Is there a way to run things like this with an AMD graphics card? Every Stable Diffusion project I've seen seems to be CUDA focused.

That's because Stable Diffusion is built with PyTorch. Which isn't optimized for anything but CUDA. Even the CPU is a second class citizen there. Let alone AMD or other graphics.

Not saying PyTorch doesn't run on anything else. You can but those will lag and some will be hackish.

Looks like Nvidia is on its way to be the next Intel.

> GPGPU stands for General-purpose computing on graphics processing units.

- "PyTorch OpenCL Support" https://github.com/pytorch/pytorch/issues/488

- Blender re: removal of OpenCL support in 2021 :

> The combination of the limited Cycles split kernel implementation, driver bugs, and stalled OpenCL standard has made maintenance too difficult. We can only make the kinds of bigger changes we are working on now by starting from a clean slate. We are working with AMD and Intel to get the new kernels working on their GPUs, possibly using different APIs (such as CYCL, HIP, Metal, …).

- https://gitlab.com/illwieckz/i-love-compute

- https://github.com/vosen/ZLUDA

- https://github.com/RadeonOpenCompute/clang-ocl

AMD ROCm: https://en.wikipedia.org/wiki/ROCm

AMD ROcm supports Pytorch, TensorFlow, MlOpen, rocBLAS on NVIDIA and AMD GPUs: https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni...

RadeonOpenCompute/ROCm_Documentation: https://github.com/RadeonOpenCompute/ROCm_Documentation

ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :

> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.

ROCmSoftwarePlatform/gpufort: https://github.com/ROCmSoftwarePlatform/gpufort :

> GPUFORT: S2S translation tool for CUDA Fortran and Fortran+X in the spirit of hipify

ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP:

> HIP is a C++ Runtime API and Kernel Language that allows developers to create portable applications for AMD and NVIDIA GPUs from single source code. [...] Key features include:

> - HIP is very thin and has little or no performance impact over coding directly in CUDA mode.

> - HIP allows coding in a single-source C++ programming language including features such as templates, C++11 lambdas, classes, namespaces, and more.

> - HIP allows developers to use the "best" development environment and tools on each target platform.

> - The [HIPIFY] tools automatically convert source from CUDA to HIP.

> - * Developers can specialize for the platform (CUDA or AMD) to tune for performance or handle tricky cases.*