ConceptWeaver: Weaving Disentangled Concepts with Flow

Pre-trained flow-based models excel at synthesizing complexscenes, yet lack a direct mechanism for disentangling and customizingtheir underlying concepts from one-shot real-world sources. To demystifythis process, we first introduce a novel differential probing technique toisolate and analyze the influence of individual concept tokens on the ve-locity field over time. This investigation yields a critical insight: the gen-erative process is not monolithic but unfolds in three distinct stages. Aninitial Blueprint Stage establishes low-frequency structure, followed bya pivotal Instantiation Stage where content concepts emerge with peakintensity and become naturally disentangled, creating an optimal windowfor manipulation. A final concept-insensitive Refinement Stage thensynthesizes fine-grained details. Guided by this discovery, we proposeConceptWeaver, a framework for one-shot concept disentanglement.ConceptWeaver learns concept-specific semantic offsets from a single ref-erence image using a stage-aware optimization strategy that aligns withthe three-stage framework. These learned offsets are then deployed dur-ing inference via our novel ConceptWeaver Guidance (CWG) mechanism,1 ∗Equal contribution; † Project Lead; ‡ Co-corresponding author.which strategically injects them at the appropriate generative stage. Ex-tensive experiments validate that ConceptWeaver enables high-fidelity,compositional synthesis and editing, demonstrating that understandingand leveraging the intrinsic, staged nature of flow models is key to un-locking precise, multi-granularity content manipulation. We will releaseour code at https://github.com/JasperChennn/ConceptWeaver.