Stable Diffusion 1.5 on Apple M5 Ultra (512 GB)

Can the Apple M5 Ultra (512 GB) run Stable Diffusion 1.5?

Yes, it runs great

Runs great. At FP16 it needs about 2.8 GB of 384 GB usable and takes about 4.1 s per image.

Best precision: FP16
Memory mode: Everything on GPU
Time per image: 4.1 s
Output: 512×512, 25 steps

Best app to run it on the Apple M5 Ultra (512 GB)

On macOS

Draw Things

Desktop app · runs on Draw Things (Metal FlashAttention)

Native Mac app with its own fast Metal engine and quantized downloads.

Open Stable Diffusion 1.5 in Draw Things

ComfyUI Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.
Invoke

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Stable Diffusion 1.5

Original weights · Hugging Face

Full-precision files from Stability AI / Runway, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Stability AI / Runway. The weights themselves download from Hugging Face.

github.com/CompVis/stable-diffusion (opens in a new tab)

The original Stable Diffusion code from CompVis; the v1.5 weights are on Hugging Face.

git clone https://github.com/CompVis/stable-diffusion

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP16, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great: How well it runs on the Apple M5 Ultra (512 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	15.8 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.2 / 15
Match score	55.9 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every precision on the Apple M5 Ultra (512 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	2.8 GB	384 GB	4.1 s	Everything on GPU	Runs great	35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 FP16 · 4.3 s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 FP16 · 5.0 s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB FP16 · 4.9 s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER FP16 · 2.3 s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP16 · 1.6 s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 Ti 16GB FP16 · 3.2 s · Runs great

Quick answers

How much memory does Stable Diffusion 1.5 need on the M5 Ultra 512GB?

At FP16 it needs about 2.8 GB. The M5 Ultra 512GB has 384 GB usable.

How long does Stable Diffusion 1.5 take on the M5 Ultra 512GB?

About 4.1 s per image at FP16 and default settings, an estimate scaled from a reference timing by this hardware’s compute speed.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.