Here are the most practical and up-to-date ways (as of 2025–2026) to run Stable Diffusion locally on a Mac, especially on Apple Silicon (M1 / M2 / M3 / M4 chips). These methods use the unified memory + GPU acceleration via Metal / MLX / MPS.

The easiest and fastest options for most users in 2025–2026 are the native Mac apps.

Easiest & Recommended Options (Best for Beginners / Fast Setup)

1. Draw Things (App Store – free, very well-optimized)
   • Native Mac/iOS app, excellent Metal performance
   • Often considered the best balance of speed + features on Apple Silicon right now
   • Supports SD 1.5, SDXL, many Flux variants, ControlNet, LoRAs, upscaling, inpainting, etc.
   • Completely offline after model download
   • How to get started:
     1. Open the Mac App Store → search for “Draw Things: AI Art Generator”
     2. Install (free)
     3. Open the app → it will offer to download models (start with Realistic Vision, Juggernaut XL, or Flux.1-dev if your Mac has ≥16 GB RAM)
     4. Type a prompt and generate
2. Diffusion Bee (still good, one-click installer)
   • Download from: https://diffusionbee.com
   • Very simple, no terminal needed
   • Good speed on M1–M4 (≈20–45 seconds per image depending on model/resolution/RAM)
   • Supports recent models (SDXL, some Flux versions via updates)
   • Great choice if you want the absolute minimum setup

More Powerful / Advanced Options

If you want extensions, ControlNet, IP-Adapter, many LoRAs at once, or Flux.1 full power:

• Automatic1111 WebUI (A1111 / Forge variant) Still very popular, huge ecosystem, but requires terminal setup.Requirements
  • macOS 13.5+ (Ventura or later)
  • Apple Silicon Mac (M1 or newer)
  • Preferably ≥16 GB RAM
  Quick install steps (2025–2026 style)
  1. Install Homebrew if you don’t have it
     • /bin/bash -c “$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)”
  2. Install dependencies
     • brew install cmake protobuf rust python@3.11 git wget
  3. Clone the repo (use Forge fork for better Mac performance in many cases)
     • git clone https://github.com/lllyasviel/stable-diffusion-webui-forge.git
     • cd stable-diffusion-webui-forge
  4. Launch (important flags for Apple Silicon)
     • First launch (downloads ~6–10 GB)
     • ./webui.sh –skip-torch-cuda-test –no-half-vae –opt-sdp-attention
     • Later launches can use:
     • ./webui.sh –opt-sdp-no-mem-attention –no-half-vae
  5. Wait → browser opens at http://127.0.0.1:7860 Download models into models/Stable-diffusion/ (e.g. from Civitai)

Performance notes: 512×512 SD 1.5 images usually take 8–30 seconds on M2/M3 16–32 GB machines.

4. ComfyUI (node-based, very powerful, increasingly popular)

• Excellent for complex workflows (Flux.1, ControlNet++, etc.)
• Often fastest for Flux on Mac via MLX backend
• Install guide: Search “ComfyUI Mac MLX” or use Stability Matrix (one-click launcher that handles Comfy + A1111 + InvokeAI)

Quick Recommendation Table (2025–2026)

Goal	Best Choice (2025–2026)	Setup Difficulty	Speed on M3/M4 (16–32 GB)	Features
Easiest & good performance	Draw Things	Very easy	Excellent	Very good
One-click, classic	Diffusion Bee	Easiest	Very good	Good
Maximum extensions & community	A1111 / Forge	Medium	Good	Excellent
Complex workflows, Flux	ComfyUI (+ MLX backend)	Medium–Hard	Excellent (Flux)	Best for advanced
Pretty UI + offline	InvokeAI or DiffusionBee	Easy–Medium	Good	Good

Quick Tips for Best Results on Mac

• Use 16 GB RAM or more (8 GB works but is slow / limited to small models)
• Prefer SDXL / Flux.1-dev-fp8 quantized models for newer Macs
• Generate at 512–768 resolution first → use hires fix / upscaler
• Close other heavy apps (Chrome with 30 tabs kills performance)

If you’re just starting, install Draw Things from the App Store right now — it’s free, looks native, and you’ll have your first image in <5 minutes.

Which Mac model do you have (chip & RAM)? That’ll help narrow down the absolute fastest option for you.

Here are some well-crafted FAQs tailored for users wanting to run Stable Diffusion locally on a Mac (especially Apple Silicon M1/M2/M3/M4 models) in 2026. These address the most common questions, confusions, and pain points based on real user experiences.

Stable Diffusion on Mac – Frequently Asked Questions (2026 Edition)

Q1: What’s the easiest way to run Stable Diffusion on a Mac right now?

A: For most people, Draw Things (free on the Mac App Store) is the easiest and best-optimized option. It’s a native Mac app that uses Apple’s Metal/ML framework, so it runs fast and smoothly on M-series chips with minimal setup. Just install, let it download a model (like Juggernaut XL or Flux fp8), and start generating. If you want something even simpler with a one-click installer, try Diffusion Bee (download from diffusionbee.com). Both work offline after the initial model download.

Q2: Which Mac is good enough to run Stable Diffusion well? A:

• 8 GB RAM (base M1/M2 Air): Works, but slow and limited to small models like SD 1.5 or LCM variants (~30–60+ sec/image).
• 16 GB RAM (most common recommendation): Solid sweet spot — good speed with SD 1.5, SDXL, and lighter Flux models (~10–40 sec/image depending on resolution).
• 24–32 GB+ RAM (M2/M3/M4 Pro/Max): Excellent — handles Flux.1-dev, high-res, multiple LoRAs/ControlNets comfortably (~5–25 sec/image). More GPU cores = faster generation (e.g., M3 Max > M3 > M2). Avoid Intel Macs — they don’t get good acceleration.

Q3: Why is generation so slow on a Mac compared to Windows PCs with NVIDIA?

A: Macs use Apple’s unified memory + Metal acceleration (no CUDA). NVIDIA GPUs are still faster for raw diffusion workloads, especially at high resolutions or with complex extensions. On a modern M3/M4 with 16–32 GB, expect 5–40 seconds per image (vs. 1–5 sec on a good RTX card). Use quantized/fp8 models, lower steps (20–30), and resolutions like 768×768 + hires fix for best speed.

Q4: Should I use Draw Things, Diffusion Bee, A1111/Forge, or ComfyUI? A:

• Draw Things → Best for beginners/fast native performance/simple UI. Great ControlNet, LoRA, inpainting support.
• Diffusion Bee → Easiest one-click setup, good for quick tests. Fewer advanced features.
• A1111 / Forge → Huge ecosystem (extensions, scripts), but slower and more error-prone on Mac unless using optimized forks (e.g., Forge with –no-half-vae flags).
• ComfyUI → Most powerful/flexible (especially for Flux + workflows), often fastest for newer models via MLX backend, but steeper learning curve (node-based). Start with Draw Things → move to ComfyUI if you want maximum control.

Q5: I get errors/glitches/black images when switching models in A1111 or Forge — how to fix?

A: Common on Mac due to precision/memory issues. Add these launch flags: –no-half-vae –opt-sdp-no-mem-attention –skip-torch-cuda-test (or –no-half for full fp32 if still unstable, though slower). Restart the UI after changing models. Use Forge fork instead of classic A1111 — it’s usually more stable on Apple Silicon.

Q6: Can I run the latest models like Flux.1 on Mac?

A: Yes! Flux.1-dev (fp8/quantized versions) runs well on 16 GB+ Macs via:

• Draw Things (native support, very fast)
• ComfyUI with MLX backend (often the fastest) Avoid full unquantized Flux on <32 GB — it’ll be very slow or crash. Download fp8/gguf variants from Hugging Face or Civitai.

Q7: How do I get more speed / better quality on my Mac? A:

• Use lower steps (20–30) + good samplers (Euler a, DPM++ 2M Karras).
• Start at 512–768 resolution → use hires fix/upscaler.
• Close other apps (Chrome eats unified memory fast).
• Prefer Core ML / MLX / Metal-optimized tools (Draw Things, ComfyUI-MLX).
• For A1111/Forge: always use –opt-sdp-attention or similar Mac-friendly flags.

Q8: Is it worth buying a high-end Mac (M4 Max, 64 GB+) just for Stable Diffusion? A:

Not really — unless you do heavy Flux/ControlNet workflows or plan to train models. A mid-range M3/M4 with 16–32 GB gives great value. For ultimate speed, many Mac users still remote into a cheap Windows PC with an RTX 3060/4070/4090 via Parsec or similar for generation, while keeping the Mac for editing/prompting.

These cover ~80–90% of the questions Mac users ask in forums/Reddit in 2025–2026. If you have a specific Mac model (e.g., M3 Pro 18 GB) or issue you’re hitting, share details and I can give more targeted advice!