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Comment by gzer0 | Hacker News Reader

The difference between an M2 air (8gb/512gb) versus an M1 pro (16gb/1tb) is much more than I expected.

  * M1 pro (16gb/1tb) can run the model in around 3 minutes.
  * M2 air (8gb/512gb) takes ~60 minutes for the same model.

I knew there would be some throttling due to the m2 air's fanless model, but I had no idea it would be a 20x difference (albeit, the m1 pro does have double the RAM. I don't have any other macbooks to test this on).

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That's probably due to swapping due to the 8GB of RAM. People who have run Stable Diffusion on M2 airs with 16 GB of RAM seem to get performance that is in line with their GPU core count.

Correct. We've been seeing 8GB is super slow, >=16GB is fast. We'll add that to the prerequisites.

Unscientifically that puts the M1 Pro GPU at about 25% of the performance of a RTX 3080.

Not too shabby...

EDIT - this comment implies it's much faster: https://news.ycombinator.com/item?id=32679518

If that's correct then it's close to matching my 3080 (mobile).

img2img runs in 6 seconds on my GeForce 3080 12 GB. 6+ it\s depending on how much GPU memory is available. If I have any electron apps running it slows down dramatically.

what args are you passing to img2img?

Curious about:

1. Image size

2. Steps

3. What your numbers are for text2img

4. (most importantly) are you including the 30 seconds or so it takes to load the model initially? i.e. if you were to run 10 prompts and then divide the total time by 10, what are your numbers?

Re 4 the lstein repo gives you an interactive repl, so you don't have to reload the model on every prompt.

I also have a 3080 and as far as I remember (not at my pc right now) it was 3-10 secs for img2img 512px cfg13 50 steps batch size 1 dimm sampler.

It's likely that a significant fraction of the perf difference between Apple' GPUs and NVIDIA GPUs is due to NVIDIA's CUDA being high optimized, and Pytorch being tuned to work with CUDA.

If Pytorch's metal support improves and Apple's Metal drivers improve (big ifs), it's likely that Apple's GPUs will perform better relatively to NVIDIA than they currently do.

Neither of these should take minutes. Try Heun sampler, 8 steps, Karras noise schedule. Should be possible to get good images in 11 secs (or 10 secs if you go down to 7 steps). measurements admittedly from M1 Max. https://twitter.com/Birchlabs/status/1565029734865584143?s=2...

I would assume it is the memory. The test command from the discussed link runs in slightly over 2 minutes on my M2 Air (16gb). How long does it take for yours?

I suspect the lack of RAM is the issue here.

I suspect that the M2 air is thrashing the disk pretty aggressively. Diffusion models rerun the same model once per step, so for a generation with 50 steps, you copy the entire model in and out of memory 50 times. That's going to kill performance.

I believe the model is copied into ram once upon calling StableDiffusionPipeline, unless the mac implementation partially loads the model due to only having 8G of ram.

It's only copied to VRAM once when implemented correctly.

M1 is a unified memory system and doesn't have VRAM.

I know, I was just adding context.

just water cool it! https://www.youtube.com/watch?v=9DyUitTVWlw

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