DALL-E VS DALLE2-pytorch

Not sure if this is the right place to post this but I'm having an issue installing DALL-E from github. I'm using this video as a tutorial and downloaded the DALL-E file from here. I got the the part of setting up a docker thing to run it but I'm getting the error shown in this picture:

well part of it is: https://github.com/openai/DALL-E

Following the read me, I ran

I am trying to run a program from GitHUb (Dall-E; https://github.com/openai/dall-e). My first step was to run pip install DALL-E in the Terminal, which worked fine.

Code for https://arxiv.org/abs/2102.12092 found: https://github.com/openai/DALL-E

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.