DALLE-pytorch VS DALLE2-pytorch

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

I was showing people Dalle2 last year and telling them how much of an impact an open source solution was going to have on, well, everything to do with art and design. (At the time Stable Diffusion had not released, not even the leak, and all hopes was on https://github.com/lucidrains/DALLE2-pytorch)

Stable diffusion is the model, MJ will use a variant and DALL-E is the old version (we have our own implementation from our distinguished fellow Lucidrains here: https://github.com/lucidrains/DALLE2-pytorch)

Sadly I don't think so. From what I understand the architecture is fixed to 1024x1024 pictures.

The code is open source, the model is not I believe. https://github.com/lucidrains/DALLE2-pytorch

Phil Wang has been fairly prolific at creating open source implementations of these text to image models. For example, here is the dalle-2 repo https://github.com/lucidrains/DALLE2-pytorch

There's already an open-source implementation of DALL-E 2 (https://github.com/lucidrains/DALLE2-pytorch) and a pretrained model for it should be released within this year.

Also true for Google's Imagen, which should be even better than DALLE-2 (and faster) https://github.com/lucidrains/imagen-pytorch.

This is possible because the original research papers behind both DALLE-2 and Imagen were publicly released.

The paper describing the model is public and has been implemented here, but that's not the hard part. The model likely requires months of compute and dozens of gigabytes of VRAM to train and run, likely costing several hundred thousand dollars.