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UniTune: Text-Driven Image Editing by Fine Tuning an Image Generation Model on a Single Image

2022-10-17 23:46:05
Dani Valevski, Matan Kalman, Yossi Matias, Yaniv Leviathan

Abstract

We present UniTune, a simple and novel method for general text-driven image editing. UniTune gets as input an arbitrary image and a textual edit description, and carries out the edit while maintaining high semantic and visual fidelity to the input image. UniTune uses text, an intuitive interface for art-direction, and does not require additional inputs, like masks or sketches. At the core of our method is the observation that with the right choice of parameters, we can fine-tune a large text-to-image diffusion model on a single image, encouraging the model to maintain fidelity to the input image while still allowing expressive manipulations. We used Imagen as our text-to-image model, but we expect UniTune to work with other large-scale models as well. We test our method in a range of different use cases, and demonstrate its wide applicability.

Abstract (translated)

URL

https://arxiv.org/abs/2210.09477

PDF

https://arxiv.org/pdf/2210.09477.pdf


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