Stable Diffusion XL
The 3.5B-parameter base model that is still the most widely-supported open image model on Earth — every UI, every ControlNet, every LoRA trainer targets SDXL first, and it runs on far less VRAM than FLUX.
Last verified 2026-07 · Page created; VRAM tiers verified against Stability AI's own SDXL guidance and widely-reported community minimums.
What it is good at
SDXL 1.0, released July 2023, is a 3.5B-parameter UNet-based diffusion model — architecturally older and smaller than FLUX, but its head start means it has the deepest ecosystem of any open image model: ComfyUI, Automatic1111, and Forge all treat it as a baseline, and the LoRA/ControlNet/fine-tune community around it dwarfs anything built for newer models. Its two-encoder CLIP text conditioning (77-token limit per encoder) is less prompt-literate than FLUX's T5 encoder, but the tooling maturity and speed more than compensate for most use cases.
License is CreativeML Open RAIL++-M — open for commercial and personal use, with a short list of prohibited-use restrictions baked into the license itself (rather than a blanket non-commercial clause like FLUX.1 [dev]).
The hardware it wants
SDXL is dramatically lighter than FLUX: 8 GB of VRAM is comfortable for full-precision (FP16) generation at its native 1024x1024 resolution, and 6 GB is workable with attention-slicing/optimization tricks most UIs enable by default. 12-16 GB gives faster generation and headroom for larger batches or an SDXL Refiner pass, but is a comfort upgrade, not a requirement — this is the image model to pick when GPU budget is the binding constraint.
VRAM by quantization
Image models don’t quantize the same way LLMs do — practical VRAM guidance instead:
Runs on
Unified-memory machines — Apple Silicon Mac Studio/MacBook Pro (Max/Ultra chips) and AMD Strix Halo mini-PCs — share system RAM and GPU memory in one pool, so a 64–128 GB configuration can hold models that would otherwise need multiple discrete GPUs. Throughput is bandwidth-bound like any GPU; it isn’t modeled in the table above yet.
Recommended builds
Three ways to size a machine for Stable Diffusion XL, from the smallest GPU that fits to the no-compromise option with headroom for the next model up.
Build it, or rent the compute?
Owning the hardware is a one-time cost that keeps paying off the more you actually use it — every run after the first is free, the data never leaves your machine, and the GPU still does everything else a PC does (gaming, editing, the rest of your workload) between AI sessions. Renting cloud GPU time trades that upfront cost for pay-as-you-go pricing with no idle hardware to maintain, which wins for occasional, bursty, or much-larger-than-consumer-VRAM jobs. For a model that fits comfortably in the VRAM tiers above and gets used regularly, owning the GPU tends to be the cheaper path over time — for anything larger than a single card can hold, renting is often the more practical starting point.
Frequently asked questions
Can I run SDXL on a 6 GB GPU?
Yes, workably — with the attention-slicing and half-precision optimizations most local UIs (ComfyUI, Automatic1111) apply automatically. 8 GB is the more comfortable floor for consistent full-resolution generation.
Should I use SDXL or FLUX.1 dev?
SDXL if your GPU is 8-12 GB, you want the widest LoRA/ControlNet ecosystem, or you need faster iteration. FLUX.1 dev if you have 12 GB+ (quantized) or 24 GB (full quality) and want stronger prompt adherence and photorealism — they are not strictly better/worse, just different tradeoffs.
Do I need the SDXL Refiner model too?
No — it is optional. The base model alone produces full-quality 1024x1024 images; the Refiner adds a second denoising pass some workflows use for extra detail, at the cost of additional VRAM and generation time.