PALP: Prompt Aligned Personalization of Text-to-Image Models

Moab Arar^1,2 Andrey Voynov¹ Amir Hertz¹ Omri Avrahami^1,3
Shlomi Fruchter¹ Yael Pritch¹ Daniel Cohen-Or^1,2 Ariel Shamir^1,4
¹ Google Research ² Tel Aviv University ³ The Hebrew University of Jerusalem ⁴ Reichman University

Paper

TL;DR

Prompt aligned personalization allow rich and complex scene generation, including all elements of a condition prompt (right).

Abstract

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 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.

How does it work?

Personalization: We achieve personalization by fine-tuning the pre-trained model using a simple reconstruction loss. We used DreamBooth-LoRA and TextualInversion personalization methods; however, any personalization method would work.

Prompt-Alignment: To keep the model aligned with the target prompt, we use score sampling to pivot the prediction towards the direction of the target prompt, e.g., ”A sketch of a cat.”

Comparison to previous works

PALP excels with complex prompts with multiple elements, including style, ambiance, places, etc. For each subject, we show a single exemplar from the training set, conditioning text, and comparisons to Dreambooth and Textual-Inversion, Custom-Diffusion, NeTI, and P+ baselines.

Art Inspired Personalization

PALP can generate scenes inspired by a single artistic image by ensuring alignment with the target prompt, ""An oil painting of a [toy / cat]"."

Single-Image Personalization

PALP achieves high fidelity and prompt-aligned even from a single reference image. Here we present a un-curated results of eight randomly sampled noises.

1 / 5

Vermeer

2 / 5

Sketch

3 / 5

Pop-art

4 / 5

Line-art

5 / 5

Caricature

Reference

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Multi-subject Personalization

PALP support multi-subject personalization. Here are sample results for the cat and toy subjects. The conditioned prompts are provided under the generated images.

BibTex

 @article{arar2024Palp,

    title={PALP: Prompt Aligned Personalization of Text-to-Image Models},

    author={Arar, Moab and Voynov, Andrey and Hertz, Amir and Avrahami, Omri and Fruchter, Shlomi and Pritch, Yael and Cohen-Or, Daniel and Shamir, Ariel},

  
    year={2024}

  }