disco-diffusion VS imagen-pytorch

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,

If google doesnt, someone like lucidrains probably would implement it, just like he did for imagen and muse.

lucidrains/imagen-pytorch: Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch (github.com)

Hi Can someone guide me a little, as to how i can use LAION dataset to train my imagen model? like how i can download the data, and in which format it should be fed to https://github.com/lucidrains/imagen-pytorch code?

If we were to put money into training something, I'd hope we use a better model, like Imagen.

I've been messing around with this open-source implementation. You can get a pretty good idea of the model size by just copying the parameters from the paper.

I'm waiting on this implementation/training of imagen: https://github.com/lucidrains/imagen-pytorch

I'm honestly surprised that they trained a StyleGAN. Recently, the Imagen architecture has been show to be both easier in structure, easier to train, and even faster to produce good results. Combined with the "Elucidating" paper by NVIDIA's Tero Karras you can train a 256px Imagen to tolerable quality within an hour on a RTX 3090.

Here's a PyTorch implementation by the LAION people:

https://github.com/lucidrains/imagen-pytorch

And here's 2 images I sampled after training it for some hours, like 2 hours base model + 4 hours upscaler:

https://imgur.com/a/46EZsJo