Image generation is the one local AI workload where the number on the model card is the least useful number on the page. A diffusion model is a pipeline, not a file, and the part people size their card against is often not the part that fills it.
This guide gives the real totals, and it puts licensing first, because the most capable open image models are the ones you are least allowed to use.
The mistake people bring from running LLMs
A language model is one file. An image model is three: the diffusion transformer, one or more text encoders, and a VAE decoder. The text encoder is the one that ruins the budget.
FLUX.1-dev at Q4_0 is 6.79GB, which sounds like an easy fit on a 12GB card. Then it needs T5-XXL, which ComfyUI ships at 9.79GB in fp16 or 4.89GB in fp8, plus CLIP-L at 0.25GB. The honest total at fp16 is about 16.8GB, not 6.79GB.
| Component | Size |
|---|---|
| T5-XXL text encoder, fp16 | 9.79 GB |
| T5-XXL text encoder, fp8 | 4.89 GB |
| T5-XXL text encoder, Q4_K_M GGUF | 2.90 GB |
| CLIP-L text encoder | 0.25 GB |
The encoder quantises too, and quantising it is usually the highest-value move available: going from fp16 to a Q4_K_M GGUF saves 6.9GB, which is more than most people save by downgrading the diffusion model itself.
Two mitigations matter as much as the numbers. Most tools can run the text encoder, produce the conditioning, then unload it before the diffusion steps begin, so peak memory is lower than the sum. And CPU offload for the encoder is cheap here in a way it never is for an LLM, because the encoder runs once per prompt rather than once per token.
Licences first, because this is where the traps are
More than any other category on this site, the strongest open image models are not open in the sense people assume. Every FLUX repository below is also access-gated.
| Model | Params | Licence | Commercial use |
|---|---|---|---|
| Z-Image-Turbo | 6.15B | Apache 2.0 | Yes |
| FLUX.2-klein-4B | 3.88B | Apache 2.0 | Yes |
| FLUX.1-schnell | 11.89B | Apache 2.0 | Yes |
| Qwen-Image | 20.43B | Apache 2.0 | Yes |
| SDXL base 1.0 | 2.57B | OpenRAIL++ | Yes, with use restrictions |
| SD 3.5 Large | 8.15B | Stability AI Community | Conditional |
| SD 3.5 Medium | 2.47B | Stability AI Community | Conditional |
| FLUX.1-dev | 11.90B | FLUX.1-dev non-commercial | No |
| FLUX.2-klein-9B | 9.08B | FLUX non-commercial | No |
| FLUX.2-dev | 32.22B | FLUX non-commercial | No |
| Krea-2-Turbo | 12.82B | Krea 2 Community | Conditional |
| Ideogram 4 | 9.28B | Ideogram 4 non-commercial | No |
The pattern is worth stating plainly. FLUX.1-dev, the single most downloaded serious open image model, is non-commercial. So is the whole FLUX.2 line except the 4B Klein build. Black Forest Labs’ commercially usable models are FLUX.1-schnell and FLUX.2-klein-4B, and both are the smaller, faster, lower-fidelity members of their families.
If the output is going anywhere near a client deliverable, a product, or a page with ads on it, your realistic shortlist is Z-Image-Turbo, FLUX.2-klein-4B, FLUX.1-schnell, Qwen-Image and SDXL.
By VRAM tier
Sizes below are the diffusion model only, from published GGUF blobs. Add the encoder from the first table.
8GB. Z-Image-Turbo at Q4_K_M is 5.02GB, or 3.64GB at Q2_K, and it is Apache 2.0. Pair it with a Q4_K_M T5 encoder and you are at roughly 7.9GB, which fits with the encoder unloaded before sampling. SDXL remains the other genuine 8GB option at 2.57B parameters, and its ecosystem of fine-tunes is still the largest of any model here by a wide margin.
12GB. Z-Image-Turbo at Q8_0 is 7.22GB, or FLUX.2-klein-4B at 3.88B parameters if you want the newer architecture with a commercial licence. SD 3.5 Large at Q4_0 is 4.77GB and fits comfortably, subject to its community licence.
16GB. FLUX.1-dev at Q4_0 (6.79GB) plus an fp8 encoder is about 11.9GB, which is the first tier where the full FLUX pipeline is relaxed rather than tight. Non-commercial only. FLUX.1-schnell is the same size class and Apache 2.0, at fewer sampling steps and lower detail.
24GB. Z-Image-Turbo at BF16 is 12.31GB and SD 3.5 Large at F16 is 16.29GB, so this is where you stop quantising the diffusion model at all. Qwen-Image becomes practical at Q2_K to Q3_K_M, 7.06 to 9.68GB, though at 20.43B parameters its higher quants stay out of reach.
32GB and up. Qwen-Image at Q4 and above, or FLUX.1-dev at F16 (23.80GB) with an fp16 encoder. Qwen-Image at BF16 is 40.87GB and does not fit a consumer card.
What to actually run
If the work is commercial: Z-Image-Turbo. It is Apache 2.0, ungated, 6.15B parameters, and its GGUF ladder runs from 3.64GB to 12.31GB, so it scales across every tier on this page. Being a turbo model it targets few sampling steps, which makes it fast on modest hardware.
If it is personal work and you want maximum fidelity: FLUX.1-dev, accepting the non-commercial licence and the gated download. This is the model most of the community output you have seen was made with.
If you want the largest fine-tune ecosystem: SDXL. Older and lower fidelity out of the box, but the volume of LoRAs and checkpoints built on it still exceeds everything else here, and for style-specific work a good fine-tune usually beats a better base model.
One thing this guide does not do is rank these models by output quality. No primary benchmark was verified for this update, and image quality comparisons are unusually subjective and prompt-dependent. The ordering above is by what fits, what you are licensed to do, and what the architecture is built for. Treat any confident quality ranking you read elsewhere, including a leaderboard, as weaker evidence than generating twenty images from your own prompts.
For the rest of the local stack, the VRAM index covers chat, coding, vision and the rest, and which quantization to use explains why none of the Q4 builds above are actually four bits per weight.