This case study details how MEUX’s designers leveraged Stable Diffusion to mass‑produce high‑quality ad images for Baidu App, overcoming the scalability challenges of personalized advertising through prompt engineering, template design, and human‑AI collaboration.
Since July, the author has been developing AIGC projects and observed dramatic image quality improvements driven by rapid advances in large models, open‑source plugins, and deeper understanding of generation pipelines and parameters, prompting a comprehensive summary of the Cyber project’s solutions, industry landscape, and front‑end responsibilities.
Fin2.0, NetEase Cloud Music’s AI‑driven design assistant, combines text‑to‑image, text‑to‑icon and text‑to‑copy generation with an internal Stable Diffusion engine and streamlined prompt templates, enabling non‑designers like a colleague to create high‑quality promotional banners in hours while avoiding external costs and data‑security risks.
This article explains why AI‑generated faces from StyleGAN are hard to distinguish, introduces an online game for testing realism, and provides practical visual cues—such as water spots, background errors, asymmetric glasses, hair artifacts, and teeth anomalies—to reliably identify fake images.
