How to create QR codes with Stable Diffusion

QR code technical details

The QR code was created in 1994 by the Japanese company Denso Wave. Their work became so popular and useful that it became internationally standardized under the ISO/IEC 18004 standard. This standard sets out the technical specifications and requirements for the creation and reading of these codes.

The specification is used by individuals and companies to implement all these details and to invent software libraries capable of creating, reading and interpreting these codes. Not all implementations are the same, hence some of these codes can only be read by certain QR code reading applications. Therefore, it does not make sense that in order to read it you have to download a new application. The main advantage of QR codes is compatibility.

Installing the model

Many articles use two units of ControlNet: one unit with the control_v11f1e_sd15_tile model and another unit with the control_v1p_sd15_brightness model. This configuration gives impressive results but the scanning usually does not work and the codes no longer look like codes.

In this case we are going to use a single unit of ControlNet loaded with the control_v1p_sd15_qrcode model.

Download this model (.safetensors file) and its configuration (.yaml file with the same name) from the following HuggingFace repository: https://huggingface.co/DionTimmer/controlnet_qrcode.

Put these two files in the models/ControlNet/ folder that you will find inside the directory where you have Automatic1111 installed.

Many thanks to Dion Timmer for creating this fantastic model.

Configuration parameters

With the original QR code and the ControlNet model installed correctly, we proceed to start Automatic1111 and access the img2img tab.

screenshot with the configuration

The parameters we need to configure are:

• Checkpoint: To your liking. Recommendation: dreamshaper_631BakedVae.safetensors. Remember to use a 1.5 model if you use control_v1p_sd15_qrcode or a 2.1 model if you use control_v1p_sd21_qrcode.
• Positive prompt: It does not need to be very long or specific as you will see in the examples below. Some prompts are more aggressive and destroy more parts of the code, so you will have to play with other values.
• Negative prompt: Same as the positive prompt, enter something basic like ugly, disfigured, low quality, blurry, nsfw, watermark, text.
• img2img tab: Select the image of the QR code that you have generated.
• Width / Height: The value you have selected when creating the QR code. A value of 512x512 should be sufficient.
• Denoising strength: This value is perhaps the most important. The optimal value is usually 0.58 although if your prompt is destroying too much QR code you can lower it to 0.55. If the QR code barely changes its design, you can raise it to 0.65. Outside of those values you won't have much luck.

The rest of the parameters can be left by default. I have used the following:

• Sampling method: Euler A.
• Sampling steps: 30.
• CFG Scale: 7.

As for the ControlNet configuration dropdown, you must configure unit 0 (the first one) as follows:

• Single Image: Select the original QR code image again.
• Enable: True. Activate the unit or it will do nothing.
• Pixel Perfect: True. Enable this option.
• Control Type: All. Leave it as it is by default.
• Preprocessor: none. No need to preprocess the QR code.
• Model: Select control_v1p_sd15_qrcode. In my case I am using the model with hash: 9c780d03, but this should not matter.
• Control Weight: The default value of 1 is fine. Between 0.9 and 1.3 there are also good results but it is only useful when you want to fine tune the result as much as possible.
• Starting Control Step: 0. We let ControlNet condition the image from the beginning of the generation.
• Ending Control Step: 0.9. This means that ControlNet will condition the result until the generation has reached 90%. The remaining 10% will give Stable Diffusion total freedom to refine the details as it pleases. If you see that your prompt is destroying too much information from the QR code you can adjust it up to 1. This way ControlNet will try to preserve the QR code until the end.
• Control Mode: Balanced.
• Resize Mode: Unnecessary if it is the same size. You can leave it at Just Resize.

After this configuration we start the generation and we will get our QR code generated with Stable Diffusion.

Results

Here are a few examples of QR codes that I have generated for you to compare the results.

Outpainting

Remember that thanks to the outpainting technique you can further enhance the image so that the QR code is integrated into something larger.

Manual check

You can use the following tool to check the QR code from this article. It is not perfect but it is a quick way to check a QR code (sometimes a valid code can appear as invalid).

Automatic check

To improve the checking process here is a Python script that uses 3 different QR code reading libraries to filter the codes that are valid in all libraries at once.

This method is more reliable than the manual one since each code is checked 3 times, but more QR codes will also be discarded for not having full compatibility (it's what we're looking for, right?).

import os
import sys
import glob
import shutil
import cv2
from pyzbar import pyzbar
import zxing

if len(sys.argv) < 2 or not sys.argv[1]:
  print('Missing first argument (text or url)')
  print('Usage: python check.py "https://www.felixsanz.dev"')
  sys.exit()

def read_qr_code_with_cv2(file):
  try:
    detector = cv2.QRCodeDetector()
    img = cv2.imread(file)

    value = detector.detectAndDecode(img)[0]

    return value
  except:
    return

def read_qr_code_with_pyzbar(file):
  try:
    img = cv2.imread(file)

    value = pyzbar.decode(img)[0].data.decode()

    return value 
  except:
    return

def read_qr_code_with_zxing(file):
  try:
    detector = zxing.BarCodeReader()
    value = detector.decode(file).parsed

    return value
  except:
    return

PASS = '\033[32m'
FAIL = '\033[31m'
RESET = '\033[0m'

text = sys.argv[1]

for file in glob.glob('./images/*.png'):
  cv2_code = read_qr_code_with_cv2(file)
  cv2_result = (cv2_code == text)
  cv2_color = PASS if cv2_result else FAIL

  pyzbar_code = read_qr_code_with_pyzbar(file)
  pyzbar_result = (pyzbar_code == text)
  pyzbar_color = PASS if pyzbar_result else FAIL

  zxing_code = read_qr_code_with_zxing(file)
  zxing_result = (zxing_code == text)
  zxing_color = PASS if zxing_result else FAIL

  print(f'[{cv2_color}cv2{RESET} {pyzbar_color}pyzbar{RESET} {zxing_color}zxing{RESET}] {file}')

  if (cv2_result and pyzbar_result and zxing_result):
    shutil.copy2(file, file.replace('/images/', '/valid/'))

This script works as follows:

1. It takes care of reading the .png images that are inside the ./images folder.
2. It checks the validity of the QR code using the opencv-python, pyzbar and zxing libraries. You must pass as first (and only) argument the text or URL with which you created the QR code, so that it can be verified that the result of the reading is the same.
3. If the QR code is valid in all 3 implementations, the script copies the image to the ./valid folder.

To install this script clone the repository https://github.com/felixsanz/felixsanz_dev, go to this article's folder, start a virtual environment and install the dependencies:

git clone https://github.com/felixsanz/felixsanz_dev
cd felixsanz_dev/articles/how-to-create-qr-codes-with-stable-diffusion

python -m venv .venv

source .venv/bin/activate # Unix
.venv\Scripts\activate # Windows

pip install -r requirements.txt

Next copy the .png images of the QR codes you want to check into the ./images folder. Add all the ones you have generated, without fear!

Run the script, you will get these results:

python check.py "https://www.felixsanz.dev"

[cv2 pyzbar zxing] ./images/00218-2724904587.png
[cv2 pyzbar zxing] ./images/00235-4081443668.png
[cv2 pyzbar zxing] ./images/00159-2755401775.png
[cv2 pyzbar zxing] ./images/00130-3223553027.png
[cv2 pyzbar zxing] ./images/00029-3235345478.png

The library names appear in green or red depending on the library result. When an image shows all 3 libraries in green, it will be copied to the ./valid folder.