I have been recently testing the new version 0.3.0 on my M1 Pro but I found that following the steps from How to use Stable Diffusion in Apple Silicon (M1/M2) the execution times for CPU and MPS are on average for similar prompts:
Has anyone tested it too ?
Hi @polodealvarado! Your CPU numbers are very similar to the ones I get in my M1 Max, but as reported in the page you mentioned, the speed I see is much faster when using the GPU. Would you mind sharing a couple of details so I can try to take a look? These would be useful:
- The amount of RAM your computer has.
- The version of PyTorch you installed.
- Your macOS version.
- A small code snippet, only if you made any changes to the example we provided.
Thanks a lot!
HI! @pcuenq, thank you for answering.
Here you have all the details and more:
- RAM: 16 GB
- GPU cores: 16
- macOS version: 12.5.1
- Python version: 3.9.13
- Diffuser version: 0.3.0
- Torch version: 1.13.0.dev20220908
I have been using the same code without touching it. On the other hand, I tried another jupyter notebook from this repository and the results are quite similar (cpu works better than mps).
I am following this thread, running mps backend. @pcuenq
That’s a very interesting thread! They specifically say that random operations are not yet optimized; however, diffusers’ code generates random latents in CPU when using the
I’ll do some testing, thanks!