Some say art is better when there’s mystery, but my view is that knowing how AI art is made is the key to making even better AI art. The VQGAN + CLIP technology is now in a good state such that it can be used for more serious experimentation. created a fork of that Notebook which has a user-friendly UI, to which I became aware of how far AI image generation has developed in a few months.įrom that, I forked my own Colab Notebook, and streamlined the UI a bit to minimize the number of clicks needs to start generating and make it more mobile-friendly. Twitter accounts like and which leverage VQGAN + CLIP with user-submitted prompts have gone viral and received mainstream press. The core CLIP-guided training was improved and translated to a Colab Notebook by Katherine Crawson/ and others in a special Discord server.
Then open source worked its magic: the GAN base was changed to VQGAN, a newer model architecture Patrick Esser and Robin Rombach and Björn Ommer which allows more coherent image generation. The first implementation was Big Sleep by Ryan Murdock/ which combined CLIP with an image generating GAN named BigGAN.
Since CLIP is essentially an interface between representations of text and image data, clever hacking can allow anyone to create their own pseudo-DALL-E.
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