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Given a triplet $\\{\\mathbf{a}$, $\\mathbf{a}'$, $\\mathbf{b}\\}$, the goal is to generate $\\mathbf{b}'$ such that $\\mathbf{a} : \\mathbf{a}' :: \\mathbf{b} : \\mathbf{b}'$. Recent methods adapt text-to-image models with a single Low-Rank Adaptation (LoRA) module, but they face a fundamental limitation: attempting to capture the diverse space of visual transformations within a fixed module constrains generalization. Inspired by recent work showing that LoRAs in constrained domains span meaningful, interpolatable semantic spaces, we propose LoRWeB, which specializes the model for each analogy task in a single inference pass. LoRWeB dynamically composes learned transformation primitives, informally, choosing a point in a \"space of LoRAs\". 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Inspired by recent work showing that LoRAs in constrained domains span meaningful, interpolatable semantic spaces, we propose LoRWeB, which specializes the model for each analogy task in a single inference pass. LoRWeB dynamically composes learned transformation primitives, informally, choosing a point in a \"space of LoRAs\". 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