Story2Hallucination VS 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)

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