ControlNet for QR Code: Let the QR code "hide" in the picture

Recently, a project called ControlNet for QR Code has attracted a lot of attention. This amazing AI technology can convert stylized images into QR codes that can be scanned and recognized. The origin behind this project, the training process and more results of image generation are very interesting.

ControlNet for QR Code is a project developed by a researcher in the field of AI. He created a parametric QR code generator qrbtf.com with his classmates in his sophomore year.

With the rise of the Stable Diffusion model and ControlNet, the project founders had a whim and tried to use the diffusion model to generate ordinary images into QR codes.

The training of the ControlNet for QR Code project requires a lot of data and computing power support. Among them, the training data ranges from 80,000 to 3 million, and the training time is even as high as 600 A100 GPU hours. Fortunately, the project team obtained the support of Google TPU v4 in the previous JAX Sprint event, and completed the training of 3 million images very quickly.

After a combination of Checkpoint + LoRA + QR Code ControlNet, this project successfully generated a variety of QR codes with recognition functions. These QR codes include traditional Chinese patterns, ukiyo-e style, two-dimensional and illustration style, ink style, watercolor style, three-dimensional style, abstract style and PCB style, etc.

In the future, the model release and technical documentation of the ControlNet for QR Code project will be published on the official website of aigc.ioclab.com.

The ControlNet for QR Code project successfully realized the conversion of stylized images to QR codes, demonstrating the great potential of AI technology in the field of digital images. We have reason to expect more breakthroughs in generative AI technology in the future, bringing more convenience and beauty to our daily lives.