stable-diffusion VS stable-diffusion-webui-feature-showcase

Yes, you can install it with Python! https://github.com/lstein/stable-diffusion works with macOS, and you can control all the common parameter via their WebUI or CLI :)

I'm using lstein fork ("dream") and when I create an image from the terminal, it also writes back to the terminal like this:

> Whenever I ask for something like ‘seamless tiling xxxxxx’ it kinda sorta gets the idea, but the resulting texture doesn’t quite tile right.

Getting seamless tiling requires more than just have "seamless tiling" in the prompt. It also depends on if the fork you're using has that feature at all.

https://github.com/lstein/stable-diffusion has the feature, but you need to pass it outside the prompt. So if you use the `dream.py` prompt cli, you can pass it `"Hats on the ground" --seamless` and it should be perfectly tilable.

From https://github.com/lstein/stable-diffusion

Stable Diffusion Dream Script: This is the original site/script for supporting macOS. I found this soon after Stable Diffusion was publicly released and it was the site which inspired me to try out using Stable Diffusion on a mac. They have a web-based UI (as well as command-line scripts) and a lot of documentation on how to get things working.

I’m pretty sure it works for M2 as well - basically the newer ARM-based Macs. The instructions to get it working are detailed! https://github.com/lstein/stable-diffusion

A1111 https://github.com/AUTOMATIC1111/stable-diffusion-webui-feature-showcase

Here's a quick rundown on a few of the most popular ones with links. I started out using CMDR2, which is very easy to get running as a newbie. Then I kind of graduated to NMKD because I wanted something a little more mainstream but still easy to use. Then, I finally decided I was hungry for all the strange and exotic bells and whistles that SD had to offer me, and so I installed Automatic1111. I also wanted something that would work well with my 4GB GTX 1650 laptop card, because that's considered "low ram" and kind of on the edge for running SD- Automatic1111 fit the bill there, too.

All generations were on the "Berry's Mix" model, which is made by combining NAI-final, Zenith's F111, r34 and SD1.4 according to this recipe. I used 30ish steps when generating images and inpainting, but 70-80 steps when outpainting because I read here that outpainting really benefits from extra steps. When outpainting I would generate 2-4 versions and pick the least broken one, then tidy up with inpainting.

sounds like "outpainting", one of the very first features listed on 1111 repo with some instructions: https://github.com/AUTOMATIC1111/stable-diffusion-webui-feature-showcase

I have no idea what you're talking about. Just get Automatic1111