Stability AI
Stable Diffusion 2.1 (GGUF)
SD 2.1 in GGUF format. Better quality than 1.5.
About This Model
Stable Diffusion 2.1 (GGUF) is a lightweight version of the popular text-to-image generation model developed by Stability AI. With only 0.86 billion parameters, this model is significantly smaller than its full-sized counterparts but still delivers impressive results for generating high-quality images from textual descriptions. The architecture, based on unet-diffusion, allows it to produce detailed and contextually relevant images, making it suitable for a wide range of creative applications such as digital art, design mockups, and content creation.
In its size class, Stable Diffusion 2.1 (GGUF) punches well above its weight. Despite having fewer parameters, it maintains a balance between computational efficiency and output quality. This makes it an excellent choice for users who may not have access to high-end GPUs or those looking to run the model on more modest hardware. The model requires only 2.7 GB of VRAM, which means it can be deployed on a variety of devices, including laptops and mid-range desktops. For individuals or small teams who need a reliable and efficient text-to-image generator without the need for extensive resources, this model is highly recommended.
Check Your Hardware
See which quantizations of Stable Diffusion 2.1 (GGUF) your hardware can run.
Quantization Options
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q8_0 | 8 | 2.163 GB | 2.66 GB | 3.16 GB | 95% |
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.
Frequently Asked Questions
How much VRAM do I need to run Stable Diffusion 2.1 (GGUF)?
Stable Diffusion 2.1 (GGUF) requires 2.66GB VRAM minimum with Q8_0 quantization. For full precision, you need 2.66GB VRAM.
What is the best quantization for Stable Diffusion 2.1 (GGUF)?
Q4_K_M offers the best balance of quality and VRAM usage. Q8_0 is near-lossless if you have enough VRAM.