How to Use Stable Diffusion: Complete Beginner’s Guide (2026)

Stable Diffusion is one of the most powerful open-source AI image generators available today. Unlike cloud-based alternatives like DALL-E or Midjourney, Stable Diffusion runs locally on your computer, giving you complete control over your image generation workflow with no subscription fees.

This guide walks you through everything from installation to creating your first images, writing effective prompts, and mastering advanced techniques that produce professional-quality results.

TL;DR — Quick Start

1. Easiest option: Use Stable Diffusion WebUI (AUTOMATIC1111) for a browser-based interface
2. Cloud option: Try RunDiffusion or Google Colab notebooks if your GPU isn’t powerful enough
3. Minimum specs: NVIDIA GPU with 6GB+ VRAM, 16GB RAM, 20GB storage

What Is Stable Diffusion?

Stable Diffusion is a latent diffusion model developed by Stability AI that generates images from text descriptions. Released as open source in August 2022, it has since become the backbone of countless AI art applications.

Key advantages over competitors:

Feature	Stable Diffusion	DALL-E 3	Midjourney
Cost	Free (local)	$20/mo (ChatGPT Plus)	$10-60/mo
Open Source	Yes	No	No
Local Processing	Yes	No	No
Custom Models	Unlimited	No	No
NSFW Filters	Optional	Strict	Strict
Batch Generation	Unlimited	Limited	Limited
Fine-tuning	Yes	No	No

System Requirements

Before installing Stable Diffusion, make sure your computer meets these minimum requirements:

Minimum Specs

• GPU: NVIDIA GPU with 6GB VRAM (GTX 1060 6GB or better)
• RAM: 16GB system RAM
• Storage: 20GB free space (more for models)
• OS: Windows 10/11, Linux, or macOS (Apple Silicon supported)

Recommended Specs

• GPU: NVIDIA RTX 3060 12GB or better
• RAM: 32GB system RAM
• Storage: 100GB+ SSD
• OS: Windows 11 or Ubuntu 22.04+

AMD and Apple Silicon Users

• AMD GPUs: Supported through DirectML on Windows or ROCm on Linux
• Apple Silicon (M1/M2/M3/M4): Supported through MPS (Metal Performance Shaders), though slower than NVIDIA GPUs

Method 1: Install AUTOMATIC1111 WebUI (Recommended)

AUTOMATIC1111’s Stable Diffusion WebUI is the most popular interface. Here’s how to set it up:

Step 1: Install Python 3.10

Download Python 3.10.x from python.org. During installation, check “Add Python to PATH.”

Step 2: Install Git

Download Git from git-scm.com and install with default settings.

Step 3: Clone the Repository

Open Command Prompt or Terminal and run:

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

Step 4: Download a Model

Download the Stable Diffusion XL (SDXL) base model or SD 1.5 model from HuggingFace or CivitAI. Place the .safetensors file in the models/Stable-diffusion/ folder.

Popular models to start with:

• SDXL 1.0: Best overall quality, requires 8GB+ VRAM
• SD 1.5: Lighter, runs on 4-6GB VRAM
• Realistic Vision: Photorealistic images
• DreamShaper: Versatile artistic style

Step 5: Launch the WebUI

Run the launch script:

Windows:

webui-user.bat

Linux/macOS:

./webui.sh

The first launch downloads required dependencies (15-30 minutes). Once ready, open http://127.0.0.1:7860 in your browser.

Method 2: Use ComfyUI (Node-Based)

ComfyUI offers a node-based workflow that gives you more control over the generation pipeline.

Installation Steps:

1. Clone the repository: git clone https://github.com/comfyanonymous/ComfyUI.git
2. Install requirements: pip install -r requirements.txt
3. Place models in models/checkpoints/
4. Run: python main.py

ComfyUI is better for:

• Complex workflows with multiple models
• Consistent batch processing
• Advanced techniques like IP-Adapter and ControlNet
• Reproducible pipelines

Method 3: Cloud-Based Options (No GPU Required)

If you don’t have a powerful GPU, several cloud options are available:

Service	Cost	Setup Time	Best For
Google Colab	Free (limited)	5 min	Testing
RunDiffusion	$0.50/hr+	Instant	Quick sessions
Paperspace	$0.07/hr+	10 min	Power users
Vast.ai	$0.10/hr+	5 min	Budget option

Writing Effective Prompts

The quality of your prompts directly determines the quality of your images. Here’s how to write prompts that get great results.

Basic Prompt Structure

A good prompt follows this pattern:

[Subject], [Medium], [Style], [Artist reference], [Quality tags], [Lighting], [Color palette]

Example:

portrait of a young woman in a garden, oil painting, impressionist style, inspired by Monet, masterpiece, best quality, soft golden hour lighting, warm earth tones

Positive Prompt Tips

1. Be specific: “red 1967 Ford Mustang convertible” beats “red car”
2. Include quality tags: masterpiece, best quality, highly detailed, sharp focus
3. Specify the medium: digital painting, photograph, watercolor, 3D render
4. Mention lighting: studio lighting, golden hour, dramatic shadows, soft ambient light
5. Add composition details: close-up, wide angle, bird's eye view, rule of thirds

Negative Prompts

Negative prompts tell Stable Diffusion what to avoid. Essential negative prompts include:

worst quality, low quality, blurry, deformed, ugly, duplicate, mutation, extra limbs, bad anatomy, bad hands, watermark, text, signature

Prompt Weighting

Control emphasis using parentheses and colons:

• (important detail:1.3) — increases weight by 30%
• (less important:0.7) — decreases weight by 30%
• ((very important)) — double emphasis (1.1 × 1.1 = 1.21)

Key Generation Settings

Understanding these settings helps you control output quality:

Sampling Steps

• Recommended: 20-30 steps for most samplers
• More steps = more detail but slower generation
• Diminishing returns beyond 40 steps

CFG Scale (Classifier-Free Guidance)

• Range: 1-30 (recommended 7-12)
• Low (1-5): More creative, less prompt adherence
• Medium (7-9): Balanced results
• High (10-15): Strict prompt following, may look oversaturated

Sampler Selection

Sampler	Speed	Quality	Best For
Euler a	Fast	Good	Quick tests
DPM++ 2M Karras	Medium	Excellent	General use
DPM++ SDE Karras	Slow	Excellent	Detailed work
DDIM	Fast	Good	Consistent results

Resolution

• SD 1.5: 512×512 native, upscale after
• SDXL: 1024×1024 native
• Always generate at native resolution, then upscale

Advanced Techniques

img2img (Image to Image)

Transform existing images using text prompts. Useful for:

• Changing art styles
• Adding details to sketches
• Color correction and enhancement

Set Denoising Strength between 0.3-0.7 for best results. Lower values preserve more of the original.

Inpainting

Edit specific areas of an image while keeping the rest unchanged:

1. Upload an image to the Inpainting tab
2. Paint over the area you want to change
3. Write a prompt describing the replacement
4. Set denoising to 0.6-0.8 for natural blending

ControlNet

ControlNet lets you guide image generation using reference images for:

• Canny Edge: Preserve outlines and structure
• OpenPose: Match human poses
• Depth: Maintain spatial relationships
• Scribble: Generate from rough sketches

LoRA (Low-Rank Adaptation)

LoRAs are small model add-ons that teach Stable Diffusion specific styles or subjects:

1. Download LoRA files from CivitAI
2. Place in models/Lora/ folder
3. Use in prompts: (0.8 = weight)

Upscaling

For print-quality images, upscale your outputs:

• ESRGAN 4x: General upscaling
• Real-ESRGAN 4x+: Photorealistic images
• 4x-UltraSharp: Maximum detail preservation

Use the “Extras” tab in WebUI to upscale by 2x or 4x.

Workflow: From Idea to Final Image

Here’s a practical workflow for creating polished images:

1. Generate at native resolution (512×512 or 1024×1024) with 20-25 steps
2. Generate multiple seeds — create a batch of 4-8 images
3. Pick the best candidate from the batch
4. Refine with img2img at 0.3-0.4 denoising strength
5. Fix details with inpainting (faces, hands, backgrounds)
6. Upscale 2-4x using ESRGAN or similar
7. Post-process in Photoshop or GIMP if needed

Troubleshooting Common Issues

“CUDA out of memory”

• Lower resolution or batch size
• Enable --medvram or --lowvram flags in launch settings
• Close other GPU-intensive applications

Blurry or Low-Quality Results

• Increase sampling steps to 25-30
• Use quality-focused negative prompts
• Try DPM++ 2M Karras sampler
• Ensure CFG scale is 7-9

Distorted Faces and Hands

• Enable “Restore Faces” option (uses CodeFormer)
• Use ADetailer extension for automatic face fixing
• Add “detailed hands, detailed face” to prompts

Slow Generation

• Use Euler a sampler for fastest results
• Reduce steps to 20
• Enable xformers: add --xformers to launch arguments
• Use FP16 precision (default on most setups)

FAQ

Is Stable Diffusion free to use?

Yes. The model is open source and free for personal and commercial use. You only pay for hardware (your GPU or cloud rental). There are no per-image fees or subscriptions required.

Can my computer run Stable Diffusion?

You need an NVIDIA GPU with at least 6GB VRAM for comfortable usage. AMD GPUs and Apple Silicon Macs are supported but slower. If your hardware doesn’t meet requirements, cloud services like Google Colab offer free GPU access.

What’s the difference between SD 1.5, SDXL, and SD 3?

SD 1.5 is lighter and has the largest ecosystem of custom models. SDXL produces higher-quality 1024×1024 images but needs more VRAM. SD 3 (released 2024) added improved text rendering and better composition but has a more restrictive license.

Is it legal to use Stable Diffusion images commercially?

The model license generally permits commercial use, but always check the specific license of the model checkpoint you’re using. Some fine-tuned models on CivitAI may have additional restrictions.

How do I get photorealistic results?

Use a photorealistic checkpoint model (like Realistic Vision or JuggernautXL), include quality tags in your prompt, add camera-specific terms like “shot on Canon EOS R5, 85mm lens, f/1.4”, and use appropriate negative prompts to avoid artifacts.

Conclusion

Stable Diffusion puts professional-grade AI image generation in your hands without ongoing subscription costs. While the initial setup takes some effort, the flexibility and control you gain are unmatched by any cloud-based service. For more insights, check out our guide on How to Use GitHub Copilot.

Start with AUTOMATIC1111 WebUI for the easiest experience, experiment with different models from CivitAI, and practice writing detailed prompts. Within a few hours of experimentation, you’ll be creating images that rival paid services.

For your next steps, explore ControlNet for precise image control, try training a LoRA on your own images, or experiment with ComfyUI for advanced workflows.