1. [Publications](/publications)
2. 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

  ![](/sites/default/files/styles/wide/public/publications/teaser.jpeg?itok=H5TwWozb)

 Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided models to turn *our* cat into a painting, or imagine a new product based on *our* favorite toy? Here we present a simple approach that allows such creative freedom.

Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new "words" in the embedding space of a frozen text-to-image model. These "words" can be composed into natural language sentences, guiding *personalized* creation in an intuitive way. Notably, we find evidence that a *single* word embedding is sufficient for capturing unique and varied concepts.

We compare our approach to a wide range of baselines, and demonstrate that it can more faithfully portray the concepts across a range of applications and tasks.



 ## Authors



Rinon Gal (NVIDIA)

Yuval Alaluf (Tel Aviv University)

[Yuval Atzmon](/person/yuval-atzmon)

Or Patashnik (Tel Aviv University)

Amit H. Bermano (Tel Aviv University)

[Gal Chechik](/person/gal-chechik)

Daniel Cohen-Or (Tel Aviv University)

 

 

 ## Publication Date



Tuesday, August 2, 2022

 

 ## Published in



[ICLR 2023](https://iclr.cc/virtual/2023/oral/12700)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

[Computer Graphics](/research-area/computer-graphics)

[Computer Vision](/research-area/computer-vision)

 

 

 ## External Links



[project page](https://textual-inversion.github.io/)

 

 

 ## Award



Top 25%