Multimodal editing large models have demonstrated powerful editing capabilities across diverse tasks. However, a persistent and long-standing limitation is the decline in facial identity (ID) consistency during realistic portrait editing. Due to the human eye's high sensitivity to facial features, such inconsistency significantly hinders the practical deployment of these models. Current facial ID preservation methods struggle to achieve consistent restoration of both facial identity and edited element IP due to Cross-source Distribution Bias and Cross-source Feature Contamination. To address these issues, we propose EditedID, an Alignment-Disentanglement-Entanglement framework for robust identity-specific facial restoration. By systematically analyzing diffusion trajectories, sampler behaviors, and attention properties, we introduce three key components: 1) Adaptive mixing strategy that aligns cross-source latent representations throughout the diffusion process. 2) Hybrid solver that disentangles source-specific identity attributes and details. 3) Attentional gating mechanism that selectively entangles visual elements. Extensive experiments show that EditedID achieves state-of-the-art performance in preserving original facial ID and edited element IP consistency. As a training-free and plug-and-play solution, it establishes a new benchmark for practical and reliable single/multi-person facial identity restoration in open-world settings, paving the way for the deployment of multimodal editing large models in real-person editing scenarios. The code is available at https://github.com/NDYBSNDY/EditedID.
Figures & tables
Figure 1: EditedID Optimized Multimodal Editing Large Models (Academic/Industrial): ID consistency capability. ID-Sim (same ID >0.7 , green border; otherwise red) indicates identity preservation in challenging scenarios: Non-focused, Multi-view, Occluded Faces, Partial-ID Multi-persons.
Figure 2: Enhanced by EditedID: ID preservation in Multimodal Editing Large Models. Comparison of repair effects and innovations. DiffBIR pre-repair triggered by Facial Corruption (FC) severity.
Figure 7: Qualitative evaluation of EditedID vs. Industrial/Academic facial reconstruction models with training cost comparison (data size, GPU). Edited image from six multimodal editing large models. Yellow boxes indicate edited elements (IP) that need to be preserved in the repaired ID.
Figure 14: Quantitative evaluation of reconstruction consistency (PSNR) under different adaptive mixing weight λ0 settings. The consistency threshold between reconstructed and original images is 25dB (higher PSNR indicates better quality). Results demonstrate that λ0 = 0.04 achieves optimal performance across different diffusion steps.
Figure 15: Qualitative evaluation of reconstruction consistency (PSNR) under different adaptive mixing weight λ0 settings. Optimal performance is achieved at λ0 = 0.04.
Figure 16: Impact of different diffusion steps and DPM-Solver++ proportions on the reconstruction results. [s1,s2] : starting and ending diffusion steps for DPM-Solver++ invocation. Results show that consistent input-reconstruction alignment is maintained within just 6 diffusion steps when applying DPM-Solver++ at [0,3] .
Figure 17: Inpainting results under different fusion weight w^ across various scenarios. w^ : fusion weight for overlapping regions of original identity image I1 ; 1- w^ : fusion weight for overlapping regions of edited image I2 . Green color indicates physically plausible ranges (e.g., hat above hair), and red color indicates implausible ranges (e.g., hair above hat). Total diffusion steps are 6.
Model
ID-Sim↑
CLIP-S↑
I-Reward↑
Full Model
0.73
28.14
1.82
w/o Alignment
0.57
26.92
1.72
w/o Disentanglement
0.63
26.48
1.69
w/o Entanglement
0.61
25.33
1.78
Baseline
0.21
19.32
1.03
Appendix
Table 7: Quantitative ablation study results
Method
ID Consistency↑
Facial Fidelity↑
IP Preservation↑
Rosberg et al. (2023)
3.34 ± 0.31
3.83 ± 0.07
4.19 ± 0.03
Ye et al. (2023a)
3.16 ± 0.97
2.13 ± 0.94
2.11 ± 0.33
Zhao et al. (2023)
3.77 ± 0.52
3.62 ± 0.83
3.41 ± 0.43
Han et al. (2024)
3.61 ± 0.02
3.51 ± 0.40
3.48 ± 0.45
Lin et al. (2024)
3.11 ± 0.90
4.02 ± 0.75
3.21 ± 0.70
Wang et al. (2025)
4.40 ± 0.33
3.55 ± 0.02
3.62 ± 0.73
Appendix
Table 8: The participants were asked to rate: (1) ID Consistency, (2) Facial Fidelity, and (3) IP Preservation. The perfect score is 5.
Method
Diffusion Steps
Total Time (s)
GPU Mem (GB)
Prompt-to-Prompt
50
∼ 8.9
∼ 13.6
Null-text Inversion
50
∼ 56.5
∼ 16.2
EditedID (ours)
6
∼ 7.0
∼ 16.5
Appendix
Table 9: Approximate inference cost of different methods for reference.
Layer
Resolution
Replace Self-attn (SSIM)
Replace Cross-attn (CLIP-S)
SD1.4
SDXL
SD1.4
SDXL
-
-
0.22
0.24
15.22
15.14
Down
32×32
0.62
0.67
19.70
21.32
16×16
0.58
0.65
18.51
20.09
Mid
8×8
0.52
0.56
16.74
19.21
Up
16×16
0.63
0.72
20.37
23.11
Appendix
Table 10: Attention mechanism analysis.
Method
Cumulative MSE ( ×10−3) ↓
maxJump ( ×10−3 )↓
latent L2 ( ×10−2 )↓
PSNR (dB)↑
DDIM-only
18.5 ± 3.2
1.9 ± 0.6
1.7 ± 0.4
28.6 ± 0.9
DPM-only
24.2 ± 4.5
3.7 ± 0.9
2.5 ± 0.6
27.2 ± 0.8
Hybrid-fragmented
47.1 ± 5.0
8.9 ± 2.2
4.3 ± 0.8
20.1 ± 1.1
Hybrid-global (ours)
17.8 ± 2.6
2.1 ± 0.5
1.8 ± 0.3
29.0 ± 0.9
Appendix
Table 11: Stability of the hybrid solver.
Method
ID-Sim
ImageReward
Clean → EditedID (w/o DiffBIR)
0.74 ± 0.03
1.82 ± 0.05
Degraded → DiffBIR only
0.36 ± 0.07
1.65 ± 0.08
Degraded → DiffBIR → EditedID
0.71 ± 0.04
1.78 ± 0.05
Appendix
Table 12: Impact of DiffBIR preprocessing on identity preservation and image quality.
Editing Attribute
EditedID Architecture
IP (CLIP-S ≥ 0.75)
ID (ID-Sim ≥ 0.70)
Single
full
92%
94%
w/o gating
78%
74%
Dual
full
87%
85%
w/o gating
69%
66%
Three
full
84%
81%
w/o gating
58%
62%
Appendix
Table 13: Effect of attentional gating in multi-region editing.
Centre for Vision, Speech and Signal Processing (CVSSP) University of Surrey Guildford, United Kingdom · School of Artificial Intelligence and Computer Science Jiangnan University Wuxi, China