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However, existing methods relying on rigid spatial masks or localized attention often struggle with the \"stability-plasticity dilemma,\" particularly failing in tasks that require complex structural deformations, such as identity-preserving age transformation. To address this, we present IdGlow, a mask-free, progressive two-stage framework built upon Flow Matching diffusion models. In the supervised fine-tuning (SFT) stage, we introduce task-adaptive timestep scheduling aligned with diffusion generative dynamics: a linear decay schedule that progressively relaxes constraints for natural group composition, and a temporal gating mechanism that concentrates identity injection within a critical semantic window, successfully preserving adult facial semantics without overriding child-like anatomical structures. To reso","title":"IdGlow: Dynamic Identity Modulation for Multi-Subject Generation","url":"https://arxiv.org/abs/2603.00607","vendor":"arxiv_cs_ai"},"summary":"arXiv:2603.00607v2 Announce Type: replace-cross \nAbstract: Multi-subject image generation requires seamlessly harmonizing multiple reference identities within a coherent scene. However, existing methods relying on rigid spatial masks or localized attention often struggle with the \"stability-plasticity dilemma,\" particularly failing in tasks that require complex structural deformations, such as identity-preserving age transformation. To address this, we present IdGlow, a mask-free, progressive two-stage framework built upon Flow Matching diffusion models. 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