deep-vector-quantization VS DALL-E

How the codebook is initialised makes a big difference, if more codes are used at the start then they often stay in use but it can be finicky to figure that out. I've found initialising codes normally with small standard deviation (e.g. 0.01) to help (so codes lie on a hypersphere). Failing that, here is an example with k-means initialisation https://github.com/karpathy/deep-vector-quantization .

The context is, that I am trying to learn a discrete vocabulary of latent codes, i.e. have a discrete learnable embedding to sort of quantize the otherwise continuous latent outputs of the encoder that are then used to reconstruct the input image via the decoder cf. this code snippet. So the idea is not to generate random sampled from noise but to learn an efficient notebook, i.e. bottleneck that captures the essentials of the data set. The decoder then outputs a prob distribution for every pixel over the 255 possible values 8 bit images can take o. The KL (assuming a uniform prior to encourage uniform use of all possible vocabulary entries) is currently weighted with 1.

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