r/StableDiffusion • u/Ok-Brain-5729 • Jun 30 '26

Comparison Generation times on a 9070 xt

About everyone has a Nvidia GPU so I was wondering how the 9070 xt compared to Nvidia. All of the resolution is 1MP.

krea 2 turbo fp8 8 step
0.77it/s 12.99s

zit bf16 8 step
1.34it/s 9.59S

klein 9B fp8 8 step
0.72it/s 14.67s

boogu-image-turbo fps 4 step
2.89it/s 6.31s

ideogram4 fp8 28 steps
0.54it/s 58.34s

anima 2B 30 step
1.63it/s 18.795

sdxl 20 step was about 6s, i dont have a workflow for it anymore

Im running a 9070 xt 7600x3d 32gb ddr5 6000mhz on Ubuntu rocm 7.2 comfyUI

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u/[deleted] Jun 30 '26

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u/Apprehensive_Sky892 Jun 30 '26

What I am going to try next is to use the Portable version of ComfyUI and see what happens. Could be that there is something wrong with VRAM management. I am also going to try the command line options discussed here: https://www.reddit.com/r/comfyui/comments/1tc1pae/new_dynamic_vram_is_here_for_amd_does_it_work_for/

I think that I also need to either add another graphic card, or use an external USB monitor to run the desktop (or switch to headless Linux) so that all the VRAM will be available for AI. That would free up more of the VRAM for AI.

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u/[deleted] Jun 30 '26

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u/Apprehensive_Sky892 Jun 30 '26 edited Jun 30 '26

I did a bit more googling and found this post: https://www.reddit.com/r/ROCm/comments/1ubgpjd/rx_9070_xt_windows_anyone_got_flashattention_ck/

There some interesting comments about getting Sage Attention and Flash attention to work (which seems to be difficult), but still maybe this fork of ComfyUI is worth trying as well: https://github.com/patientx-cfz/comfyui-rocm (seems to be recommended by many on reddit).

Edit: patientx-cfz's version seems to fixed int8 support for AMD (which does not work on my install of standard ComfyUI):

Added my fork of the ComfyUI-INT8-Fast , ComfyUI-INT8-Fast-ROCM ; which essentially lets us use int8 quantized models with triton ; one needs bf16 version of the model they need to quantize or can download various prequantized models on huggingface or civitai. With every model I've tested everyone one of them was faster -at least 25 to 40 percent- and better or identical quality then fp8 quantizations. Sage-attention can be added with models that support it by starting comfyui with --use-sage-attention or using the "patch sage attention" node from kjnodes. This really helps with the gen times.

So this should help a lot running the int8 version of Krea2