Local Image Generation in 2026: FLUX or SDXL, Which Should You Pick?

The most important fact about local image generation in 2026 is that there is no single winner: run the same prompt on one 24GB GPU and FLUX wins prompt adherence and realism, SDXL wins the biggest LoRA ecosystem and lowest VRAM, and Qwen-Image wins in-image text. So serious local artists keep both FLUX and SDXL installed and switch by task. The big picture is clear: for quality and instruction-following pick FLUX.1 [dev] (12B), for style, customization, and 8GB cards pick SDXL (3.5B), and when you need text pick Qwen-Image. In short: don't standardize on one; choose by axis (quality, ecosystem, text).

In one line: local image generation has no single winner - FLUX for quality and realism, SDXL for the LoRA ecosystem, low VRAM, and commercial use, and Qwen-Image for in-image text; pick by axis.

In plain terms: picking an image model is like picking a camera lens. Just as portrait, wide, and macro are separate, realistic portraits want FLUX Krea, a specific style wants an SDXL fine-tune, and signage/typography wants Qwen-Image. Trying to shoot everything with one lens is the mistake; swapping to fit the task is what pros do.

What differs in quality and text?#

Quality goes to FLUX; text is decided by architecture. FLUX.1 dev's 12B DiT leads with finer detail, natural light, and skin texture, especially on human anatomy like fingers and close-up portraits over SDXL and SD 3.5. For realism specifically, FLUX.1 Krea (refined on under a million ultra-photorealistic images) looks noticeably more natural than dev - while base FLUX leans graphic and illustrative, good for concept art. For text, FLUX is legible, SD 3.5 is passable (T5-XXL), and SDXL garbles it - for bilingual typography like Korean, Qwen-Image is the reference.

2026 local image models - strengths, VRAM, license (public benchmark, measured snapshot) · columns: Model, Strength (measured/observed), VRAM / license · 출처 Hax hax.moche.ai/en/p/1107?ref=ai_answer

2026 local image models - strengths, VRAM, license (public benchmark, measured snapshot) · columns: Model, Strength (measured/observed), VRAM / license · 출처 Hax hax.moche.ai/en/p/1107?ref=ai_answer

Model | Strength (measured/observed) | VRAM / license
FLUX.1 dev (12B) | prompt adherence, realism, text | fp8 ~12GB / non-commercial
FLUX.1 Krea | top photorealism | ~12GB / dev-family
SDXL (3.5B) | 5,000+ LoRAs, lowest VRAM | ~8GB / commercial OK
SD 3.5 Large | mid quality, composition | FP16 ~18GB / limits apply
Qwen-Image (20B) | in-image text, bilingual | 20B-class / commercial OK

목차

Where are the speed and VRAM lines?

What's easy to miss in ecosystem, license, and running?

So what's the 2026 local image recommendation?

Where are the speed and VRAM lines?#

Step count and quantization drive them. On an RTX 4090, SDXL defaults to 25-30 steps but hits 4 steps at ~0.3-0.5s with Turbo/Lightning LoRAs. FLUX.1 dev runs 20-28 steps at about 12s per image (slow but fine for single shots), while schnell's 4 steps is best for iteration. VRAM is SDXL ~8GB, FLUX fp8 ~12GB, SD 3.5 Large FP16 ~18GB, but headline numbers exclude the text encoder, so budget more in a real pipeline. The sweet spot is 16GB (room for ControlNet and LoRAs). A 12GB 4070 runs FLUX dev fp8 at 30-60s per image.

What's easy to miss in ecosystem, license, and running?#

The LoRA ecosystem, the commercial license, and ComfyUI wiring. SDXL has 5,000+ LoRAs on CivitAI alone (FLUX ~500, SD 3.5 ~50), which is why it "refuses to die." For commercial use, FLUX.1 dev forbids selling output (non-commercial), so choose SDXL or FLUX.1 schnell. The standard runner is ComfyUI, but FLUX has no negative prompt and its CFG behaves fundamentally differently from SDXL, so dropping an SDXL graph onto FLUX yields broken, washed-out results - always start from a FLUX-specific template.

So what's the 2026 local image recommendation?#

The key is don't standardize on one; choose by priority axis.

Quality and instruction-following: FLUX.1 [dev] (12GB+); realism-specialized is Krea. Base tone is illustrative, good for concept art.

Ecosystem, low VRAM, commercial: SDXL (most LoRAs/ControlNet, 8GB, commercial OK) or FLUX schnell (commercial, ultra-fast).

Text and bilingual: Qwen-Image. For speed, Z-Image Turbo (6B) or FLUX.2 klein (4B). Run on ComfyUI and re-validate per task.

Related reading: ComfyUI로 이미지·영상 만들기: 우리가 직접 굴리며 잰 운영 회고, ComfyUI란? 노드로 조립하는 이미지·영상 생성 파이프라인

Related reading: 로컬 이미지 생성(SDXL·Flux), 5분 시작 가이드, 로컬 이미지 생성 SDXL vs Flux, 직접 돌려본 속도·품질

Reference links

ComfyUI (node-based runner)

black-forest-labs/flux (FLUX official)

Stability AI (SDXL, SD 3.5)

Qwen-Image (in-image text)

SDXL paper (latent diffusion)

Note: figures like speed (12s or 0.3-0.5s per image), VRAM (8-18GB), and LoRA counts (5,000+) are 2026 public benchmarks and community observations that vary greatly by GPU, resolution, steps, and quantization (not permanent). FLUX dev is a non-commercial license that forbids selling output, so verify before commercial use. Image models and speed optimizations move fast (e.g., Z-Image, FLUX.2), so this is reviewed quarterly.