Does FLUX Already Know How to Perform Physically Plausible Image Composition?
Authors: Shilin Lu
Zhuming Lian
Zihan Zhou
Shaocong Zhang
Chen Zhao
Adams Wai-Kin Kong
Venue: ICLR 2026 Poster (2026)
Keywords: Image Editing
Image Composition
Diffusion Models
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Image composition aims to seamlessly insert a user-specified object into a new scene, but existing models struggle with complex lighting (e.g., accurate shadows, water reflections) and diverse, high-resolution inputs. Modern text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential physical and resolution priors, yet lack a framework to unleash them without resorting to latent inversion, which often locks object poses into contextually inappropriate orientations, or brittle attention surgery. We propose SHINE, a training-free framework for Seamless, High-fidelity Insertion with Neutralized Errors. SHINE introduces manifold-steered anchor loss, leveraging pretrained customization adapters (e.g., IP-Adapter) to guide latents for faithful subject representation while preserving background integrity. Artifact-suppression guidance and adaptive background blending are proposed to further eliminate low-quality outputs and visible seams. To address the lack of rigorous benchmarks, we introduce ComplexCompo, featuring diverse resolutions and challenging conditions such as low lighting, strong illumination, intricate shadows, and reflective surfaces. Experiments on ComplexCompo and DreamEditBench show state-of-the-art performance on standard metrics (e.g., DINOv2) and human-aligned scores (e.g., DreamSim, ImageReward, VisionReward). Code is available at https://github.com/ZhumingLian/SHINE

Published as a conference paper at ICLR 2026
DOES FLUX ALREADY KNOW How TO PERFORM PHYSICALLY PLAUSIBLE IMAGE COMPOSITION?
Shilin Lu" Zhuming Lian Zihan Zhou', Shaocong Zhang',
Chen Zhao", Adams Wai-Kin Kin Kong'
"Nayyang Technological University, *Nanjing University
(shilin002, chuming001, zihan010, shaocong001] Jθe ntu. edu. sg 602024710020 smail.mmaml.mmmmmmmmm..mm.mm.a.m www.iiiinn..uuuuc..m adamskongantu.eduu.g
Figure 1: Showcase of our training-free free image composition method, SHINE, This gallery highlights SHINE's ability seamlessly integrate subjects into complex scenes, including low-light condi- tions, intricate shadows, and water reflections.
ABSTRACT
Image composition aims to seamlessly insert a user-specified object into new scene, but existing models struggle with complex lighting (e-g.. accurate shad- ows, water reflections) and diverse, high-resolution inputs. Modern text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential physical and res- olution priors, yet lack framework to unleash them without resorting to latent inversion, which often locks object poses into contextually inappropriate orienta- tions, brittle attention surgery. We propose SHINE, training-free framework for Seamless, High-fidelity Insertion with Neutralized Errors. SHINE introduces manifold-steered anchor loss, leveraging pretrained customization adapters (e.g., IP- IP-Adapter) to guide latents for faithful subject representation while preserving background integrity. Degradation suppression guidance and adaptive background blending are proposed to further eliminate low-quality outputs and visible seams. To address the lack of rigorous benchmarks, we introduce ComplexCompo, fea-
"Equal Contribution.

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Published as a conference paper at ICLR 2026
turing diverse resolutions and challenging conditions such as low lighting, strong illumination, intricate shadows, and reflective surfaces. Experiments on Complex- Compo and DreamEditBench show state-of-the art performance on standard met- rics (e.g., DINOv2) and human-aligned scores (e.g.. DreamSim, ImageReward, Vesion Code is available at http://giiibb.ooo//uumnnnii/////
INTRODUCTION
Image composition, which places a user-specified object into a new scene, is demanding image editing task. Despite the breathtaking progress of multimodal foundation models (e.g., GPT-5 (Ope- nAI, 2025), Gemini-2.5 (Gemini2.5, 2025), SeedEdit/Doubar (Shi et al., 2024b), and Grok-4 (gro, 2025)), these generic models still struggle with image composition. Typical failures include impre- cise object placement, inconsistent lighting, and the subject's 's identity drift (see Fig, 2). These limita- tions indicate that, as of now, massive multimodal pre-training alone has not yet endowed them with sufficient compositional ability for this task. A natural response has been to train specialized models. Yet building large-scale, high-quality, nulti-resolution triplet datasets (object, scene, composite) is prohibitively costly. As a result, most composition models are fine-tuned from base models (e.g., IL.UX.I-dev (Black Forest Labs, 2024a), FL.UX. (Black Forest Labs, 2024c), SDXL. (Podell et al., 2024)) synthetic data generated via inpainting or augmentations (Chen et al., 2024c; Yang et al., 2023; Song et al., 2023; Wang et al., 2025a; He et al., 2024).
These models, however, face two main limitations (see Fig. 6): (i) Lighting realism. They strug- gle to achieve natural composition under complex lighting conditions, such as accurate shadow generation water reflections for the inserted subject. (ii) Resolution rigidity. They are tied to fixed resolution, necessitating downsampling or cropping when applied to varied, high-resolution background images, which degrades generation quality. Notably, such issues absent in the base models, implying that the underlying physical priors are present but are not effectively exploited by fine-tuned variants. The degradation largely stems from low-quality synthetic datasets, which inherit flaws from inpainting models that often mis-handle shadows and reflections, producing implausible edits, hallucinated content, or incomplete object removal (Yu et al., 2025c; Winter et al., 2025).
There have been prior training-free attempts to exploit the priors of text-to-image (T2I) models for advancing image composition, but they fall short for two main reasons.
(i) Inversion bottle-
necks. Most methods (Lu et al., 2023d; Pham et al., 2024; Yan et al., 2025; Li et al., 2024b) de- pend on accurate image inversion (Song et al., 2021; Lu et al., 2022; Mokady et al., 2023). In practice, inversion constrains the inserted object to the pose of its reference image, often resulting in contextually inappropriate orientations. Morcover, inversion is less effective for classifier- free guidance (CFG) distilled models (e.g.. FLUX), where elevated inversion errors degrade identity preservation. (ii) Fragile attention surgery. Many training-free free approaches rely on attention ma- nipulation (Lu et al., 2023d; Yan al., 2025; Li et al., 2024b). While compatible with the joint self-attention in Multimodal Diffusion Transformers (MMDiT) (Peebles & Xie, 2023), these meth- ods inherit the instability and hyperparameter sensitivity (Lu al., 2023d), limiting their robustness.
To bridge these gaps we present SHINE, a training-free framework for Seamless, High-fidelity Insertion with Neutralized Errors (see Fig. 1). SHINE comprises three innovations: (i) Manifold- Steered Anchor (MSA) loss, which leverages pretrained open-domain customization adapters (c.g.. IP-Adapter (Ye et al., 2023)) steer noisy latents toward faithfully representing the reference subject while preserving the structural integrity of the background. (ii) Degradation-Suppresion Guid- ance (DSG) that steers sampling away from low-quality distributions. (iii) Adaptive Background Blending (ABB) that eliminates visible seams along mask boundaries.
Existing benchmarks primarily comprise background images with a fixed resolution of 512 512 pixels. To evaluate performance across diverse, high-resolution, and demanding scenarios, we in- troduce ComplexCompo, a benchmark that includes varied resolutions, both landscape and portrait orientations, and complex conditions such as low lighting, intense illumination, intricate shadows, and water reflections. Extensive experiments on ComplexCompo and DreamEditBench (Li et al., 2023b) demonstrate that SHINE achieves state-of-the-an (SOTA) performance, surpassing base- lines on standard metrics (c.g., DINOv2 (Oquab et al., 2024)) and human- aligned metrics (c.g., DreamSim (Fu et al., 2023), ImageReward (Xu et al., 2023), Rewidd (Xu et al., 2024)).
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Condition
Grok 4
Gemini 2.5
SeedEdit/Doubao
GPT-5
Ours
Figure 2: Image composition from advanced multimodal models under three challenging conditions:
backlighting, shadows, and water surfaces. Refer to Appendix H for prompt details.
RELATED WORK
This section reviews prior work on image composition. A more comprehensive discussion, covering image composition, general image editing, and subject-driven generation, is offered in Appendix A. Classical image composition splits into sub-tasks (Niu et al., 2021) such as object placement (Azadi et al., 2020; Zhang et al., 2020a), blending (Wu et al., 2019; Zhang et al., 2020b), harmonization (Cao al., 2023; Lu al., 2023b), and shadow generation (Hong al., 2022; Sheng et al., 2021), 1), typ- ically handled by separate models. Diffusion models have shifted the field toward unified frame- works, either training-based or training-free. Training-based approaches fine-tune diffusion models with curated datasets, adding grounding layers, controllability signals, or identity-preserving super- vision from image video sets (Wang et al., 2025a; Chen al., 2024c; Yang et al., 2023; Song al., 2023: Lu et al., 2023c). However, they often bias model priors and struggle with complex lighting due the lack of large-scale real-world triplets. Training-fre approaches avoid retraining by ma- nipulating inversion and attention during inference, enabling flexible test-time adaptation (Yan et al., 2025; Li al., 2024b; 2023b; Lu al., 2023d; Pham et al., 2024). Yet these methods remain fragile: strong injections preserve identity but fix unnatural poses, while weaker ones improve realism the cost of fidelity, reflecting a core trade-off between identity preservation and natural composition.
METHOD
Image composition seeks to integrate a subject into a designated area of a background image while preserving the integrity of the surrounding scene. This process typically requires three inputs: (I) one or more reference images of the subject {a: .22
saab ). (2) a background image ", and (3) user-provided mask Muser specifying the insertion region within the background.
Our framework built on three core components: Manifold-Steered Anchor (MSA) loss, Degradationn-Sppressioo Guidance (DSG), and Adaptive Background Blending (ABB). Impor- tantly, the design is model-agnostic and requires only standard features modern generative mod- els: MSA loss assumes that the base model supports either personalization finetuning or provides access to a pretrained personalization adapter, DSG uses self-attention maps, and ABB relies on text- image cross-attention. These mild assumptions enable seamless integration into existing pipelines without architectural changes. We present main results with FLUX, while additional experiments on SDXL (Podell al., 2024), SID3.5 (Esser al., 2024), and PixArt (Chen al.) provided in Appendix E. The complete algorithm is shown in Algorithm I.
3.1 NON-INVERSION LATENT PREPARATION
In training-free free diffusion based image composition (Lu et al., 2023d; Pham et al., 2024; Yan et al., 2025; Li al., 2024b), it is common to start from noisy latent. Existing training-fiee free frameworks typically rely on image inversion, where the initial noisy latent is constructed by copying the inverted latent of the subject image into designated region of the background image's inverted latent.
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MSA Uptrizztton
DSG Sampling
Adppise Blending
DOG ABU
One Danoising Step
Overviow of Imegg Componiioon Precess
1)
Juint S:lf
[2 xjou-aa))
elant The slon grey coler with
Attention
bely and blook eyer, and inr arne and lege one white
Image Inpainting
Rie K
Ving
Multi-Straen Black
Qui Kiz Var
VLM
Ss-aollss #6
2 (o(
Juist Salf
Attention
Q
King
Ving
of f- 2
V., vo (a e, ) 2g
Comupted Mulli Block
Inversion
Maniisti--Sssrrll (MSA)
Degiddation Suppression Guidince (DSG)
Figure 3: Overview of the proposed framework. (a) The noisy latent is created by inpainting the background with VLM-derived object description, then adding Gaussian noise. (b) Manifold- Steered Anchor (MSA) loss guides noisy latents toward faithfully capturing the reference subject (red arrow), while preserving the structural integrity of the background. Concretely, it enforces that the prediction of the optimized latent 2 on the adapter-augmenter model's manifold remains close to the prediction of the original latent 2 on the base model's 's manifold. (c) Segradation-Suppressee Guidance (DSG) constructs a negative velocity pointing toward low-quality regions by blurring Qimg and, in CFG-like manner, steers the trajectory away from this low-quality distribution.
However, this copy-paste strategy constrains the inserted object the exact pose of its reference image, often leading to contextually inappropriate orientations in the composed result. Moreover, inversion suboptimal for CFG-distilled models (e.g.. FLUX), as introduces higher inversion errors that compromise subject identity preservation.
To address these limitations, abandon inversion and instead perform a one-step forward diffusion to obtain the noisy latent. As illustrated in Fig. 3(a), we use vision-language model (VLM) (Xue al., 2024; Chen al., 2024d; Liu et al., 2024) caption the subject image and leverage this caption, along with image inpainting model (Li et al., 2024c; Ju et al., 2024; Zhuang et al., 2024; Black Forest Labs, 2024b), to generate the image which the subject attached, denoted as miit The noisy latent is encoded in the VAE space as z and perturbed to timestep /. < T via one- step forward diffusion, following the flow matching formulation: 2₁ = (1 -ot)= init + dee, where (/(0 1).
3.2 SANIFOL--STERREE ANCHOR LOss
The Hanifold-Steered Anchor (MSA) loss is designed optimize the noisy latent z; (from Sec. 3.1) during the denoising process, steering it toward a reference subject while preserving the structural integrity of the original image. The key intuition to leverage the prior knowledge embedded in pretrained open-domain customization adapters (or alternatively, personalized LoRAs) such as IP- Adapter (Ye et al., 2023). PuLID (Guo et al., 2024), and InstantCharacter (Tao et al., 2025), to intervene directly in the diffusion trajectory. Specifically, the MSA loss is defined as:
min (2,) = ,, -0---g//e sg(ü.]
=b))
where G. vo(E,, I c) serves fixed anchor, preserving the structure of the background image at a given noise level t, with 2, held constant as the original noisy latent. vg(-) denotes the velocity predicted by the frozen T2I model 9, while vo 20+20(.) represents the velocity predicted by the T2I model augmented with an adapter A6. zuh is the latent the subject image. The text prompt c is from the VLM's description of init and ag[-] indicates the stop-gradient operation.
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Normal
Blur Q img
Blur K mm
Blur Ving
Blur 1xt
Blur Kxx
Blur Vizi
Prompt:
dog running on the beach.
sjadifklkax Low quality;
Prompt: Two children are playing with a ball the park
Figure 4: Left: Robustness of FLUX. Right: Impacts of blurring different features in FLUX.
The MSA loss motivated by the observation that optimizing latent representation against frozen generative model implicitly projects the latent onto the model's learned data manifold (Meng et al., 2021; Kim al., 2022; Graikos al., 2022; Feng et al., 2023). The generator serves as an implicit prior, guiding gradient descent toward the manifold's 's basin of attraction.
For instance, when a generative model G(w) is trained solely on cat images, its outputs are confined to the cat-image manifold. Thus, approximating a dog image & by solving min [G(ww -ddog yields G(w"") that remains cat image, but with structural features (e.g., pose or outline) aligned to Edog- The result is the projection of the dog image onto the cat manifold, not genuine dog image.
2dog 1
Analogously, MSA loss is designed to achieve two goals simultaneously. (1) It seeks an optimized noisy latent 2 that remains within the manifold of the adapter-augmented model when conditioned on the subject zdu (2) It encourages the adapter's prediction on this latent z to align with the base the original latent 2t, i.c., Ve (2 t, C, =bb)) z uo (z, f, c) (see Fig. 3(b)).
model's 's prediction Since the velocity prediction estimate of the clean image that encodes essential structural information (Zheng et al., 2023), this alignment preserves the spatial layout and background details inherited from the original image.
T2I model on a noisy latent 2; can also be interpreted as a coarse
The gradient of LMSa with respect to 2, is:
2 (0oo)))2(2000eee (0v0-00(2,,
sg(u.)) Owg so t,
V (zi =
Ōz.
The Jacobian term necessitates backpropagation through the MMDiT, which is computationally expensive. However, this scenario is analogous to Score Distillation Sampling (SDS) (Poole al.), where research shows that omitting the Jacobian term yields an effective gradient for optimization with diffusion models. Thus, we adopt the same strategy for optimization.
3.3
DEGRADDATIONSUPPRESSION GUIDANCE
MSA loss effectively facilitates the insertion reference objects. However, due to the inherent stochasticity of the denoising and optimization process, the results sometimes suffer from degraded visual quality, manifesting as oversaturated colors and reduced identity consistency (see Fig. 5). To address this, we introduce ing (Schramowski et al., 2023), defined as:
SerradattionSSppressiiii Guidance (DSG), inspired by negative prompt-
Dz U0+ t, sbb) + ((v0+50(2z:) c, zubl) U (Zz, t, c, x zubb)).
dag
(3)
neg where 08+49 denotes a negative velocity prediction that guides the generation toward low-quality nng
regions. A key challenge is the design of meaningful negative velocity prediction V9+ within MMDiT-based architectures. In our experiments with FLUX, we observed that using nonsensical text prompts explicit negative prompts fails to introduce degradation. The generated images main high-fidelity (Fig. 4(a)). suggesting that text-based negative prompting is ineffective for FLUX.
In our setting, the ideal negative velocity UgL should target directions that preserve the semantic content and spatial layout while lowering perceptual quality. To achieve this, we investigate whether manipulate FLUX's internal representations to construct such targeted degradation signal.
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W/o ABB
W ABBB
(8)
(b)
(c)
(d)
(e)
Figure 5: Comparison of rectangular-nan blending and Adaptive Background Blending (ABB).
Boundary regions (pink dashed boxes) are enlarged for clarity. Zoom in for details.
In FLUX, both multi-stream and single-stream blocks compute joint self-attention over concatenated text and image tokens as follows:
h softmax (19ras Qimgl Kixts King]
//V dx
[
Vingl.
where [Qra: Qimgl represents the concatenation of text and image queries. and similarly for keys and values. To identify an effective manipulation strategy, we systematically perturb different com- ponents in the attention mechanism (i.e., Qix- K Vix. Qimg. K mg and Ving) and observe their impact on generation quality.
As shown in Fig. 4(b), our findings are follows:
1. Blurring Qixis K or Vaa has negligible impact on semantic fidelity and visual quality.
2. Blurring Vang severely disrupts the output distribution, leading to unintelligible images.
3. Blurring King moderately impacts quality, while the image remains visually acceptable.
4. Blurring Qime yields pronounced degradations while preserving structural integrity, making it the most effective lever for constructing negative velocity.
Based on these insights, we construct the negative velocity prediction o0 Eqn. 3 by blurring Qime within FLUX (see Fig. 3(c)). Moreover, we show that blurring Ving mathematically equiv- alent to blurring the self-attention weights, whereas blurring Kimg or Vimg is not (see Appendix for the proof). This equivalence is consistent with the fact that attenuating self-attention activations suppresses informative interactions and thus degrades image quality (Lu et al., 2024a).
3.4 ADAPTIVE BACKGROUND BLENDING
Previous methods typically rely on user-provided mask Muser to preserve the background during each denoising step. blending as 2 = M user C 2₁ + (1 Muser) @ z/ but this often introduces visible seams along mask boundaries (see the first row of Fig. 5).
To address this limitation, we propose Adaptive Background Blending (ABB), defined as
2 = M ④ + (1- M O 2
M = 1(t 7) D(M alin) + 11t <T M user
(5)
where Muser is the user mask, while atm is derived by binarizing the cross-attention maps corre- sponding to subject tokens. These maps can be obtained by either averaging across layers or select- ing the most informative layer via lightweight analysis (details in Appendix D). The operator D(-) performs dilation and extracts the largest connected component, ensuring robustness to noise.
Compared to Muser M is more spatially precise, particularly for elongated or irregularly shaped objects that do not fully occupy a rectangular region. As illustrated in the second row of Fig. 5, method produces smoother transitions by replacing the rigid user mask with the semantically guided mask. This refinement better preserves the surrounding scene, enabling seamless integration between generated content and the original background. However, applying this method throughout

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Published as a conference paper at ICLR 2026
Algorithm The Image Composition Process of SHINE.
Input: background latent 208 VAE((z")), subject latent 2 VAE(zzuu)), a inpainted latent zinit VAE (Inpainting [a "
M user VLM(x*"bi))) user mask Muser. user.
Output: The composition latent Z0-
1: Zt, (1 o1 mir J,C,, where (0,
2: for l,...,, do -ttered Anchor (MSA) Optimization if't > r then
3:
//
4:
5:
D; - (0 Rt, c,
6:
for j
do
7:
2, 2t Muser user V 10000000,,, sg(ü:
8:
end for
9:
end if
10:
// Seggraaation-suppesssset
Guidance (DSG)
v; A, -- 1 10++0022,1,, 2ubi)
db 1 U: 10 22, t,, c,bb))
12:
13:
= + + (0:-1-0 02
deg
14:
2,
bg (1- 0 O2-1E, where (0,1)
15:
// Adaptive Background Blending (ABB)
16:
M +- MaCConcceeeComooonn((iil
2 2)))
17:
M - 1 {t. > 7 M + 1ft S r Muser
2c-1 M @ --1 (1- M 2
19:
end for
20:
return 2g
the denoising process may truncate object shadows or reflections. Through empirical evaluation, find that leveraging = during the initial denoising steps (t >> r) sufficiently mitigates visible seams along mask boundaries, ensuring high-fidelity scene coherence.
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Benchmark. Current benchmarks primarily consist of background images with fixed resolution of 512 x 512 pixels. To assess performance across diverse, high-resolution, and complex scenarios, we introduce ComplexCompo, benchmark built upon DreamEditBench (Li et al., 2023b). DreamEdit- Bench includes 220 (subject, background, bounding box) pairs designed for 512 x 512 resolution. In contrast, ComplexCompo features 300 composition pairs with varying resolutions, encompass- ing both landscape and portrait orientations, and incorporates challenging conditions such as low lighting. intense illumination, intricate shadows, and water reflections. The background images are sourced from OpenImage (Kuznetsova et al., 2020). Further details are provided in Appendix G.
Metrics. Previous methods primarily adapt CLIP-I (Radford al., 2021) and DINOv2 (Oquab al., 2024) to assess subject identity consistency. However, these features capture high-level semantic information that may not fully align with human perception of finer-grained attributes. Thus, we further incorporate instance retrieval features (IRF) from (Shao & Cui, 2022) and DreamSim (Fu al., 2023), which better align with human judgments. An analysis of identity consistency metrics provided in Appendix I. For overall image quality, we use ImageReward (IR) (Xu et al., 2023) and VewionReward (VR) (Xu et al., 2024), fine-grained reward models that more accurately reflect human preferences. To more comprehensively evaluate composition quality, further include three UnifiedReward variants (Wang et al., 2025c:d) and HPSv3 (Ma et al., 2025b) in Appendix Back- ground consistency is measured using LPIPS (Zhang al., 2018) and SSIM (Wang et al., 2004).
Implementation Details. our experiment, we used FLUX.1-dey 12B-parameter flow matching model, the base model, combined with InstantCharacter (Tao al., 2025) as the adapter (Addi-

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Published as a conference paper at ICLR 2026
Condition
AnyDoor
UniComb
EEdit
Ours-LoRd Durs-Adapte
Figure 6: Qualitative comparison of our method with multiple baselines in challenging scenarios, drawn from our benchmark dataset. More qualitative comparisons are available Appendix O.
tional results on SDXL, SD3.5, and PixArt are presented in Appendix E). Our approach also supports per-concept LoRA (Hu et al.), which requires test-time tuning (Ruiz et al., 2023) but delivers supe- rior identity consistency compared an open-domain adapter, making it ideal for scenarios demand- ing precise identity preservation. The denoising schedule consists of 20 steps, with the inpainted image perturbed to timestep 15 and denoising initiated from that point. We Flux.1 Fill (Black Forest Labs, 2024b) as the inpainting model and BLIP-3 (Xue et al., 2024) as the VLM. Additional details and hyperparameters are provided Appendix J.
4.2 EXPERIMENTAL RESULTS
We compare our method with two categories baselines: (1) Training-based methods (6 in total): UniCombine (Wang et al., 2025a), AnyDoor (Chen al., 2024c), Paint by Example (PBE) (Yang et al., 2023), ObjectStitch (Song et al., 2023), MADD (He et al., 2024), and DreamCom (Lu et al., 2023c); (2) Training-free methods (5 in total): EEdit (Yan et al., 2025), TIGIC (Li al., 2024b), DreamEdit (Li et al., 2023b), TF-ICON (Lu et al., 2023d), and TALE (Pham et al., 2024).
As shown Tab. both variants of our method surpass all baselines on DreamEditBench across human preference aligned metrics (i.c.. DreamSim, IR, VR), which are the most critical indicators metrics, all methods achieve comparable results, with differences
of quality. For background so small they are imperceptible to the human eye. On the more challenging ComplexCompo dataset, which includes non-square resolutions and intricate scenes, most methods experience notable per- formance drop, yet our approach consistently remains the top performer. From Fig. 6, it is evident that while AnyDoor achieves high scores on many identity metrics, the model tends to copy and paste the subject into the scene, resulting in unnatural compositions and lower image quality scores. In contrast, our method excels at naturally composing objects challenging conditions (c.g., low- light settings, water surfaces, and scenes with complex shadows). Appendix F provides user study.
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Table 1: Comparison of composition performance across two benchmarks. The best result in each column is highlighted in bold, while the second-bes is underlined. Metrics shown in pink are those specifically trained to better align with human preferences. Abbreviations: IRF= Instance Retrieval Features; IR- = ImageReward; VR RisionReward: URE-
Trintes
Ideeity
Crisutrayy
Hadigrored
Image
Model
Modld
IMPS
5SM
VR.
URRE-1
Thes. FII (llbck Fome Labe, 2000b)
ILUX
017325
0.5155
015754
1152255
002/00
12:876
11:577
1,5927
3.552
21:3812
MADD tlee al. 2014)
A33
D130
01118
0.377
1138111
0.332
.1.345
2/7013
1,4655
13.5345
CAgooSitci (Seeg
2003)
3023)
0.5709
0.500
0.563
00200
0.253
0.1575
3.5033
5.953
15:9356
Ang (Clia ot 3024g)
5D
DISO
05183
07283
0.714
0.376
00251
0.884
0.551
3.3936
8.4807
19/2889
Dram
LaaCaslkee (Waag et d 2025g)
FLUX
LRRA
0.008
07353
17500
0.386
00050
08987
0.4565
36008
8.5415
21,250
Ed
TKUIL al, 2650)
533
01711
0130K
00264
05531
013332
25871
3.2670
17.0550
(200)
TALacC/ham st 2014:
V
433
03886
00m
0301
01130
11158
0.375
180784
TF
0.17
0.816
DreanBlt 30036)
5D
LRA,
17700
07151
0,507
05755
51775
6,005
15.636
(Ycec 3025
FLEX
0 0058
02500
04433
0.0m
6883
0.555
00316
15500
0.5600
Oues Adkger
FLUX
Adkeer
0 5066
02115
0 10000
00226
3,221
18501
22
Tlas P1I (Blokk Tome Labe 300/h)
TEEX
B5435
1-118
15311
53300
187713
MADD 3024)
00211
45568
2,033
130557
OticoSatci
2005)
0.5515
04517
DecusrCou claid al, 3023g
5D
LRRA
D64E
0,560
0.278
03102
00360
01382
00075
1.455
7.554
I5.145
AenDeex (Clia ut. 3024g)
5D
DINO
0
0
0.330
0.405
00259
0.200
0.350
3321)
55700
153550
Gample--
PMEE-Yinng 2012)
SB
77577
0150
0030
113159
14110
18-557
(200)
TIGRC-0.. al, 2000)
V
XD
B1911
B5379
0.36
00639
-111
1841701
TF-CON
2023.)
0,000
0.2266
34013
0.258
15,2366
ExcanEll (1i it al, 20036)
V
SD
LRRA, VIT
07314
0,512
0.509
0.570
09465
0.730
01312
1.531
8.634
15,9534
Eaahi 310h)
ILLXX
0
0.797
R8226
0706
013433
8100
19:3448
Oun- LakA
ILLX
LakA
(L799
0.726
(L7689
1L3542
0.633
174386
1.991
93408
11,8136
Table 2: Ablation study examining the impact of key components on DreamEdiiBench.
Method
MSA
DSG
ABB
Subject Mdntity Consistency
Background
Image Quality
CLIP-I
DINOv2
IRF
DreamSim
LPIPS
SSIM
IR
VR
Config A
0.7328
0.6745
0.5154
0.5233
0.0166
0.9076
0.5577
3.5997
Condg B
V
x
X
0,7814
0.7204
0.7414
0.3951
0.0172
0.9075
0.5455
3,5952
Config C
0.7528
0.6941
0.6533
0.4436
0.0178
0.9038
0.5633
3.6130
Condig D
0.7421
0.6814
0.6158
0.5127
0.0210
0.9010
0.5595
3.6109
Config E
X
V
V
0.7481
0.6987
0.6647
0.4317
0.0218
0.8971
0,5850
3.6277
Config F
0,8084
0.7429
0.7609
0.3756
0.0231
0.8991
0.5459
3.6023
Config G
0.8077
0.7375
0.7589
0.3762
0.0182
0.9037
0.5745
3.6191
Ours-Adapter
V
0.8086
0.7415
0.7702
0.3730
0.0236
0.8959
0.5709
3.6232
4.3 ABLATION STUDY
We validate our design choices through ablation (see Tab. 2 and Fig. 7). The results highlight three key insights. First, MSA loss notably improves subject identity consistency. Second, DSG boosts IR and VR scores by steering denoising away from cow-quality regions. Finally, ABB effectively suppresses visible seams along mask boundaries. While this improvement is readily apparent in visual comparisons (Figs. 5, 7), it is less well captured by quantitative metrics, since LPIPS and SSIM primarily assess structural similarity rather than perceptual smoothness.
5 CONCLUSION
We introduced SHINE, a training-free framework for seamless and high-fidelity image composi- tion with pretrained T21 models. SHINE integrates Manifol--Steered Anchor Loss Degradation- Suppression Guidance, and Adaptive Background Blending to ensure precise subject placement and artifact-free synthesis across diverse resolutions and lighting conditions. To enable rigorous evalu- ation, we proposed ComplexCompo, a benchmark for challenging composition scenarios. SHINE achieves state-of tf-te-of-the-ate the-art results on both ComplexCompo and DreamEditBench.
Limitations. Our method reliably converges to the correct subject identity through MSA optimiza- tion even when the inpainted subject's 's appearance deviates substantially from the reference (see Fig. 8(a)). However, when the inpainting prompt specifies an incorrect color, the final inpainted re- sult tends to inherit and preserve this erroneous color (see Fig. 8(b)). On the other hand, the similarity between the inserted object and the user-provided object depends on the quality of the customization adapter used. As shown in Tab. 1, because LoRA performs test-time tuning for individual concepts, it generates subjects that are more similar to the target than those produced by pretrained customization adapters, resulting in higher subject identity consistency metrics in the composition. While current customization adapters already perform well, the potential of our method will con- tinue to improve as advancements are made in the field of open-domain customization adapters.
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Config
Config
Config
Config
Config
Config
Config G
Ours
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
X
DSG
X
DSG
DSG
X
DSG
DSG
X
DSG
DSG
DSG
X
ABB
X
ABB
X ABB
ABB
ABB
ABB
X
ABB
ABB
Figure 7: Qualitative ablation study comparing different variants of our framework.
Reference Subjects
Inpainting
Results
Our Results
(a) Successful cases despite flawed inpainting
(b) Failure cases from incorrect impainting color
Figure 8: Composition inherit erroneous colors if the inpainting prompt specifies an incorrect color.
ETHICS STATEMENT
Our framework provides an accessible way for people without professional artistic skills to cre- ate image compositions. While this technology offers significant benefits, it also carries the risk of misuse for malicious purposes, such as harassment or spreading misinformation. Additionally, our framework relies pretrained large-scale T2I models, which may inadvertently introduce social and cultural biases. Therefore, using these models raises ethical concerns and requires careful con- sideration. We therefore urge users exercise caution and use this tool responsibly for appropriate purposes only.
REPRODUCIBILITY STATEMENT
We have taken several measures to ensure the reproducibility of our results. Algorithm 1 presents the pseudocode of our method. Details of the implementation for the main experiments provided in Sec. 4.1, while the hyperparameter configurations are listed Appendix J. The source code and the ComplexCompe dataset will be released publicly. This work adheres the reproducibility requirements set by ICLR.
ACKNOWLEDGMENTS
This research is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Trust Tech Funding Initiative. Any opinions, findings and conclu- sions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and Infocomm Media Development Authority.
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Appendix
Table of Contents
A Related Work
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A.1 Image Composition
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A.2 General Image Editing.
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A.3 Suhject-Driven Generation
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B Visualizing the Impact Adaptive Background Blending
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C Equivalence of Query Blurring and Attention Weight Blurring
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C.I. Blurring the Query Matrix
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C.2 Blurring the Key and Value Matrices
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C.3 Implementation Details of Gaussian Blurring
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Evaluating Cross-Attention Map Accuracy via IoU
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E Experiments with SDXL, SD3.5, and PixArt
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F User Study
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G Benchmark Details
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Prompts Proprietary Foundation Models
I Subject Identity Metries Analysis
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J Implementation Details
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K Discussion on Inversion vs. One-Step Forward Diffusion
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L Additional Image Quality Evaluation Using UnifiedReward and HPSv3
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M Further Analysis DSG in SD3.5
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N Runtime Comparison
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Additional Qualitative Results
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LLM Usage Statement
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A RELATED WORK
A.1 IMAGE COMPOSITION
Image composition involves integrating specific objects and scenarios from user-provided photos, often guided by text prompts. Traditionally, this process divided into sub-tasks (Niu et al., 2021), including object placement (Azadi et al., 2020; Chen & Kae, 2019; Lin et al., 2018; Tripathi et al., 2019: Zhang et al., 2020a), image blending (Wu al., 2019; Zhang et al., 2020b), image harmoniza- tion (Cao et al., 2023; Lu et al., 2023b; Zhang et al., 2020b; Cong et al., 2020: Cun & Pun, 2020;
Jiang al., 2021: Xue et al., 2022), and shadow generation (Hong et al., 2022; Liu et al., 2020; Sheng et al., 2021; Zhang et al., 2019), each typically handled by distinct models and pipelines. However, with the rise of diffusion-based generative models, recent approaches have shifted toward unified frameworks that address all sub-tasks simultaneously. These methods are broadly classified into training- -ased and training-free approaches.
Training- based methods fine-tune foundational models using datasets tailored for image composi- tion. Early methods like Paint by Example (Yang et al., 2023) and ObjectStitch (Song et al., 2023) use CLIP to encode subject features, ensuring high semantic similarity between inserted objects and reference images. These approaches use image augmentation to create training datasets, enabling effective training. GLIGEN (Li et al., 2023c) incorporates grounding information into new trainable layers of pre-trained diffusion model via a gated mechanism. ControlCom (Zhang et al., 2023a) integrates 2-dim indicator vector to improve controllability. DreamCom (Lu et al., 2023c) and Mure- ObjectStitch (Chen et al., 2024a) fine-tune models with small sets of reference images to preserve subject identity. AnyDoor (Chen al., 2024c), IMPRINT (Song et al., 2024), and E-MD3C (Pham et al., 2025) leverage DINOv2 to enhance identity fidelity and control over shape and pose, drawing supervision from video data. MimicBrush (Chen et al., 2024b) similarly uses video-derived super- vision for imitative editing. In contrast, MADD (He et al., 2024), ObjectMate (Winter et al., 2025), and OmniPaint (Yu et al., 2025c) employ image inpainting models to generate higher-quality train- ing datasets compared to those based on image video augmentation. Multitwine (Tarrés et al., 2025) enables the integration of multiple objects, capturing interactions from simple positional relationships to complex actions requiring reposing. DreamFuse (Huang al., 2025) uses a Po- sitional Affine mechanism to embed foreground size and position into the background, fostering effective foreground backkuuoudd interaction through shared attention. Insert Anything (Song al., 2025a) and UniCombine (Wang et al., 2025a) introduces a FLUX-based multi-conditional gener- ative framework that handles diverse condition combinations. However, these methods often bias the generative priors of base models toward curated datasets, resulting in unnatural compositions, such as implausible object-environment interactions (e.g., missing or unrealistic shadows and reflec- tions). This stems from the absence of large-scale, high-quality, multi-resolution, real-world triplet dataset comprising an object, a scene, and the object seamlessly integrated into the scene, which is expensive to produce.
Training-free methods, on the other hand, modify the inference process of pre-trained models to achieve composition without additional training. Early approaches like TF-ICON (Lu et al., 2023d) leverage accurate image inversion to lay the groundwork for composition, achieved through com- posite self-attention map injection. TALE (Pham et al., 2024) and PrimeComposer (Wang et al., 2024c) build on TF-ICON to enhance identity preservation and background-objer style adaptation. TIGIC (Li et al., 2024b) focuses on preserving non-target areas during composition. Thinking Out- side the BBox (Canet Tarrés et al., 2024) enables unconstrained image compositing, unbound by input masks. FreeCompose (Chen et al., 2024e) employs pipeline of object removal, image har- monization, and semantic composition. DreamEdit (Li et al., 2023b), UniCanvas (Jin et al., 2025), and Magic Insert (Ruiz et al., 2025) use test-time tuning to fine-tune models during inference. Add- it (Tewel et al.) enables text-guided object insertion on FLUX, where users describe objects via text prompts instead of reference images. EEdit (Yan et al., 2025) recently improves TE-ICON on FLUXX, introducing step-skipping to reduce time costs and spatial locality caching to minimize redundancy. However, training- freee methods rely on precise image inversion and fragile attention surgery, which can lock inserted objects into the exact pose of the reference image, leading to awkward or contextu- ally inappropriate orientations. Attention manipulation often causes instability and hyperparameter sensitivity, as feature attention map injection does not always preserve subject identity. This cre- ates a trade-off: stronger injection preserves identity but results in unnatural poses, while lighter injection yields more natural poses but compromises identity.
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A.2 GENERAL IMAGE EDITING
Recent and significant advancements in text-to-image generative models have enhanced numerous applications (Zhou et al., 2023; 2024a;b;c; 2025a;b; Wang et al., 2025b; Wang & Chen, 2025a;b;
Zhao et al., 2024a; 2025b;a; 2024b; 2026; Li et al., 2025; Gao et al., 2024; 2025; Zhu et al., 2024; Lu et al., 2024b; Ren et al., 2025; Yu et al., 2025b). Instruction-base rapidly. Early systems relied on modular, two-stage pipelines: a multimodal language model first produced textual prompts, spatial guidance, synthetic instruction- -mmage pairs, and a separate dif- fusion model then executed the edit- as InstructEdit (Wang et al., 2023), InstructPixxPPix (Brooks et al., 2023), MagicBrush (Zhang et al., 2023b), and BrushEdit (Li et al., 2024c). Recent work has shifted toward tightly integrated, instruction-centre architectures. Models such as SmartEdit (Huang et al., 2024), X2I (Ma et al., 2025a), RPG (Yang et al., 2024), AnyEdit (Yu et al., 2025a), and UI- traEdit (Zhao et al., 2024c) embed routing, task-aware objectives, and fine- ane-grained controls directly into the network, yiclding higher fidelity and more precise manipulation.
image editing has evolved
frameworks (e.g., OmniGen (Xiao et al., 2025), ACE (Han et al.), ACE++ (Mao et al., 2025), Lumina-Omni OmniLV (Pu et al., 2025), Qwen2VL-Flux (Lu, 2024), Drea- mEngine (Chen et al., 2025), MetaQueries (Pan et al., 2025). Hidream-El (HiDream-ai, 2025)) treat editing as one capability of end-to-end vasion-language model, often fusing language embed- dings with diffusion latents to provide context-aware, pixel-level control. Efficiency has advanced in parallel: ICEdit (Zhang al., 2025) couples LoRA with mixture-of-expprts tuning and optimized noise initialization, while SuperEdit (Ming et al., 2025) relies on higher-quality data and contrastive supervision sustain performance at lower cost. Looking ahead, large foundation models such as Gemini (Gemini2.5, 2025) and GPT-5 (OpenAI, 2025) already show strong visual reasoning and coherent, instruction guided image generation. Yet, despite extensive multimodal pre-training they still fall short on image composition: object placement remains hard to control, lighting is often inconsistent, and subjects can drift in identity.
Unified generation-and-edittnn
A.3 SURJECT-DRIVEN GENERATION
Extensive research has explored subject-driven image generation, in which the output must not only portray the contexts described by the text prompt but also faithfully include the specific subject supplied by reference images. Methods in this area are divided into two categories-test test-ttmme fine- tuning customization and zero-shot customization according to whether extra training is needed for each new subject. Our framework accommodates both categories, so we provide two corre- sponding variants in the main paper.
Test-time fine-tuning methods (Gal et al., 2022; Ruiz et al., 2023) adapt a pre-trained T21 model to small set of reference images (typically 3 to 5 images). Although this step adds computational cost and latency, it offers the greatest flexibility for diverse customization requirements. Such meth- ods are commonly grouped into three subclasses: data regularization, weight regularization, and loss regularization. In the data-regularizzttion family, DreamBooth (Ruiz et al., 2023) limits overfit- ting by generating superclass images with the base T2I model and training on both reference and regularization images; Custom Diffusion (Kumari et al., 2023) improves regularization quality by retrieving real images; and Specialist Diffusion (Lu et al., 2023a) applies extensive data augmen- tation. reeghtt-egullarization approaches (Gal al., 2022: Hu al.: Han al., 2023; Qiu et al., 2023) confine updates to carefully chosen parameters, such as the subject-specific text embeddings or the singular values of weight matrices. Loss-eegulaiization approaches, including Specialist Dif- fusion (Lu et al.. 2023a), MagiCapture (Hyung et al., 2024), and FaceChain-SuDe (Qiao et al., 2024), introduce objective terms that respectively maximize CLIP-space similarity between generated and reference images, disentangle identity and style via masked facial reconstruction, or encourage cor- rect superclass classification.
Zero-shot image customization methods avoid subject-specific fine-tuning at inference time but rely on extensive pre-training. For general subject customization, InstantBooth (Shi et al., 2024a) adds visual encoder that captures coarse-to-fine image features from the references; BLIP- Diffusion (Li et al., 2024a) fine-tunes BLIP-2 (Li et al., 2023a) to extract multimodal subject repre- sentations; ELITE (Wei et al., 2023) maps reference images into hierarchical textual tokens through global and local networks; and Song et al. (Song et al., 2025b) enhance textual control by remov- ing the projection of visual embeddings onto textual embeddings. For facial customization, Instan-
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tID (Wang et al., 2024b) isolates facial regions from reference images to extract appearance and structural cues. For style customization, InstantStyle (Wang et al., 2024a) identifies style-con coytocconto layers and injects IP- Adapter features (Ye et al., 2023) into those layers to achieve style transfer. In- stantCharacter (Tao et al., 2025), IP-Adapter (Ye et al., 2023), and Pul.I) (Guo et al., 2024) have each released versions compatible with the FL.U model.
B VISUALIZING THE IMPACT OF ADAPTIVE BACKGROUND BLENDING
Although our loss function aims to find a new latent within the manifold learned by the adapter, en- couraging the adapter-augmenter T2I model's predictions to closely match those of the base model on the original noisy latent, this early-stage optimization primarily preserves structural elements rather than fine details. Consequently, discrepancies in fine-grained features often arise between the masked and unmasked regions. As illustrated in Fig. 9, we compare the composite images generated using our Adaptive Background Blending (ABB) method with those produced via direct background blending using the rectangular user mask. For clarity, we enlarge the boundary areas of each image (highlighted in pink dashed boxes) to better reveal differences.
w/o ABB
w/ ABB
w/O ABB
w/ ABB
(c)
11)
Figure 9: Comparison of composites from Adaptive Background Blending (ABB) method and direct blending with a rectangular mask. Boundary regions within pink dashed boxes are enlarged for clarity. Please zoom in to details.
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C EQUIVALENCE OF QUERY BLURRING AND ATTENTION WEIGHT BLURRING
Consider 2D Gaussian filter G applied convolution operation, denoted by @ The self-attention weights are computed as QK*, where Q. K R with n being the sequence length and d the embedding dimension. We explore the effect of applying a Gaussian blur to the attention weights and its equivalence to blurring the query matrix.
C.I BLURRING THE QUERY MATRIX
Blurring the self-attention weights QK with 2D Gaussian filter G can be expressed as:
G + (QK*),
where @ denotes 2D convolution. Due to the linearity of convolution, there exists a Toeplitz matrix B E RRxx such that the convolution operation can be represented as matrix multiplication:
G (QK") B(QK")
Using the properties of matrix multiplication, we can rewrite this as:
B(QK")= = (BQ)K*.
(8)
Since the convolution operation is linear, applying the Gaussian filter G to the rows of Q yields:
BQ = G @ Q
Thus, we obtain:
G @ (QK - (G & Q)K
(10)
This establishes that blurring the query matrix Q with G is mathematically equivalent to applying the same blur to the self-attention weights QK This equivalence suggests that query blurring can be used as computationally efficient proxy for smoothing attention weights, potentially reducing the need for direct manipulation of the attention matrix.
C.2 BLURRING THE KEY AND VALUE MATRICES
In contrast, applying the Gaussian blur to the key matrix K does not yield similar equivalence. Consider the convolution applied to K. The resulting attention weights become:
QG @ K) Q(BK) Q.
(11)
Since T + B for general Toeplitz matrix derived from Gaussian filter, we have:
QK BT B(QK)).
(12)
Thus, blurring the key matrix K does not produce an equivalent effect to blurring the attention weights QK* A similar argument applies to the value matrix V. as the output of self-attention, (QKT)V, involves V in a post-uuutiliication step, and convolution on V does not commute with the attention weight computation in the same manner.
C.3 IMPLEMENTATION DETAILS OF GAUSSIAN BLURRING
For the 2D Gaussian filtering step, adopt the implementation provided by the kornia library. Following standard engineering practice, we set the kernel radius to r 3o, since three standard deviations capture approximately 99.7% of the Gaussian mass. Consequently, the kernel size is chosen as the nearest odd integer to 2r = 6a. In all our experiments we use a = 10 (see Tab. 5).
The procedure is applied to query embeddings by reshaping them into spatial feature maps. perform- ing Gaussian smoothing, and then mapping them back into the sequence domain prior to attention computation. The full workflow is given below:
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Algorithm 2 Implementation Details of 2D Gaussian Blurring
Input: query matrix Q E BxLDx key K, value V, spatial dimensions (H, W), Gaussian standard deviation
Output: Attention output O.
// Reshape query into spatial tensor
2: 2sp + Reshape( Q, (B, H, H,W,
3: Qsp f- Permute Qu: (0, (,3,),2)
BBxxxxx
4: // Construct Gaussian kernel size
5: k + 60
6: k + k (k: mod 2) +
Ensure odd kernel size
7: kernel_size + (ki k)
8: (0,00)
9: // Apply 2D Gaussian smoothing
10: 2x kormaa.filtess gausssiin blur2d(Qup: kernel size, σ)
11: // Reshape smoothed queries back to sequence form
Q' + Permute Sp. (0, 2,, 1))
13: Q' + Reshape Q² L,)
14: // Compute attention
15: A < softmax
Q'K'
VD
AV
17: return O
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Figure 10: The loU is calculated between the mask produced from each block and the ground-truth mask which is obtained by segmenting the final generated images using SAM. The loU for each block is averaged over 100 images.
D EVALUATING CROSS-ATTENTION MAP ACCURACY VIA IOU
To identify the most accurate cross-altention maps that reflect the location of the generated object, we first create 100 prompts containing a main subject (e.g.,"t a dog is sleeping on couch") using GPT-5. These prompts are then used generate 100 images with FLUX. 1-ee, employing 20 de- noising steps. Cross-attention maps are extracted from 19 multi-stream (or double-strean blocks and single-stream blocks across denoising steps. The maps are averaged over the 20 steps and subsequently normalized and binarized, resulting in total of 57 binary masks.
To determine which of these 57 masks is the most accurate, we compute the Intersection over Union (IoU) between each mask and a ground-truth mask obtained by segmenting the final generated im- ages using SAM (Ravi et al., 2025). The IoU for each block is averaged over the 100 generated images. The results are presented in Fig. 10, showing that the cross-attention maps from the last multi-stream (or double-stream) block achieve the highest segmentation accuracy. A visualization of the cross-attention maps from all blocks is provided in Fig. 11.
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Prompt: baby is crawling on soft carpet
Orig
Double
Double 2
Double
Double
Double
Double
Double 7
Double
Double
Double 10
Double 11
Double
Double
Double 14
Double
Double
Double
Double 18
Double 19
Single
Single
Single 3
Single 4
Single
Single
Single 7
Single 8
Single
Single 10
Single 11
Single 12
Single 13
Single 14
Single 15
Single 16
Single 17
Single 18
Single 19
Single 20
Single 21
Single 22
Single 23
Single 24
Single 25
Single 26
Single 27
Single 28
Single 29
Single 30
Single 31
Single 32
Single 33
Single 34
Single 35
Single 36
Single 37
Single 38
Figure 11: Visualization of aross-attention maps from different MMDiT blocks of FLUX.I-dew.
E EXPERIMENTS WITH SDXL, SD3.5, AND PIXART
Our framework introduces a novel, model-agnostic approach for enhancing generative architectures, anchored by three synergistic components: Sanifold-Steerel Anchor (MSA) loss, Degradation- Suppression Guidance (DSG), and Adaptive Background Blending (ABB). These components meticulously designed to leverage ubiquitous features modern generative models, ensuring seamless integration without requiring architectural modifications. Specifically, MSA utilizes ei- ther LoRA-based personalization or a pretrained personalization adapter, DSG capitalizes on widely available self-attention maps, and ABB harnesses text-image cross-attention maps, a staple in most text-to-image pipelines. This design ensures broad applicability across diverse generative models.
In the main text, we showcase the effectiveness of our approach on Flux, leading open-source model. To establish its generalizability, we conduct experiments on SDXL (Podell et al., 2024), SD3.5 (Esser et al., 2024), and PixArt (Chen et al.) across DreamEditBench and ComplexCompp The results are presented in Tab. 3. On BrenhEditBench, our MooRA-basee variant with Flux achieves state-of-the of-tth--rttttttte performance in subject identity preservation, while delivering superior image quality. Similarly, on ComplexCompo the Flux-LoRA configuration excels in identity consistency and im- age fidelity. Notably, the framework's benefits extend beyond single model family: both SDXL and
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PixArt-E exhibit substantial performance gains, affirming the approach's 's generality and adaptability across diverse generative architectures.
Table 3: Comparison of compositional performance across two benchmarks with different base models. The best result in each column is highlighted in bold, while the second-best is underlined. Metrics shown in pink are those specifically trained to better align with human preferences. Abbre-
viations: IRF: Instance Retrieval Features; IR ImageReward: VR VisionReward.
Bench
Method
Base Model
Subject Identity Consistency
Background
Image Quality
CLIP-11
DINOv2
IRF
DreamSim
LPIPS
SSIM
t
VR
Flux.1 Fill
FLUX
0.7328
0.6745
0.5754
0.5233
0.0166
0,9076
0.5577
3.5997
DreamEdit-
Ours Adapter
SDXL
0.7944
0.7334
0.7659
0.3761
0.0238
0.8922
0,5621
3.6158
Rench
Ours-Adapter
SD335
0.8054
0.7407
0.7699
0.3745
0.0234
0.8937
0.5701
3.6187
(220)
Ours LoRA
PixArt-3
0.8098
0.7445
0.7798
0.3612
0.0251
0.8775
0,5842
3.6198
Ours-Adapter
FLUX
0.8086
0.7415
0.7702
0.3730
0,0236
0.8959
0.5709
3.6234
Ours-LoRA
FLUX
0.8125
0.7452
0.7900
0.3577
0.0271
0.8847
0.5906
3.6161
Flux.1
FLUX
0.7108
0.0232
0.7442
3,5737
Complex-
Ours-Adapter
SDXL
0.7657
0.7084
0.6862
0,4457
0.0457
0,7612
0.3894
3.5987
Compo (300)
Ours- Ours-Adapter
SD3.5
0.7701
0,7091
0.6977
0.4173
0.0401
0.7784
0,4091
3.6021
Ours LoRA
PixArt-3:
0.7924
0.7287
0,7311
0.3603
0,0424
0.7698
0.42777
3,5988
Ours-Adapter
FLUX
0.7721
0,7107
0.6764
0.4294
0.0404
0.7789
0,4090
3.6020
Ours-LoRA
FLUX
0,7999
0.7384
0.7659
0,3542
0,0430
0.7634
0.4246
3.5951
F
USER STUDY
We conduct study involving 50 participants. Each participant asked to complete 50 rank- ing tasks. In each task, they were shown 13 composition results generated by different methods, along with reference subject image.
To ensure a balanced evaluation, 25 of the tasks were randomly sampled from DreamEditBench and the remaining 25 from ComplexCompo. Participants were asked to rank the results based on two key criteria: (1) subject identity consistency and (2) composition realism. A lower rank (e.g., 1st) indicates better composition result, while higher rank (e.g.. 13th) reflects less favorable outcome.
We summarize the average ranking scores for each method in Tab, Our method received the most favorable rankings from the majority of participants, demonstrating its effectiveness in producing high-quality compositions.
G BENCHMARK DETAILS
Our benchmark consists of 300 triplets, each comprising subject image, background image, and bounding box. The subject images are identical to those used in DreamEdiiBench (Li al., 2023b; Ruiz et al., 2023), while the background images are sampled from the OpenImages dataset (Kuznetsova et al., 2020). These backgrounds exhibit variety of aspect ratios and reso- lutions, including landscape and portrait formats, such as 768 x 1088, 768 1072, 768 x 1024, 768 x 1152, 1024 x 768, 1152 768, 1200 768, 848 768, and 1360 x 768. The bounding boxes are manually designed to ensure that the size and placement of the inserted subjects are contextually appropriate and visually plausible. The benchmark will be released publicly.
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Table 4: Average ranking scores from the user study on image composition methods. Lower is better.
Method
Training-Fres Free
Average Ranking (Lower is Better)
MADD
x
12.44
ObjectStitch
x
11.80
DreamCom
X
6.66
AnyDoor
X
4.12
UniCombine
X
2.94
PBE
X
4.94
TIGIC
9.74
TALE
9.06
TF-ICON
8.36
DreamEdit
6.36
EEdit
10.76
Ours-Adapter
2.30
Ours-LoRA
1.52
H PROMPTS FOR PROPRIETARY FOUNDATION MODELS
To perform image composition with proprictary, general-purpose multimodal foundation models (e.g., GPT-5 (OpenAI, 2025), Gemini 2.5 Pro (Gemini2.5, 2025), SeedEdit/Doubao (Shi et al., 2024b), and Grok (gro, 2025)), we upload three images: (1) Subject image; (2) Background image; and (3) Mask image defining the insertion region.
We then issue prompt of the following form (with the resolution and coordinates adjusted for each case):
Please insert the object from the first uploaded image into the second image. The target region for insertion defined by the mask in the third image. For reference, the resolution of the second image is 1152 x 768, and the bound- ing box for placement is specified by the top-left and bottom-right coordinates: (x1 = 550, 1/1 = 600, 700, y/2 = 750). The inserted object should retain the same identity and appearance as the first image. The final composite should appear realistic, natural, and physically plausible.
I SUBJECT IDENTITY METRICS ANALYSIS
In our experiments we found that widely-used subject-identity metrics such as CLIP-I (Radford al., 2021) and DINOv2 (Oquab et al., 2024) correlate poorly with human preferences. Because they focus almost exclusively on semantic similarity, they ignore appearance changes introduced by lighting, shadows, reflections, and surrounding context. Fig. 12(b) presents several image pairs produced by AnyDoor (left) and by our method (right); the corresponding CLIP-I (f) (Radford et al., 2021), DINOv2 (1) (Oquab et al., 2024), IRF (1) (Shao & Cui, 2022), and DreamSim (4) (Fu al., 2023) scores are shown beneath each image, with the better score highlighted in red. Although the AnyDoor results are visibly less realistic and less consistent, they nevertheless receive higher CLIPPII and DINOv2 scores, and in most cases higher IRF scores, demonstrating that these measures do not faithfully capture compositional quality.
A reliable metric should recognise the same object whether it is underwater (see Fig. (2(b)((8), (17), (20))), in shadow (see Fig. (2(b)((9), (13), (19)I), partially occluded (see Fig. (2()) or situated in low-light or back-lit scene (see Fig. (2(b) (14), (15), (16)]). Among the metrics we evaluated, only DreamSim, which was designed to align more closely with human perception, consistently exhibits this desired behaviour.
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Subject images for evaluating identity consistency
CLIP-I: GAZAE
0,779W
CLIP-1:0. 0,0.006
CLIPE 0.3744
CLIP-E 0.3662
CLIP-I
0.790
O 7004
DINOv2:
0.6912
DINOVE
DINO<2: 0.6058
IRF
0.8964
IRF 0.7813
DreantSim: 0.36M3
DreamSium: 0.3375
DreamSim: 0.396]
DecanSim. 0.3216
DreamSSun: 0.3018
DecunSSi:: 0,2877
DreamSim: 0.479
DreanSium: 0.2943
(2)
(3)
CLIP 0.891 DINOx2:
0,5452
CLIPA 0.9146
0.8975
CLIFE 0.8633
CLIP 0.8477
CLIP-I 0.262
0,7521
IRF: 2915
0,808
IRF:0 RPP00.9399
IRF:
IRF: 1960
IRP: 0,772
IRF: 8041
DreamSim: 0,2947
DreamSim: 0.2833
DreamSim: 0,2439
DecamSim. 0.2199
DreamSau: 0.3427
DecumSim: 0.3160
DreamSim: 0.3499
DreamSium: 0.2135
CLIP 0.8799 DINO o. 1668
CLIP 0,5208
CLIP E: 9ORN DINO<2: O 7225
CLIP 0,8589
CLIP& O. 7900 DINO<2: 8,6271
CLIP J: CLO0D9
CLIP E 0,5638 DINOv2: 0.9944 IRF 0.0655
CLIP J: 0.159
DENOw2: O. 2.7233
DINOx2: 0,6000
DINOx2: 0,5849 IRF: 0,5596
DINO<t: 8,6579
0.7V3
LRF: 1178114
IRF: 0.9126
IRF: 0.9005
IRF 0,5975
IRF: 0.7865
DreamSim: 0,3775
DreamSinn
DreamSim: 0.2786
DecamSim: 0.2729
DreamSiin: 0.5784
DeaumSim: 0,4033
DreamSin: 0.337
DreamSim: 0.3181
(10)
CLIP- 0.811
CLIPE 0.845
CLIP-I: 87400
CLIPE 0.188
CLIP-I: 0.894
CLIPE 0.7129
0,7775
DINOV2 7372
DINO12: o 0,7001
DENOv2, 0.7740
DINO12: 0,6272
DINOV2 0.6196
0.7495
0,323
IRF:
DizunSimn
DecamSin::
Dic 0,3588
DreamSim
Dezen
DecamSim
(13)
(14)
(15)
CLIP-I-O.R20
DINO
0,6355
IRF: 0.5574
0.7:9
IRF: 0,7077
IRF: 0,7277
IRE: 0.3161
DreamSim:0.kk 0.4944
DreamSine 0.1156
DremSSm: 0,3254
DreamSin: 0.3081
DremSim: 0,335
DreamSine 0.3327
CLIP-L 0.9141
CLIP 0.8818
CLIP-I: 0.915
CLIP-E 0,9955
CLIP-I: 0.855
CLIP E: 0,8115
DINOx2: 0/7564
DENOw2: 0.6614
DINO 0,7568
DENOv2: 0.6728
OINOxx:00.169
DINOv2: 0.6629
0,9269
IRF: 09130
IRF:
O.F:0.8490
DreamSins 0.2340
DreamSine
(19)
(20)
(21)
(b) Comparison of Identity Consisteney Metries
Figure 12: Comparison of Subject Identity Metrics. (a) Reference subject images used for metric calculations. (b) Image pairs generated by AnyDoor (left) and our method (right), with correspond- ing CLIP-I (t), DINOv2 (t), IRF (1), and DreamSim (L) scores displayed below each image; the better score is highlighted in red. Despite AnyDoor's 's results appearing less realistic and consistent, they often achieve higher CLIP--I, DINOv2, and IRF scores, indicating that these metrics may not reliably reflect compositional quality. In contrast, DreamSim provides a more reliable assessment.
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Table 5: Hyperparameters Our Frameworks. Bin Thresh Binarization Threshold; #iter Number of Iterations.
Denousing Seoup
Manifold Stecred Anchor Loss
Degradation Suppression Guadance
Adapiie Background Blending
Variant
Total stops
Stat step ft
Step Range
Learning Rite
Niters
Step Range
Scale
Blur
Step Raage
Hin Thresh
Dilation
(2" ++ 0
-
-
(6:
1)
Kemel Size
Ours Adagter
19 0
14
14 14-+ 12 13-+ 12
300, 750, 1000
10
14 0
0.5
10
13 -
0.2
3
Ours LoRA
0
300
15
0.7
10
0.4
J IMPLEMENTATION DETAILS
The hyperparameters used in our frameworks are summarized in Tab. 5. Under Denoising Setup, "Total steps" refers to the full diffusion/noising schedule, which specifies a sequence of 20 values of ar across timesteps I. However, our method does not begin denoising at the first timestep. As shown in Algorithm 1, it starts 4 = 14 (Ours-Adapter), resulting in 15 denoising steps total. Under MSA loss, "Step Range" indicates the subset of denoising steps which MSA optimization is applied. For the adapter setting, the MSA loss is applied only to the first three denoising steps (from 14 more tailored prior than the generic adapter, allowing it to converge in fewer steps and with lower overall compute.
12). In addition, since LoRA is trained for specifie subject, it provides
Each baseline is implemented according to the configuration settings recommended in its original publication. The repositories utilized for each baseline are listed below:
1. MADD: http://gthhlooooknkkkuoooeooooor
2. ObjectStitch: http://ghhhllcooooooo////cccohcp
3. DreamCom: DreanCom:htpp://gigien http://gihhhooooomo///////nnnnnn
4. AnyDoor: https//githhh.commmadiiiia///oo
5. UnfCCmniie.iiiigi/tcooooooooo/on
6. PBE: http://iitbbboooooo//aaaaanu/uut
7. TIGIC: https//giitaabcco//zccal////CCCc
8. TALE: http:///ggtbbbooom//////am33s11a
9. TF-ICON: http://ggtthbbccmmm/hiiiiiLLL---
10. DreamEdit: http:///tttii.ooo//////////ttttn
11. EEdit: http://ggithbbbooooyyyyyyy/////a
K DISCUSSION ON INVERSION VS. ONE-STEP FORWARD DIFFUSION
We provide an expanded discussion of our design choice between inversion and one-step forward diffusion to better clarify the motivation behind our approach. In training-free image editing, both inversion and one-step forward diffusion are commonly used obtain noisy latent that serves as the starting point for subsequent optimization or denoising. In our framework, we intentionally adopt one-step forward diffusion a practical substitute for inversion. Our method does not depend on the initial latent to preserve object identity. Instead, the MSA loss extracts object-specific information from the adapter/L.oRA and injects it into the latent during the editing process.
This design choice is motivated by practical observation: many modern models are distilled for speed, making accurate inversion difficult to achieve practice. When inversion cannot reliably encode object identity, its benefit becomes limited. In such cases, noisy latent that still retains enough background structure is sufficient as a starting point. For this reason, we adopt one-step forward diffusion a pragmatic replacement for inversion, rather than theoretically equivalent alternative. It offers two advantages: (i) it avoids the accuracy limitations of inversion on distilled models, making it more broadly applicable, and (ii) is faster than performing inversion.
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Table 6: Image Quality Evaluation Results using HPSv3 and Rnir variants.
Unte wer.ettlkkkkeeee wew.-geeeeehhhc
Areiago
Auerag
Quiliy
Arstaee
MADD 3004
11,0127
13.9148
10:1103
euSttch 2003)
10:054
14.9221
18884
23 1..99
18 18:5--5
30,250
12 115114
20.7231
25.0311
348912
03:1132
348727
15
(TTa 3014
84867
23.356
174125
35.9713
304000
19:204
193965
31,3633
6.594
210014
33.533
25:3330
Untireeec (Wung c.l. 30300
024.3
22.4443
12.98
23 1862
11.231
211997
31 111
233713
01.040
22/481
21.480
192381
215633
213431
25.234
HHRE lJ 21240)
32805
11:133
14.90
150013
11.8%
133300
104633
17:296
181833
1C:000
10224
11.000
21685
203335
15.115
TNCCNN dlect de 38850
72843
204480
13335
15300
20.1177
100111
16.711
30870
15.570
21200
20.184
25.035
FRS (V 2005)
6:681
15 111190
1015
4.900
11 165
10 0.50
116)
13794
11337
10.030
Ches- Lak
13345
11:84
16.1166
11
73985
1143
257590
256163
MARD Nel0
miia shrague 2003)
8.118
16:155
13177)
11:4330
14:90
11300
1600
15:99
19:000
118116
14:191
19
AnyDve (Clcs cill 2004
8.7703
217833
131113
20:5577
18:330
13.47
15 11407
110500
1316
183055
17.443
213467
155335
11.8%
Gordle- Cuego
FRL Dhing anul 3013)
4.521
18:111
13130
210013
11397
54 43M
110055
11135
19631
18900
11364
213003
13640
11,070
TALE (Purcit 2000)
8.731
16:094
11385
17050)
150000
23386
18553
18630
16750
15 111
3180
16656
187055
3.57
1000
21.10:
bani 200300
838034
112200
133770
133954
13.5%
15:431
135835
E2Ai 2003)
8:333
23.801
197748
22.001
13
228332
223952
11.99%
28,1536
21.000
22/421
22.274
2037133
20,5447
13465
11,9532
153775
220030
23349
31,123
228031
31.218
21002
24,468
77.990
22.835
19.230
EEdit also proposes an elegant strategy, termed inversion skipping, to accelerate the initialization process. In EEdit, the initial latent is obtained via an inversion procedure involving model predic- tions, while inversion skipping significantly reduces the number of required steps. In contrast, our one-step forward diffusion does not perform inversion at all. The initial latent is produced by directly sampling noise and adding it to the clean latent at chosen timestep, without any model prediction. This makes our initialization computationally lighter. Because FLUX-Dev is a CFG-distilled model, its inversions are relatively imprecise, which we believe contributes to the weaker subject-identity preservation observed in EEdit. Our method is therefore designed to avoid reliance on accurate in- version in such distilled settings.
L ADDITIONAL IMAGE QUALITY EVALUATION USING UNIFIEDREWARD AND HPSV3
To provide a more comprehensive assessment of composition quality, we further evaluate the meth- ods using three variants of UnifiedReward (Wang et al., 2025c:d) (UnifiedReward-22.6) 10ed..00--eee33---- UnifiedReward- Edit-qwen3vl-8h FaaeedRRewar--Ediii-weev//// and UnifiedReward- wwwaaarr--hii-ggeeeee think-qqwe--7)) as well as HPSv3 (Ma et al., 2025b). The results are summarized in Tab. 6.
M FURTHER ANALYSIS ON DSG IN SD3.5
We conduct experiments on SD3.5 by replacing our DSG mechanism with standard negative prompt- neg ing. Specifically, in Eqn. 3, we substitute the negative velocity V'-Ae with the velocity obtained using a variety of commonly used negative prompts. The four sets of negative prompts used are:
1. distorted, deformed, glitch, artifacts
2. undefined shapes, bad anatomy, unnatural pose
3. low quality, worst quality, low resolution, blurry, out of focus
4. AI artifacts, melted objects, strange textures
The quantitative results on the DreamEditBench dataset are presented in Tab. 7.
Key Insights These results lead to the following key conclusions:
1. Performance Sensitivity: The performance of standard negative prompting is sensitive to the specific wording used (e.g., Prompt 2 performs better than Prompt 4). This confirms that the effectiveness of heuristic negative prompts relies heavily on manual, often tedious, prompt engineering.
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Table Comparison negative prompting versus DSG on SD3.5 on the DreamEditBench.
Method
CLIP f
DINO
IRF 1
DreamSim І
LPIPS 4
SSIM f
IR
VR f
Ours-SD3.55 3D3.5-Adapier (w/ NP
0.7934
0.7311
0.7608
0.3918
0.0331
0.8812
0.5837
3.5714
Srns-SD3.5-Adppeee (w/ NP
0.8029
0.7388
0.3781
0.0259
0.5726
3.6102
Durs-SD3.55AAie (W/ NP
0.7958
0.7329
0.7623
0,3892
0.0315
0.8839
0.5814
3.579
Ours-SD3.5-Adgin Adapier (w/ NP 4)
0.7887
0.7281
0.7574
0,3976
0.0368
0.8765
0,5882
3.5548
Ours-SD3.5-Adapter (w/ DSG)
0.8054
0.7407
0.7699
0.3745
0.0234
0.8937
0.5701
3.6187
2. Adaptive Guidance:
Standard negative prompts generate a degradation direction that is decoupled from the specifies of the input image. In contrast, DSG constructs an image- specific low-quality direction by blurring the attention component, making it significantly more adaptive, stable, and effective across diverse inputs without requiring any manual prompt tuning. DSG consistently outperforms all tested standard negative prompts across metrics.
This analysis confirms that DSG provides a superior, more robust, and automated mechanism for degradation suppression compared traditional negative prompting, in models like SD3.5 where negative prompts are generally effective.
N RUNTIME COMPARISON
The wall-clock runtime at a 512 x 512 resolution on an H100 GPU is summarized the table below. In implementation, inference and reduce GPU memory usage. Additionally, Ours-Adapter can also be run on a 24-GB GPU by enabling CPU offloading.
applied qint8 quantization all LLUX-based methods to accelerate
Table Runtime and memory usage comparison of various image composition methods a 512 512 resolution.
Training
Base Model
External Model
Time
Peak Memory
Method
Free
(s)
(MB)
MADD (He et al., 2024)
SD
DINO
45.73
11708
ObjectStitch (Song et al., 2023)
x
SD
VIT
6.63
8268
DreamCom (Lu et al., 2023c)
x
SD
LoRA
9.87
3388
AnyDoor (Chen et al., 2024c)
x
SD
DINO
8.61
18612
UniCombine (Wang al., 2025a)
X
FLUX
LoRA
11.98
22711
PBE (Yang et al., 2023)
X
SD
3.52
10842
TIGIC (Li et al., 2024b)
SD
10.82
21640
TALE (Pham et al., 2024)
SD
8.03
23524
TF-ICON (Luu al., 2023d)
SD
24.55
20670
DreamEdit (Li et al., 2023b)
SD
LORA, VIT
99.83
19298
EEdit (Yan al., 2025)
FLUX
60.31
26546
Ours-Adapter
FLUX
Adapter 38.29
32552
Ours-LoRA
FLUX
LoRA
18.08
23519
O ADDITIONAL QUALITATIVE RESULTS
We offer more qualitative assessment results, including visualizations of baselines, presented in Figs. 13 to 18.
P LLM USAGE STATEMENT
We used large language models for text polishing and grammar correction during manuscript prepa- ration. No LLMs were involved in the design of the method, experiments, or analysis. All content has been carefully verified and validated by the authors.
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Condition
MADD
ObjectStitch
DreamCom
AnyDoor
UniComb
PBE
Figure 13: (Part of 2) Qualitative comparison our method against baselines in challenging scenarios.
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TIGIC
TALE
TF-ICON
DreamEdit
EEdit
Ours-LoRA Ours-Adapter
Figure 14: (Part of 2) Qualitative comparison of our method against baselines in challenging scenarios.
34

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Published as a conference paper at ICLR 2026
Condition
MADD
ObjectStitch
DreamCom
AnyDoor
UniComb
PBE
Figure 15: (Part of 2) Qualitative comparison our method against baselines in challenging scenarios.
35

---

Published as a conference paper at ICLR 2026
TIGIC
TALE
TF-ICON
DreamEdit
EEdit
Ours-LoRA Ours-Adapter
Figure 16: (Part of 2) Qualitative comparison of our method against baselines in challenging scenarios.
36

---

Published as a conference paper at ICLR 2026
Condition
MADD
ObjectStitch
DreamCom
Anoo
UniComb
PBE
Figure 17: (Part of 2) Qualitative comparison of our method against baselines in challenging scenarios.
37

---

Published as a conference paper at ICLR 2026
TIGIC
TALE
DreamEdit TF-ICON
EEdit
Ours-LoRA Ours-Adapter
DOOPIC
Figure 18: (Part of 2) Qualitative comparison of our method against baselines in challenging scenarios.
38


Reviews:
[{"review_id": "4Y6kjwNRTb", "reviewer": "Reviewer_6v7A", "summary": "This paper addresses the challenge of physically plausible image composition, particularly the failure of existing models to handle complex lighting, shadows, and reflections. It propose SHINE, a \"training-free\" framework that is effectively a test-time optimization process. \nThe framework consists of three main components: \n1) Manifold-Steered Anchor (MSA) Loss, a guidance loss that uses a pretrained adapter (e.g., IP-Adapter) and the base model's predictions as an anchor to steer the latent variable; \n2) Degradation-Suppression Guidance (DSG), a form of negative guidance achieved by blurring specific query features ($Q_{img}$) inside the DiT to avoid artifacts; \n3) Adaptive Background Blending (ABB), a technique that uses cross-attention maps in early sampling steps to create smoother blends. \n\nAdditionally, the authors contribute a new benchmark for evaluating composition in complex lighting conditions.", "questions": "1.  Compute Cost Quantification: Could you provide a fair, wall-clock time comparison on identical hardware, e.g., how much time does SHINE (with $k=10$) take to generate one 1024x1024 image, versus the time required by baselines like AnyDoor or UniCombine?\n2.  DSG Generalizability: You claim in Appendix E that DSG applies to SDXL, SD3.5, and PixArt. Was the \"blur $Q_{img}$\" heuristic applicable *out-of-the-box* to all these architectures, or did each one require a *new* round of brute-force experimentation to find a *different* hack (e.g., \"blur $K_{txt}$\")?\n3.  Pre-processing Failure Rate: I am curious about the actual failure rate of your [VLM + Inpainting pre-processing pipeline], when running the 300-sample ComplexCompo benchmark.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  Good Qualitative Results: The qualitative results presented (e.g., Fig. 1, 6) are impressive, demonstrating a high degree of physical realism in challenging scenarios.\n2.  Benchmark Contribution: The newly proposed ComplexCompo dataset is a solid contribution, offering a valuable tool for future research.", "weaknesses": "1.  Impractical Computational Cost: The \"training-free\" claim is misleading, as the method is a test-time optimization with *extreme computational costs*. Algorithm 1 and Table 5 indicate that it requires $k=10$ optimization iterations *per* denoising step $t$. For a 20-step process, this amounts to ~*200* forward and backward passes. On a 12B parameter model like FLUX, this cost is computationally prohibitive and makes any comparison against single-forward-pass baselines impractical.\n2.  Brittle Pre-processing Pipeline: To avoid image inversion, the paper introduces a (Sec 3.1) pre-processing pipeline that depends on *two* external models: a VLM for captioning and an inpainting model for creating $x^{init}$.  If the VLM caption is wrong (e.g., wrong color), the entire composition fails (as indicated in Sec 5-Limitation). It seems that this merely trades one failure point (inversion) for two new, uncontrolled failure points (VLM failure, inpainting failure)?\n3.  Limited Novelty and Model-Specific:\n    * MSA Loss: Re-naming a known technique (SDS) applied between an adapter and a base model does not constitute a significant novel contribution.\n    * DSG: This is an empirical, model-specific hack (via brute-force experimentation) for the FLUX architecture, it seems to lack a theoretical justification and guarantee of generalizability.", "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": "wQqaPIXlJ2", "reviewer": "Reviewer_5xsu", "summary": "The paper presents SHINE, a training-free strategy for image composition using FLUX. Since previous inversion-based and attention manipulation–based approaches struggle to achieve robust image composition, the authors introduce Non-Inversion Latent Preparation, Manifold-Steered Anchor (MSA) Loss, Degradation-Suppression Guidance (DSG), and Adaptive Background Blending (ABB). FLUX.1-dev is a CFG-distilled model, which causes high inversion errors. The subject image is captioned by a VLM, and an inpainting model creates an initial image based on the caption. Building on this, the authors perform a one-step forward diffusion to obtain and manipulate the latent. The Manifold-Steered Anchor (MSA) Loss utilizes the velocity predicted by the original noisy latent as anchor noise, along with the adapter-guided velocity prediction using the subject image’s latent. Degradation-Suppression Guidance (DSG) is inspired by negative prompting and constructs a negative velocity prediction by blurring the attention component $Q_{img}$ of FLUX. Adaptive Background Blending (ABB) selects between the attention mask and a user-provided mask depending on the timestep. Experiments demonstrate the effectiveness of the proposed framework for image composition, both quantitatively and qualitatively. The authors also propose ComplexCompo, a benchmark for challenging composition scenarios.", "questions": "1. In L260-262, how are the attention components systematically perturbed? Which 2D Gaussian filter $G$ (kernel size) is used?\n2. How much time and GPU memory are required to generate a composited image using FLUX.1-dev + Adapter and + LoRA? Also, for the other baselines?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The design of the Manifold-Steered Anchor Loss, which takes into account the flow-matching process between FLUX.1-dev and the adapter model (InstantCharacter), is well-motivated and technically sound.\n2. The in-depth analysis of the attention blocks in FLUX.1-dev (Sec. C & D) and the derivation of the subsequent improvement, Degradation-Suppression Guidance, are particularly interesting.\n3. The Adaptive Background Blending method is simple yet powerful, and it appears broadly applicable to various image editing tasks.\n4. The paper offers numerous insights drawn from a comprehensive set of dense experiments.\n5. The proposed methods are model- and adapter-agnostic, making them broadly applicable across different architectures and integration settings.", "weaknesses": "Overall, the paper is still a strong and well-executed, however, it could be further improved if the authors provide additional details and clarifications on certain points regarding the following aspects:\n\n1. In L159-161, the term “inpainted background image” appears somewhat inappropriate in this context. Phrases such as “scene image” or “image to which the subject is attached” would be more accurate and better convey the intended meaning.\n\n2. In L186-187, the authors state that they abandon the inversion process and instead apply one-step forward diffusion. I interpret this as implying that “the effect of inversion can be approximated by directly adding noise corresponding to a one specific timestep”. If this interpretation is correct, a theoretical justification should be provided. Additionally, the authors should clarify how this approach differs from the inversion-skipping technique used in EEdit [1].\n\n3. While numerical improvements are observed in Config A and C of the ablation study, it remains unclear whether DSG truly acts as a negative guidance vector when blurring the attention component $Q_{img}$ derived from the text embedding $c$ extracted by the VLM. Although FLUX is a CFG-distilled model where non-sensical or explicit negative prompts are often ineffective (as shown in Fig. 4(a)), this behavior may differ in other MM-DiT–based models such as SD3.5. It would strengthen the paper to include an analysis comparing simple negative prompting and Degradation-Suppression Guidance in SD3.5 to better quantify their differences.\n\n4. Please specify which inpainting model (e.g., Brushedit, BrushNet, PowerPaint, Flux.1 fill-dev) and which VLM (e.g., BLIP-3, InternVL, LLaVA-1.5) were used in the proposed frame work and evaluation.\n\n5. In Alg. 1, the origin of $z^{bg}$ is unclear. Is it defined as $z^{bg} = (1 - M^{user}) \\odot z^{init}$ and $z^{subject} =  M^{user} \\odot z^{init}$? Please clarify this in the text or Alg. 1.\n\n6. In Tab. 1 & 3, it would be beneficial to include results for Config A (the input image generated by the inpainting model). This addition would help readers better understand the performance gap and the impact of subsequent modules at a glance.\n\n[1] EEdit : Rethinking the Spatial and Temporal Redundancy for Efficient Image Editing, ICCV 2025", "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": "Ln7cSZfGGM", "reviewer": "Reviewer_Yk7s", "summary": "SHINE is a training-free framework designed for seamless, high-fidelity insertion with neutralized errors. It introduces the manifold-steered anchor loss, which leverages pretrained customization adapters to guide latent representations, ensuring accurate subject representation while maintaining the integrity of the background. Additionally, SHINE incorporates degradation-suppression guidance and adaptive background blending to prevent low-quality results and visible seams, enhancing the overall output quality.\nIn response to the lack of rigorous benchmarks in this domain, the authors propose ComplexCompo, a benchmark suite that tests the framework under challenging conditions.", "questions": "See Weakness.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The article presents a clear expression of its ideas, with a thorough visualization analysis of the motivation and proposed modules.\n\n2. The experiments are comprehensive, and the results appear highly promising.\n\n3. The work makes notable breakthroughs in addressing issues related to lighting realism and resolution rigidity in image synthesis.", "weaknesses": "1. As shown in Table 1, the method performs well in Subject Identity Consistency, but there is room for improvement in Background Retention. Could you provide further insights and possible approaches for enhancing this aspect?\n\n2. In the Image Quality evaluation, the models used for assessment are somewhat limited. Could you consider incorporating more diverse and widely-adopted image quality evaluation models, such as UnifiedReward [1] and HPSv3 [2], to provide a more comprehensive analysis?\n\n[1] Unified Reward Model for Multimodal Understanding and Generation. 2025\n\n[2] HPSv3: Towards Wide-Spectrum Human Preference Score. 2025", "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)