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Existing personalization methods may\ncompromise personalization ability or the alignment to complex textual prompts.\nThis trade-off can impede the fulfillment of user prompts and subject fidelity.\nWe propose a new approach focusing on personalization methods for a\nsingle prompt to address this issue. We term our approach prompt-aligned\npersonalization. While this may seem restrictive, our method excels in\nimproving text alignment, enabling the creation of images with complex and\nintricate prompts, which may pose a challenge for current techniques. In\nparticular, our method keeps the personalized model aligned with a target\nprompt using an additional score distillation sampling term. We demonstrate the\nversatility of our method in multi- and single-shot settings and further show\nthat it can compose multiple subjects or use inspiration from reference images,\nsuch as artworks. 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Papers
arxiv:2401.06105

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

Published on Jan 11, 2024
· Submitted by AK on Jan 12, 2024
#2 Paper of the day
Authors:
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Abstract

Prompt-aligned personalization improves text alignment and enhances personalized image creation with complex prompts by using score distillation sampling.

AI-generated summary

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.

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