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}

Text2QR是一种创新方法，利用稳定扩散模型的先进技术，同时实现用户定义的美学和扫描鲁棒性，通过引入QR Aesthetic Blueprint (QAB) 模块确保了美学QR码的稳定生成，并通过Scannability Enhancing Latent Refinement (SELR) 迭代地在潜在空间中优化输出，提升了扫描的稳定性。该方法充分利用了稳定扩散模型的强大生成能力，在图像美观度和QR码可扫描性之间找到了平衡点，实现了视觉吸引力与美学QR码的实用性的无缝融合，并在实验证明优于先前的方法。

Text2QR: 融合美化定制与扫描稳定性的文本导向QR码生成