IdGlow: Dynamic Identity Modulation for Multi-Subject Generation

• URL: http://arxiv.org/abs/2603.00607v1
• Date: Sat, 28 Feb 2026 11:56:34 GMT
• Title: IdGlow: Dynamic Identity Modulation for Multi-Subject Generation
• Authors: Honghao Cai, Xiangyuan Wang, Yunhao Bai, Tianze Zhou, Sijie Xu, Yuyang Hao, Zezhou Cui, Yuyuan Yang, Wei Zhu, Yibo Chen, Xu Tang, Yao Hu, Zhen Li,
• Abstract summary: We present IdGlow, a mask-free, progressive two-stage framework built upon Flow Matching diffusion models.<n>In the supervised fine-tuning (SFT) stage, we introduce task-adaptive timestep scheduling aligned with diffusion generative dynamics.<n>In the second stage, we design a Fine-Grained Group-Level Direct Preference Optimization (DPO) with a weighted margin formulation to simultaneously eliminate multi-subject artifacts.
• Score: 23.20674988897558
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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. 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 resolve attribute leakage and semantic ambiguity without explicit layout inputs, we further integrate a badcase-driven Vision-Language Model (VLM) for precise, context-aware prompt synthesis. In the second stage, we design a Fine-Grained Group-Level Direct Preference Optimization (DPO) with a weighted margin formulation to simultaneously eliminate multi-subject artifacts, elevate texture harmony, and recalibrate identity fidelity towards real-world distributions. Extensive experiments on two challenging benchmarks -- direct multi-person fusion and age-transformed group generation -- demonstrate that IdGlow fundamentally mitigates the stability-plasticity conflict, achieving a superior Pareto balance between state-of-the-art facial fidelity and commercial-grade aesthetic quality.

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