DirectML VS text2image-gui

Only reason I am using the DirectML fork of Automatic1111 is because I am on Windows and pytorch hasn't caught up to RocM 6.

DirectML is fully supported path on Windows and is support by Microsoft et al. (https://github.com/microsoft/DirectML).

Everyone is moving off Cuda as quickly as possible not because the other are better, per se, but because it is easier and cheaper.

See: https://github.com/microsoft/DirectML/issues/400

ONNX (https://onnx.ai/ https://github.com/onnx/onnx) is an alternative to the basic CUDA model, using Direct-ML ( https://learn.microsoft.com/en-us/windows/ai/directml/dml-intro https://github.com/microsoft/DirectML), which is a microsoft-backed open approach. That is what has allowed AMD cards, even slightly older ones, to join in on the machine learning fun.

It's really shocking that AMD fails to extend support natively.

Workarounds such as DirectML claim to be the answer in unifying people with NVIDIA or AMD GPUs, but thus far it hasn't, with issues such as [this](https://github.com/microsoft/DirectML/issues/58) constantly popping up.

As nicolaslem points out, Arch does have community packages for ROCm, but that, unsurprisingly fails to lend support to many consumer GPUs. The best community support I have come across are [rocm-opencl](https://copr.fedorainfracloud.org/coprs/mystro256/rocm-openc... [rocm-hip](https://copr.fedorainfracloud.org/coprs/mystro256/rocm-hip/) for Fedora maintained by [mystro256](https://github.com/Mystro256), who is a single AMD employee.Thanks to him, my AMD GPU (Radeon 6800XT) hasn't completely gone to waste, and I was able to tinker with some things (Gaming isn't really up my alley).

Lately however, after beginning to work on DGX V100s and A100s, and using my older laptop with a GTX 1650, it was apparent how simple setting up CUDA was, and how easily I could experiment with it on my consumer card. Many have spoken about similar stories, and here's mine. Really hope AMD does a whole lot more, and doesn't exclusively keep their powerful GPUs for gaming.

You can use a NVidia gpu or the cpu to do the training but the cpu training is very very slow. For AMD graphics cards like the AMD Radeon VII the only solution is pytorch_directml but unfortunately there appears to be a bug that stops it working nn-morse and torch-directml memory leak? · Issue #355

Github: https://github.com/microsoft/DirectML

I read github

With the AMD guide

You can find NMKD here, and the readme should be quite simple to get it to work on your own machine for a basic SD setup: https://github.com/n00mkrad/text2image-gui

So assuming you are suing the "NMKD" GUI i found an existing issue on the github page: The Issue.

Source code is available here if you want to check that out, the download for the precompiled program is on itch.io.

The 1024x572 was from a comment talking about NMKD ,that mentions using OptimiseSD may run on less than 4GB.

Be sure to read the system requirements and the special AMD GPU info page.

Easiest way I have seen so far, working well for me. https://github.com/n00mkrad/text2image-gui/blob/main/DreamBooth.md