disco-diffusion VS shared-tensor

Disco-diffusion, a framework like Stable, which came out about 13 months ago: https://github.com/alembics/disco-diffusion

Runner-ups are Craiyon (for being more "creative" than SD), Disco Diffusion, minDALL-E, and CLIP Guided Diffusion.

From the Disco Diffusion GitHub, "“A frankensteinian amalgamation of notebooks, models and techniques for the generation of AI Art and Animations.”

Google Colab is probably the easiest way to run DD. To find the most recent version go to the GitHub page and then open the link to the Colab. Initially, you'll probably just want to experiment with the prompts. But there's also Zippy's Disco Diffusion Cheatsheet v0.3 which can be a useful place to learn more.

You can probably port Disco Diffusion pretty easily. It’s available on Google Colab, so should be straightforward. Their GitHub is: https://github.com/alembics/disco-diffusion

https://www.reddit.com/r/DiscoDiffusion/, https://github.com/alembics/disco-diffusion. As far as I'm aware the only way to use this is via Google Colab. Rather difficult to use because of this.

https://github.com/alembics/disco-diffusion,

This needs distributed training...

Years ago I made a shared tensor library[1] which should allow people to do training in a distributed fashion around the world. Even with relatively slow internet connections, training should still make good use of all the compute available because the whole lot runs asynchronously with highly compressed and approximate updates to shared weights.

The end result is that every bit of computation added has some benefits.

Obviously for a real large scale effort, anti-cheat and anti-spam mechanisms would be needed to ensure nodes aren't deliberately sending bad data to hurt the group effort.

[1]: https://github.com/Hello1024/shared-tensor