How to Run Stable Diffusion Locally (NSFW)
Stable Diffusion WebUI Forge is the easiest “first PC install” for most beginners: clone the repo, run webui-user.bat, wait for dependencies, then drop .safetensors files into the right folders. GPU: NVIDIA + current drivers is the path with the most tutorials. NSFW: nothing is “unlocked by magic” - you’re downloading community weights and running them locally, so keep it legal and age-appropriate.
The Models
1. 1) Prerequisites
Top PickDo drivers + PATH first - it prevents spooky errors later.
Architecture: Windows + NVIDIA + Python 3.10 + Git · VRAM: GPU-dependent · Best for: Smooth tutorial path before you touch models
Open on Civitai →Clone repo → run webui batch → wait for first-run downloads.
Architecture: WebUI fork · VRAM: 6-12 GB+ typical (depends on model and res) · Best for: First local install with a normal UI
Open on Civitai →Wrong folder = invisible model - follow Civitai’s file guide.
Architecture: Filesystem layout · VRAM: N/A · Best for: Checkpoints vs LoRA vs VAE hygiene
Open on Civitai →Start conservative - prove stability, then expand.
Architecture: SD 1.5 / SDXL / Flux-class (later) · VRAM: Resolution × model size · Best for: Smoke test before NSFW downloads
Open on Civitai →Why This Matters
SD means Stable Diffusion. VRAM is your GPU’s fast memory - it’s the usual bottleneck for image size and model size. CUDA is NVIDIA’s GPU compute stack; most beginner guides assume an NVIDIA card because the install path is smoothest. This page walks you from zero to a first local image, with adult-capable workflows in mind - you’re responsible for lawful use.
The Steps
1. Prerequisites (do this before you “install SD”)
Check you’re on Windows 10/11, 64-bit, with an NVIDIA GPU for the standard path.
| Architecture | VRAM | Best For |
|---|---|---|
| PC + NVIDIA GPU | 6 GB+ (tight) to 12 GB+ (more comfortable) | Following WebUI tutorials |
Install Python 3.10.x (add to PATH), Git for Windows, and the latest NVIDIA driver from NVIDIA’s site. Put the WebUI on an SSD if you can - model loads hurt on a slow disk.
2. Download Forge (WebUI)
Clone Stable Diffusion WebUI Forge - it’s the beginner-friendly WebUI we recommend.
| Architecture | VRAM | Best For | |---|---|--/| | Gradio WebUI | Same as your GPU | txt2img + extensions |
Open a terminal in the folder where you want the project, then clone the repo from GitHub (lllyasviel/stable-diffusion-webui-forge). Run webui-user.bat. First launch can take 20-30+ minutes while it downloads dependencies - that’s normal.
3. Put models in the right folders
Think “checkpoint = base brain,” LoRA = small add-on.
| File type | Where it goes (Forge/A1111-style layout) |
|---|---|
.safetensors checkpoints |
models/Stable-diffusion |
| LoRA | models/Lora |
| VAE (optional) | models/VAE |
| Textual inversion | embeddings |
After copying files, refresh the checkpoint list in the UI (or restart). CivitAI’s community article “Where do I put the files?” is the classic reference if you forget.
4. Make your first image (SFW test first)
Prove the pipeline works before you chase exotic models.
| Checkpoint | VRAM | Best For |
|---|---|---|
| Any common SD 1.5 or SDXL base you can hold | Match file to VRAM | Smoke test |
Pick a checkpoint in the UI, set resolution modestly (especially on 8 GB), then generate. If you OOM (out of memory), reduce resolution or enable --medvram / --lowvram in your launch args (trade: speed).
If you want a local-first bundle without assembling Python, Git, and CUDA by hand, LocalForge AI is built to cut setup friction - you still pick models and stay in control.
5. Troubleshooting (the boring stuff that fixes 80%)
VRAM errors are usually not “broken AI” - they’re a math problem.
- OOM: lower resolution, close other GPU apps, try
--medvramor--lowvram(A1111 wiki explains the tradeoffs). - Slow generation: expect that on small GPUs - seconds-per-image is not universal.
- Wrong folder: 90% of “it doesn’t show up” is the file path.
Quick Comparison
| Path | What it is | Good for |
|---|---|---|
| Forge | WebUI fork | Beginners + extensions |
| ComfyUI portable | Node UI + batch files | You want workflows later |
| Cloud-only | Not local | Privacy tradeoff |
What to Do Next
- Want the cluster overview? Local NSFW Setup Guide - ranks stacks and VRAM bands.
- Flux? Flux Uncensored Local - quant formats and honest VRAM floors.
- Phone? SD on Android (NSFW) - what actually runs on-device.
Verdict
Install Forge, place models in the standard folders, and prove a small image before you download 20 checkpoints. If you’re stuck on environment setup, LocalForge AI is the “less time wrestling installs” lane - still local, still your responsibility.
Easiest Way to Run Uncensored Stable Diffusion Locally (2026)
The 'easiest way' depends on what you're willing to skip. Here's the honest ladder, fastest to most flexible:
- Fooocus - download, double-click, generate. No Python install, no Git clone. Best if you've never opened a terminal. Limited to SDXL/Pony, no Flux. ~10 minutes to first image.
- LocalForge AI - same one-click flow as Fooocus, but ships Forge plus uncensored CivitAI checkpoints already in the right folders. $50 once. Same ~10 minutes.
- Forge (manual) - the Steps section above. Install Python 3.10 + Git, clone the repo, run
webui-user.bat. ~30-60 minutes including the first dependency download. Free. Largest model ecosystem of the three. - ComfyUI portable - unzip and run. Skips the Python install but the node graph is the real time tax - plan a few hours of tutorials before you're productive. Best if you eventually want Flux or video.
The 'uncensored' part isn't a setting you flip on - it's the default. None of these tools ship with a content classifier. You add 'uncensored' by picking an uncensored CivitAI checkpoint and dropping it in models/Stable-diffusion. The same Forge install runs SFW or NSFW depending on which file you load.
For a deeper image-by-image walkthrough of the manual path, the NSFW Stable Diffusion Setup Guide covers each click.