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Piece it Together

eladrich.github.io · 654 words · saved by 1 readers

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 gene

1Tel Aviv University, 2Bria AI Using a dedicated prior for the target domain, our method, Piece it Together (PiT), effectively completes missing information by seamlessly integrating given elements into a coherent composition while adding the necessary missing pieces needed for the complete concept to reside in the prior domain. Abstract 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…

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