If you are picking an image generation model for a local rig in 2026, your real choice is between three families: Stable Diffusion 1.5 (the lightweight veteran), SDXL (the 1024 native step up), and Flux (the new king of quality, with the heaviest VRAM bill). This article walks through what each one is good at, what it costs, and which is the right fit for your hardware.

If you only want the headline:

• 6–8 GB VRAM: SD 1.5
• 10–12 GB VRAM: SDXL
• 16 GB+ VRAM: Flux (with caveats), or SDXL with heavy LoRA / ControlNet stacks
• 24 GB+ VRAM: Flux comfortably, or experiment with whatever ships next

The rest is the why.

A 60-second history

Stable Diffusion 1.5 dropped in October 2022 as the open-source moment that put generative AI on home GPUs. It is small (4 GB FP16 weights), trained at 512 × 512, and the entire fine-tuning ecosystem — every LoRA, every checkpoint mix, every ControlNet — calibrated itself around SD 1.5 first.

SDXL (Stable Diffusion XL) released in July 2023, bumping native resolution to 1024 × 1024, doubling weight count to ~3.5 B parameters across the base + refiner pair, and substantially improving prompt adherence and photorealism. SDXL is the workhorse of the “good enough quality, reasonable VRAM” tier today.

Flux (Flux.1 dev / schnell / pro by Black Forest Labs, the team behind the original Stable Diffusion) launched in August 2024. Flux is a different architecture — a flow-matching transformer rather than a UNet — and beats both SD 1.5 and SDXL on prompt following, hands, text rendering, and fine detail. It is also dramatically heavier: ~12 B parameters.

By 2026 each of these still has a real audience. They have not replaced each other; they serve different sweet spots.

VRAM and speed comparison

Model	Native resolution	Parameters	FP16 VRAM (model alone)	4-bit VRAM	First-gen speed (16 GB GPU)
SD 1.5	512 × 512	~860 M	~2 GB	~1 GB	1–3 sec
SDXL base	1024 × 1024	~3.5 B	~7 GB	~3.5 GB	4–10 sec
SDXL Turbo	512 × 512	~3.5 B	~7 GB	~3.5 GB	<1 sec (1–4 steps)
Flux dev	1024 × 1024	~12 B	~24 GB	~10 GB	15–40 sec
Flux schnell	1024 × 1024	~12 B	~24 GB	~10 GB	4–10 sec (4 steps)

These are weight-only numbers. In practice you also need a few GB for the VAE, encoders, and working memory. SDXL on a 12 GB card is comfortable; Flux dev on a 12 GB card requires the quantized variants (flux-dev-fp8 or GGUF builds), which run but are noticeably slower.

For background on what 4-bit and 8-bit quantization actually do, see our quantization explainer — the core ideas apply to image models too.

Quality: where each one shines

SD 1.5: the LoRA universe

SD 1.5’s quality at the base level is below SDXL and far below Flux. But its fine-tuning ecosystem is unmatched. Civitai alone lists tens of thousands of LoRAs, checkpoints, and embeddings calibrated for 1.5 — covering anime styles, photoreal humans, specific artists, specific characters, specific lighting setups. If you want a particular look, there is probably a LoRA for it on 1.5 already.

For artistic generation, illustrations, anime, or any style that has been heavily explored by the fine-tuning community, SD 1.5 with a curated LoRA stack still produces results that often beat what you would get from base SDXL or even Flux at “first try.” It is also fast enough that iteration is pleasant.

SDXL: the photoreal default

SDXL out of the box produces clearly better photorealism than SD 1.5: better skin, better hair, better hands (most of the time), much better prompt adherence. The native 1024 × 1024 resolution alone removes the upscaling step that 1.5 workflows usually need.

The SDXL ecosystem is mature. Custom checkpoints like JuggernautXL, DreamShaperXL, and RealVisXL are excellent general-purpose photoreal bases. SDXL LoRAs and ControlNets are widely available, and almost every workflow tutorial you find online assumes SDXL today.

For 90% of users on 12–16 GB cards, SDXL hits the sweet spot — high quality, large enough ecosystem, manageable VRAM, fast enough iteration.

Flux: when only the best will do

Flux dev (the open-weights variant; pro is API-only) clearly leads on:

• Hands and anatomy — the long-running embarrassment of diffusion models is much better.
• Text rendering — Flux can produce legible text in images far more reliably than SD or SDXL.
• Prompt adherence — Flux follows complex compositions and multi-subject prompts more reliably.
• Fine detail at native resolution — sharper, less smudgy.

The cost is real:

• VRAM: Flux dev FP16 is 24 GB. You need a 4090 / 5090 / A6000 / 6000 Ada to run it comfortably. On 16 GB cards, fp8 quantization is required and quality takes a small hit.
• Speed: Flux dev needs 20–28 sampling steps for best quality, taking 15–40 seconds per image even on top hardware. Flux schnell (the distilled fast variant) generates in 1–4 steps but with a quality drop.
• Fine-tuning is harder. The transformer architecture trains differently than UNets; LoRA quality is improving but the ecosystem is younger.

Flux is the right choice when image quality is the goal and hardware can keep up.

Fine-tuning ecosystem comparison

Aspect	SD 1.5	SDXL	Flux
LoRA count on Civitai	100,000+	30,000+	growing fast (5,000+)
Custom checkpoints	thousands	hundreds	dozens
ControlNet support	universal	universal	partial (some types)
LoRA training VRAM (rank 32, 1024 res)	~10 GB	~16 GB	~24 GB
IP-Adapter support	universal	universal	partial
Inpainting models	strong	strong	improving

The trend: SDXL’s ecosystem reached parity with SD 1.5 in 2024–2025; Flux is approaching parity now in 2026 but still trails on niche or stylistic fine-tunes.

Which one for which workflow

Goal: maximum visual quality, you have 24 GB+ VRAM
  → Flux dev (fp16 if 24 GB, full fp16 if 32+ GB)

Goal: best photoreal photos, 12–16 GB VRAM
  → SDXL with a custom checkpoint like JuggernautXL or RealVisXL

Goal: anime, illustration, artistic styles, character LoRAs
  → SD 1.5 with the right LoRA stack
  (or SDXL if you want higher resolution out of the gate)

Goal: speed-iterate during prompt design
  → SDXL Turbo or Flux schnell — both designed for 1–4 step generation

Goal: legible text in images (logos, signs, posters)
  → Flux is the only realistic option

Goal: Limited VRAM (6–8 GB)
  → SD 1.5 (or SDXL with --medvram flag in A1111 / sequential offload in ComfyUI)

For more practical setup help, see our ComfyUI on Windows guide — all three model families load the same way.

What about SD3, SD3.5, the newer models?

Stability AI released SD3 and SD3.5 in late 2024 / early 2025. They are technically competitive with Flux at a similar parameter scale, but adoption has been slower because of their commercial license terms (free for non-commercial; paid for commercial use beyond a revenue threshold). The Flux dev license is more permissive (non-commercial) and Flux schnell is fully Apache 2.0 — both reasons the open-source community gravitated to Flux as the SD3 successor in practice.

If you are building a commercial product, the licensing differences matter. For personal use, either family works.

Bottom line for your wallet

If you are buying a card specifically for image generation in 2026:

• Budget pick (under $400): RTX 4060 Ti 16GB or RTX 5060 Ti 16GB — the VRAM is the thing. 16 GB lets you run SDXL comfortably and Flux dev with fp8 quantization. Either is vastly better than the 8 GB options at similar prices.
• Sweet spot ($800–1200): RTX 4080 Super or RTX 5070 Ti / 5080 — fast enough for daily Flux work plus headroom for image + LLM running concurrently.
• No-compromise ($1500+): RTX 4090, 5090, or used 3090 with 24 GB — Flux at full quality, multiple LoRAs, big batch sizes.

For the math behind those VRAM numbers and how to estimate your headroom, the VRAM guide is the LLM-focused companion piece — same math applies.

The interesting truth is that for a lot of users, SDXL at 16 GB is enough. The marginal quality gain to Flux is real but smaller than the marginal cost of doubling your VRAM. Spend the difference on a good case fan instead.