Abstract: 'Creative'' remains an inherently abstract concept for both humans and diffusion models. While text-to-image (T2I) diffusion models can easily generate out-of-domain concepts like ''a blue banana'', they struggle with generating combinatorial objects such as ''a creative mixture that resembles a lettuce and a mantis'', due to difficulties in understanding the semantic depth of ''creative''. Current methods rely heavily on synthesizing reference prompts or images to achieve a creative effect, typically requiring retraining for each unique creative output---a process that is computationally intensive and limits practical applications. To address this, we introduce CreTok, which brings meta-creativity to diffusion models by redefining ''creative'' as a new token, \texttt{}, thus enhancing models' semantic understanding for combinatorial creativity. CreTok achieves such redefinition by iteratively sampling diverse text pairs from our proposed CangJie dataset to form adaptive prompts and restrictive prompts, and then optimizing the similarity between their respective text embeddings. Extensive experiments demonstrate that \texttt{} enables the universal and direct generation of combinatorial creativity across diverse concepts without additional training (4s vs. BASS's 2400s per image), achieving state-of-the-art performance with improved text-image alignment ($\uparrow$0.03 in VQAScore) and higher human preference ratings ($\uparrow$0.009 in PickScore and $\uparrow$0.169 in ImageReward). Further evaluations with GPT-4o and user studies underscore CreTok's strengths in advancing creative generation.