feed_forward_vqgan_clip VS CLIP-Guided-Diffusion

WOMBO I suspect uses the feed forward inferential approach to VQGAN + CLIP (instead of finetuning, predict the final z latent vector for a given text input) which is why their outputs are less sophisticated: as a result there are many deployment optimizations you can do to speed that up, which may be complicated.

The system used to produce these images is unlike most other VQGAN+CLIP systems because it uses a neural network trained by the developer(s) instead of an iterative process. This system is known to have a "formula" for image layout.

Hello, some news. For those who are interested, I released new models (release 0.2) that you could try and you might find them better (depending on the prompt) than the current one(s), also the problem that was mentioned by /u/Wiskkey is less visible (object parts appearing systematically on top-left), but still not 100% solved, there is still a common global structure that can be identified, but it's more centered on the image. The Colab notebook was updated to use the new models.

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

-clip guided diffusion https://github.com/nerdyrodent/CLIP-Guided-Diffusion

Nerdy Rodent has a Github repo for this; it should work fine from the Anaconda command line: https://github.com/nerdyrodent/CLIP-Guided-Diffusion

As far as I understand, VQGAN is not a guided diffusion model. I've been using a slightly tweaked version of https://github.com/nerdyrodent/CLIP-Guided-Diffusion for diffusion. Once you get it set up the interface is pretty much what you might expect: