---
tags:
  - stable-diffusion-xl
  - text-to-image
  - gguf
  - quantization
  - unet
  - vae
  - clip
license: mit
base_model: stabilityai/stable-diffusion-xl-base-1.0
datasets:
  - stabilityai/stable-diffusion-xl-base-1.0
  - stabilityai/stable-diffusion-xl-refiner-1.0
model_creator: stabilityai
model_type: stable-diffusion-xl
task: text-to-image
timestamp: 2025-03-06

---

# SDXL GGUF Quantized Model

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.

## Quantization 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.

## 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**.

## 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)

## Acknowledgments

This model is based on **Stable Diffusion XL** by [Stability AI](https://stability.ai/) and has been quantized for improved accessibility across various hardware configurations.

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

---