DALLE-pytorch
VQGAN-CLIP
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DALLE-pytorch | VQGAN-CLIP | |
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20 | 67 | |
5,493 | 2,563 | |
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2.5 | 0.0 | |
2 months ago | over 1 year ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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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
VQGAN-CLIP
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📚 Tutorials & 🎨 AI Art Generation Tool List Mega Thread
VQGAN-CLIP
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Which is your favorite text to image model overall?
I've screwed with many text-to-image models over the past couple of years, and I found that while I currently enjoy Stable Diffusion's coherency, I have a soft spot for the ImageNet model used by default for VQGAN+CLIP. It easily approaches the uncanny valley when generating people or animals, but makes for great abstract backgrounds and wallpapers. I already have nostalgia for generating images with it on my CPU overnight.
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Stable Diffusion Announcement
For someone only tangentially familiar with this space, how is this different than e.g. https://github.com/nerdyrodent/VQGAN-CLIP which you can also run at home? Is it the quality of the generated images?
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Medieval Noir - VQGAN-CLIP - COCO Checkpoint
Used https://github.com/nerdyrodent/VQGAN-CLIP
- Once have access, do you run it on your computer or over the internet on Open-AI's computers?
- How to get AI imaging effect in Premiere pro
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A Guide to Asking Robots to Design Stained Glass Windows
I don't have any of the DALL-Es but I do have a couple from github [1], [2] which gave these outputs[3]
[1] https://github.com/nerdyrodent/VQGAN-CLIP
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How not to waste $1600?
If you want to try your hand at buggering your whole system - try playing with AI image generation as it uses all possible computer assets :D . There is a lot of forms and installations for those but I VQGANs from github the easiest. Problem is that some require familarity with shell, python and in some cases - you need to enable the Linux subsystem in Windows (is it called a subsystem? it is not exactly a VM). This one is the easiest to install out of all I tried. But I liked the results of Pixray most but I wrecked it. I use this one nowadays.
- Ask HN: Is there a publicly available (not private beta) text-to-image API?
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Got a Machine Learning Algorithm to depict Aphex
For those that are interested, I used VQGAN-CLIP, specifically this GitHub repository
What are some alternatives?
DALL-E - PyTorch package for the discrete VAE used for DALL·E.
CLIP-Guided-Diffusion - Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
DALLE-mtf - Open-AI's DALL-E for large scale training in mesh-tensorflow.
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
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
DALLE-datasets - This is a summary of easily available datasets for generalized DALLE-pytorch training.
imagen-pytorch - Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
waifu2x - Image Super-Resolution for Anime-Style Art
CoCa-pytorch - Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch
stable-diffusion - A latent text-to-image diffusion model