IME, the bottleneck when using diffusion models isn't storage space or memory, it's generation time. Lots of models will run on 8-12 GB 1080-generation GPUs onwards, or on Macs with similar memory, which are probably the bottom end from a GPU power perspective anyway. I also note that these models are marginally slower than the small FLUX.2 model they're based on.

Okay, maybe this allows running a local model on something that has a reasonably powerful GPU and limited memory, like an iPhone, but is that really a common requirement?

There are many problems I want to work on which require billions of tokens. These are completely inaccessible without corporate project sponsorship at the moment. An asic generation machine which can pump out a few 10s of thousands of tokens per second at opus4.6 quality is more than sufficient.

Isn't SD XL 3.5B? And the refiner model is even larger. Those can run on an iPhone 13 Pro.

Led me to wonder what happens if a domain gets a new owner, and they want to petition Apple to remove the block.

I do wonder how these compare to existing image generation models. I've tried https://github.com/alichherawalla/off-grid-mobile-ai for a while but I find the image generation models rather lacking.