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johnfn

For those as keen as I am to try this out, I ran these steps, only to run into an error during the pip install phase:

> ERROR: Failed building wheel for onnx

I was able to resolve it by doing this:

> brew install protobuf

Then I ran pip install again, and it worked!

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In the troubleshooting section it mentions running:

    brew install Cmake protobuf rust

To fix onnx build errors. I had the same issue.

What kind of speed does this run at? Eg. How long to make a 512x512 image at standard settings?

On my M1 Pro MBP with 16GB RAM, it takes ~3 minutes.

I haven't installed from this link specifically, but I used one of the branches on which this is based a few days ago, so the results should be similar.

On a first-gen M1 Mac mini with 8GB RAM, it takes 70-90 minutes for each image.

Still feels like magic, but old-school magic.

On an M1 Pro 16GB it is taking a couple minutes for each image.

Is that the difference in graphics performance between the M1 and M1 Pro or did the other person do something wrong? 70-90 minutes seems nuts

Might be the RAM difference. RAM is shared between CPU and GPU on the M1 series processors.

My 16gb M1 Air was initially taking 13 minutes for a 50 step generation. But when I closed all the open tabs and apps it went down to 3 minutes.

Looks like RAM drastically affects the speed.

I have the M1 8GB I mentioned in my first comment, and the M1 Pro 16GB I mentioned in my second component, side-by-side. However, the first one was running a Stable Diffusion branch from earlier in the week, so I replaced using the same instructions. The only difference now is the physical hardware.

The thing to understand is that the 8GB M1 has 8GB. When I run txt2img.py, my Activity Monitor shows a Python process with 9.42GB of memory, and the "Memory Pressure" graph spends time in the red zone as the machine is swapping. While the 16GB M1 Pro immediately shows PLMS Sampler progress, and consistently spends around 3 seconds per iteration (e.g. "3.29s/it" and "2.97s/it"), the 8GB M1 takes several minutes before it jumps from 0% to 2% progress, and it accurately reports "326.24s/it"

So yes, whether it's M1 vs M1 Pro, or 8GB vs 16GB, it really is that stark a difference.

Update: after the second iteration it is 208.44s/it, so it is speeding up. It should drop to less than 120s/it before it finishes, if it runs as quickly as my previous install. And yes, 186.04s/it after the third iteration, and 159.22s/it after the fourth.

Sounds entirely like a swap-constrained operation. You need ~8gb of VRAM to load the uncompressed model into memory, which obviously won't work well on a Macbook with 8gb of memory.

My first-gen M1 MacBook Air with 16GB takes just under 4 minutes per image. Running top while it's generating shows memory usage fluctuating between 10GB and 13GB, so if you're running on 8GB it's probably swapping a lot.

Installed from this link on a MacBook Pro (16-inch, 2021) with Apple M1 Pro and 16GB. First run downloads stuff, so I omit that result.

I had a YouTube video playing while I kicked off the exact command in the install docs, and got: 16.84s user 99.43s system 61% cpu 3:08.51 total

Next attempt, python aborted 78 seconds in! Weird.

Next attempt, with YouTube paused: 16.31s user 95.48s system 65% cpu 2:49.45 total

So around three minutes, I'd say.

A little over three minutes on a first-gen M1 iMac with 16GB.

It looks like memory is super-important for this (which isn't all that surprising, really...).

Looks like I'm getting around 4s per iteration on my M1 Max. At 50 iterations, that's 200 seconds.

M1 Max (32gb) is around 35 seconds per image.

Getting around 4 minutes per image on M1 MacBook Air 16GB

Hm, taking 2 hours on my M1 MacBook Air 16GB and it's clearly swapping. Are you using model v1.4? Or any other memory optimization that you applied?

For 512x512 on M1 MAX (32 core) with 64 GB RAM I'm getting 1.67it/s so 30.59s with the default ddim_steps=50.

I've gotten 1.35it/s that corresponds to 38s, but I've the M1 Max with the 24 cores GPU (the "lower end" one).

I just set up a similar thing on my own a bit differently than OP did.

Apple M1 Max with 10-core CPU, 32-core GPU, 16-core Neural Engine - Takes 38 seconds as well up to 46 when it gets hotter.

Can anyone give comparison with Nvida gpu in terms of performance?

On my M2 Air, 16G, 10 CPU cores, the default command as in the installing instructions takes like 2m20s.

MacBook Air M2 8 CPU 8GB the example apple image took 35mins. Guess I'll wait for now.

You clearly doing something wrong as I get about 3 minutes per image on m1 mac mini.

But yeah, at this stage most of guides are early hacks and require individual tweaking. It is quite expected that people get varying results. I assume in a week or a month situation will get much better and much more user-friendly.

I just had to:

> brew link protobuf --overwrite

Don't blindly run this command unless you understand what you're doing.

Python dependency hell in a nutshell. Impossible to distribute ML projects that can easily be ran.

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