Optimizing ID Consistency in Multimodal Large Models:  Facial Restoration via Alignment, Entanglement, and Disentanglement
Authors: Yuran Dong
Hang Dai
Mang Ye
Venue: ICLR 2026 Poster (2026)
Keywords: Identity preservation
Facial reconstruction
Multimodal Large Models
Fashion Image Editing
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: 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.

Published as a conference paper at ICLR 2026
OPTIMIZING ID CONSISTENCY IN MULTIMODAL LARGE MODELS: FACIAL RESTORATION VIA ALIGN- MENT, ENTANGLEMENT, AND DISENTANGLEMENT
Yuran Dong, Hang Dai', Mang Ye*
National Engincering Research Center for Multimedia Software School of Computer Science, Wuhan University, Wuhan, China { (dongyuran, daihang, yemang e
Optimized Multimodal Editing Models: GPT-40 Flocd1 Kontext (Indutrial), In-Contex: Edit, InstructFis2Fi (Academmk]
enhancing preservation
Mln-focused
Editadio
Flus.1 EditedID
Occluded Face
Itpkakc (D023)
Plug EditedID
Partial-ID ID Multi person
GPT-40 plus [2025-3]
Plug EditediO
Figure I: EditedID Optimized Multimodal Editing Large Models (Academic/Industrraa))) ID consis- tency capability. ID-Sim (same ID 0.7. green border; otherwise red) indicates identity preserva- tion challenging scenarios: Non-focused, Multi-view, Occluded Faces, Partial-ID Multi-persons.
ABSTRACT
Multimodal editing large models have demonstrated powerful editing capabili- ties across diverse tasks. However, persistent and long-standing limitation is the decline in facial identity (ID) consistency during realistic portrait editing. Due to the human eyc's high sensitivity to facial features, such inconsistency significantly hinders the practical deployment 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 taarraanggmmmmm---tannnnneeeeeee Alignment-Disentanllen Disentanglementt Entanglement framework for robust identity-specifi facial restoration. By systematically analyzing diffusion trajectories, sampler be- haviors, and attention properties, we introduce three key components: 1) Adap- tive mixing strategy that aligns cross-source latent representations throughout the diffusion process. 2) Hybrid solver that disentangles source-specific identity at- tributes and details. 3) Autentional 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 con- sistency. As training-free free and alug-and-play solution, it establishes new benchmark for practical and reliable single/muuttiperron facial identity restora- tion 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 http://hhh.b..com//////E//////E
"Corresponiing authors.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Recent years have witnessed growing interest in Multimodal Editing Large Models Zhang et al. 2025: Liu et al. ): Black Forest Labs (2025); OpenAl (2025); Deng et al. (005)) Wu et al.
2025 owing to their broad applicabilitt and practical value. These models function by interpreting
user instructions to enable image editing. Although these models are effective for identity preserva- tion in cartoons, they degrade significantly with complex long-instruction real real-person fashion edit- ing. Detailed prompts like "make him wear a light gray jacket with Black-framed glasses" frequently induce facial artifacts. Academic models, such as In-ContextEdiit Zhang et al. (2025) and Instruct- Pix2Pix [Brooks et al. (2023), suffer from limited fine-tuning data, affecting facial feature extraction and causing progressive deterioration of facial edits with long instructions. Industrial models, such GPT-4o Plus OpenAl (2025), Qwen-Image-Eöde Wu et al. (2025) and Flux.1 Kontext Black Forest Labs (2025) prioritize LLM-driven textual controllability but neglect facial geometric constraints, producing random facial identities. Given the high sensitivity of human perception facial features, even slight deviations in identity can render the results unusable. However, due to the confidentiality of real-world facial datasets, task-specific fine-tuning is often impractical. Multimodal editing large models face the unngsiaading challenge of achieving reliable identity preservation when performing complex, long- long-instruction edits Dong Ye (205);;); Wang
Ye (204)) real- real-person scenarios.
Repetiing Coltee Image:
EditedID
Aresenation
Origina/DD
"fore" "focewih glasses"
il Blndd Image Nattoration
Editig
0.23
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Train: free
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Muttimedal
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Input
Large Model Fadal Damage
Longe Maded Pogg Editedio
Besting Repair Methods
Enhanced Preservation Multimddal Editing Large Models EditeolD
[3) Our Framework: Pipeline and Innovations
Figure 2: Enhanced by EditedID: ID preservation in Multimodal Editing Large Models. Comparison of repair effects and innovations. DifiBIR pre-repair triggered by Facial Corruption (FC) severity.
To optimize the ID) consistency capability of existing multimodal editing large models, shown in
Fig. (2). our goal achieve plug-and-play facial identity reconstruction. This involves restoring facial consistent with the Original II) while preserving edited Element IP (e.g., the color, size, or
texture of accessories such as glasses) consistent with the Intermediate ID. In Fig. 2 (1), existing four types of facial consistency methods each exhibit limitations in achieving this objective:
Cross-source Distribution Bias Leads to: i) Detail Loss in Identity Preservation. Identity- preserving approaches Ye et al. (2025): Ohayon et al. (2024); Ye et al. (202aa) often degrade pho- torealism by blurring fine facial details and introducing cartoon-lik artifacts. This degradation pri- Ye Ye
marily stems from Cross-source Distribution Bias. For instance, IP-Adapter coarse- grasied identity features (learned from limited data) with high-resolution features from base diffusion models, leading distribution mismatches and distortion artifacts. ii) Original ID Lost in Blind Restoration. Generic facial restoration methods Hu et al. (2025); Ito et al.] (2025); Lin et al.) 2024 focus on facial super-resolution but neglect identity consistency. Although these models gen- erate clear textures and plausible structures, Cross- source Distribution Bias between the generated facial features and the original distribution leads random, -D--eifpeeccfic facial reconstruction.
(203a merges
Cross-source Feature Contamination Leads to: iii) Element IP Loss in Identity Fusion. Fusion- based methods Yao et al.] (2025): Nam et al. (024); Chen et al. (2024); Ye et al. (2023b) act as relaxed feature transfer mechanism. During cross-source feature (original facial and edited ele- ment) fusion, such methods often suffer from inter-feature contamination, leading to loss of fine- grained edited attributes. For instance, the "black-framed" style of glasses is frequently lost, as these methods prioritize element-level consistency over accurate element attribute preservation. iv) Edited Facial Noise in Face- Swapping. Face-swapping methods Ye et al. (2025), Waan et al. (2025;; Wang (2024); Liu et al.) (2024); Li et al. (2024) exhibit high sensitivity to artifacts in edited facial. Effective under normal facial conditions, their performance degrades significantly in the presence
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Published as a conference paper at ICLR 2026
of geometric structural distortions, where Cross-source Feature Contamination (original ID and edited facial) and leads to loss of the original ID in swapping.
To address these limitations, draw inspiration from 3D facial processing methods Li ct al. 2023), which separate specific elements (c.g., glasses and faces) from cross-source objects and re-simulate physical interactions (e.g., lighting, position) to combine them into novel composites. This in- spires the core principle of our proposed Edited (Fig. 2 (2) and (3)) in 2D scenarios: Align- wwwpreattnnnneeeee--tnnnnngeeeee
ID-consistent fa- cial reconstruction method. Specifically, to mitigate Cross-source Distribution Bias, we introduce Adaptive Mixing for dual-ID (Original and Intermediate ID) latent Alignment; to isolate Cross- source Feature Contamination in aligned identities and preserve personalized element features, we propose Hybrid Solver for dual-II) latent Disentanglement. Finally, via an Attentional Gating mechanism, we Entangle facial attributes from the Original ID with edited elements from the Inter- mediate ID. Refer to Appendix for related work. Our contributions are as follows:
EditedID is diffusion-inversion-bee
Trajectory Insight Adaptive Mixing: By revealing the soltiiso and controllability of diffusion trajectories, we propose Adaptive Mixing a cross-object feature fusion approach that mitigates Cross-source Distribution Bias avoid abrupt transitions and artifacts.
Sampler Analysis Hybrid Solver: By leveraging DDIM identity retention and DPM-Solver++ detail enhancement properties, we design Hybrid Solver-a aloeee--aggaalitiiiiiiiin global-timestep hybrid sampling method that isolates Cross-source Feature Contamination while preserving original identity-detail features.
Attention Mechanism Attentional Gating: By uncovering the distinct roles of attention in diffusion processes, we introduce Attentional Gating-an specified element control mechanism that preserves single-clement structures and balances multi-element interactions during entanglement.
Identity-Consisten Restoration Paradigm: We propose a novel, training-free trajectory Align- ment framework for woaldworld single- and multi-facial restoration, leveraging the Disentanglement and Entanglement of cross-source semnanic--pecciic elements within pre-trained diffusion models.
METHODOLOGY
features
tt: Unconditional Embedding
A
Fragmented-
C: "foce with giosses'
(0-1)
foce
...
Timastop Method
Preset
z(0
pose
Up self cross
Twtth
Token
visual attention map X
self cross
self cross
Eg
Global-
noise
tollo
Outout
Preset
Timestop
Method
tokens keys
tokens values
(1) U-Net (each diffusion step)
(2) Text Token to Visual Attestnn
[3) Timestep Setting: Fragmented Global
Figure 3: Preliminaries: (1) U-Net (2) cross-attention.
Hybrid solver: (3) Timestep Init. (Sec 2.3
Preliminaries. Diffusion models synthesize an image (0) by iteratively denoising random noise 2(T) over T steps. The inversion process diffuses real image 2(0) to noise 2 while the recon- struction path reverses it via a denoising trajectory. Null-text optimization Mokady et al. (2023) bridges the deviation between reconstruction and inversion by introducing an unconditional embed- ding (11) achieving accurate reconstruction trajectories of real images g(() R 2()), This process is training-free. At each step i, U-Net predicts noise & = (0(=(1), t) using 2(t), conditional embedding C, and (f)); the sampler then updates (t--)) accordingly in Fig. 11).
Diffusion Samplers: DDPM Ho al. (2020) adopts Markov chain (e.g., T = 1000), incurring high cost. DDIM Song et al. (2200) accelerates sampling via non-Marke non-Markovian steps (c.g., T 50). It reconstructs g(0) from 2 and deterministically computes 2(k), A < t. DPM-Solver++ Lu al. (2022) treats reverse diffusion an ODE, solved via high-order Taylor approximations. This enables rapid convergence with realistic details (details in Appendix[A.. A.2
U-Net Attention: U-Net includes cross-attention layers (Fig, 3[2)) that map text tokens visual attention maps A. Query Q is projected from image features 200, while Key K and Value V from
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C. Prompt-to- proptttp Hertz al. (2022) manipulates attention maps for feature transfer. Null-text extends this to real- real-image editing via reconstruction trajectory.
Given source identity 11 and edited image 12 (e.g., with black glasses), the goal is to reconstruct a new image Ly that preserves 4 's identity while retaining 12's semantics. For degraded 12, first apply DifíBIR Lin et al. (2024) for super-resolution enhancement.
Problem Statement:
2.1
MOTIVATION
Disentangte:
Reconstruction via DOIM
(Tonal difiuion sopps 10 simlonly
Reconstructton
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Intiiaitzaion:
05
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ortension mapa
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[07, ()7.0))
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(0,07)
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(3) Pilot
Latent Initialization Blas and Trajectory (Single- Gingle-Image Reconstruction)
Investigartng Selt Attentit
Figure 4: (1) Limitations of the Pilot Study and analysis of three contributing factors: (2) (4).
Pilot Study. We investigate Prompt-to-Prone and Null-text for identity-consisten restoration of edited images, focusing on feature disentanglement and fusion via wross-attention. In Fig. (1), La- tent Initialization of I3 is achieved by using either 21 or 22 (T) which are obtained through DDIM Sampler inversion of Dual-ID (I, and I2). During the diffusion process of Ia. Cross-Attention maps "f-e"face". I2 "glaassss""" are fused to guide cross-source feature transfer. However, la exhibits asymmetric feature retention- starting from 2(T) preserves ID (face) but loses edited at- tributes (glasses), and vice versa. Significant artifacts also emerge in Iz. Key factors include:
1) Latent Initialization Bias: Different initial latents (z)²) (T) 22 (T) produce incompatible attention
maps, leading to poor fusion. Mixing latents (0.5z) (2) - 0.52, (T) amplifies artifacts due to diffusion nonlincarity (Fig. (2)- To reduce cross-source latent space bias, explore the controllability of diffusion trajectories. Fig. (2)-bottom, trajectories are perturbed via text embedding modi- fications noise injection (a E 0.05, 0.5)). Higher PSNR Fardo et al. (016) indicates greater reconstruction-origee original similarity (z(0) (0)), consistency threshold: 25 dB. Similar PSNR under different perturbations suggest identical reconstructions across trajectories. Our reveal key findings:
Observation 1 Trajectory: i) Multi-solution. Multiple trajectories can achieve same output (same PSNR) if fixed target 2("), demonstrating multi-path solvability (a 0.25 or tert text5) of (0)), ii) Controllability. 2(T) and evolution jointly shape trajectories; controlled perturbations (a) allow trajectory alteration without degrading fidelity (PŠNR dB).
2) DDIM Sampler Loss Detail: DDIM sampler introduces bias in detail fidelity, which is exac- erbated by cross-source fusion and leads to artifacts. Fig. 43) compares ID similarity (Deng et al. 0.7 same ID) between DPM-Solver++ and DDIM under different reconstruction steps.
2019
Observation - Sampler: i) DDIM: Identity over Details. More diffusion steps (> 50) with deterministic path preserves identity consistency (ID sim: 0.84), but detail loss due to 1st-order smoothing and eтror accumulation. ii) DPM-Solver++: Details over Identity. Fewer diffusion steps (< 10) with high-order Taylor expansion produce high-fidelity details, yet path deviation and prior interference cause identity loss (Avg. ID sim: 0.59), Detailed analysis in Appendix A.3
3) Cross-Attention Weak Constraints: Cross-source feature transfer via cross-attention alone yields weak constraints and ignores spatial coupling in self-attention. We analyze self vs. cross roles by manipulating attention maps ("squirrel" replace "lion") under varied strengths in Fig. (4):
Observation - Attention: i) Self: Single-Element Structure. Self-attention encodes single- element structure; increasing self-strength enforces shape transfer (quor->qqiirre). ii) Cross:
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Multi-Element Interaction. Cross-attention encodes multi-element interaction; increasing strength enhances semantic interactions (lion eating hamburger). iii) Balance: Coordinated
cross/self- tuning is essential for structure-interaction trade-offs and coherence outcomes.
These observations motivate us to solve three core challenges, solved in Sec.
Sec.
Sec.
C1: How to align dual-ID diffiusion trajectories to reduce cross-source latent initialization bias?
C2: How to integrate DDIM and DPM-Solve+++ identity-detal preserving disentanglemem!?
C3: How to coordinate tross/seff-attentionn for structure-interaattin balanced entanglement?
Alignment: Dual-D Latent Alignment via Adaptive Mixing
Disentanglement Dual-ID Disentangjament
Tour-pointed star: vestor
Tie-poiinted star: Latant vector via DOIM
2
23-43
shared
lasent origin
NO
Independent Algned Invertinn
Inttaa reconstruction
Chanttnggee recomdraction
0
Yest
Intermeslote lD]
Reconstruction
Figure 5: Dual-ID alignment (Left) and disentanglement (Right).
2.2
ALIGNMENT: DUAL-ID LATENT ALIGNMENT VIA ADAPTIVE MIXING
Linear blending the invert trajectories of the source images 1 and /2 by combining their latent codes #1 and 2 with fixed weighty leads to two critical failures: 1) Early inversion (near 2(0)), exces- sively abrupt averaging of latents causes significant degradation of source-specific features; 2) Late inversion (near 2(²'),, lack of adaptivity results in overlapping trajectories, leading to cross-source feature contamination. To address C1 based on Observation 1: Multi-solutie and Controllability of trajectory, we propose Adaptive Mixing with learnable weights X (Fig. 5 The A dynamically balances the contributions of 2(t) and a!a), optimized via gradient descent to minimize alignment loss (in red):
Lalign = =11 ((t)
Vh
(1)
where 7) = 0.01 is the learning rate, A E [0, 0.5). During inversion, latent codes (2 and 2(") are updated independently (noise predicted separately for and Ig ):
(1+1) = (1- - de 200)
2(t)
((+1)
Ar) 200 + A 2(()
Lower Å initial values yield smoother trajectory alignment. To ensure alignment of 2 and 22 in latent space at t = T, we enforce constrained convergence later diffusion steps:
(0
(+1) 2/
((+1) (2 + )/2, A = 0.5.
This yields unified initialization E(T) with two smooth merging paths (free from abrupt loss vari- ations), which preserve original features while harmonizing latent trajectories. By storing disentan- gled source-specific attributes along the paths, our method achieves latent space alignment, mitigates Cross-source Distribution Bias, and retains individuality (e.g., facial identity (I1) and glasses (Iz)).
2.3 DISENTANGLEMENT: DUAL-ID DISENTANGLEMENT VIA HYBRID SOLVER
After aligning the initial latent space (TT) for dual identities, we disentangle original and interme- diate IDD features from this shared initialization point for selective token fusion. Extending null-text
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optimization, we optimize distinct null-text embeddings
per ID to minimize MSE be- Sc=1
tween reconstructed latents 2; (t-11) and aligned states =!"- s!(-1),
-((-1)) - 2- 0,0 C.).
Free
(4)
Unlike single image null-text optimization, our reconstruction objective Free integrates joint MSE losses for both I and I2 reconstructions 1. 2), conditioned embeddings C.. Exclusive DDIM
sampling updates subsequent latents via: 2 -((-1) 2 (z) ! C.). Under trajectory alignment constraints, this amplifies detail degradation and artifacts in the reconstructed IDs.
To resolve C2 with Observation 2-DDIM: Identity over Details, DPM-Solver++: Details over Identity- we propose Hybrid Solver that dynamically invokes DDIM DPM-Solver++ during 1((11) prediction harness complementary advantages:
DP 2 C.), [21, DDIM (1) .0.0) otherwiss...
(5)
where i and 52 denote start/end steps for DPM-Solver++ invocation. Empirical analysis (Appendix A.8) reveals optimal reconstruction strategy: DDIM invocation in early steps (near T(T) establishes robust ID feature preservation, while DPM-Solver++ activation in the late steps (near z0)) actively repairs and enhances the textural details from DDIM-retained features. Our hybrid solver optimizes diffusion timestep sampling, enabling high-fidelity ID reconstruction in few steps while resolving the long-standing efficiency-fided -iieelity trade-off in DDIM sampling. Furthermore, it ensures identity- detail consistent disentanglement for dual-ID, preventing Cross-source Feature Contamination.
To ensure feature alignment when using the same sampler at matching noise levels, the invocation DPM-Solver++ should be symmetric across both the inversion and the reconstruction stages in Fig. 5
Symmetry Constraint:
Timestep Continuity:
During the inference of 2("-1), the sampler requires a consistent sequence of timesteps corresponding to the full diffusion step length t = T. However, different samplers compute their timestep sequences differently (see Appendix [A.4p. In our initial attempt, we adopt fragmented scheduling approach, where the diffusion trajectory is partitioned into fixed intervals, with DDIM applied over [0, 51), DPM-Solver++ over [$1. s2]. etc.
As shown in Fig. 3), employing 11-ttp reconstruction schedule (DDIM for steps 0-4; DPM- Solver++ for steps 5-10) under symmetric inversion leads to uncontrolled latent divergence at
transition boundary (L = 4). This discontinuity resulted in chromatic aberrations and significant
reconstruction errors (2) (0) A (()) in Fig. 3-too..
To resolve this, we propose a global timestep pre-setting strategy that ensures temporal continuity across the entire [0 - T' trajectory. Specifically, we first pre-compute the full timestep sequences for both schedulers: DDIM: (To, -I. DPM-Solver++: ( o0o-1). we dynamically assign the timestep at each diffusion step as:
oo E s2]
limestep
Tt. otherwise.
As shown in Fig. 33) -bbottm, this unified scheduling resolves discontinuities at critical transition points (e.g.. / = 4), enabling smooth latent evolution and high-fidelity photorealistic identity recon- struction. The improved alignment between (0) and =(") validates the effectiveness of our hybrid- sampler scheduling in mitigating reconstruction artifacts.
2.4 ENTANGLEMENT: MULTI-ELEMENT (IP) ENTANGLEMENT VIA ATTENTIONAL GATING
As shown in Fig. 6. to address C3, we initiate generation from shared latent E(T), reconstructing I, 1z, and target In under null-text constraints g6 and g During parallel diffusion, we replace attention maps of 13 with self-attention and cross-attention maps of / for semantic tokens.

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Published as a conference paper at ICLR 2026
Entanglement: Multi-Element Entanglement via Attentional Gating
face
New tokon woggtdng
"face with glosses"
'Joce
2:71
Jrice
"Joce with glasses*
stared
Recilaee
Joce with glosses"
Arpatred
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Mark Selective Self-tmenniin Reclagement
Token Selective Cross Attemtion Replacement
plosses
Reconstruction
Figure 6: Multi-clement entanglement: Mask/Token-selectie self/cross- replacement.
Mask-Selective Self-Attention Replacement. In the step I, based on Observation 3, self-attention preserves Single-Element Structures, we introduce a semantic-guided mask mechanism for self- attention feature selection. Specifically, spatial masks Mi (e.g., "face") and M2 (e.g.. "glasses") are applied to self-attention maps S1 (from I1) and S2 (from I2) to isolate target regions:
- (00 W. o S3 Wa.
For non-target regions Wa, the original self-attention map Sy of target image Ig is retained. The effective regions Wi and W2 for filtering S₁ and S2 are defined as:
Mi (1 - M2) + ú (M1 M2), W2 M2 n M,) + (1 u) (M1 M2), W3 1- (M, M2).
W1
Physical interpretation: Exclusive zones of M1 /M2 retain full self-attention from S1/S2. Over- lapping zones apply weighted fusion with coefficient w. The entangled 5 undergoes
malization for valid attention distributions, with replacement confined to down/mid U-Net layers to preserve inter-element generability (Analysis in Appendix A.5))
Teken-Selective Cross-Attention Replacement. At step f, based on Observation 3, cross- cross-attention facilitates Multi-Element Interactions, we utilize visual attention maps ( (Fig. 3)) to selectively entangle features between 1 and Ig- Let A1, Ag, .Ag denote cross-attention maps for 11, I2. Ia. respectively. The replacement rule can be defined as:
5 [ = (IET) A [t] + +1 ((ETT) [i] + 1 (UET..ETT) [i.1]-
where i,j E J0, 76] (maximum length of text token sequence). Target token maps (e.g., "face" from 41 "glasses" " from 12) are replaced throughout t E [0, 77 to ensure semantic coherence. 1 fcondition
Indicator function (1 if true, else 0); T1.T: Target token sets for source images; (A()) [t]: Cross- attention map for the i-th token. By integrating BlendDiffusion / Avrahami et al. (2022), our frame- work maintains source-specific structural priors and facilitates token-wise, context-aware interac- tions, enabling idemtity-consistent restoration without extra training.
EXPERIMENT
Experimental setup in Appendix A.6 and hyperparameter sensitivity analysis in Appendix A.8
Comparisons. As shown in Fig. and Tab. [ we compare EditedID with SOTA methods using samples see Appendix A.6 for details. The comparison spans four categories: PrrntttpPreservingg Identity Fusion, Blind Restoration, and Face- Face-Swapping. We use three metrics: ID-Sim (ID simi- larity), CLIP-S (edited element IP preservation), and I-Reward Etuman-expeccation compliance ex- cluding artifacts). Fig. includes challenging scenarios (details in Appendix [.7]:: focused (single face area > 10% of image, Lines 1,2,4), multi-angle and complex lighting (Line 3), multi-person ID-specific optimization (Line 4), multi-element IP preservation (Line 5), and non-focused scenes (single face area < 10% of image, Line 6). Key observations are summarized as follows:
EditedID resolves the trade-off between Original ID and edited Element IP preservation. Due Cross-source Feature Contamination between the Original ID and Element IP, existing methods

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Table 1: Comparison with ID Preservation Methods: ID
Table 2: Comparison with Multi- modal Editing Large Models. In- Con denotes In-ContextEdit.
Similarity (Deng et al, (2019). ) IP Preservation (CLIP-S Hessel al. ). Human Preference (I-Reward Xu al. (2023).
ID Preservation Method ID-Simt CLIP-S1 I-Reward
Multimodal Model
ID-SimT
Rosberg al. (2023)
0.36
25.71
1,45
InstructPix2Pix
0.37
Ye al.
2023a
0.35
20.42
1.02
In-ContextEdit
0.56
Zhao
(2023
0.49
25.85
1.53
GPT-4o Plus
0.58
Han ct al.
2024)
0.40
26.13
1.61
Flux.1 Kontext
0.55
Lin ct al. 2024)
0.34
25,43
1.65
Doubao
0.63
Wang et al.
(2025)
0.63
26.11
1,56
Dreamina AI
0.64
Baliah et (2025)
0.41
27.63
1.68
BAGEL
0.50
Seepl'aceSwap Al (2025
0,52
28.02
1.69
Swen-Image-Bll
0.52
Ye al. (2025)
0.65
26.11
1.73
In-Con w/ EditedID
0.72
EditedID (Ours)
0.73 10.27
28.14
1.82
Doubao w/ EditedID 0.75 0.12
Suc. GPU
A1B0K8
closed source
0TX4600X
Conbort 26
GIV do
Con't hande MURHDD
Canth MUR--DI
MuriiDe
onggna lo
Edited Image har doows
FaceGancer
Ip-Adeperr
DeIGR
DeepSwwp
Editeclo
MAI Io:
Originat
Edited mage bew www.TutttWToeeee...oo rallec
DIHBIR
FaceDancer, Ip-Adapper, DeepSwap
Editedio
Figure 7: Qualitative evaluation of EditedID vs. Industiall/ccaemmic 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.
exhibit limitations: IP-Adapter often leads to facial collapse, DiffBIR corrupts IP (e.g.. transform leopard-print frames into plain black), and FaceDancer fails to preserve high-dimensional identity features. By isolating Cross-source Feature Contamination through Hybrid Solver and Attentional Gating, EditedID achieves robust and controllable identity migration while faithfully preserving all edited IP attributes, c.g., patterns, logos, colors. Quantitative evaluations demonstrate consistent improvements over SOTA methods, with average gains of 0.27 in ID-Sim and 2.43 in CLIP-S.
2 EditedII demonstrates robustness in Real-world scenarios. Existing methods often suffer from performance degradation in real-world scenarios (e.g., profile views, challenging lighting, multi- person scenes, etc.), due to being trained or fine-tuned on facial datasets dominated by professional frontal portraits. The resulting Cross-source Distribution Bias restricts their learnable knowledge and generalizability under real-world scenarios: IP-Adapter fails on 45e profile views, while DeepSwap loses edited IP elements and exhibits identity drift under occlusion. In contrast, EditedID mitigates Cross-source Distribution Bias via Adaptive Mixing, achieving stable, urtifact-free reconstruction in practical scenarios ceenaris--yieldingg yielding average I-Reward improvement of 0.27 over baselines.
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Existing methods struggle with Multi-IDs restoration. Due the inherent limitations in the ap- plication scenarios assumed by existing methods (FaceDancer, IP-Adppeer, DeepSwap), they strug- gle to perform parallel multi-person ID restoration. In contrast, EditedID enables concurrent ID- consistent facial recovery across multiple icenariss-includinng multi-person focused, non-focused, ID-specific optimization, and multi-element IP innieevvationtthruuee parallel identity restoration on facial patches. It also enhances original ID resolution in low-resolution defocused scenarios.
In Fig. 7top, EditedID requires neither complex tunnn//inngr nor labor-intensive data collec- tion, achieving training-free implementation with a single GPU. Additionally, to further evaluate the ID consistency enhancement by EditedID on multimodal editing large models, we compare it against academic Brooks et al. (2023); (Zhang et al. (2025) and industrial state-of th- models Black For- est Labs (2025); O (2025): Deng et al. (2025) ; Wu et al. (2025); [ByteDance (2025a (b) in Tab. 2 Clearly, existing large models exhibit facial identity degradation (III-Sim < 0.7) under vari- ous challenging scenarios. This is primarily due to interference from excessive text tokens during long-instruction multi-subject editing, which dilutes the model's focus on visual features and dis- rupts the retention of original identity priors, often resulting in randomly generated IDs. Notably, EditedID offers flexible compatibility with different large models through external optimization, avoiding model-specific fine-tuning and its associated data/resource costs. After optimization, the academic model En-CottextEdit achieves 0.16 gain in ID-Sim, while the industrial model Doubao improves by 0.12. These results demonstrate both the effectiveness and compatibility of EditedII) in enhancing ID consistency for multimodal editing large models.
Inference Time 62
Single ID Double Tripic ID:
Orginai Editd Irage
Basdie
Alge
Gevontoegla
Eraingie
DCC
Offface
DinSwap
DeepSwap
Ed bed scep)
Figure 8: Per Image Time: Single vs. Multi IDs.
Figure 9: Qualitative Ablation Evaluation.
Efficiency. We evaluate the inference efficiency of EditedID in both single- and multi-ID scenarios (see Fig. 8). For the single-ID case, EditedID achieves average reconstruction time of approxi- mately 4.2 seconds, which is about 6x faster than the diffusion-based DiffFace (details in Appendix A. While baselines exhibit exponential time growth in multi-IDs scenarios, EditedID maintains constant inference time with parallel architecture regardless ID count.
Ablations. EditedII is evaluated through ablation (Fig. and Appendix A.9) on a baseline of null- text inverted real images with pompp-to-promp identity fusion. Without alignment introduces identity mismatches, artifacts, and abrupt transitions, highlighting the role of Adaptive Mixing in mitigating Cross-source Distribution Bias during diffusion. Without disentanglement produces artifacts or facial distortions, validating the Hybrid Solver's importance in balancing sampling strate- gies to isolate Cross-source Feature Contamination while preserving identity and details. Without entanglement causes loss of edited attributes (Blements IP e.g., hats/glasses), underscoring the necessity of Attentional Gating to maintain single-element structures and control multi-element in- teractions during diffusion. In summary, EditedID is not simple aggregation of modules, but a coherent diffusion optimization framework. Its effectiveness stems from principled improve- ments-Adaptiiv Mixing, Hybrid Solver, Attentional Gating- that optimize the pretrained diffusion process without introducing unnecessary complexity. Ablation studies confirm that each technique provides an essential refinement to diffusion dynamics, enabling stable and interpretable identity reconstruction. Essentially, EditedID is stable, general diffusion manipulation mechanism.
4 CONCLUSION
By reutilizing diffusion dynamics for cross-source feature fusion, we propose EditedID-ap training- free and resource-efficient framework for ID-consistent face restoration. We reveal the limitations of multimodal large models (requiring extensive data/compuutationnuumann efforts) in preserving ID consistency; plugging in EditedID significantly enhances ID-consistent capability of multimodal editing large models, demonstrating the real-world applicability and effectiveness of EditedID in addressing the ID consistency challenge. Furthermore, with its high ID consistency, Edited[ID

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serves pre-edit/post-edit facial dataset calibration method to significantly expand existing facial datasets- a single facial sample can yield multiple edited versions, thereby alleviating a long- standing limitation in facial ID consistency for multimodal large models-dee scarcity and fidentiality. Overall, by revealing new insights into diffusion Trajectories, Samplers, and Attention mechanisms, believe EditedlI) will underpin future broader applications and academic search in multimodal fusion, deformation-rreistari editing, and fine-grained attribute preservation.
ETHICS STATEMENT
This work studies identity-consister facial restoration and editing techniques. While the proposed method is designed for benign applications such as image restoration, creative content generation, and research purposes, acknowledge that such technologies could be misused (e.g.. for imper- sonation or privacy invasion). To mitigate such risks, our work does not provide tools for identity acquisition unauthorized identity manipulation. The method operates only user-provided im- ages and does not incorporate any identity recognition or retrieval components. All experiments are conducted on publicly available datasets data with appropriate usage permissions. We emphasize that this research is intended to advance controllable and responsible image generation. We encour- age future users and developers comply with applicable laws and ethical guidelines, obtain proper consent when handling personal data, and avoid malicious applications.
ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China under Grants (T2541022, 62361166629), and the Key Research and Development Project of Hubei Province (2022BADI75 2022BCA009). It is also partially supported by the WHU-Kingsoft Joint Lab. Ad- ditional support was provided by the National Natural Science Foundation China (Grant No. 62571380).
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Reviews:
[{"review_id": "YArJvFolnA", "reviewer": "Reviewer_43M9", "summary": "This paper introduces a training-free framework for improving facial fidelity and identity consistency in personalized image synthesis. To begin with, it points out two limitations in existing methods, including the cross-source distribution bias that models are trained with different data (e.g., with different image resolutions), and the cross-source feature contamination that the identity features may be affected by those of the edited elements. Subsequently, the proposed framework mainly incorporates three strategies to address these limitations. First, it proposes an adaptive mixing strategy that utilizes a learnable combination weight to adaptively merge the latents of the original image and the edited image, yielding a unified representation to tackle the cross-source distribution bias. Second, a hybrid solver is introduced to optimize the null-text embeddings of the original image and the edited image,  to balance identity preservation and detail editing. Finally, an attentional gating strategy is adopted to fuse both the null-text embeddings and generate the final result. Extensive experiments have been conducted to validate the proposed strategies, and the proposed method outperforms several open-source and closed-source large models.", "questions": "1. DDIM and DPM-Solver++ are used in the disentanglement stage, have any other solvers been considered?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper is insightful, and its motivation for balancing facial identities and edited elements is clearly stated by multiple observations from pilot studies.\n\n2. The proposed framework is training-free, can be employed with a consumer-grade GPU, and its results are considerable.\n\n3. The proposed components are validated via solid experiments.", "weaknesses": "1. The inference cost of the proposed method has not been reported and may be large, as it requires three stages of optimization (alignment, disentanglement, and entanglement).\n\n2. The overlapping region fusion weight $\\hat{w}$ varies in different scenarios. Although two principles for setting the weight have been provided in the Appendix,  it is still inconvenient for ordinary users to configure it to achieve fine-grained controls.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "y130S2NJAk", "reviewer": "Reviewer_1CnC", "summary": "The paper proposes EditedID, a training-free, plug-and-play pipeline for identity-consistent face editing/restoration from edited portraits. It comprises (i) Alignment via adaptive mixing of two inversion trajectories, (ii) Disentanglement via a Hybrid Solver that mixes DDIM and DPM-Solver++, and (iii) Entanglement via Attentional Gating that replaces attention maps under masks/tokens (e.g., face with glasses). Experiments report improvements on ID-Sim (ArcFace cosine), CLIP-S, and ImageReward; efficiency claims include 6 steps and ~4.2s per image with constant time under multi-ID.", "questions": "See Weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "- Clear practical target (ID drift in edited portraits) and a coherent three-stage pipeline mapping to common diffusion operations.  \n- Sampler insight into DDIM vs DPM-Solver++ is distilled into an actionable hybrid schedule; the paper gives explicit step ranges [s_1,s_2] in code-style detail.    \n- Training-free at dataset level with wide editor compatibility (academic and industrial editors).  \n- Ablations (module removal) show separable contributions on ID-Sim / CLIP-S / ImageReward.", "weaknesses": "1.  The ``self-attn keeps single-element structure / cross-attn handles multi-element semantics`` is plausible but untested across U-Net variants/resolutions; current evidence is largely qualitative. Provide attention-map statistics across layers/architectures.  \n2.  The method described in this article was tested on the outdated U-Net and its migration performance was not tested on the modern DiT architecture. Currently, DiT has become mainstream in the field of image editing and generation.\n3. Training-free” vs instance-level optimization (definition gap). Alignment uses learnable \\lambda_t updated by gradient descent per image; disentanglement also optimizes distinct null-text embeddings per ID. This is per-image optimization, not “no learning.” Authors must formally define “training-free” (no dataset-level updates vs. no parameter learning at all) and include these optimization costs in timing.    \n4. Hybrid-solver theory is under-justified. The paper splices DDIM and DPM-Solver++ with a “global-timestep preset,” but offers no stability/consistency analysis for solver-mixing across schedules (error accumulation, reversibility, path homotopy).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YDZ6TQy1YT", "reviewer": "Reviewer_c3re", "summary": "This paper addresses the long-standing issue of facial identity inconsistency in multimodal editing large models during real-person portrait editing. To solve these problems, the authors propose EditedID—a training-free, plug-and-play framework based on the \"Alignment-Disentanglement-Entanglement\" paradigm. EditedID optimizes diffusion model dynamics through Adaptive Mixing, Hybrid Solver and Attentional Gating. Extensive experiments on challenging scenarios show EditedID achieves state-of-the-art performance: it outperforms SOTA ID preservation methods by 0.27 in ID similarity and 2.43 in edited element preservation, while maintaining efficiency. It also enhances ID consistency for existing multimodal models (e.g., boosting In-ContextEdit’s ID-Sim by 0.16).", "questions": "- Have you conducted preliminary experiments on DiT-based architectures? If so, how did you modify the \"Attentional Gating\" module to fit token-wise attention? If not, what technical challenges do you anticipate in adapting EditedID to DiT, and what solutions are planned?\n- If DiffBIR (the pre-processing tool for degraded images) introduces artifacts (e.g., incorrect facial contours), how does EditedID mitigate this? Is there a plan to integrate EditedID with restoration models end-to-end to avoid error propagation?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper directly addresses a practical bottleneck—ID inconsistency in real-person editing—that limits multimodal models’ deployment in fields like fashion, film, and portrait design. Unlike prior work focusing on frontal face scenarios, it tackles open-world challenges (non-focused faces, occlusions, multi-person) that are more relevant to real use.\n- The framework is grounded in rigorous analysis of diffusion dynamics, including trajectory controllability, sampler complementarity and attention roles.\n- EditedID requires no additional training data or model fine-tuning, making it compatible with pre-trained multimodal models (e.g., GPT-4o Plus) without resource-intensive retraining. This lowers deployment costs and enhances scalability.\n- The experiments cover both quantitative and qualitative evaluations, with comparisons across 4 method categories and 6 challenging scenarios. Ablation studies also validate the necessity of each component, ensuring the framework’s interpretability.", "weaknesses": "- The paper mentions using DiffBIR for super-resolution pre-processing of degraded edited images (e.g., blurry faces), but it does not discuss how DiffBIR’s own errors (e.g., texture distortion) affect EditedID’s final results.\n- The paper exclusively validates EditedID on UNet-based pre-trained models (e.g., Stable Diffusion v1.5), while failing to demonstrate its effectiveness on Diffusion Transformer (DiT-based) models—the current mainstream in diffusion research and industrial deployment (e.g., Flux.1 Kontext’s advanced variants). DiT architectures differ fundamentally from UNet in attention computation (token-wise vs. spatial-wise), which raises critical questions: Can the \"Attentional Gating\" module—designed for UNet’s spatial attention—adapt to DiT’s token-based attention? Will the \"Hybrid Solver’s\" timestep scheduling (optimized for UNet’s noise prediction) remain effective in DiT’s transformer-driven denoising process? This gap limits the framework’s applicability to cutting-edge models, as DiT now dominates scenarios requiring high efficiency and large-scale feature modeling.\n- Typo: I suppose the word 'BiffBIR' in Line 164 should be 'DiffBIR'.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zXaqN59cXi", "reviewer": "Reviewer_ey3S", "summary": "This paper presents EditedID, a novel, training-free, and plug-and-play framework designed to solve the critical problem of facial identity (ID) inconsistency in multimodal large editing models. The authors identify that current models, while powerful, degrade significantly when editing realistic human portraits, especially with complex instructions. This failure is attributed to two core issues:\n1.  Cross-source Distribution Bias: A mismatch between the original image's facial features and the edited model's feature distribution, leading to detail loss or the generation of a completely different, random identity.\n2.  Cross-source Feature Contamination: The bleeding of features between the original face and the edited element (e.g., glasses), which causes the model to either lose the specific attributes of the edit (e.g., \"black-framed\" glasses become generic glasses) or fail entirely when the edit introduces facial artifacts.\n\nTo solve this, EditedID proposes a three-stage Alignment-Disentanglement-Entanglement process. It works by:\na. Alignment (Adaptive Mixing): Mitigates distribution bias by aligning the latent representations of the original face (Original ID) and the edited, ID-inconsistent image (Intermediate ID) throughout the diffusion trajectory.\nb. Disentanglement (Hybrid Solver): Isolates feature contamination by using a novel sampler that combines DDIM (to retain the core identity) and DPM-Solver++ (to enhance fine details), effectively separating the person's identity from the edited element.\nc. Entanglement (Attentional Gating): Selectively fuses the desired features, combining the restored facial attributes from the Original ID with the specific edited elements (Element IP) from the Intermediate ID.\n\nThe authors claim this framework achieves state-of-the-art performance in preserving both the original facial ID and the integrity of the edited elements, establishing a new benchmark for practical, real-world portrait editing.", "questions": "1.  Potential for High Complexity and Latency. The proposed framework, involving trajectory mixing, a hybrid solver, and an attentional gating mechanism, sounds significantly more complex than a standard diffusion pass. This could introduce substantial computational overhead and increase inference time, which might limit its practical \"plug-and-play\" utility.\n2.  Ambiguity in \"Element IP\" Preservation. The mechanism (Attentional Gating) must reliably distinguish between a desired edited element (like glasses) and an undesired artifact (like a distorted nose) from the intermediate image. It's unclear how robust this \"entanglement\" is, especially if the edit itself is complex and affects multiple facial regions.\n3.  Lack of Detail on Multi-Person Handling. While the paper claims to support multi-person restoration, it's unclear how it handles multiple IDs, interactions between subjects, or the allocation of edited elements to the correct person.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  Novel and Systematic Framework: The proposed \"Alignment-Disentanglement-Entanglement\" framework is intuitive and systematically tackles the identified issues. Instead of a single \"trick,\" it's a comprehensive process where each component (Adaptive Mixing, Hybrid Solver, Attentional Gating) has a clearly defined role inspired by analysis of diffusion trajectories, samplers, and attention.\n2.  High-Impact Problem: The paper addresses a widely acknowledged limitation of current generative models. \n3.  Strong Problem Diagnosis: The authors provide a clear and insightful analysis of why existing methods fail, breaking the problem down into the distinct concepts of \"Cross-source Distribution Bias\" and \"Cross-source Feature Contamination.\" This clear diagnosis provides a strong motivation for their multi-part solution.", "weaknesses": "1.  Potential for High Complexity and Latency. The proposed framework, involving trajectory mixing, a hybrid solver, and an attentional gating mechanism, sounds significantly more complex than a standard diffusion pass. This could introduce substantial computational overhead and increase inference time, which might limit its practical \"plug-and-play\" utility.\n2.  Ambiguity in \"Element IP\" Preservation. The mechanism (Attentional Gating) must reliably distinguish between a desired edited element (like glasses) and an undesired artifact (like a distorted nose) from the intermediate image. It's unclear how robust this \"entanglement\" is, especially if the edit itself is complex and affects multiple facial regions.\n3.  Lack of Detail on Multi-Person Handling. While the paper claims to support multi-person restoration, it's unclear how it handles multiple IDs, interactions between subjects, or the allocation of edited elements to the correct person.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)