title: PALP: Prompt Aligned Personalization of Text-to-Image Models
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.
paper_authors._id: 65a0bd799185dcca3061a974
paper_authors.name: Moab Arar
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paper_authors.fullname: Moab Arar
paper_authors.type: user
paper_authors.user: moabarar
paper_authors._id: 65a0bd799185dcca3061a975
paper_authors.name: Andrey Voynov
paper_authors.status: admin_assigned
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paper_authors.fullname: Andrey Voynov
paper_authors.type: user
paper_authors.user: avoin
paper_authors._id: 65a0bd799185dcca3061a976
paper_authors.name: Amir Hertz
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paper_authors.fullname: Amir Hertz
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paper_authors.user: amirhertz
paper_authors._id: 65a0bd799185dcca3061a977
paper_authors.name: Omri Avrahami
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paper_authors.fullname: Omri Avrahami
paper_authors.type: user
paper_authors.user: omriav
paper_authors._id: 65a0bd799185dcca3061a978
paper_authors.name: Shlomi Fruchter
paper_authors.status: admin_assigned
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paper_authors._id: 6335ae887fcbeb615b0f9924
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paper_authors.fullname: Shlomi Fruchter
paper_authors.type: user
paper_authors.user: shlomif
paper_authors._id: 65a0bd799185dcca3061a979
paper_authors.name: Yael Pritch
paper_authors._id: 65a0bd799185dcca3061a97a
paper_authors.name: Daniel Cohen-Or
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-12T10:02:04.337Z
paper_authors._id: 628507161949ebcae8e24ec3
paper_authors.avatarUrl: /avatars/008ecb3daa4c8187b5f339f1176b3c39.svg
paper_authors.fullname: Daniel Cohen-Or
paper_authors.type: user
paper_authors.user: cohenor
paper_authors._id: 65a0bd799185dcca3061a97b
paper_authors.name: Ariel Shamir
paper_title: PALP: Prompt Aligned Personalization of Text-to-Image Models
paper_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.
paper_ai_keywords: prompt-aligned personalization score distillation sampling term