What is Stable Diffusion?
Stable Diffusion is a family of open-source AI models that turn text prompts into images. It was first released in August 2022 by Stability AI in collaboration with researchers at LMU Munich (the CompVis group) and Runway, and it's the model that kicked off the modern wave of consumer AI image generation.
What makes Stable Diffusion different from Midjourney, DALL·E, Imagen and the other big-name image generators is that the model weights are public. Anyone can download them, run them on their own hardware, fine-tune them on their own data, and ship products built on top of them. That single decision — open weights — created an entire ecosystem of fine-tunes (like Juggernaut XL), LoRAs, ControlNets, and front-ends that no closed model has matched.
How does it work?
Stable Diffusion is a latent diffusion model. The short version: it starts with pure random noise and, over a series of denoising steps (usually 20–30), gradually shapes that noise into an image that matches your prompt.
In more detail, the pipeline has three parts. A text encoder (CLIP, or in newer versions a T5 transformer) turns your prompt into a vector that captures its meaning. A U-Net (or in SD3, a Diffusion Transformer) takes random noise plus that text vector and predicts what noise to remove at each step. Repeat that 20–30 times and the noise resolves into a coherent image — but in a compressed latent space, not at full resolution. Finally, a VAE decoder turns the final latent into the full-resolution PNG you see.
Doing the heavy lifting in latent space is the trick that makes Stable Diffusion fast enough to run on a single consumer GPU. The latents are roughly 8× smaller per dimension than the output image, so each denoising step is around 64× cheaper than working at full resolution.
The Stable Diffusion versions
There have been four major generations. Each is a different architecture, not just a bigger version of the last.
| Version | Released | Native res. | Architecture | Status |
|---|---|---|---|---|
| SD 1.5 | Oct 2022 | 512×512 | U-Net + CLIP | Mature, huge fine-tune ecosystem, still widely used |
| SD 2.x | Nov 2022 | 768×768 | U-Net + OpenCLIP | Effectively skipped — tightened filters broke prompt compatibility |
| SDXL | July 2023 | 1024×1024 | Larger U-Net + dual text encoders | Current commercial standard for open-weight image tools |
| SD3 / 3.5 | 2024–2025 | 1024×1024 | Diffusion Transformer (MMDiT) | Stronger prompt comprehension and text rendering; smaller ecosystem so far |
The version that matters most in practice is SDXL. It's the resolution and architecture the bulk of the open-source ecosystem is built on, including Juggernaut XL. SD 1.5 is still everywhere despite being three years old, because the fine-tune library on Civitai is enormous and it runs on almost any GPU. SD 2.x was effectively skipped by the community. SD3 is impressive on prompt comprehension and text rendering, but the ecosystem is smaller and the original licence terms made some commercial users cautious.
Stable Diffusion vs Midjourney, DALL·E and FLUX
Each of the major image models makes different tradeoffs.
Midjourneyis closed and runs on subscription. The output is consistently beautiful out of the box with very little prompt engineering needed — its default aesthetic is the strongest of any model. The cost: you can't run it yourself, you can't fine-tune it, and the content licence depends on your tier.
DALL·E 3(via ChatGPT) is closed, integrated into ChatGPT's prompt-rewriting layer, and excellent at following long, complex prompts. Weak on photorealism compared to SDXL fine-tunes and FLUX.
FLUX(by Black Forest Labs, founded by ex-Stability researchers) is the newest serious contender. FLUX Pro is closed and API-only; FLUX.1 [schnell] and [dev] are open-weight. FLUX has better text rendering and stronger prompt comprehension than SDXL, at the cost of larger model size — typically 16+ GB of VRAM versus SDXL's 8 GB.
Stable Diffusion (and SDXL fine-tunes specifically) wins on photorealism per dollar, ecosystem maturity, and the fact that you can actually own and modify the model. For photographic-style work at sensible hardware requirements, an SDXL fine-tune like Juggernaut XL is hard to beat.
Fine-tunes: where Juggernaut XL fits
The base SDXL model is competent at everything and excellent at nothing. The interesting work in the SDXL ecosystem happens in fine-tunes: people take the base model and continue training it on a specialised dataset to push its output in a particular direction.
The major SDXL fine-tunes you'll see referenced:
- Juggernaut XL (by RunDiffusion) — photorealism. The most-downloaded SDXL fine-tune by a large margin.
- RealVisXL — also photorealism, slightly different aesthetic.
- DreamShaper XL — semi-realistic, artistic illustration, fantasy.
- AnimagineXL — anime and stylised character art.
- Pony Diffusion XL — character work with strong prompt adherence.
We run Juggernaut XL Lightning on the homepage generator because it's the best general-purpose photorealistic option in the SDXL ecosystem and the Lightning variant lets us serve generations in 6–12 seconds without giving up much quality. See the full tool roadmap for what's coming next.
What is Juggernaut XL?
Juggernaut XL is a fine-tune of SDXL by RunDiffusion, trained specifically to fix the things stock SDXL gets wrong in photographic work — plasticky skin, flat lighting, lifeless eyes, broken hands. Among open-source SDXL models, it's widely considered the strongest general-purpose option for photorealism. Current version at time of writing is v10 / Ragnarok.
For the full breakdown and a free playground, see the Juggernaut XL generator.
Lightning, Turbo, LCM: the speed variants
Standard SDXL needs 25–30 diffusion steps to converge on a clean image. That's slow for an interactive web tool — eight to fifteen seconds even on fast GPUs.
Several distillation techniques produce variants that converge in 4–8 steps instead:
- SDXL Lightning (ByteDance) — distillation using progressive adversarial training. Quality holds up well at 4 steps, nearly identical at 8.
- SDXL Turbo (Stability AI) — adversarial distillation aimed at single-step inference. Faster but visibly lower quality than Lightning at equivalent step counts.
- LCM (Latent Consistency Models) — a different distillation method that works as a LoRA you can apply to any SDXL checkpoint, including fine-tunes.
We run the Juggernaut Lightning SDXL variant, which combines the Juggernaut XL fine-tune with Lightning distillation. Same Juggernaut look, generated in roughly a third of the time.
How to write good Stable Diffusion prompts
Stable Diffusion responds well to specific, structured prompts. Vague prompts get vague results; specific prompts get usable ones.
A workable template for photorealistic work:
[subject], [pose or action], [setting], [camera/lens], [lighting], [style references]
For example: “A woman in her 40s laughing in a sunlit kitchen, mid-action, 50mm lens at f/1.8, golden hour through a window, photojournalism style, sharp focus on eyes.”
Things SDXL-family models respond well to: focal lengths (35mm, 50mm, 85mm), film stocks (Portra 400, Kodak Gold), lighting descriptors (golden hour, soft window light, harsh midday sun), and shot types (wide, medium, close-up). Things to avoid: long strings of vague adjectives (“beautiful, amazing, stunning, gorgeous”), and contradictions (“cartoonish realistic photograph”).
A short negative prompt usually helps for portraits: “blurry, low quality, deformed hands, extra fingers, watermark, text.” Don't stuff it — long negative prompts hurt more than they help. If faces come out slightly blurry or warped even after prompting, our upcoming Face Restoration tool is designed to fix exactly that in one click.
Hardware: do you need a GPU?
To run Stable Diffusion locally: yes, and a reasonable one. SDXL needs around 8 GB of VRAM to run comfortably. SD 1.5 will run on 4 GB. SD3.5 Large wants 16+ GB. FLUX is even heavier.
If you don't have a suitable GPU, you have three options: pay for cloud GPU time (RunPod, Vast.ai), use a free demo on Hugging Face Spaces (limited, often queued), or use a hosted wrapper like this one — free trial, no setup, no waiting for downloads. We also have a step-by-step guide on how to use Stable Diffusion if you're just getting started.
Is Stable Diffusion free? Licensing explained
The model itself is free to download and use. Stable Diffusion is released under variants of the CreativeML Open RAIL-M licence, which allows commercial use, modification, and redistribution. The restrictions in the licence are around harmful use (CSAM, defamation, etc.) rather than commercial terms.
SD3 was initially released under a more restrictive Stability AI Community Licence with a $1M revenue cap, which slowed its adoption; the terms have since been relaxed.
Juggernaut XL specifically is under CreativeML Open RAIL++-M and is free for commercial use. You own what you generate.
Try Stable Diffusion in your browser
You don't need a GPU or a Python install to try Stable Diffusion. Open the Text2Pixel playground and generate your first image in your browser. The trial works with no signup. After that, free credits are granted on signup; pay-as-you-go credit packs are available if you need more — no subscription. If you want a deeper dive, our guide to what Stable Diffusion is covers the technical side in more detail.