IC-Custom: Diverse Image Customization via In-Context Learning
Authors: Yaowei Li
Xiaoyu Li
Zhaoyang Zhang
Yuxuan Bian
Gan Liu
Xinyuan Li
Jiale Xu
Wenbo Hu
yating liu
Lingen Li
Jing Cai
Yuexian Zou
Yancheng He
Ying Shan
Venue: ICLR 2026 Poster (2026)
Keywords: image customization
image generation
image editing
diffusion model
diffusion transformer
Primary area: generative models
TL;DR: IC-Custom is designed for diverse image customization scenarios, including: position-aware (e.g., product placement) and position-free (e.g., IP creation) customization.

Abstract: Image customization, a crucial technique for industrial media production, aims to generate content that is consistent with reference images. However, current approaches conventionally separate image customization into position-aware and position-free customization paradigms and lack a universal framework for diverse customization, limiting their applications across various scenarios. To overcome these limitations, we propose IC-Custom, a unified framework that seamlessly integrates position-aware and position-free image customization through in-context learning. IC-Custom concatenates reference images with target images to a polyptych, leveraging DiT's multi-modal attention mechanism for fine-grained token-level interactions. We propose the In-context Multi-Modal Attention (ICMA) mechanism, which employs learnable task-oriented register tokens and boundary-aware positional embeddings to enable the model to effectively handle diverse tasks and distinguish between inputs in polyptych configurations. To address the data gap, we curated a 12K identity-consistent dataset with 8K real-world and 4K high-quality synthetic samples, avoiding the overly glossy, oversaturated look typical of synthetic data. IC-Custom supports various industrial applications, including try-on, image insertion, and creative IP customization. Extensive evaluations on our proposed ProductBench and the publicly available DreamBench demonstrate that IC-Custom significantly outperforms community workflows, closed-source models, and state-of-the-art open-source approaches. IC-Custom achieves about 73\% higher human preference across identity consistency, harmony, and text alignment metrics, while training only 0.4\% of the original model parameters.

Published as a conference paper at ICLR 2026
IC- IC-CUSTOM: DIVERSE IMAGE CUSTOMIZATION VIA IN-CONTEXT LEARNING
Yaowei Li Xiaoyu Li; Zhaoyang Zhang?, Yuxuan Bian", Gan Liu", Xinyuan Li Jiale Xu², Wenbo Hu², Yating Liu Lingen Li", Jing Cai², Yuexian Zou 1,4 Yancheng He², Ying Shan?
ADSP Lab, School of ECE, Peking University ²ARC Lab, Tencent PCG Teeneent
4 "Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology
Thhe Chinese University of Hong Kong TTsinghua University
Project Page:
https://liyyoweit sstu. htw.://wwwoooss.u...u..........e htt.haa..ooprroeettttC_oooosss
Position-aware Customization
HETRUX
NETFLEX
AMUSE MYMELODY KUROMI
AMUSE
MYMELODY
KUROMI
Position-free Customization
the forest
On the lake
On the blanket
Figure 1: Visualization of IC-Custon results. Our method supports diverse image customization scenarios, including position-aware (location-specified editing conditioned on mask) and position- free (ID-consistent generation guided by text) customization.
ABSTRACT
Image customization, a crucial technique for industrial media production, aims to generate content that consistent with reference images. However, current approaches conventionally separate image customization into position-aware and position-free customization paradigms and lack universal framework for diverse customization, limiting their applications across various scenarios. To overcome these limitations, we propose IC-Custom, a unified framework that seamlessly in- tegrates position-aware and position-fre free image customization through in in-context learning. IC-Custom concatenates reference images with target images to a polyp- tych, leveraging DiT's multi-modal attention mechanism for fine- graiigr token- level interactions. We propose the In-context Multi-Modal Attention (ICMA)
Project Lead & Corresponding Authors

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Published as a conference paper at ICLR 2026
mechanism, which employs learnable task-oriented register tokens and boundary- aware positional embeddings to enable the model to effectively handle diverse tasks and distinguish between imputs in polyptych configurations. To address the data gap, we curated a 12K identity- connsisttttttt dataset with 8K real-world and 4K high-quality synthetic samples, avoiding the overly glossy, oversaturated look typ- ical of synthetic data. IC-Custom supports various industrial applications, includ- ing try-on, image insertion, and creative IP customization. Extensive evaluations on our proposed ProductBench and the publicly available DreamBench demon- strate that IC-Custom significantly outperforms community workflows, closed- source models, and state-of-the-arr open-source approaches. IC-Custom achieves about 72% higher human preference across identity consistency, harmony, and text alignment metrics, while training only 0.4% of the original model parameters.
INTRODUCTION
Image customization, which ensures that generated content remains consistent with the identity of reference images, has enabled applications such as image insertion (Chen et al., 2024a;b; Mao al., 2025; Song et al., 2025). IP creation (Ruiz et al., 2023b; Ye et al., 2023b; Tewel et al., 2024; Tan et al., 2024; Mou et al., 2025), and visual try-on (Wang et al., 2024; Guo et al., 2025; Xu et al., 2025). These capabilities are vital for industrial media production, supporting consistent content creation across diverse visual contexts.
Early image customization methods (Ruiz et al., 2023a; Gal et al., 2022; Avrahami et al., 2023) relied on per-instance optimization, which was time-consuming. Subsequent approaches (Ye et al., 2023a; Chen al., 2024b;a) added control branches to mre-trained diffusion models to inject identity information from reference images. However, these methods were constrained by model architecture and scalability issues, resulting in suboptimal performance. Recently, by leveraging the modeling inductive bias of DiT architectures (Peebles & Xie, 2023b; Esser et al., 2024b; Labs, 2024a), image conditions multi-modal attention mechanisms, without the need for additional branches. This enables image customization methods to exhibit powerful emergent capabilities (Song al., 2025; Mou et al., 2025; Labs, 2024b;e; Tan et al., 2024; Mao et al., 2025).
be directly input as sequences, interacting with noisy tokens through
Despite these advances, existing methods still face sig- nificant challenges in maintaining consistent identity across diverse user requirements and customization see- narios (see Tab. 1): (1) They typically treat image cus- tomization as two separate tasks. In position-aware cus- tomization, an reference identity is inserted into masked regions of a fill-in image. position-free customiza- tion. edentity-consister
Table I: Comparison of IC-Custom with previous image customization meth- ods (Labs, 2024b;c; Tan et al., 2024; Song al., 2025; Mou et al., 2025; Hurst et al., 2024b). The checkmarks and crosses indicate task compatibility.
images are generated from text prompts. (2) They provide limited support for diverse mask types, often confusing user-drawn with precise masks, e.g., treating coarse hand-drawn regions as exact boundaries. These limitations hinder the development of unified frameworks capable of flexibly handling diverse customization requirements, forcing separate models for each scenario and limiting the development of robust, comprehensive identity representations.
Model
Pusition-aware
Position-free
precise user-dnawn
FLUX workflow Omini
x
Insert Anything
DreanO GPT-4o
IC-Custom
To this end, we propose IC-Custom, a unified framework that seamlessly integrates position- aware and position-free image customization, enabling flexible and identity-consistent eustomiza- tion across diverse scenarios (see Fig. 1). Specifically, we first employ a diptych format by concate- nating the reference identity image with the fill-in image (either partially or fully masked), yielding a unified representation that allows the model to handle diverse customization settings within a sin- gle framework. Building on DiT's multi-modal attention, we further introduce a novel In-Context Multi-Modas Attention (ICMA) module that more effectively transfers identity information from the reference image to the fill-in image and enables comprehensive customization across diverse sce- narios. The ICMA module features two key innovations: (1) Three types of learnable, task-oriented
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register tokens to specify the customization type position-aware customization (with precise or user-drawn masks) and position-free customization aalooiiiiioo-lloiiining the model to adapt its behavior based on requirements. (2) Two types of learnable positional embeddings to represent spatial rela- tionships: Reference Embeddings (RE) for the reference identity image and Fill Embeddings (FE) for the fill-in image, helping the model clearly differentiate input boundaries in the diptych format.
To enable effective training of our unified framework, we curated a high-quality dataset CustomData, consisting of both real-world and synthetic samples. Specifically, we curated 8K identity-consisten diptychs from real-world sources and an additional 4K synthetic diptychs, resulting in total of 12K diptychs. This comprehensive dataset enables our model learn robust identity representations across diverse contexts and viewpoints, while also addressing the limitations of previous methods that overly rely on synthetic data and often produce lookiii results.
To extensively evaluate the performance of our method, we use ProductBench and Dream- Bench (Ruiz et al., 2023a) to assess both position-awes awarr and position-free customization capabilities. ProductBench is our manually curated benchmark for position-aware customization, consisting of 40 identity-consisent images with an even distribution of rigid and non-rigid objects, along with their corresponding precise and user-drawn masks. We also use DreamBench to evaluate position- free customization performance. Extensive subjective and objective evaluations demonstrate that IC- EE-Custom outperforms community workflows, the closed-source GPT-4o (March 25, 2025), and state-of-the-ae open-source approaches. Notably, IC- "C-Custom achieves a 72% higher human prefer- across identity consistency, harmony, and text alignment metrics, while training only 0.3% of the parameters of the pre-trained FLUX model.
In summary, contributions are as follows:
We propose unified framework that seamlessly integrates position-aware and position-free image customization via in-context formulation.
We introduce the ICMA module, which enables flexible image customization through learnable taie-trie register tokens and boundary-awand positional embeddings.
We curate a dataset from real-world sources, addressing the limitations of existing methods that rely synthetic data, which often produce artificialllooingg results.
We demonstrate that method outperforms existing approaches across range of metrics, sur- passing community workflows, closed-source models, and tatte-of-theanrd open-source methods.
PRELIMINARIES
MM-DIT Architecture. Recent state-of-the-ard generative diffusion models, such as SD3 (Esser et al., 2024b) and FLUX (Labs, 2024a), leverage the MM-DiT architecture (Peebles & Xie, 2023a), which integrates a Multi-modal Attention (MMA) mechanism with Rotary Position Embedding (RoPE) a central component. This design enables the concurrent processing of noisy image tokens X, E Rnxd and text tokens Cr E R as shown in Eq.
(R(Q) R(K)T
MMA ([X;;r)) softmnx
R(V).
(1)
Vd
Here, Q, K, and V are derived from the projection of the concatenated input [X:; Cr] [n(n-))xd with the operator R applying RoPE to Q and K to encode positional information.
Flow Matching, The model is trained within the Rectified Flow (RF) (Liu et al., 2022). The Continuous Normalizing Flow (CNF) is formalized as the following ODE:
d X, = v(Xe, t)dt Xi - - WE E [0, 1].
(2)
di
Here, given clean latent variable constructed via linear interpolation:
Xo Pdata and a Gaussian noise sample X, ~ N/ 1), X,
X, 1X, + (1-1)xo.
VI E [0,
(3)
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RE
Input
VAE
Single Block
Random
Multi-Scene Inputs
Single Block
[REF-SC (CE-SCEEE) A cat sitting blanket. [TAGGE--CCENE]] A cat sitting table.
PE
Single
Encoder
Double Book
Double Biock
Double Biock
Redux
Encoder
RE Reference Embeddings
FE Fill Embeddings
PE
Rotary Position Embedding
ICMA module
Frceon
Trainable
Figure 2: Model overview. (1) Our model takes in-context diptych inputs together with redux embeddings and text prompts. (2) During training, it randomly chooses to mask either the entire fill-in image (position-free customization) or only partial regions (position-aware customization) to produce diverse in-context latents. (3) The ICMA module, equipped with task-oriented register tokens and boundary-aware positional embeddings (see Sec. 3.2), integrated into the architecture. We train LoRA adapters on the ICMA module while unfreezing the input layers.
Subsequently, the Conditional Flow Matching (CFM) loss (Lipman et al., 2023) is employed to train a velocity filed prediction model ti:
LCFM = E e-p((t), (o.1) (T), Cr,
Cr, - -x)
(4)
Here, t sampled from a Logit-Normal Distribution (Esser et al., 2024a) with the probability den- sity function p(t) exp(-0.5-10 (logit() -a) /o²), where logit(() log --ク From the Logit-Normal
o/22 (1-f)+
Distribution definition, Y logit(f) (N,, with μ 0 and = under the RF
DiT-based Image Customization Methods Recent state-of-the-art DiT-based image customiza- tion methods (Chen et al., 2024c; Mao al., 2025; Wu et al., 2025b; Song et al., 2025; Mou al., 2025), integrate reference image conditions directly into the input via concatenation, instead of using additional network branches. This method unifies reference and other conditions into a sin- gle sequence, improving integration during flow matching. However, these methods typically train position-aware and position-free customization tasks separately, without explicitly addressing their potential unification. In position-aware tasks, the identity's location is specified using mask, while position-free tasks leverage textual guidance to generate adentity-consisten content, For instance, ACE+ (Mao al., 2025) and OmniControl (Tan al., 2024) train separate LoRA adapters, In- sertAnything (Song et al., 2025) is specifically trained for position position-aware aware tasks, and DreamO (Mou al., 2025) and UNO (Wu al., 2025b) are designed for position-fii tasks.
3 METHOD
As shown Fig. 2, we introduce IC-Custom, a novel approach that presents a unified framework for comprehensive image customization, detailed in Sec. 3.1. At its core, IC-Custom leverages In-Context Multi-Modal Attention (ICMA) to effectively adapt to diverse customization scenarios, described in Sec. 3.2. Additionally, tomization tasks, sourced from both roal-world and synthetic data, with image resolutions exceeding 800x800 pixels, outlined in Sec. 3.3.
curate high-quality dataset for comprehensive
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3.1 IN-CONTEXT DIPTYCH CUSTOMIZATION
Motivation. Formally, position-aware customization can be framed as a reference-guided image filling task, represented as p(X C₁ Cr.M) M), where X denotes the customized output, Ci denotes the reference identity image. Cr represents the image to be filled, and M denotes the mask spec- ifying the filling position. In contrast, position-free customization is viewed as a reference-guides text-to- text-to-image task, formalized as p(X Cr, CT). Since position-fri free customization can be regarded as a special case of image filling where M and Cp are set to zero, we unify both paradigms under the formulation p(X Cr M,
Diptych Framework and Training Strategy. Based the unified formulation above, in- troduce an in-context' diptych format to unify diverse input conditions and support this paradigm. Specifically, we concatenate the reference identity image C1 with the fill-in image Cr. in diptych layout, then encode them jointly as tokens to enforce simultancous modeling and generation. The model is trained with the following CFM loss:
CCFM = E Ewp(tt) X, (0,1) (X,, Cr) |I
M). Cr.t) - (X, Xo) [
where Xo [Ci; Cr] denotes the width-wise diptych concatenation of the reference identity image and the fill-in image, X, computed according to Eq. 3, and X Xo @ M,, with C indicating element-wis wise multiplication. Here, [X,, XO" MM represents the channel-wise concatenation of these three components. The text condition Cr provides scene descriptions for both the reference identity image and the fill-in image. separated by the placeholders [REF-SCENE] and [TARGET-SCENE] Notably, during training, instead of requiring triplets (C1, Cr. X), where Cr and X typically differ in identity, we use two images of the same identity and X Cr. enabling the model predict Cp conditioned on M and X hence Eq. defines X, [C1: Cr. rather than X,- [C1; X].
Based on this formulation, once paired data {C1, Mr, Cr are available, the model can be trained two complementary modes without collecting separate datasets or designing distinct model struc- tures. Specifically, setting Cr and M to zero (i.e., a global mask) corresponds to position-free customization, while using nonzero (localized) masks for Cp and M enables position-aware cus- tomization. Thus, single paired dataset suffices to support both capabilities through simple varia- tions in training inputs.
3.2 IN- IN-CONTEXT MULTI-MODAL ATTENTION
Challenges. Although our pipeline seamlessly adapts diverse customization settings, it still faces several challenges. (1) Task-type ambiguity: for example, under position-aware customization settings, the model often misinterprets user-drawn masks as precise boundaries, generating content that fully fills and strictly follows the mask shape. (2) Image-boundar confusion: in diptych pre- diction settings (Eq. 5), the model struggles differentiate between reference and target regions, leading to undesirable edge artifacts.
Proposed ICMA. To address these issues, we propose In-Context MultiMMdal Attention mod- ule (ICMA), variant of the multi-modal attention mechanism. As illustrated in Fig. 3 (a), ICMA incorporates two key design innovations: (1) learnable task-oriented register tokens to explicitly in- dicate the customization type (precise masks, user-drawn masks, or position-free); and (2) learnable boundary-aware positional
seoeedddinn--omppris Reference Embeddings (RE) and Fill Embed- dings (FE)-to encode spatial relationships between the reference identity image and the fill-in image. Formally, the ICMA mechanism operates as follows:
P(x) x x+++ [ER; R(z),
Q = [P(Q)); Qr Dr+R(Q0 R(Qr),
K [P((i); Kr+ R(K))) r.),
(6)
V = [VT; VT: rıl.
h' = (HHA(Q,KV))
Here, "in-context" refers concatenating images and jointly conditioning on their captions.
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data
86
Cyt
RE Reforence Emiediings
Synthetic
Ewbedsings
Embeddings
Multi-Head Attention
Secoes
Tritsable
Coneat
O
MLP
MLP
In- In-Conextt Multi- Modiil Attention (ICMA)
(b) Examples
Figure 3: (a) In-Context Multi-Modal Attention (ICMA). ICMA incorporates learnable task- oriented register tokens and boundary-aware positional embeddings (RE, FE) into the multi- modal attention of MM-DiT (Peebles & Xie, 2023a) to specify customization types and delin- eate input boundaries. (b) Training data examples. High-quality identity-consistent quadruples {G,, Cr, M, J from real-world and synthetic data; for clarity, text descriptions OT are omitted.
where [:] denotes diptych concatenation, R(.) denotes rotary position encoding (Su et al., 2024); gaxd Q1, K2, Vi E Raxd and Qr, Kr, Vr E I Rixd are the query, key, and value matrices for image and text tokens, respectively; ER, &p the learnable Reference and Fill embeddings; r; E mx denotes the i-th learnable task-oriented register token; and MHA(-) is the Multi-Head Attention operation. Our proposed ICMA module replaces the multi-modal attention layers in both the double- block and single-block components of the original FLUX.1 I MM-DiT architecture (Labs, 2024a).
3.3 IN-CONTEXT CUSTOMIZATION DATA CURATION
Data Collection. The scarcity of high-quality customization data remains a critical bottleneck in developing robust customization models. Existing approaches (Tan et al., 2024; Wu et al., 2025b; Li et al., 2025) rely predominantly on synthetic data for training; however, such data often struggles to preserve identity consistency and photorealistic quality, thereby limiting model effectiveness.
To address this challenge, we introduce CustomData, a high-quality customization dataset designed for both authenticity and diversity. We curate nearly 8K idessity-consistent realistic image pairs from e-commerce platforms, covering real-world scenarios such as clothing try-on, cosmetics, fur- niture, electronics, accessories. home decor, and personal care products, with resolutions ranging from 800 x 800 to 3000 x 3664 pixels. To further enrich the dataset and extend coverage beyond commercial products,
add 4K high-quality, identity connisttt synthetic pairs carefully filtered from the SynCD 1024 x 1024 subset (Kumari et al., 2025), resulting in a comprehensive dataset of 12K {Cr, Cr, M, CT) samples (see Fig. 3(b) for visualization; symbol definitions in Sec. 3.1).
Data Processing, Our filtering process applies three rules: (1) exclude items whose DI- NOv2 (Oquab et al., 2023) feature similarity between Ci and Cp below 0.2: (2) discard pairs composed entirely of backgrac images; and (3) ensure Cp is not a blank-background im- age. These rules improve identity consistency and reduce ambiguity. We then use Qwen- VL2.5 (Bai et al., 2025) to auto-generate captions for CustomData (system prompt in Appendix Sec. J) and Grounded SAM (Ren et al., 2024) to obtain ground-truth masks, while randomly generating user masks under predefined rules to support model training (see Appendix Sec. L for details).
4 EXPERIMENTS
4.1 EXPERIMENTS SETUP
Implementation Details, IC-Custom builds on the pre-trained text-to-image model FLUX.1- Fill (Labs, 2024b). We train LoRA (Hu al., 2022) (rank 64) on the first 10 layers of both single and double blocks, while directly fine-tuning the image and text input layers. In total, only 49, 49.26M pa- rameters trainable-juss 0.4% of the original FL.UX model's 12B parameters (19 double and 38

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Table 2: Quantitative results on position-aware and position-free image customization. Evalu- ation on ProductBench (precise/user-dravn masks) and DreamBench shows that IC-Custom consis- tently outperforms existing methods across all objective metrics (higher better t). Baselines: FLUX.1 workflow (Labs, 2024b;c), OminiCtr/DDeemmO/nss al., 2025; Song et al., 2025), GPT-4o (Hurst et al., 2024b).
Anything (Tan et al., 2024; Mou
ProductBench
DreamBench
Method
Precise Mask
User-drawn Mask
Position-free
DINO-I
CLIP-1 1 CLIP-T
DINO-11
CLIP-1
CLIP-T
DINO
CLIP t CLIP-T
FLUX.1 workflow
60.80
81.66
31.13
62.26
81.60
31,29
OminiCtrl
$7.93
76.06
31.31
48.29
75.85
36.82
DreumO
62.98
78.86
31.25
57.69
76.33
36.24
Insert Anything
62.71
81.65
31.24
61.21
81.75
31.44
GPT-40
61 |44
78.53
30.72
62.05
79.87
30.58
54.31
77.38
36.33
IC-Custom (Ours)
63.14
81.92
31.75
63.28
81.95
31.80
65.67
83.19
36.88
Table 3: (a) Human-study results on image customization quality (higher is better). (b) Abla- tion studies on Productßench. Abbreviations: Zero-shot zero-shot inference without fine-tuning: w/o IL = without training Input Layers; w/o RD without using Real Data for training; w/o UM without using User-drawn Mask for training; w/o TR = without -orinte Register tokens; w/o PE without Boundary-awwa aware Positional Embeddings.
(a) Human-study results
(b) Ablation on ProductBench
Method
Cousistency
Harmony
Text Aligament
Mudels
Precise Mask
Uwr-dryon Mack
DINO-I
CLIP-11 CLIP-T
DINO-I f CLIP-I CLIP-T
FLUX. 1 workflow
3.2%
5.3%
Zemosher
77.55
79.84
31,20
OminiCrr
1.5%
2.1%
6.3%
wil IL.
62.00
81.52
31.36
62.13
51.33
31.64
DivamO
5,4%
3.2%
10.1%
w/b RD
62.38
81881
31.62
62.71
81.85
31.22
Insert Anylhing
6.8%
6.5%
wib UM
Wil TR
62.65
63.00
81.42
81.82
31.58
31.43
61.30
63.07
$1.44
81.28
31.33
31.64
GPT-40
4.6%
7.5%
21 4%%
web PF:
62.99
81.31
31.42
63.08
81.40
31.30
IC-Custom (Ours)
78.5%
75.4%
62.2%
$1,95
single blocks). Unlike prior methods (Song et al., 2025; Mou et al., 2025) that train LoRA on all lay- ers (e.g., DreamO (Mou al., 2025) trained 707M parameters), our approach drastically cuts train- ing cost. The model is optimized on our 1 12K dataset for 20K iterations using Adam (Loshchilov & Hutter, 2017) with a learning rate of 5 x 10-5 and batch size of 4. To handle diverse resolu- tions, we employ a data- buttabbckktinn strategy that groups samples by size (e.g.. 800x800, 1024x 1024, x024 1280, x280 1280, 1 1504x 1504) so each batch has uniform input dimensions. We also present a web application and inference pipeline in Appendix M.
Benchmarks. To assess our model's performance in both position-aware and position-free cus- tomization settings, we evaluate on our proposed ProductBench and the open-source Dream- Bench (Ruiz al., 2023a) benchmark. ProductBench contains 40 high-quality, identity-consistent items with resolutions exceeding 1024 x 1024 pixels. Each item includes paired images and cor- responding masks, with no overlap with our training data. We use SAM (Kirillov et al., 2023) to annotate precise masks and manually create user-drawn masks. The dataset is evenly divided into rigid and non-rigid categories, covering diverse domains such as clothing try-on, accessories, bags, furniture, toys, and perfume, specifically designed to evaluate position-aware customization. DreamBench comprises 30 items, each with 5-6 identity-consisten images and used to evaluate position-free customization. We take the first image of each item as the reference. Additionally, use Qwen-VL2.5 (Bai et al., 2025) to generate in-context textual descriptions for both benchmarks. For ProductBench, we directly prompt it to caption the diptych input, whereas for DreamBench we prompt it to creatively generate new scene descriptions. (see Appendix Sec. K for details)
Metrics. Follow established methods (Ruiz et al., 2023a; Wu et al., 2025b), consider objec- tive evaluation metrics across two aspects: identity consistency, and text alignment.
Identity Consistency: We calculate the DINO-I Score (Oquab et al., 2023) and CLIP-I (Radford et al., 2021) Score between reference images and generated images to assess identity preservation. Text Alignment: We use the CLIP-T score (Radford et al., 2021) to evaluate the model's instluction-folloiiing ability.
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bi User drown Moss
Reference
Fili-in Image
OminiCtrl
DreamO
FLUX workflow InsertÄnything
GPT-4o
Ours
Figure 4: Qualitative comparison of position-aware customization under precise-mask and user-drawn-mask settings. OminiCtrl and DreamO lack support for fill-in inputs. IC-Custom achieves high-quality customization with harmonious lighting, shadows, and perspectives.
Reference
FLUX workflow InsertAnything
OminiCtri
DreamO
GPT-4o
Ours
Figure 5: Qualitative comparison on position-free customization. IC-Custom achieves more realistic, coherent, and detailed customization. Red circles highlight incorrect regions details.
We also incorporate subjective evaluation metries: identity consistency, harmony, and text alignment to assess the performance customization models.
Baselines. We compare our approach against several strong baselines, including the community FLUX.1 workflow (FLUX.1-FH) with FLUX.1-Redux. (Labs, 2024b;c), saate-of-the-aee art DiT-based open-source methods OminiCtrl (Tan et al., 2024), DreamO (Mou al., 2025). and Insert Any- thing (Song et al., 2025), well as the commercial system GPT-4o (Hurst et al., 2024a) (March 25, 2025). Among them, FLUUX.1 workflow and Insert Anything are primarily designed for position- aware customization, whereas OminiCtrl and DreamO target position-free customization. Beyond evaluating each method in its native setting, we also adapt the other baselines to complementary cestarros-feddiin blank fill-in images to FLUX.1 workflow and Insert Anything to approximate position-free free customization, and prompting OminiCtrl and DreamO with text descriptions of the identity embedded in the fill-in image scene to approximate position-awand awarre customization. GPT-4o, in contrast, a unified vasion-language system. We therefore provide with alternating image-text pairs and explicit instructions perform each customization mode.
4.2 POSITION-AWAR CUSTOMIZATION
Quantitative Comparisons. Tab. 2 reports quantitative results ProductBench using both pre- cise and user-drawn masks. IC-Custom achieves state-of-the-al identity consistency and text align- ment, particularly under the more practical user-drawn mask setting (e.g., DINO-I 63.28 62.26).
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Reference
Fill-in Image
Durs
Zoro
w/o IL
w/o RD
w/o UM
w/o TR
w/o PE
Figure 6: Ablation Visualization. Qualitative results show that our model preserves identity con- sistency while enabling harmonious customization. Abbreviations are as follows: Zero-shot zero- shot inference without fine-tum mhe-tunnng w/o IL = without training Input Layers; w/o RD = without using Real Data for training; w/o UM without using User-drawn Mask for training, w/o TR without Trik-oriented Register tokens; w/o PE without Boundary-awaid Positional Embeddings.
Although the adapted OminiCtrl, DreamO, and GPT-4o achieve reasonable scores, they essentially regenerate images rather than perform reference-based image filling (see the following paragraph). Despite being specifically designed for position-aware customization, FLUX.1 workflow and Insert Anything still underperform compared with method.
Qualitative Comparisons. Fig. 4 presents qualitative comparisons on ProductBench. OminiC- trl, DreamO, and GPT-4o tend to regenerate entire images rather than perform position-awar cus- tomization, for example in the precise-mask mask try-on case (second row) where the human's 's face is completely altered. FLUX.1 workflow and Insert Anything also produce noticeable artifacts and weaker identity preservation compared with our model. Moreover, under the user-drawn mask set- ting. our method generates content with harmonious size, shape, and appearance instcad of merely filling the mask region. Thanks to its unified in-context formulation, IC-Custor delivers position- aware customization with harmonious lighting, shadows, textures, and materials. More visual results provided in Appendix Sec. H.
4.3 POSITION-FREE CUSTOMIZATION
Quantitative Comparisons. FLUXX1 workflow and Insert Anything lack position-free customiza- tion capability and, even after adaptation, merely replicate the reference (see the following para- graph), so we exclude them. As shown in the DreamBench section of Tab. 2, OminiCtrl shows poor identity consistency (low DINO-I and CL.IP-I), while DreamO and GPT-4o, though strong, still lag behind our approach. Trained on a high-qualit mix of real and synthetic data with a unified customization representation, our method achieves state-of-the-ar performance across all metrics.
Qualitative Comparisons. Figure 5 presents qualitative comparisons in the position-free setting. FLUX.1 workflow and Insert Anything fail to achieve true position-free customization, tending in- stead to replicate the reference identity image. OminiCtrl and DreamO produce results that are less realistic and less coherent than ours, while GPT-4o, despite strong instruction-followee sometimes loses fine- mne-griined identity details. contrast, IC-Custom consistently generates diverse, harmonious, and ddeetitt-consisterr results. More visual results are provided in Appendix Sec. H.
capabilities,
4.4 HUMAN EVALUATION
We conducted a user study with 20 participants on 50 randomly selected samples from both position- aware and position-free subsets. For each sample, participants were asked to identify the best- performing model across three dimensions: identity consistency, harmony, and text alignment. As shown in Tab. 3(a), method receives the highest human preference across all three dimensions compared with existing approaches. As FLUX.1 I workflow and Insert Anything only take images as input, exclude them from the rating of text alignment.

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w/o
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Figure 7: Effect of Boundary-awarr Positional Embeddings. Without Boundary-aware Posi- tional Embeddings (PE), position-free customization can produce blurred or ambiguous boundaries between the reference and generated content. Incorporating these embeddings sharpens boundaries.
4.5 ABLATION STUDIES
We present ablation studies of IC-Custom Tab. 3(b), examining model architecture, training data sources, and training strategies. We first establish zero-shot performance baseline. We then validate several key design choices: Without training the DiT image and text input layers (w/o IL), the model struggles transfer the pre-trained diffusion prior customization tasks, especially under user-drawn mask settings; Training solely on synthetic data (w/o RD) weakens identity consistency and realism; Omitting user-drawn mask data during training (w/o UM) substantially reduces performance on free-form masks; @ Removing Task-oriented Register tokens (w/o TR) or Boundary- Positional Embeddings (w/o PE) also degrades performance.
Qualitative results in Fig. 6 confirm these findings: all ablated variants introduce artifacts or shape distortions, whereas our full model preserves identity while naturally integrating it into the scene, yielding harmonious lighting and perspective. While the ablation variants exhibit different imper- fections, representative case w/o TR: Fig. 6 (second row) shows unwanted structures form- ing around the imprecise user-drawn mask boundaries, where background completion is expected. Adding TR helps alleviate this by enhancing the model's ability to distinguish and adapt to different task types. We also observe that, in position-free customization, performing flow matching on both the reference and output images tends to blur their boundaries-aae issue alleviated by incorporating Boundary-aware Positional Embeddings (see Fig. 7). We also include ablation studies under differ- ent conut-condiiioning settings in Appendix Sec. B. IC-Custom remains robust and reliable across all input-conditioning modes.
5 DISCUSION
This paper presents IC-Custom, a flexible and effective framework for image customization. Our approach introduces four key contributions: (1) an in-context customization paradigm that unifies position-free and position-aware image customization; (2) a novel In-Context Multi-Modal Atten- tion (ICMA) mechanism adapt to different customization settings; (3) high-quality identity- consistent dataset sourced primarily from real-world images; and (4) an evaluation benchmark with balanced distribution rigid and non-rigid customization tasks. Extensive experiments demon- strate that IC-Custom achieves state-of-the-art performance across multiple metrics.
Despite these achievements, our method does not explicitly model viewpoint, lighting, geometry, or other 3D scene properties, which we plan to address in future work. We also provide an initial exploration of multi-reference customization preliminary study geometric consistency, Appendix G illustrates the cross-domain customization capability our model, and Appendix D demonstrates multi-round iterative customization per- formance. We also include representative failure cases and corresponding analyses in Appendix I.
Appendix F. In addition, Appendix E presents a
ACKNOWLEDGMENTS
This work is supported by NSFC (No. 62176008), Tencent University Relations (Tencent AI Lab RBFR2024006) and Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology (Grant No. 2024B1212010010
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Appendix
CONTENTS
A Related Work
16
A.1 Diffusion Models
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A.2 Image Customization
16
Ablation on Distinct Input Conditioning Modes
16
C Comparison with ACE++
17
D Multi-Round Iterative Customization
17
Geometric Consistency Assessment via 3D Reconstruction
18
Preliminary Study on Multi-Reference Customization
18
G Cross-Domain Customization Capability
20
Additional Visualization Results
20
I Failure Cases
20
J Automated Captioning for Data
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K Automated Captioning for Benchmark
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Training Strategy: Mask Sampling and Augmentation
22
M Web Application
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N Ethics Statement
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Reproducibility Statement
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LLM Usage Statement
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A RELATED WORK
A.1 DIFFUSION MODELS
Recent advances in diffusion models (Sohl-Dickstein et al., 2015; Ho et al., 2020) have set new benchmarks in generative modeling, surpassing traditional approaches such as Variational Autoen- coders (VAE) (Kingma et al., 2013) and Generative Adversarial Networks (GANs) (Goodfellow ct al., 2020). As a result, many state-of-the-ar generative methods (Dhariwal & Nichol, 2021; Ho et al., 2020; Nichol et al., 2021; Ramesh al., 2022) now rely diffusion models as their core framework. Early approaches utilized a U-Net (Ronneberger et al., 2015) architecture with cross-attention, achieving competitive performance and efficiency. The open-sourcing of Stable Diffusion (Rombach et al., 2022) has significantly accelerated research in this area. More recently, diffusion models have been further advanced through the integration of transformer architectures, as demonstrated by models like SD3 (Esser et al.. 2024b), FLUX (Labs, 2024a), CogVideo (Yang et al., 2024b) and WAN (Wan et al., 2025). These 2017) have shown even greater performance and are now widely applied to range of tasks, includ- ing depth estimation (Bochkovskii al., 2024; Yang et al., 2024a; Hu et al., 2025), editing (Labs et al., 2025; Wu et al., 2025a), compositing (Labs, 2024b; Yang et al., 2025) and more.
hybrid models (Vaswani et al.,
A.2 IMAGE CUSTOMIZATION
Image customization is typically accomplished by integrating additional control signals from refer- ence images into text-to-image foundation models. One line of work (Wu et al., 2025b; Li et al., 2025; Hurst al., 2024a; Mou al., 2025; Tan et al., 2024; Chen et al., 2024c) focuses on position- free customization, directly generating centity-consistent images based on input reference images and text, as seen in GPT-4o (Hurst et al., 2024a), DreamO (Mou et al., 2025), and OminiControl (Tan et al., 2024). However, these methods struggle with position-aware customization, particularly when a masked source image is provided, as they cannot preserve the unedited regions. In contrast, meth- ods like Insert Anything (Song et al., 2025) and the FLUX.1-Fii 1-F----Fi---ddun workflow (Labs, 2024b) specialize in position-awas aware customization, inserting subjects into masked source images, but lack the capability for position-free customization. Concurrent works such ACE++ (Mao et al., 2025) and FLUX.1 Kontext (Labs et al., 2025) share similar ideas with our approach, yet differ in innova- tive technical details. In this work, we propose flexible framework that can address both position- aware customization and position-free customization. We also propose a data curation pipeline to collect high-quality real image data from different product images. Benefiting from this framework and high-quality data, our model achieves highly identity consistent customization, which can be used in real production.
B ABLATION ON DISTINCT INPUT CONDITIONING MODES
Our method supports not only the in-context diptych reference but also the integration of both the Redux Encoder and the Text Encoder. To assess how IC-Custom behaves under different combi- nations of textual and reference-based inputs, we conduct a comprehensive ablation across four input-conditioning configurations within the position-awan aware customization setting. Full condi- tions (default setting), @ Redux-only (no text), 2 Text-only (no Redux reference), Diptych-only (neither text nor Redux; relies solely on the in-context diptych reference). Table 4 summarizes the quantitative comparison on ProductBench under both precise masks and user-drawn masks. Across all four input modes, IC-Custom achieves stable and reliable customization performance. Even the diptych-only configuration yields competitive results, indicating that the in-context diptych framework already provides strong customization capability, while the Redux and text inputs act as optional boosters. As shown in Fig, 8, the qualitative comparisons further validate that our method supports all four copdittondiiiniin modes: the full full-conditions setting is the most stable overall, yet the reduced-input variants also perform strongly.
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Table Comparison of IC-Custom under four input-conditioning modes in the position-aware setting. Across all configurations, the model exhibits consistent and robust performance.
Setting
Precise Mask
User-drawn Mask
DINO-I
CLIP-It
CLIP-T
DINO-I
CLIP-1
CLIP-T
Full conditions (default)
63.14
81.92
31.75
63.28
81.95
31.80
Redux-only
63.34
81.49
31.37
63.78
81.86
31.10
Text-only
62.96
81.32
31.42
63.04
81.43
31.29
Diptych-only
62.97
81,41
31.41
63.18
81.77
31.30
Reference
Fill-in Image
Diptych-only
Redux-only
Text-only
Conditions
Se
Ciics
CIO
MINCHIP
Figure 8: Qualitative comparison of IC-Custom under four input-conditioning modes in the position-aware setting. Full conditions yield the most stable results, while the reduced-inpur modes (Redux-only, text-only, diptych-only) remain strong and coherent.
C COMPARISON WITH ACE++
ACE+ (Mao al., 2025) is a concurrent work proposing the Long-context Condition Unit (LCU), conceptually similar to our in-context diptych. However, ACE+ focuses on four separate domain- specific tasks and trains distinct LoRA adapters for each, rather than unified model handling both position-aware and position-free frre customization. Moreover, unlike our framework, ACE+ does not incorporate the innovative ICMA module. For a fair comparison on ProductBench, we directly use ACE++'s publicly released subject LoRA adapters to evaluate its performance under our bench- mark. As shown Tab. and Fig. 9, our model consistently produces more visually coherent customization results, showing superior perspective, lighting, and shape fidelity while operating as a single unified model rather than multiple task specific LoRA adapters.
and
D MULTI-ROUND ITERATIVE CUSTOMIZATION
In the position-aware customization setting, our framework inherently composable, allowing multi-round, iterative refinement of the generated content. As illustrated in Fig. 10, IC-Custom
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Table 5: Comparison with ACE+ (Mao et al., 2025) ProductBench. Metries under Precise
Mask (left) and User-drawn Mask (right); higher is better (t).
Precise Mask
User-drawn Mask
Method
DINO-1
CLIP-1
CLIP-T
Method
DINO-1
CLIP-1
CLIP-T
ACE++
60.68
81.34
31.64
ACE++
61.26
81.16
31,42
Ours
63.14
81.92
31.75
Ours
63.28
81.95
31.80
Precuse Mosk
Dj User-drown Mask
Image
Reference
Image
Figure 9: Qualitative comparison with ACE+ (Mao et al., 2025). Our method produces more adentity-consisterd and harmonious customization results. We compare our unified framework with ACE+ on ProductBench.
can perform GPT-style, multi-object customization by sequentially applying the model multiple rounds. We begin by generating initial customization result based on the in-context diptych ref- erence. This intermediate output can then reused as a new reference input subsequent rounds, enabling the model to introduce additional objects refine fine ene-grained appearance details step-by- step. By iteratively composing these rounds, users can achieve complex, multi mujtii customization scenarios while preserving spatial coherence and identity consistency throughout the process.
E GEOMETRIC CONSISTENCY ASSESSMENT VIA 3D RECONSTRUCTION
To explicitly validate whether the customized results generated by method maintain geometric consistency with the reference identity, we conduct a 3D reconstruction analysis using VGGT (Wang al., 2025). Specifically, the recoverability of the underlying 3D structure. As illustrated in Figure 11, VGGT effectively aggregates geometric cues from the generated distinct views, successfully reconstructing 3D point clouds with faithful shapes. Notably, despite the absence of explicit 3D modeling, these results indicate that our method not only preserves visual semantics but also achieves robust geometric alignment with the reference subject. Nevertheless, we acknowledge that our current framework does not support explicit control over the pose of the customized results. Such precise viewpoint controllability pertains to the domain of generative rendering, which lies outside the scope of this paper and is reserved for future work.
feed both the reference and generated images into VGGT to assess
F PRELIMINARY STUDY ON MULTI-REFERENCE CUSTOMIZATION
Benefiting from the learnable task-oriented register tokens and boundary-aware positional embed- dings introduced in our In-Context Multi-Modal Attention (ICMA), our model can accurately distinguish customization types and the boundaries between inputs and outputs. This naturally ex- tends multi-reference customization, where multiple reference images of the same identity (but from different scenes) are provided not as multi-image fusion, but as separate context cues. By aggregating information from multiple references, model better preserves identity fidelity and
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laptop placed on wonden sable.
IC-Cuntom:
Human:
backpack wooden laptop.
Customs
Replace the backpack.
IC
Custom::
Figure 10: Illustration of multi-round iterative customization: add laptop add backpack - replace backpack, demonstrating IC-Custom's 's extended application for multi-object customization.
fine details. To support this setting, we concatenate multiple reference images with the fill-in noise input and introduce an additional index embedding in the boundary-aware positional embeddings differentiate reference indices. Formally, we extend the diptych structure polyptych, modi- fying Eq. to Xo [C1; Cr, .r....., Cr. Additionally. to distinguish different references, we draw inspiration from DreamO (Mou al., 2025) by introducing learnable Index embeddings. The key difference is that we add these embeddings in the ICMA module rather than the input tokens. Specifically, we extend the function P(x) in Eq. as P(x) = + [R + E₁ &p + R(x), where ε E i...m//x are the learnable Index embeddings, &R pmxd and & E ggd are the learnable Reference and Fill embeddings. We also curated multi-reference dataset containing 2K real-world and 2K synthetic polyptychs for training. Fig. 12, our multi-refere refti-refereece approach aggregates infor- mation from multiple references (e.g., different viewpoints) to better preserve object identity details and textures. This preliminary exploration highlights the broader capability and scalability of our unified customization model, and plan further explore this direction in future work.
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Reference
VGGT
Reference
VGGT
3D Points
Geametry Consitercy
Figure 11: Evaluation of Geometric Consistency. Visual comparison of 3D reconstructions de- rived from reference and generated images.
Multi-Reference Customization
VS
Figure 12: Multi- Relli-RRfeeence Customization. By aggregating multiple reference images of the same identity from different environments and viewpoints, our model preserves richer details and textures. For example, when a single reference view omits the glasses' temples, the model must hallucinate them; with multiple viewpoints including the temples, it reconstructs the object more completely.
G CROSS-DOMAIN CUSTOMIZATION CAPABILITY
To further validate the Cross-Domale Customization Capahility of our method, we conducted exper- iments position-aware customization tasks across multiple domains, including cross-style, cross- pose, and cross-object scenarios. Fig. highlight the robustness and flexibility our method in handling wide range of cross-domall customization tasks. However, clarify that explicit style- content disentanglement not a primary objective of our work. As such, is challenging to specify reference style image input achieve more specific style-driven customization.
H ADDITIONAL VISUALIZATION RESULTS
Figure shows additional position-fre customization results, where our model seamlessly gener- ates novel scenes that preserve the reference identity based on text descriptions. Figure 18 presents additional position- aware customization results, demonstrating its ability to accurately insert edit images with different materials and textures while maintaining identity consistency.
I FAILURE CASES
While method demonstrates robust performance across diverse scenarios, acknowledge spe- cific limitations. We present several failure cases where the performance of the model degrades under certain conditions.
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Cross Style, Pross-Pose
Cross-Style, Cross-Pose
Cross-Style, Cross- Pose, Cross Object
Cross-Style Style, Cross-Pore Cross-Object
Figure 13: Qualitative results of cross-domale customization. Demonstrating the effectiveness of our method in handling cross-style, cross-pose, and cross-object customization tasks.
Gusomitaaion
Reference
inll Image
Mask
Output
Mask
Dutput
Mask
Output"
Fill-n Image
Mask
Output
Fill-in Image
Mask
Outout
Position free Custoniazion
Reference
Output
Reference
Output
Reference
Output
Figure 14: Failure cases. We present several failure cases that highlight the limitations of method and suggest areas for potential improvement.
As illustrated in Fig. 14, in position-aware customization, challenges arise when the user-provided mask ambiguous, extremely incomplete, or excessively small. For instance, in the first row of Fig. 14, an ambiguous or incomplete mask leads to the generation of erroneous content instead of the intended duckling; however, dilating the mask into rectangular bounding box significantly improves performance. Furthermore, as shown in the second column of the second row, when the mask excessively small, the model fails to inject the target subject entirely due insufficient position guidance.
In position-free customization, model's performance is hindered when the reference identity lacks essential details or features complex textures. For example, in the position-free customiza- tion row of Fig. 14, the texture patterns of the generated cat are not perfectly consistent with the reference image, indicating difficulty in faithfully reconstructing high- figh-freuencc details for com- plex subjects. We aim to further investigate these boundary conditions and enhance the robustness of our framework in future work.
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Human:
I've shown you images that contains the same object. Create a short description of this 2-scene image. Each scene detail should be scene's description.
image' the second image' the images are unordered describe each independently without referencing their order. Focus describing the key visual contents, effects, colors, textures and styling details of each object. The descriptions (SCENE-1), [SCENE-2) should completely independent and should reference each other. The destription should exceed 512 wards. Please strictly follow this format without any additional content.
[SCENE 11, [SCENE indicating start
use phrases
[Qwwn-VL]]:
[SCENE-1] 1] The Murad Clarifying Toner bottle is set against neutral beige background. The bottle is upright, and the label faces forwa dearly displaying the product information. golden capsules scattered around battle, adding touch contrast warm toned background. The lighting saft and highlighting the transparency the bottle and the vibrant blue of the label.
[SCENE-2] The Murad Clarifying Toner bottle placed light blue background, creating a cool-toned settiigg. bottle also upri ght, and the label visible, showing the product name and description. The background features a subtle pattern of water droplets, ambiance serene and refreshing, complementing
hydrating theme essociated product's purpose.
nhancing
toner.
Figure 15: Example of automated text prompt annotation. A concatenated pair of identity- consistent images fed into Qwen-VL2.5 (Bai al., 2025) with custom-designed instructions to generate corresponding captions for our data.
J AUTOMATED CAPTIONING FOR DATA
We Qwen-VL2: (Bai et al., 2025) to automatically generate text annotations for our data. Specifically, each concatenated pair of identity-consiseent images is fed into Qwen-VL2.5 with custom-designed instructions generate captions, illustrated in Fig. 15.
K AUTOMATED CAPTIONING FOR BENCHMARK
Our benchmark consists of parts: ProductBench for evaluating position-aware customization and DreamBench (Ruiz et al., 2023a) for evaluating position-free customization. For ProductBench, we apply the captioning approach described in Sec. to generate input captions. For DreamBench, which targets position-fire free customization, we provide the reference image together with prompts designed to elicit creative yet dlentity-consister outputs; an example of this prompting strategy shown in Fig. 16.
L TRAINING STRATEGY: MASK SAMPLING AND AUGMENTATION
To enhance model flexibility and robustmess, we randomly sample mask types during training: position-ate aware masks with a probability of 0.6 and position-free masks with 0.4. Within the position- aware cases, we further draw user-drawn masks with 0.75 probability and precise masks with 0.25, assigning higher probabilities to harder tasks to provide more training iterations. In addition, we convert precise masks from Grounded SAM into user-drawn masks via standard morghoooyphy operations such as dilation, erosion, opening, and closing.
M
WEB APPLICATION
We implement& a web application using Hugging Face Gradio to provide a simple and seamless interface for both position-free and position-aware customization (see Fig, 19 and Fig. 20). Users
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Human:
You be shown a scene with a prominent object Your task is to place this object into completely different and imaginative scene. The goal to ensure the object integrates harmoniously within this new environment, taking on new meaning or purpose that teels natural to the setting. vivid 128
(Qwen-VL]:
ginger tabby cat sits a rural field, surrounded by sprawling field go whest under a radiant mid-mominn sky. The sun casts a gentle shim its fur. The cat's yellow-gren green eyes scan the horbon, as light breeze tousles its whiskers and ruffles the wheat below. the distance, small idyllic setting.
splash
(Ours):
Figure 16: Example of DreamBench captioning and generated output. We illustrate our prompt- ing process for DreamBench, where a reference image and custom instructions are provided vision-language model to generate creative, identity cdentity-consistent captions. The figure also shows an example image generated by our method using the curated reference and caption.
first select customization mode and upload reference image. In the position-aware mode, they choose mask type (precise user-drawn), upload the fill-in image, optionally refine the mask (via SAM for precise masks or manual brushing for user-drawn masks), and provide an optional text prompt before running the model. In the position-free mode, users directly supply text prompt describing the desired scene or use the built-in VLM-based prompt auto-generation tool prior to execution. This web application provides simple, unified interface for both position-aware and position-free customization, enabling users to interactively explore our model's 's capabilities with minimal setup. We will release the full code and the web application open source to support reproducibility and community adoption.
N ETHICS STATEMENT
This work complies with the ICLR Code of Ethics.? 3 Our study does not involve human animal subjects, personally identifiable information, or sensitive demographic attributes. All datasets are either publicly available or internally curated, and will be verified for proper licensing prior to open- sourcing. We also adopt the SafeChecker from the Diffusers FLUX. framework to filter potentially harmful outputs (e.g., sexual, violent, toxic content) and apply similar precautions during data collection to minimize such content. We adhere to established research integrity practices, including reproducibility, transparency, and proper attribution of prior work.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, we provide detailed descriptions our data preparation and processing in Sec. 3.3, and implementation details in Sec. 4.1, including training hyperparameters, evaluation
httpss///ccr.. http::///tec..cooooooc///offfofe
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protocols, and baselines clarification, In Appendix Sec. J, we also describe the prompts used when preparing data with the modlti-modal language model. We will release our code and models under appropriate licenses to facilitate full reproducibility.
P LLM USAGE STATEMENT
In preparing this paper, used large language models (LLMs), including ChatGPT (Hurst et al., 2024a) and Gemini (Team et al., 2023), solely as writing-assissancee tools. Specifically, we first drafted the content ourselves and then used LLMs with prompts such "You are expert in academic writing. Please help me refine and rephrase the text to make it more professional, fluent, clear, and readable." We then manually reviewed and revised all LLM outputs ensure that the text accurately reflects our intended meaning. No part of the research design, experiments, analysis, or results was generated by LLMs; their use was limited to improving clarity and readability of the manuscript. We, the authors, take full responsibility for the content of this paper.
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Staiing ot refeccion
pand. Surrounded by pirk widflwwrrs
on the book.
.Troughh Ruch Jungle.
piaced Sandy boach...
Suffounded by ancient beoks...
8m
Surny garden..
-rsslig beach lowe...
Seriounded by neally orianged Fresh while Hes..
coft sola-
marble fake.
weathered
Figure 17: Additional visualization results on position-free customization. Our method success-
fully maintains identity consistency while generating diverse scenes and poses.
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CAFO
ASHI
LEO MESSI
LEO MESSI
Emirares
Emirate
Figure 18: Additional visualization results on position-aware customization. Our method cessfully maintains identity consistency while seamlessly integrating subjects into diverse lighting, styles, and poses in target scenes.
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IC- IC-Custom
Transfor Al: Ar-powered customization
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Figure 19: Web App Position-awars mode. Users upload reference image and a fill-in image, choose the mask type (precise user-drawn), optionally edit refine the mask, add an optional text prompt, and then run the model to perform position-aware customization.
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IC-Custom
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Figure 20: Web App Position-free free mode. Users upload a reference image, provide a text prompt describing the desired scene or use the built-in VLM prompt generator, and then run the model to perform position-free customization.
28


Reviews:
[{"review_id": "6U5M3PoiAm", "reviewer": "Reviewer_uenq", "summary": "The authors propose IC-Custom, a unified framework integrating position-aware (mask-guided editing) and position-free (text-guided identity generation) image customization. It adopts an in-context diptych format by concatenating reference and fill-in images into a unified input and introduces ICMA module, which incorporates task-oriented learnable register tokens and boundary-aware embeddings to resolve task ambiguity and boundary confusion. They also curate CustomData (12K samples: 8K real-world e-commerce diptychs, 4K filtered synthetic ones) to avoid unrealistic synthetic data flaws, with strict filtering (DINOv2 similarity ≥0.2) and auto-annotation (Qwen-VL2.5 for captions, Grounded SAM for masks). Experiments results show the effectiveness of the proposed method.", "questions": "How the model behaves when it lacks clear text guidance?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper addresses the long-standing gap of isolated position-aware and position-free image customization by proposing a unified framework. \n2. The method balances performance and efficiency: built on pre-trained FLUX.1-Fill, it only trains 0.4% of parameters via LoRA fine-tuning, while outperforming baselines on ProductBench and DreamBench.\n3. Overall, the writing is clear.", "weaknesses": "1. Limited Scenario Generalization: IC-Custom only validates single-object customization. Its performance in multi-object scenarios (e.g., inserting a backpack and a laptop simultaneously) remains untested.\n2. The paper does not mention any performance in the absence of text input. Position-aware customization relies on text to define identity and scene constraints. How the model behaves when it lacks clear text guidance is unclear.", "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": "NfXEFGcYsi", "reviewer": "Reviewer_YcXu", "summary": "This paper proposes IC-Custom, a unified framework for both position-aware (e.g., image insertion, try-on) and position-free (e.g., text-guided generation) image customization. It reformulates both tasks through a diptych in-context formulation, processed by a new In-Context Multi-Modal Attention (ICMA) module with task-register tokens and boundary-aware positional embeddings. A related dataset (CustomData) and a new benchmark are also introduced. IC-Custom achieves strong SOTA results while fine-tuning only 0.4 % of the base model.", "questions": "1. How do task tokens learn to specialize—via supervision or implicitly from data mixing?\n\n2. Can IC-Custom handle multi-reference or style-blending scenarios?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ Elegant unification: A clear and practical integration of position-aware and position-free customization within one architecture, avoiding the need for separate specialized models.\n\n+ Well-designed ICMA mechanism: Task tokens and boundary embeddings effectively address task ambiguity and spatial confusion, with strong ablations supporting their contribution.\n\n+ Comprehensive evaluation: Extensive comparisons show consistent superiority over GPT-4o and recent open-source baselines, supported by high-quality qualitative examples.", "weaknesses": "+ The “In-context learning” framing is somewhat overstated—the model is trained end-to-end rather than showing adaptive few-shot behavior.\n\n+ There is no explicit modeling of 3-D or geometric consistency; limited robustness tests for large viewpoint changes.", "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": "IjLfvd8bmc", "reviewer": "Reviewer_JiRo", "summary": "The paper introduces IC-Custom, a unified framework for integrating position-aware (mask-guided insertion) and position-free (text-guided generation) image customization tasks using in-context learning on Flux1. architectures. It proposes an In-Context Multi-Modal Attention (ICMA) module with learnable task-oriented register tokens and boundary-aware positional embeddings to handle diverse scenarios. The authors curate a 12K identity-consistent dataset and evaluate on a new ProductBench for position-aware tasks and DreamBench for position-free, demonstrating superior performance over baselines OmniCtrl, DreamO, Insert Anything, and GPT-4o in metrics such as DINO-I, CLIP-I/T, and human preferences.", "questions": "1. Could the author share more specific details on the computational resources used, such as the type and number of GPUs, as well as the total GPU hours required for the 20K iterations?\n2. In the ablation studies, why do the results show only a minor drop when real data is excluded? How was the synthetic data filtered to ensure it matches the quality of real-world data?\n3. The metrics from the user study are significantly higher than those of the baselines; could the author clarify why IC-Custom outperforms them to such a large extent?\n4. How does IC-Custom perform when faced with more challenging or extreme customization scenarios, such as very complex images, unclear user input, or ambiguous mask regions?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Novel unification of two customization paradigms using in-context learning on DiT, with innovative ICMA incorporating task-specific register tokens and positional embeddings to handle ambiguity.\n\n2. Robust dataset curation (12K samples, real+synthetic) and comprehensive evaluations, including ablations that validate key components.\n\n3. Intuitive diptych formulation and clear training strategy enable seamless handling of diverse tasks without separate models.\n\n4. Advances efficient, identity-consistent generation for practical applications, outperforming SOTA with minimal parameters trained.", "weaknesses": "1. No quantitative analysis of inference time, failure cases beyond visuals. The paper does not address how long the model takes to generate results, nor does it provide a comparison to baseline models in terms of speed. \n\n2. Unclear Handling of Multi-Reference Customization. The paper briefly mentions multi-reference customization as future work, but does not provide sufficient detail on how this will be incorporated into the current framework. \n\n3. Limited Exploration of Dataset Diversity. The paper mentions that the authors curated a dataset with 12K identity-consistent samples, combining real-world and synthetic data. However, there is limited discussion about the diversity of this dataset, particularly with respect to edge cases.", "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": "SxhpeRIKU8", "reviewer": "Reviewer_pHSt", "summary": "This paper presents IC-Custom, a unified framework for both position-aware and position-free customized generation. The approach formalizes the task under a universal equation, $p(\\hat{X}|C_{I},C_{I'},M,C_{T})$, and utilizes an In-Context Diptych Format where the reference identity image and the fill-in image are concatenated and jointly encoded into token sequences using the DiT architecture. The key technical contribution is the In-Context Multi-Modal Attention (ICMA) module, which incorporates learnable task-oriented register tokens to explicitly signal the customization type and Boundary-aware Positional Embeddings to help delineate the input regions in the diptych configuration.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper proposes a unified framework that seamlessly integrates position-aware and position-free image customization, addressing the key limitation of non-unified, task-specific approaches found in prior work.\n2. The diptych framework and its associated training strategy are well-designed and appear reasonable for achieving the model's unified capability.\n3. The motivation for using both task-oriented register tokens and boundary-aware positional embeddings within the architecture is clear.", "weaknesses": "1. The paper lacks sufficient qualitative ablation studies to clearly validate the effect of the Task-oriented Register tokens (TR) and Boundary-aware Positional Embeddings (PE). While quantitative ablation results are provided in Table 3(b), the observed metric values are not significantly compelling compared to the full model design. More visualizations for the ablation studies should be provided to demonstrate the necessity and efficacy of these modules.\n2. The Redux Encoder is integrated into the framework; it remains unclear whether the resulting performance in identity consistency is highly reliant on the embeddings provided by this Redux Encoder.", "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)