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Textual Inversions for personalized Text-to-Image generation
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site name
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2026-03-03 14:22:19
raw text
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion Rinon Gal 1,2 , Yuval Alaluf 1 , Yuval Atzmon 2 , Or Patashnik 1 , Amit H. Bermano 1 , Gal Chechik 2 , Daniel Cohen-Or 1 , 1 Tel Aviv University, 2 NVIDIA Paper Code We learn to generate specific concepts, like personal objects or artistic styles, by describing them using new "words" in the embedding space of pre-trained text-to-image models. These can be used in new sentences, just like any other word. Our work builds on the publicly available Latent Diffusion Models Here are some more generated examples. We hope they are cool enough to convince you it's worth reading on :) Abstract Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised ...
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