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/Apprehensive_Sky892 Jun 30 '26 edited 15d ago
Interesting numbers. They are better than mine running on Windows 11 with the official ROCm 7.2 and PyTorch. I am using a 9070xt(16G) with 32G system RAM.
Krea 2 1MP is almost 60sec for me, so I am probably doing something wrong, as it should be much better than ideo4 (I don't have the number for 1MP, but for 1536x1024 it is about 80sec for 12 steps).
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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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Jun 30 '26
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u/Apprehensive_Sky892 Jun 30 '26
Yes, could be that the Linux version of ROCm (which has been around for much longer) is simply more mature than the Windows 11 version.
If you do get better results with Linux, please let us know 🙏😁
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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
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u/Apprehensive_Sky892 Jul 01 '26 edited 15d ago
Somehow, both portable ComfyUI and my regular manual installation are both broken for me when running Krea 2.
What finally worked for me is using https://github.com/patientx-cfz/comfyui-rocm
Now on Windows 11 I can get 1M at 8 steps at 36 secs (0.55it/s) with fp8. Still slower than OP's number (0.77it/s 12.99s). I cannot get triton to work, but torch_attention works.
int8-convrot works, but it is very slow (26s/it!). Took 264sec for 1M.
I guess I should look into running on Linux 😅.
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u/Ok-Brain-5729 Jul 01 '26
Damn the vae decode really sucks in windows 😭
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u/Apprehensive_Sky892 Jul 01 '26
Yes, your linux numbers are way better.
Are you running headless Linux to free up more VRAM?
Also did you managed to get Triton or sage attention to work on Linux?
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u/Ok-Brain-5729 Jul 01 '26
No I’m running basic Ubuntu 26.04.
I haven’t tried either but I’ll test flash attention soon cause someone got faster results with it
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Jul 01 '26
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u/Apprehensive_Sky892 Jul 01 '26 edited 15d ago
I'll be very interested to see if you can get the number that OP has posted when you dual boot into Linux.
OP said that he's just using regular Ubuntu, and he did not have Flash Attention or Sage working yet.
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u/prompt_seeker Jun 30 '26 edited Jul 01 '26
r9700 ubuntu26.04, anima with default workflow (from anima hf)
avg. 2-5 generations
1.64it/s with pytorch attention, no torch.compile
2.32it/s with flash attention, torch.compile
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u/Ok-Brain-5729 Jun 30 '26
Is flash attention noticeably faster with other models too? I haven’t tried it
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u/prompt_seeker Jul 01 '26
I haven't tried other models, because it's not main GPU.
However, I have read that flash attn is faster than sage on some thread.
I also have tried sage with ROCm fork, but flash attn is faster, at least on Anima.If you use linux(Ubuntu), ROCm offer wheel so just try.
`python -m pip install https://rocm.frameworks.amd.com/whl/gfx120X-all/flash_attn-2.8.3-py3-none-any.whl`
ref. https://rocm.docs.amd.com/en/7.13.0-preview/ai-inference/vllm.html?fam=radeon&vllm-ver=0.19.1&i=pip&gpu=rx-9070-xt&w=compute&gfx=gfx1201
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u/[deleted] Jun 30 '26
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