stable_diffusion.openvino VS stable-diffusion

it might be the --xformers flag, try getting rid of that since your not using cuda you wouldn't be able to run it with xformers and you could also try --use-cpu all ... you can also check this out .. https://github.com/bes-dev/stable_diffusion.openvino .. it's probably your best option if your using CPU, which if your PC Graphics are using Intel UHD 620 then you don't have a GPU and an optimized CPU inference would be best to run

SD can be run on a CPU without a GPU. I know for certain it can be done with OpenVINO. In fact, on some i7s, it will run at around 3 seconds per iteration. There was a reddit SD thread a while back saying it can be done with Automatic111. Also, soe recent threads on problems with AMD GPUs suggest Automatic1111 is using the CPU rather than the intended GPU. (Fortuanely, I have a GPU, so I don't have to deal with it myself!)

I'm not actually entirely convinced that it's even using the GPU. Radeontop shows 0% utilization while the images are generating. Additionally, the listed iteration speed should be impossibly slow for any GPU; it says 26.58s/it, which is slower than just running on a CPU.

iGPU's are in short not supported. There's this repo that may or may not help you, but even if it did I wouldn't expect much.

You can also run it in windows native with openvino, there is a barebones webui for it as well in one of the forks.Requires setting cpu to gpu in one the files. https://github.com/bes-dev/stable_diffusion.openvino

In https://github.com/bes-dev/stable_diffusion.openvino/blob/master/stable_diffusion_engine.py

" We ended up using three different Stable Diffusion projects for our testing, mostly because no single package worked on every GPU. For Nvidia, we opted for Automatic 1111's webui version(opens in new tab). AMD GPUs were tested using Nod.ai's Shark version(opens in new tab), while for Intel's Arc GPUs we used Stable Diffusion OpenVINO(opens in new tab). "

use or update this one : https://github.com/hlky/stable-diffusion it has all the samplers, and if you want perfect faces, try k_euler_a

try this one https://github.com/hlky/stable-diffusion you need at least a 1050 to run it tho

https://github.com/hlky/stable-diffusion/issues/153 With 36 comments and tons of before and after comparisons, which are now deleted

I wanted to try hlky version (https://github.com/hlky/stable-diffusion) , due to the WebUI and integration with upscaling models. It should also have the option to be optimized for low VRAM. To avoid getting a green square I have to add the parameters "--precision full --no-half". When I run a prompt, even with the smallest image size, I immediately get a CUDA memory error. Interestingly, without these parameters there isn't any memory error (but, of course, the result is a green square)

Made using https://github.com/hlky/stable-diffusion

Any chance to get this integrated into something like hlky's web ui?

Linux Guide her. Jeg har også Linux, men jeg valgte å sette det opp på Windows boksen min fordi driverne til Nvidia kortet på Linux ikke er helt sammarbeidsvillig når det kommer til å justere viftene etter sensorene i kortet (så jeg må sette det manuelt).

I'm seeing GFPGAN essentially remove all texture from faces, and I only want to use it on the eyes. Any thoughts on how to do this? I am using hlky/stable-diffusion now but I have no issues running a different repo/fork if needed and using command line.