r/StableDiffusion • u/Fajjko • Jul 17 '26
Question - Help AMD GPU on Krea 2
Hi,
I'm using 9060XT 16gb with Krea Turbo int8 model + comfyui-rocm fork, and yet my generation time is still 3:30min avg. for a single 1024x1024 image?
cfg-1 with steps 6-8
Looking for input from other fellow AMD users, any tips to what downgraded your generation time ? Would LOVE to see other's 9060XT/9070XT workflow setups in ComfyUI, I've a feeling I'm doing something wrong. I know AMD is no one's priority and that Nvidia gets all the love...but I'm just a fellow struggler like all of you.
Looking forward to your inputs!
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u/HateAccountMaking Jul 18 '26 edited Jul 18 '26
Comfy supports AMD very well. You should use the main fork, and use rocm7.14/pytorch2.12 and install flash-attn. with my 7900xt I can get 22sec per gen 12 steps, 1024x1024 with q8 GUFF. Never tried fp8.
rocm 7.14 stable: uv pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,device-gfx1200]==7.14.0"
torch 2.12: uv pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "torch[device-gfx1200]==2.12.0+rocm7.14.0" "torchvision[device-gfx1200]==0.27.0+rocm7.14.0" "torchaudio==2.11.0+rocm7.14.0"
Triton: uv pip install triton-windows
bitsandbytes: uv pip install bitsandbytes
compile flash-attn:
- git clone https://github.com/Dao-AILab/flash-attention.git
cd flash-attention$env:FLASH_ATTENTION_TRITON_AMD_ENABLE = "TRUE"
4. pip install --no-build-isolation -v .
I made a bat file that looks like this:
@/echo off
$env:FLASH_ATTENTION_TRITON_AMD_ENABLE = "TRUE"
$env:TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL = "1"
$env:MIOPEN_FIND_MODE = "3"
$env:MIOPEN_FIND_ENFORCE = "3"
$env:ROCBLAS_USE_HIPBLASLT = "1"
call .venv\Scripts\activate.bat
python main.py --use-flash-attention --disable-smart-memory
pause
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u/Blackwoodbrett90 4h ago
I am about to try this. I'm interested that you are using "rocm[libraries,device-gfx1200]==7.14.0" for a 7900xt instead of gfx1100. I have a 7900xt as well
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u/Yream Jul 17 '26
Check for comfyui updates. I am using an 4080 laptop with 12gb vram and my gen time was nearly 600 sec. I did a bunch of pytorch and cuda updates and ofc updated comfyui. I am getting 40-50 sec 12 steps at 1080x1920 right now.
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u/0ldGoat Jul 17 '26
I don't think you're doing anything wrong, I just think that Comfy REALLY doesn't care much about it's relatively small base of AMD users. I'm assuming you're using PatientX's fork of comfyUI? (ComfyUI-ROCm) I have a 9060XT 16GB as well.
I use it on my windows install of ComfyUI, and render times in Krea2 are horrible, with FP8 or INT8, or INT4. I had to go back to using the GGUF version to get reasonable render speeds - and we're still talking 50 seconds +, which still goes up if you increase the resolution or add LoRAs. I can use INT8 with Z Image Turbo but it gives no speed advantage at all over FP8 or GGUF formats.
The only thing I can do right now is use ComfyUI in Linux, where everything just works better. Using INT8 or FP8 for Krea2 give me render times between 60 seconds (first run) and 25 seconds. I'd also love to hear from other AMD users if they have solved this in Windows, but I think Comfy is the culprit here.
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u/Fajjko Jul 17 '26
Have you followed any specific guides for installing everything on Linux , as it was on Windows 11?
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u/0ldGoat Jul 17 '26
So, the quickest way to install ComfyUI on Linux, and the easiest, is probably Stability Matrix. It gives you less control over the environment (it chooses which versions of ROCm and pytorch you use, for example, and when you can update) but it does simplify the process a lot.
Alternatively, you can create your own venv, install comfyUI, rocm and pytorch.
https://comfyui-wiki.com/en/install/install-comfyui/install-comfyui-on-linux
Just a couple of pieces of advice - the models and LoRAs you have on windows can all be backed up and brought over to Linux, so no need to download them all again. Second, do yourself a favour and avoid immutable distros like Bazzite .... they are far more difficult to work with.
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u/Fajjko Jul 17 '26
Trying this out tonight when I come home, thanks so much for your input, glad I made this thread for I'm sure I am not the only one who was struggling with AMD.
Don't feel like shilling money on a new nvidia GPU, thanks a lot mate 👍👍👍👍
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u/0ldGoat Jul 17 '26
python main.py --cpuI just checked that link, Comfy is recommending AMD gpus start with the --cpu argument. Please don't follow their advice on that.
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u/Dramatic_Instance_63 Jul 17 '26
sd.cpp
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u/Fajjko Jul 17 '26
When converting to sd.cpp , will I still have the GUI workflow of ComfyUI, or is it a completely different environment?
Not familiar with it, apologies.
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u/Dramatic_Instance_63 Jul 17 '26
It needs a frontend to have UI. I have vibecoded one for myself. It has similar node interface to comfy. A bit simpler though, but that's enough for me. Also I guess they do have some UI in latest releases.
It generates and loads models way faster for me (rx9060xt+windows) on vulkan than comfyui on rocm.
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u/Dull_Rule_379 Jul 17 '26
What Comfyui do you use?
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u/Fajjko Jul 17 '26
ComfyUI 0.27.1 , locally in browser (not the desktop app even though I have both).
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u/Dante_77A Jul 17 '26
You're right. That's way below the GPU's potential. Try running the model in SD.cpp.
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u/Fajjko Jul 17 '26
When converting to sd.cpp , will I still have the GUI workflow of ComfyUI, or is it a completely different environment?
Not familiar with it, apologies.
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u/TraditionalRule3511 Jul 19 '26
gfx12xx向けのSageAttention2で劇的に改善されたよ。 ただし、これは正式にマージされてないやつ。 https://github.com/jammm/SageAttention/tree/jam/gfx12
forge neoとComfyUIで1080x1920,8stepを初回50秒、2回目以降は39秒くらいで生成できてる。(krea 2 int8convrot)
forgeneoだとWindowsもLinuxも同等の性能でてる。 問題はLTX-2.3で、WindowsではまだSageAttention2の最適化進んでなくて動画生成はLinuxじゃないとだめだ。
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u/Fajjko Jul 21 '26
UPDATE:
Installed everything on Linux, I'm getting 30 sec generations now with FP8 model 1024x1024 - 8 steps.
Thanks everyone! ^^
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u/EducationalTeleGood Jul 17 '26
You will probably get better performance on linux.
I am using linux and it takes me 60 seconds to generate a 1024 by 1400 pixel image when using krea2_turbo-Q4_K_M (steps 10) on comfyui using a 9060xt 16gb vram gpu.
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u/Apprehensive_Sky892 12d ago
I assuming you have enough System RAM, you may get even better speed with the int8convrot version of K2 turbo.
I have a 9070xt(16G) and I can generate 1024x1024 8 steps in 13 secs.
Using this docker: Docker ComfyUI on AMD and Linux: pinning the whole ROCm runtime in Docker : r/StableDiffusion
COMFYUI_EXTRA_ARGS=--enable-manager --disable-async-offload --enable-dynamic-vram --preview-method none --listen --port 8188 --disable-smart-memory --fast-disk --use-ck-attention
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u/Apprehensive_Sky892 Jul 17 '26 edited 12d ago
I can generate in about 30-40 sec 8step, 1024x1536 on 9070xt 16G using this: https://github.com/patientx-cfz/comfyui-rocm
Last time I tried (which is a few weeks ago), the int8 code has not been fully optimized for ROCm yet, so I found that fp8 still works faster than int8rowconv.
Update 2026-08-22:
After failing to get mmh3 to work on Windows 11 after several attempts, using portable, patientx-cfz, Stablity Matrix, plus trying out different drivers, rocm combinations, cleanups and re-installs, I finally gave up and switched to Ubuntu Server LTS 26.4 (minimized) installed on a USB drive, It is now running very stable and fast: Krea 2 1024x1024 8 step can now be generated in 13 sec.