If you have a GPU with >4GB of VRAM and you want to run this locally, here's a fork of the Stable Diffusion repo with a convenient web UI: https://github.com/hlky/stable-diffusion It supports both txt2img and img2img. (Not affiliated.) Edit: Incidentally, I tried running it on a CPU. It is possible, but it took 3 minutes instead of 10 seconds to produce an image. It also required me to hack up the script in a really…
I do runs at 384px by 384px, with batch size of 1. Sampling method has almost no impact on memory. Using k_euler with 30 steps renders an image in 10 to 20 seconds. The biggest thing that affect rending speed is the steps and the resolution, so 512x512 with C 50 using ddim is much slower than 256x256 with C 25 using k_euler.
The sampling methods run mostly in the same timelines, but the k_euler one can produce viable output at lower C values, meaning it is faster than the rest.
Don't add gfpgan in the same pipeline, as it takes more vram.
I'm running it on Windows 10 with latest drivers. I set the python process to Realtime priority in task manager (makes a slight difference!). Have not tried it on Linux.