Since the dawn of Trading Card Games, the genre has grown into a multi-billion-dollar industry engaging millions of analog and digital players worldwide. Popular TCGs rely on regular updates, balance adjustments, and rotating constraints to sustain engagement. Yet, as metagames stabilize, predictable strategies dominate and viable card options diminish, often resulting in repetitive and impaired player experiences. This paper investigates the use of Large Language Models and Image Diffusion Models for Procedural Content Generation of TCG cards, addressing these challenges by enabling a personalized infinity of card designs. Modern generative AI not only enables large-scale content creation but could even introduce procedural relatedness, fostering unique connections between players and their cards. We present a pipeline combining player-centric co-creation, fine-tuned embeddings, local LLMs, and Diffusion Models to generate dynamic, personalized cards while potentially expanding creative range. We evaluated the pipeline in a user study with 49 participants who generated 196 Pokémon card samples. Participants rated aesthetics and representativeness of visuals and mechanics, and provided qualitative feedback. Results show high satisfaction and indicate that most participants successfully realized their own ideas through prompt adjustments. These findings lay groundwork for future content generation systems and alternatives to conventional metagame evolution through procedural relatedness.

翻译：自集换式卡牌游戏诞生以来，该品类已发展成涵盖数百万模拟与数字玩家的价值数十亿美元产业。主流TCG依赖定期更新、平衡性调整及轮换限制机制来维持用户参与度。然而随着元游戏趋于稳定，可预测策略占据主导地位，有效卡牌选择范围不断缩小，常常导致玩家体验重复化且受损。本文探究通过大语言模型与图像扩散模型实现TCG卡牌程序化内容生成的可行性，旨在利用个性化无限卡牌设计应对上述挑战。现代生成式AI不仅支持大规模内容创作，更能引入程序化关联机制，培养玩家与其卡牌间的独特情感纽带。我们提出结合玩家中心协同创作、微调嵌入向量、本地LLM及扩散模型的完整流水线，在动态生成个性化卡牌的同时拓展创意边界。通过49名参与者生成196张宝可梦卡牌样本的用户研究进行流水线评估，参与者对视觉表现力与机制代表性进行评分并提供定性反馈。结果表明该系统获得高度满意度，多数参与者能通过提示词调整成功实现自身创意构想。这些发现为未来内容生成系统及通过程序化关联替代传统元游戏演化路径奠定了理论基础。