there was a cool one posted to /g/ earlier https://i.jollo.org/ePE3JHak.png
This was created using this guide:-
https://learn.thinkdiffusion.com/creating-qr-codes-with-cont...
I was speaking to him when he created it!
How to make a QR code with Stable Diffusion
71–75 of 75 posts
Re: How to make a QR code with Stable Diffusion
#72Unfortunately, they don't look as nice as the originals that were shown a few days ago, I wonder how they did that. https://mp.weixin.qq.com/s/i4WR5ULH1ZZYl8Watf3EPw They talk about a custom QR codes ControlNet.
Re: How to make a QR code with Stable Diffusion
#73Unfortunately, they don't look as nice as the originals that were shown a few days ago, I wonder how they did that. https://mp.weixin.qq.com/s/i4WR5ULH1ZZYl8Watf3EPw They talk about a custom QR codes ControlNet.
Follow along! https://learn.thinkdiffusion.com/creating-qr-codes-with-cont...
This is not using a custom Controlnet for QR codes.
Re: How to make a QR code with Stable Diffusion
#74The funny thing is that no-one AFAICT has realized that the same content can be encoded in different-looking QR codes. Beside the obvious (different error-correction levels), the content itself can be changed while maintaining its semantic meaning (e.g. " https://example.com/foo ", "HTTPS://EXAMPLE.COM/foo", or "HtTpS://eXaMpLe.CoM/foo" are all semantically identical) and even the QR encoding itself can be tweaked (e…
Also not sure the diffusion model has been taught yet that it could in some cases choose to deliberately ignore a (small) fraction of some of the elements/blocks, if it helps with composition. Or even better just wire up a QR decoder to the loop and automatically reject images that don't decode correctly, and let the model sort out how far it can go.
You'd need to inject a lot of noise (and different kinds of noise, and different implementations of QR decoders) before you present the image to the QR decode, to make sure you are still producing a robust QR code.
Re: How to make a QR code with Stable Diffusion
#75Earlier quoted context omitted.
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 mult…
> 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?