Run It

Generating Images Locally

Stable Diffusion, running entirely on your own hardware - what tool to start with, what VRAM actually gets you, and where new images and models come from.

14 minute read

A different ecosystem than Ollama

Everything else in this pillar runs on Ollama. Image generation doesn't - it's a separate world built around Stable Diffusion and newer families like Flux, open models trained specifically to turn text prompts into images, with their own tools and their own hardware demands.

If Building a Full PC for Local AI was on your radar because of image generation specifically, this is the guide that cashes in that reasoning.

The good news: it's just as free and just as private as everything else here. Nothing about a prompt or a generated image leaves your machine.

Picking an interface

Four tools dominate local image generation, and they trade off simplicity against control:

Tool	Best for	Tradeoff
Fooocus	Getting started with minimal setup	Fewer advanced controls
Forge	A faster, actively maintained AUTOMATIC1111 fork	Still wants a Python environment
AUTOMATIC1111	The most widely documented option, huge community	Requires Python and a bit more setup
ComfyUI	Full control over the generation pipeline	A real learning curve - node-based, not beginner-first

Start with Fooocus. It bundles sensible defaults and a simple interface, so you're generating images in minutes rather than debugging a Python environment first. Download it from its GitHub releases page, extract it, and run the included launcher - no separate install steps beyond that on Windows.

On Mac or Linux, or if you outgrow Fooocus: Forge and AUTOMATIC1111 both work well cross-platform and install the same way - a normal git clone plus a setup script, the same shape as most of the tools covered elsewhere on this site:

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

The first run downloads several gigabytes of dependencies automatically - that's normal, and only happens once. Forge follows the same pattern from its own repository, and is worth trying first if generation speed matters to you - it's built as a faster fork of the same codebase.

VRAM and speed

Image generation is more VRAM-hungry per second of use than chat, because it's doing dense image math instead of predicting one token at a time.

4GB Minimum Runs older SD 1.5 models at modest resolutions.

8GB Comfortable Handles SDXL, the current standard model family, well.

12GB+ Power user Room for larger batches and higher resolutions.

CPU-only generation works, technically - but where a chat response takes seconds, a CPU-only image can take several minutes. If you're doing this regularly, a GPU stops being optional the way it can be for chat.

Getting a model

Fooocus and AUTOMATIC1111 both need at least one model checkpoint - the actual trained weights, separate from the interface itself. Hugging Face hosts the official releases and is the most straightforward source for a first model. Download the checkpoint file and drop it into the interface's models folder (Fooocus and AUTOMATIC1111 both show you this path on first run).

Stable Diffusion isn't the only family worth knowing - Flux is the other major open option, and it genuinely closes most of the remaining quality gap to cloud image tools. It's a heavier model with steeper VRAM demands than SDXL, so it's worth starting with a Stable Diffusion checkpoint to confirm your setup works end to end before stepping up to Flux.

Community model sites vary in moderation. Sites like CivitAI host a huge range of community-trained models, including some with adult content mixed into general listings - use official Hugging Face releases if you'd rather not deal with that at all, and pay attention to what you're downloading either way.

What actually goes wrong

"CUDA out of memory" errors

Your GPU doesn't have enough VRAM for the resolution or batch size you asked for. Lower the resolution first - most interfaces also have a low-VRAM mode (AUTOMATIC1111's launch flags include --medvram and --lowvram) that trades speed for fitting into less memory.

Generation is extremely slow

Confirm the tool is actually using your GPU, not silently falling back to CPU - this happens most often when GPU drivers are outdated or missing, the same failure mode covered in Building a Full PC for Local AI. Check the interface's console output on startup; it usually states which device it's running on.

Images come out completely wrong or corrupted-looking

This is almost always a mismatched model type - loading an SDXL checkpoint with settings meant for SD 1.5, or vice versa. Check that the checkpoint's expected settings (resolution, in particular) match what the interface is configured for.

Common questions

Does Ollama do image generation too?

No. Ollama is built for text models. Image generation runs on a separate ecosystem - Stable Diffusion and the tools built around it - installed and run independently of Ollama.

Can I do this without a GPU?

Technically yes, but expect minutes per image instead of seconds. Image generation leans on a GPU far more heavily than text models do - see Building a Full PC for Local AI if this is your main reason for wanting one.

Go deeper

This guide covers one solid path. Here's where to go if you want something different.

r/StableDiffusion The center of gravity for local image generation - active, current, and where new models get discussed first. Building a Full PC for Local AI If image generation is the main reason you want a GPU, this covers the hardware reasoning.

Keep exploring See everything else worth knowing, whenever you actually want it.

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