{/* banner prompt: A playful but polished 3D render of a stylized RTX 3060 as a small factory on a simple background, using matte clay-like materials in a color story of sage green, cream, and terracotta; a conveyor belt on the left carries misshapen, glitchy figures, while a belt on the right carries perfectly formed photorealistic objects. */}

Eighteen months ago I wrote a tutorial on
[mastering human portraits with Stable Diffusion](/blog/2025/02-06-mastering-human-portraits-with-stable-diffusion-ai/).
Reading it back is a little painful. It opens with "trust me, this is hard," and
its list of search keywords reads like a bug report: *two heads, extra heads,
deformed face, extra eyes, disfigured, duplicate.* Those weren't edge cases —
they were what you fought on every render.

This week I ran open-source image generation on the **exact same computer**. Same
RTX 3060, same 12GB of VRAM. The only thing that changed is the software, and the
gap is hard to overstate.

## The machine (deliberately unchanged)

It's the same box from our [local-LLM
benchmark](/blog/2026/05/11-64gb-ram-12gb-vram-the-honest-local-llm-benchmark/):

- **CPU:** Intel Core i7-12700F
- **RAM:** 64GB DDR4
- **GPU:** NVIDIA GeForce RTX 3060 — 12GB VRAM
- **OS:** Debian 13

Holding the hardware constant is the whole point. Any improvement you see is the
open-source ecosystem getting better, not a new graphics card.

## Early 2025: Stable Diffusion 1.5, 512 pixels, and a war on anatomy

Back then, the state of the art you could actually run at home was Stable
Diffusion 1.5. My old parameters tell the story: 512×512, Euler a, CFG 7. To get
anything lifelike you leaned on community finetunes (RealisticVision, ChilloutMix,
F222), stacked LoRAs, and wrote paragraph-long negative prompts to *suppress* the
model's habit of adding a second head or a sixth finger. Then you generated a
dozen and kept the one that wasn't broken. It worked, but it was a craft — and a
grind.

## Mid 2026: the same card, a different league

Here's a portrait from the same 3060 today, straight out of the model —
no inpainting, no ControlNet, no clean-up:

![A photoreal portrait generated on the RTX 3060 in 2026](https://www.tyolab.com/api/tynion/images/f9f8fb60-d635-1048-cb10-c3492d7a4c4a/file)

Three things changed the game. **Flux** and modern **SDXL** finetunes replaced
SD 1.5 as the open baseline. They render natively at 1024px and up, and they get
hands, faces and proportions right the first time far more often than not.

They also learned to spell — the single clearest "we crossed a line" moment.
Readable text in an image was essentially impossible in 2025:

![A shopfront with legible hand-painted signage, generated locally](https://www.tyolab.com/api/tynion/images/3fa3d0ac-f3af-f6e7-952e-27b04f9b545e/file)

And the range on one card is wide — photoreal wildlife and clean anime styles
from the same machine, same afternoon:

![A photorealistic wildlife shot from the same GPU](https://www.tyolab.com/api/tynion/images/adbceadb-bc09-46f0-c8c7-bd76f312361f/file)

![A detailed anime illustration from the same GPU](https://www.tyolab.com/api/tynion/images/3d737b80-9cbb-186f-ce2c-c496fb918589/file)

## Then and now, side by side

<Table data={
    {
        headers: ['', 'Early 2025', 'Mid 2026'],
        rows: [
            ['Best model you can run', 'Stable Diffusion 1.5 + finetunes', 'Flux, SDXL finetunes'],
            ['Native resolution', '512 x 512', '1024px and up'],
            ['Anatomy', 'Fighting extra heads, hands, eyes', 'Right on the first try, mostly'],
            ['Text in the image', 'Gibberish', 'Often readable'],
            ['Keepers per ten tries', 'One or two', 'Most of them'],
            ['Effort per shot', 'Long negative prompts, LoRAs, inpainting', 'A plain-English sentence'],
            ['Video', 'Not on this card', 'A few seconds, locally'],
        ]
    }
} />

## The part that was pure science fiction in 2025: video

The same RTX 3060 now generates **video**. Wan 2.2 and LTX-Video produce short
text-to-video and image-to-video clips locally — a five-second scene in a few
minutes, on a card that two years ago struggled with a single 512px still. The
next open model on our list, MiniMax H3, even adds a synchronised soundtrack.

## What actually changed — and why it matters

It wasn't the GPU. Three shifts in the open-source world did the work:

- **Better base models.** Flux and SDXL are simply generations ahead of SD 1.5.
- **Quantisation.** Techniques like fp8 and 4-bit weights shrink 12-billion-parameter
  models enough to fit inside 12GB of VRAM with quality mostly intact.
- **Smarter memory management.** The tools now offload the heavy text encoders to
  system RAM, so the GPU only holds what it must.

Put together, a consumer card that launched years ago runs models that didn't
exist when you bought it — because the community learned to make them fit.

For a small business or a hobbyist, that's the real headline: the *same* modest
hardware budget now buys studio-grade stills and short video, from software
that's free and runs on your own desk. Every image in this post was generated on
that one 3060; you can browse and remix the prompts in our
[gallery](https://www.tyolab.com/prompts).

*Next up: putting MiniMax H3 — open-weights video with native audio — on the very
same card, and seeing how far 12GB can really go.*


cf0945211accc8aaf67c2caeafb240cb56b92b472152b7fc8a83a573245a77ac

A hands-on before-and-after: how open-source text-to-image models improved on the exact same RTX 3060 12GB between early 2025 and 2026 — Stable Diffusion 1.5 vs Flux and SDXL finetunes, resolution, anatomy, text rendering, hit-rate, and video.

We ran open-source image generation on the same RTX 3060 we used 18 months ago. The hardware is identical; the results are unrecognisable — from 512px portraits fighting extra heads to 1024px Flux, near-perfect anatomy, readable text, and even video.

Tag: Flux

Tag: Flux

Two Years of Open-Source Image AI on One RTX 3060