Story2Hallucination
StyleCLIP
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Story2Hallucination | StyleCLIP | |
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13 | 23 | |
146 | 3,896 | |
-0.7% | - | |
0.0 | 0.0 | |
about 3 years ago | 11 months ago | |
Jupyter Notebook | HTML | |
GNU General Public License v3.0 only | MIT License |
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Story2Hallucination
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test
(Added Feb. 5, 2021) Story2Hallucination.ipynb - Colaboratory by bonkerfield. Uses BigGAN to generate images/videos. GitHub.
- Skrev in "Stockholm" i en AI-generator. Fick fram denna. Känns rimligt ändå
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Some AI tools I've picked up, and tips.
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.
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Text to Image Generation
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
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[P] Visualizing evolution of Text-to-Image generation algorithms side by side by generating video from song-lyrics (X-LXMERT v/s AlpehImage/Dall-E)
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.
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Story2Hallucination renders a world of dragons from an AI dungeon game
Here you go: https://github.com/lots-of-things/Story2Hallucination/
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[D] Will machine learning enable a single person to make a blockbuster movie like Avengers: endgame within 6 months?
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.
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Story2Hallucination render of my latest meme (check comments for links)
Story2Hallucination github by u/bonkerfield : GITHUB LINK
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Story2Hallucination on another AI Dungeon game and now I have a script to visualize to GIF while playing at the same time.
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.
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Short video generated by Story2Hallucination
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)
StyleCLIP
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A History of CLIP Model Training Data Advances
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.
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[D] What is the largest / most diverse GAN model currently out there?
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.
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test
(Added Feb. 15, 2021) StyleCLIP - Colaboratory by orpatashnik. Uses StyleGAN to generate images. GitHub. Twitter reference. Reddit post.
- I am David Bau, and I study the structure of the complex computations learned within deep neural networks.
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Dragon Age Origins Companions as Photorealistic People.
I used StyleCLIP. I purchased some Google Colab time to use their GPUs. I'll probably do some more later this week.
- Turning BDO characters into blursed people with AI
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I used AI to generate real life for honor character faces
Link for Styleclip
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AI-generated 'real' faces of CGI characters - description in comments
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.
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AI Generated Art Scene Explodes as Hackers Create Groundbreaking New Tools - New AI tools CLIP+VQ-GAN can create impressive works of art based on just a few words of input.
Combining these methods with CLIP allows you to generate images based on text. This one uses a face generator. https://github.com/orpatashnik/StyleCLIP
- [D] How to save latent code edited from StyleClip.
What are some alternatives?
deep-daze - Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun
encoder4editing - Official implementation of "Designing an Encoder for StyleGAN Image Manipulation" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02766
big-sleep - A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Technique was originally created by https://twitter.com/advadnoun
compare_gan - Compare GAN code.
deep-music-visualizer - The Deep Visualizer uses BigGAN (Brock et al., 2018) to visualize music.
NVAE - The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)
DALLE-pytorch - Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
stylized-neural-painting - Official Pytorch implementation of the preprint paper "Stylized Neural Painting", in CVPR 2021.
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
Voice-Cloning-App - A Python/Pytorch app for easily synthesising human voices
alias-free-gan - Alias-Free GAN project website and code