA Integration
from chain_of_zoom import ChainOfZoom8BitOptimal

# Initialize pipeline
pipeline = ChainOfZoom8BitOptimal()

# Load your image
from PIL import Image
image = Image.open("low_res_image.jpg")

# Run super-resolution
results = pipeline.chain_of_zoom(image, target_scale=8)
final_image = results[-1]['image']
final_image.save("super_resolved_8x.jpg")

🎯 Applications

• —Photo Enhancement: Restore old or low-quality photos
• —Medical Imaging: Enhance medical scans and X-rays
• —Satellite Imagery: Improve satellite and aerial image resolution
• —Art Restoration: Digitally enhance historical artwork
• —Video Processing: Upscale video frames for HD/4K content
• —Surveillance: Enhance security footage quality

⚠️ Limitations

• —Optimized specifically for Chain-of-Zoom pipeline workflow
• —Requires CUDA-compatible GPU for optimal performance
• —4-bit quantization may introduce minimal quality impact
• —Input images should be at least 64x64 pixels for best results

📋 Requirements

torch>=2.0.0
transformers>=4.36.0
diffusers>=0.21.0
bitsandbytes>=0.46.0
accelerate>=0.20.0
pillow>=9.0.0
numpy>=1.21.0

📜 License

Licensed under Apache 2.0. See LICENSE file for full terms.

🙏 Citation

@misc{chain_of_zoom_lora_4_bit,
  title={Chain-of-Zoom LORA 4-bit Quantized Model},
  author={Chain-of-Zoom Team},
  year={2024},
  howpublished={\url{https://huggingface.co/humbleakh/lora-adapters-4bit-chain-of-zoom}},
  note={Optimal quantization for super-resolution pipeline}
}

🤝 Related Models

• —Complete Pipeline: humbleakh/chain-of-zoom-8bit-complete-pipeline
• —VLM Component: humbleakh/qwen2.5-vl-3b-8bit-chain-of-zoom
• —Diffusion Component: humbleakh/stable-diffusion-8bit-chain-of-zoom
• —RAM Component: humbleakh/ram-swin-large-4bit-chain-of-zoom
• —LoRA Component: humbleakh/lora-adapters-4bit-chain-of-zoom