Generate AI Images on Your Own Computer

Yes, you can generate AI images on your own computer: no subscription, no credits, no upload queue. If you have a PC with a decent graphics card, you already own the hardware. This guide explains what local image generation actually requires, which models are worth your disk space, and how to go from zero to your first image without touching a terminal.

Why generate images locally?

Cloud image generators are convenient, but they come with trade-offs that add up fast:

• Cost. Credit systems and monthly caps punish exactly the thing that makes image generation fun: iterating. Locally, your hundredth attempt costs the same as your first: nothing.
• Privacy. Every prompt you type into a cloud service is a prompt on someone else's server. Family photos you're restyling, product mockups for an unannounced launch, personal art projects. Locally, none of it leaves your machine. (More on this in local AI vs. cloud AI privacy.)
• No watermarks, no gatekeeping. The output is a plain image file on your disk. Yours.
• It works offline. On a plane, in a dead zone, during an outage: your image studio doesn't care.

The catch used to be setup pain: Python environments, model weights scattered across download sites, cryptic error messages. That part has genuinely improved.

What hardware do you need?

You don't need a workstation. Rough rules of thumb:

• Windows or Linux PC with a GPU. This is the route this guide covers, and 6 to 8 GB of VRAM is enough to start. SDXL checkpoints want 6 to 8 GB, FLUX wants 8 to 10, and the heaviest models on the list want 24.
• Apple Silicon Mac. M-series chips can generate images, but the tooling is separate from everything else in this article. The LU Labs desktop app has a Mac build, a beta for Apple Silicon on macOS 14 or newer, and its Create tab makes images on the Mac itself without ComfyUI; the browser Studio is the other route. What a Mac does and does not handle is in what a Mac can run locally in 2026.
• Disk space. A single SDXL checkpoint is about 6.5 GB. FLUX 2 Klein is 11 GB once you count its autoencoder and text encoder, and FLUX.1 in FP8 is about 21 GB the same way. Plan for 20 to 40 GB if you want a small collection.

If you're unsure where your machine lands, the same logic that applies to language models applies here; our guide on how much RAM you need for local AI walks through it.

The models worth knowing

You'll see a lot of names thrown around. Three groups cover most of what a newcomer needs, and the sizes below are the actual download sizes in the LU Labs model manager.

SDXL fine-tunes: the cheapest way in

SDXL is the workhorse of open image generation: 1024x1024 native resolution and a huge ecosystem of community fine-tunes. Juggernaut XL V9 and RealVisXL V5 are the photorealistic ones people mean when they say a local image made them look twice, and each is a single 6.5 GB file that includes its own autoencoder and text encoder. Both run on 6 to 8 GB of VRAM, which is the lowest bar on this page. Start here if your card is modest or if you mostly want to learn how prompting behaves.

FLUX.1: the prompt-follower

FLUX.1 is known for rendering the scene you actually described, including awkward things like text in the image and objects placed relative to each other. Schnell is the fast variant and dev is the slower, sharper one. Either wants 8 to 10 GB of VRAM, and in FP8 the full package comes to about 21 GB on disk, because the diffusion model is 16.1 GB and the T5 text encoder adds 4.6 GB on top.

FLUX 2 Klein: the current generation, and smaller

FLUX 2 Klein is the newer architecture, and the interesting part is that it went down in size rather than up. The 4B diffusion model is 7.2 GB, its autoencoder is 0.3 GB and its text encoder is a 3.5 GB build of Qwen 3 4B, so 11 GB in total against FLUX.1's 21. It still wants 8 to 10 GB of VRAM. If your card is in that range and you are choosing one model to download, this is the one.

Two ways to run them

On a graphics card, essentially all of this runs through ComfyUI, the de facto standard engine for Stable Diffusion and FLUX. It is enormously powerful, node-based, and famously intimidating to set up by hand: a Python environment, a set of custom nodes, and model files that have to land in exactly the right subfolder.

You can absolutely assemble that yourself, and tinkerers do. If you'd rather skip the plumbing, LU Labs is a free desktop app that, on Windows and Linux, installs and manages ComfyUI for you. It reads your card, lists only the models that fit it, downloads every file a model needs into the right place, and gives you a prompt box instead of a node graph. When ComfyUI falls back to the processor or refuses to start, the app names the reason rather than showing you a stack trace. Unlimited generations, no watermarks.

Getting your first good image

Whichever tool you use, the workflow is the same:

1. Start with a fast model. Learn prompting on something that responds in seconds, not minutes.
2. Describe the image, not the vibe. "A ceramic mug on a wooden desk, morning light through a window, shallow depth of field" beats "cozy aesthetic mug pic". Subject, setting, lighting, style (in that order) is a reliable skeleton.
3. Change one thing at a time. Reroll the same prompt a few times first; models are non-deterministic, and your third attempt may nail what your first missed.
4. Graduate to a bigger model for finals. Draft fast, then re-run your best prompt on a photorealistic model for the keeper.

Common beginner traps: prompts stuffed with twenty comma-separated keywords (pick the five that matter), expecting perfect hands or legible long text from smaller models (larger models do better), and judging a model on one generation (always sample a few).

Local vs. cloud: an honest comparison

Cloud services still hold two real advantages: the absolute frontier of image quality lives on data-center hardware, and there's zero download wait. If you generate three images a year, a website is fine.

Local wins on everything else: unlimited iteration, privacy, cost, offline use, and full ownership of the output. And the gap in quality has narrowed to the point where, for most everyday purposes, the model on your laptop is simply good enough that the cloud's advantages stop mattering.

Many people land on a hybrid: local for the daily 95%, cloud for the occasional heavy job. That's exactly the model LU Labs uses: local is free forever, with optional hosted plans if you ever want a cloud boost.

Start generating

The whole loop (install, download a starter model, first image) takes about as long as making coffee:

1. Download LU Labs for Windows or Linux (free). The Mac beta makes images in Create without ComfyUI, so these steps are the PC route.
2. Let it check your card and suggest an image model that fits.
3. Type a prompt and generate. Iterate as much as you like; it's your machine.

If you want the broader picture of running AI on your own hardware (chat models, coding agents and video too), start with the features overview, or with local AI video generation in 2026 if moving pictures are what you came for. Your computer is more capable than you think.

Sources

• Model file sizes, VRAM requirements and the ComfyUI backend: the model bundles shipped in the LU Labs desktop app, version 2.6.8