rocm-build VS InvokeAI

Scripts to help with building rocm and hip. It will also help work out dependencies. You will need to modify the scripts for them to work and not all are required. https://github.com/xuhuisheng/rocm-build

Short answer no. Long answer "in theory" yes. I tried this [1] but gave up as building rocm + deps takes up to 6h :/ Official statement [2]

[1] https://github.com/xuhuisheng/rocm-build

I am in the same boat with a gfx03 card. What patch did you use? The ones here? https://github.com/xuhuisheng/rocm-build

I also tried to compile pytorch with its Vulkan backend, but ended throwing the towel as LDFLAGS are a mess to get right (I successfully compiled it, but that was only part of the build chain, and decided I had better things to spend time on). I wonder how that would perform; ncnn works pretty decently.

Unofficial black magic is available: https://github.com/xuhuisheng/rocm-build/tree/master/navi10 (pytorch 1.12.0 is outdated but can run SD)

also have an RX570, currently running latest Tensorflow and ROCm 4.1. had to recompile some parts of ROCm 4.1 libraries to get tensorflow to work. mostly followed this guide: https://github.com/xuhuisheng/rocm-build/tree/master/gfx803

Probably not, since I have no idea what you're talking about. I've just been using the models that InvokeAI (2.3, I only just now saw there's a 3.0) downloads for me [0]. The SD1.5 one is as good as ever, but the SD2 model introduces artifacts on (many, but not all) faces and copyrighted characters.

[0] https://github.com/invoke-ai/InvokeAI

I actually used the rocm/pytorch image you also linked.

I'm not sure what you're pointing to with your reference to the Fedora-based images. I'm quite happy with my NixOS install and really don't want to switch to anything else. And as long as I have the correct kernel module, my host OS really shouldn't matter to run any of the images.

And I'm sure it can be made to work with many base images, my point was just that the dependency management around pytorch was in a bad state, where it is extremely easy to break.

> Anyways, hopefully this PR fixes the immediate issue: https://github.com/invoke-ai/InvokeAI/pull/5714/files

It does! At least for me. It is my PR after all ;)

The data being flagged is not in that github repo, it's fetched from elsewhere and I don't fancy spending time looking for it. The alert is for 'Sirefef!cfg' which has been reported as a false positive with a bunch of other stable diffusion projects (https://www.reddit.com/r/StableDiffusion/comments/101zjec/trojanwin32sirefefcfg_an_apparently_common_false/, https://www.reddit.com/r/StableDiffusion/comments/xmhukb/trojan_in_waifudiffusion_model_file/, https://github.com/invoke-ai/InvokeAI/issues/2773 )

I haven’t tried it recently, but InvokeAI runs on Mac. Invoke. I used to run on my MacBook, but have since gotten a Win laptop.

There are already a number of local, inference options that are (crucially) open-source, with more robust feature sets.

And if the defense here is "but Auto1111 and Comfy don't have as user-friendly a UI", that's also already covered. https://github.com/invoke-ai/InvokeAI

https://github.com/invoke-ai/InvokeAI should work on your machine. For LLM models, the smaller ones should run using llama.cpp, but I don't think you'll be happy comparing them to ChatGPT.

Stable Diffusion. /r/stablediffusion There are many tutorials on how to set it up locally and use it. InvokeAI is the easiest way to set it up. https://github.com/invoke-ai/InvokeAI

InvokeAI