GoKawiilI use Draw Things a lot on Apple hardware, and one thing has always bothered me about Automatic1111: it feels slower than it should.
Not unusably slow. Just slow enough that you notice it.
On my M3 Pro, a short five-step DPM++ SDE generation in Automatic1111 was typically landing somewhere around 8–10 seconds. Draw Things had already shown me that Stable Diffusion on Apple Silicon could feel much more immediate than that.
So I wanted to see how much of that gap was actually necessary.
There was one important constraint: I did not want to replace Automatic1111.
I wanted the same WebUI, checkpoints, LoRAs, samplers, extensions, API, prompt syntax, and general workflow. I wasn't interested in converting everything to Core ML and building another inference engine around it. The goal was much narrower:
How fast can Automatic1111 get if we make the parts that matter behave more like native Apple software?
The answer, at least for the workloads I'm running, is quite a bit faster.
The same class of generation that was taking roughly 8–10 seconds on my M3 Pro is now generally landing between 3 and 7 seconds. And 13-20, now landing 8-10 on my M1 Mac Mini.
Those are observed ranges across my current workloads, not a controlled benchmark claiming a universal 2x improvement. There is also an important distinction between the runtime improvements and NGMS, which actually reduces the amount of guidance work being performed.
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