PALP: Prompt Aligned Personalization of Text-to-Image Models
Moab Arar Andrey Voynov Amir Hertz Omri Avrahami Shlomi Fruchter Yael Pritch Daniel Cohen-Or Ariel Shamir
Content creators often aim to create personalized images using personal
subjects that go beyond the capabilities of conventional text-to-image models.
Additionally, they may want the resulting image to encompass a specific
location, style, ambiance, and more. Existing personalization methods may
compromise personalization ability or the alignment to complex textual prompts.
This trade-off can impede the fulfillment of user prompts and subject fidelity.
We propose a new approach focusing on personalization methods for a
\emph{single} prompt to address this issue. We term our approach prompt-aligned
personalization. While this may seem restrictive, our method excels in
improving text alignment, enabling the creation of images with complex and
intricate prompts, which may pose a challenge for current techniques. In
particular, our method keeps the personalized model aligned with a target
prompt using an additional score distillation sampling term. We demonstrate the
versatility of our method in multi- and single-shot settings and further show
that it can compose multiple subjects or use inspiration from reference images,
such as artworks. We compare our approach quantitatively and qualitatively with
existing baselines and state-of-the-art techniques.