DALLE-pytorch VS DALLE-datasets

Here you go: https://github.com/lucidrains/DALLE-pytorch

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

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.

- "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)

Here's the DALLE-pytorch git repo.

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?

Vielleicht bei https://github.com/lucidrains/DALLE-pytorch und https://github.com/kobiso/DALLE-reproduction

A large part of the results that can be achieved with such models is thanks to data. Large amount of data. Today the largest open dataset for (image, text) pairs are in the order of 10M (see https://github.com/robvanvolt/DALLE-datasets ), which is enough to train okay models, but not enough to reach the best performance. Having a public dataset with hundred of millions of pairs could help a lot to build these image+text models.