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.
Figures & tables
Figure 1: Qualitative results of IdGlow on two multi-subject generation tasks. Given a set of reference portrait images (left), IdGlow generates high-fidelity group photos (right) that faithfully preserve each individual’s identity while producing coherent, aesthetically pleasing scenes. Bottom: Task 2—age-transformed group generation, where adult identities are transformed into child-like appearances while maintaining discriminative facial features. Top: Task 1—direct group fusion.
Figure 2: The architecture of IdGlow-DiT. The model processes variable numbers of reference identities through a unified encoding strategy, forming a concatenated multi-ID sequence. A key innovation is the Dynamics-Aware Gating Module (highlighted in orange), which modulates the intensity of the identity sequence based on the diffusion timestep t and the specific task ( e.g . , age transformation curves). These gated features are injected into the main generation process via Dual-Stream DiT Blocks, where they serve as keys/values in a specialized Multi-ID Cross-Attention layer, interacting with the noisy latent queries.
Figure 3: Task-specific prompt synthesis via the Image-Edit-Prompt model. Given a set of input images and a structured VLM prompt with spatial instructions, our fine-tuned Qwen 3 VL model automatically generates a detailed, spatially precise prompt that specifies subject positions, appearance attributes, and scene composition. This generated prompt then guides the diffusion model to produce a coherent group photo with correct spatial arrangement and identity preservation.
Figure 4: Dynamics-aware identity modulation tailored to specific generative tasks. We design dynamic loss schedules aligned with the spectral evolution of the diffusion process ( t : 1.0→0.0 ). (a) Loss Annealing for Group Generation : high identity weight in early stages to establish identity foundation, gradually relaxed for harmonious lighting and pose. (b) Temporal Gating for Age Transformation : identity constraints selectively activated only during the semantic window ( t∈[0.3,0.6] ), suppressed at early stages for child-like structure and reduced at late stages for smooth skin texture.
Method
Static
Dynamic
FS
Aes
FS
Aes
FastComposer [ 35 ]
0.16
2.34
0.18
2.94
Nano banana pro
0.66
6.48
0.70
6.21
Qwen-Image-Edit-2511 [ 34 ]
0.52
4.69
0.59
5.02
HunyuanImage [ 3 ]
0.48
5.22
0.52
6.35
Seedream [ 7 ]
0.57
5.71
0.60
5.95
Table 1: Quantitative comparison on Task 1 (Direct Group Fusion) under static and dynamic prompts. FS = FaceSim, Aes = Aesthetic Score. Best results in bold , second best underlined .
Method
Static
Dynamic
FS
Aes
FS
Aes
FastComposer [ 35 ]
0.13
2.35
0.14
2.81
Nano banana pro
0.32
6.51
0.32
6.59
Qwen-Image-Edit-2511 [ 34 ]
0.23
5.21
0.24
5.48
HunyuanImage [ 3 ]
0.26
6.01
0.28
6.26
Seedream [ 7 ]
0.28
6.35
0.29
6.53
Table 2: Quantitative comparison on Task 2 (Age-Transformed Generation) under static and dynamic prompts. FS = FaceSim, Aes = Aesthetic Score. Best results in bold , second best underlined .
Task
Configuration
FaceSim
Aesthetic
Task 1
SFT w/o ID Loss
0.45
6.25
SFT w/ Constant ID Loss
0.59
5.83
SFT w/ Loss Annealing
0.64
6.09
+ Fine-Grained DPO
0.72
6.21
Task 2
SFT w/o ID Loss
0.12
6.71
SFT w/ Constant ID Loss
0.18
6.22
Table 3: Ablation study: Effect of dynamic identity loss modulation and DPO. “Constant” uses fixed Ω(t)=1.0 . “Task-Adaptive” denotes loss annealing for Task 1 and temporal gating for Task 2.