title: Piece it Together: Part-Based Concepting with IP-Priors
summary: Advanced generative models excel at synthesizing images but often rely on
  text-based conditioning. Visual designers, however, often work beyond language,
  directly drawing inspiration from existing visual elements. In many cases,
  these elements represent only fragments of a potential concept-such as an
  uniquely structured wing, or a specific hairstyle-serving as inspiration for
  the artist to explore how they can come together creatively into a coherent
  whole. Recognizing this need, we introduce a generative framework that
  seamlessly integrates a partial set of user-provided visual components into a
  coherent composition while simultaneously sampling the missing parts needed to
  generate a plausible and complete concept. Our approach builds on a strong and
  underexplored representation space, extracted from IP-Adapter+, on which we
  train IP-Prior, a lightweight flow-matching model that synthesizes coherent
  compositions based on domain-specific priors, enabling diverse and
  context-aware generations. Additionally, we present a LoRA-based fine-tuning
  strategy that significantly improves prompt adherence in IP-Adapter+ for a
  given task, addressing its common trade-off between reconstruction quality and
  prompt adherence.
paper_authors._id: 67d44cb6bb362707e0c548b9
paper_authors.name: Elad Richardson
paper_authors._id: 67d44cb6bb362707e0c548ba
paper_authors.name: Kfir Goldberg
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:13:52.604Z
paper_authors._id: 6381e8c72a85b6714b7eb5f7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6381e8c72a85b6714b7eb5f7/GTqChXYVI1Q11btXn1X36.jpeg
paper_authors.fullname: kfir goldberg
paper_authors.type: user
paper_authors.user: kfirgold99
paper_authors._id: 67d44cb6bb362707e0c548bb
paper_authors.name: Yuval Alaluf
paper_authors._id: 67d44cb6bb362707e0c548bc
paper_authors.name: Daniel Cohen-Or
paper_title: Piece it Together: Part-Based Concepting with IP-Priors
paper_summary: Advanced generative models excel at synthesizing images but often rely on
  text-based conditioning. Visual designers, however, often work beyond language,
  directly drawing inspiration from existing visual elements. In many cases,
  these elements represent only fragments of a potential concept-such as an
  uniquely structured wing, or a specific hairstyle-serving as inspiration for
  the artist to explore how they can come together creatively into a coherent
  whole. Recognizing this need, we introduce a generative framework that
  seamlessly integrates a partial set of user-provided visual components into a
  coherent composition while simultaneously sampling the missing parts needed to
  generate a plausible and complete concept. Our approach builds on a strong and
  underexplored representation space, extracted from IP-Adapter+, on which we
  train IP-Prior, a lightweight flow-matching model that synthesizes coherent
  compositions based on domain-specific priors, enabling diverse and
  context-aware generations. Additionally, we present a LoRA-based fine-tuning
  strategy that significantly improves prompt adherence in IP-Adapter+ for a
  given task, addressing its common trade-off between reconstruction quality and
  prompt adherence.
paper_ai_summary: A generative framework integrates user-provided visual components using a flow-matching model and LoRA-based fine-tuning to create coherent and context-aware compositions.
paper_ai_keywords: generative framework visual components flow-matching model IP-Prior IP-Adapter+ LoRA-based fine-tuning prompt adherence domain-specific priors