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I also committed to have everything supported in the app available in https://github.com/liuliu/swift-diffusion repository, and making that an open-source CLI tool that other stable-diffusion-web-ui can choose as an alternative backend. The reason is because this implementation, while behind PyTorch on CUDA hardware, are about 2x if not more on M1 hardware (meaning you can reach somewhere around 0.9 it/s on M1, and better on M1 Pro / Max / Ultra (don't have access to these hardwares)).
Yes, Android devices tend to have bigger RAMs, making running 1024x1024 possible (this is not possible at all on iPhones, which could peak around 5GiB memory with my current implementation, some serious engineering required to bring that down on iPhone devices). The problem is I am not sure about speed. I would likely switch to NCNN (https://github.com/Tencent/ncnn) as the backend which have a decent Vulkan computing kernel support. It is definitely a possibility and there is a path to do that.
I've been using Diffusion Bee on my Mac, and it's just gained the ability to import models (which it converts), but it is unpickling to do so— but barely. It unpickles, figures out what sort of data is in every data file and then computes what it wants from them on its own. I would love it to not use unpickling at all, so my intention is if I can figure it out, to write a script to decode the pickle file (with Fickling or otherwise) and then just do the weight calculation/assignment.
I've been using Diffusion Bee on my Mac, and it's just gained the ability to import models (which it converts), but it is unpickling to do so— but barely. It unpickles, figures out what sort of data is in every data file and then computes what it wants from them on its own. I would love it to not use unpickling at all, so my intention is if I can figure it out, to write a script to decode the pickle file (with Fickling or otherwise) and then just do the weight calculation/assignment.
Got the no-unpickling weight extractor working, you can see it here. Currently everything is in the two no_pickle_ files, but I'll probably be pushing a version up that puts them into convert_model.py and fake_torch.py, with an option passed to convert_model determining whether unpickling is used. I made another branch (visible from my GitHub profile) with a proper restricted unpickler, that the forthcoming push will merge into this.