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Go to lstein/stable-diffusion for all the best stuff and a stable release. This repository is my testing ground and it's very likely that I've done something that will break it. (by magnusviri)
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Nope. There are instructions for Windows, Linux and Apple Silicon in the readme: https://github.com/AUTOMATIC1111/stable-diffusion-webui
There's also this other fork, which also has a Colab notebook ready to run, and it's way, way faster than the KerasCV version: https://github.com/TheLastBen/fast-stable-diffusion
(It also has a many, many more options and some nice, user-friendly GUIs. It's the best version for Google Colab!)
Not M1 comparible but I'm working on testing various GPU vs M1 comparisons, with a few accessible cloud providers. My impression is times should be the same, but it's nice to hear other real-world stats for M1 with SD. Makes me really want to rent the Hetzner M1 now.
Which repo or build are you using BTW, is it the one related to this readme?
https://github.com/magnusviri/stable-diffusion/blob/main/REA...
While AUTOMATIC is certainly popular, calling it the most active/popular would be ignoring the community working on Invoke. Forks don’t lie.
https://github.com/invoke-ai/InvokeAI
On intel MacBookPro, CPU-only, the original one[1] using pytorch only utilized one core. A tensorflow implementation[2] with oneDNN support which utilized most of the cores ran at ~11sec/iteration. Another OpenVINO based implementation[3] ran at ~6.0sec/iteration.
[1] https://github.com/CompVis/stable-diffusion/
[2] https://github.com/divamgupta/stable-diffusion-tensorflow/
[3] https://github.com/bes-dev/stable_diffusion.openvino/
Nope. There are instructions for Windows, Linux and Apple Silicon in the readme: https://github.com/AUTOMATIC1111/stable-diffusion-webui
There's also this other fork, which also has a Colab notebook ready to run, and it's way, way faster than the KerasCV version: https://github.com/TheLastBen/fast-stable-diffusion
(It also has a many, many more options and some nice, user-friendly GUIs. It's the best version for Google Colab!)
On intel MacBookPro, CPU-only, the original one[1] using pytorch only utilized one core. A tensorflow implementation[2] with oneDNN support which utilized most of the cores ran at ~11sec/iteration. Another OpenVINO based implementation[3] ran at ~6.0sec/iteration.
[1] https://github.com/CompVis/stable-diffusion/
[2] https://github.com/divamgupta/stable-diffusion-tensorflow/
[3] https://github.com/bes-dev/stable_diffusion.openvino/
> This is by far the most popular and active right now: https://github.com/AUTOMATIC1111/stable-diffusion-webui
While technically the most popular, I really wouldn't call it "by far". This one is a very close second (500 vs 580 forks): https://github.com/sd-webui/stable-diffusion-webui/tree/dev