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.
Personalized person image generation requires preserving subject identity across both local facial details and broader appearance cues. Existing methods typically emphasize only one level of identity information, leading to an inherent trade-off between facial fidelity and overall appearance consistency. To address this, we first propose a simple dual-branch baseline that unifies global appearance control and local facial control within a shared generation framework. This simple combination of different branches yields promising results, but suffers from instability in practice due to uncoordinated branch contributions. To this end, we propose Dynamic Balancing Scaling (DBS), a fine-tuning strategy for improving face and appearance identity coordination. DBS consists of two components: adaptive temporal gating, which dynamically modulates branch contributions along the denoising trajectory, and region-aware optimization, which improves the coordination of facial, appearance, and global supervision. Together, these designs alleviate persistent face-branch over-dominance and encourage more effective appearance-aware guidance. We also introduce Pexels-100, a benchmark for evaluating holistic identity consistency in personalized person generation. Experiments show that DBS achieves a better trade-off between facial fidelity and appearance consistency than existing open-source baselines, while providing a controllable basic framework for holistic identity modeling.
Multi-person identity-preserving generation requires binding multiple reference faces to specified locations under a text prompt. Strong identity/layout conditions often trigger copy-paste shortcuts and weaken prompt-driven controllability. We present AnyPhoto, a diffusion-transformer finetuning framework with (i) a RoPE-aligned location canvas plus location-aligned token pruning for spatial grounding, (ii) AdaLN-style identity-adaptive modulation from face-recognition embeddings for persistent identity injection, and (iii) identity-isolated attention to prevent cross-identity interference. Training combines conditional flow matching with an embedding-space face similarity loss, together with reference-face replacement and location-canvas degradations to discourage shortcuts. On MultiID-Bench, AnyPhoto improves identity similarity while reducing copy-paste tendency, with gains increasing as the number of identities grows. AnyPhoto also supports prompt-driven stylization with accurate placement, showing great potential application value.
Multi-subject image generation aims to synthesize images that faithfully preserve the identities of multiple reference subjects while following textual instructions. However, existing methods often suffer from identity inconsistency and limited compositional control, as they rely on diffusion models to implicitly associate text prompts with reference images. In this work, we propose Hierarchical Concept-to-Appearance Guidance (CAG), a framework that provides explicit, structured supervision from high-level concepts to fine-grained appearances. At the conceptual level, we introduce a VAE dropout training strategy that randomly omits reference VAE features, encouraging the model to rely more on robust semantic signals from a Visual Language Model (VLM) and thereby promoting consistent concept-level generation in the absence of complete appearance cues. At the appearance level, we integrate the VLM-derived correspondences into a correspondence-aware masked attention module within the Diffusion Transformer (DiT). This module restricts each text token to attend only to its matched reference regions, ensuring precise attribute binding and reliable multi-subject composition. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the multi-subject image generation, substantially improving prompt following and subject consistency.
Yijia Xu, Zihao Wang, Haokun Gui +1
Peking University · Hong Kong University of Science and Technology