Diffusion models cannot generate a qr code by itself. The article even shows you need to generate the base image yourself. What you are proposing would mean bruteforcing the QR codes until we see a better shape.
I wouldn't call generating a couple of semantically equivalent (but fully valid and 0 error) QR codes and giving each of them as inputs to diffusion model brute forcing.
Even the normal AI image generation process often involves some trial and error. Generating a bunch of variants by changing random seed or slightly tweaking prompt keywords and choosing a result which looks the best. Doing multiple attempts with multiple different inputs doesn't seem more crazy than doing multiple attempts with same input and hoping for better results.
> I wouldn't call generating a couple of semantically equivalent (but fully valid and 0 error) QR codes and giving each of them as inputs to diffusion model brute forcing.
If you have 20 letters in your URL, and you give the model each of 2^20 different ways to capitalise them, isn't that pretty close to brute-forcing?
It would be at least possible to write a python plugin for the 'automatic1111' ui which allows you to simply input the QR text, and have it generate images based on a variety of possible configurations in batch so you can go through and pick the one which is most aesthetically pleasing.
edit: It would also be beneficial to have a script which automatically distorts the image in various ways and checks to see if it would be recognized by a QR code scanner, and give a score so you can see how likely it would be to be scannable in various conditions.
Without CRC calculation you could just check which square correspond to which letter and possibly change the letter casing.
But with CRC it will leads to squares changing in the CRC area.
Yes, that's the whole point. I did not claim you can control every single module in the QR. I just said that you can get different QR codes for the same semantic content.
