Local Image Generation on Mac: What Fits Your RAM (2026)

Your Mac can generate images locally: SD 1.5 on any 8GB machine, FLUX on 16GB and up, and the new FLUX.2 models on 32GB+. The binding constraint is unified memory. Here is exactly what fits each RAM tier, with file sizes verified from HuggingFace, and which app to use.

By ModelFit Team · Published 2026-06-12 · Updated 2026-10-15
TL;DR

Install Draw Things (free, App Store): it runs SD 1.5, SDXL, FLUX.1, FLUX.2 and more on a custom Metal engine. 8GB Mac: SD 1.5. 16GB: SDXL + 4-bit FLUX. 32GB: FLUX.1 Q8 and FLUX.2 klein. 64GB: full-precision FLUX.1 and the 20B-class 2026 models. macOS needs roughly 5GB headroom on top of the model file (our estimate; file sizes below are HuggingFace-verified).

Table 1

Which image models fit your Mac's RAM?

On Apple Silicon, unified memory plays the role VRAM plays on a GPU: the model must fit in RAM alongside macOS (budget ~5GB headroom, our estimate). The file sizes below were verified from each model's HuggingFace repository on 2026-06-12 (Qwen-Image 2.1 Turbo rows on 2026-10-15).

Mac RAMFits comfortablyBorderlineRecommended app
8GBSD 1.5 (4.27GB)SDXL (6.94GB): expect swapping; FLUX not practicalDraw Things or Mochi Diffusion (Core ML / Neural Engine)
16GBSD 1.5, SDXL, SD 3.5 Medium (5.11GB + 4.89GB fp8 text encoder)FLUX.1 4-bit (MFLUX pipeline ~9.85GB), FLUX.2 klein 4B and Qwen-Image 2.1 Turbo mflux 4-bit (10.6GB bundle): work, but little headroomDraw Things; MFLUX 4-bit for FLUX
32GBFLUX.1 dev GGUF Q8 (12.7GB), FLUX.2 klein 4B (7.75GB), SD 3.5 Large (16.5GB), Qwen-Image Q4 (12.1GB), Qwen-Image 2.1 Turbo mflux 8-bit (18.5GB)FLUX.2 klein 9B (18.2GB weights), Qwen-Image Q8 (21.8GB)Draw Things for speed; ComfyUI Desktop for GGUF workflows
64GB+FLUX.1 bf16 (23.8GB), Qwen-Image Q8 (21.8GB), Qwen-Image 2.1 Turbo bf16 (32.5GB repo), FLUX.2-dev GGUF Q4-Q6 (19.3-27.4GB)FLUX.2-dev bf16 (64.4GB) does NOT fit. Needs 96-128GBComfyUI (GGUF) or MFLUX (native MLX)

File-size sources: SD 1.5, SDXL, SD 3.5 Medium, SD 3.5 Large, FLUX.1, FLUX.1 GGUF quants, FLUX.2 klein 4B, FLUX.2-dev GGUF, Qwen-Image GGUF, Qwen-Image 2.1 Turbo, its mflux 4-bit and 8-bit bundles.

Which app should you use?

  • Draw Things: best for most people

    Free · App Store

    A custom native Metal engine (not a Python wrapper), one-click model downloads, and support for everything from SD 1.5 to FLUX.2, Qwen Image and LoRA training on-device. Local generation is unlimited and free; the optional subscription only covers cloud compute. drawthings.ai

  • ComfyUI Desktop: best for power users

    Free · open source · Apple Silicon beta

    The node-based standard now ships a native Apple Silicon desktop app (beta). Runs GGUF-quantized FLUX.1 and FLUX.2 via the ComfyUI-GGUF node, which is the key to fitting big models in limited RAM. A community benchmark on an M4 Pro Mac mini found it roughly 20% slower than Draw Things on identical hardware (heyuan110.com). comfy.org

  • MFLUX: best native-MLX option (CLI)

    Free · open source

    A line-by-line MLX port of FLUX with built-in 4-bit/8-bit quantization and a --low-ram flag. Tracks new releases fast: FLUX.2 klein, Z-Image, Qwen Image and more. Per its own benchmark table, FLUX.1 schnell generates a 1024×1024 image in under 15 seconds on an M2 Ultra and about 30 seconds on an M3 Pro with the 4-bit pipeline (mflux README).

  • Mochi Diffusion: best Neural Engine efficiency

    Free · open source

    Runs Core ML models on the Neural Engine. The app itself uses ~150MB of RAM per its README. Back under active development (v6.0, Feb 2026) with FLUX.2 Klein support. GitHub

Skip DiffusionBee. It was the classic beginner pick, but it has not shipped a release since August 2024 and lags badly on modern models. Most "Stable Diffusion on Mac" listicles still recommend it, but that advice is two years stale.

FLUX.2 on Mac: what runs in 2026?

FLUX.2 is open-weight, and the practical Mac question is which variant fits. FLUX.2 klein 4B (Apache 2.0, released January 2026) is the sweet spot: 7.75GB of weights, and Black Forest Labs states it fits in about 13GB of memory, workable on a 16GB Mac and comfortable on 32GB. The 9B klein (18.2GB weights, ~29GB per BFL) wants 32GB+. The full FLUX.2 [dev] is a 32B model whose bf16 checkpoint is 64.4GB. On Macs that means GGUF quants (Q4 at 19.3GB, Q6 at 27.4GB) on a 64GB machine, plus its large text encoder.

Sizes from the official FLUX.2-klein-4B, FLUX.2-klein-9B and FLUX.2-dev repos, GGUF quants from city96.

Does Qwen-Image 2.1 Turbo run on a Mac?

Yes, through community ports only. Qwen-Image 2.1 Turbo (published 2026-10-09) is the 8-step distilled build of Qwen-Image 2.1, and the official repository is 32.5GB, of which the Qwen3-VL text encoder alone is 17.5GB. Qwen ships no Apple Silicon path, so Macs use the MLX (mflux) bundles: 10.6GB at 4-bit, borderline on a 16GB Mac, and 18.5GB at 8-bit, comfortable on 32GB. A GGUF denoiser at Q4_K_M is 4.20GB, but it still needs the encoder and VAE alongside it. The checkpoint stays under the Qwen Research License: research and evaluation only.

Sizes from the official repo, the mflux 4-bit and 8-bit bundles and unsloth's GGUF ladder. Every quant, the encoder options and the runtime caveats: Qwen-Image 2.1 Turbo on a Mac.

How fast is image generation on Apple Silicon?

All figures below are community-published measurements, attributed to their source. ModelFit does not run its own benchmarks:

  • SD 1.5, 512×512: ~5-10s on an M4 Pro Mac mini (per heyuan110); ~8-15s on M2 Pro in Draw Things (per rentamac.io)
  • SDXL, 1024×1024: ~20-40s on M4 Pro, 25 steps (per heyuan110)
  • FLUX.1 dev Q6, 1024×1024, 20 steps: ~50-90s on M4 Pro in ComfyUI (per heyuan110)
  • FLUX.1 schnell (2 steps): under 15s on M2 Ultra, ~80s on M3 Pro non-quantized (per the mflux benchmark table)

The pattern: schnell-class models are interactive on any recent Pro/Max chip, dev-class models are a coffee-sip wait, and base-M-chip 8GB machines should stay on SD 1.5.

Frequently asked questions

Can I run FLUX on a Mac?

Yes, via Draw Things, ComfyUI or MFLUX. On 16GB use 4-bit quantization (~9.85GB pipeline); 32GB runs Q8; full bf16 (23.8GB) wants 64GB. FLUX.2 klein 4B is the newer efficient option at 7.75GB.

What is the best app for local image generation on Mac?

Draw Things for most users (free, fast Metal engine, every major model). ComfyUI Desktop for node workflows and GGUF quants. MFLUX if you prefer a native-MLX command line. Avoid DiffusionBee, unmaintained since 2024.

How much RAM do I need for Stable Diffusion or FLUX on a Mac?

8GB: SD 1.5. 16GB: SDXL and quantized FLUX. 32GB: FLUX.1 Q8, FLUX.2 klein, SD 3.5 Large. 64GB: full-precision FLUX.1 and 20B-class models like Qwen-Image.

Is local image generation on Mac as good as Midjourney or DALL-E?

FLUX-class open models produce results competitive with cloud services for most use cases, with no subscription, no content filter surprises, and full privacy. The trade-offs are generation speed (seconds to a minute-plus locally) and needing enough RAM for the bigger models.

Related guides

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