Kavli Affiliate: Jing Wang
| First 5 Authors: Fu Feng, Yucheng Xie, Jing Wang, Xin Geng,
| Summary:
Creativity, both in human and diffusion models, remains an inherently
abstract concept; thus, simply adding "creative" to a prompt does not yield
reliable semantic recognition by the model. In this work, we concretize the
abstract notion of "creative" through the TP2O task, which aims to merge two
unrelated concepts, and introduce CreTok, redefining "creative" as the token
$texttt{<CreTok>}$. This redefinition offers a more concrete and universally
adaptable representation for concept blending. This redefinition occurs
continuously, involving the repeated random sampling of text pairs with
different concepts and optimizing cosine similarity between target and constant
prompts. This approach enables $texttt{<CreTok>}$ to learn a method for
creative concept fusion. Extensive experiments demonstrate that the creative
capability enabled by $texttt{<CreTok>}$ substantially surpasses recent SOTA
diffusion models and achieves superior creative generation. CreTok exhibits
greater flexibility and reduced time overhead, as $texttt{<CreTok>}$ can
function as a universal token for any concept, facilitating creative generation
without retraining.
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