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I've been running SD on AMD hardware since day one. It's a little bit of a hassle compared to NVIDIA for sure, but I attribute that to a decade or two of somewh

I've been running SD on AMD hardware since day one. It's a little bit of a hassle compared to NVIDIA for sure, but I attribute that to a decade or two of somewhat shady (and very successful) moves to make ML researchers need/want to use NVIDIA. AMD is behind ROCm, and, development is active. So, maybe you can clarify what you mean by "abandoned"? I'm not feeling it. https://en.m.wikipedia.org/wiki/ROCm https://en.m.wikipedia.org/wiki/ROCm

FloatArtifact 4y ago
Any benchmarks out there preparing Nvidia versus AMD?
anigbrowl 4y ago
ROCm's great for data centers, but good luck finding anything about desktop GPUs on their site apart from this lone blog post: https://community.amd.com/t5/instinct-accelerators/exploring-amd-vega-for-deep-learning/ba-p/416328 https://community.amd.com/t5/instinct-accelerators/exploring... There's a good explanation of AMD's ROCm targets here: https://news.ycombinator.com/item?id=28200477 https://news.ycombinator.com/item?id=28200477 It's currently a PITA to get common Python libs like Numba to even talk to AMD cards (admittedly Numba won't talk to older Nvidia cards either and they deprecate ruthlessly; I had to downgrade 8 versions to get it working with a 5yo mobile workstation). YC-backed Ivy claims to be working on unifying ML frameworks in a hardware-agnostic way but I don't have enough experience to assess how well they're succeeding yet: https://lets-unify.ai https://lets-unify.ai I was happy to see DiffusionBee does talk the GPU in my late-model intel Mac, though for some reason it only uses 50% of its power right now. I'm sure the situation will improve as Metal 3.0 and Vulkan get more established.