ContextGen: Contextual Layout Anchoring for Identity-Consistent Multi-Instance Generation
Authors: Ruihang Xu
Dewei Zhou
Fan Ma
Yi Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Models
Controllable Generation
Multi-Instance Generation
Identity Preservation
Attention Mechanisms
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Multi-instance image generation (MIG) remains a significant challenge for modern diffusion models due to key limitations in achieving precise control over object layout and preserving the identity of multiple distinct subjects. To address these limitations, we introduce **ContextGen**, a novel Diffusion Transformer framework for multi-instance generation that is guided by both layout and reference images. Our approach integrates two key technical contributions: a **Contextual Layout Anchoring (CLA)** mechanism that incorporates the composite layout image into the generation context to robustly anchor the objects in their desired positions, and **Identity Consistency Attention (ICA)**, an  innovative attention mechanism that leverages contextual reference images to ensure the identity consistency of multiple instances. To address the absence of a large-scale, high-quality dataset for this task, we introduce **IMIG-100K**, the first dataset to provide detailed layout and identity annotations specifically designed for Multi-Instance Generation. Extensive experiments demonstrate that ContextGen sets a new state-of-the-art, outperforming existing methods especially in layout control and identity fidelity.

Published as a conference paper at ICLR 2026
CONTEXTGEN: CONTEXTUAL LAYOUT ANCHORING FOR DENTITY-CONSISTENT MULTI-INSTANCE GENER- ATION
Ruihang Xu, Dewei Zhou, Fan Ma; Yi Yang ReLER Lab, CCAL, Zhejiang University
(ruihangxu, 2ww1999, yangyics (2 edu.cc fl #1owerfan524@ gma com
Reference Images
Nano Banana
GPT-4o
8
Reference Images
Generate with Layout
Generate with Layout 2
Lacun
Nameplate
mode
Figure I: Representative showcases of our work. Upper panel: Our multi-subject sutt--subbect-diiven generation results versus existing open-source SOTA (OmniGen2) and proprictary models (Nano Banana, GPT- 4o). Lower panel: Our layout-to-image generation examples using different layouts.
ABSTRACT
Multi-instance image generation (MIG) remains significant challenge for modern diffusion models due to key limitations in achieving precise control over object layout and preserving the identity of multiple distinct subjects. To address these limitations, we introduce ContextGen, a novel Diffusion Transformer framework for multi-instance generation that is guided by both layout and reference images. Our approach integrates two key technical contributions: a Contextual Layout Anchoring (CLA) mechanism that incorporates the composite layout image into the generation context to robustly anchor the objects in their desired positions, and Identity Consistency Attention (ICA), an innovative attention mechanism that leverages contextual reference images to ensure the identity consistency of multiple instances. To address the absence of large-scale, high-quality dataset for this task, we introduce IMIG-100K, the first dataset to provide detailed layout and identity annotations specifically designed for Multi-Instance Generation. Extensive experiments demonstrate that ContextGen sets new state-of-the-art outperforming existing methods especially in layout control and identity fidelity. Our code is available at lettt::::// /tthttithhhu con/ nenhang/ ContextGen
"Corresponding Author.

---

Published as a conference paper at ICLR 2026
INTRODUCTION
Diffusion-based models (Ho et al. 2020p have significantly expanded the horizons of image cus- tomization, with many recent systems (e.g., FLUX (Labs) 2024b)) adopting the Diffusion Transformer (DiT) (Peebles & Xie 2022) framework for its enhanced generation quality. Recent developments in subject-driven image generation, such as OmniGen2 (Wu et al. 2025b), and layout-to-image synthesis, exemplified by AS-Diffusion (Wang et al. 2025 , have further broadened the scope of customization, enabling control over both content and composition in generated images.
However, current methods face three fundamental limitations: (1) Inadequate position control, where existing layout guidance fails to achieve accurate spatial precision for user-specified arrange- ments; (2) Weak identity preservation, as subject-driven approaches struggle to maintain fine details across multiple instances, particularly with increasing number of reference images.
(3) Lack of high-quality training data, as existing datasets do not provide large-scale, precisely aligned pairs of reference images and layout annotations for multi-instance scenarios. These deficiencies collectively hinder the simultaneous achievement of compositional accuracy and identity fidelity.
To address these challenges, we propose ContextGen, novel DiT-based framework that enables multi- muulii-nsttance generation by unifying two key modalities. First, we use composite layout image for precise spatial control. As shown in the setup stage of Fig. 2 this layout image can either user-provided or automatically synthesized. Second, we integrate reference images to overcome the limitations of layout-only generation, such as instance information loss due to overlaps. By incorporating these modalities into a unified contextual framework, ContextGen achieves both precise spatial control and high instance-level identity consistency.
Our work introduces three key innovations and contributions: (1) Contextual Layout Anchoring (CLA), which leverages contextual learning to anchor each instance at its desired position by incorporating the layout image into the generation context, thereby achieving robust layout control; and (2) Identity Consistency Attention (ICA), a novel attention mechanism which propagates fine- grained information from contextual reference images their respective desired locations, thereby preserving the identity of multiple instances. Complementing these mechanisms is an enhanced position indexing strategy that systematically organizes and differentiates multi-image relationships. (3) A large-scale, hierarchicalll-structuue bounding boxes and identity-matched matched references to directly address the current data scarcity in Image- laage-guided Multi-instanre Image Generation, with hierarchical samples shown in Fig. 3
dataset, IMIG-100K, which we curate with annotated
Our method achieves state-of-the-art performance across three benchmarks. On (1) COCO- MIG (Zhou et al., 2024b), it improves instance-level success rate by +3.3% and spatial accuracy (mloU) by +5.9% over prior art. For (2) LayoutSAM-Eii Evaal (Zhang et al. 2024).. attains the highest scores in texture and color fidelity, demonstrating superior detail preservation. Most notably, on (3) LAMICBench++ (Chen et al. 2025b), our approach outperforms all open-source models by +1.3% average score and surpasses commercial systems like GPT-4o in identity retention (+13.3%). These gains validate CLA's layout robustness and ICA's effectiveness in multi-instance scenarios.
In summary, key contributions
follows:
ContextGen: A novel DiT-based framework with Contextual Layout Anchoring (CLA) for robust layout control and Identity Consistency Attention (ICA) for precise identity preservation.
IMIG-100K: The first large-scale, high quality, and hicrarchieally-sttreuurr dataset for image- guided multi-instance generation, which provides detailed layout and identity annotations.
SOTA Performance: We achieve state-of-the-arr results, outperforming existing methods (both
open source and proprietary) especially in layout control and identity preservation.
RELATED WORK
2.1 DIFFUSION MODELS
Diffusion models have evolved from UNet architectures (Ho et al. 2020 Rombach et al., 2022)
to transformer-be based approaches like DiT (Peebles & Xie 2022). enabling scalable multimodal
generation as seen in Stable Diffusion 3 (Esser et al. 2024). FLCX (Labs) 2024b) further advanced this by unifying visual and textual inputs through multi-modal attention mechanism.
2

---

Published as a conference paper at ICLR 2026
Sctup
Text Embed
Noise Imsgge
Layout Eminge
Reference Imaage
Instance Wise Podition Indesing
Layout Image Composition
Reference
Biuaz
16
10
MM Token Sequence
CLA Ciniesteal Layout
ICA Coneniency
Opt. I Customize
DiT-Blacks
Designed manually
Double Arta Blocks
Single Attn Blocks x19
ICA
T
Single Blocks x19
Opt. Automatic Stacked automarically by sorting algorishms
DIT-Block
Details
Sequence
Lincar
LoRA
Vasile
MM-Alteation
1 Too Tikert
Ohes Ance
Ref Tokes
Bbos
FEN
LoRA
Laywst
Bles Ovailag
Figure 2: Overview of ContextGen. Left (Setup Stage): Options composite the Layout Image. Middle (Model Core): Central generation architecture using FLUX DiT-Blocks. Right (Attention Mechanisms): Details of MM-Attention components (Position Indexing, CLA and ICA).
2.2 INSTANCE-LEVEL CONTROLLABLE IMAGE GENERATION
GLIGEN Li al. 2023) pioneered the layout-to-image generation paradigm. Follow-up studies utilizing UNet-based methods like InstanceDiffusion (Wang et al., 2024) and MIGC (Zhou et al.) 2024a), or DiT-based approaches like EliGen (Zhang et al. 2025a) and 3DIS (Zhou et al.] 2024c). have demonstrated enhanced capabilities in handling multiple instances. Current sfate-of-the-ate frameworks like OmniGen2 (Wu et al.] (0255) and DreamO (Mou et al. (0255) process multi- subject conditions via integrated token sequences but face identity degradation with many subjects. While MS-Diffusion (Wang et al. (2025) and LAMIC (Chen et al. 2025b) combine reference-driven generation with layout control, challenges remain in layout precision and identity consistency.
METHOD
3.1 PRELIMINARIES
Multimodal Diffusion Transformers (MM-DiT) Recent architectures have replaced modality- specific cross-attention with unified multimodal processing. The MM-Attention operation concate- nates image tokens Uimage and text embeddings tex into single sequence T [ Uimage enabling joint self-attention across modalities. Stable Diffusion 3/3.5 (Esser et al. 2024 stability.ai 2024)
and FLUX (Labs 2024b), treat all modalities within a shared latent space. The framework naturally supports in-context learning by allowing arbitrary interleaving of visual and textual tokens, while maintaining stable gradient flow across modalities during end-to-end training.
Position Indexing and Attention Mask in MM-Attention To address the coanuutationinvvriiann of the Transformer architecture, Rotatory Position Embedding (RoPE) (Su al. 2023) was introduced to encode relative positional information. Adapting this for unified multimodal space, the FI.UX.1- Dev architecture proposes a novel extension of RoPE that employs ternary position encoding scheme. This scheme assigns position index p, (m, i, to each token in the sequence. The first component m is set to and is retained for further use. For text tokens, the spatial coordinates (i, j) fixed (0, 0), while for image tokens, they correspond to the spatial coordinates (i, in the 2D noise latent space. This set of position indices {p:} for the sequence forms position index matrix P.
The unified attention mechanism, which is controlled by the attention mask M, integrates this positional information through RoPE. Specifically, the rotation matrix R is computed by applying the RoPE formulation to the position index matrix P, process denote R Rotate(P). This
3

---

Published as a conference paper at ICLR 2026
resulting matrix R is then utilized to apply rotation the query (Q) and key (K) embeddings
before the dot- dot-product calculation. The attention is then calculated as:
(RK) O M V.
MM-Attm Q. K, V) softmax
(I)
vd
where @ denotes element-wise multiplication. In the FLUX.1 series, a self-attention mechanism is employed where queries, keys, and values are all derived from the unified token sequence T, and M is an all- all-True matrix, enabling full attention across all tokens.
3.2 CONTEXTUAL ATTENTION WITH LAYOUT ANCHORING AND IDENTITY PRESERVATION
Contextual Conditioning with Layout and Reference Images Recent studies in image-to-image (12T) tasks have demonstrated the effectiveness of using diptych, side-by-side reference image pair, to guide diffusion models (Shin et al.) 2024: Song et al., 2025 Zhang et al. 2025c). Building upon this, our framework introduces novel layout control strategy by integrating a composite layout image into the generation context. This can be done either by a user-defined composition, which offers greater control and often more aligned with specific user intent, or by our automated sorting algorithm (mentioned in Appx. A based on the occlusion ratio of all instances. This composite diptych serves as the primary input for our Contextual Layout Anchoring (CLA) mechanism, which is designed to enforce a robust spatial structure by anchoring objects to their desired locations.
However, relying solely on this composite layout image presents challenges. Appx. B.5 in scenarios with instance overlap, the process of compositing may result in information loss or detail degradation. To address this, we integrate the original, high-fidelity reference images alongside the diptych. The unified token sequence T mentioned in Sec. 3.1 is constructed as:
illustrated in
teext timage Maayaut tyef,
t
(2)
Our Identity Consistency Attention (ICA) mechanism incorporates these tokenized reference images (tref, } into the context to preserve instance-specific attributes and details, effectively mitigating the issues of detail loss in overlapping regions, thereby ensuring complementary relationship between the robust layout guidance from CLA and the precise detail preservation from ICA.
Contextual Layout Anchoring (CLA) Inspired by the functional specialization observed in DiT layers (Zhou et al. 2025b) Zhang et al., 2024). we propose a hierarchical attention architecture to process the unified token sequence. As shown in the middle panel of Fig. 2 the CLA mechanism operates in the front and back layers, focusing primarily on global context and structural composition. The CLA mask, detailed the right panel of Fig. 2 ensures broad communication across the text, image, and layout modalities. Using the token sets defined (T = (tiex) I [iiaage }, L= [tayout): and R==
= (tret,,. }) and reference bounding boxes (B,) ) the attention mask for CLA is defined as:
N
McLA(g k) = (e (TUZUC)) URR x (TUR.))
==1
where q and l are arbitrary tokens from the query and key sequences respectively, and [-] denotes the indicator function.
Identity Consistency Attention (ICA) While the front and back layers perform global spatial anchoring, we introduce the ICA mechanism the middle layers facilitate detailed, instance-level identity injection. As detailed in the right panel of Fig. 2 ICA operates by applying a specialized attention mask, MICA, for tokens located within specific bounding box. For a query token g E Bn, the attention mask is defined as:
MICA (g. k) 1 (a, (c) (IB.X ((g E MALA 1 $ U B
(4)
n=
==1
The core function of MICA is the forced connection between q and its corresponding reference tokens Rn, ensuring reliable identity transfer. Tokens outside any bounding box (i.e., background) default to the mask used by CLA. This hierarchical strategy effectively transitions our framework from global layout control refined instance-level identity preservation.
4

---

Published as a conference paper at ICLR 2026
1. Basic Part
II. Complex Part
III. Flexible Part
Figure 3: Image Samples of IMIG-100K Dataset.
Instance- Wise Position Indexing The ternary position encoding scheme described Sec. 3.] was extended in FLUX. 1-Knttext (Labs et al.) 2025) handle image editing, where the first component of the position index, m, was set to for edit tokens. Inspired by this work and other existing work (Wu et al. 2025c) that shows providing distinct and non-overlapping position indices for each image sequence sigmificantly improves the model's ' ability to differentiate between various images, propose refined position encoding strategy to systematically structure the relationships within our unified token sequence T (Eq.
The primary noise latent Uimuge retains the original (0, (,)) indexing, ensuring spatial coherence within the target image. Auxiliary Part: Tokens from auxiliary inputs, including layout image and reference images, assigned a unique index. They are indexed (1, W,, + H, + +). where W,, Σx= wy and H, = Σi= hg are cumulative offsets aggregating the dimensions of all preceding conditioning images. This guarantees unique positional identifiers for each conditioning image, when they are concatenated.
Basic Part:
The effectiveness of this refined strategy is validated by our ablation study in Appx. B.2 This approach allows the attention mechanism to distinguish between tokens from the noise latent and auxiliary inputs, well differentiate between tokens from various conditioning images.
3.3 I0IG-100K: AN IMAGE-GUIDED MULTI-INSTANCE-GENERAA
DATASET
High-fidelity image-guided multi-instance generation is severely limited by the lack of suitable training data. While existing large-scale datasets (Lin et al. 2013 Deng et al. 2009) provide diverse instances, they often lack the aesthetic quality and annotation granularity required for modern diffusion models. Conversely, recent subject-driven datasets (Tan et al. 2025 Xiao et al. 2024a) exhibit high visual quality but are limited by their low instance multiplicity per image. A brief survey on related datasets is provided in Appx. C.1 To bridge this gap, we introduce IMIG-100K, large-scale dataset created using the FLUX framework (Labs) 2024b). This dataset specifically designed to support multi-instance generation by providing high-resolution, high-fidelity data with precise layout and reference images.
Dataset Structure and Key Features robustly train the diverse capabilities required for identity- consistent multi-instance generation, the IMIG-100K dataset is systematically structured into three specialized sub-datasets. These subsets collectively facilitate the comprehensive training of our framework, with examples shown in Fig.
1. Basic Instance Compasition (50K samples): This subset focuses on foundational compositional skills. The ground truth images are generated by the eext-to-image model 1- Labbs 2024b), and we derive reference images using detection and segmentation models (Liu et al. 2023 Ravi et al., 2024 Dai et al. 2025). These reference images undergo minimal post-processing, including basic lighting adjustments.
Complex Instance Interaction (50K samples): Designed for more complex scenarios with up to 8 instances per image, this subset's 's data construction is similar to the basic part. However, the reference images are semantically edited to simulate real-world interactions, including occlusion, viewpoint rotation, and object pose changes.
5

---

Published as a conference paper at ICLR 2026
3. Flexible Composition with References (10K samples): Unlike the previous two subsets, this unique subset is designed to train the model's robustness in handling low lwwconsistency inputs. We first generate individual reference instances using the FLUX.I-Dev model. These are then composited into ground truth scenes by subject-driven models (Wu et al. 2025e] Mou et al. 2025), allowing for much greater degree of flexibility and transformation in the composited instances relative to their original references. A key step involves rigorous filtering to ensure
identity consistency from the references (Guo al. 2021 Oquab et al. 2023).
All textual prompts generated by advanced large language models (DeepSeek Al 2025 Comanici et al. 2025, OpenAl| 2024), ensuring diverse and high-quality descriptions. Further details on the dataset are provided Appx. C.2
EXPERIMENTS
4.1 EXPERIMENTAL SETTING
Training Details We initialize the model with 1-UXX.-KKotextt (Labs et al. 2025 without intro- ducing additional parameters and fine-tune using LoRA (Low-Rante Adaptation) (Hu et al. (021) with LoRA Rank 512. We perform training on 4x NVIDIA A 100 GPUs with total batch size of 16. The model is tuned on the three hierarchical sub-datasets described in Sec. 3.3 for 5K steps, employing the Prodigy optimizer (Mishchenko & Defazio 2024) with its default learning rate. We also employ Direct Preference Optimization (DPO) (Rafailov et al. 2024) (detailed in Sec. (44) to refine text-visual alignment and user preference. These enable the model to evolve from mastering simple compositions to synthesizing complex multi-instance scenes.
Benchmark Datasets We employ three distinet benchmark datasets for evaluation.
(1) LAMICBench++: A specialized benchmark for evaluating identity preservation and feature con- sistency in subject-driven generation. We extend the multi- composition benchmark from LAM- ICBench (Chen et al. 2025b), aggregating multi-category reference images (humans, animals, objects, etc.) from established datasets including X VerseBench (Chen al.] (025a), DreamBench++(Peng ct al. 2025) and MS-Bench (Wang et al. 2025). In particular, we construct a dataset of 160 cases total, including 50 cases with 2 reference images, 40 with 3, 30 with 4, 20 with 5, and 20 with over 5 reference images. These cases are divided into two categories: Fewer Subjects (5 3 reference images) and More Subjects (2 4 reference images). In this benchmark, we adapt and slightly modified the four evaluation metrics from the original work: (1) Global text-image consistency wel.uuuiisnnnsseereeee
(TTC) evaluated through
(VQA) (Ye al., 2024). with approximately 2K questions (4-12 per item): (2) Object preservation (IPS) (Liu et al.] 2023 Oquab et al.] 2023); (3) Facial identity retention (IDS) (Guo et al. (022);; (4) Aesthetic quality (AES) (Schuhmann) 2023).
(2) COCO-MIG (Zhou et al. 2024b): A benchmark designed to evaluate spatial and attribute accu- racy in layout-to-image generation, comprising 800 images from COCO Dataset (Lin et al., 2015) with annotaedd instances. The evaluation metrics include: (I) Global and instance level success rate (SR and I-SR) determined by spatial accuracy (mloU) and color correctness; (2) Multi-scale semantic consistency through global and local CLIP Scores (G-C and L-C).
(3) MayoutSAM-EEt (Zhang et al.) 2024): An open-set benchmark for layout-to-image evaluation, featuring 5K prompts with exhaustive entity-level annotations, from which we filter 1K samples with sufficiently large bounding boxes for reliable instance evaluation. We adapt the original work's met- rics: (1) Fine-grained entity accuracy (spatial, color, textural, shape) evaluated using MLLM (Yao) et al.
2024); (2) Holistic quality metries: CLIP Score for semantic alignment and Pick Score (Kirstain
et al.
2023 for human preference.
4.2 BASELINES
We compare our method against a comprehensive set of state-of-the-art baselines across relevant domains. For layout-to-image generation, we include pioneering works such LAMIC (Chen et al. 2025b) and MS-Diffusion (Wang et al., 2025). To evaluate spatial control, we also bench- mark against CreatiLayout (Zhang et al. 2024), EliGen (Zhang et al. 2025a),, MIGC (Zhou et al. 2024b). 3DIS (Zhou et al. 2024c). InstanceDiffuse (Wang et al., 2024), and GLIGEN (Li et al. 2023). In the domain of subject-driven generation, we benchmark against OmniGen2 (Wu et al.

---

Published as a conference paper at ICLR 2026
Table 1: Quantitative results on LAMICBench++. Performance rankings: bold (highest), underline (second highest), way. underline (third highest). The benchmark provides complete manual annota- tions for all method requirements: layout-aware methods (*) use our pre-annotated bounding boxes, while single-image-ddinne methods (1) use our manually composited layout images.
Fewer Subjects
More Subjects
Method
AVG
ITC
AES
IDS
IPS
AVG
ITC
AES
IDS
IPS
AVG
LAMIC"
42.27
50.26
37,02
74.17
50.93
28.29
50.84
24.63
60.87
41.16
45.61
XVerse
77.65
53.79
39.47
71.25
60.54
43.48
47.68
15.26
56.12
40.63
50.29
MIP-Adapter
87.22
56.50
6.63
68.40
54.69
71.88
58.38
1.12
61.10
48.12
51.28
UNO
89.86
58.04
17.53
75.34
60.19
77.25
58.90
7.83
62.94
51.73
55.58
MS-Diffusion
89.13
57.67
12.45
75.49
58.69
78.46
59.65
9.06
69.75
54.23
56.35
DreamO
90.14
56.56
33.84
71.44
63.00
78.49
57.86
14.53
60.07
52.74
57.31
Qwen-Image-Fil Image-Fditt Fdiit
93.63
57.97
17.71
73.30
60.65
86.35
59.57
9.32
65.26
55.13
57.57
OmniGen2
95.40
57.58
32.17
73.14
64.57
89.69
58.49
15.15
69.31
58,16
61.08
FLUX.1-Konan K-ontex!
90.16
54.87
42.65
77.87
66.39
90.30
56.08
27.91
70.93
61.31
63.33
Ours*
92.54
57.50
35.86
81.23
66.78
89.89
52,18
30.42
73.35
63.21
64.66
Closed-Source Commercial Models
GPT-4o
97.63
59.52
28.49
79.53
66.29
95.37
62.77
17.12
72.64
61.98
63.71
Nano Banana
96.58
58.48
34.36
80.87
67.57
95.48
60.81
16.67
74.11
61.77
64.11
Ours*
92.54
57.50
35.86
81.23
66.78
89.89
59.18
30.42
73.35
63.21
64.66
Inputs
DreamO
Qwen
OmniGen2
Kontext
GPT-4o
Nano Banana
Ours
piselated
bouquett flowrrs in a your, with a donut and the table.
little zirl is sleting with a Sphyns cat, a dog and piggy bank.
Figure 4: Qualitative results on LAMICBench+++
2025b), DreamO (Mou et al.
2025). UNO WW et al. 2025c). Xerse (Chen al. 2025a) and MIP-
Adapter (Huang et al. 2024) to specifically assess identity preservation. For a cutting-edge bench- mark, we highlight the latest proprietary models, including Nano Banana (formally named Gemini 2.5 Flash Image, Google's latest multimodal model) (DeepMind) 2025) and GPT-4o-Image (OpenAl), as well leading open-source models like Owen-Image- Edit (Wu et al., 2025a) and FLUX.1-
Kontext (Labs et al. 2025
4.3 COMPARISON
Identity Preservation and Overall Quality Quantitative results on LAMICBench++ in Tab. show that our method excels object preservation and facial identity retention. In Fewer Subjects, we achieve the highest IPS with competitive IDS. This advantage amplifies in More Subjects, while other open-source models experience significant drops these metrics. Compared to closed-source models (GPT-4o and Nano Banana), show strategic trade-off: while slightly trailing in ITC and AES, we outperform them significantly in both IPS and IDS. This balanced performance yields our superior overall benchmark score (64.66 vs 63.71/64..1 I), demonstrating exceptional capability in preserving both objects and identities simultaneously.
Fig. 4 demonstrates our method's superior performance in preserving both content and style across diverse scenarios. Our approach consistently maintains accurate object relationships and fine details where other methods fail evident in the precise rendering of facial identities (old man's wrinkles),
7

---

Published as a conference paper at ICLR 2026
Table Quantitative results COCO-MIG and LayoutSAMM-Eall Image-guided methods (*) use our pre-generated images by FLUX.1-Dev (Labs 2024b).
COCO-MIG Results
LayoutSAM-Eval Results
Method
SR
I-SR
mloU
G-C
L-C
Spatial
Color
Texture
Shape
CLIP
Pick
GLIGEN
4.25
29.56
27.44
25.21
20.90
77.35
54.86
59.38
57.75
26.68
21.53
LAMIC*
1.25
13.56
21.17
21.82
18.71
77.27
69.04
69.96
68.74
23.49
21.91
MS-Diffusion
4.50
28.22
34.69
25.50
20.77
85.41
73.94
76.08
75.21
26.92
22.22
InstanceDiifrusion
23.00
60.28
54.79
25.77
21.91
86.39
71.39
76.73
75.37
26.36
20.96
3DIS
18.88
55.44
49.35
23.72
20.40
88.34
80.97
82.52
81,30
26.75
21.89
CreatiLayout
19.12
54.69
48.96
26.22
20.70
93.59
77.43
79.62
78.89
27.99
22.44
MIGC
27.75
66.44
56.96
26.21
21.47
86.04
71.07
74,88
73.37
25.50
21.10
EliGen
26.00
64,12
59.23
24.92
20.58
94.05
83.84
87.31
87.01
26.89
22.27
Ours
33.12
69.72
65.12
25.86
21.87
93.96
87.44
89.26
88.36
27.26
22.47
Layout
MS-Diffusion CreatiLayout
EliGen
Inst
3DIS
MIGC
Ours
COW
COW
COW
table sofa
Figure 5: Qualitative results on COCO-MIG. We use red dashed box to indicate the missing,
merged, dislocated and incorrectly attributed instances.
Layout
MIGC
3DIS
InstDiff
MS-Diffusion
EliGen
CreatiLayout
Ours
The sky is clear blue with few clouds drifling by... In the distance, some modern buildings can be seen. Phrases: Modern outdoor leisure area with palm trees, water feature, and string lights. 2) Tall, mujestic palm trees dominate scene. 3) serene pond surrounded by greenery. 4) Abundant, thriving green foliage.
Caption:
Caption: ... Two glasses with thick foam. placed metal rack, with of beer taps above Phrases: 1) glass filled with jumber-colored beer, topped with white foam. A beer glass with frothy head and "Radical
Beer for Radical People" sticker. 3) Beer bottles with detailed labels, blurred background. 4) bottle of beer with ivellow cap and dark liquid, accompanied by a foamy glass filled with the same beverage. 5) Two beer bottles with yellowi caps
Figure 6: Qualitative results on LayoutSAM-Eval.
object features (shape of the vase, appearance of piggy bank, color and texture of Sphynx cat). More qualitative results provided in Appx. D.I. Beyond fidelity, our model also exhibits high generative flexibility. A qualitative illustration of this adaptability is included Appx. B.4 showcasing the model's ability modify subjects' postures and attributes comply with complex interactions given by text prompts (e.g., inter-subject interactions or dynamic scenes), thus proving does not rigidly "transfer" the references.
Layout Control and Attribute Binding Tab. shows our method achieves superior layout control with the highest correctness on COCO-MIG. Direct comparison with text-guided L.2I is infeasible due to differing input modalities, yet our image- image-guided guided approach provides more detailed and robust attribute binding. Crucially, compared to existing image-guided techniques, we lead both layout fidelity and LayoutSAM-Eva color/texture accuracy.
8

---

Published as a conference paper at ICLR 2026
Qualitative analysis, presented in Fig. highlights two key capabilities of our method. First, approach effectively handles instance overlap, a common challenge for existing methods which often leads to attribute leakage instance missing/merginn Second, layout control, allowing it to synthesize a coherent and well-structured image from source images that may lack consistency. Additionally, demonstrated in Fig. 6 our method performs robustly on complex text prompts, accurately reflecting fine-grained textual details in the generated image while preserving precise layout control. More qualitative results are provided in Appx. D.2 and D.3
method exhibits superior spatial
4.4 ABLATION STUDY
Attention Mechanism Variations Across DiT- Blocks We perform an ablation study to inves- tigate the contribution of the ICA mechanism within our hierarchical attention architecture. We empirically divide the 57 DiT-blocks into three groups: FR-19 (first 19 blocks), MID-19 (middle 19 blocks), and BK-19 (last 19 blocks). The quantitative results on LAMICBench++ are summarized in Tab.
Table Ablation study on applying ICA to dif- ferent DiT-Blocks. F, M, B denote FR-19, MID- 19. BK- 19 blocks respectively. Gray line denotes the method w/o CLA.
F
M
ITC
AES
IPS
AVG
V
V
V
83.16
53.80
22.70
72.45
58.03
V
V
v
91.54
58.41
24.19
74.46
62.15
V
V
91.14
57.36
26.08
74.17
62.19
Crucially, the baseline configuration (gray line Tab. B that omits the CLA mechanism shows substantial decline in performance across all metrics, reinforcing the indispensability of CLA for effective multi-instance generation.
V
91.42
57.76
26.57
74.39
62.53
V
91.20
57.00
30.80
77.63
64.16
90.26
58.35
31.27
76.99
64.22
91.55
58.85
31.10
75.64
64.28
91.38
58.24
32.72
76.32
64.66
Prior work (Zhou et al. 20255) has demonstrated that MID-19 blocks have the most significant influence on instance-specifie attributes. In alignment with this finding, our experiments confirm that applying the ICA mechanism selectively to the MID-19 blocks yields the highest average score of 64. .66 and the best IDS score of 32.72. Furthermore, the results in Tab. 3 Suggest some potential functional focus across the FLUX-DiT layers, with detailed analysis provided in Appx. [B6
Importantly, while the value of the ICA may not be fully reflected by macro-level metrics alone, the ICA component is indispensable for preserving fine-grained identity details in scenes involving instance overlaps, as detailed in Appx. B.5
DPO Fine-tuning Analysis To mitigate the model's 's tendency to rigidly copy layout images while neglecting instance adaptation (e.g., pos-
Table 4: Ablation study on DPO 3.
DPO
ITC
AES
IDS
IPS
AVG
ture, lighting), we employ Direct Preference Op-
100
91.32
57.97
22.36
74.54
61.55
timization (DPO) (Rafailov et al. 2024), utiliz-
250
91.44
57.58
24.49
75.01
62.13
ing target images as preferred samples and lay- images as less preferred. We first conduct ablation study on LoRA Rank 256 to determine the optimal coefficient (results summarized in Tab. (4. The optimal 3 (1000) was subsequently applied our final DPO fine-tuning, which uses
500
91.33
57.57
25.01
74.92
62.21
750
91.13
57.22
25.89
75.45
62.42
1000
91.03
57.10
26.83
75.71
62.67
1500
90.35
56.69
26.88
75.91
62.45
w/o DPO
86.84
54.19
32.37
76.78
62.55
the higher capacity LoRA Rank 512. The qualitative validation for the final model is provided in Appx. B.3.3
Tab. and further studies in Appx. B.3 reveals three key findings:
Improved Composition: ITC and AES metrics consistently show improvement across tested values, demonstrating DPO's effectiveness in enhancing overall subject composition.
Controlled Trade-off:: Identity metrics (IDS and IPS) exhibit a slight and controlled decrease, with degradation scaling monotonically with the regularization strength B.
Optimal Configuration: The setting of B = 1000 yields the highest overall average (AVG 62.67). This configuration successfully navigates the adelity-flexiible trade-off, outperforming both the non-DPO baseline (62.55) and other tested 3 settings.
This validates DPO's ability navigate the adelity-flexibiii trade-off.

---

Published as a conference paper at ICLR 2026
5 CONCLUSION
In this work, we presented ContextGen, a novel Diffusion Transformer-base framework for highly controllable multi-instance generation. The foundation of our approach a unified contextual token sequence that integrates text, layout information, and multiple reference images, enabling com- prehensive understanding the generation task. To tackle the twin challenges of precise spatial control and identity preservation, we introduced two dedicated mechanisms: the Contextual Layout Anchoring (CLA), which effectively enforces robust spatial structure using a composite layout image, and the Identity Consistency Attention (ICA), which ensures fine-grained mastance-specific attribute preservation via a constrained attention strategy. Furthermore, our work involved an exploration of attention mechanism specialization across DiT layers, which informed the design of our hierarchical attention architecture. We also contributed the large-scale, hieearchically-struetie dataset, IMIG-100K, complete with detailed layout and identity annotations, accelerate future research in this domain. Extensive quantitative and qualitative evaluations confirm that ContextGen consistently outperforms state-of-te oottte----h--rrr models, validating the efficacy and robustness of our pro- posed mechanisms. We believe this work provides a robust and scalable foundation for highly customizable image generation systems.
ACKNOWLEDGMENTS
This work was supported by National Natural Science Foundation of China (U2336212) and the Fundamental Research Funds for the Central Universities (226-202500080).
REFERENCES
Fadi Boutros, Meiling Fang, Marcel Klemt, Biying Fu, and Naser Damer. Cr-fiqa: Face image quality assessment by learning sample relative classifiability. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5336-5 June 2023.
Bowen Chen, Mengyi Zhao, Haomiao Sun, Li Chen, Xu Wang, Kang Du, and Xinglong Wu. Xverse: Consistent multi-subject control of identity and semantic attributes via dit modulation. arXiv preprint arXiv:2506.2144 2025a.
Yuzhuo Chen, Zchua Ma, Jianhua Wang, Kai Kang, Shunyu Yao, and Weiming Zhang. Lamic: Layout-aware multi-image composition via scalability of multimodal diffusion transformer. arXiv preprint arXiv:2508.0044 004477, 2025b.
Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, and Inderjit Dhillon. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities, 2025. URL atttps://aree org/ //b///////// /2507
06261
Ming Dai, Wenxuan Cheng, Jiang jiang Liu, Sen Yang, Wenxiao Cai, Yanpeng Sun, and Wankou Yang. Deris: Decoupling perception and cognition for enhanced referring image segmentation through loopback synergy, 2025. URL //tpr//gs../..///////////////o//
org /ttp///wwww../////////.0.0000 01738
Google Deep.ind. Gemini 2.5 flash image: State-of-the-art image generation and editing model. Technical report, Google, 2025. URL /ttp:tttppp///ttrsee.goggggeee com/
deepmind ottp://e......rr.c....e2cenna...
For-
merly known as "Nano-Banaza
DeepSeek-Al. Deepseek-v3 technical report, 2025. 19437
2242
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hier- archical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, 2009. doi: N0.T109/CVPR.2000990 200.5500888..
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English,
10

---

Published as a conference paper at ICLR 2026
Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach. Scaling rectified flow trans- formers for high-resolution image synthesis, 2024. URL https://arxiv.ort hwtp//iiorr//aaaaaoa//aaa/ 3244 03206
2403
Jia Guo, Jiankang Deng, Alexandros Lattas, and Stefanos Zafeiriou. Sample and computation redistribution for efficient face detection. arXiv preprint arXiv:2105.04771 2021.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In
H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (eds.), Advances in Neural Information Processing Systems, volume 33, pp. 6840-6851. Curran Associates, Inc.,
2020. URL /ttpsss http://iiiinnnneerrisssoooo///aa haappperrrleeeeeeeeeeeeeeeeeeeee wwwpss/pppppee/e22.22. F111/4/0ccccc5331000771111111113
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang. and Weizhu Chen. Lora: Low-rank adaptation of large language models, 2021. URL https
Junjie Hu, Tianyang Han, Kai Ma, Jialin Gao, Hao Dou, Song Yang, Xianhua He, Jianhui Zhang, Junfeng Luo, Xiaoming Wei, and Wenqiang Zhang. Positionic: Unified position and identity consistency for image customization, 2025. URLLhtts://axxiv/ htt.:/orggaabb////eee
aba/aaass550 18661
Qihan Huang, Siming Fu, Jinlong Liu. Hao Jiang, Yipeng Yu, and Jie Song. Resolving multi- condition confusion for finetuning-free personalized image generation, 2024. URL https
//arxiv.erg/abss.
17920
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Pick- a-pic: An open dataset of user preferences for text-to-image generation, 2023. URL https
//axiiv. org/ abs 2305 01569
Black Forest Labs. Flux.1-fill-dv: Diffusion-based image inpainting model. https: hucgingface.
1at.aa///.....1111--00.-.---. 2024a. Accessed: 2025-
09-24.
Black Forest Labs. Flux.
2024b.
Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, Kyle Lacey, Yam Levi, Cheng Li, Dominik Lorenz, Jonas Müller, Dustin Podell, Robin Rombach, Harry Saini, Axel Sauer, and Luke Smith. Flux.1 kontext: Flow matching for in-context image generation and editing in latent space, 2025. btRb:ts:: arxiv. org abs 1
Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. CVPR, 2023.
Tsung-Y Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. Microsoft coco: Common objects in context, 2015. URL htttttt////aaattti org abs 1405. 0312
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang. Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al. Grounding dino: Marrying dino with grounded pre-trate te-ttrrining for open-set object detection. arXiv preprint wxXiv:2303.05499 2023.
Konstantin Mishchenko and Aaron Defazio. Prodigy: An expeditiously adaptive parameter- learner. In Forty-first International Conference on Machine Learning, 2024. URL https:/7
openreview www.rreview.nee//feee www.teeuu/u/r//ee dd==:]pssnOoup
Chong Mou, Yanze Wu, Wenxu Wu, Zinan Guo, Pengze Zhang, Yufeng Cheng, Yiming Luo, Fei Ding, Shiwen Zhang, Xinghui Li, al. Dreamo: A unified framework for image customization. arXiv preprint arXiv:2504.1691: 2025.
OpenAl. Addendum to gpt-4o system card: Native image generation. semanticscholast
URL https://api
OpenAl. Gpt-4o system card, 2024. URL https://arxiv.. /240 .12766
11

---

Published as a conference paper at ICLR 2026
Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo. Mare Szafraniee, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaacldin El-Nouby, Russell Howes, Po- Yao Huang, Hu Xu, Vasu Sharma, Shang- Beang-Wen Li, Wojeiech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision, 2023.
William Peehles and Saining Xie. Scalable diffusion models with transformers. arXiv preprint arXiv:2212.09748, 2022.
Yuang Peng, Yuxin Cui, Haomiao Tang, Zekun Qi, Rumpei Dong, Jing Bai. Chunrui Han, Zheng Ge, Xiangyu Zhang, and Shu-Tao Xia. Dreambench++: A human-aligned benchmark for personalized image generation. In The Thirteenth International Conference on Learning Representations, 2025.
q thub.
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model, 2024. URL
https:://arxi..or///abbb abwpg///b////a 100.231....10..0
Nikhila Ravi, Valentin Gabeur, Yuan- Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao- Yuan Wu, Ross Girshick, Piotr Dollár, and Christoph Feichtenhofer. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.000 00714, 2024. URL https://arxiv. org/ abs/ htwg////////////////////////
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684-10695, June 2022.
Christoph Schuhmann. predictor, 2023.
Improved aesthetic predictor: Clip+mlp aesthetic score https: com/ www.sstoosschhhaaa...
URL
improved- wwweredd-esstetii--peeettieeee
GitHub repository. Accessed: 2025-09-09.
Xiaolong Shen, Jianxin Ma, Chang Zhou, and Zongxin Yang. Controllable 3d face generation with conditional style code diffusion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 4811-4 2024.
Chaehun Shin, Jooyoung Choi, Heeseung Kim, and Sungroh Yoon. Large-scale text-to-image model with inpainting is zero-shot subject-driven image generator. arXiv preprint arXiv:2411.15466 2024.
Wensong Song, Hong Jiang, Zongxing Yang, Ruijie Quan, and Yi Yang. Insert anything: Image insertion via in-context editing in dit, arXiv preprint 15XX::2504.11009 2025.
stability.ai.
Stable diffusion
3.5.
news/
2024.
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding, 2023. URL /
09864
Zhenxiong Tan, Qiaochu Xue, Xingyi Yang, Songhua Liu, and Xinchao Wang. Ominicontro12: Efficient conditioning for diffusion transformers, 2025. URL 2503. 08280
Xierui Wang, Siming Fu, Qihan Huang, Wanggui He, and Hao Jiang. Mi-diffusion: Multi- subject zero-shot image personalization with layout guidance. In The Thirteenth International Conference on Learning Representations, 2025. id=BJgPOwyOek
wwweeeiiww....... ew net. ft//m/rrmmm
Xudong Wang. Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, and Ishan Misra. Instancediffu- sion: Instance-level control for image generation, 2024.
12

---

Published as a conference paper at ICLR 2026
Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, Yuxiang Chen, Zecheng Tang, Zekai Zhang, Zhengyi Wang, An Yang. Bowen Yu, Chen Cheng, Dayiheng Liu, Deqing Li, Hang Zhang, Hao Meng, Hu Wei, Jingyuan Ni, Kai Chen, Kuan Cao, Liang Peng, Lin Qu, Minggang Wu, Peng Wang, Shuting Yu, Tingkun Wen, Wensen Feng, Xiaoxiao Xu, Yi Wang, Yichang Zhang. Yonggiang Zhu, Yujia Wu, Yuxuan org/abs
Cai, and Zenan Liu. Qwen-image technical report, 2025a. 2508. 02324
Chenyuan Wu, Pengfei Zheng. Ruiran Yan, Shitao Xiao, Xin Luo, Yueze Wang, Wanli Li, Xiyan Jiang, Yexin Liu, Junjie Zhou, Ze Liu, Ziyi Xia, Chaofan Li, Haoge Deng, Jiahao Wang, Kun Luo, Bo Zhang, Defu Lian, Xinlong Wang. Zhongyuan Wang, Tiejun Huang, and Zheng Liu. Omnigen2: Exploration to advanced multimodal generation. arXiv preprint arXiv:2506. 18871, 2025b.
Shaojin Wu, Mengqi Huang, Wenxu Wu, Yufeng Cheng, Fei Ding, and Qian He. Less-to Less-to-more general- ization: Unlocking more controllability by in-context generation, arXiv preprint arX1vv2504.021660 2025c.
Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xingrun Xing, Ruiran Yan, Chaofan Li, Shuting Wang. Tiejun Huang, and Zheng Liu. Omnigen: Unified image generation, 2024a. URL
mittps://ett //axxiv/aivvv....
org/ awtp//a///// 2409. 11340
Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xingrun Xing, Ruiran Yan, Shuting Wang, Tiejun Huang, and Zheng Liu. Omnigen: Unified image generation. arXiv preprint
Mxii::4409,11444 2024b.
Tao Xin. Facexlib: Ready-to-use face-related functions current stoa open-source methods.
https://githun: http://hwi....ooiitttttttoeeee/e wwinttaoofaceeiiii.i.ea 2022. access time: 2025-11-15.
Yunqiu Xu, Linchao Zhu, and Yi Yang. Gg-editor: Locally editing 3d avatars with multimodal large language model guidance. In Proceedings of the 32nd ACM International Conference on Multimedia, pp. 10910-10915 2024.
Yunqiu Xu, Linchao Zhu, and Yi Yang. Mc-bench: benchmark for multi-context visual grounding in the era of milms. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 17675-17687 2025.
Yi Yang, Jingkuan Song, Zi Huang, Zhigang Ma, Nicu Sebe, and Alexander G. Hauptmann. Multi-feature fusion via hierarchical regression for multimedia analysis. IEEE Transactions on Multimedia, 15(3):572-581, 2013. doi: 10.1 (0.1009/MM.2201..222.52
Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. Minicpm-v: A gpt-4v level milm on your phone. arXiv preprint arXiv:2408.00 01 01000 2024.
Jiabo Ye, Haiyang Xu, Haowei Liu, Anwen Hu, Ming Yan, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou, mplug-ow13: Towards long image-sequence understanding multi-modal large language models, 2024. URL httpstt:::// arxiv. org/ abs 2408 04840
Hong Zhang, Zhongjie Duan, Xingjun Wang, Yingda Chen, and Yu Zhang. Eligen: Entity-level controlled image generation with regional attention, 2025a. URL http:tts//a.xx...rr////// 2501. 01097
Hui Zhang, Dexiang Hong, Tingwei Gao, Yitong Wang, Jie Shao, Xinglong Wu, Zuxuan Wu, and Yu- Gang Jiang. Creatilayout: Siamese multimodal diffusion transformer for creative layout-to-image generation. arXiv preprint arXiv:2412.03859, 2024.
Zechuan Zhang, Zhenyuan Chen, Zongxin Yang, and Yi Yang. Are image-to-video models good zero-shot image editors?, 2025b. URL /ttpp::////ttt http:://axxvvv.. www.wwwwwwwww...//oo//.... 19945
Zechuan Zhang, Ji Xie, Yu Lu, Zongxin Yang, and Yi Yang. In-context edit: Enabling instructional image editing with in context generation in large scale diffusion transformer, 2025c. URL https?
7/ rxxiv wwt.oor/abbbs/0//// 20690
13

---

Published as a conference paper at ICLR 2026
Dewei Zhou, You Li, Fan Ma, Zongxin Yang, and Yi Yang. Mige+:: Advanced multi innti-instaane generation controller for image synthesis, 2024a. URL http::t/axxx...oo// wwwpaiiii...raaab.ooo abs 2407 02329
Dewei Zhou, You Li, Fan Ma, Xiaoting Zhang, and Yi Yang. Mige: Multi-instance generation controller for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 0818-6822 2024b.
Dewei Zhou, Ji Xie, Zongxin Yang, and Yi Yang. 3dis: Depth- driven decoupled instance synthesis for text-to-image generation. arXiv preprint arXiv:2410. 12669, 2024c.
Dewei Zhou, Mingwei Li, Zongxin Yang, Yu Lu, Yunqiu Xu, Zhizhong Wang, Zeyi Huang, and Yi Yang. Bidedpo: Conditional image generation with simultaneous text and condition alignment, 2025a. URL/tttps: // www.aaxivvvv.........aa..a. abs 2511. 19268
Dewei Zhou, Mingwei Li, Zongxin Yang, and Yi Yang. Dreamrenderer: Taming multi-instance attribute control in large-scale text-to-image models, 2025b. URL
org/
abs/2503. 12885
14

---

Published as a conference paper at ICLR 2026
A MORE IMPLEMENTATION DETAILS
A.1 BASE MODEL SELECTION
In our framework. different diffusion backbone models exhibit variations under iie-tuning-ffe -ree settings for achieving high-fidelity multi-instance generation. We evaluated three variants of the FLUUX family of models as potential backbones: FL.UX.1-Dee (a general image generation model) (Labs) 2024b), FLUX. 1-Fill (a local inpainting model) (Labs 2024a), and FLUX. I-Kontext (an editing generation methods without layout control (Wu al. 2025c] Hu et al. 2025) without attention masks have successfully utilized FLUX.1- Dev, our experiments showed a significant limitation: without additional tun-ttnnng, FLUX.1-DD 1-Dev failed produce coherent images when an attention mask was applied. In contrast, both FLUX.I-FiN and --Koneet demonstrated the ability to generate images correctly with the attention mask. Among these two, FL.UX.I-Kontee 1-Kontext exhibited noticeably superior capacity for identity preservation. Therefore, we chose FLUX.I-KAN I-Knteext as the backbone, as it fits better with our framework's 's attention- masking strategies and identity preservation goals. To isolate the impact of the backbone, we re-implemented a baseline method MS-Diffusion on FLUX. 1 --ontext backbone for ablation studies in Appx. [B.7] And we conducted qualitative comparisons with other state-of-the-arr methods built upon equivalent FLUX-Family backbones in Appx. D.I
model) (Labs et al. 2025). While existing muuli-suuject-drie
A.2 DETAILS OF COMPOSITING LAYOUT IMAGE
Our Contextual Layout Anchoring (CLA) mechanism relies meticulously constructed composite layout image to achieve robust spatial control. The design of this mechanism is conceptually inspired by the principles of multi-feature learning (Yang et al. 2013 which suggests that combining evidence from heterogeneous feature representations can lead to more robust semantic understanding. This process involves two key steps: determining the optimal composition order for all instances and then precisely placing each instance onto the canvas.
correct composition order is crucial for multi-instance synthesis, especially when handling oc- clusions and complex overlaps. We propose a dynamic sorting algorithm, Instance Layering Prioritization, which first handles explicit containment relationships by prioritizing instances whose masks are completely contained within another's. s. For all other candidate instances, we use hybrid priority scoring system to simulate the natural layering of objects. We utilize a pre-processing step to obtain each instance's 's precise effective area (Ravi et al. 2024). The priority score P, is calculated as:
Pj = a A(iintaacce) +3. (1- - (inttattanee,, instancee,) + A RandomFactor, JØ
where A(instance;) is the area instance i, (instta,ee,, instance,) is the Intersection over Union between instances, and a, B, À are hyperparameters.
The proposed hybrid priority scoring system is designed to simulate general, high-probability compo- sition orders for model training. The introduction of the random factor enhances data diversity and model robustness during training. Despite the Identity Consistency Anchoring (ICA) mechanism, the overlap relationships can still influence the final generated image. Thus, for inference, user-provided layout offers a more direct and customized form of control.
B MORE DETAILS AND ANALYSIS ON EXPERIMENTS
B. EXPERIMENTAL DETAILS ON LAMICBENCI++
As shown in Tab. I we benchmark our method against several strong baselines, including single- image-editing models (Wu et al. 2025a Labs et al. 2025) and closed-soures commercial mod- els (OpenAl 2024 Deep 2025). Since these two categories models require different input modalities and instruction methods, we prepared distinct inputs for fair evaluation:
15

---

Published as a conference paper at ICLR 2026
Table 5: Ablation study on different position indexing strategies.
Position Indexing Strategies
ITC
AES
IDS
IPS
AVG
w/o x and offsets
90.53
57.92
21.54
74.57
61.14
w/o y offsets
90.22
57.60
21.01
74.02
60.71
w/o x offsets
91.53
57.87
26.71
74.59
62.67
w/o editing token
90.95
58.11
32.64
75.28
64.25
w/ offsets and editing token
91.38
58.24
32.72
76.32
64.66
Lingle-Image-Edieee Models These models primarily designed for single-image editing, meaning they must process all instances combined within single input image. We thus provided our manually composited layout images, ensuring minimal overlap across instances to avoid ambiguity. The following prompt template was used: "Use the objects or humans in the image to create new image that shows (PROMPTJ'. Preserve object features and human identities (if any, including facial details). You may fill in the background with appropriate details to achieve a natural and aesthetically harmonious result."
Closed-Source Commercial Models These are general-purpose multi-modal models that accept multiple input images. To guide them to perform the specific preserving identities, relied on a dedicated prompt alongside the references. The prompt template was: "Generate a high-quality image of "PRRMPPY'. Use the provided references, preserve object features and human identities (if any, including facial details)."
generation task while
B.2 ABLATION ON POSITION INDEXING
As specified in Sec. 3.2 we extend the position indexing strategy from prior work (Wu et al., 2025c which uses non-overlapping indices for explicit spatial delineation of objects, crucial for multi- instance distinction. The ablation results on LAMICBench++ are presented in Tab. 5 Our findings validate the importance of the positional encoding components:
The Necessity of Explicit Spatial Separation: The results strongly confirm the importance of explicit spatial separation achieved via x and y offsets. Removing any offset component (e.g.. w/o x and y offsets) significantly degrades all metrics, confirming the necessity of these offsets for the non-overlapping indexing mechanism. Its superiority is evidenced by the large margin observed in the IDS, suggesting better token distinction across different reference images in token sequence.
Role of Editing Token: Our complete strategy includes "editing token" (setting the first component the position index triplet to 1). The ablation w/o editing token (setting it to 0) shows minimal performance difference in the final metrics. However, omitting this token actually resulted in a certain degree of gradient instability during the initial phase of training, which justifies its inclusion for a more stable optimization process.
B.3 DETAILS OF DPO FINE-TUNING
B.3.1 QUALITATIVE RESULTS OF DPO FINE-TUNING PROCESS
The visualization in Fig. 7 illustrates the efficacy of Direct Preference Optimization (DPO) in enhancing image generation, particularly by mitigating the issue of rigidly copying the layout image with blank backgrounds. To demonstrate this, we intentionally select an input and seed configuration that typically results in minimal background scene.
The initial result on the left of Fig. 7 correctly anchors the main subject but suffers from the blank background issue, failing to render any environmental context. As the fine-tuning process advances, the model begins to introduce more naturalistic details, first by generating a realistic shadow and subsequently by adding simple yet coherent background. Upon convergence, the final image the right features a rich, detailed background, effectively demonstrating DPO's ability to enrich the overall scene while strictly preserving the subject's 's layout.
Fig. shoss how the DPO 3 parameter affects the generation quality. A high 3 value may limit the model's ss capacity for meaningful tuning, whereas overly low & value can cause the model to
16

---

Published as a conference paper at ICLR 2026
follow the preference data too aggressively, risking loss of the subject's identity during convergence (as seen with the leather bag when = 50). Our results validate that when 3 is correctly calibrated, DPO substantially improves image quality with minimal compromise to the subject's identity.
Step 0
DPO Fine-tuning Process
Results during the DPO fine-tuning process 100, Rank 256).
1500
1000
750
500
250
100
50
DPO BL
(b) Results the same input and fixed seed DPO fine-tuning (Rank 256).
Figure 7: Results for the same input and fixed seed of DPO fine-tuning.
B.3.2 IMAGE QUALITY ASSESSMENT ON DPO FINE-TUNING
In order fully assess the impact by DPO fine-tuning on image quality, we report the quality assessment results in Tab. 6 However, the FID score on LayoutSAM-Eval may not be highly referential, not only because FID less sensitive fine-grained image details, but also because the calculation process necessitates severe distortion by resizing the structurally diverse ground-truth images of this benchmark, thus failing to reflect the objective quality of the generated outputs. As shown in Fig. [ the model with the best FID score LayoutSAM-Evai exhibits less satisfactory overall visual effect, specifically in background richness, light and shadow rendering, and fine detail processing, compared the results obtained using a smaller DPO B value.
To better assess the overall visual quality and fidelity of the generated images, despite the metrics AES and FID score from the benchmarks, conducted sets of inputs and seeds from each of the two benchmarks (LAMiCBencc++ and LayoutSAM-Evee For each sampled set, generated results using 6 different DPO B's, alongside result from the model without DPO fine-tuning. This yielded total of 7 images for comparison per set. 10 reviewers were asked select two images with the best quality and two images with the worst quality in each group of images. Each "best quality" selection added one point to image's score, while each "worst quality" selection deducted one point. The results are normalized and recorded at User Preference metric in Tab. [ The comparison results further demonstrate that DPO fine-tuning will enhance image quality, and a smaller 3 (a more aggressive DPO) generally leads to better quality.
study. In this user study, sampled 20
Table Image quality assessment results on benchmarks and user study.
DPO 8
AES
FID
User Preference
100
57.97
55.97
0.54
250
57.58
55.60
0.37
500
57.57
55.32
0.16
750
57.22
55.53
0.03
1000
57.10
55.65
-0.11
1500
56.69
55.70
-0.43
w/o DPO
54.19
55.93
-0.56
B.3.3 QUALITATIVE VALIDATION ON FINAL MODEL.
Following the ablation study in Tab. 4 we applied the optimal DPO setting (8 = 1000) to our
final ContextGen model, which utilizes LoRA Rank 512. The qualitative results presented in Fig.
17

---

Published as a conference paper at ICLR 2026
Figure 8: Qualitative Comparison: DPO 3 500 (FID-Optimal) vs. DPO 3 = 100. The comparison showcases the model fine-tuned with the FID-optimal setting (DPO 3 500, upper panel) versus a more aggressive setting (3 = 100, lower panel). All corresponding images were generated with identical inputs and random seeds. The columns specifically grouped demonstrate the quality differences: Columns 1-3 highlight background richness; Columns 4-5 illustrate light-and- shadow rendering; and the final two samples reveal differences in fine identity details. Zoom for more details.
Figure 9: Qualitative comparison final Model (Rank = 512): before vs. after DPO Fine- tuning. The upper panel shows images generated before DPO fine-tuning, and the lower panel shows the results after. All corresponding images were produced using the same random seed.
strongly validate that DPO fine-tuning significantly enhances the visual quality and overall fidelity of the outputs from this high-rank configuration. This enhancement is particularly evident in several key aspects: increased richness in background details, improved flexibility in instance composition and more natural rendering of light and shadow.
B.4 RESULTS AND ANALYSIS ON MODEL'S GENERATION FLEXIBILITY
The model's final output represents a crucial trade-off between Fidelity (adherence to reference image identities and given layout) and Flexibility (the ability to incorporate text descriptions, especially for inter-subject interactions). The richness of input text prompt significantly modulates this balance. When simple minimal prompt is provided, the model inherently prioritizes high fidelity, leaning towards preserving the exact details and posture defined by the reference images. Conversely, by enriching the prompt with complex descriptions and detailed interactions between subjects, the model will exhibit greater flexibility in the generation results.
Fig. 10 demonstrates how prompt complexity influences the generation outcome while the layout image remains constant. For instance, in the first panel of Fig. [0] when the action is described as "having a conversation". the model exhibits flexibility by removing the sword from the pixelated figure, a detail conflicting with the context. Furthermore, when the prompt is "in fierce dual", the figure's 's posture is greatly modified align with the dynamic description.
B.5 ICA: AN INDISPENSABLE COMPONENT HANDLING IDENTITY IN OVERLAPPED REGIONS
Although a pure CLA method (using only a layout image as contextual input) exhibits scores similar strategies with ICA in the aggregated results of Tab. 3 the critical necessity of the ICA component
18

---

Published as a conference paper at ICLR 2026
References
Layout Image
Results with different prompts
Having pleasant conversation
Clashing blades in fierce duel
Moets
Eyes fixed tenderly on the dog and stroke its fur
Intently studying the piggy
Sits together
Figure 10: Flexible compositions with different prompts.
Inputs
w/ ICA
w/o ICA
Layout 1
man wearing headphones, with his head rilted the side. The logo on the headphones clearly visible.
Layout
Details
Figure 11: Results of overlapping layout on methods with and without ICA.
explicitly demonstrated when handling layouts with overlaps. For generating layout involving overlapping instances, ICA is critical because the reference images in the token sequence provide extra identity information for the regions being occluded by other instances, mitigating identity loss in these overlapping areas.
scenarios: For Layout subtle details like the red logo the headphones are successfully preserved by the method with ICA but completely abandoned by the method without ICA. Conversely, in the occluded scene of Layout 2, the ICA method better retains fine-grained facial identity fold compeed to the non-ICA method, which loses these crucial details.
Fig. II illustrates this effect
as the color of the eyes and the presence of the nasolabial
However, these overlapping cases are relatively rare in the current benchmark, and the differences on the image lead to limited overall impact on the macro-level evaluation indicators. But these qualita- tive results demonstrate that ICA is indispensable component for ensuring identity preservation in such cases, And its absence significantly impacts the final image's visual quality and detailed fidelity.
B.6 ANALYSIS ON LAYER-WISE ICA ABLATION
As shown in Tab. 3 applying ICA to different layers (grouped by First-19, Mid-19 and Back- 19) reveals an interesting result. Our ablation study revealed a gap between two distinct sets of strategies: the first set, comprising {F + MM + B, F M, B}. consistently yielded lower scores, while the second set, {F, w/o CLA, M B, M). achieved closely clustered higher scores. This result suggests the existence of intrinsic properties within the FLUX-DiT layers, indicating potential functional difference where performance is sensitive to the placement of attention mechanism.
F-19: Strategy to Minimize Modality Interference. Prior work CreatiLayout (Zhang et al. 2024) extending the F-19 blocks has shown powerful control in both spatial and attribute. And as
19

---

Published as a conference paper at ICLR 2026
stated this work, no further modification is made to the other blocks (Single Stream Blocks, i..., Mid-19 and Back- 19 blocks) in order to reduce the potential interference among modalities (i.e., text, layout, and image). It does make sense in considering the Front-19 blocks contains huge amount of parameters compared to the others. Adding ICA to these blocks is sufficient, while applying ICA more blocks lead some side effect like identity distortions. This aligns with observation that F' + M + B and F' + B strategies underperform others.
M-19: Critical Role in Attribute Binding. Prior work DreamRenderer (Zhou et al. 2025b demonstrates through ablation study that Mid-19 blocks play a relatively more critical role in attribute binding. Our ablation study validates the finding, for applying ICA to Mid-19 blocks yields the best performance in our task on benchmark score.
F-19 Protection and Similar Performance. The four strategies, F, M, M + B, and no ICA, yield relatively close performance scores because they all avoid posing further disruptions to the Front-19 19 blocks. And we can derive from this result that these four strategies do not have very significant difference in overall performance.
B-19: Detrimental Impact on Final Refinement. Conversely, applying ICA solely to the Back- 19 blocks proves detrimental to performance, which is also aligned with the ablation study in DreamRenderer. For the Back-19 blocks serve as final and sensitive post-processing layers in each noise prediction step. Introducing attention mask exclusively at this late stage without any modifications the earlier blocks will affect the refinement process, leading to problems like artifacts and blurring identity.
We choose Mid-19 from the last four strategies with very close score for the following reasons:
Applying ICA the Mid-19 blocks demonstrates advantage LAMICBench++ average score over other strategies.
Performance:
Indispensability of ICA: The analysis in Appx. B.5 has revealed the indispensability of ICA.
Efficiency: Considering that the computational cost of applying the attention mechanism scales with both the number of blocks and the block capacity (such as F-19 blocks with much denser parameters), we selected the Mid- 19 configuration for a relatively efficient approach achieve better performance.
B.7 MS-FLUX: A CONTROLLED CASE STUDY OF STRATEGY FAILURE ON STRONG BACKBONE
Previous image generation methods based on older diffusion models (e.g., Stable Diffusion) have shown considerable improvements over their base architectures. For instance, MS-Diffusion (Wang al. 2025), built on SDXL, achieved image-guided layout control via its Grounding Resampler mechanism based on SD-Unet cross attention architecture. This Resampler fuses reference im- age content with explicit spatial and phrase information into multi-modal prompt tokens to guide generation.
To quantify the contribution of ContextGen mechanisms and assess their synergistic fit with the FLUXX backbone, we implement a competitor MS-FLUX. This implementation adapts a similar Grounding Resampler architecture onto the FLUX-DiT. We fine-tuned MS-FLUX with all the Grounding Resampler's 's parameters trained and LoRA Adapter (R=512) applied to the DiT blocks. After 50K training steps on IMIG-100K, we evaluate this model on COCO-MIG and LAMICBench+# for comparison, the results of which are presented in Tab. 7 This result demonstrates severely poor identity preservation coupled with complete degradation of layout control of MS-FLUX. The method of integrating reference images, phrases and layout information together text-prompt-like token sequence, which works on SD-Unee, works poorly on FLUX-DIT. In summary, this confirms that despite the strong contextual capability of FLUX. 1-Kontext (FLUX-DiT) over old backbones, not all methods (even those that seem reasonable in principle or already worked on previous backbones, just like this Grounding Resampler in MS-D MS-Diffusion) will work FLUX backbone. This further suggests that our specialized mechanisms provide a more correct design for achieving better layout the FLUX backbone.
control and ceenity-consistenny
To ensure complete transparency regarding our comparative experiments and to fully document the controlled competitor, we include the implementation code details of MS-FLUX below. The code
20

---

Published as a conference paper at ICLR 2026
Table 7: Quantitative result of MS-FLUX on key metires of LAMICBench++ and COCO-MIG.
Method
SR
I-SR
mloU
IDS
IPS
MS-FLUX
0.38
7.3
18.71
3,20
60.22
MS-Diffusion
4.50
28.22
34.69
9.06
69.75
Ours
33.12
69.72
65.12
32.72
76.32
involves three core stages: Resampler initialization, multimodal input encoding, and feature injection into the Transformer.
Resampler Initialization and Input Preparation. The Grounding ng Resampler initialized within custom 1 ghtni inModd e to match the dimensions FLUU Transformer. During the training step, image, layout, and phrase information are prepared.
Initialization (in the IigntningModule's setup method) from -projection import Resampler
self. gea..grruuddngrrresmpeerr Resampler (
dim=1280,
depth=4,
din_head=64,
heads=20,
nun_queries=512,
mmbeeddigg_dim=self...m
outpout_dimmsllFFFoox. pe. ot fet_fauurrss
ff_mult=4,
latent_ini__moe==grooe ng",
seraase_mbbeddings.
wtw.beddingg.diissi....... dww.seif..e.ecetww flux pipe. wtw.eeee..o.....oooee.........e dim,
toto (self. dtype)
self. www.gggddnggreeanngnee www.eempeeerrrruueeeeeeeeee requireeuuurereee (True) train
Encoding Reference and Spatial Information. The weteeeerefeeeeeeeeeeeeeeeeeeeeee function encodes the reference images into latents. Concurrentyy, instance phrases and bounding boxes encoded and assembled as "Grounding Keywords".
Preparing inputs within the Custom Lightning Model'a step () method
I 1. Extract and normalize boxes
phrases = [inst ["phrase"] for inst in cistance_info(( boxes [inst ["bbox"] for inst in instance info[0]] instance_info(0]] boxes torch.stack (boxes) (eseef.ddiiiie boxes boxes / torch. tensor ([width, height, width, height), cal.cosseff.eviice
self.target_dtypp
tyypo=aelf.target_dtt
# 2. Encode Instance Phrases using FLUX's s text encoder
phrase_input_ids = []
for phrase in phrases:
.... (Tokenizer call)
phrase iepee_nput__iid app.dd (npptsseeiauuutt...tpppp input_iii id)
phrase_input _ds ids torch (phrase input_ids ids)
phrase_input_ids phrase_input_ion www.itiissiiiiiieeedd....
view phrase_input_ids. shappp
trrasseembedds - felf. text_encoder (phrase_input_ide. (tt.l....d..otl..eee.... pooler_cutput
# 3. Assemble Grounding Keywords
grounding_kwarge =
"boxes": boxes,
"phrase_embeds" phrase_embeds. "drop_groundinggtoeen [0],
4. Prepare reference image latenta
21

---

Published as a conference paper at ICLR 2026
reference info dict prepare reference latents
(calling prepare_referrnce__aaee encodes the reference Images into latents)
instance_latenta reference_infe innf_diictttiiiiii ["inssttnn ["iisstace_lattetaaa [0] instance_ latentss torch. (instance latents, dim=0)
Resampler Processing and Feature Injection. The Instans (visual features) and grounding__warrd (spatial and phrase features) are passed the Resampler. The Resampler's out- put then concatenated into the token sequence input FLUX Transformer layer.
latents
# Calling the Grounding Resampler
img wwwppppotpppt.e.e.. wwwppp_tiideeenset____eee
hidden statts self. wwwoonniingreeannneeeeeeeee
x=instance latents, Lateetss,, grounding wow.a//u.wwwwag.ssrrouuoou.nigg kwargss
wwg_ppromp__hiddensstteeess hmn_proomps_hidde_sttttees reshape (bs, -1, img
imn_prront__hdden_sttttt shape [
Injecting the Resampler output into the custom Transformer forward pass
transfozmer_out
self=self.fluse ses.rssdf.fuuupppppp.taaeeeeeeee
hidden_statte==x_tt
.... (standard FL.X arguments)
# <-- Resamplee
Output Injection
txt ids=text_idss _ds,
img ids=ing_ide
img wwwppppoopiiieeeee sisetttdddiimmg_em pronittgppropp....m ids, beint_attenttio__aarrsse return_dict-False,
Context Aggregation. In FLuxTransformer22bee foroaaodeloooowrdddeeeeoo the Resampler's output _ittddd_dttttsss))
Borgg_preepp____ pttpppppttttteeeen_ee__ee hidden states) represents fused image, bounding box, and phrase information. This feature vector concatenated with the standard text seaccoddr__iddee_eeee to form a unified, comprchensive context for the Transformer.
Context Aggregation Logic (inaide the custom Pluutraassforree22Meeeffffo
# 1. Process standard text tokens
encoder Sellddnn_statee se_f.contert_eneedeer rendodd hidden states)
2. Integrate the MS-FLUX Resampler Output
torch. cat
ing_prroms__hidde__staeeee
dim-1)
# 3. Update corresponding position indices
ids = torch. (txt_ids, ids, ing_pprmpt__ds ims_ids), dim=0) (ids)
image_rotary_emb
(Encoder hidden states are then passed to the double transformer blocks)
Inference Time Injection. During inference, the pre-caleulated Resampler output consistently passed into the Transformer every denoising step.
Inference logic in the custom pipeline forward function
se_e
22

---

Published as a conference paper at ICLR 2026
(Standard Transforme: call if reference_ is None)
if reference_dict None
else FLuxtrannffomer72bbbboLLooooa
solf=scof..cransfommee
# <- Inference Injection
timestep=timestep / 1000,
(other arguments)
return _ettrn_diit-taaeeeeeeeeeeett dict=False,
} [0]
C MORE DETAILS ON MULTI-INSTANCE DATASET
C.I BRIEF SURVEY AND DISCUSSION ON EXISTING MULTI-INSTANCE DATASETS
Various approaches have been adopted to construct multi-instance datasets. Prior works like OmniGen series (Wu et al. 20256 Xiao et al. 2024b) and MS-Diffusion (Wang et al. 2025) use a cross- verification method match and pair group images and corresponding individual images from web images or video frames. And some methods begin to leverage synthetic data for training. For example, UNO (Wu et al. 2025c) employs co-evolutionary data generation and training process using the model itself, and XVerse (Chen et al., 2025a) add data generated by FLUX.1-De as supplementary high-aesthetic- quality images.
However, the existing datasets contain some limitations as robust training resources for spatial-aware multi-instance generation.
Insufficient Instance Count and Lack of Layout Annotations. Though datasets from general- purposed generation methods or these above mentioned subject-driven works often exhibit a relatively high overall quality, identity consistency and large overall scale, the data with more than 3 subjects is rather limited. Critically, none of these datasets provide detailed spatial or layout annotations.
Restricted Subject Variation between Reference and Target. MS-Diffusion's dataset, while featuring more subjects per image and layout annotations, has a crucial limitation mentioned in the original paper: the low identity matching rate during cross-verification process across the video frames often necessitates using parts of the ground-truth image directly as reference image, which will limit the necessary variations and meaningful transform between the subjects in reference and target.
To address these limitations, we propose the pipeline in Sec. 3.3 to construct our own IMIG-100K dataset. We choose the synthetic data for the following reasons:
Real-World Data Scaling Challenges. The collection and matching process in real-worke data is relatively less ficxible and more difficult to scale in both diversity and quantity.
Successful Precedent Synthetic Data Usage. The successful practice previous work, such as UNO, has demonstrated the effectiveness of evolving synthetic data in training robust subject-driven generation models.
C.2 MORE DETAILS OF IMIG- 100K DATASET
Sec. 3.3 provides general introduction the synthesis and architectures of our dataset. More details and samples of our IMIG-100K Dataset are presented as follows.
Data Diversity We have designed multiple prompt generation templates to include more scenes and styles. These templates randomly combined and fed to LLMs maximize the diversity of the resulting image generation prompts. We also use different LLMs (i.e. DeepSeck v3 (DeepSeck-AI) 2025), Gemini 2.5 Flash (DeepMind) 2025) and GPT-4o (OpenAl 2024h) for lexical and stylistic
23

---

Published as a conference paper at ICLR 2026
Realistic
Fantasy
Cyberpunk
Illustrative
Anime
Figure 12: Samples with multiple styles in IMIG Dataset.
variations. Besides the realistic style (natural style), illustrated in Fig. 12] also include extra styles like fantasy and anime, which account for more user scenarios. The proportion of natural scenes remains high, accounting for approximately 85% to 90% of the total data.
Detailed Spatial Annotations Every instance's is meticulously annotated with detailed spatial information, including the instance phrase, its valid area's bounding box and mask, and its correspond- ing position within the ground image. The third subset of HUGG-100K---
flexible composite
featufeatures more detailed annotations. Recognizing that many user scenarios only provide a facial
avatar instead of half or full-body image, we use face-related tools (Xin) 2022 Guo et al. 2021) to restore and standardize the faces in reference images into aligned facial images, shown in Fig. 14 Face pairs in the ground-truth images (composited images) and corresponding reference images strictly filtered by face quality and similarity. Crucially, the positions of valid faces are annotated (indicated by the green round bounding boxes in Fig. across composited images, corresponding reference images and aligned face images. This enables a precise position and scale alignment when positioning and sizing reference instances in canvas in training process, instead of relying very rough bounding box of the entire instance.
Overall Quality Our dataset has undergone strict quality assessment (Kirstain et al. 2023 Boutros et al., 2023) for both reference images and ground-truth images. And we leverage MLLMs to ensure the semantic consistency between reference images and generated captions. This aligns with recent efforts in multi-context understanding (Xu et al. (025) and multimodal condition alignment (Zhou al. (222aa), which both emphasize maintaining logical and semantic integrity across complex visual inputs. Additional samples illustrating the quality and annotation detail are provided in Figs. 13 and
D MORE RESULTS AND DEMOS
D.1 QUALITATIVE RESULTS COMPARING ACROSS FLUX-BASED
BASED METHODS
Several FLUX-based methods included in comparison LAMICBench+#: vanilla FLUX.1- Kontext, LAMIC (based on FLUX..1 Konttee), UNO and DreamO (both based on FLUX.. FLUV..-Dev). Given that these competitors and our method base highly similar (or identical) foundational backbones, we provide additional qualitative results in Figs. 15 and 16 for direct and fair visual comparison.
D.2 MORE RESULTS COCO-MIG
Full quantitative result COCO-MIG benchmark is shown in Tab Our method establishes new state-of-the-art on all key metrics, achieving the highest average Success Rate (2%) and mloU (65))2%) among all compared methods. Notably, in complex, high-instance--ome couttannee-cc scenarios. For L,, Ls. and Lg levels. our method significantly outperforms all baselines with Success Rate of 28.12% 23.12%. and 24.38 %. respectively. This demonstrates the robust scalability of our tierarchical architecture to maintain both layout and identity control in intricate scenes. While competitors show slightly higher scores individual metrics
performance advantage is most pronounced
24

---

Published as a conference paper at ICLR 2026
(a) Samples basic part
(b) Samples complex part
Figure 13: Samples of the basic and complex part of IMIG-100K.
Annotated Ground-Truth Images
Instances
w/ ID
Aligned Faces
Instances w/o ID
Figure 14: Samples of the flexible composite part of IMIG-100K. The faces are annotated in green round bounding boxes.
25

---

Published as a conference paper at ICLR 2026
Inputs
LAMIC
DreamO
UNO
Kontext
Ours
basketball front of vintage television.
A dog chasing butterfly in the garden.
A piggy bank is on polished table, and Rolls-Royce hood ornament also the polished table.
A little girl playing an figurine.
A boy is discovering ring in the forest.
A woman is playing with dog.
An old meets pixelated warrior. Figure 15: Qualitative results across FLUX-based methods on LAMICBench++.
26

---

Published as a conference paper at ICLR 2026
Inputs
LAMIC
DreamO
UNO
Kontext
Ours
A motorcycle is parked next to house, with a roll of film the ground.
lion is sitting an armehair, watching vintage computer.
A little girl standing beach, holding teapot the left hand, A flamingo float is on her right.
BUID
LIGHT
man is wearing headphones, listening music, with a beer can on table.
A little girl sitting with Sphynx cat, dog and piggy bank,
An old man with sweater is playing guitar. pineapple, and cherry blossoms tree are nearby.
An old is sitting in armchair, watching tiger vintage television, while cat is sleeping on his lap. A vase with flowers is on table beside him, and a rubber duck is on the floor. A woman is standing behind him.
Figure 16: Qualitative results across FLUX-based methods on LAMICBench++.
27

---

Published as a conference paper at ICLR 2026
Table Full quantitative result on COCO-MIG. According to the count of generated instances, COCO-MIG is divided into five levels: L2, La, Ls, Ls, and Lg- L; means that the count of instances needed generate the image is i.
Success Rate( t
Method
Global Clip
Local Clip
Avg
L2
L3
L4
Ls
La
LAMIC
21.82
18.71
1.25
6,25
0.00
0.00
0.00
GLIGEN
25.21
20.90
4.25
16.88
4.38
0.00
0.00
0.00
MS-Diffusion
25.50
20.77
4.50
13.75
5.62
2.50
0.62
0.00
CreatiLayout
26.22
20.70
19.12
46.25
30.63
11.88
4.38
2.50
3DIS
23.72
20.40
18.88
51.88
19.38
10.62
4.38
8.12
InstanceDifffssion
25.77
21.91
23.00
52.50
24.38
16.88
10.62
10.62
EliGen
24.92
20.58
26,00
50.00
39.38
22.50
10.00
8.12
MIGC
26.21
21,47
27.75
53.75
34.38
21.88
11.25
17.50
Ours"
25.86
21.87
33.12
52.50.
37,50
28.12
23.12
24.38
Instance Success Rate(
mloU f
Method
Avg
L2
Lg
L4
Lg
Ls
Avg
L2
Lg
L4
L
La
LAMIC
13.56
28.12
19.17
13.75
21.17
31.67
25.79
20.68
18.08
18.25
GLIGEN
29.56
41.88
31.67
27.19
27.38
27.81
27.44
37.35
29.17
25.31
26.42
25.56
MS-Diffusion
28.22
37.81
33.12
28.12
25.75
24.69
34.69
41.15
36.38
34.57
32.36
33.70
CreatiLayoul
54.69
67.19
63.33
56.09
50.25
48.96
48.96
56.32
55.38
49.42
46.22
45.28
3DIS
55.44
71.56
61.88
55.47
48.25
52.81
49.35
61.29
53.80
49.88
44.27
47.01
InstanceDiffusion
60.28
71.25
61.67
59.38
57.00
59.27
$4.79
65.76
57.21
53.33
51.43
53.72
EliGen
64.12
69.69
72.50
66.56
61.62
58.54
59.23
64.61
66.10
61.59
56.74
$4.50
MIGC
66.44
74.06
67,29
67.03
63.25
65.73
56,96
63.84
57.60
56.95
54.01
56.82
Ours*
69.72
70.94
69.58
72.19
68.38
68.85
65.12
66.20
66.19
66.84
63.78
64.19
like Global Clip (CreatiLayout (Wu et al., 2025c)) or Local Clip (InstanceDiflussion (Wang et al. 2024)), our approach achieves superior overall balance. The consistently high mloU scores across all complexity levels and our leading Instance Success Rate on the most challenging cases further validate our model's ability to master both precise instance placement and high-fidelity attribute preservation.
More qualitative results on COCO-MIG are shown in Fig. Our method demonstrates clear qualitative advantage over existing models on the COCO-MIG benchmark. In the first example, other methods fail correctly generate the blue vase, with issues ranging from incorrect position to instance merging. Our model, in contrast, precisely renders the vase intended. Similarly, for the "green potted plant", our approach correctly applies the "green" attribute to the pot, whereas competitors fail to do This highlights superior ability to handle holistic subject identity. Furthermore, while some baselines correctly generate the requested objects in the third and fourth examples, their outputs often lack aesthetic harmony and visual coherence. Our method consistently produces images that are not only accurate but also visually pleasing and well-composed. These results underscore two key advantages of our framework: (1) Compared to other image-guided methods like MS- MS-Diffusiin (Wang et al. 2025), our approach offers significant superiority in layout control (2) Our dedicated identity preservation mechanism provides more robust and reliable subject fidelity than the attribute- based control of text- rext-guided methods, particularly in intricate, multi-instane scenes.
D.3 MORE QUALITATIVE RESULTS ON LAYOUTSAM-EVAL
Additional qualitative results are presented in Fig. 18 Evidently, our method exhibits superior overall visual quality and realism compared to all existing approaches. While other methods (especially those reliant on text-guided layout-to-ings tonettte-mmgeee generation) struggle with preserving fine- ane-grained at- tributes such as the specific text on the building in the first example and the exact color of the man's 's shorts the second- our approach faithfully preserves the user's intended details to the greatest extent, excelling in both layout control and attribute binding.
28

---

Published as a conference paper at ICLR 2026
Layout
MS-Diffusion Cyout
EliGen
InstDiff
3DIS
MIGC
Ours
Figure 17: More qualitative results on COCO-MIG.
Layout
MIGC
3DIS
InstDiff
MS-Diffusion
EliGen
CreatiLayout
Ours
with large glass curtain walls and yellow text that reads "RYERSON UNIV UNIVÉRSITY ÉRRITY" .... there are several frees with yellow leaves surrounding environment includes other buildings, shops, and vehicles. as well pedestrians on street. Phrases: Large bellow on glass building.. 2)
Caption:
stands agrinst cityscape hackdrop. 3) Modem university building with glass facade and distinct geometric pattems. 4) Lush green with sellowing leaves.
wheelbarrow filled with branches. deer stands next him, seemingly observing the man's actions background is a lush green environment, with trees and grassland... Phrases: 1) Man in navy blue shirt and khaki shorts pushing wheelbarrow. 2) A green wheelbarrow with metal frame, black tires, and visible wear. A deer with large antlers grazes on grass. 4) A doer with brown for and Wiite antlers.
Caption: where man in blue uniform is working park, pushing
Figure 18: More qualitative results on LayoutSAM-Era
29

---

Published as a conference paper at ICLR 2026
E LIMITATIONS AND FUTURE WORK
While our framework demonstrates state-of-the-art performance multi-instance generation, is without limitations. primary challenge stems from our model's strong emphasis on identity preservation. When inconsistencies exist between the provided reference images or between the images and the text prompt, our model tends to prioritize maintaining the identities of the reference subjects. This can sometimes lead to a lack of flexibility in adjusting attributes such as lighting, color, or pose, which may compromise the overall visual harmony and text-image consistency of the final output. This trade-off between identity fidelity and contextual flexibility represents an important area for future research. In the future, we plan to explore more dynamic attention mechanisms that can better balance these competing demands, allowing for more flexible style and attribute transfer. Drawing on zero-shot consistency priors (Zhang et al., 2025b), we also aim to incorporate broader generative knowledge improve the overall harmony of multi-instance scenes. Furthermore, while ContextGen focuses on 2D image synthesis, extending
consistent control to 3D assets remains promising direction (Xu et al. 2024; Shen et al. 2024).
F THE USE OF LARGE LANGUAGE MODELS
During the preparation of this manuscript, Large Language Models were used a general-purpose writing assistant tool. Specifically, LLMs were employed to polish the language and refine the clarity of the text. The authors take full responsibility for the content of the paper.
30


Reviews:
[{"review_id": "Vm4mMM2GVK", "reviewer": "Reviewer_pU4T", "summary": "This paper addresses the challenging task of multi-instance image generation (MIG), which requires simultaneous control over the layout of multiple objects and the preservation of their distinct identities. The authors propose ContextGen, a novel Diffusion Transformer (DiT) based framework. The framework features two core innovations: 1) Contextual Layout Anchoring (CLA), which uses a \"composite layout image\" as context to precisely anchor object positions; and 2) Identity Consistency Attention (ICA), a novel attention mechanism that leverages reference images to maintain high-fidelity identity for multiple instances. Furthermore, to address the lack of high-quality training data in this domain, the authors have constructed and introduced a large-scale, hierarchically-structured dataset, IMIG-100K. The experimental evaluation is extensive. On three different benchmarks (LAMICBench++, COCO-MIG, and LayoutSam-Eval), ContextGen achieves state-of-the-art performance, outperforming not only all open-source models but also top-tier proprietary models (like GPT-4o and Nano Banana) on key identity preservation metrics.", "questions": "Please referring Weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Empirical Results: As mentioned, the paper's experimental results are its greatest strength. It comprehensively outperforms all competitors (both open-source and proprietary) on three distinct and challenging benchmarks. The ability to handle multiple (>3) subjects, shown in Tables 1 and 4, is a capability that other models clearly lack.\n2. High-Quality Dataset Contribution: The release of IMIG-100K  is a major contribution. The authors not only created a dataset but thoughtfully designed it in three subsets (Basic, Complex, Flexible) to support the model's progressive training curriculum .\n3. Architectural Design: The hierarchical attention design is excellent. Rather than blindly applying attention masks, the authors discovered through experiments (Table 3) that different layers of the DiT indeed have functional specializations—CLA is applied to the first/last blocks for structure, while ICA is applied to the middle blocks for identity details.", "weaknesses": "1. Confusing DPO Ablation: This is the only confusing part of the paper. Table 3  shows the best (non-DPO) model (F+M+B) achieving an AVG score of 64.66. However, in Table 4, the \"w/o DPO\" baseline has an AVG score of only 62.55. Both scores are presumably from LAMICBench++, but they do not match. The authors claim DPO provides a benefit (62.67 vs 62.55) , but this seems to be a degradation compared to the 64.66 score in Table 3. The authors need to clarify this discrepancy.\n2. Reliance on Base Model: This work is a LoRA fine-tune on a very strong (and until recently, non-public) base model, FLUX.1-Kontext. This is not a flaw, but rather an efficient research methodology. However, it does mean the success is built, in part, on the inherent in-context capabilities of the Kontext model. The authors are transparent about this in Appendix A.1 , which is good.\n3. Dataset Limitations: The IMIG-100K dataset is synthetic, meaning it may not fully capture the entire complexity and long-tail distribution of real-world imagery.", "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": "IVTQdIuRk8", "reviewer": "Reviewer_CnRS", "summary": "The authors introduce ContextGen, a novel Diffusion Transformer (DiT) framework designed to tackle the significant challenges of multi-instance image generation (MIG). The method aims to solve two key problems: poor layout control and the failure to preserve the identities of multiple distinct subjects. The framework integrates two primary technical innovations: 1) a Contextual Layout Anchoring (CLA) mechanism to precisely position objects, and 2) an Identity Consistency Attention (ICA) mechanism that uses reference images to maintain the identity of each instance. Recognizing a lack of training data, the authors also created IMIG-100K, a new large-scale dataset for this task. The paper reports state-of-the-art performance in control precision, identity fidelity, and image quality.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The work's primary strength is its direct and simultaneous attack on the two most critical failure points in multi-instance generation: layout control and identity preservation.\n- The paper proposes two clear, novel technical components (CLA and ICA) integrated into a modern DiT framework. The Identity Consistency Attention, in particular, appears to be a sophisticated mechanism tailored specifically for the multi-subject challenge.\n- The introduction of the IMIG-100K dataset is a valuable contribution in its own right. A large-scale, hierarchically-structured dataset with detailed layout and identity annotations addresses a major resource gap and will be beneficial for the wider community.", "weaknesses": "- Fidelity vs. Generative Diversity Trade-off: The model appears to struggle with the balance between preserving identity and generating novel depictions. As suggested by the results (e.g., Figure 4, rows 1 and 3), the generated instances often exhibit a \"copy-paste\" artifact, adhering so rigidly to the reference images that they lack meaningful diversity. This suggests a potential overfitting to the reference context and limits the model's true generative flexibility.\n- Sub-optimal Performance on Key Metrics: Despite the SOTA claims, the method does not achieve top performance across all evaluated metrics. This performance gap may be linked to the newly introduced IMIG-100K dataset. By relying heavily on synthetic data, the model may not learn the full complexity and nuances of real-world image distributions, which could be limiting its generalization and robustness. Incorporating more authentic, real-world images into the training curriculum might be necessary to close this gap.", "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": "gf3NreB6Cx", "reviewer": "Reviewer_4W69", "summary": "The paper advances multi-instance generation (MIG) with not only precise layout control, but identity preservation. To simultaneously achieve  these two goals, the authors devise CLA and ICA to manipulate attention masks for better identity and location understanding. Moreover, they also propose a large-scale dataset to facilitate future research in this field. Extensive experiments on three large-scale benchmarks demonstrate their method's efficacy.", "questions": "Please refer to \"weaknesses\"", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) The proposed dataset, IMIG-100K, has a significant meaning to the IMIG field.\n\n(2) The overall training process is clear and could be easily followed.\n\n(3) Comparison with ``Closed-Source Commercial Models'' in Tab.1 strongly demonstrates the efficacy of proposed method.", "weaknesses": "Major:\n(1) The inference process is not clear on pure \"layout-to-image\" benchmarks. Based on Fig. 2, the ref images seems to be mandatory for model's inference. However, the included layout-to-image benchmark (COCO-MIG and LayoutSAM-eval) only provide layout and instance captions. How to perform inference on these benchmarks with yoour model? Is it possible to infer your model without any reference images? Please clarify this.\n\n(2) Instance-Wise Position Indexing is one of the contributions for this work, but I cannot see any ablation study about this.\n\n(3) In Fig.7, the generated image exhibits noticeable artifacts (crab legs on the bag) with DPO. Therefore, it is necessary to report metrics about image quality (FID or IS). The authors can compute FID and report the result on LayoutSAM-eval as CreatiLayout has also reported FID on this benchmark.\n\n\nMinor:\n(1) Typo: Tab.1 #L338, \"FLUX\" is mis-spelled.\n\nRef.\n[a] CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation. In ICCV'25.", "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": "Iz6hXrqhpY", "reviewer": "Reviewer_LWAe", "summary": "This paper introduces ContextGen, a novel Diffusion Transformer-based framework designed for multi-instance image generation, a task that requires precise control over the layout and identity of multiple subjects. The authors identify key challenges in existing methods, namely inadequate position control, weak identity preservation, and a lack of high-quality training data. To address these, they propose a model guided by both layout and reference images, featuring two main technical contributions: Contextual Layout Anchoring (CLA) for spatial control and Identity Consistency Attention (ICA) for identity preservation. Furthermore, they introduce a new large-scale dataset, IMIG-100K, with detailed layout and identity annotations. The experimental results show that ContextGen achieves state-of-the-art performance, outperforming existing open-source and proprietary models on several benchmarks. The strength of the paper lies in the successful integration of these components to produce a highly effective system. However, the work is weakened by concerns about the novelty of its individual methodological components and the conclusiveness of the ablation studies used to validate them.", "questions": "1.  Could you provide an ablation study that demonstrates the specific benefit of using the composite layout image as an additional input? For instance, a comparison with a model variant that receives only the noise image and the individual reference images, without the pre-composited layout image. This would help clarify the contribution of this specific design choice.\n\n2.  Could you provide a more detailed analysis of the ICA ablation results in Table 3? Specifically, please elaborate on why the performance variations are so small among the last four configurations and justify the final choice. More importantly, please explain the large and systematic performance gap (especially in the IDS metric) between the first three configurations and the last four. What does this reveal about the properties and effectiveness of the ICA mechanism?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 3, "contribution": 1, "strengths": "The primary strength of this work is its empirical success. The authors have successfully engineered a complete system that achieves a new state-of-the-art in the complex task of layout-controlled, multi-instance image generation. The combination of a large, well-curated dataset (IMIG-100K), a strong foundation model (FLUX.1-Kontext), a tailored architecture (CLA and ICA), and a robust fine-tuning strategy (including DPO) has proven to be highly effective. The quantitative results across multiple benchmarks, particularly the comprehensive comparison in Table 1, demonstrate a clear advantage over a wide range of baseline methods in overall performance. This indicates that the holistic approach and the specific combination of techniques are well-suited for the problem at hand.", "weaknesses": "Despite the strong empirical results, the paper has several weaknesses concerning its methodological contributions and the clarity of their validation.\n\n1.  **Limited Novelty in Core Mechanisms:** Beyond the ICA mechanism, the core methodological contributions appear to be adaptations of existing techniques. Contextual Layout Anchoring (CLA) seems to be a general strategy for multi-reference generation where each reference's attention is constrained, a concept that is not entirely new. The idea of using a composite layout image as an explicit input is interesting, but the paper lacks a specific ablation study to demonstrate its effectiveness compared to a baseline without it. Similarly, the use of Direct Preference Optimization (DPO) is an application of an existing fine-tuning technique. While Table 4 shows it is effective for this task, it does not represent a novel methodological contribution from this work.\n\n2.  **Unconvincing Ablation Study for ICA:** The novelty of the Identity Consistency Attention (ICA) mechanism is the paper's most significant claim, but its validation in Table 3 is not fully convincing. While the final proposed model (applying ICA to the middle blocks) achieves the highest average score and IDS, the performance differences among the last four configurations are marginal and likely within the noise margin. The configuration that completely omits ICA (row beginning with \"ITC = 90.26\") performs almost as well, which raises questions about whether ICA provides a truly significant or necessary improvement. Furthermore, there is a large, unexplained gap in the IDS metric between the first three configurations and the last four. The paper does not offer an explanation for this pattern, making it difficult for the reader to understand the specific properties or impact of the ICA mechanism. Overall, the ablation study does not provide compelling evidence for the effectiveness of ICA.\n\n3.  **Overstated Contribution of the Dataset:** While the effort in creating the IMIG-100K dataset is substantial, the claim that it is the \"first large-scale, hierarchically-structured image-guided multi-instance generation dataset\" is questionable. The pipeline described—using generative models to create images and segmentation models to extract references—has become a common practice in the field, with similar efforts seen in works like OmniGen2, Qwen-Image, and MS-Diffusion. As such, while the dataset is a valuable asset, its creation does not constitute a significant research innovation.\n\n4.  **Confounded Baseline Comparisons:** The main results in Table 1 are difficult to interpret due to a lack of controlled variables. The proposed method benefits from a stronger base model (FLUX), a larger and task-specific training dataset (IMIG-100K), and an additional DPO tuning stage, whereas many baselines (e.g., MS-Diffusion) use older, less capable base models and different data. This makes it impossible to attribute the performance gains specifically to the novel CLA and ICA mechanisms. While it is understood that re-implementing all baselines under identical conditions is costly, this confounding of variables limits the scientific insight that can be drawn from the results. The paper demonstrates that a better system can be built with better components, but it fails to provide clear evidence for the isolated impact of its core architectural innovations.", "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)