StyleCLIP VS Story2Hallucination

While CLIP on its own is useful for applications such as zero-shot classification, semantic searches, and unsupervised data exploration, CLIP is also used as a building block in a vast array of multimodal applications, from Stable Diffusion and DALL-E to StyleCLIP and OWL-ViT. For most of these downstream applications, the initial CLIP model is regarded as a “pre-trained” starting point, and the entire model is fine-tuned for its new use case.

I'm currently building a fork for StyleCLIP global directions which allows you to control multiple semantic parameters simoultaneously to generate and edit an image with StyleGAN and CLIP in realtime. I want to showcase its potential as a design tool. Unfortunately, GAN weights are trained on very domain-specific (faces, cars, churches) data. This makes them inferior to modern diffusion models which I can use to generate whatever comes to mind. Although I know we won't have a GAN-based DALL-E counterpart anytime soon, I still would love to use my system with weights that can output a wide variety of things.

(Added Feb. 15, 2021) StyleCLIP - Colaboratory by orpatashnik. Uses StyleGAN to generate images. GitHub. Twitter reference. Reddit post.

I used StyleCLIP. I purchased some Google Colab time to use their GPUs. I'll probably do some more later this week.

Link for Styleclip

So, I watched this Corridor Crew video on generating realistic faces from CG characters, and I wanted to try it out on the RDR2 models. The github link for the original work is here. If you guys are interested I can generate the faces of more characters from RDR2 and RDR1. I can even try some from RD Revolver.

Combining these methods with CLIP allows you to generate images based on text. This one uses a face generator. https://github.com/orpatashnik/StyleCLIP

(Added Feb. 5, 2021) Story2Hallucination.ipynb - Colaboratory by bonkerfield. Uses BigGAN to generate images/videos. GitHub.

Third which is pretty new to me is the Story2Hallucination. Which takes text, (say your story) and uses Google Deep Sleep to create a visual using AI to generate images for what the story is describing. And example can be found here.

I ran some Lovecraft through Story2Hallucination[1] which uses Big Sleep to make videos from text.

The results were quite something - https://m.imgur.com/tfWLsSR

[1] https://github.com/lots-of-things/Story2Hallucination

Using Story2Hallucination: https://boredhumans.com/music_videos/Story2Hallucination_withwords.mp4 (made with https://github.com/lots-of-things/Story2Hallucination). The problem with it was that I had no easy way to match the timing of the lyrics on the screen with the real singing. So I then made a new version at https://boredhumans.com/music_videos/Story2Hallucination_nowords.mp4 where I edited the code so it did not show the words at all. But it is somewhat boring to watch.

Here you go: https://github.com/lots-of-things/Story2Hallucination/

This is basic attempt at that: https://github.com/lots-of-things/Story2Hallucination . It turns the text you write into a dream-like series of images converted into an animated GIF. It does not look real, but it is a start.

Story2Hallucination github by u/bonkerfield : GITHUB LINK

I've added a slightly simpler notebook to Story2Hallucination that can render GIFs on Google Colab. Note that the story text has to be fairly short or it will make a gigantic unrenderable GIF.

Credit to https://github.com/lots-of-things/Story2Hallucination/ and onwards - I converted the script to run in regular python, and on windows through WSL+CUDA (Though, the windows tweaks seem to have caused other issues, Will probably have to roll back and dualboot on this device to do more)