IdGlow: Dynamic Identity Modulation for Multi-Subject Generation

TL;DR

IdGlow is a novel diffusion-based framework that enhances multi-subject image generation by addressing stability and identity preservation through a two-stage, mask-free approach with adaptive scheduling and context-aware prompt synthesis.

Contribution

The paper introduces IdGlow, a two-stage, mask-free diffusion model with adaptive scheduling and preference optimization for improved multi-subject image generation.

Findings

Achieves a better balance between facial fidelity and aesthetic quality.

Effectively preserves identity during age transformation tasks.

Reduces artifacts and semantic ambiguities in multi-subject images.

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.