metadata

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: 2025-03-06T00:00:00.000Z

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 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!