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Stable Diffusion 4-bit Quantized for Chain-of-Zoom

📋 Model Description

4-bit quantized Stable Diffusion components optimized for super-resolution

This model is part of the Chain-of-Zoom 4-bit Quantized Pipeline - a memory-optimized version of the original Chain-of-Zoom super-resolution framework.

🎯 Key Features

4-bit Quantization: Uses BitsAndBytes NF4 quantization for 75% memory reduction
Maintained Quality: Comparable performance to full precision models
Google Colab Compatible: Runs on T4 GPU (16GB VRAM)
Memory Efficient: Optimized for low-resource environments

📊 Quantization Details

Method: BitsAndBytes NF4 4-bit quantization
Compute dtype: bfloat16/float16
Double quantization: Enabled
Memory reduction: ~75% compared to original
Original memory: ~12GB → Quantized: ~3GB

🚀 Usage

# Install required packages
pip install transformers accelerate bitsandbytes torch

# Load quantized model
from transformers import BitsAndBytesConfig
import torch

# 4-bit quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16
)

# Model-specific loading code here
# (See complete notebook for detailed usage)

📈 Performance

Quality: Maintained performance vs full precision
Speed: 2-3x faster inference
Memory: 75% reduction in VRAM usage
Hardware: Compatible with T4, V100, A100 GPUs

🔧 Technical Specifications

Created: 2025-06-08 16:30:09
Quantization Library: BitsAndBytes
Framework: PyTorch + Transformers
Precision: 4-bit NF4
Model Size: 0.00017261505126953125 MB

📝 Citation

@misc{chain-of-zoom-4bit-diffusion,
  title={Chain-of-Zoom 4-bit Quantized Stable Diffusion 4-bit Quantized for Chain-of-Zoom},
  author={humbleakh},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/humbleakh/stable-diffusion-4bit-chain-of-zoom}
}

🔗 Related Models

⚠️ Limitations

Requires BitsAndBytes library for proper loading
May have slight quality differences compared to full precision
Optimized for inference, not fine-tuning

📄 License

Apache 2.0 - See original model licenses for specific components.

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