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 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

Text2QR: Harmonizing Aesthetic Customization and Scanning Robustness for  Text-Guided QR Code Generation

Latent Consistency Models (LCMs) have achieved impressive performance in
accelerating text-to-image generative tasks, producing high-quality images with
minimal inference steps. LCMs are distilled from pre-trained latent diffusion
models (LDMs), requiring only ~32 A100 GPU training hours. This report further
extends LCMs' potential in two aspects: First, by applying LoRA distillation to
Stable-Diffusion models including SD-V1.5, SSD-1B, and SDXL, we have expanded
LCM's scope to larger models with significantly less memory consumption,
achieving superior image generation quality. Second, we identify the LoRA
parameters obtained through LCM distillation as a universal Stable-Diffusion
acceleration module, named LCM-LoRA. LCM-LoRA can be directly plugged into
various Stable-Diffusion fine-tuned models or LoRAs without training, thus
representing a universally applicable accelerator for diverse image generation
tasks. Compared with previous numerical PF-ODE solvers such as DDIM,
DPM-Solver, LCM-LoRA can be viewed as a plug-in neural PF-ODE solver that
possesses strong generalization abilities. Project page:
this https URL

通过将 LoRA 蒸馏应用于包括 SD-V1.5、SSD-1B 和 SDXL 的稳定扩散模型，扩展了潜在一致性模型 (LCMs) 的潜力，减少内存消耗，提高图像生成质量，并将通过 LCM 蒸馏得到的 LoRA 参数作为稳定扩散加速模块命名为 LCM-LoRA，该模块可以直接插入各种经过微调的稳定扩散模型或 LoRAs 中，从而成为多样的图像生成任务中可广泛应用的加速器。