Redefining in Dictionary: Towards an Enhanced Semantic Understanding of Creative Generation

Given the challenge atively generated using . Furthermore, this that diffusion models face in directly generating creativity, meta-creativity enables direct concept combinations without existing methods typically rely on synthesizing reference requiring additional training, much like generating "a prompts or images to achieve creative effects. This significantly reduces both time and computational instance, to combine "Lettuce" and "Mantis" creatively, complexity compared to state-of-the-art (SOTA) ConceptLab [43] merges tokens representing these concepts creative generation methods, such as ConceptLab [43] (4s into a new composite token, while BASS [22] uses predefined vs. 120s per image, 30 speedup) and BASS [22] (4s vs. sampling rules to search for creative outcomes from a 2400s per image, 600 speedup), while maintaining linguistic large pool of candidate images. Further each generation, which leads to high computational costs evaluations using GPT-4o [1] and user studies indicate superior and limited practicality for online applications. In contrast, performance of CreTok in terms of integration, originality, "a blue banana" can be generated directly without additional and aesthetics, underscoring its effectiveness in fostering training, due to its clear and concrete semantics, especially combinatorial creativity. Inspired by this, we may Our contributions are as follows: (1) We propose Cre-ask: Can we awaken the creativity of diffusion models by Tok, a method designed to enhance models' meta-ability enhancing their semantic understanding of "creative"? To by enabling a enhanced understanding of abstract and ambiguous achieve this, we propose CreTok, which redefines "creative" adjectives (e.g., "creative" or "beautiful") through as a new specialized token, , allowing it their redefinition as new tokens. This redefinition we redefine the abstract term "creative" within our proposed enhances the model's semantic understanding for CangJie dataset for the TP2O task, and introduce combinatorial creativity, as shown in Figure 1c. Specifically, text-to-image (T2I) models and creative generation methods CreTok builds on the definition of "creativity" from in terms of computational complexity, human preference the TP2O task [22] for combinatorial object generation, ratings, text-image alignment, and other key metrics. ") and human-like creativity, a critical yet underexplored aspect an adaptive prompt (e.g., "A photo of a mixture"). of AI research [28, 29].