Huge win for AMD users who've been stuck with suboptimal performance! The automatic ROCm selection during installtion is a nice touch since manually configuring GPU backends can be a pain. I'm curious how this compares to DirectML perfomance on the same hardware, since that was the previous workaround. The cross-attention flag support in 7.1.1 should help a lot with memory-intensive workflows.
Should be a night-and-day performance improvement with ROCm vs DirectML. With DirectML, I had a W7800 go toe-to-toe with a GTX 1060 (not a typo) on Windows. You should get close to a 10x performance boost, at least.
I’ve been running some initial tests on my system, and it’s working very smoothly and perfectly stable with Python 3.12.11.
As a side note : I noticed a significant performance boost when running it on my manual ComfyUI setup with Python 3.13.9. I don't know if it is just the Python version.
I vaguely remember the comfyui README saying somewhere that some dependencies don't support py3.13 yet so py3.12 was recommended, is this still the case or does it work with no issues already?
I guess there's no "no issues" option for AMD users yet. For example, I haven't tested the above version for training yet (Flux FP16/BF16), but I have a lot of trouble with my py3.13.
Huge win for AMD users who've been stuck with suboptimal performance! The automatic ROCm selection during installtion is a nice touch since manually configuring GPU backends can be a pain. I'm curious how this compares to DirectML perfomance on the same hardware, since that was the previous workaround. The cross-attention flag support in 7.1.1 should help a lot with memory-intensive workflows.
Should be a night-and-day performance improvement with ROCm vs DirectML. With DirectML, I had a W7800 go toe-to-toe with a GTX 1060 (not a typo) on Windows. You should get close to a 10x performance boost, at least.
A huge thank you for providing such a great tool!
I’ve been running some initial tests on my system, and it’s working very smoothly and perfectly stable with Python 3.12.11.
As a side note : I noticed a significant performance boost when running it on my manual ComfyUI setup with Python 3.13.9. I don't know if it is just the Python version.
in seconds
Py3.13.9 Py3.12.11 #1...Py3.12.11 #2
1........89,43.........317,63.............176,56
2........87,9...........211,21............223,56
3........88,53..........201,74.............318,05
4........84,86.........189,76.............219,25
AVG..87,6975.....230,085...........239,39
%.......100,00 .....262,36.............272,97 %
system:
Win 11
AMD Ryzen 5 7500F 6-Core Processor
AMD Radeon RX 7800 XT
64GB RAM.
test workflow:
https://openart.ai/workflows/aura_111_111/fantasy-elf-sdxl-cinematic-nsfwsfw-portrait-workflow-hires-fix-vae-ksampler-workflow/JiDA2LC9sKQnvBUbNFRQ
I vaguely remember the comfyui README saying somewhere that some dependencies don't support py3.13 yet so py3.12 was recommended, is this still the case or does it work with no issues already?
I guess there's no "no issues" option for AMD users yet. For example, I haven't tested the above version for training yet (Flux FP16/BF16), but I have a lot of trouble with my py3.13.
Please support NVFP4 Models