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Ambigrams are calligraphic designs that have different meanings depending onthe viewing orientation. Creating ambigrams is a challenging task even forskilled artists, as it requires maintaining the meaning under two differentviewpoints at the same time. In this work, we propose to generate ambigrams bydistilling a large-scale vision and language diffusion model, namely DeepFloydIF, to optimize the letters' outline for legibility in the two viewingorientations. Empirically, we demonstrate that our approach outperformsexisting ambigram generation methods. On the 500 most common words in English,our method achieves more than an 11.6% increase in word accuracy and at least a41.9% reduction in edit distance.

AmbiGen: Generating Ambigrams from Pre-trained Diffusion Model