SDXL-GGUF Open-source AI Painting Model - Different Quantization Adapts to Diverse Hardware to Generate Exquisite Images

SDXL GGUF

Developed by HyperX-Sentience

GGUF-format quantized version of Stable Diffusion XL, offering different quantization levels to accommodate various hardware configurations.

Text-to-Image Open Source License:MIT #GGUF Quantization #Low VRAM Optimization #Modular Components

Downloads 2,189

Release Time : 3/6/2025

Model Overview

This model is a quantized version of Stable Diffusion XL, supporting text-to-image tasks and including UNet, VAE, and CLIP components, suitable for diverse hardware environments.

Model Features

Multiple Quantization Levels

Offers Q4_K_S, Q5_K_S, and Q8 quantization levels to suit different hardware configurations.

Component Separation

UNet, VAE, and CLIP components are provided separately for easier optimization and compatibility adjustments.

Hardware Adaptability

Supports various configurations from low VRAM environments to high-performance hardware.

Model Capabilities

Text-to-Image

Image Generation

Multi-level Quantization Inference

Use Cases

Creative Design

Art Creation

Generate high-quality artistic images

Produces artistic images based on text descriptions

Advertisement Design

Quickly generate advertising materials

Creates appealing advertisement images based on product descriptions

Education

Teaching Assistance

Generate educational illustrations

Produces intuitive diagrams based on teaching content

🚀 SDXL GGUF Quantized Model

This repository offers a quantized version of Stable Diffusion XL in the GGUF format. It enables flexible deployment according to hardware capabilities and provides separate components for better optimization.

🚀 Quick Start

This repository contains a quantized version of Stable Diffusion XL in the GGUF format. The model has been converted to different quantization levels, including Q4_K_S, Q5_K_S, and Q8, allowing for flexible deployment based on hardware capabilities. The UNet, VAE, and CLIP components are provided separately for better optimization and compatibility.

✨ Features

The model is quantized into different levels (Q4_K_S, Q5_K_S, Q8), enabling flexible deployment based on hardware capabilities.
UNet, VAE, and CLIP components are provided separately for better optimization and compatibility.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

This model can be used with any GGUF-compatible inference engine, such as ComfyUI, Kohya's SDXL GGUF loader, or custom scripts supporting GGUF-based SDXL inference.

📚 Documentation

Quantization Details

Property	Details
Component	Available Quantization
UNet	Q4_K_S, Q5_K_S, Q8
VAE	FP16
CLIP	FP16

Files & Structure

sdxl-unet-q4_ks.gguf
sdxl-unet-q5_ks.gguf
sdxl-unet-q8.gguf
sdxl-vae-fp16.safetensors
sdxl-clip-fp16.safetensors

Each quantization level offers a trade-off between speed and quality. Q4_K_S provides the highest speed but lower quality, while Q8 retains more details with higher VRAM usage.

Hardware Requirements

Q4_K_S: Suitable for low-VRAM environments (2GB+)
Q5_K_S: Balanced performance and quality (3GB+ VRAM recommended)
Q8: Best quality, requires higher VRAM (4GB+ recommended)

🔧 Technical Details

This model is based on Stable Diffusion XL by Stability AI and has been quantized for improved accessibility across various hardware configurations.

📄 License

The model is licensed under the MIT license.

For support and discussions, feel free to open an issue or reach out on Hugging Face forums!

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