QWE AI Academy Installing Stable Diffusion on Windows solves a specific problem: unlimited AI image generation without per-image cloud fees, without usage limits, without sending prompts to third parties. Local installation gives you that. Cost? Your time. Fighting Python versions. Git commands you’ve never used.
Once it’s running, you’re done paying. But most tutorials skip the decision that determines whether you’ll love or hate the next two hours: which interface to install.
Pick Your Interface First
AUTOMATIC1111 and ComfyUI are completely different tools. Pick wrong, you’re uninstalling everything and starting over.
AUTOMATIC1111 (A1111) looks like Photoshop – tabs, dropdowns, sliders. Type prompt, click Generate, get image. According to comparison benchmarks, it’s beginner-friendly but slower. Setup: 15-30 minutes if nothing breaks.
ComfyUI looks like Blender – nodes connected by wires. Each step (load model, encode prompt, denoise, save) is a separate box you wire together. Community reports show 15-75% faster on the same hardware, especially 6-8GB GPUs. Learning curve? 10-20 hours before workflows click.
Never used Stable Diffusion? Start with A1111. You can switch later. Used it before or enjoy node-based tools? ComfyUI’s speed advantage matters.
Pro tip: Both interfaces use the same models. Install A1111 first, learn prompting basics, then add ComfyUI later. They coexist on the same PC – just separate folders.
What Your PC Actually Needs
The specs everyone repeats are partially wrong. Here’s what happens when you don’t meet them.
Windows 10 or 11. Windows 7/8 won’t work – Python dependencies require newer system libraries (as of 2026).
GPU: NVIDIA cards with 4GB VRAM generate 512×512 images, slowly. 8GB handles 768×768 and basic LoRAs. 12GB+ is where ControlNet and high-res workflows become comfortable. According to hardware guides, RTX 3060 (12GB) hits the sweet spot for price-to-capability in 2026. AMD cards? Work via DirectML but community feedback consistently reports slower speeds and more troubleshooting.
RAM: 16GB system RAM is the floor. 8GB? You’ll hit swap constantly. Generation crawls.
Storage: 20GB minimum. Base install ~10GB, each model checkpoint 2-7GB. Plan to collect models? Budget 100GB+.
CPU barely matters. Any Intel i5 or Ryzen 5 from the last 5 years works. GPU does 95% of the work.
Install Python – The Part That Breaks
Python 3.10.6. Not 3.11, not 3.12, despite what the Python website suggests.
Why? Dependencies like xformers and some extensions haven’t caught up to 3.11+, according to the AUTOMATIC1111 repository. Will 3.11 work? Sometimes. GitHub discussions show users report minimal gains (~0.1 iteration/sec) and broken extensions. Not worth it.
Two ways:
Option A:Microsoft Store – search “Python 3.10”, install. Auto-manages PATH variables. Easiest for beginners.
Option B: Download 64-bit installer from python.org. During install, check “Add Python 3.10 to PATH”. Forget this? “python is not recognized” errors later.
Verify: Open Command Prompt (Windows key, type cmd), type python --version. Should see Python 3.10.something.
The Version Trap
Already have Python 3.11 or 3.12? Windows might default to that even after installing 3.10.6. Webui installer fails with “incompatible Python version” errors.
Fix: Uninstall newer versions first (Control Panel → Add or Remove Programs), reboot, install 3.10.6. Annoying. Saves two hours later.
Git and Repository Clone
Git downloads Stable Diffusion’s code from GitHub. Install from git-scm.com. Accept defaults.
Create a folder for Stable Diffusion. Watch out: Use a path without spaces. C:stable-diffusion works. C:UsersJohn SmithAI Projects causes errors – scripts choke on spaces.
Open that folder, click address bar, type cmd, hit Enter. Command prompt opens there.
Type this:
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.gitDownloads ~1GB into a new folder called stable-diffusion-webui. Takes 2-5 minutes.
Download a Model First
Stable Diffusion doesn’t include a model. You need at least one checkpoint file before the interface works.
Navigate to stable-diffusion-webuimodelsStable-diffusion. You’ll see a text file: “Put Stable Diffusion checkpoints here.”
Head to Hugging Face, download v1-5-pruned-emaonly.safetensors (~4GB). Place it in that folder. This is the base SD 1.5 model – reliable, well-documented, runs on 4GB GPUs (as of 2026).
Launch and Wait
Go back to stable-diffusion-webui folder. Find webui-user.bat, double-click.
Black terminal window opens. Text scrolls fast. This is normal. It’s creating a virtual environment, installing ~3GB of Python libraries. First launch: 5-10 minutes. Don’t close the window – looks frozen sometimes but it’s working.
Done? You’ll see: Running on local URL: http://127.0.0.1:7860
Open browser, go there. Stable Diffusion interface loads.
Type “a cat wearing sunglasses”, click Generate. Image appears after 10-30 seconds? You’re done. Errors? See next section.
When Installation Fails
“RuntimeError: Couldn’t install torch”
Python version is wrong or path has spaces. Check version: python --version in cmd. Not 3.10.x? Uninstall, reinstall. Path has spaces? Move entire folder to C:sd, try again.
“CUDA out of memory”
GPU doesn’t have enough VRAM for that image size. Lower resolution (try 512×512), close browser tabs, or edit webui-user.bat to add flags. Open file in Notepad, find set COMMANDLINE_ARGS=, change to set COMMANDLINE_ARGS=--medvram, save, relaunch.
“Python 3.11 incompatible” warning but 3.10.6 is installed
Windows finds 3.11 first in PATH. Add --skip-python-version-check to COMMANDLINE_ARGS to bypass the warning. Won’t fix actual dependency errors – just hides the message. Better: uninstall 3.11.
webui-user.bat closes immediately
Git or Python isn’t installed correctly. Open cmd, type git --version, then python --version. Either fails? Reinstall that tool, reboot.
| GPU VRAM | Comfortable Resolution | Recommended Flag |
|---|---|---|
| 4GB | 512×512 | –lowvram |
| 6-8GB | 768×768 | –medvram or –xformers |
| 12GB+ | 1024×1024+ | –xformers |
What Just Happened
Most tutorials don’t explain what the installer did. When something breaks later, you’re lost.
The webui-user.bat script created a virtual environment – a contained Python setup that won’t conflict with other projects. All dependencies live in stable-diffusion-webuivenv. Need to reinstall? Delete that venv folder, run webui-user.bat again.
It installed PyTorch (neural network library), xformers (memory optimization), Stable Diffusion model loader, ~50 other packages. Entire stack is pinned to versions that work together – this is why Python 3.11 breaks things. One incompatible library cascades into “module not found” errors.
Ever wonder why tutorials are so specific about Python 3.10.6? That’s the version where all these dependencies align. 3.11 changes internal APIs just enough to break xformers.
Generate Your First Image
Open http://127.0.0.1:7860. Text box (prompt), negative prompt box below, sliders on right.
Top-left dropdown: select your model (v1-5-pruned-emaonly if you followed steps above).
Type: “a landscape painting of mountains at sunset”
Leave everything default. Click orange Generate button.
Watch terminal. Progress bar: 0%| | 0/20 [00:00<?, ?it/s]. The it/s number is iterations per second – higher is faster. RTX 3060 gets ~8-10 it/s at 512×512. Generation: 2-5 seconds per image on mid-range GPUs, 10-30 seconds on budget cards (as of early 2026).
Done? Image appears. See a mountain landscape? You’re running Stable Diffusion locally.
Settings to Change Now
Before you generate 500 images and hate the defaults:
Click Settings (top) → Saving images. Change filename pattern. Default is a hash like 00234-1234567890.png. Set to include prompt: [seed]-[prompt_spaces]. Now files are named 123456-a-cat-wearing-sunglasses.png. You can find them later.
Settings → User interface: enable “Show image creation progress every N sampling steps”, set N to 1. You’ll see the image form in real-time instead of staring at a progress bar. Sounds minor. Changes everything.
Settings → Stable Diffusion: Enable xformers if you have an RTX GPU. Cuts VRAM usage 20-30%, slightly speeds generation.
Click Apply settings, restart UI.
ComfyUI vs A1111 in 2026
As of early 2026, the community is split. AUTOMATIC1111 is still most-used, but ComfyUI adoption is growing fast. Why? It handles SDXL and Flux models more efficiently. Benchmarks show 45% faster on 8GB GPUs, 75% faster on 6GB cards. More aggressive memory management.
The catch: you’re wiring nodes. Used Blender’s shader editor or Unreal’s Blueprints? You’ll adapt quickly. If not? Expect a weekend of tutorials.
You can install both. They don’t conflict. Try A1111 first. If you’re waiting 30 seconds per image on an 8GB card, add ComfyUI and compare.
FAQ
Can I use Stable Diffusion with an AMD GPU?
Yes, but slower and buggier. You’ll use DirectML on Windows or ROCm on Linux. Community reports show generation times 2-3x longer than equivalent NVIDIA cards. Some extensions won’t work. Already have AMD? Try it. Buying hardware specifically for this? Get NVIDIA.
Do I need to keep the command prompt window open while generating images?
Yes. To restart later, double-click webui-user.bat again – launches in seconds after first install.
Why does my first image take 60 seconds but the second one takes 5 seconds?
First generation loads the model into VRAM (10-20 seconds). Subsequent images reuse the loaded model. Normal behavior. Every image takes 60 seconds? Your GPU is too slow for that resolution – drop to 512×512 or upgrade.