sd-extension-system-info VS tomesd

I've been using both SD and various LLM on linux without any issue and have done so for months. Windows support is also starting to roll out slowly, with koboldcpp-rocm recently giving me 20-25+t/s for a13B even on windows. you can see what SD performance is like on sites like these. those numbers roughly match what i get on my RX6800 as well (8t/s).

That seems a lot worse than a 2060 SUPER with PyTorch in A1111.

https://vladmandic.github.io/sd-extension-system-info/pages/... (search for 2060 SUPER)

But usually A1111 users use benchmark on this extension https://github.com/vladmandic/sd-extension-system-info

AMD SD = Setup Diaster? it was quite troublesome googling the few linux/amdgpu/rocm/sd vers/configs/params posts online. Also the whole PC may hang during generation which is bad for the harddisk. Your card is way more powerful so may not hang like mine. People are getting 8it/s https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html

According to system info benchmark, M40 is like 1-2 it/s and P4 is barely better than that.

Anyway to properly "benchmark" the impacts different switches on your image generation speed, it is better to use the benchmarking utility from extension https://github.com/vladmandic/sd-extension-system-info (it also creates a very handy table of results from other users at https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html for you to compare with.

I can see that the top performance benchmark results on SD WebUI Benchmark Data (using RTX 4090), are obtained through WSL2 running Automatic1111 on a Linux dist and Python 3.10.11, along with PyTorch 2.1.0.dev+cu121 (like benchmark id: 4126)

You should be able to compare based on the published benchmarks, just replicate the settings based on what's reported https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html

They show up to 5.4 times greater: you can see his results in the image on the github repo here: https://github.com/dbolya/tomesd

Another optimization extension you can use as well is token merging which has reported around 5.4x faster image generation.

It merges redundant tokens: https://github.com/dbolya/tomesd So it can make the generation slightly faster.

Mods patched - Tomesd - Pillow-SIMD - OpenCV-CUDA (WIP) - Removed some unused imports and startup checking - Improved performance with reduced VRAM usage (tested on txt2img only) - Added a new option to use external RealESRGAN with --external-realesrgan

Were the 40 it/s perhaps achieved with ToMe?

Done! Proposal

This feature comes from this project: https://github.com/dbolya/tomesd

GitHub - dbolya/tomesd: Speed up Stable Diffusion with this one simple trick!