Using that ControlNet QR code model to generate scannable images, it actually helps a bit with the success rate.

I hopped on that trend of generating art QR codes with SD a while back. Started off brute-forcing it with Automatic1111—fun to mess around with, but the success rate for actually scannable codes was only about half, like 10 gens and 5 were duds. Then I stumbled on a dedicated qrcode-control model on Hugging Face, the v1p sd15 and v2 versions, and things turned around.

I ran the whole pipeline on Colab’s free tier—first generate a standard QR code yourself, and don’t forget the image has to be square. Then prep a base image or its URL, set up your positive and negative prompts, and it spits out a result in a minute or two. If you wanna try 1.5, just comment out the two lines at the bottom of the script and uncomment the 1.

5 ones. For batch generation, I usually use a Python package to make the codes directly; no need for that if you’re just doing one-offs. The dev’s still working on wrapping a Gradio UI around the script—once that’s done, it should be way more convenient.

50% success rate is pretty realistic tbh.

I’ve fallen into that square crop trap before.

Yo, is the free tier enough to run this?

Dedicated ControlNet is way better than raw generation.

Gonna save this workflow once the Gradio UI pops up.

For batch generation, you still gotta rely on scripts to churn out the codes.

Got a link?

put the QR code dead center with extra margin, bump the error correction to H, success rate can climb well above 50%.