I’ve been using InvokeAI:
https://github.com/invoke-ai/InvokeAI
Great support for M1, basically since the beginning. The install is painless.
Release video for InvokeAI 2.2: https://www.youtube.com/watch?v=hIYBfDtKaus
Stable Diffusion with Core ML on Apple Silicon
111–120 of 184 posts
Re: Stable Diffusion with Core ML on Apple Silicon
#112Earlier quoted context omitted.
It reminds me "Teach Yourself C++ in 21 days". You just need to quickly learn Swift (which you will use exactly nowhere after this task). It's astonishing how ungrateful people are. Even writing documentation for the software is quite a time-consuming action - writing the software itself is much more time-consuming. So you are looking at some free software, that gives you the ability to play with StableDiffusion in 2…
I think you completely misunderstood what I was saying. I wasn’t saying that the author of DiffusionBee should make a SwiftUI application. In fact I said the opposite in that I agree that the person who expected a native app is entitled. I was however refuting the person I was responding to who said making a native app is a huge undertaking, because learning SwiftUI is fairly quick. That’s not to say that the maintai…
It is still kind of toxicity: “cool, you did it, but you could do it better - I could do it better, just out of time”.
Don't be toxic to don’t get that hostility.
Re: Stable Diffusion with Core ML on Apple Silicon
#113Man, this takes a ton of room to do the CoreML conversions - ran out of space doing the unet conversion even though I started with 25GB free. Going on a delete spree to get it up to 50GB free before trying again.
All hail Grand Perspective back in the day, not sure who is carrying the "what's wasting my disk space" torch for free these days. Edit: still alive! https://grandperspectiv.sourceforge.net/
I suspect it was virtual memory - the CoreML conversion progress was at 32Gi at one point and there's only 16GB in this laptop. That would explain why it was consuming 30Gi+ of disk space when the output CoreML models only totalled 2.5Gi.
Re: Stable Diffusion with Core ML on Apple Silicon
#114Earlier quoted context omitted.
Last nail in the coffin for DALL·E.
Not really, everyone will have their own flavor on how to rapidly train the model. Dall-e et. al will still be able to bandwagon off of all the free ecosystem being built around the $10M SD1.4 model that is showing what is possible. E.g. Dall-e could go straight to Hollywood if their model training works better than SD’s. The toolsets will work
source for the $10m number? i havent heard that one before, everyone just keeps parrotting the 600k single run number that is obviously misleading
Re: Stable Diffusion with Core ML on Apple Silicon
#115Earlier quoted context omitted.
automatic1111 does have an M1 workaround in the wiki, but it is incorrect it's correct enough that if you know your way around a CLI, git, and package management you can figure it out
It sucks to have to figure it out, any person who figures it out should submit a PR on the very outdated Apple Silicon readme.
you cant send a PR on a wiki right?
also wonder if anyone did a blogpost yet
Re: Stable Diffusion with Core ML on Apple Silicon
#116Macbook Air M1 / 16GB RAM took 3.56 to generate an image, this is pretty wild
> 3.56 to generate an image
3.56 seconds?
Re: Stable Diffusion with Core ML on Apple Silicon
#117How come you always have to install some version of pytorch or tensor flow to run these ml models? When I'm only doing inference shouldn't there be easier ways of doing that, with automatic hardware selection etc. Why aren't models distributed in a standard format like onnx, and inference on different platforms solved once per platform?
In the professional context (apart of individual apps distributed by small creators / indiehackers) usually models are run using standardized runtimes in native code (C++ usually), using runtimes TensorRT (for Nvidia Devices), onnxruntime (agnostic), etc.
Re: Stable Diffusion with Core ML on Apple Silicon
#118How come you always have to install some version of pytorch or tensor flow to run these ml models? When I'm only doing inference shouldn't there be easier ways of doing that, with automatic hardware selection etc. Why aren't models distributed in a standard format like onnx, and inference on different platforms solved once per platform?
Seconded, I wish for a way to work with ML models using native code rather than through some Python scripting interface. I believe TensorFlow is there with C++, but it works only with C++ and not through FFI.
If you are okay with using nvidia-ecosystem, check out tensor rt.
Re: Stable Diffusion with Core ML on Apple Silicon
#119What are some good resources to get into working with this and learning the basics around ML to get some fundamental understanding of how this works?
Re: Stable Diffusion with Core ML on Apple Silicon
#120Would it be possible to run 2 SD instances in parallel on a single M1/M2 chip?
One on the GPU and another on the ML core?