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BlazeObsidian t1_ium8sru wrote

I haven’t tried out the performance yet but it appears PyTorch supports the apple silicon processors now as a separate device named ‘mps’ similar to cuda for Nvidia gpus. There is also a tensor flow plugin that can be separately installed to take advantage of the apple chips

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papinek t1_iums091 wrote

Works very well. I use Stable Diffusion on Mac M1 and using mps its blazing fast

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caedin8 t1_iunmrya wrote

By blazing fast he means as fast as a gtx 1060. My 3070 is 5x faster than my M1 Pro

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papinek t1_iunqbum wrote

Well on my M1 it takes using mps 20 seconds to generate image using SD and 30 steps. Using CPU it would be lots of minutes per one image. So I would say it works on M1 well.

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caedin8 t1_iunqou5 wrote

I don't even think you can use CPU to make images using stable diffusion, but maybe you can.

Yeah my M1 Pro takes about 25-30 seconds per image, some of that has to do with configuration. But my RTX 3070 cranks them about in about 4 to 5 seconds per image.

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papinek t1_iunrbv0 wrote

Yes you can switch to CPU. Which then takes like 5-10 minutes per picture. So the gain using mps is big.

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Hobit104 t1_iunyudt wrote

That wasn't their point though.

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BlazeObsidian t1_iumubsj wrote

Did you run into memory issues ? I assumed it wouldn’t work with only 8 gigs unified memory.

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papinek t1_iunm0b5 wrote

I have 32GB but never run into issue. Next to SD I run Photoshop, Intellij Idea and Chrome with 20 tabs and it was always enough.

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BlazeObsidian t1_iunmgfn wrote

Hmm. Might give it a try. Usually I use colab. If there isn’t much of a difference during inference, local is better

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TheEdes t1_iuqcu3t wrote

Pytorch supports it but there's still some bugs here and there, you might also find that a function or its gradient isn't implemented yet on some architectures.

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