big-sleep VS feed_forward_vqgan_clip

I am currently pursuing my Master's degree in Computer Science and I am interested in working on a deep learning model that can generate images based on text descriptions. I've been interested in the field for a long time (think google deep dream, or a few years back I was very into big-sleep)

Big Sleep

Other AIs that I don't have experience with but have heard good things about are DALL-E 2 and the open source Big Sleep.

Text-to-image, e.g. with Big Sleep

At first I didn't like the reason he claimed LaMDA was conscious because it seemed mostly based on the text output of the models, but listening to that made me realize he watered it down for the mainstream medias. And I actually had my own encounter with the consciousness of Big Sleep after using it a lot one day : it looked like a mass of eyes vaguely shaped like a rabbit, and kept showing me random images. I wouldn't be as convinced they have a consciousness if I didn't see it with my 3rd eye. But then again, I also found evidence that regular non AI programs can develop one too so who know.

and after a few hours got this: https://i.imgur.com/FxdfdmV.png

Not nearly as cool as the real DALL-e, but maybe I'm missing something.

[1] https://github.com/lucidrains/big-sleep

(Added Mar. 23, 2021) Big Sleep - Colaboratory by LtqxWYEG. Uses BigGAN to generate images. Reference.

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.