Story2Hallucination
DALLE-pytorch
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Story2Hallucination | DALLE-pytorch | |
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13 | 20 | |
146 | 5,493 | |
-0.7% | - | |
0.0 | 2.5 | |
about 3 years ago | 2 months ago | |
Jupyter Notebook | Python | |
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)
DALLE-pytorch
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The Eleuther AI Mafia
It all started originally on lucidrains/dalle-pytorch in the months following the release of DALL-E (1). The group started as `dalle-pytorch-replicate` but was never officially "blessed" by Phil Wang who seems to enjoy being a free agent (can't blame him).
https://github.com/lucidrains/DALLE-pytorch/issues/116 is where the discord got kicked off originally. There's a lot of other interactions between us in the github there. You should be able to find when Phil was approached by Jenia Jitsev, Jan Ebert, and Mehdi Cherti (all starting LAION members) who graciously offered the chance to replicate the DALL-E paper using their available compute at the JUWELS and JUWELS Booster HPC system. This all predates Emad's arrival. I believe he showed up around the time guided diffusion and GLIDE, but it may have been a bit earlier.
Data work originally focused on amassing several of the bigger datasets of the time. Getting CC12M downloaded and trained on was something of an early milestone (robvanvolt's work). A lot of early work was like that though, shuffling through CC12M, COCO, etc. with the dalle-pytorch codebase until we got an avocado armchair.
Christophe Schumann was an early contributor as well and great at organizing and rallying. He focused a lot on the early data scraping work for what would become the "LAION5B" dataset. I don't want to credit him with the coding and I'm ashamed to admit I can't recall who did much of the work there - but a distributed scraping program was developed (the name was something@home... not scraping@home?).
The discord link on Phil Wang's readme at dalle-pytorch got a lot of traffic and a lot of people who wanted to pitch in with the scraping effort.
Eventually a lot of people from Eleuther and many other teams mingled with us, some sort of non-profit org was created in Germany I believe for legal purposes. The dataset continued to grow and the group moved from training DALLE's to finetuning diffusion models.
The `CompVis` team were great inspiration at the time and much of their work on VQGAN and then latent diffusion models basically kept us motivated. As I mentioned a personal motivation was Katherine Crowson's work on a variety of things like CLIP-guided vqgan, diffusion, etc.
I believe Emad Mostaque showed up around the time GLIDE was coming out? I want to say he donated money for scrapers to be run on AWS to speed up data collection. I was largely hands off for much of the data scraping process and mostly enjoyed training new models on data we had.
As with any online community things got pretty ill-defined, roles changed over, volunteers came/went, etc. I would hardly call this definitive and that's at least partially the reason it's hard to trace as an outsider. That much of the early history is scattered about GitHub issues and PR's can't have helped though.
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Thoughts on AI image generators from text
Here you go: https://github.com/lucidrains/DALLE-pytorch
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[P] DALL·E Mini & Mega demo and production API
Here are some other implementations of Dalle clones in Pytorch by various authors in the ML and DL community: https://github.com/lucidrains/DALLE-pytorch
- New text-to-image network from Google beats DALL-E
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[Project] DALL-3 - generate better images with fewer tokens through clip guided diffusion
If in general DDPM > GAN > VAE, why do transformer image generators all use VQVAE to decode images? Wouldn't it be better to use a diffusion model? I was wondering about this and started experimenting with different ways to decode vector-quantized embeddings with a diffusion model - see discussion here After a lot of trial and error I got something that works pretty well.
- Still waiting for dall-e
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Ask HN: Computer Vision Project Ideas?
- "Discrete VAE", used as the backbone for OpenAI's DALL-E, reimplimented here (and other places) https://github.com/lucidrains/DALLE-pytorch (code for training a discrete VAE)
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Crawling@Home: Help Build The Worlds Largest Image-Text Pair Dataset!
Here's the DALLE-pytorch git repo.
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(from the discord stream) I'm so hyped for this game. This generation is really good.
I am very excited, when AI Dungeon was released and seeing them filtering stuff, I thought that one day there will be an open source version of this without filters, the same goes for any future open sourced GPT-X. Now if we can get to train an open source DALL-E too and integrate it on NovelAI. Wouldn't that be even more awesome?
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Wann habt Ihr euch das letzte Mal wie ein Kind über eine Sache gefreut?
Vielleicht bei https://github.com/lucidrains/DALLE-pytorch und https://github.com/kobiso/DALLE-reproduction
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
DALL-E - PyTorch package for the discrete VAE used for DALL·E.
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
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
deep-music-visualizer - The Deep Visualizer uses BigGAN (Brock et al., 2018) to visualize music.
StyleCLIP - Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral)
DALLE-datasets - This is a summary of easily available datasets for generalized DALLE-pytorch training.
stylized-neural-painting - Official Pytorch implementation of the preprint paper "Stylized Neural Painting", in CVPR 2021.
imagen-pytorch - Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
Voice-Cloning-App - A Python/Pytorch app for easily synthesising human voices
CoCa-pytorch - Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch