DALLE-pytorch
vit-pytorch
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DALLE-pytorch | vit-pytorch | |
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20 | 11 | |
5,493 | 18,006 | |
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2.5 | 7.3 | |
2 months ago | 9 days ago | |
Python | Python | |
MIT License | MIT License |
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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
vit-pytorch
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Is it easier to go from Pytorch to TF and Keras than the other way around?
I also need to learn Pyspark so right now I am going to download the Fashion Mnist dataset, use Pyspark to downsize each image and put the into separate folders according to their labels (just to show employers I can do some basic ETL with Pyspark, not sure how I am going to load for training in Pytorch yet though). Then I am going to write the simplest Le Net to try to categorize the fashion MNIST dataset (results will most likely be bad but it's okay). Next, try to learn transfer learning in Pytorch for both CNN or maybe skip ahead to ViT. Ideally at this point I want to study the Attention mechanism a bit more and try to implement Simple Vit which I saw here: https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/simple_vit.py
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What are the best resources online to learn attention and transformers?
For code implementation, check out this git repo. It contains fairly straightforward PyTorch implementations for various ViT papers with references.
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Training CNN/VIT on very small dataset
For ViT’s specifically, there’s been a good amount of research trying to extend ViT’s to work on small datasets without a large amount of pre-training (which comes with its own host of issues such as the best way to fine tune such a huge model). One paper which comes to mind is ViT’s for small datasets (https://arxiv.org/abs/2112.13492), which has an implementation in lucidrain’s repo here: https://github.com/lucidrains/vit-pytorch
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Transformers in RL
Here's a pytorch implementation of ViT https://github.com/lucidrains/vit-pytorch
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[P] Release the Vision Transformer Cookbook with Tensorflow ! (Thanks to @lucidrains)
looks great Junho! i've linked to it from https://github.com/lucidrains/vit-pytorch like you asked :)
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Will Transformers Take over Artificial Intelligence?
Sure thing. Also if you're getting into transformers I'd recommend lucidrains's GitHub[0] since it has a large collection of them with links to papers. It's nice that things are consolidated.
[0] https://github.com/lucidrains/vit-pytorch
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[D] Surprisingly Simple SOTA Self-Supervised Pretraining - Masked Autoencoders Are Scalable Vision Learners by Kaiming He et al. explained (5-minute summary by Casual GAN Papers)
nah, it is really simple. here is the code https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/mae.py
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[D] Training vision transformers on a specific dataset from scratch
lucid rains VI has all of what you may need in a clean API
- Can I train a tranaformer for image classification on Google colab??
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[R] Rotary Positional Embeddings - a new relative positional embedding for Transformers that significantly improves convergence (20-30%) and works for both regular and efficient attention
I've attempted it here https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/rvt.py but those who have tried it haven't seen knock out results as 1d. Perhaps the axial lengths are too small to see a benefit
What are some alternatives?
DALL-E - PyTorch package for the discrete VAE used for DALL·E.
MLP-Mixer-pytorch - Unofficial implementation of MLP-Mixer: An all-MLP Architecture for Vision
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
convolution-vision-transformers - PyTorch Implementation of CvT: Introducing Convolutions to Vision Transformers
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
reformer-pytorch - Reformer, the efficient Transformer, in Pytorch
DALLE-datasets - This is a summary of easily available datasets for generalized DALLE-pytorch training.
performer-pytorch - An implementation of Performer, a linear attention-based transformer, in Pytorch
imagen-pytorch - Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
efficient-attention - An implementation of the efficient attention module.
CoCa-pytorch - Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch
Compact-Transformers - Escaping the Big Data Paradigm with Compact Transformers, 2021 (Train your Vision Transformers in 30 mins on CIFAR-10 with a single GPU!)