SDXL Turbo (GGUF) by Stability AI is a 3.5 billion parameter text-to-image generation model designed for efficient local deployment. This model excels in generating high-quality images from textual descriptions, making it a powerful tool for creatives, artists, and developers who need to produce detailed and visually appealing content without the need for cloud services. The unet-diffusion architecture ensures that the model can handle complex scenes and styles, providing a balance between speed and quality that is often hard to achieve in smaller models.
Compared to other models in its size class, SDXL Turbo stands out for its efficiency and performance. It punches well above its weight, offering results that are comparable to larger, more resource-intensive models while requiring significantly less VRAM. This makes it an excellent choice for users with mid-range GPUs, as it operates efficiently within a 5.0 GB VRAM range. The availability of Q5_0 quantization further enhances its performance on lower-end hardware, ensuring that a wide range of users can benefit from its capabilities. Ideal for hobbyists, small-scale projects, and professionals looking for a reliable local solution, SDXL Turbo is a versatile and accessible option for text-to-image generation.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q5_0 | 5 | 3.5 GB | 5 GB | 7 GB | 85% |
How to run SDXL Turbo (GGUF)
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Official Hugging Face pipeline. Best quality & sampler control.
🤗 Diffusers home →- 1
Install
pip install diffusers transformers accelerate torch - 2
Run
from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo").to("cuda") img = pipe("a futuristic city").images[0]Pipeline class auto-detects (StableDiffusion, FluxPipeline, etc.).
Community benchmarks
Real seconds-per-image reports from people running SDXL Turbo (GGUF) on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Try It — Diffusion Generation Demo
Click "Generate" to watch how Flux.1 creates an image from noise. Real outputs from RunThisModel.com.
"A cozy wooden cabin in snowy mountains at golden hour sunset"
"A friendly humanoid robot reading a book in a library"
"Gourmet sushi platter, professional food photography"
"Woman scientist in a modern lab, natural lighting"
"Snow leopard on mountain peak at dawn, golden rim light"
"Cyberpunk city at night, neon signs, rain reflections"
Animation simulates the diffusion denoising process at recorded generation speed. Actual generation requires GPU hardware or cloud service.
how much VRAM do I need to run SDXL Turbo (GGUF)?
SDXL Turbo (GGUF) requires 5 GB VRAM minimum with Q5_0 quantization. For full precision you need 5 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run SDXL Turbo (GGUF)?
To run SDXL Turbo (GGUF), you need a GPU with at least 5.0 GB of VRAM. The exact VRAM requirement can vary slightly depending on the quantization level used.
Is SDXL Turbo (GGUF) good for coding?
SDXL Turbo (GGUF) is primarily designed for image generation, not coding. It may not be suitable for text-based programming tasks.
SDXL Turbo (GGUF) vs Llama 3.1 8B?
SDXL Turbo (GGUF) has 3.5 billion parameters and is optimized for fast image generation, while Llama 3.1 8B is a larger language model with 8 billion parameters, better suited for text generation tasks.
Can I run SDXL Turbo (GGUF) on a Mac?
Yes, you can run SDXL Turbo (GGUF) on a Mac as long as your Mac has a compatible GPU with at least 5.0 GB of VRAM.
How much VRAM does SDXL Turbo (GGUF) need?
SDXL Turbo (GGUF) requires at least 5.0 GB of VRAM, with the exact amount depending on the quantization level used.
Is SDXL Turbo (GGUF) censored?
The content generated by SDXL Turbo (GGUF) is not inherently censored, but it adheres to the community guidelines set by Stability AI.
Is SDXL Turbo (GGUF) commercial-use allowed?
Yes, SDXL Turbo (GGUF) is licensed under the stability-community license, which allows for commercial use, provided you adhere to the terms of the license.
SDXL Turbo (GGUF) context length?
The context length for SDXL Turbo (GGUF) is unknown, as it is primarily an image generation model and does not rely on text context in the same way as language models.
Does SDXL Turbo (GGUF) support function calling?
No, SDXL Turbo (GGUF) does not support function calling as it is designed for image generation and not for executing code or functions.
SDXL Turbo (GGUF) quantization options?
SDXL Turbo (GGUF) supports various quantization levels, including INT8 and FP16, which can help reduce VRAM usage and improve performance.
Can SDXL Turbo (GGUF) run on CPU?
While SDXL Turbo (GGUF) can technically run on a CPU, it is highly recommended to use a GPU for better performance and faster image generation.
SDXL Turbo (GGUF) fine-tuning?
SDXL Turbo (GGUF) can be fine-tuned for specific image generation tasks, but this process requires a dataset and computational resources.
SDXL Turbo (GGUF) system requirements?
To run SDXL Turbo (GGUF), you need a system with at least 5.0 GB of VRAM, a compatible GPU, and sufficient CPU and RAM to handle the workload.
SDXL Turbo (GGUF) performance benchmark?
SDXL Turbo (GGUF) can generate images in near-instant time, typically within a few seconds, depending on the GPU and quantization level used.
SDXL Turbo (GGUF) for RAG?
SDXL Turbo (GGUF) is not designed for Retrieval-Augmented Generation (RAG) as it focuses on image generation rather than text retrieval and augmentation.
SDXL Turbo (GGUF) for agents?
SDXL Turbo (GGUF) can be integrated into agent systems for generating visual content, but it is not designed for conversational or decision-making tasks.
SDXL Turbo (GGUF) for coding vs general?
SDXL Turbo (GGUF) is specialized for image generation and is not suitable for coding or general text-based tasks.
SDXL Turbo (GGUF) vs ChatGPT?
SDXL Turbo (GGUF) is an image generation model, while ChatGPT is a language model designed for text-based conversations and tasks.
SDXL Turbo (GGUF) download size?
The download size of SDXL Turbo (GGUF) is approximately 3.5 GB, depending on the quantization level and format.
Best quant for SDXL Turbo (GGUF)?
The best quantization level for SDXL Turbo (GGUF) depends on your specific needs. INT8 provides a good balance between performance and quality, while FP16 offers higher quality at the cost of more VRAM usage.