  In the digital era, QR codes serve as a linchpin connecting virtual and
physical realms. Their pervasive integration across various applications
highlights the demand for aesthetically pleasing codes without compromised
scannability. However, prevailing methods grapple with the intrinsic challenge
of balancing customization and scannability. Notably, stable-diffusion models
have ushered in an epoch of high-quality, customizable content generation. This
paper introduces Text2QR, a pioneering approach leveraging these advancements
to address a fundamental challenge: concurrently achieving user-defined
aesthetics and scanning robustness. To ensure stable generation of aesthetic QR
codes, we introduce the QR Aesthetic Blueprint (QAB) module, generating a
blueprint image exerting control over the entire generation process.
Subsequently, the Scannability Enhancing Latent Refinement (SELR) process
refines the output iteratively in the latent space, enhancing scanning
robustness. This approach harnesses the potent generation capabilities of
stable-diffusion models, navigating the trade-off between image aesthetics and
QR code scannability. Our experiments demonstrate the seamless fusion of visual
appeal with the practical utility of aesthetic QR codes, markedly outperforming
prior methods. Codes are available at \url{https://github.com/mulns/Text2QR}
