Piece it Together: Part-Based Concepting with IP-Priors
Authors: name: Elad Richardson, name: Kfir Goldberg, name: Yuval Alaluf, name: Daniel Cohen-Or

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.