This paper explores the potential for AI to exhibit creativity comparable to humans. It introduces the concept of *Relative Creativity*, which assesses whether an AI model can produce outputs indistinguishable from those of a hypothetical human creator. The paper also introduces *Statistical Creativity*, a method for quantifying AI's creative abilities by comparing it to existing human creators. The authors argue that AI can achieve human-like creativity if it can effectively fit a sufficient amount of conditional data, including artworks and their creation conditions. The study provides practical guidelines for training AI models to foster creativity and discusses the implications for the responsible development and application of AI. The theoretical framework established in this paper offers a comprehensive approach to understanding, evaluating, and enhancing creativity in AI models.This paper explores the potential for AI to exhibit creativity comparable to humans. It introduces the concept of *Relative Creativity*, which assesses whether an AI model can produce outputs indistinguishable from those of a hypothetical human creator. The paper also introduces *Statistical Creativity*, a method for quantifying AI's creative abilities by comparing it to existing human creators. The authors argue that AI can achieve human-like creativity if it can effectively fit a sufficient amount of conditional data, including artworks and their creation conditions. The study provides practical guidelines for training AI models to foster creativity and discusses the implications for the responsible development and application of AI. The theoretical framework established in this paper offers a comprehensive approach to understanding, evaluating, and enhancing creativity in AI models.