WithAnyone: Toward Controllable and ID Consistent Image Generation
Authors: Hengyuan Xu
Wei Cheng
Peng Xing
Yixiao Fang
Shuhan Wu
Rui Wang
Xianfang Zeng
Daxin Jiang
Gang YU
Xingjun Ma
Yu-Gang Jiang
Venue: ICLR 2026 Poster (2026)
Keywords: AIGC
ID-Consistent Generation
Primary area: generative models

Abstract: Identity-consistent (ID-consistent) generation has become an important focus in text-to-image research, with recent models achieving notable success in producing images aligned with a reference identity. Yet, the scarcity of large-scale paired datasets—containing multiple images of the same individual—forces most approaches to adopt reconstruction-based training. This reliance often leads to a failure mode we term copy-paste, where the model directly replicates the reference face rather than preserving identity across natural variations in pose, expression, or lighting. Such over-similarity undermines controllability and limits the expressive power of generation.
To address these limitations, we (1) construct a large-scale paired dataset, MultiID-2M, tailored for multi-person scenarios, providing diverse references for each identity; (2) introduce a benchmark that quantifies both copy-paste artifacts and the trade-off between identity fidelity and variation; and (3) propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity. These contributions culminate in WithAnyone, a diffusion-based model that effectively mitigates copy-paste while preserving high identity similarity. Extensive experiments—both qualitative and quantitative—demonstrate that WithAnyone substantially reduces copy-paste artifacts, improves controllability over pose and expression, and maintains strong perceptual quality. User studies further validate that our method achieves high identity fidelity while enabling expressive, controllable generation.

Published as a conference paper at ICLR 2026
AA WITHANYONE: TOWARD CONTROLLABLE AND
ID CONSISTENT IMAGE GENERATION
Hengyuan Xu
Wei Cheng
Peng Xing² Gang Yu?:
Yixiao Fang² Xingjun Ma 1i
Shuhan Wu²
Rui Wang
Xianfang Zeng²
Daxin Jiang"
Yu-Gang Jiang'
Fudan University
StepFun
With
WilhAnngome
WithAnyone's Friends:
Figure 1: Showcases of WithAnyone. WithAnyone is capable of generating high quality, controllable, and ID consiisitt images by leveraging ID DD-ootrasttvve training on the proposed MultilD-2M dataset. IDs above are authors' and authors' friends'.
ABSTRACT
consistent consisstt))) generation has become an important focus in text-to-image research, with recent models achieving notable success in producing images aligned with reference identity. Yet, the scarcity of large-scale paired Mitasets--cntainig multiple images of the same individual-foree most ap- proaches to adopt reconstruction-balt failure mode we term copy-paste, where the model directly replicates the reference face rather than preserving identity across natural variations in pose, expression, lighting. Such over-similarity undermines controllability and limits the expressive power of generation. To address these limitations, we (1) construct large-scale paired dataset MultilD-2N tailored for multi-person scenarios, providing diverse references for each identity; (2) introduce benchmark that quantifies both copy- paste artifacts and the trade-off between identity fidelity and variation; and (3) propose a novel training paradigm with contrastive identity loss that leverages paired data to balance fidelity with diversity. These contributions culminate in WithAnyone, diffusion-bar model that effectively mitigates copy-paste while preserving high identity similarity. Extensive experiments bothh qualitative and quantitative-demnnstee that WithAnyone substantially reduces copy-paste ar- tifacts, improves controllability over pose and expression, and maintains strong perceptual quality. User studies further validate that our method achieves high identity fidelity while enabling expressive, controllable generation. Our project is fully open-sourced at http:///////TTTTT/////// BErTPS:/DOBY-XX..CTHHHI WTw./////..T.OO WITHANYONE
training. This reliance often leads to

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Published as a conference paper at ICLR 2026
INTRODUCTION
With the rapid progress of generative AI, controllable image generation via reference images or image
prompting (Ruiz al. 2023 Hertz et al. 2022 Zhang et al., 2023a Xiao & Fu 2024| Hu al.
2025b Wu et al. 2024a) and identity-consistent [ID-consistent generation ( (Ye et al. 2023 Guo
et al. 2024, Wang et al. 2024c; Diang et al. 2025aa] Cheng al. 2025 Zhang et al. [025]] He et al.
2024) have achieved remarkable advances: modern models can synthesize portraits that closely match the provided individual. Recent efforts (Cheng et al. 2025 Chen et al., 2025) push resemblance toward near- pear-perfect reproduction. While pursuing higher similarity seems natural, beyond a certain point, excessive fidelity becomes counterproductive.
In real photographs of the same person, identity sim- ilarity varies substantially due to natural changes in
Face Similarity
0,77
0.46
0.46
0,30
pose, expression, makeup, and illumination (Fig. [ By contrast, many generative models adhere to the reference image far more rigidly than this natural range of variation. A Although such over-optimization may seem beneficial, it suppresses legitimate varia- tion, reducing controllability and limiting practical usability. We term this failure mode the copy-paste artifact: rather than synthesizing an identity in a flexible, controllable manner, the model effectively copies the reference image into the ouiput (see Fig. 2 In this work, we formalize this artifact, develop met- rics to quantify it, and propose novel training strat- egy to mitigate it.
Real Image Pairs
Ours
UniPortrait PuLID
InstentID
UMD
Face Similority
0.4
0.6
0.8
1.0
Mitigating paytee artifacts is fundamentally con- strained by the lack of suitable training data. While numerous large-scale face datasets exist (Liu et al.
Prompt:
blonde lady, natural makeup
Input
InstantiD
PULID
WithAryone
GT
2015, Stacchio et al./ 22200 Chu et al. [024] Zhang al. 2015: Jiang et al. 2025b; Zhong al. 20118 Wang et al. 2025), they remain ill-suited for con- trollable multi-identity generation. Critically, few datasets provide paired references for each iden- tity-muliiple images of the same person across di- verse expressions, poses, hairstyles, and viewpoints. As a result, most prior work resorts to single-person. reconstruction-based training (Guo et al. [2224] Wang
X
Copy
Dustonlaed
Figure 2: Our Observation. Natural variations, such as head posc, expression, and makcup, may cause more face similarity decrease than expected. Copying reference image limits models' ability to respond to expression and makeup adjustment prompts.
al. 2024c). where the reference and target coincide. This setup inherently promotes copying and ex- acerbates copy-paste artifacts. Constructing datasets with multiple references per identity, particularly in group photos, and developing methods to effectively exploit such data remain open challenges.
In this work, we introduce large-scale open-source Multi-ID dataset, MultilD-2M, together with a comprehensive benchmark, MultilD-Benchh designed for intrinsic evaluation of multi-identity image generation. MultilD-2M contains 500k group photos featuring 1-5 recognizable celebrities. For each celebrity, hundreds of individual images are provided as paired references, covering diverse expressions, hairstyles, and viewing angles. In addition, 1.5M unpaired group photos without references are included. MultilD- Bench establishes standardized evaluation protocol for multi- identity generation. Beyond widely adopted metrics such as ID similarity (Schroff et al.) 2015] Deng et al. 2019). it quantifies copy-paste artifacts by measuring distances between generated images, references, and ground truth. Evaluation on 12 state-of-the-ar customization models highlights a clear trade-off between ID similarity and copy-paste artifacts (see Fig, 5).
Furthermore, we present Withhnyone, a novel identity customization model built on the FLUX (Bat- ifol et al. 2025) architecture, of art identity similarity (with regard to target image) while substantially reducing copy- paste, thereby breaking the long-oben oong-observed trade-off between fidelity and artifacts. This advance is enabled by paired-training strategy combined with an ID contrastive loss enhanced with a large negative pool, both made possible by our paired dataset. The labeled identities and their reference
step toward mitigating copy-paste artifacts. WithAnyone maintains
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images enable the construction of extended negative pool (images of different identities), which provides stronger discrimination signals during optimization.
In summary, main contributions are:
MultilD-2M: A large-scale dataset of 500k group photos containing multiple identifiable celebrities, each with hundreds of reference images capturing diverse variations, along with 1.5M additional unpaired group photos. This resource supports pre-training and evaluation multi-identity generation models.
comprehensive benchmark with standardized evaluation protocols for identity customization, enabling systematic and intrinsic assessment of multi-identity image generation methods.
MultilD-Bench:
A novel customization model built on FLUX that achieves state-of-the- performance, generating high-fidelity multi-identity images while mitigating copy-pace paste artifacts and enhancing visual quality.
WithAnyone:
2 RELATED WORK
Single-ID Preservation.
preserygp image generation is a core topic in customized syn-
thesis (Wang al.) 2024a; Huang et al. 2024, Arar al. 2024 Jones et al., [024: Kumari al.
2024 Zeng et al. [023]] Arar et al. [2033] Ma et al. 2024; Valevski et al. 2023 Wang et al. 2024b
Yan et al. 2023 Xiao et [025| Wu et al. 2024bb Wang et al. 2024d. Chen et al.
2024 Hyung
al. 2024 Papantoniou et al. (024). Many methods in the UNet/Stable Diffusion
inject learned Ronneberger
embeddings (e.g., CLIP or Arc ArcFace) via cross-attention or adapters (Ho et al. 2020
jet al.) [2015
| Qian et al., 2024 Ye al. [2023] Radford et al.
2021 Ren al. 2023). With the rise
of DiT-style backbones (Peebles X0233023] Esser et al.
2024 Labs 2024) (c.g., SD3, FLUX),
progress II) preservation like Pul.I)) (Guo al. (024). also attracts great attentions.
Multi-ID Preservation. Multi-ID preservation remains relatively underexplored. Some works target spatial control of multiple identities (Kim et al., 2024] He et al.] [2024] /Zhang et al. 2025). while others focus on identity fidelity. Methods such as X Veese (Chen et al. 2025) and UMO Cheng
ct al. 2025) use VAE-derived face embeddings concatenated with model inputs, which can produce level copy-paste artifacts and reduce controllability. DynamicII (Hu et al. 2025a achieves improved controllability but constrained by limited task-specific data and eva evaluation standards.
Other general-purpose customization and editing models (Parmar al., 2025 Mou et al., [025
Patashnik et al. 2025 Wu et al. [025d]] Xiao et al., 2024 Wu et al. [022bb c Batifol et al. 2025
[ Wu
al.) (025a) can also synthesize images containing multiple identities, but their ID similarity
often compromised for generality.
ID-Centric Datasets and Benchmarks. Although numerous single-ID datasets Karras et al. 2017: Wang et al. 2025) and multi-ID collections (Chu et al., (2024] Jiang al. 2025b) exist, paired reference images are scarce, so reconstruction remains the dominant training objective for multi-ID datasets. Representative datasets are listed in Table 4 Evaluation protocols are underdeveloped: several works (c.g., PuLID (Guo et al.] (2024). UniPortrait (He et al.) 22024). and others (Xiao et al.) 2025| Zhang et al., 20255) construct test sets by sampling identities from CelebA (Liu 2015). which undermines reproducibility. To address this, we release a curated multi-ID benchmark with standardized splits and comprehensive metrics facilitate future research.
3 MULTTID-2M: PAIRED MULTI-PERSOO DATASET CONSTRUCTION
MultilD-2M is a large-scale multi-person dataset constructed via a four-stage pipeline: (1) collect single-ID ID images from the web and construct clean reference bank by clustering ArcFace (Deng et al. 2019) embeddings, yielding I- reference images across ~3k identities (averaging 400 per ddnntity;; (2) retrieve candidate group photos via multi-name and scene-awan queries and detect
faces; (3) assign identities by matching ArcFace embeddings to single-ID cluster centers using cosine
Exxcluded from experiments due unavailability of code and pretrained models.
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1. Single-ID
2. MultHD Collstion
3. ID Image Paring Embedding Retrievel
4. Post-processing Fitaring Labeling
Data Construction
Collection Clustering
GIA 4D Al
momes
collaeX
Spcsermone
sappping
Detaseta
Single-ID Subses 1M
Open Catases
MulHD Subeet 2M
Paired Subeet SOOK
and Style Subeet 10K
1. Reconstruction Pretrain with Foxdd Promot
Reconstruction Pretrain with Caption
Paired Tuning Midtrain with Pair & Recon.
Quality Tuning Quality Style Tuning
Training Stages
Bround Trush
Refononccts)
Referencets)
Figure 3: Overview of WithAnyone. It builds a large-scale dataset, MultilD-2M, constructed through four-step pipeline: (1) collect and cluster single-ID data based on identity similarity; (2) gather multi-ID data via targeted searches using desired identity names with negative keywords for filtering; (3) form image pairs by matching faces between single- ID and multi-ID data; and (4) apply post processing for quality control and stylization. Training proceeds in four stages: (1) pre-train single-ID ID, multi-ID, and open open-domain images with fixed prompts; (2) train with capeecn supervision; (3) fine-tune with ID ID-paired data; and (4) perform quality tuning using a curated high-quality subset.
similarity (threshold 0.4); and (4) perform automated filtering and annotation, including Recognize Anything (Zhang et al. (2023b), aesthetic scoring (discus0434 (023), OCR-based watermark/logo removal, and L...-based caption generation (Bai et al.) 2025). The final corpus comprises ~500k identified multi-ID images with matched references from the reference bank, as well ~ 11.5M additional unidentified multi-ID images for reconstruction training, covering ~55 unique identities, with diverse nationalities and ethnicities.
Note that all images were collected from publicly accessible sources via search engines with explicit Creative Commons (CC) filters, restricted publicly known figures, and limited to licenses permitting reuse and derivative works. We excluded content with restrictive or unclear terms and did not use private login-restricted sources. In addition, personal names explicit identity labels were included in training, as individuals are represented only through anonymized internal identifiers. Further details of the data construction pipeline and dataset statisties are provided in Appendix C
MULTIID- COMPREHENSIVE ID CUSTOMIZATION EVALUATION
MultilD-Benncl is unified benchmark for group-photo (multi-ID) generation. It samples rare, long- tail identities with no overlap to training data, yielding 435 test cases. Each case consists of one ground-truth (GT) image containing 1-4 people, the corresponding 1-4 reference images as inputs, and prompt describing the GT. Detailed statistics are provided in Appendix C
Evaluation considers both identity fidelity and generation quality. Let t. g denote the face embed- dings of the reference identity, the target (ground-truth), and the generated image, respectively. We define similarity between two embeddings as Sim (a, b). specifically we term the generated image's face similarity with regard to GT as Simgr, and to reference as SimRef.
a T b
g
g
Sim (a, b) = |a]
SimgT =
Sim
(1)
[bl'
[g]
|||]]
|gg
|
Prior works (Zhang et al., 2025 He et al. 2024 Guo et al. 2024 Cheng et al. 2025) has largely reported only SimRef: which inadvertently favors trivial capy-paste: directly replicating the reference appearance maximizes the score, even when the prompt specifies changes in pose, expression, or viewpoint. contrast, MultilD-Bent -Bnch uses SimGr- the similarity to the ground-truth identity described by the prompt-as as the primary metrie. This design penalizes excessive copying when natural variations (c.g., pose, expression, occlusion) are expected, while rewarding faithful realization of the prompted scene.
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ArcFace Brench
IPA Cross Attendion
Negative Pool
GT-aligned ID Loss
DiT Blocks
Embedding
ID Contrastive with negatke camples
Siglip-Brench
cos
a Model Architecture
b Training Objectives
Figure 4: (a) Architecture of WithAnyone: Each reference is encoded by both face- recognition network and a general image encoder, yielding desiitydiisrimminatie signals and complementary mid-level features. Face embeddings Training Objectives of WithAnyone: addition the diffusion loss, we incorporate an ID contrastive loss and a ground truth aligned ID loss, which together provide consistent and accurate identity supervision.
restricted attend only image tokens within their corresponding face regions. (b)
We define the angular distance as lab arccas b)) (geodesic distance on the unit sphere). The Copy-Paste metric is given by
Mcr(g r) 0gt fgr E (--1,
(2)
(Btr E)
where small constant for numerical stability. The metrie thus captures the relative bias of toward the reference r versus the ground truth t, normalized by angular distance of r and A score of means g fully coincides with the reference (perfect copy-paste), while means full agreement with the ground truth.
We additionally report identity blending, prompt fidelity (CLIP I/T), and aesthetics; formal definitions and further details are provided in Appendix D
5 WITHANYONE: CONTROLLABLE AND ID-CONSISTENT GENERATION
Building on the scale and paired-reference supervision of the MuliID-2M, we devise training strategies and tailored objectives that transcend reconstruction enable robust, identity-conditioned synthesis. This rich, identity-labeld labeled supervision not only substantially improves identity fidelity but also suppresses trivial copy-paste artifacts and affords finer control over multi-identing composition. Motivated by these advantages, we introduce WithAnyone recipe designed for controllable, high-fidelitt multi-ID generation. Architectural schematics and implementation details are provided in Fig. aad Appendix E.
a unified architecture and training
5.1 TRAINING OBJECTIVES
Diffusion Loss. We adopt the mini-batch empirical flow- now-matching loss. For each batch, we sample a data latent .i Pdatas Gaussian noise mo (0, I), and timestep (. (d(0, 1). We then form the interpolated latent 24 = =(1 - 1)z0 + tx and regress the target velocity (x1 .n):
Lait ||vo (99) c()))
(of -600) 11
where (i) denotes the conditioning signal.
ID Loss. Since ArcFace embedding requires landmark detection and align- ment, directly extracting landmarks from Tyen is unreliable because generated images are obtained through noisy diffusion one-step denoising. Prior methods compromise: PortraitBooth (Peng et al.) 2024) applies the loss only at low noise levels (f < 0.25), discarding supervision at higher noise, while PuLID (Guo al. 2024) fully denoises generated results significant computational cost. In contrast, we align the generated image using GT landmarks, thereby avoiding noisy landmark extrac- tion. We minimize the cosine distance between GT-aligned ArcFace embeddings of the generated and ground-truth (GT) faces:
cos(g. where g and are ArcFace embeddings of the generated and GT images. This design (1) enables applying the ID loss across all noise levels, (2) incurs negligible overhead throughout training, and (3) implicitly supervises generated landmarks. Ablation studies (Sec. 6.3) demonstrate more accurate
LI
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identity measurement and substantially improved identity preservation. Further explaination and notations are provided in Appendix E.
ID Contrastive Loss With Extended Negatives. To further strengthen identity preservation, introduce an ID contrastive loss that explicitly pulls the generated image closer to its reference images in the face embedding space while pushing it away from other identities. The loss follows the InfoNCE (Oord et al. 2018) formulation:
LeL log exp(cos(g. t)/r)
exp(cos(g: g. m)))/T)
where t is the embedding of the target, n, embeddings of M negatives from different identities, and 7 is a temperature hyperparameter. This formulation relies on ID-labeled datasets, which make
possible to draw thousands of negatives per sample from the reference bank, thereby greatly enriching the diversity of negative examples.
The overall training objective is weighted of the above losses:
L Laitt + AD + CCL
(6)
where Å④ and ÀcL are hyper- papee--armeetess controlling the contributions of the ID loss and contrastive loss, respectively. Both are set to 0.1 across all training phases described below.
5.2 TRAINING PIPELINE
Copy-paste artifacts largely arise from reconstruction-only training, which encourages models to replicate the reference image rather than learn robust identity-conditione generation. Leveraging our paired dataset, we employ a four-phase training pipeline that gradually transitions the objective from reconstruction toward controllable,
synthesis.
Phase I: Reconstruction pre-training with fixed prompt. We begin with reconstruction pre- training to initialize the backbone, this task is simpler than full identity-conditioned generation and can exploit lcage unlabeled data. For the first few thousand steps, the caption is fixed to a constant dummy prompt (e.g., "two people"), ensuring the model prioritizes learning the identity- conditioning pathway rather than drifting toward text-conditioned styling. The full MultilD-2N used in this phase, which typically lasts for 20k steps, at which point the model achieves satisfactory identity similarity. To further enhance data diversity, CelebA-HQ (Karras et al. 2017). FFHQ (Karras) al. 2019), and subset of FaceID-6M (Wang al. 2025) are also incorporated.
Phase 2: Reconstruction pre-training with full captions. This phase aligns identity learning with text-conditioned generation and lasts for an additional 40k steps, during which the model reaches peak identity similarity.
Table Quantitative comparison on the single-person subset of MultilD-Bench and OmniContext. and indicate the first-, second-, and third-best performance, respectively. For Copy-Paste ranking, only cases
with Sim(GT) > 0.40 considered.
a MultilD-Benn Bench
b OmniContext Single Character Subset
Identity Metries
Generation Quality
Quality
Overall
Method
Method
SimrGT)
SimtRel)
CLIP-11
CLIP-T
PF
SC
Overall
Omnitien2
DreanO
OmniGen
0.454
0.365
0.388
0.475
0.602
0.142
0.248
0.793
0.757
0.780
0.317
01.331
4,877
4.991
5.059
DreamO
OmniGen
7,50
8.13
7,09
5.52
5.47
7.02
FLUX Kontext
0.324
0.408
0.009
0.755
0.527
5.319
OmniGen2
Quen-Tmago- Edit
0.334
0.400
0.93
0.776
0.316
5.056
FLUX. Kontext
7.72
8.60
7.94
GPT-4o Natve
0.425
0,579
0.178
0.794
0.311
53344
Qwen- Iman--mage-- Edit
7.66
8.16
7.51
UNO
0.304
4.923
GPT-4o Native
7.98
9.06
8.12
USO
UNO
7.22
7.72
7.04
0.458
0.732
0.359
0.783
4,800
USO
7.88
UniPortrait
0.447
0,677
0.265
0.793
0.319
5.018
UMO
6.56
7.92
6.79
ID-Pisch
0.426
B.633
0.231
0.792
0,512
4.000
InfU
0.139
0,630
0.233
0.772
0.328
$.359
UniPortrait
6.00
5.55
PaLID
0.452
0.705
0.315
0.779
0.305
4,839
ID-Patch
N/A
N/A
N/A
InstatD
0.164
0.734
0.337
0.764
0.295
5.255
InfU
7.69
4.62
4.70
4,783
PaLID
6.62
6,83
5.78
0.999
NIA
InstantlD
4.89
5.49
4.35
NIA
NIA
Ours
Phase 3: Paired tuning, To suppress trivial copy paste behavior, we replace 50% of the training samples with paired instances drawn from the 500k labeled images in Multill-2M. For each paired

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GPT-4o
Copy-Past Papy-Paste
Copy-Paste
DmniGen
OmniGen2
ID-Patch
InfU
Ours
0.84
Uni UnrPorta
GPT-4B
DreamO
ID-atthh
UniPortrait
0.32
PuLID
InfeattD
DreamD
0.42
0445
0.44
0.45
0.48
0.0
0.35
0.40
Sim(GT)
Sim(GT)
a Single-ID subset
Multi-ID subset
Figure 5: Face Similarity vs. Copy-Paste Effect (y-axis descending order). Except for WithAnyone, all other models lie approximately fitted curve, reflecting clear trade-off off between face similarity and copy-paste. The upper-right corner represents the desired region.
sample, instead using the same image as both input and target, we randomly select one reference image from the identity's reference and another distinet image of the same identity the target. This perturbation breaks the shortcut direct duplication and compels the model to rely on high-level identity embeddings rather than low-level copying.
Phase 4: Quality tuning. Finally, we fine-tune on a curated high-quality subset augmented with generated stylized variants (i) enhance perceptual fidelity and (ii) improve style robustness and transferability. This phase refines texture, lighting, and stylistic adaptability while preserving the strong identity consistency established in earlier phases.
EXPERIMENTS
In this section, we present a comprchensive evaluation of baselines and our WithAnyone model on the proposed MultilD-Bence
Baselines. We evaluate two categories of baseline methods; general customization models and face
customization methods. The general customization models include OmniGen (Xiao al. (024)).
OmniGen2 (Wu et al.] (2025b). Qwen- Laann--mmee-Edii (Wu et al. 2025a), FLUX. Kontext (Batifol et al.
2025 UNO (Wu al. 2025d), USO (Wu et al. (025c), UMO (Cheng et al.) 2025), and
native GPT 4m-mma (OpenAl] 2025).. The face customization methods include UniPortrait (He
et al.
2024), ID-Patch (Zhang al. 2025), PuLID (Guo et al.) (2024) (referring its FLUX (Labs)
2024) implementation throughout this paper), and InstantID (Wang et al.) 2024c). All models
were evaluated on the single-person subset of the benchmark, while only those supporting multi-ID generation were additionally tested on the multi-person subset. Further implementation details are provided in Appendix F.I.
Table 2: Quantitative comparison on the multi-person subset of MultilD- Bench.. and indicate the first-, second-, and third-best performance, respectively. For Copy-Paste Paste ranking, only cases with Sim(G'I > 0.35 are considered. GPT exhibits prior knowledge of identities from TV series in subsets with than two IDs, leading abnormally high similarity scores.
2-people Subset
b 3--and-4peopll Subset
Idmitiy
Quality
Quality
Method
Sin(CT)
SionRe01
CP
BM
CLIP-11
CLIP-T1
Aes
Methol
Sin)(TT)
SlacRef)
CP
BM]
CLIP
CLIPTT
Ars
DieuzOO
OmnnGGedd
0.281
0.351
0.081
6.112
0.66
0:334
4.547
OmniGaa2
01288
01534
0.89
0.01
0/36
0.229
4.666
carT
0.332
0.400
0981
0.02
0336
0.318
5,678
CETT
0.403
06:54
6642
686
0315
0.230
5A6
0.214
0043
4.30
0.65
0.85
0017
0.319
4,500
0.0%
0317
0.309
UnaPotuii
0.367
0.001
0.254
01005
0.300
03313
$1187
UnPootrai:
0.343
0.517
0.175
0.048
0.38
0.313
5250
PD-Pashh
0.300
0.13
0.183
0,855
0.367
03366
4671
ID-Padd
0.370
0.543
0.195
0.00
0.81
0.200
4.547
0166
0.71
6.1 QUANTITATIVE EVALUATION
The quantitative results are reported in Tables I and We observe clear trade-off between face similarity and popte artifacts. As shown in Fig. most methods align closely with a regression curve, where higher face similarity generally coincides with stronger copy-paste. This indicates that many existing models boost measured similarity by directly replicating reference facial features rather than synthesizing the identity. In contrast, WithAnyone deviates substantially from this curve,
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Input Ref
Kontext
Oweningödk
UniPortraf:
PuUD
InstantiD
WithAryone
Prompt: "a Noman wearing a white hooded jacket with a black inner garment. hair is styled loosely, and has minimal makeup, The woman is posting with her head slightly tilted, showtzssing calm and composed demeanor Her expression neutral.
Prompt: "a woman long, flowingg dynamically. She white geometric patterned top with shaud-lile drape. posture is poised, showcasing elegant jewelry and subile smille. The background features blurzed circular pattern in shades of gray.
Prompt: black jacket holding microphone, appears
speaking, with
slightly open
talking. styled straight..
GT
Input Ref
DreamO
GPT
UniPortrait
PDtohh
UMD
WithAryone
Prompt:' sult bolding coffee mug
head ber hand. They appear
kitchen, looking surpeised. standing,
Prompt: couple posing together.
light
Iloth are smiling and standing close, with the man's is arm around the woman, indicating friendly or intimate relationship.
Prompt: "three people, two women and one man, posing closely together. The woman on the left wears white blazer, while the younger woman in front has strupless top. The mon has white shart. All are smillng warmly the cameru.
Prompt: "four people dressed white shirts posing together: The group includes three males and two females, with one male and one female in the center: They are smiling and standing closely, sugresting a family close-lus group. The attire is casual and courdimated.
Figure 6: Qualitative Results of Different Generation Methods. The text prompt extracted from the ground-truth image shown the leftmost side.
achieving the highest face similarity with regard to GT while maintaining a markedly lower copy-paste score.
WithAnyone also achieves the highest score among ID -pesciiiicc reference models on the OmniCon- text (Wu al. (0255) benchmark. However, VLMs (Bai et al., 2025 OpenAl (2025) exhibit limited ability to distinguish individual identities and instead emphasize non-identity attributes such as pose, expression, or background. Despite that general customization and editing models often outperform face customization models on OmniContext, WithAnyone still has best performance among face customization models.
6.2 QUALITATIVE COMPARISON
To complement the quantitative results, Fig. presents qualitative comparisons between our method, state-of-the-an general customizzation/edting models, and face customization generation models.
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Table 3: Ablation study. and indicate the first-, second- and third-best performance, respectively. We ablate paired data training phase (w/o Phase 3), the G'T-aligned landmark ID loss (w/ GT-Aligg), and the of extended negative samples in InfoNCE (w/o Ext. N. and model trained on FFHQ only.
Face Sm
0.71
0.54
0.38
0.10
LTL
Pred Link
Nowe Lavel
0.2
0.4
0.6
Identity Metries
Generation Quality
Ablation
Sim(Gi)
Sim(K) 7
CP
CLIP-I
CLIPTT 1 Acs
Phases
0.755
10
wlo GT-Align
0.385
0.549
0.175
0.763
0.317
4.754
Loss
Ext.
0.368
0.455
0.074
0.740
0.304
4.984
Data
FPHQ only
0.224
0.246
0.027
0.688
0.330
5.039
Figure 7: Comparison of GT-aligned and prediction-aligner landmarks.
Ours
Full Setting
0.405
0,551
0.161
0.770
0.321
4.883
shows that identity consistency remains a significant weakness of general customization or editing models, consistent with our quantitative findings. Many VAE-based approaches where references are encoded through VAE, such as FLUX.1 Kontext and DreamO tend to produce faces that either exhibit copy-paste artifacts or deviate markedly from the target identity. A likely reason is that VAE embeddings emphasize low-level features, leaving high-level semantic understanding to the diffusion backbone, which may not have been pre-trained for this task. ID-specific reference models also struggle with copy-paste artifacts. For example, they fail to make the subject smile eye gaze. In contrast,
when the reference image is neutral and often cannot adjust head pose WithAnyone generates flexible, controllable faces while faithfully preserving identity.
6.3 ABLATION AND USER STUDIES
To better understand the contribution of each component in WithAnyone, we conduct ablation studies on the training strategy, the GT-aligned ID loss, the InfoNCE-based ID loss, and our dataset. Due to space constraints, we report the key results here, with additional analyses provided in Appendix G
Aes
CP
As shown in Table the paired-data fine-tuning phase re- duces copy-paste artifacts without diminishing similarity to the ground truth, while training on FFHQ performs sig- nificantly worse than demonstrates that the GT-aligned ID loss lowers denoising at low noise levels and yields higher-variance, more informative gradients at high noise, thereby strengthening identity learning. By ablating extended negatives, leaving only 63 negative samples from the batch (originally extended to 4096), the effectiveness of ID contrastive loss is greatly reduced. More ablation results can be found Appendix G
Sim
Dura
UNO
Paast
Unipantra al
Grmiee
curated dataset. Fig. further
Figure 8: User study. Bigger bubbles indi- cate higher ranking and better performance.
We conduct user study to evaluate perceptual quality and identity preservation. Ten participants were recruited and asked rank 230 groups generated images according four criteria: identity similarity, presence of copy-paste artifacts, prompt adherence, and aesthetics. The results, shown in Fig. indicate that our method consistently achieves the highest average ranking across all dimensions, demonstrating both stronger identity preservation and superior visual quality. Moreover, the copy-paste metric exhibits a moderate positive correlation with human judgments, suggesting that it captures perceptually meaningful artifacts. Further details of the study design, ranking protocol, and statistical analysis are provided in Appendix H
CONCLUSION
Copy-paste artifacts are a common limitation identity customization methods, and samelsiiyly metrics often exacerbate the issue by implicitly rewarding direct copying. In this work, identify and formally quantify this failure mode through MultilD-Benn and propose targeted solutions. We curate MultilD-2N - and develop training strategies and loss functions that explicitly discourage trivial

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replication. Empirical evaluations demonstrate that WithAnyone significantly reduces copy-paste paste artifacts while maintaining and in many cases improving srewinng-deentttyyy similarity, thereby breaking the long-standing trade-off between fidelity and copying. These results highlight a practical path toward more faithful, controllable, and robust identity customization.
ETHICS STATEMENT AND DISCLAIMER
Data Source. The images used this work were collected exclusively from publicly accessible sources through search engines that provide explicit filtering based on Creative Commons (CC) licensing. Our dataset focuses on publicly known figures, and we restricted data collection to images released under CC licenses that explicitly permit reuse and derivative works (e.g.. CC-BY, CC- BY-SA, or CCO where applicable). We excluded content with restrictive terms such as "NoAI", non-derivative, or unclear licensing conditions. No private datasets, regginrestricted sources, or personally sensitive images were used. These license-based permissions provide authorization consistent with creator-defined reuse terms, and we follow applicable copyright and data-usage regulations to ensure responsible research practice.
Anonymization. To further mitigate privacy and misuse risks, we applied an anonymization procedure to the dataset processing and training pipeline. No personal names, textual identifiers, explicit identity labels were included during training. All individuals are represented solely through internal numeric identifiers and corresponding ID embeddings, without any direct linkage real-world names or metadata. The model therefore operates on abstract identity representations rather than explicit personal information, reducing the risk of unintended identity disclosure.
Potential Ethical Risks and Mitigation. Identity-consistnnt image generation is inherently dual- use. While enabling legitimate applications such creative media and virtual avatars under proper authorization, it may also facilitate identity cloning, impersonation, misattribution, or deceptive synthetic media-particularty if applied without consent. To mitigate these risks, the models released strictly for non-commercial academic research; training data is limited to publicly known figures under reuse-permin pense-permtted licenses; and no personal names or explicit identity labels are used in training. We further recommend that downstream deployments implement consent verification, authorization controls, disclosure or watermarking mechanisms, and abuse monitoring. Responsible use must comply with applicable legal, institutional, and ethical standards.
Disclaimer. The WithAnyone models and associated datasets (the "Project") are provided solely for research and non-commereial use under the FLUX.1 [dev] Non-Commercial License v1.1.1. All base models and third-party components remain subject to their original licenses. Any underlying content derived from publicly available sources remains the property of its respective rights holders, and no ownership, endorsement, or additional rights are claimed or granted. The Pruject is provided "as is" without warranties of any kind, express implied. Users are solely responsible for ensuring compliance with all applicable laws, regulations, and third-party rights. The providers of this Project shall not be liable for any claims, damages, or losses arising from its use. Under circumstances shall the authors or the affliated organization be liable for any claims, damages, losses, or other liabilities arising from or related to the use of the Dataset.
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A FAMILY OF WITHANYONE
FLUXX,1 comprises family of models, including FLUX.11 Kontext (Batifol et al. 2025) and FLUX.11 Krea (Labs) 2025). Krea is rext-to-imaee model with improved real-person face generation, whereas Kontext an image-editing model that excels at making targeted adjustments while preserving the rest of the image. However, reported in Table T Kontext shows limited consistency with the reference face identity.
Our method, WithAnyone, downstream tasks like face swapping. As illustrated in Fig. / WithAnyone effectively injects identity information from the reference images into the target image.
be seamlessly integrated into Kontext for the face customization
The overall training pipeline follows the procedure described in Sec. 5 with a single modification: the input image provided Kontext (whose tokens concatenated with the noisy latent each denoising step) is set to the target image with the face region blurred.
WithAnyone Kontext
WithAnyone Kontext
Figure 9:
Application of WithAnyone- Kontext. Marrying editing models, WithAnyone is capable of face
editing given customization references.
B GALLERIES OF WITHANYONE
We show more results of WithAnyone in Fig. 10
Fig.
11 and Fig. 12
C MULTIID-2M CONSTRUCTION DETAILS
To fill in the void left by the lack publicly available multi-ID datasets, a data constraction pipeline proposed create large-scale dataset of multi-person images with paired identity references for identities on the data record. Based on this pipeline, 500k group photo images are collected, featuring 3k identities, each with hundreds of single-ID reference images. Another 1M images that cannot be identified are also included in the dataset for image reconstruction training purpose for image reconstruction training purpose.
C.1 DATASET CONSTRUCTION PIPELINE
The pipeline contains four steps, shown in Fig. The detailed pipeline are as follows.
Single-ID images. To construct ID reference set, single-ID images were collected from the web using celebrity names as search queries on Google Images. For each image, facial features were extracted with ArcFace (Ren al. 2023). , ensuring that only images containing exactly one face were retained. To remove outliers, DBSCAN (Schubert et al., (217)) clustering was applied to the embeddings for each celebrity, resulting in set of cluster centers and hundreds reference images identity. This process established reliable reference set for each unique identity. Human review confirms the accuracy of the ID bank built in this step.
Multi-ID images. To achieve best searching efficiency, group photos obtained using more complex queries that combined multiple celebrity names, keywords indicating the number of people (e.g., "two celebrities"), scene descriptors (e.g., "award ceremony"), and negative keywords filter out irrelevant results. ArcFace embeddings were extracted for these images, yielding large pool of candidate multi-ID images. At this stage, the dataset comprised more than 20 million images.
Retrieval. To provide ID reference for the multi-ID images, it is necessary to retrieve the IDs on it. All single-IID cluster centers were aggregated into embedding matrix. For each detected face
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Reference
Reference
Reference
Reference
Figure 10: Galleries of Single-ID Generation.
in every multi-ID image, its ArcFace embedding was compared to all single-ID ID cluster centers to determine identity. The similarity between two embeddings was calculated as:
sim(idi, id2) (fdi),
(7)
where id, and id2 denote faces, and f is the ArcFace embedding network.
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Figure 11: Galleries of 2-person Generation.
Each face in a multi-ID image was assigned the identity of the single-ID cluster center with the
highest similarity, provided the similarity exceeded predefined threshold (0.5). This approach enabled accurate and automated identity assignment group images and facilitated retrieval of corresponding reference images.
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Figure 12: Galleries of 3-to-4-person Generation.
Filtering and labelling. To further improve dataset quality, a series of annotation and filtering steps were applied. The Recognize Anything model (Zhang et al.) (023b), an aesthetic score predictor (discus0434) 2023), and other auxiliary tools were used for annotation. Images with low aesthetic scores or those identified as collages rather than genuine group photos were excluded. Optical Character Recognition (OCR) tools detected watermarks and logos, which cropped out when possible; otherwise, the images were discarded. Finally, descriptive captions were generated for the images using large language model, enriching the dataset with textual information.
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Appearance Count
Range: to 4273B
USA
European descent
China
Multiple US
Korea
Europe
Asian
15k
Japan
descent
CN
Others
Single
Median: 2407
Canada
African
descent
Celebrities
Count
ID Appearance.
b Nationality Distribution.
Benchmark Distribution.
Figure 13: Overview of Dataset Distributions. (a) ID appearance distribution for the subset of one nation: the X-axis represents celebrities, sorted by the number of images in which they appear. (b) Nationality distribution: celebrities dataset from 10 countries, with most data sourced from China and the USA. (c) Word cloud most frequent words the captions.
So far, dataset with three parts is obtained: (1) 1M single-II) images reference bank, single-II) cross-paired training: (2) 500k paired multi-ID images with identified persons; (3) 1M unpaired multi-ID images, which can be used for training scenario without the need of references, such as reconstruction.
C.2 DATASET STATISTICS
Comprehensive statistics of the dataset are provided in Fig. 14, including the distribution of na- tionalities, the count of appearances per identity, and word cloud illustrating the most frequent terms in the generated image captions, offering insights into the diversity and richness of the dataset. long-tail distribution is observed in the count of appearances per identity in Fig. 14a with few identities appearing frequently while many others are less common. This provide diverse identities, as well as perfect test dataset without identity interaction with the training set. Fig. [4b]and and Fig. ill]stuttee MultilD-2N 's nationality distribution and action diversity respectively, The comparison between the proposed dataset and existing multi-ID datasets are listed in Table highlighting MultilD-2M' outstanding volume and paired references.
Table 4: Statistic Comparison for Ceentit Datasets. #Img refers to total scale of the dataset; #Paired
refers to paired group image number; #lmg/ / ID indicates number of reference image for each single ID; #ID
Img means number of IDs appears on group photos.
Dataset
#Img
#Paired
#Img ID
#ID Img
IMAGO Stacchio et al.
2020
80k
0
0
MHP
Chu et al. 2024)
5k
0
PIPA
Zhang al. 2015
40k
40k
cross
10
Humankef (Jiang et al.
2025b
36k
36
1+
1 - 14
Celebrity Together Zhong
2018
194k
0
0
-
PhotoMaker (Li et al. 2024 (closed source)
1.3M
1.3M
100
1
MultilD-2M
2M
500k
100+
1 1
D BENCHMARK AND METRICS DETAILS
Most existing methods are evaluated on privately curated test sets that are seldom released, and even when datasets are shared, the accompanying evaluation protocols vary widely. For example, ID-Patch (Zhang et al. (025) and UniPortrait (He et al. 2024) measure identity similarity using ArcFace embeddings, whereas UNO (Wu et al. 2025d) relies DINO (Oquab et al.) 2023) and CLIP similarity scores. This heterogeneity together with the common practice of reporting only the cosine similarity between matched ArcFace embeddings fails capture more nuanced insights and can even encourage degenerate behavior in which models produce images that are effectively "copy-pastes" of the reference photos.
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BOWTIE
TUXEDO
UNI
necklace
-emebaaci acing arm ar oound foueleng Moring
Srnme around Segags
gazing
grivng
Smiling
hat
ruing
clasped
res
pose
Profensional
arrinng
facing tes
woMno
Special
alttyeeless
leaning
COMIIS
Interestion
sated
dancing
en lur turtennee
leans
crossed
Clothes
Upper
hoodie
gentrree
Actions
ppointing
speaking
gloves HENEEENN
heels
yor PIgE
TOP
Normal
bloyse
Cardigan
leather
boot
sitting
walking
atroligfeet srre
Focused
Exprasiin
Single
Statio
S stands
gestures
laughing
Ryons BORDA
Boleupeee hests
oodr ooun aeeae seated posture wears puont
Mendsoinn jreciat Sulpphddrr
Clothes & Accessories Distribution.
Action Distribution.
Figure 14: Distribution of Clothes and Action Labels of Proposed Dataset.
In this work, MultilD-Bench is introduced as unified and extensible evaluation framework for group photo (multi-ID) generation. standardizes assessment along two complementary axes: (i) identity fidelity (preserving each target identity without unintended copying and blending), and (ii) generation quality (semantic faithfulness to the prompt/ground truth and overall aesthetic quality).
The data used in MultilD- Benhh are drawn from the long-tail portion of MultilD-2MM 2M. We first select the least frequent identities and gather all images containing them. To prevent information leakage, the training split filtered ensure zero identity overlap with the benchmark set. The final benchmark contains 435 samples; each sample provides 1-4 reference identities (with their images), a corresponding ground-truth image, and text prompt describing that ground-truth scene.
Identity Blending, In the similarity matrix, the off-diagonal elements correspond to the similarity between different identities. The average of the diagonal elements is used as the metric for identity fidelity, and the average of the off- off-diagonal elements serves as the metric for identity blending, as in Eq.
MBIa (00,
Σ
Σ j-ij
cos(gr, tz)
N2
where di is the embedding of the i-th face in the generated image xº, and tj is the embedding of the j-th face in the ground-truth image x². A lower value indicates less unintended blending between different identities, which is desirable.
Generation quality. The overall generation quality is evaluated based on CLIP-I and CLIP-T, which the de facto standards for evaluating the prompt-following capability (Radford al. 202 , employed to measure the cosine similarity in the CLIP embedding space between the generated image and the ground truth image caption. Additionally, an aesthetic score model (discus0434 2023) used to assess the aesthetic quality of the generated images.
E MODEL FRAMEWORK DETAILSS
We follow prior work Ye et al., 2023 Guo et al. 2024) and integrate lightweight identity adapter into the diffusion backbone. Identity embeddings are injected by cross-attention so that the base generative prior is preserved while controllable identity signals are added.
Face embedding. Each reference face first encoded by ArcFace, producing x 512 identity embedding. To match the tokenized latent space of the DiT backbone, this vector is projected with a multi-layer perceptron (MLP) into 8 tokens of dimension 3072 (i.c., an 8 x 3072 tensor). This tokenization provides sufficient capacity for the cross-attention layers integrate identity cues without overwhelming the generative context.
Controllable attribute retention. Completely suppressing coky behavior not always desirable: users sometimes expect certain mid-level appearance attributes (c.g.. hairstyle, accessories) to be preserved. ArcFace focuses on high-level, dientty-discriniiaatve geometry and texture cues but
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omits many mid-level semantic factors. To expose controllable retention of such attributes when needed, we optionally incorporate SigLIP (Zhai et al. 2023) as a secondary encoder. SigLIP provides more semantically entangled representations, enabling selective transfer of style-relevant traits while ArcFace anchors identity fidelity.
Attention mask and location control. To further improve identity disentanglement and precise localization in the generated images, an attention mask and location control mechanism are incorpo- rated. Specifically, ground-truth facial bounding boxes are extracted from the training data and used to generate binary attention masks. These masks are applied to the attention layers of the backbone model, ensuring that each reference token only attends to its corresponding face region in the image, providing location control at the same time.
Feature injection. After each transformer block of the DiT backbone, we inject face features through a cross-attention modulation:
H' = H + Au softmax (HW)) + +M) (EWv),
(9)
vd
where H denotes the current hidden tokens, E the stacked face-embedding tokens, and WQ. WK. Wy the projection matrices; dl is the query/key dimension, and Asa = 1.0 during training. When SigLIP is enabled, its tokens are processed by parallel cross-attention with independent scaling coefficient.
E.1 DETAILS OF THE GT-ALIGNED ID LOSS
To compute the ID Loss, the predicted velocity must first be converted into a fully denoised latent representation, which is then decoded into the pixel space. Typically, this process requires around 20 iterative denoising steps, leading to an unacceptable computational overhead. PortraitBooth (Peng al. 2024) mitigates this issue by applying the loss only low noise levels (t 0.25), effectively discarding supervision at higher noise levels. In contrast, PuLID (Guo et al. (024) performs full denoising with four iterative steps, incurring sigmificant computational cost.
Since WithAnyone is based on FLUX and employs flow-matching diffusion objective, where the model directly predicts the velocity field, the denoised latent can be obtained single step. Let zo denote pure noise and æ1 the clean latent. The noisy latent input at timestep during training is given by:
24 = (1 -ot-taa t)a1 + t.o.
(10)
where i is the sampled timestep. The model predicts the velocity corresponding za 21 Therefore, the predicted denoised latent can be expressed as:
f = to.
(11)
Decoding è into pixel space yields the reconstructed image used for identity (ID) feature extraction in the face recognition network.
We find this implementation both highly efficient and effective. The visual quality of the predicted images during training is illustrated in Fig. 7 Since single-step denoising introduces minimal computational overhead, the ID Loss can be applied throughout the entire training process, rather than being restricted to the fine-tuning stage as PuLID (Guo et al. 2024). as in PortraitBooth (Peng et al., 2024
or only in low timestep
Denoting the face recognition model as f(-,) (Areface Deng et al. 2019). in our case), and the coupled detection model as 9(.) (RetinaFace (Deng et al. (0200), the generated image as G. and the ground-truth image as T, embedding extraction should be performed as follows:
g = f() G), t= =T() where g( ()) and g( (T) are the detected landmarks, and f(:))) extracts the aligned face embedding. The IDD loss in Guo et al. (2024) and is computed as:
(12)
Lia = 1 (g(() G), t = ((g(T))T)
(13)
And our GT-aligned ID loss is computed as:
Lia 1 cos ( (T), G), t (OTT)))))
(14)
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GT
Input Ref
Kantaxt
Guaninnfalt
UniPortrait
PuLlD
InstantiD
WithAnyone
Figure 15: Qualitative Results of Low Quality References, WithAnyone is robust when presented
low-quality references like low-resolution, motion blur, len-flare.
Input Ref
DreamO
GPT
UniPortrait
UMO
WithAnyone
Rejected
Prompt: "They go shopping together.
Prompt: "They are watching show together.
Prompt: "They pose together photo, each with playful expression, front of blue backdrop.
Figure 16: Qualitative Results on Selebrrty-Referrnnees, WithAnyone is robust enough to generalize to non-celebrity identities. Cases are from OmniConetxt
F EXPERIMENTAL DETAILS
F.1 IMPLEMENTATION DETAILS
WithAnyone trained 8 NVIDIA H100 GPUs, with batch size of 4 on each GPU. The learning rate is set to le -4 and the AdamW optimizer is employed with a weight decay of 0.01. The pre- training phase runs for 60k steps, with fixed prompt used during the first 20k steps. The subsequent paired-tuning phase lasts 30k steps: 50% of the samples use paired (reference, ground-truth) data, while the remaining 50% continue reconstruction training. Finally, a quality/style tuning stage of 10k steps is performed with a reduced learning rate of 1 10-5
For the extended ID contrastive loss, the target is used as the positve sample, while other IDs from samples in the same batch serve negative samples. With the global batch size of 32, this yields less than hundred negative samples. Extended negative samples are drawn from reference bank. If this of the 3k ID in the reference bank, we simply omit its ID and draw the
ID is identified from other IDs. If this ID is not identified, then makes things easier all the IDs in the reference bank can be used negative samples.
For other baseline methods, official implementations and checkpoints (or API) are used with default settings. Methods tested MultilD- Bench and real-human subset of OmniContext (Wu et al.
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2025b).
OmniContext uses Vision-Language Models (VLMss to evaluate the Frompt-follownet (PF) and subject-consitenne (SC) of generated images. For reproducibility, the VLM is fixed to Owen2.5-VL (Bai et al. 2025). ID-Patch (Zhang et al. 2025 requires pose condition, and we use the
ground-truth pose for it.
Single face embedding model may induce biased evaluation on ID similarity, thus average three de-facto face recognition models' consine similarity to compute the overall ID similarity metric, namely ArcFace (Deng al. 2019), FaceNet (Schroff' et al. 2015), and AdaFace (Kim et al. 2022).
F.2 MORE QUALITATIVE RESULTS
Results on Low Quality References. We show more results on low-quality reference images in While many ID-preserving methods even copy the low-quality artifacts like blurriness and flare into the generated images, WithAnyone successfully preserves the identity while generating high-quality images.
Fig. [1]
Results on Non-Celebrity References. We show more results on non-celebrity reference images in Fig. I6. WithAnyone shows consistent demonstrating its generalization ability beyond celebrities commonly found in training data.
ability on non-celebrity references,
F.3 MORE DISCUSSION ON THE QUANTITATIVE RESULTS
The performance of GPT on our 3-and-4-people subset offers useful validation of our copy-paste metric, as shown in Table 2 This subset largely comprises group photographs from TV series that GPT may have encountered during pretraining, so GPT attains unusually high identity-similant scores both to the ground truth (GT) and to the reference images. Actually, in one case GPT even generates an ID from the TV series that not present in the reference images. This behaviour approximates an idealized scenario in which model fully understands and faithfully reproduces the target identity: similarity to GT and to references are both high, and the copy-paste measure-the difference between distances to GT and to references references-approatteee approaches zero. These observations consistent with our metric design and support its ability to distinguish true identity understanding from trivial copy-and- aayy-and-pass and-paste replication.
We report the experimental limit in Table If one model completely copy the reference image, Simer 0.521, Simnet 1.0, and copy-paste is 0.999, which aligns with the theoretical limit 1.0 of copy-paste.
The prompt-following ability is measured by CLIP-I and CLIP-T in our benchmark, and is judged by VLM in OmniContext. WithAnyonegains state-of-the-art performance in both metrics, and is ranked the highest in our user study. However, the credibility of CLIP scores and the aesthetic scores may be debated. as they are not always consistent with human perception.
F.4 MORE QUANTITATIVE RESULTS
Table 5: Quantitative comparison on MultilD-Bench with different face recognition models. indicate the first, second, third performance respectively. For Copy-Pastt ranking, only cases with GGT) higher than a threshold are compared. Thresholds for ArcFace, AdaFace and FaceNet are 0.35, 0.30, 0.45 respectively.
ArcFace
AdaFace
FaceNet
Method
Sim(GT)
Simi(Re
CP |
Sim(GT)
Sim(Ref)
CP
Sim(GT)
SimiRee)
CP
DreamO
0.315
0.476
0.197
0.286
0.455
0.177
0.480
0.607
0.151
OmniGen
0.296
0.453
0.186
0,277
0.462
0.187
0.464
0.673
0.250
OmniGon2
0.193
0.269
0.069
0.197
0.275
0.078
0.461
0.516
0.070
GPT
0.261
0.314
0.059
0.245
0.310
0.059
0.491
0.576
0.092
UNO
0.130
0.174
0.045
0.151
0,194
0.039
0.408
0.469
0.056
UMO
0.278
0.420
0.168
0.266
0.439
0.173
0.448
0.610
0.188
UniPortrait
0.356
0.542
0.224
0.307
0.525
0.217
0.440
0.330
ID-Patch
0.302
0.440
0.162
0,284
0.459
0.175
0.470
0.644
0.209
0.544
0.095
Three face recognition models are used to compute the ID similarity metric in the main paper. We report the individual scores of each model in Table The results consistent across ArcFace (Dngg
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10 Loss
Copy-Paste
0.0
0.1
0.20
Sig
Big Sig
08 Sig
0.4
Sig
Sig
300
600
0.25
Step
0.55
0.60
Q.62
0.84
Sim(Ref)
Figure 17: ID Lass Curves with Ax InfoNCE Loss. 0.1 is 0.1x InfoNCE Loss without extended negative samples, and 0.1+ is 0.1x InfoNCE Loss with tended negative samples.
Fig gure 18: Trade-off Curves with Ax Siglip and x)x ArcFace signal.
al. 2019) and AdaFace Kim al. (2022), while FaceNet Schroof et al. (2015) shows slightly more similar lar scores among different methods. Overall, WithAnyone achieves SOTA performance in SOTA face recognition models.
G ABLATION STUDY DETAILS
Table 6: Model Performance After Each Training Phases. Scores of model checkpoints after each training phases are shown in this table. For stage 1, we present results both with fixed prompt (like "two people", "a portrait"). without fixed prompt (original caption of training record).
Identity Metries
Generation Quality
Phase
Ablation
Sim(G) f
Sim(R)
CP
CLIP-11
CLIP-T
Aes 1
w/o fixed txt
0.277
0.350
0.066
0.727
0.337
4.715
Phase
w fixed txt
0.305
0.428
0.104
0.758
0,315
5.082
Phase
0.406
0.625
0.239
0.755
0.307
4.955
Phase
0.409
0.572
0.168
0.765
0.324
4.854
Phase 4
0.405
0.551
0.161
0.770
0.321
4.883
In this section, systematically evaluate the impact of training strategy, GT-aligned ID-Loss, InfoNCE ID Loss, and our dataset construction. User study is also conducted to validate the consistency of the proposed metrics with human perception, well as evaluate the human preference on different methods.
SigLIP signal. SigLIP Zhai al. 2023) signal introduced retain copy-paste effect when user tend to retain the features from reference images like hairstyle, accessories, etc. As shown in Fig. [8 increasing the SigLIP signal weight effectively amplifies the copy-paste effect while simultaneously boosting ID similarity to the reference images exacesy expected, since stronger SigLIP guidance enforces tighter semantic alignment and transfers more fine-graine -na-irr appearance cues (e.g., hairstyle. accessories, local textures).
Training strategy. Results after each training phases are shown in Table To our insight, 1) phase gives the model the basic ability of ID-preserving generation, and by fixing the prompt this process is accelerated. 2)ahe further improves the ID consistency and reaches the highest similarity and copy-paste scores. 3) Phase 3 significantly reduces the copy-paste issue while maintaining (even sightly improving) the Sim GT. 4) Phase 4 maintains the quantitative scores while making the model capable of generating style-varied images.
Fig, 19 shows the effect of phase 4 quality/style tuning. This phase effectively enables the model to generate images with diverse styles while preserving identity consistency.
Dataset construction. To validate the effectiveness our dataset, we trained a model on FFHQ (Kar ras et al.) (019)) using reconstruction training for the same number of steps. As shown in Table 3 the FFHQ-trained model performs poorly across all metrics. This likely stems from FFHQ's limited diversity and size, it contains only 70k face-only portrait images.
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Cartzon
Sketch
Piael Art
Water Color
Before
After
Figure Qualitative Results Before and After Stylized Quality Tuning (Phase 4). Training phase greatly improves the capability of generating stylized results.
Figure 20: User Study Interface.
GT-aligned ID-Loss. We validate the GT-aligned ID-Loss with a simple experiment that visualizes predicted faces at different denoising time steps during training. As shown in Fig. 7 at low noise levels the GTT-aligned ID-Loss substantially lower than the loss computed using detiictioonaalinnne landmarks, indicating that aligning faces to ground-truth landmarks reduces denoising error and yields a more accurate identity assessment. At high noise levels the GT-aligned ID-Loss shows greater variance, producing stronger and more informative gradients that help the model learn identity features.
InfoNCE Loss. The InfoNCE loss with extended negative samples is crucial for the convergence in the early training stage. We conduct a toy experiment with 1000 training samples, and record ID Loss curves with no InfoNCE loss, 0.1x InfoNCE loss without extended negatives, and 0.1x InfoNCE loss with extended negatives. As shown in Fig, [7] ID loss fits a lot faster with InfoNCE loss with extended negatives, demonstrating its effectiveness accelerating training convergence. also largely increases the ID similarity score, shown in Table
H USER STUDY DETAILS
Our study is conducted with the same data samples and generated results in our quantitative experiments. Due to a tight financial budget, we randomly select 100 samples from single-perse subset, 100 samples from 2-people subset, and all samples from 3-and-4 people subset. 10 participants
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recruited for the study, all of whom are trained with a brief tutorial to understand the task and evaluation criteria.
We illustrate the interface used in user study in Fig. 20
Table 7: Correlation Statistics Between Machine Ranking and Human Ranking, Reported values include
Pearson's r, Spearman's p. and Kendall's 7 with corresponding p- p-values.
Dimension (N)
Pearson (p)
Spearman p (p)
Kendall (p)
Copy-Pasie
0.4417 -89
0.4535 (1.26e-50)
0.3405 (1.10c-46)
ID Sim
0.3254 (1.54c-200 26)
0.3237 (2.91e-22) 26)
0.2423 (1.11c-25)
H.1 CORRELATION ANALYSIS
We analyze the correlation between our proposed metrics and user study results. As shown in Table 7 our copy-paste metric shows a moderate positive correlation with user ratings on copy-paste effect.
H.2 PARTICIPANT INSTRUCTIONS
We provide the instructions for training the participants the following table.
I PROMPTS FOR LANGUAGE MODELS
I Large language models (L..Ms) and vision- rision-language models (VLMs) are used various stages of our work, including dataset captioning and OmniContext evaluation.
1.1 DATASET CAPTIONING
Besides the system prompt, we design 6 different prompts to generate diverse captions for each image. 1 prompt is randomly selected for each image during captioning.
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Participant Instructions and Evaluation Procedure
Data souree and task overview.
Five different methods generated images under the following conditions:
A single prompt that describes the "ground truth image."
Between 1 and people in the scene (most examples contain 1-2 people).
For each trial you will be shown the ground truth image, input images, and generation instruction. Then you will observe five generated group-photo results (one per method) and rank them according several evaluation dimensions. Use 5-star scale where stars best and star worst. Please read the input image(s) and the editing instruction carefully before inspecting the generated results.
Evaluation procedure (per-image ranking).
Rank each generated image individually on the following criteria.
Identity similarity
How well do the person(s) in the generated image resemble the person(s) in the ground truth image?
Rank images by their resemblance the ground truth image: the more the generated person(s) look like the original reference, the higher the rating.
When judging identity similarity, ignore factors such as image quality, rendering artifacts, or general aesthetics. Focus only on how much the person(s) resemble the original reference(s). Also, try to assess resemblance to the ground truth image whole, rather than comparing to any single separate "reference person n."
Important:
Copy-and-paste effeet (excessive mimiery the reference)
Generated images should resemble the original reference but should not direct copies of an individual reference photo.
Evaluate whether the generated person appears to be directly copied from one of the reference images. Consider changes (or lack thereof) expression, head pose and orientation, facial expression/demeanc and lighting/shading.
The lower the degree of direct copying (i.e., the less looks like a pasted replica), the better. Rank according the amount of change observed in the person(s): more natural variation (less copy-paste) should be ranked higher.
Prompt following
Does the generated image reflect the content and constraints specified by the promp- t/instruction?
Rank images by prompt fidelity: the more faithfully the image follows the prompt, the higher the ranking.
Aesthetics
Judge the overall visual quality and pleasantness of the generated image (c.g., smoothness of rendering, harmonious body poses and composition).
Rank images by aesthetic quality: higher perceived visual quality receives higher ratings.
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Full Prompts for Dataset Captioning (6 variants)
System Prompt: You are an advanced vision- -aiigage model tasked with generating accurate and comprehensive captions for images.
Prompt 1: Please provide brief description of the image based these guidelines:
1. Describe the clothing, accessories, or jewelry worn by the people in detail.
2. Describe the genders, actions, and posture of the individual in detail, focusing on what they are doing.
3. The description should be concise, with maximum of 77 words.
4. Start with 'This image shows'
Prompt 2: Offer short description of the image according to these rules:
1. Focus on details about clothing, accessories, or jewelry.
2. Focus on the gender, activity, and pose, and explain what the people is doing.
3. Keep the description within 77 words.
4. Begin the description with "This image shows"
Prompt 3: Please deseribe the image briefly, following these instructions:
1. Provide detailed description of the clothing or jewelry the person may be wearing.
2. Provide detailed description the two persons' gender, actions, and body position.
3. Limit the description to no more than words.
4. Begin your description with 'This image shows'
Prompt 4: Describe the picture briefly according these rules:
1. Provide detailed description of the clothing, jewelry, or accessories of the individu- als.
2. Focus on the two persons' gender, what they are doing, and their posture.
3. Keep the description concise, within limit of 77 words.
4. Start your description with "This image shows'
Prompt 5: Provide short and precise description of the image based on the following
guidelines:
1. Describe what the person wearing or any accessories.
2. Focus on the gender, activities, and body posture of the person.
3. Ensure the description is no longer than 77 words.
4. Begin with "This image shows'
Prompt 6: Briefly describe the image according to these instructions:
1. Provide precise description the clothing, jewelry, other adornments of the people.
2. Focus on the person's 's gender, what they are doing, and their posture.
3. The description should not exceed 77 words.
4. Start with the phrase 'This image shows'
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Modified Prompt for OmniContext Evaluation (Face Identity Focus)
Rate from to 10:
Evaluate how well the facial features in the final image match those of the individuals in the original reference images, as described in the instruction. Focus strictly on facial identity similarity; ignore hairstyle, clothing, body shape, background, and pose.
Task:
Scoring Criteria
0: The facial features completely different from those in the reference images.
1-3: The facial features have minimal similarity with only or two matching elements.
4-6:
The facial features have moderate similarity but several important differences remain.
7-9: The facial features are highly similar with only minor discrepancies.
10: The facial features are perfectly matched to those the reference images.
Pay detailed attention these facial elements:
Eyes: Shape, size, spacing, color, and distinctive characteristics of the eyes and eyebrows.
Nose: Shape, size, width, bridge height, and nostril appearance.
Mouth: Lip shape, fullness, width, and distinctive smile characteristies.
Facial structure: Cheekbone prominence, jawline definition, chin shape, and forehead structure.
Skin features: Distinctive marks like moles, freckles, wrinkles, and overall facial texture.
Proportions: Overall facial symmetry and proportional relationships between features.
Example: If the instruction requests combining the face from image onto another pose, the final image should clearly show the same facial features from the source image.
Important:
For each significant facial feature difference, deduct at least one point.
Ignore hairstyle, body shape, clothing, background, pose, or other non-facial ele- ments.
Focus only on facial similarity, not whether the overall instruction was followed.
Scoring should be strict-high scores should only be given for very close facial matches.
Consider the level detail visible in the images when making your assessment.
Editing instruction: <i <inttrnctio>>
30


Reviews:
[{"review_id": "GXP6vrKwS2", "reviewer": "Reviewer_YRvY", "summary": "This paper makes a strong and timely contribution to controllable, identity-consistent image generation by introducing WithAnyone, a diffusion-based framework trained with a novel contrastive identity loss and a ground-truth–aligned ID loss to mitigate “copy-paste” artifacts—where models overfit to reference faces instead of generalizing identity", "questions": "Generalization to Non-Celebrity or Low-Quality Data: Since MultiID-2M primarily contains celebrity faces with high-quality, well-lit imagery, how does WithAnyone perform on non-celebrity or in-the-wild datasets with lower resolution, occlusions, or varied lighting conditions? Would the model’s performance degrade significantly outside of this curated domain?\n\nDataset Ethics and Bias: Although the paper addresses ethical data sourcing and the exclusion of minors, could the authors elaborate on how potential biases (e.g., nationality, ethnicity, or gender imbalance) in MultiID-2M might impact the model’s fairness and generalization? Have any bias analyses or mitigation strategies been conducted?\n\nReproducibility and Accessibility: The paper states that only a small subset of MultiID-2M is included in the supplementary material. Are there concrete plans or timelines for releasing the full dataset and benchmark, or alternative means (e.g., data access agreements) to allow other researchers to replicate results?\n\n\nValidity of the Copy-Paste Metric: The proposed Copy-Paste (MCP) score depends on cosine distances in face embedding space. How sensitive is this metric to the choice of embedding model (e.g., ArcFace vs. ElasticFace or CurricularFace etc)? What about face verfic?ation metrics, e.g., FMR10000? Have the authors verified that the metric aligns with perceptual similarity judgments beyond the limited user study?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper’s main strengths lie in its clear problem formulation, methodological thoroughness, and comprehensive evaluation. It identifies and formalizes the “copy-paste” artifact in identity-consistent image generation, offering a concrete metric to quantify this often-overlooked issue. The introduction of the MultiID-2M dataset and MultiID-Bench benchmark represents a valuable infrastructural contribution, enabling more systematic and reproducible research on multi-identity generation. The proposed WithAnyone framework integrates a ground-truth–aligned identity loss and a contrastive loss with extended negatives in a diffusion-based pipeline, leading to measurable improvements in controllability and identity preservation. Extensive experiments, strong ablation studies, and user evaluations convincingly support the claimed benefits, while the paper’s clear presentation, detailed appendix, and ethical considerations reflect a high level of polish and maturity in execution.", "weaknesses": "The proposed “copy-paste” failure mode, while intuitively appealing, overlaps conceptually with previously discussed overfitting and reconstruction issues in personalization models such as DreamBooth or PuLID, making the novelty somewhat incremental rather than fundamental.\n\nThe dataset MultiID-2M, though large, is primarily composed of celebrity data scraped from the web, raising concerns about bias, consent, and representativeness; furthermore, the lack of public access to the full dataset severely limits reproducibility and undermines the paper’s claim to openness. Evaluation metrics like the Copy-Paste score depend heavily on specific embedding spaces (e.g., ArcFace), which could bias results toward models aligned with that embedding and may not fully capture perceptual similarity.\n\nThe use of a similarity metric is clearly insufficient to justify identity consistency. Additional face verification metrics are needed, EER, FRM1000 etc.", "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": "sXeBG9EK45", "reviewer": "Reviewer_wdT7", "summary": "This paper terms the failure mode of the reconstruction-based diffusion training where it fails to produce diversity as **copy-paste**, such that it focuses too much on fidelity and cannot adapt to human preference and diversity requirement. This paper proposes a well annotated MultiID-2M benchmark to provide diverse references for each identity under multi-person scenarios, addressing balance between identity preservation and generation diversity with a contrastive identity loss (and corresponding metrics to measure performance).", "questions": "Please see Weaknesses for full details. In summary, weakness 1 and 2 need clarifications and updating manuscript to more precisely summarize the motivation and contribution of this paper, and weakness 3 needs additional experiments and explanations to help audience better understand the proposed method, e.g. which components/properties contribute the most. I am happy to raise my rating if my concerns are well addressed, especially for weakness 3, because I think such ablations provide new understanding and shed light on future work, adding strength to this paper.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. There are previous benchmark papers which propose datasets of similar functionalities, but MultiID-2M is larger in scale (2M images) and uniquely focuses on group photos with labeled identities, beyond objects, anime characters, and human faces. The rich annotation also enables fine-grained control aspects. Thus, I think the high quality of MultiID-2M is the main strength/contribution of this paper and cannot be substituted by simply assembling a few previous datasets.\n\n2. Using contrastive loss directly on reference images sounds clever to me. There are previous work which adds contrastive loss to diffusion model, e.g. Customcontrast [1] pulls representations of the same subject closer. WithAnyone links target, generated, and reference images together via reconstruction loss + contrastive loss, which repurposes contrastive learning in another way, but I think this paper is among the first papers to leverage contrastive learning here, which sounds interesting.\n\n[1] Chen, Nan, et al. \"Customcontrast: A multilevel contrastive perspective for subject-driven text-to-image customization.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 2. 2025.", "weaknesses": "1. The intuition/motivation from the ''copy-paste'' effect has been proposed and studied [2,3], where they also leverage multiple images per identity to resolve overfitting and target leakage that leads to copy-paste effect. Then, it will be better for the authors to compare their datasets and proposed methods to demonstrate the improvements from this work -- I agree MultiID-2M is more comprehensive and unique as I summarized in strength 1, but it will be good to discuss it in the manuscript to better illustrate the motivation and improvement. \n\n2. Following weakness 1, since it is a grounded problem with previous studies, this paper shares some similarities in their approach and evaluation. For example, [2,3] use multiple different images of the same identity as the target image, and [3] also proposes metrics to measure the alignment/balance between fidelity and diversity. This weakens the novelty of this work for the motivation part.\n\n3. I hope there can be more ablation studies to demonstrate what makes this method works better, besides the good MultiID-2M data. A. I see ablation on number of negative examples for InfoNCE, but I think it is more of parameter tuning, can the authors show results w/o contrastive loss at all? B. Compared to previous work, is the capability of learning from MultiID reference images brought by larger models than initially SD 1.4/1.5? I am curious about what contributes the most to make WithAnyone works well on MultiID-2M, whether it is due to contrastive loss or larger model's expressivity. I suggest try baseline minus CL loss, baseline with reduced model size, and baseline minus CL loss and with reduced model size.\n\n[2] Li, Zhen, et al. \"Photomaker: Customizing realistic human photos via stacked id embedding.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024.\n\n[3] Tang, Haoran, et al. \"Retrieving conditions from reference images for diffusion models.\" arXiv preprint arXiv:2312.02521 (2023).", "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": "sG4YPHwYeb", "reviewer": "Reviewer_VDLA", "summary": "The paper studies identity-consistent image generation conditioned on a reference face image and a text prompt. The authors argue that current identity-preserving generators often fall into a \"copy-paste\" failure mode: instead of synthesizing a coherent new face, they directly paste the reference face into the generated scene, leading to visible artifacts in pose, expression, and lighting mismatch.\n\nTo address this, the authors introduce (1) a large-scale paired dataset containing multi-person images together with target face identities, aimed at training models that handle multi-person composition; and (2) a new evaluation benchmark with metrics designed to distinguish between naive face copy-paste and proper identity transfer, by explicitly comparing the generated face to both the provided reference and the intended target identity.\n\nBuilding on these resources, the paper proposes a four-phase finetuning pipeline based on the Flux image generation model, following the PuLID architecture, to improve multi-identity conditioning and reduce copy-paste behavior. Empirically, the finetuned model WithAnyone is reported to perform competitively with SOTA approaches in both single-person and multi-person generation settings.", "questions": "How Ground-truth-Aligned ID Loss is computed is still unclear to me. How do authors obtain the generated face image to compute this loss at high noise level? Is it to estimate an approximate $z_0$ or to run the whole denoising process to obtain a clean $z_0$? I would expect a more detailed explanation as the current description in the paper doesn't distinguish WithAnyone with existing methods in obtaining generated images.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper makes a conceptual step forward by explicitly distinguishing between the reference face and the intended target face, moving toward more controllable identity-consistent person generation.\n\n2. It contributes a large-scale paired dataset with diverse identities and multi-person compositions, which is likely to be valuable for future work on identity-conditioned generation.\n\n3. It proposes new evaluation metrics designed to capture the copy-paste failure mode in identity-consistent generation, offering a more diagnostic way to assess whether a model is truly preserving identity rather than naively pasting faces.\n\n4. The proposed model achieves strong results and is competitive with SOTA methods in both single-person and multi-person generation settings.", "weaknesses": "1. *Single-person quality not clearly superior*. In the single-subject setting, the qualitative comparisons do not convincingly demonstrate an advantage of WithAnyone. In particular, the claim that the method avoids copy–paste style artifacts would be more credible if shown under challenging conditions (extreme lighting, large pose changes, strong facial expressions). The revision should include more targeted visual examples in these stress cases.\n\n2. *Underpowered user study*. The user study is based on only 10 raters, which limits statistical reliability and generalizability. A larger participant pool is needed, ideally with demographic diversity and significance reporting.\n\n3. *Limited ablations*. The ablation analysis is incomplete. It would be valuable to (i) report performance after each training phase/stage to show incremental gains, and (ii) include a baseline trained with only the diffusion/denoising loss to isolate the benefit of the proposed objectives.\n\n4. *Unclear ID contrastive loss formulation*. Equation 5 defines the ID Contrastive Loss using features from the reference image rather than the generated (target) image, but the paper does not justify this design choice or discuss alternatives. The rationale and expected effect on identity preservation need to be explained.\n\n5. *Inconsistent table highlighting*. The visual highlighting in Tables 1 and 2 appears misleading. For example, in Table 1(a), Qwen-Image-Edit seems to have the best CP score among non-ground-truth methods, but WithAnyone is marked as “best.” The authors should fix or clarify the ranking/formatting so that highlighted cells actually correspond to the strongest scores.", "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": "qnzuJE3iUL", "reviewer": "Reviewer_6GUJ", "summary": "To address the conflict between identity consistency and controllability in text-to-image generation—where existing models tend to suffer from the \"copy-paste\" issue —the authors construct the large-scale MultiID-2M dataset, which contains 500k group photos with paired references and 1.5 million unpaired group photos. They also design MultiID-Bench, a benchmark for quantifying copy-paste artifacts and the trade-off between ID fidelity and variation. Additionally, the authors propose the WithAnyone model based on the FLUX architecture. By leveraging an ID contrastive loss (with an extended negative sample pool) and a ground-truth-aligned ID loss, combined with a four-stage training pipeline (reconstruction pre-training with fixed prompts, reconstruction pre-training with full captions, paired fine-tuning, and quality tuning), WithAnyone reduces copy-paste artifacts while maintaining high ID similarity. Experiments validate its advantages in pose/expression controllability and perceptual quality.", "questions": "1. All cases in this paper are based on celebrity images. What are the results when applying the model to ordinary people or synthetic portraits?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. Solid experiments: A large number of data experiments and qualitative results support the observations presented in this paper.\n2. Promising research direction: Most existing works on ID consistency focus on ID consistency itself, while systematic research on editability-related topics is lacking. This paper conducts in-depth exploration of this idea, proposing corresponding data-driven solutions and relevant benchmarks. I believe the research community should invest more efforts in this area to achieve a better trade-off between similarity and editability, rather than overemphasizing similarity.", "weaknesses": "1. Lack of innovation in the experimental design: Most of the methods adopted have been explored by previous studies (e.g., contrastive loss), or only involve minor modifications based on prior work (e.g., GT-Align).\n2. The experimental results are suboptimal: The reduction in similarity is quite significant. For instance, the second ID from the right in the bottom image of Figure 12 does not closely resemble the reference identity.", "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)