Diffusion brings different set of trade offs, and as you can see it improves speed but I would expect it increases compute required for generation. But this is hard to say for sure without knowing their exact sampling process.
Interestingly we have opposite direction in case with gpt-4o, OpenAI made autoregressive image generation model and it seems it works great.
If the "output quality per compute" is roughly the same for diffusion and auto-regression (is it? I have no idea...), then diffusion will be much more efficient for local inference because the same amount of compute can be packed into a much shorter time period.
I think "output quality per compute" will be loss for diffusion models, but it might be similar (or even better?) for "output quality per number of parameters". Which will still make it better for local inference.
However autoregressive models also have own way of dealing with low compute utilization - it's speculative decoding. You can use smaller (and faster) model to generate bunch of different possible continuations and verify all of them at once. I think Eagle3 for example achieves ~8 tokens per iteration speedup this way (and to be frank I believe it can be even better).