stable-diffusion-webui-directml VS triton

I'm getting this error using (https://github.com/lshqqytiger/stable-diffusion-webui-directml/issues), freshly installed. I'm running it on an AMD RX 6700 XT with 12gb of vram. Generating a single image at default settings (512x512, 20 steps, etc.) I can do simple prompts (i.e. "kitty cat") but as soon as I add a couple more tags, I get the aforementioned error message, usually 20-30% into generating an image. I went through this thread (https://github.com/lshqqytiger/stable-diffusion-webui-directml/issues/38) and tried every solution I saw, most of them being variations of adding --medvram --precision full --no-half --no-half-vae --opt-split-attention-v1 --opt-sub-quad-attention --disable-nan-check to the commandline arguments. What else might I be able to try? Thanks.

i use automatic 1111 you can find the installation on the github , this branch https://github.com/lshqqytiger/stable-diffusion-webui-directml and it works fine although the speed is what it is, i also have a old GPU.

I'm using this one: https://github.com/lshqqytiger/stable-diffusion-webui-directml

- https://github.com/lshqqytiger/stable-diffusion-webui-directm - starting webuser-ui with COMMANDLINE_ARGS=--opt-sub-quad-attention –disable-nan-check - AMD 8GB Radeon Pro WX7100

I'm running this fork (https://github.com/lshqqytiger/stable-diffusion-webui-directml) on a pc with a Ryzen 5700x and a Radeon RX 6700 XT 12 GB Video Card.

I am using direct-ml fork

Im running Stable diffusion on my 6900XT, and I feel like its way slower than normal. Using the updated Webui https://github.com/lshqqytiger/stable-diffusion-webui-directml.

This refers to the use of iGPUs (example: Ryzen 5 5600G). No graphic card, only an APU. The DirectML Fork of Stable Diffusion (SD in short from now on) works pretty good with AMD. But not only with the GPUs, but also with only-APUs without GPUs.

cd .. git clone https://github.com/ROCmSoftwarePlatform/triton.git -b release/pytorch_2.0 cd triton/python pip3 install cmake pip3 install -e .

Someone I know returned their 4080 (had a horrible coil whine he said) and yesterday his new 7900XTX came in and he did some testing. Now he can't use xformers and he did not have the sdp optimization on (iow no optimizations) using 5.5.0 beta on docker (that hurts a bit too) he was getting about 16it/s for 512sq and at 768sq he was getting 5.25ish it/s. I had him try with the SDP but optimization but docker is new to him and for some reason I saw no gains, or losses, when it was used (as if docker ignored it). His next test will be for training (which is why he got the card and I will as well). Another thing that hurts is no Triton but here is what he told me yesterday "regarding the 7900 XTX. Inference is fine, around 16 it/s. I couldn't get the training to work, mostly because of what I assume is a bug with the ROCm fork of Triton that's currently in development ( https://github.com/ROCmSoftwarePlatform/triton )."