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arXiv · 2506.24085

Imagine for Me: Creative Conceptual Blending of Real Images and Text via Blended Attention

Wonwoong Cho·Yanxia Zhang·Yan-Ying Chen·David I. Inouye
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Abstract

Blending visual and textual concepts into a new visual concept is a unique and powerful trait of human beings that can fuel creativity. However, in practice, cross-modal conceptual blending for humans is prone to cognitive biases, like design fixation, which leads to local minima in the design space. In this paper, we propose a T2I diffusion adapter "IT-Blender" that can automate the blending process to enhance human creativity. Prior works related to cross-modal conceptual blending are limited in encoding a real image without loss of details or in disentangling the image and text inputs. To address these gaps, IT-Blender leverages pretrained diffusion models (SD and FLUX) to blend the latent representations of a clean reference image with those of the noisy generated image. Combined with our novel blended attention, IT-Blender encodes the real reference image without loss of details and blends the visual concept with the object specified by the text in a disentangled way. Our experiment results show that IT-Blender outperforms the baselines by a large margin in blending visual and textual concepts, shedding light on the new application of image generative models to augment human creativity.

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Wonwoong Cho, Yanxia Zhang, Yan-Ying Chen, David I. Inouye. 2025-07-14. Imagine for Me: Creative Conceptual Blending of Real Images and Text via Blended Attention. https://arxiv.org/abs/2506.24085

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