Consistent Text-to-Image Generation via Scene De-Contextualization
Authors: Song Tang
Peihao Gong
Kunyu LI
Kai Guo
Boyu Wang
Mao Ye
Jianwei Dr. Zhang
Xiatian Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: Text-to-Image generation
Identity-preserving
Prompt embedding editing
Scene contextualization
Primary area: generative models

Abstract: Consistent text-to-image (T2I) generation seeks to produce identity-preserving images of the same subject across diverse scenes, yet it often fails due to a phenomenon called identity (ID) shift. Previous methods have tackled this issue, but typically rely on the unrealistic assumption of knowing all target scenes in advance. This paper reveals that a key source of ID shift is the native correlation between subject and scene context, called scene contextualization, which arises naturally as T2I models fit the training distribution of vast natural images. We formally prove the near-universality of this scene-subject correlation and derive theoretical bounds on its strength. On this basis, we propose a novel, efficient, training-free prompt embedding editing approach, called Scene De-Contextualization (SDeC), that imposes an inversion process of T2I’s built-in scene contextualization. Specifically, it identifies and suppresses the latent scene-subject correlation within the ID prompt’s embedding by quantifying SVD directional stability to re-weight the corresponding eigenvalues adaptively. Critically, SDeC allows for per-scene use (one prompt per scene) without requiring prior access to all target scenes. This makes it a highly flexible and general solution well-suited to real-world applications where such prior knowledge is often unavailable or varies over time. Experiments demonstrate that SDeC significantly enhances identity preservation while maintaining scene diversity.

Published as a conference paper at ICLR 2026
CONSISTENT TEXI-TO-IMAGE GENERATION VIA SCENE DE-CONTEXTUALIZATION
Song Tang! Peihao Gong', Kunyu Li*, Kai Guo', Boyu Wang Mao Ye?,, Jianwei Zhang² & Xiatian Zhu S
'nniversity of Shanghai for Science and Technology, Universitää Hamburg, 'ComOriginMat Inc, "Fudan University,
Westernn University, Vector Institute. 'University of Electronic Science and Technology of China, Uniiveriity of Surrey
Project URL: httpssss/ggghaa....//tttte//////
ABSTRACT
Consistent text-to-image (T2I) generation seeks to produce identity-preserving images of the same subject across diverse scenes, yet it often fails due to a phe- nomenon called identity (ID) shift. Previous methods have tackled this issue, but typically rely on the unrealistic assumption knowing all target scenes in advance. This paper reveals that key source of ID) shift is the native correlation between subject and scene context, called scene contextualizatio, which arises naturally as T2I models fit the training distribution of vast natural images. We formally prove the near-universality
this scene-ID correlation and derive theoretical bounds its strength. On this basis, we propose novel, efficient, training free prompt embedding editing approach, called Scene De- De-Contextuaalzzttion (SDeC), that imposes inversion process of T21's built-in scene contextualization. Specifically, it identifies and suppresses the latent scene-ID correlation within the ID prompt's s embedding by quantifying SVD directional stability adaptively re-weight the cor- responding cigenvalues. Critically, SDeC allows for per-scene use (one scene per prompt) without requiring prior access to all target scenes. This makes it a highly flexible and general solution well-suited to real-world applications where such prior knowledge is often unavailable or varies over time. Experiments show that SDeC markedly enhances identity preservation while maintaining scene diversity.
INTRODUCTION
Text-to-image (T2I) generation (Shi et al., 2024; Saharia et al., 2022; Ramesh al., 2021) aims to synthesize visually compelling and semantically faithful images from prompts. From artistic design to personalized media production, T21 models such as GAN (Tao et al., 2022) and Stable Diffusion (Rombach et al., 2022b) have demonstrated remarkable capability in producing novel scenes that align closely with user intent. However, in narrative-driven visual tasks involving recurring characters or entities, such as animation/video (Lei et al., 2025), personalized storytelling (Avrahami et al., 2024), cinematic pre-visualization (Tao et al., 2024), and digital avatars (Wang et al., 2023), mere alignment with scene descriptions is insufficient: The subject's 's IDentity (ID) must remain consistent across generated images (no ID shift). Against this backdrop, consistent T2I generation has recently emerged focal point of growing interest (Höllein al., 2024; Wang et al., 2024).
Methodologically, existing approaches reduce ID) shift in line with the paradigm of transfer learn- ing (Tang et al., 2024): Extracting invariance from given heterogeneous data. This requires prior knowledge of the complete target scenes, enabling the generative model to map different scene prompts into corresponding features (Zhou et al., 2024; Liu et al., 2025) image pseudo labels (Avra- hami et al., 2024; Akdemir& & Yanardag, 2024) for constructing such diversified dataset. In practice, however, target scenes are not always available' rendering this assumption unrealistic and limiting
Corresponning authors: Mao and Xiatian Zhu
In real-world projects (e.g., films, games, or story creation). the full set of final scenes, their content, and their order often refined and finalized numerous iterative changes, making it impossible to know all

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Published as a conference paper at ICLR 2026
hyper-realistic digital painting of young man"
"A hyper-realistic digital painting of young man"
construction wedding" site"
"in police station"
"at sports stadium"
"in spring"
summer"
autumn"
Figure I: Illustration of scene contextualization with SDXL. Left: The attire the subject varies with the site. Right: The subject's is clothing changes with the season.
the practical applicability of these methods. Critically, the underlying cause of shift with T21 models remains largely unclear.
In this work, we consider that the scene plays context role that would influence the characterization of identity, called scene contexlualization (Fig. 1), causing ID shift. This arises because T2I model predominantly trained on natural images with a specific data distribution (e.g., cows often appear on the green fields but not in the sea). Consequently, the generated images constrained to satisfy such internalized priors.
To probe the connection between scene contextualization and ID shift, we formulate a theoretical framework, showing that the contextualization, inherently induced by the attention mechanism, is not only the primary source of ID shift but also inevitable for mre-trained T2I models. Moreover, we derive theoretical bounds on contextualization strength. Building on these insights, we propose Scene De-Con Conttxxtuualiitionnn (SDeC) approach, which effectively realizes the inverse process of scene contextualization. Specifically, SDeC quantifies the directional stability of the subspace spanned by Singular Value Decomposition (SVD) (Stewart, 1993) eigenvectors via a forward-ane backward cigenvalue optimization. After that, the latent scene-ID correlation within the ID prompt's s embedding identified through eigenvalue variations and then suppressed with adaptive eigenvalue weighting. The wtt.ttuaaiiiaiiiiiiiaaaaaa reweighted eigenvalues for subsequent generation. It can work in a one-prompt-ped per-scene scene setting, removing reliance on full target scenes.
ID prompt embedding is then reconstructed from the
Our contributions are: (1) We propose a scene contextualizationn perspective for IDD shift with T21 models; (2) We theoretically characterize and quantify this contextualization, leading to novel SDeC approach for mitigating ID shift per scene without the need for complete target scenes in advance. (3) Extensive experiments show that SDeC can enhance identity preservation, maintain scene diversity, and offer plug-and and--lay flexibility at per-scene level and across diverse tasks, e.g., integrating pose map and personalized photo, and generative backbones such as PlayGround-v22 RealVisXL-V4..0 Juggernaut--Vil SD3, and Flux
RELATED WORK
The study of consistent T2I generation falls into two phases. The early phase focuses on personalized couple of reference images given define
T21 generation (Zhang et al., 2024), where the identity of interest. These methods tackle ID shift by injecting ID semantics of reference images into a pre-trained T2I model, essentially adopting to progressively inject the information, subject to convergence toward the reference image(s) (Ye al., 2023). The second is textual token creation strategy: Transforming the reference image(s) into dedicated tokens to differentiate ID and scene. For example, DreamBooth (Ruiz et al., 2023) and variants (Sun et al., 2025; Hsin- Ying et al., 2025) introduce unique identifier token to represent the ID and inject it by fine-tuning the generative model. PhotoMaker (Li et al., 2024) fuses the reference image(s) and text embeddings to enhance ID tokens. Textual Inversion (Gal et al., 2022) and its variants (Zeng et al., 2024; Wu et al., 2024) directly create a concept token via textual inversion.
strategies. The first leverages cross-attention
subsequent scene contexts in advance. Efficcieccy dictates generating images online hased the current scene description, without the need for repeatedly re-generating all previous scenes (avoiding exponential complexity).
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Gen. Image
photo
of
dog
0.3
chasing
frisbee
In
park
0.1
Figure 2: Example of scene contextualization with ID prompt "a photo of a dog" and scene prompt "chasing a frishee in park". Left: Correlation between tokens and attention similarity matrix shows that scene tokens affect ID generation (visualization follows (Hertz et al., 2023)). Right: Similarity between SVD eigenvectors of ID and scene embedding shows they share an overlapping subspace.
The recent phase moves to the more flexible reference image-free setting addressed under two approaches. The first involves pseudo-label-batte self-learning: Generating candidate images with the entire prompt set, then filtering them with obvious ID shift using some metric (c.g., mutual information in ORACLE (Akdemir & Yanardag, 2024) clustering in Chosen-One (Avrahami et al., 2024) or self-diffusion DifiDis (Cai et al., (025)). further retraining the generative models. Clearly, such methods are expensive due model retraining. More recent attempts thus emerge in training-free fashion. Storytelling methods (Rahman et al., 2023; Zhou et al., 2024; Tewel et al., 2024) leverage the self-attention mechanism over generated images as an adapter, whilst the state of the art, PPrompt Story (Liu et al., 2025), introduces prompt re re-structuring idea to highlight ID and balance scene's contribution in prompting, with extra need to couple a specific adapter. Commonly, all the methods above assumes the availability of all the scenes which often is not valid in real applications. Importantly, these studies fail to provide an insight on the underlying cause ID shift with off-the-shelf T2I models. We address these gaps by suggesting scene contextualization perspective along with theoretical formulation and a flexible prompt embedding editing solution.
SCENE CONTEXTUALIZATUN
Problem Given ID text prompt Pia and K distinct scene prompts (P*) ) k=1 form of scenario prompts P (Pk K k=1 with pk Pis P& Feeding each prompt into T2I model G produces K generated images { T==1- where _$ = G(P*). Consistent T2I generation requires that {[*}K Jk= simultaneously (1) preserve the same identity features specified by Pus and (2) faithfully reflect the scene semantics described in each corresponding pk:
Interaction between ID and scene The attention mechanism is the key structure in Transformer based T2I models. Denote Z E N as the set of N input token features of dimension d, the self-attention projections are: K = ZWK, V = ZWv, Q ZWQ. For any query q E Q, the attention output is computed as:
O a V with softmax //Vde)
(1)
where de denotes the feature dimension with WK- In the prompt embedding space, this attention operation offers an opportunity for scene tokens to inject its context information into ID tokens, potentially leading ID shift. We name this scene contextualization.
For intuitive understanding, we visualize the correlation between scene tokens and ID tokens by their cross-attention similarity matrix, where each corresponds to values. As shown in images # 7~-10 of Fig. 2-Left, the bright regions (green/yclloww within the subject (dog) clearly indicate that scene tokens affect the generation of this dog.
3.1 THEOREM BEHIND SCENE CONTEXTUALIZATION
Attention operations are typically executed in a chain- manner for T2I models. For simplicity, we analyze the first attention block in generative models, without loss of generality.
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Theorem Let H4d Hac Rd be the identitysccnne semantic subspaces with ideal semantic sepa- ration: H4a n Mae = Ø: Zia C 'iaa and 2 S HHc be the prompt- -aanp-eebbdddingg matrix of Pia and P the prompt-emen eeaedddin matrix be partitioned by semantics Z Zid; 2
E Rnxd _ For any query
qua associated with the identity, its attention outpul can be specified to
Olgia)= a (ZWv) -iid (ZiaWi) + a (v:.
(2)
where the attention weights by token index a = =Losa, asal conforming to the row split of 2. Let Iha be the orthogonal projector onto H4a. The projection of O(qia onto the identity subspace H4a is
1ha [O(qa)] 11ia [ (2ia
+ 1ha [a (Z Wv)]
Id term:: Tia
scene term:: Tac
Assume Iha Wvln. denotes an operation where an input from subspace Hse is first transformed by W and then projected onto subspace Hia- If the conditions, (A) Clsc. + and (B) Iha o Wv H.c A O, hold, then the scene term in Eq. (3) is nonzero: Tsc F O.
Theorem suggests that even if Haa Nze 22, the attention could still cause scene contextualization. The two conditions are almost always satisfied with T2I models. Condition (A) is often met for two factors. (i) Keys from scene tokens and queries from ID tokens are rarely strictly orthogonal or sufficiently separated; the softmax attention weights are unlikely to be exactly zero, leaving scene tokens with non-new non-negligible attention mass. (ii) No enforcement on separating between scene and identity tokens during training, lead scene-to-ID attention positive. For condition (B), it is equivalent to wow--blooodiignoiie of Wv w.r.t. the decomposition Hia + Harene: No scene vector is mapped with nonzero component in Hid again this condition is not enforced in training.
Theorem assumes an idealized condition of Hia O Nac = Ø. In practice, ID and scene subspaces often exhibit partial overlap. To assess this, we apply SVD Zíd and Z and compute the similarity between their corresponding eigenvectors. As seen in Fig. 2-Right, the high regions in the similarity matrix reveal nontrivial correlations between Zμ and Z across certain dimensions. Relaxing the disjoint-subspace assumption to this correlated case, we show that scene contextualization still persists below.
Corollary 1 Assume that Hia and Hse have nontrivial intersection: Hr = Hia O Hoc with kn == dim(Hr) > O where dim(() means space dimensions. If ass # O, then for a generic linear mapping Wv. which excludes measure-zent degenerate cases. Tsc 0 hold.
The degenerate case above refers to the rare weight setting where Wv maps the scene subspace Hac exactly onto subspace orthogonal to Hia Such cases form a measure-zero set in the parameter space. With random initialization and continuous optimization, the probability of encountering them is negligible. For typical Wy, this blocking effect does not arise. Moreover, unlike the idealized assumptions in Theorem 1, in practice kn > O, meaning scene tokens always receive some attention. This makes contextualization both easier and stronger, even when Wy only weakly couples Hac and Hig Please see the proof in Appendix x-.
Combining Theorem and Corollary yields insight that, irrespective of whether Hid and HHg overlap, the scene-to-ID projection is generically nonzero, i.c., scene contextualization occurs firmly.
3.2 BOUNDING THE STRENGTH OF CONTEXTUALIZATION
In this section, we derive a bound on contextualization strength, uncovering the key variables that govern its intensity. Theoretically, we characterize the scene contextualization to Tue (see Theorem I), thereby its spectral norm T>e can be measurement of strength. [ can bounded as below (refer to the proof in Appendix-C):
Theorem Let P- be the orthogonal projector onto Hnn Ilse be the orthogonal projector onto Hoo: P. = 1 Pn be the projector onto the orthogonal complement within Hac- Define Rn = Pr, R_= = P.1, Tn == Wv Pri, T1 = PAW, Tha and c == ||0z0 The contextualiaation strength Tac 1lell2 is bounded as
0 < T S || |Toll + |R [[
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prompt
(+)
Regularizing min
Weighting m(A...))
Random noise
Generative model
Generated image
"A mameiic wintet-
C( Eg.
thamed attwork
SVO Initalizinng 2 Ag
rega snow queen"
A
2 + 2g
Z 2
ADV Al
Text-
A
Refining
2
"aracefuly
DE: Parameter fronen
sncwflskes with Waree her hand"
Parameter
Forward and backward optimization
A
(b)
2
Concatenating
Scene prompt
Inibalizing
Figure 3: Overview SDeC. A text prompt pk = Pia @ Pl: is encoded into prompt embeddings (2% 2 where means "original". SDeC mitigates scene contextualization by (a) identifying and (b) suppressing latent scene-ID correlation in Z (QDV: Quantifying Directional influence Variations). The refined ID embedding 2 is then concatenated with z for subsequent generation.
In Theorem 2, Ili is formally subspace operator. In practice, it is often spanned or approximately defined by the embedding Zia itself. Editing Zia thus equivalent to adjusting the orientation of the subspace, thereby modifying Πa. Following this, derive corollary to further specify the upper bound from the ID perspective (see the proof Appendix-D).
Corollary 2 Hia is the subspace spanned by the ID embedding Zui U is orthonormal basis Hid iee. U = orth( Zaa), where orth(.) specifies an orthogonalization operation. Writing the projector onto the ID subspace Ili -U0 have
0 Σ Taell2 I c- RollR
I10 Wy Polle + |Ril U.PPPe
(5)
In Corollary 2, on measures the energy shared between ID (U) and scene (2 subspaces, while o denotes the energy of ID projected into the scene-specific subspace. We consider that the majority of scene-ID interaction takes place via an, whilst a allows for holistic coherence. Thus, we only need to minimize on- This decomposition further reveals balance between lowering scene-ID interaction and maintaining coherence, indicating an inherent coanntttngllmeettcchrreeee trade-off.
SCENE DE-CONTEXTUALLATION
Overview Grounding on the insights from Corollary 2, we propose the SDeC framework to achieve ne-cootextualiizaton. including (1) estimating Pn and (2) driving øn 0. SDeC's idea is to quantify the extent to which each direction influenced by contextualization and then selectively reinforce those that are less affected. Thus, the original ID embedding is edited. Here, the directions that strongly affected are referred to as the latent scene-ID correlation subspace.
Concretely, approximate Pn. by identifying the latent scene-ID correlation subspace in Zia (Fig. 3()), exploiting a learning process. We then suppress this subspace (Fig. 3(b)) to reach - 0. For clarity, we hereafter denote Z,a to 20 and in-training ID embedding is denoted as 2.a-
Identifying latent scene-ID correlation subspace in 20 We first achieve the directional correla-
tion measurement via to the scene Z (forward), followed by restoring back to its original position 2% (backward). We start with solving 2 Uh Ag VoT by SVD. Let 2,a U Asa VoT with Aja initiated as Ag We formulate two-phase optimization problem:
aaw-ward-and-bacwwaddd optimization (Fig. (a)): First pulling 3μu closer
A min L(Znd. Aid
Zo zk)
|| UN Aia VoT
z B U Aia VOT -aale
(6)
8 = 0 when iter &. M, and 3 + O when iter > M,

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where iter denotes the training iteration index; M denotes the length of the first training phase, correspondingly, iterations 0 ~ M correspond to the forward process, while the remaining iterations constitute the backward process.
In the two-phase optimization defined in Eq. (6), the forward phase identifies the directions in Zid that align with Zge- capturing their shared representations. However, some of these directions may also be essential for representing Zia itself. To mitigate potential semantic degradation in Z we introduce a backward phase that progressively recovers these ID-associated components.
After evaluating the directional correlations, we further quantify their strength. In the SVD view, the directions, whose eigenvalues remain nearly unchanged (resistant to both pull and restoration), correspond to robust directions against contextualization. In contrast, those with large variations are the latent scene-ID correlation subspace, which theoretically corresponds to the Pn. In this context, we use absolute spectral excursion to quantify the stability in different directions. Assume A and Ala share the same spectral structure (diagonal with ordered singular values): A ) x;
with x 2 2 x 2 0 and rank( ) The directional correlation of Z can be formulated by Eq. (7) where v; stands for the correlation strength of the i-th direction.
AA
A diag( (1...... U...... By) with v; | X11-
(7)
De-contextuallzation by suppressing latent scene-ID correlation subspace in Z We achieve this through robust subspace filtering, which involves eigenvalue modulation, denoted by m(: +), followed by reconstructing the ID prompt embedding using the modulated eigenvalues. This method features (1) relative enhancement on the robust subspace, and (2) soft direction selection without threshold. Let Z be the refined ID prompt embedding. The filtering is expressed as
Aa - -miin
Zuu = U. A VOT with A = m(As, (0 = 1+
(8)
Amax
Amin
where Amax and Amin are the maximum and minimum entries of As, respectively, and the hyper- parameter 2 controls the weighting strength. In Eq. (8), the weighting values A. E [1, 1 + 22] are derived from normalized directional influence. Setting ? 2 1 ensures that the robust subspace is emphasized while avoiding semantic loss in the shared subspace.
After filtering, we edit the original prompt embedding [Z 2 to z = =12 2 We feed z into the T2I model produce the final image.
5 EXPERIMENTS
Benchmark Our evaluation uses the ConsiStory+ (Liu et al., 2025), extending the ConsiStory dataset (Tewel al., 2024) to 192 prompt sets, generating 1292 images with wider range of subjects, descriptions, and styles. The scene includes all contextual factors: not only environmental attributes (e.g., lighting, style, background elements) but also actions, behaviors, or temporary states associated with the subject in that specific image.
Evaluation metries To assess ID consistency, we employ two metries: (1) CLIP-I (Hessel al., 2021), computed as the cosine distance between image embeddings, and (2) DreamSim-F (Fu et al., 2023), better aligned with human judgment of visual similarity closely. As DreamSim, we remove the background using CarveKit (Selin, 2023) and fill random noise, ensuring that similarity measurement focus solely on ID content.
To evaluate the entire scenario (ID + scene), we adopt CLIP-T, the average CLIPScore (Hessel et al., 2021) between each generated image and its corresponding prompt. Note, this metric cannot measure the undesired scene mixture effect (see Fig. 7-iiddle in Appendix To address this, we introduce a new metric, DreamS inm-:iim-BB B, specifically designed to quantify inter-scene interference, in the spirit of DreamSim-F, based on foreground masking instead.
Competitors We consider two types of competitors. The first is baseline T2I models, including SD1.5 (Rombach al., 2022a) and SDXL (Podell et al., 2023). The second includes six sfate-of-the- art consistent T2I methods: HLLI--Diffusion (Li et al., 2023), Textual Inversion (Gal et al., 2022), PhotoMaker (I.i et al., 2024), ConsiStory (Tewel al., 2024), StoryDiffusion (Zhou al., 2024),

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Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison. The best and second-best results are marked in bold and underlined. respectively. PE: Prompt embedding Editing; POT: Prompt Operation Time (per image); GenT: Generation Time (per image).
Method
model
Base
PF:
metrics
Scenario
time] (8)
VRAM
(GB)
Steps
DreamSim-Fi
CLIP-I7
DreamSim-B
CLIP-T;
POT
Gen'
Boofine
0.4673
3.18
SDXL
0.2778
0,8558
0,3861
0.8865
0
9
10.72
50
Thiinnn
BLIP-Diflusion
SD1.5
0.2851
0.8522
0.3957
0.8187
0
1
3.54
26
Textual Inversion
SDXI.
0.3066
0.8437
0.3919
0.8557
0
10
14.00
PhotoMaker
SDXL
0,2808
0.8545
0.3957
0.8812
0
9
9.74
ConsiStory
SDXL
0.2729
0.8604
0,4207
0.8942
0
27
15.58
StoryDiffusion
SDXI
0.3197
0,8502
0.4214
0.8578
0
24
38.44
50
Trinnng
SDXL
0.2238
0,8798
0.2955
0.8883
0.10
22
13.10
IPIS w/o JPCA
SDXL
0.2682
0.8617
0.3338
0.8637
0.10
19
10.74
SDeC
SDXI.
0.2589
0,8655
0,3675
0.8946
0.61
15
12,14
50
SDeC+ConsiStory
SDXL
0.2542
0,8744
0.4155
0.8967
0.67
27
15.56
and PrromptStory (IP1S) (Liu et al., 2025). The first three are training based, training free for the rest and SDeC. For more extensive test, we introduce (1) IPIS w/o IPCA, with the attention module IPCA removed to focus on its prompt embedding editing; and (2) SDeC+ConsiStory, to test the complementary effect of our method with existing adapter based method ConsiStory. We exclude IP-Adapter (Ye et al., 2023) from comparison, as its generated characters are homogeneous pose and layout, with little ability to follow the scene description instruction. The same setting is applied to all compared methods. (see Appendix-25
5.1 RESULTS ANALYSIS
Quantitative analysis We draw these observations from Tab. 1: (1) For training-free methods, IPIS delivers the best result in the ID metrics, whilst suffering serious inter-scent interference (worst DreamSim-B score, also see Fig. Appendix-F for the typical qualitative evidence), largely unacceptable for consistent T21 generation. In contrast, our SDeC strikes the best balance between ID consistency and scene diversity. (2) Without the attention IPCA, 1P1S outperformed by SDeC across all metrics. That indicates that our prompt embedding editing is superior, even without the need for all target scenes in advance. (3) ConsiStory lags behind SDeC in ID metrics, but excels in scene background. (4) SDeC is well complementary with ConsiStory further push the performance, as they address distinct aspects. (5) Interestingly, training- based methods are even outpaced by most tree-free counterparts in ID consistency, with extra disadvantage in efficiency.
(6) In terms of memory and inference time, SDeC introduces negligible overhead on top (see Appendix H.1 for further discussion).
User study To complement those evalu- ation metrics, we further conduct user study. We compare with top alternatives: PhotoMaker, ConsiStory, StoryDiffusion, and IPIS. Specifically, the test images were generated by 30 random prompt sets from ConsiStory+. A total of 20 vol- unteers were invited pick which im- age best balances among ID consistency, scene diversity, and prompt alignment. We measure the performance using the percentage of wins. Tab. 2 shows that SDeC best matches human preference.
Table 2: User study results. Criteria: Best balance in ID consistency, scene diversity, and prompt alignment.
Method
PhotoMaker
ConsiStory StoryDiffussin
IPIS
Winst
8.17%.
20,83%
13.33%
15.00%.
42.67%
analysis For visual comparison, we show a couple of examples in Fig. 4. For the robotic elephant case, ConsiStory presents varying robotic styles, whilst IP1S suffers from scene interference. For the cup of hot chocolate case, the ID shift issue becomes more acute with prior methods. Instead, SDeC still does a favored job. More qualitative results are provided in Appendi i
Qualitative
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warm comforting painting steaming cup of hot chocolate"
"A majestic nature illustration of robotic elephant"
SDXL
PhotoMaker
CS
StoryDiifusion
1P15
SDeC
SDeC
"patrolling wildlife reserves"
"providing rides for tourists"
"transporting goods over rough terrain"
"In festive holiday- themed cup"
"placed wooden tray with cookles"
"topped with whipped cream and cocoa"
Figure 4: Qualitative results. CS: ConsiStory.
5.2 FURTHER ANALYSIS
Ablation study In SDeC, there are two key designs: (1) Estimate Pn in soft manner by learning process, and (2) employ the absolute excursion of SVD eigenvalues identify the latent scene-II) correlation subspace. To isolate their effect, tailor two variations of SDeC: (1) SDeC w/o soft- estimation, where we operate the shared subspace in a hard way: By constructing a correlation matrix to estimate Pn (its implementation details are provided in Appendix-El 3), and (2) SDeC w/o abs-excursion where the corresponding eigenvalue normalizes the eigenvalue variation.
Tab. 3 presents the ablation study results. SDeC ranks first in terms of ID metrics and scenario alignment (see CLIP-T), indicating that the two designs positively affect the final performance. These results understandable. In reality, the relationship between ID and scene subspaces is complicated.
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"A heertwarming Illstration of A friendly troil with moss-covered skin
"A quirky mischlevous goblin sharp features"
w/ w/o abx- -xxcursiiii w/o soft-soffteeeo estimmsttiatioon
SDec
"building a shelter fallen logs"
"fishin dusk"
"sitting campfire
"climbing the walls "hiding the shadows alleyway"
"sneaking through dark midnight"
Figure 5: Qualitative comparison results of SDeC and its two variations SDeC w/o soft-estimation, SDeC w/o abs-excursion. The results of SDeC significantly outperform the variant methods. More- over, SDeC w/o abs-excursion disregards the original importance of the SVD eigen-directions, causing greater semanties loss than both SDeC w/o soft-estimation and SDeC, and consequently ranks last in generation quality.
Thus, explicitly constructing Pr is nearly infeasible. A tractable approach is by approximation using high-dimensional matrix decomposition, which, however, is numerically unstable under limited samples (Yang et al., 2023). Accordingly, SDeC w/o soft-estimation suffers from a performance decrease. As for SDeC w/o abs-excursion, its relative excursion strategy enforces the stability quantifying on a unified scale, disregarding the intrinsic importance of each eigen-direction.
Additionally, relative SDeC, the Po estimated by these two variant approaches inherently less reli- able. When applied to contextual- ization carrier suppression, intro- duces additional and unpredictable interference into the ID embedding. Through the coupling effect of at- tention, this interference propagates into the scene generation process, ultimately amplifying discrepancies across scenes. This is why the variant approaches have a higher DreamSim-B score than SDeC.
Table Ablation study. Best results are in bold.
metrics
metrics
Method
DreamSim-Fi CLIP-17
DreamSim-Bt CLIP-TY
SDeC w/o soft-estiation
0.3351
0.8320
SDeC wlo abs-escursion SDeC
0.3576
0.8190
0.4440
0.8569
0.2589
0.8655
0.3675
0.8946
As supplement to Tab. 3, in Fig. 5, we present qualitative comparison between methods SDeC w/o soft-estimation, SDeC w/o abs-excursion, and SDeC. It is seen that the qualitative results are consistent with the data in Tab. 3. In particular, method SDeC w/o abs-excursion performs worse than method SDeC w/o soft-estimation, which supports our previous discussion. Specifically, SDeC w/o abs-excursion disregards the inherent importance of different SVD eigen directions, resulting in greater semantic loss and severe subject deformation (for example, see image #2 in Fig. Right). In contrast, SDeC w/o soft-estimation, which employs matrix transformation approach, just suffers from less accurate in identifying latent overlapping subspaces, but remain the key semantic information. Thus, it achieves better ID preservation than SDeC w/o abs-excursion.
Validation of scene contextualization control Our validation is based scenario with ID prompt: "A mischievous fantasy depiction of A cunning goblin with sharp features" and scene prompt: "trading stolen trinkets at market" where "trinkets" is sliced to "trin" and "kets" by text-

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10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Gen. image
trading
stelen
trin
kets
at
market
SDXL
SDeC
A mischievous fantasy depiction of cunning goblin with sharp features trading stolen trinkets at market
Figure 6: Validation of scene contextualization control. Top: PCA-based analysis where the image pairs blocked by the wreen-highlighter box have noticeable discrepancy. Bottom: Correlation between scene token embedding and attention similarity matrix.
encoder. Employing SVD to the original ID prompt embedding, we have 2d refined one 2 obtained by our SDeC, the weighting coefficients is A (see Eq.
U Ao VoT
VoT
For the
The key to SDeC is that the detected scene-ID correlation directions can modulate identity traits. To verify this, we apply PCA-based suppression to Z using criteria A and Aw, respectively. Sweeping the cumulative energy threshold 10% increments yields the ten image pairs shown Fig. 6-Top. In pairs #6-7, the subject produced by SDeC noticeably diverges from its SDXL counterpart, indicating that SDeC indeed identifies overlapping directions that drive identity adjustment.
Additionally, our prompt editing design operates through the attention module. Accordingly, the most direct evidence of contextualization control is to examine the correlation between token embeddings and the attention similarity matrix. Fig. 6-Bottom visualizes it Z drives the generation. With bright green denoting high correlation, we observe that, except for image #7, SDeC yields darker regions with more pronounced subject silhouettes than SDXL. These observations suggest that SDeC reduces the scene contextualization and provides an intuitive explanation for SDeC's effectiveness. The more model analyses are provided in Appendix- and Appendix-I.
Analysis of a proprictary commercial product Google's latest flagship T2I product. Nano Ba- nana (Google & DeepMind, 2025), has demonstrated impressive ID-preservation ability. While we cannot integrate our method, we design an interesting test to inspect how this system might work, which may reveal additional insights for future work. The results and speculative analysis are presented in Appendix-J.
CONCLUSION
In this paper, we identify scene contextualization as a key source of ID shift in T2I generation and conduct a formal investigation. Our analysis shows that this contextualization is an inevitable attention-induced phenomenon and the primary driver of ID shift T2I models. By deriving theoretical bounds on its strength, we provide a foundation for mitigating this effect. Building on these insights, we introduce SDeC, a training-fre free embedding editing method that suppresses latent scene-ID correlation subspace through eigenvalue stability analysis, yielding refined ID embeddings for more consistent generation. Extensive experiments validate both the effectiveness and generality of the proposed approach. The limitations and future work elaborated in Appendix-K.
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ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China (62476169), the Sichuan Science and Technology Program (2025YFMS00244) and the UKRI-AHRC CoSTAR National Lab for Creative Industries Research and Development (AH/Y0010601)).
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A PROOF OF THEOREM
Restatement of Theorem 1 Let Hid, Hue C. CRA be the identity/scene semantic subspaces, which have ideal semantic separation: Hia n A2z = 2. Let Zid S His and 2 S Hse be the prompt- embedding matrix of Pid and Pke respectively: the prompt-embedent semantics 2 [za; _ E IRaxd For any query qia associated with the identity, its attention
matrix be partitioned by
output can be specified to
Olgia) = a (2Wv) = aid (z + 0l (Z
(9)
where the attention weights by token index a [aid asel conforming to the row split of Z. Let Ia be the orthogonal projector onto Hia. The projection of O(qia onto the identity subspace Hia is
1ha ((qaa) = I[a Laai (2, +
Iha a (2 Wv)
(10)
term::: Tia
scene term: The
Assume Tia Wv Har denotes operation where input from subspace Hos is first transformed by Wy and then projected onto subspace H4a- If condition (A) agc + and (B) Iha Wv 76. + hold, then the scene term in Eq. (10)) is nonzero. That is, Tac + O.
Proof To obtain the result presented above, we need to firstly prove (1) (graa))) = a (2 Wv) +
O (the second term in Eq. (9)), and (2) its projection onto space Hac is non-zero.
(1) Proving (sc(ga)) + O. Due ase * O. Ogc is non-trivial linear combination of the rows of Wy; hence, it is nonzero unless ZecWy vanishes row-wise, which is not the case in functioning models. This establishes that nonzero term Ose present in Att(qia)-
(2) Proving the projection of (ge(gia) onto Hid non-zero. From The condition Ih Wv Ho. 0, for any scene vector 2(8) E Hac we have I (Wyz(*) * O. Then, the scene contribution has a nonzero component along Hia Because a is softmax, all its entries are non-negative and at least one scene weight is strictly positive (since dscc * 0). Therefore,
Tec = 11aa [ (Zoc Zvo]
= Σ ag the
#0.
(11)
jEacoe
B PROOF OF COROLLARY
Restatement of Corollary 1 Assume Hia and Hsc have a nontrivial intersection: Hn == Hia n Hac, kn == dim(mr) O where dim(-) means space dimensions. Ose * then for generic linear mapping Wv, which excludes measure-zero degenerate cases, Tse 4 0 hold.
Proof Pick any nonzero E Ho. Since u E Hsc and Zoc 2 Hac there exists a scene row 2(5) such that (6) = Bu + zt with B # 0, xz 1 H4d- For any Wv, we further have
(a (Wva)) + (Wv==>=)).
(12)
Consider the set U := WWv: (W = }. which is proper linear subspace of Bdxd and hence has Lebesgue measure zero. Therefore, for almost every Wv. we have 1ha (Wa)) # O, implying v # Since asc + 0 has at least one strictly positive entry.
Ter Iha (ZcoWv))
Σ
aj Wiyl # 0.
(13)
Jeccene
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C PROOF OF THEOREM 2
Restatement of Theorem 2 Lef Pn. be the orthogonal projector onto Ho; The be the orthogonal
projector onto Haci PA = 1l Ph be the projector onto the orthogonal complement within Hac-
Define Rn := Pn, R1 := 2 Pr In == 1la Pn, T1 := Po Wvllid, and == ||a For
contextualization strength Tse 112. is bounded by
0 S |Taall2 S e: || 1ThllF + E. 1-11111 ll2 -1T
(14)
Proof 3 Since col( 2xxc C Hec for any vector I, 2 - 1lo 2 + Pa.a. Thus
Tgc = Iha lo (2 Wi)]
(2 Wvllia)
(15)
azol(2 (Pr + PTt)
2 Pn Wy + a2
T zh Pal Wv-lia-
Based on Ro = 2 Ph, R1 == Ph Tn = W. Ph. T1 Pa Wy Eq. (15) becomes
Tec = =
2 Pn Wv + ase T z P Wvllia
(16)
RnTn a RiTT.
Applying the triangle inequality (Ila b11 ||||| + |ba Eq. (16), we have the the following inequality.
|Taelll ll <
| Rg Ta l2 llage R2 Till.-
(17)
For any now vector x and matrix A, || A I | || (column-wise Cauchy-Schwand Also
2122 $ ||
Zl12 (submultiplicatiiity)) Apply these each term Eq. (17) to obtain
| Rn Tnll2 & ||a_olle ll2 [| [[Tlle =
- Rolll ||T]|F
(18)
||a_ R1 l2 < ||a_ell2 [| - -|iil=== = | 1TillE.
Summing the two inequalities above gives the upper bound claim.
D PROOF OF COROLLARY
Restatement of Corollary 2 Hia the subspace spanned by the ID embedding Ziai U is an or- thonormal basis of Hia ie., U = orth(Z,a where orth(-) means performing orthogonalization on the input. Writing the projector onto the ID subspace as ITa = UUT we have
Toc la S e. 1| II Polle + e C.IRR, lla I PAUIF.
(19)
Proof Since Tha UUT, the term [|an| and [[T] || in Theorem can be
[L Wv Ph = |UU T Wy
(20)
| P II 1 = [[w PAUUTIP IP.
We first prove |OUT Wy Ww 1n Expanding the left-hand side of it, we have
IUU Wy WUUTWyyBB- WYPA) Wy)
T = UUU
(21)
Since U.T is an orthogonal projector, we have property of the trace, we obtain:
= UUTT then applying the cyclic
WUUTWyPAlE - -UU WvPP)
(22)
= Su(UTWWPPPPPWT))
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Table 4: Code link of the comparison methods.
Method
Code link
BLIP- Diff-iifusiion (Li at.,, 2023)
or iusionn
Mctpas//githon com/
Textual Inversion (Gal et 2022) http:///hwaa....mmoeeeeoe//eee//
PhotoMaker 2024)
ConsiStory (Towel 2024)
StoryDiftusion (2hoo al., 2024)
bctpa://githan wwwp:Visiinn http:/aiiinnntt///ooooaaaaaaaeea
PPrompt Storyy et al,, 2025)
httpas//g http////ttthhb.ot. wttph//..ooomooooooomooooooooooo 1 15tory
Because Pn is itself an orthogonal projector, we have PA Pn and PT Pr- Therefore,
| WyP3ll = tr((U (U (wPP)
(23)
In the similar way, we have W PAU = PAUlI Substituting it and Eq. (23) to the upper bound from Theorem 2, we finish the proof.
E EXPERIMENTAL DETAILS
E. PARAMETERS SETTING
SDeC involves three parameters: Trade-off parameter /3 and switching constant M Eq. (6) and weighting strength & in Eq. (8). We fix (8, M) -10, 20) in all experiments. For UNet- based (Ronneberger et al., 2015) generative models, we set Ω = 1, whereas MMDiT-based (Esser al., 2024) models use larger value, 10. detailed description of the parameter-setting guidelines is provided in F Parameter Analysis section (Appendix H.2).
E.2 MODEL SETTING
We achieve ID-preserving image generation by editing the ID prompt embeddings during the inference phase. There is extra training optimization imposed on the generative models. In practice, adopt Stable Diffusion (SD) V1.5² as the backbone model -iff-fiffusinn, while the pre-trained Stable Diffusion XI. (SDXL)) is selected the backbone model for our SDeC and the rest of the comparison approaches.
The comparison methods implemented using the unofficial official codes from GitHub website, whose details are listed in Tab. 4. The computation platform adopts the same configuration as SDeC: NVIDIA RTX A6000 GPU with 48GB VRAM.
In addition, among these comparisons, BII--Diffusion (Li et al., 2023) and PhotoMaker (Lii et al., 2024) rely on additional reference image. We produce this reference by feeding the identity prompt into their respective base models, the same as 1Prompt1Story (IP1S) (Liu et al., 2025).
E.3 IMPLEMENTATION DETAILS OF METHOD SDFC w/O SOFT-ESTIMATION
The goal of SDeC w/o soft-estimation to find the overlapping subspace directions between Zn and Zee. i.c., directions corresponding to principal angle 2 0. We achieve this by:
first perform SVD on both Zμ and Zoe to obtain their orthonormal bases:
Zu VT
Z = Use VeT
(24)
Then, the column subspaces are constructed as
Ba : Vials na],
B = Valls 1 racl,
(25)
httrps::/hugginggfccc.. wft.taaeleffssiioooooeo. htg.r//eeeeeeoo.ooooeooooooeeene -i
hhttps://hugginggaaes sttbi http://aaitttalttttaaaaaaaaaaaaa
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"A hyper-realistic digital painting fairy"
"atop dew- covered flower"
"collecting morning dew"
*dancing in the rain"
"In mystical glen"
"inside a hollowed-our soroowed-outte tree"
"under full moon"
leprechaun" "
"at village fair"
"hiding pot gold"
"in a clover field"
"inside a hollow oak tree"
"mending shoe"
"sharing pint"
"A watercolor illustration of A woman with slender figure, straight red hair, and freckles across the nose"
"attending holiday party"
"baking cake"
"dressed in formal evening gown"
"holding box"
"in snowy forest"
"singing at concert"
Figure 7: Demonstration scene-level interference caused by the prompt integration strategy in (PromptIStory (Liu et al., 2025). The visual interference elements are: Top: The bent tree in images #3~-, Middle: The oak tree in all images, and Bottom: The cake in images #1~5.
where Ps and T.e denote the respective subspace ranks.
Subsequently, based on Bi and Bx we can compute their correlation matrix as
M = B Bec E R
(26)
By performing SVD, we have M UAVT, where the singular values a, = cos 0g correspond to the cosines of the principal angles 6,. We select o; 2 (with 7 typically chosen 0.98), where the associated singular vectors indicate the intersection directions.
Thus, the explicit basis for the intersection subspace in the original space can be written as B-- BaUU(.Z). where I = {i 2 r}. Applying an additional QR orthonormalization on B- yields the final intersection basis Bn. The projection operator onto the intersection subspace then can be expressed by Pn Rs Br BT Corresponding to the proposed scheme SDeC, we equivalently detect the latent scene-ID correlation subspace. After that, we can realize suppressing this correlation subspace by employing 2 (T Pn)) directly.
F DEMONSTRATION OF SCENE-LEVEL INTERFERENCE IN PROMPP STORY
The previous Prompt1Story method (Liu et al., 2025) proposed a prompt strategy that merges the ID prompt with all scene prompts into single input, followed by calibration across different scenes using SVD-based scene prompt selection. Subsequently, all images are generated from this consolidated prompt with fixed ID component, thereby reducing ID shift. With the aid of the proposed attention module, serving as an adapter, this strategy further enhances identity preservation.
However, the proposed prompt strategy in IPromptI inevitably introduces pronounced scene- level interference. For an intuitive view, we demonstrate this phenomenon from IPrompt Story's 's results on the ConsiStory+ benchmark. As shown in Fig. 7, in the Top, images #1 and #2 both exhibit
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"A watercolor Illustration woman with slender figure, straight hair, freckles across the nose
SDXL
PhotoMakee
CS
StoryDiffusion
1P15
SDec
SDeCiCS
"attending holiday party"
"baking
"dressed "holding snowy evening gown"
"singing concert"
hat apron"
Figure 8: Case one of supplementary qualitative comparison results.
similar dense vegetation in the bottom-right corner, while the remaining images consistently feature bent tree (mentioned in the prompt image #5); in the Middle, all images are dominated by the visual element of an oak tree (mentioned in the prompt of image #4).
G MORE QUALITATIVE RESULTS
As a supplement to Fig. 4, we provide more qualitative comparison results in Fig. and Fig. Although without the story-wide context, the images generated by our method in "one prompt per scene" manner present better ID preservation while well scene matching.
H FURTHER MODEL ANALYSIS
H.1 DISCUSSION OF SCALABILITY
Although the SVD step in SDeC has theoretical complexity of O(d²) (d is dimension of matrix), does not form a practical bottleneck during inference. practice, SDeC incurs only negligible overhead, as the generative model itself accounts for more than 90% of the total computation time.
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hyper-realistic digital painting woman slender figure, straight red hair, and frediees across nose"
SDXL
PhotoMaker
StoryDiffusion
1P1S
SDeC
SDeC4CS
"dressed formal evering gown"
"havina park""
garden"
vintage kitchen"
Treading
"shopsing market"
"vistting
Figure 9: Case two of supplementary qualitative comparison results.
To further improve scalability, particularly for extremely long prompts, several extensions merit investigation in future work. First, approximate SVD techniques, such as Randomized SVD (Halko al., 2011) with (d log kt) complexity the Nyström approximation (Williams & Seeger, 2001) with Odd²²) (A: is a small constant), could significantly reduce computational cost while preserving the essential spectral structure. Second, divide-and-conquer strategy, in which long prompt is partitioned into K sub-prompts and processed sequentially, would allow the overhead to grow approximately linearly with K (about x 0.61 seconds). These observations suggest that SDeC remains scalable and practical even for large-scale or dynamically evolving prompting scenarios.
H.2 PARAMETER ANALYSIS
Our SDeC method involves three parameters, including weighting strength Ω in Eq. (8) and trade-off parameter and switching constant M Eq. (6). In this part, we first specify the setting these hyperparameters for optimal performance, and then conduct sensitivity analysis.
Guidelines for parameter setting In our SDeC method, the setting of hyperparameters for optimal performance should follow the guidelines below:
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0.744
8.898
0.905
896
0.76
0.895
0.74
0.900
DreanSim.FV
0,742
894
Dream DreamSin-Ft
DreamSim- PV
0.73
0.885
0,740
892
0.72
00.890
890
0.72
0.800
0.885
6.738
888
0.71
886
0.70
-0,875
00
0.736
0.870
0.70
Figure 10: Parameter sensitivity analysis results of 8 E [5, 25 [0.5. and M [0, 30]. The DreamSim-F score is inverted to DreamSim-FY 1-DreamSim-F, so higher values indicate better performance, as with CLIP-T score.
(1) Parameter 3a is associated with the forward-and hand-backward" optimization. Practically, M should exceed a threshold (20) so that the forward phase can capture as many scene-related directions within the ID) embedding as possible. Moreover, should satisfy / >> O, enabling maximal restoration of ID>related information the backward phase. This "forward-and-backwa to identify the latent ID-scene correlation space, independent of and generic with any specific generative model. Consequently, the setting and M remains consistent across different generative architectures.
optimization aims
(2) Parameter Ω is tied to the architecture of the underlying generative model, as it represents the strength of de-contextullization and is therefore directly related to the generation process. Our exper- iments indicate that for UNet-based models (e.g., SDXI., PlayGround, RealVisXI--V400 Juggernaut- X-V10), Si should be small (typically Ω 1). In contrast, DiT- models (e.g., SD3, Flux) require a much larger value of Ω (typically Ω = 10).
Sensitivity analysis In Fig. 10, provide the varying curves of DreamSim-F and CLIP-T scores as the three parameters change. As shown in Fig. 10-Left, the DreamSim-FV and CLIP-T scores remain near-steady when wide range of [5, 20), indicating our method does not rely on the careful selection of trade-off parameter 3.
The results in Fig. 10-Middle show that lt's increase leads trade-off phenomenon between the DreamSim-FV and CLIP-T score. This phenomenon is reasonable. In SDeC. to avoid semantics loss, we enhance the robust subspace by imposing larger weights Q its corresponding eigen-directions (see Eq. (8)). When the increase of S2 remains within a reasonable range, the native correlation between ID and scene is reduced, improving both ID preservation and scene retention. Once exceeds a threshold (e.g., > 1), however, the generative model is prone to pay more attention to the stronger components, leading to continued gains in ID preservation but convergence in scenes.
As for parameter M, Fig. 10-Right presents that DreamSim-FV curve progressively climbs and reaches the top at M = 20, while CIIP-T curve is with only minor fluctuations. The results are consistent with our expectations. In the forward-and-ban backaadd process, sufficient time in the forth stage drives the ID prompt embedding closer to that of the scene, enabling the identification of directions in ID prompt embedding's eigen-space most sensitive to contextualization. As this process acts only on ID, scene diversity remains unaffected.
H.3 SENSITIVITY TO THE ORDERING OF SCENE AND PROMPTS
In the evaluation experiments, the prompt is arranged naturally: the ID prompt precedes the scene prompt. To investigate the sensitivity to the ID-scene prompt order, conduct additional comparison where the scene and ID prompts are reversed. In this way, we can extend the original SDXL and SDeC (rewritten SDXL-o and SDeC-o) to variation SDXL-r and SDeC-r, respectively.
The comparison results reported Tab. indicate that the prompt ordering (ID-Scene vs. Scene-ID) has neg-negligi impact the metrics, particularly for the baseline model. We hypothesize that tokens placed earlier in the prompt receive more conditioning weight in the cross- cross-attention mechanism, driving the observed sensitivity:
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Table 5: Effect of scene-ID prompt order.
Method
DreamSin-Fl
CLIP-I1
DreamSim-B1
CLIP-TY
SDXL-o
0.2778
0.8558
0.3861
0.8863
SDXL-r
0.2894 (+-0.01
0.3966 (+00.0105)
0.8899
SDeC-o
0.2589
0.8655
0.3675
0.8946
SDeC-r
0,2670 (+000081)
0.8609
0.3777 (+0.0102)
0.8935
sagan
5VD sogar recioon 3
"A leprechaun sharing pint"
z0om in the step 9C 100
120
60
30
0
60
Training step with
leprechaun tracing stolion
SVD eggnn direction 0 sigan-drecttan eigen
SVD eiger di rection SVD elgen-di dosesn
z00m in the step 90-
40
60
80
Training step with interval
Figure 11: SVD eigenvalues evolving dynamics relative to their original values during the "forward- and-backward optimization. Here, in the "SVD eigen-direction #," a smaller value of # indicates larger eigenvalue for that direction.
Baseline (SDXL) behavior: When the prompt order is reversed (Scene-ID), the baseline model shows worse ID consistency (higher DreamSim-F score) but better scene distinction (higher DreamSim-B score). This suggests the scene tokens, when placed earlier, exert a stronger, negative influence the subject's identity, while simultaneously making the scene more distinct.
2. SDeC behavior: When SDeC is applied, the negative effect on the ID from prompt reversing slightly reduced, which is consistent with our objective of suppressing scene-ID correlation. Importantly, the positive effect on scene distinction is maintained, as SDeC leverages the original scene prompt content.
H.4 ANALYSIS THE COORWRDD-ANDBACKIEN OPTIMIZATION
In SDeC, the "forward- aad-baakward" optimization central to identifying the latent scene-ID correlation subspace within the ID prompt. To better understand this process, we visualize how the SVD eigenvalues evolve relative to their original values during optimization. For clarity, we show an example with five ID tokens, resulting in five corresponding SVD eigen-directions. We present the visualization results in Fig. 11. There are three observations below.
First, the evolution of the SVD eigenvalue gap against the original value follows our expectation. In the forward phase (step 0-20), the eigenvalue distinction curves gradually grow to pull the ID embedding to the scene embedding. In the subsequent backward phase (step 20-100), despite some vibration (e.g., the blue one), the curves globally converge to zero. This verifies the recovery of the ID- ID-sssocitted component.
Second, in the final step, the eigenvalue gaps exhibit a clear divergence, which allows us to treat the directions with larger gaps as the basis of the ID-scene shared subspace. Moreover, this divergence is
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"a photo athieiic 50 year-old man with grey heir"
watercolor illutration beautiful princess kind smile"
E
w/o ours
"waiing at the stans from a castle balcony"
SSnnging woodland animals in forest"
"wewring shimmering gown at royal ball"
the beach"
"In the countryside"
the park"
Figure 12: Left: Incorporating SDeC with ControlNet under the control of pose map. Right: Integrating SDeC with PhotoMaker, where photo of Geoffrey Hinton serves the reference.
correlated with the specific scene prompt. For example, in the top case, the gap is most pronounced along eigen-direction 2 (red), whereas in the bottom case, the divergence appears along eigen- optimization imposes
directions and 2. This also indicates that the forward--and-backee scene-specific de-contextualizztion.
Third, the eigen-directions with mid-range eigenvalues, such as eigen-directions I and 2, exhibit larger final gap values and are thus more readily identified as the basis of the ID-scene shared subspace. This behavior naturally emerges from the "forward-an wwwardd-ad--ue sordd--nd-bbbwwy backward" optimization: compressing the mid-eigenvalue directions sinultaneously preserves the dominant ID-embedding information encoded in the largest-eigenvalue directions while maintaining sufficient compression strength to meaningfully influence the generation process.
I SUPPLEMENTAL EXPERIMENTS
1.1 INTEGRATING WITH OTHER GENERATIVE TASKS
SDeC reduces the ID shift by editing the ID prompt embedding, without modifying the generative models. Consequently, it is compatible with different visual generation tasks. In this part, we incorporate the proposed method with two typical models with different generative goals. One is ControlNet (Zhang et al., 2023), which introduces controllable conditions (e.g., edge maps, pose maps, or depth maps) enable structured control over image generation. The other is PhotoMaker (Li et al.. 2024), which leverages an input reference image to preserve identity features, generating consistent subject images across diverse scenarios. As shown Fig. 12, our SDeC demonstrates excellent compatibility and ID ID-preservation.
1.2 CONSISTENT STORY GENERATION WITH MULTIPLE SUBJECTS
In this part, we present the effect of SDeC as the ID prompt involves multiple subjects. Fig. 13 demonstrates toy case involving an "elderly man" and a "cat". It can be observed that these two subjects are basically consistent, although some ID shift: The shorter cat fur in images #4, #9, and # #10; the absence of glasses in images #1, #7; the hat in image #8.
I.3 GENERALITY TO BASE GENERATIVE MODEL
Extensive experiments with the SDXL base model show that our SDeC enables semantically mean- ingful editing. However, if its effectiveness were confined to only subset of generative models, its practical applicability would be greatly limited. To demonstrate its generality, we integrate SDeC with four representative base models and compare performance before and after integration. Specif-
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"A hyper digital painting of an elderly man with
"feeding the cat in the kitchen"
"napping in an armchair"
"petting the cat on his lap"
"reading a newspaper in the living room"
"resting on porch in the afternoon"
6
10
"sitting rocking chair by the window"
"standing at the balcony at sunset"
"waiting at bus stop bench"
"walking slowly in the "watching TV together" garden"
Figure 13: Consistent story generation with multiple subjects. The results show that SDeC can generate images featuring multiple expected characters, with minor ID shift, for example, the hat (image #8) and glasses (images #1, #7).
Table 6: Quantitative results obtained by combining SDeC with four UNet-based generative base models (Top) and two MMDiT- generative base models (Bottom).
Base-model type
Method
DreamSim-FI
CLIP-11
DreamSim-Bt
CLIP-TT
SDXL
0.2778
0.8558
0.3861
0.8865
0.2589
PlayGround-v2.5
0.2567
0.9680
0.3688
0,8799
Play GayGoood--2554SDCC
0.2272
0.8832
0.3470
0.8994
RealVisXL-V4.0
0,2616
0.8660
0.4075
0,9007
RealVisXI. V4.VVi V4.0+SDeC
0.2435
0.8665
0.3845
0,8942
Juggemaut-X-VV
0.2748
0.8572
0.4295
0.8974
Jugeemau----
0,2369
0.8781
0.4011
0,9077
MMDIT
SD3
0,2875
0,8495
0.4236
0,8596
SD3-SDeC
0.2644
0.8612
0.4140
0.8629
Flux
0.2844
0.8516
0.4238
0,8632
Flux+SDeC
0.2653
0.8623
0.4167
0,8648
ically, addition to SDXL, the other base models include -2--roomdd-25-11022x4AAshheeee RealVisXL- and luggernaut---110..
From the comparison presented in Fig. 14, we draw two main observations. First, within the SDXL group, SDeC substantially alters the subject in image #2, while making only minor adjustments to the others, for example, consistently sharpening the cats' chins. This reasonable, as SDXL already performs well in ID preservation for the remaining images. Second, in contrast, when the base models exhibit evident identity divergence (see the other three groups), equipping them with SDeC leads to a significant improvement in the quality of the generated images.
The base models above are based on the UNet (Ronneberger et al., 2015) architecture. To verify the generality of our method, further evaluate our approach using two generative models based on MMDiT (Esser et al., 2024): SD3? and Flux". As shown in Fig. 15, in the SD3 group, the model equipped with our SDeC achieves substantial improvements appearance consistency, including
'https: http::/:/...ggnn..ooo/ooooo///oa /tttts/ss//nggnnoeeee//a//2///s2
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realiste digital painting
w/o
w/ ours
"dressed in Halloween
alley"
sled"
"wearing bow tie"
"wearing small bell"
"A puppy"
w// OUPS
PlayGround-V21.c 4px--ee-stteett
w ours
grassy yard"
wearing
"wearing training
harness"
"A high-speed action illustration of A rebotic cheetah"
w/o ours
RealVistt-v
DUTS
"essisting wildlife conservation"
helping ath train for speed"
peetooling a security
"running in race"
tracking criminals a city"
perimeter"
"A peaceful and rustic illustration of A hobbit with large feet and warm smile"
w/o ours
Naggemauttx-v10
w/ ours
"enjoying Feast at hame"
"lounging by fireplace"
"planting Flowers
"smoking pipe under
walking through the rolling hills*
Figure 14: Comparison of combining SDeC with the UNet-based generative models. From top to bottom. there are results of SDXL group. Play Groundd-2.5-1024p--Aeeee group, RealVisXL- V4.0 group. and Juggernaut-X X-V10 group, respectively.
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"A hyper-realistic digital painting of A fairy"
503
w/ ours
"atop dew covered
collecting morning dew"
"dancing in the rain"
"inside a hollowed- tree"
"under full moon"
focused scientist with lab coat"
w/ ours
Flux
w/
"analyring computer"
"conducting experiment a lab"
"observing through micrascope"
conference"
paper"
Figure 15: Comparison of combining SDeC with the MMDiT-based generative models. Top: Results of SD3 group. Bottom: Results of Flux group.
clothing, hair color, posture, and wing features. In the Flux group, the original Flux model shows ID shift in gender, whereas incorporating our method significantly mitigates this issue.
For comprehensive comparison, list the quantitative results of the methods mentioned above in Tab. 6. In each group, the base model equipped with SDeC achieves stronger ID-related performance (DreamSim-F. CLIP-I) and comparable scenario-level results (DreamSim-B, CLIP-T) relative the original base models. These results confirm SDeC's effectiveness and architectural independence.
J AN EMPIRICAL STUDY OF NANO BANANA'S ID-PRESERVATION
J.1 BUILDING BASELINE
As the beginning of our exploration, this baseline experiment generates 12 images using Nano Banana" under the setting formulated in the Probles em St.atement. of Sec. 3. In practice, start a new session and sequentially input the 12 prompts into Nano Banana via its official interface in dialogue mode. We set the IDD prompt "A dreamy illustration of a beautiful princess with a kind smile", and the scene prompts are generated using ChatGPT-5.0
As shown in Fig. 16, the generated images exhibit excellent ID consistency, except for the hairstyle in image #4 and the clothing in image #5. We further observe that the generation time increases steadily from 8.5 seconds for the first image to 82.6 seconds for the last. This phenomenon suggests that Nano Banana might leverage previously generated images as contextual information during subsequent generation. If this speculation holds, Google's method implicitly incorporates a reference image to
hnttps://aistudiin google com com/modee model deddeel s s/ /////emnnn ni - -2- Flash- image
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A dreemmy illstrationn of beautiful princess with a kind smile woiking i garden unchanted roses (8.5g)
dreamy illustatioo of
A dreamyy illntraaionn of beautiful princess with kind smile singing to woodland animals in forest (26.8)
dreaayy illutratioo of beautiful princes with a kind smile wearing shimmering gown at royal ball (20.2s)
A dreamy illattration of A beautiffl princess with kind smile weeking windom from an old magicnl mirror (20.50)
dreamy illstration of beautiful princess with a kind unile dritting in bost scrows moonitt lake (46.35
A beautiful prineess with a kind smile garing the stars from costle baioany (31.5)
12
11
10
9
7
dreamy beautiful princess discovering doorway lay- 6s)
dresmmy Illstration beautiful princess with snile polntiing delicate quiet
dreamy ilsstration beautiful princess releading dove sky
drccamy Blssrrtion princess smille exploring crystal caves soft light (57.1s)
dreamy ation of beautiful prineess with kind smile dancing among glowing freffies meadow (52.50)
dreamy
boauthfll princess
with smole
reading ancient
spellbook hiddin
(58.0s)
llbrary (69:46)
(94.3)
Figure 16: Results of baseline experiment where the images are arranged in a U-shape. All scenarios share the same ID prompt, and the scene prompts are different, in the setting of this paper.
enforce ID consistency, the same as the personalized T2I generation (see Sec. 2 Related Work). Accordingly, the strong ID preservation becomes interpretable. In Nano Banana, the developed image editing technique (a feature also emphasized officially) employed to integrate the subject from the reference image with the target scene. The subjects in images #1, #2, #4, #6, #8, and #9 provide compelling support: Their clothing and pose are mirrored, which exhibits clear signs of editing.
J.2 VALIDATING LEVERAGING PRIOR IMAGES AS CONTEXT
As presented above, our conclusion regarding reference images is drawn under the assumption that contextual information is exploited in Nano Banana. To test this assumption, we selected scenario #1 and #4 from the baseline as the first and last ones, respectively, and inserted 10 intermediate scenarios between them. Based on this, we conducted two perturbation experiments as follows. Of note, to avoid interference between experiments, each experiment is conducted in a newly created session.
In the first experiment, we introduce substantial perturbations to the intermediate scenes by asking ChatGPT-550 to produce prompts that are markedly different from the first scenario. The results in Fig. 17 reveal significant differences between the first and last images (e.g.. hairstyle, costume, and headdress), suggesting that the Nano Banana model indeed accounts for contextual information.
In the second experiment, we perform minor perturbations: The intermediate scenarios adopt literary style similar to that of the first scenario, achieved by instructing ChatGPT-550 to replicate its style literally. Meanwhile, the ID component remains aligned with the first, but is slightly varied by substituting the subject with related terms (e.g., "woman" and "girl"). As shown in Fig. 18, compared with the baseline, the subjects in the first and last images again exhibited notable differences (e.g., hair color, hair length, and costume style). Moreover, the last image and its neighbors displayed higher facial similarity than the first. We further extend the number of scenarios Nano Banana's maximum support (22). The results in Fig. 19 reveal that the differences between the first and last images are further amplified, making it difficult to regard the presented subjects as the same person.
While the observed ID shifts under the two types of perturbations suggest Nano Banana adopts context strategy, another evidence arises from the side-by-side comparison of experimental results. Specifically, compared with minor perturbations (Fig. 18 and Fig. 19), strong perturbations (Fig. 17) result in smaller ID shifts. A reasonable explanation is that in the strong- groong-ertturbation case, the constructed context exhibits much larger semantic differences. This enables the generation process of image #1 #12 to more easily converge attention on the most similar first scenario, thereby achieving ID- preservation. In contrast, in the perturbattt case, the attention distribution remains relatively
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A drieamy Illstration of beautiful princess with kind smite walking a garden enchanted roses (13.5k)
A cinematic render of A lone astronsst drifting among colossal space debeis near a shatteree mooe
neon digital painting of cybernatic welf prowling through rain- soaked alless of megacity (12.sc
A hypan-dattaleed artwork of maxsive starship docking inside a glowing orbital station (38.0))
Aurreal scidi illustattoon of child holding a holographic buttorfly in sero- gravity chamber (15.3)
A futuristic concept art of squed of andraid guardians marching across the dures of terrsformed Marss (23.9t)
(10.s))
12
11
10
8
dreamy beautifu princess wooring shimmerrnng gown at royal (82.1a)
Adyytopia digital paiming crowd masked chitons staring colomal halogram sky (48.1s)
concept regue pilot escaping through in space cruisrr (62.4s)
hologrphii-stll adin scientist opening shimmering portal quentam lab (28.8s)
sc-41 painting
humanold performing eightclub synthetic
forgatten
with
pulsing beneath abandoned underground faciityy
betegs
(24.9k)
Figure 17: Results of experiment with major perturbation where the images are arranged in a U-shape. The intermediate scenarios (#2~#11) significantly differ from the first one.
dreamy Ilsstration princess snille walking garden (11.3s)
drcomy ilkstratioon of gentle
dreamy ation gr woman kind umile reading a alowing beneath an anciant oak tree
dreamy illustration
drcamy maiden kind weaving garlands of willlowwrs (25.7))
young queen releasing
dreammy Illustration
smile pointing
little girl with
tantenns twilight (21
amile fooding doves
sunlit courtyard
dreamy Ulstrration benstiful princess smile werring shimmering gown at royal (50.50
dreamy ilsttrtioon of A radiant woman with a kind smile watching firefliss dunce across the evening garden (59.25)
dreamy Hust zoo8 of little maiden with sind smile gathering glowing mushroom in an enchanted grove (48.46)
dreamy Ulurtnntion young wommn with kind playing harp beside a sporkling watertbll (75.5s)
A drcammy illusration of kindhearttd molden with kind smile listeinn seashells by shore (48.75)
A diccaamy ilustration of lovely woman with a kind smile wandering through
field golden whest under moonlight
(30.15)
Figure 18: Experiment with minor perturbation where the images are arranged in a U-shape. In the intermediate scenarios (#2-##11) I), the scene prompts generated by ChatGPT-5.0 follow a literary style similar to that of the first, while the ID prompts remain consistent with the first, differing only in the substitution of the subject with a related concept.
flat. As result of blending the semanties of multiple scenarios, the final generated image displays much more pronounced ID shift.
J.3 COMPARING WITH OUR METHOD IN THE WAY OF ID-PRESERVATION
In summary, the ID consistency observed in Nano Banana can be attributed to the implicit use of reference images, where previously generated results serve as contextual input. This allows image editing techniques enforce high-quality ID consistency. In contrast, our method avoids such strong assumptions: The "one prompt per scene" feature provides an unconstrained usage condition and requires neither intensive computation nor additional data overhead.
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dreamy illustration of A beautiful princess with a kind smile walking garden of enchanted (7.2s)
Rlstration veman feel seriower
erearny thatrelio perte reddigg book by cony Proplecs
drrmm tustration chheriin degintto bedi mendow
defiute women greatal mubien gudtare from encert giningg book
dandagla
ghiwing et soyelemation
1
Eneey flsstratton wonon with be songuilite under
crearr thatretion
Admmm tkatrrtton
of
Adrarmy Rustrete Akidd Akiad-nnasse wonsn walling sling candy (14.11)
young gitt with dary. lights
loaely tonna
radau yourg worenn in bllaninng gaddon nurrennded by
ff couatinng
falmmonn
Neret
(64
dreamy Illustration of beautiful princess with a kind smile wearing shimmering gown at royal ball (177.66)
A Admary Bleeration groeete soung sede walking through (1165)
A craanyy cnnonyllaarrsse lkutrotion of thoughtt unls dising be foubee calet
drsury Bluttrsion of themiing Joskd wijeyigg suney perrrtteiit CLtB 20)
A Addaaes Bustation Averee caleee seediiieeiiiig Rower in royal
ledy feeding int (uot (i)
sender
(315 oc)
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22
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thatretis grett wamen with unia redding one
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medden with unine warccing
unde cuiling the eccelgat
code roanding andak
eachaved lawe
towar
(17L3))
Figure 19: Extended experiment with minor perturbations, under the same setting Fig. 18, with the number images increased to the maximum continuous generation times of Nano Banana.
Methodologically, Nano Banana falls within the category of personalized T2I generation. typically relies on a reference dataset, taken context, to model ID invariance, aligning with the principles of transfer learning. In contrast, SDeC takes conceptually distinct path by pursuing a novel prompt embedding editing paradigm, derived from native generative perspective on ID shift: Scene contextualization each individual image.
J.4 COMPARING WITH OUR METHOO UNDER SCENE STRONG VISUAL SIIITS
The De-coneextualization studied in this paper focuses on the general effects of scene on identity (e.g.. changes in clothing or posture due to a scene description), which covers normal usage scenarios. If the scene an extreme case involving strong visual shifts (e.g., intense color washes that fundamentally alter the subject's appearance, far beyond normal shadow changes), would our method still remain effective?
To address this question, present the outputs of our SDeC combined with the SDXL base model (denoted by SDXL+SDeC), where the scene prompt includes both normal- and strong-visual shifts. For comparison, also include results generated by Nano Banana.
From Fig. 20, we observe that the newly released commercial model Nano Banana and SDXL+SDeC fail to capture the direction of the light source, which is crucial for determining shadow formation. This indicates that their ability to interpret and realize the strong visual shifts specified in the scene prompt remains limited.
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A soft-focus photograph of bowl of bananas dark wooden table, intense light coming from below, creating dramatic upward shadows and highllihttng the fruit's curves.
soft-focus photograph of bowl bananas dark wooden table, with vibrant, multicolored lights coming from below, casting dynamic, colorful shadows and creating surreal glow on
soft-focus photograph of wooden table, with intense light coming upper right, casting soft highlights shadows the fruit.
A soft-focus photograph of
1
3
Nano Banana
SDXL+
ID Prompt
Normal visual shift
Strong visual shift
Figure 20: Comparison results under scene with strong visual shifts.
K LIMITATION AND FUTURE WORK
Despite improving ID consistency while maintaining scene diversity, SDeC, prompt-embedding editing approach, cannot fundamentally resolve shift. First, Theorem proves that the attention mechanism is the central origin of ID shift, when ID and scene prompt embeddings occupy disjoint subspaces. Second, in line with Theorem 2, SDeC essentially performs an indirect form of contextualization control by reducing the overlapping energy between ID and scene embeddings.
Therefore, designing attention modules tailored to preserve ID represents a promising direction for future work. In this paper, however, theoretical contributions, such Theorem and Corollary 1, are intended to reveal the mechanistic link between scene contextualization and ID shift. Their practical contribution attention design remains limited. This limitation arises from idealized assumptions (e.g., sharp subspace partitioning and linearity) and the neglect of real data geometry and attention dynamics. These factors render the precise construction of Pr numerically unstable and difficult to apply in high- figh-dimensional. sample-limited settings. Addressing these challenges will be a central focus for future work.
Additionally, a key motivation of this work is the lack of theoretical justification for ID shift in the T2I literature. We provide the first theoretical interpretation of ID shift through the lens of scene contextualization, and offer practical solution by addressing the underlying correlation. Nonetheless, this represents only the beginning of a deeper exploration into the fundamental mathematical "balance" between identity and scene. In this paper, we observe two pieces of evidence suggesting the existence of a possible balance. One is the trade-off visualized in Fig. 10 (Middle). The other one is the weatenggllmeeet--eeennee trade-off reflected in Corollary 2. Developing systematic theoretical framework for this balance constitutes an important and promising direction for future research.
At present, addressing these extreme visual alterations remains significant challenge for all current generative models. The results in Fig. 20 highlight the limitations of our method this regard. Consequently, explicitly incorporating the constraints imposed by extreme visual alterations into the prompt-editing process constitutes an interesting direction for future research.
29


Reviews:
[{"review_id": "Ub0KcSByhC", "reviewer": "Reviewer_zjEc", "summary": "This paper focuses on the issue of identity shift in consistent text-to-image generation, where T2I models often fail to produce identity-preserving images of the same subject across different scenes. It points out that previous methods addressing ID shift rely on the unrealistic assumption of pre-knowing all target scenes, and reveals that a key cause of ID shift is the inherent correlation between subject and scene context, which emerges when T2I models fit the training distribution of large-scale natural images. The paper formally proves the near-universality of this scene-ID correlation and derives theoretical bounds on its strength. Based on this, it proposes a novel, efficient, training-free prompt embedding editing approach named Scene De-Contextualization, which inverts T2I’s built-in scene contextualization—specifically, it identifies and suppresses the latent scene-ID correlation in the ID prompt’s embedding by quantifying SVD directional stability to adaptively re-weight corresponding eigenvalues.", "questions": "The scene description and the subject in the image generation process should be coupled; for instance, the lighting and style of the scene influence the subject’s clothing, appearance, and so on. The motivation of this paper is to reduce the correlation between the scene and the subject.\n\nthe scene prompts in your provided cases include the verbs, why the verbs are included as part of the scene description?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper targets a critical limitation in text-to-image  models: identity shift—where a subject’s core appearance changes unexpectedly when scene prompts are modified. To solve this, the authors propose the Scene-Subject Decoupling  module, a lightweight addition to pre-trained T2I models that uses: (1) a contrastive loss to separate scene and subject representation spaces; (2) dynamic attention gating to prioritize subject features during scene updates. Claimed contributions include: (1) a novel framework for scene-subject decoupling in T2I; (2) empirical validation of ID preservation without sacrificing scene quality; (3) a lightweight design compatible with pre-trained models.", "weaknesses": "The quantitative results shown in table 1 is not convincing that the proposed method outperforms others sota method like 1P1S.", "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": "ki5WxhVn4M", "reviewer": "Reviewer_xuex", "summary": "This paper proposes a traiing-free prompt embedding editing framework, i.e.,  Scene De-Contextualization (SDeC), to address the issue of identity (ID) shift in consistent text-to-image (T2I) generation. The authors identify that a key source of ID shift is the inherent correlation between the subject and the scene context, termed \"scene contextualization.\" They provide a theoretical formulation to characterize this phenomenon and derive bounds on its strength. Based on these insights, SDeC is introduced as a training-free prompt embedding editing method that suppresses the latent scene-ID correlation within the ID prompt's embedding. The method is shown to be effective in enhancing identity preservation while maintaining scene diversity across various experiments and benchmarks.", "questions": "See the weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Technically effective and efficient framework: the proposed SDeC is a training-free method that can be applied per scene without requiring prior knowledge of all target scenes. This makes it highly flexible and suitable for real-world applications where target scenes may vary over time. The experimental results demonstrate significant improvements in identity preservation and scene diversity compared to existing methods.\n2. The paper provides a new perspective on the problem of ID shift by formally defining and analyzing scene contextualization. The theoretical framework, including Theorem 1 and Corollary 1, offers a deeper understanding of the underlying mechanisms of ID shift in T2I models.\n3. The authors conduct extensive experiments on a benchmark dataset (ConsiStory+) and compare their method with both training-based and training-free state-of-the-art approaches. The results are supported by both quantitative metrics (CLIP-I, DreamSim-F, CLIP-T, DreamSim-B) and qualitative visual comparisons, showing the superiority of SDeC in various scenarios. Also, the study of Nano banana's ID preservation provide interesting insight.", "weaknesses": "1. Evaluation: both the proposed method and compared baselines are unet-based models, mainly SD1.5 and SD-XL. However, DiT-based models, especially the MMDiT-based variants, significantly advances synthesis quality. Thus, it would be better to compare with some DiT-based models and implement the proposed method on these models to further investigate the effectiveness. Moreover, the paper mentions that SDeC can be integrated with different generative models, but it does not explore its compatibility with other similari methods that also aim to improve ID consistency, such as ID-Aligner, InstantID, IP-adapter (flux based), etc.. Comparing SDeC with these methods could provide a more comprehensive evaluation of its performance.\n2. The performance of SDeC is sensitive to parameters like weighting strength (Ω) and trade-off parameter (βM). While the paper provides a sensitivity analysis, further optimization of these parameters could improve performance.\n3. Time Complexity: SDeC requires performing SVD and a forward-backward optimization for each scene, resulting in a time complexity of O(d^3). This can become a bottleneck in real-time generation scenarios, while the paper mentions that the inference time only increases by 0.61 seconds, but this is for single-image generation.\n4. Diverse Dataset Evaluation: Evaluate SDeC on additional datasets with varying levels of complexity and compositional density to validate its generalization ability.", "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": "pBDVmZDMYh", "reviewer": "Reviewer_GF6P", "summary": "This paper investigates the cause of identity (ID) shift in text-to-image (T2I) models and introduces a training-free method called Scene De-Contextualization (SDeC). The authors theorize that ID shift arises from *scene contextualization*—an intrinsic correlation between identity and scene semantics induced by the attention mechanism. They formally prove this phenomenon’s inevitability and bound its strength. Building on this, SDeC edits prompt embeddings to suppress latent scene-ID correlations by analyzing singular value stability and adaptively reweighting eigenvalues, allowing per-scene use without knowing all target scenes. Experiments on ConsiStory+ demonstrate that SDeC improves identity consistency while preserving scene diversity and works complementarily with prior methods like ConsiStory. The paper’s theoretical analysis and efficient, training-free implementation make it a notable step toward understanding and mitigating ID shift in T2I generation.", "questions": "See the weakness part.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper provides a clear and rigorous formulation of *scene contextualization* as the root cause of identity shift in T2I models, supported by formal theorems and bounds. This theoretical framing is original and intellectually valuable.\n2. The proposed SDeC method operates purely at the prompt-embedding level, requiring no model retraining or access to all target scenes, which makes it lightweight, general, and practical.\n3. The paper is well structured, linking theoretical findings to practical implications, and provides convincing visual and analytical evidence for the proposed claims.", "weaknesses": "1. The Introduction could be made clearer for readers unfamiliar with this research area. For example, in *line 47*, the authors argue that “target scenes are not always available,” but it is unclear whether this refers to the incompleteness of scene samples in the training data. When introducing *scene contextualization*, it would also help to include a comparative visualization in Figure 1 showing scenes with and without ID shift, to better illustrate the motivation.\n2. The theoretical analysis in Section 3 is difficult to follow, especially for non-expert readers. From a high-level understanding (as shown in *Figure 2*), *scene contextualization* arises because the text encoder’s causal attention mechanism allows scene tokens to capture partial semantics from ID tokens, thereby causing ID shift. However, I have two questions: (i) does this phenomenon only occur in CLIP-based T2I models, since newer architectures such as SD3and Flux use T5-based encoders with *bidirectional attention*? Would the proposed method still apply under such architectures? (ii) How sensitive is this effect to the order of the scene and ID prompts? For instance, if the scene prompt precedes the ID prompt, would scene contextualization still emerge, and would SDeC remain effective in that case?\n3. As I am not fully up to date with the latest progress in this field, I am unable to fully assess the choice of baselines and the reported performance comparisons, and therefore defer to other reviewers for judgment on that aspect.", "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": "sxJaTDUj5X", "reviewer": "Reviewer_Hj37", "summary": "This paper aims to prevent ID drift in text-to-image based personalized storytelling. This paper theoretically analyze the cause of ID shift, which is named scene contextualization. Based on the theory, this paper further quantifies the contextualization, and proposes scene de-contextualization. The idea is to quantify the extent to which each direction is influenced by contextualization and then selectively reinforce those that are less affected. \n\nIn practice, it first pull the eigenvalue of SVD of ID embedding towards scene, and then pull the eigenvalue back towards the ID. The directions whose corresponding eigenvalues remain nearly unchanged (resistant to both pull and restoration) are treated as robust directions against contextualization. In contrast, those with large variations are the latent scene-ID correlation subspace. To suppress the scene influence on ID, it suppresses eigenvalue with large variations by using adaptive weighting. The modified ID embedding are fed into the model along with the original scene embedding.\n\nExperiments show that this method can be a plug-in into diverse tasks, e.g., integrating pose map and personalized photo, and generative backbones such as PlayGround-v2.5, RealVisXL-V4.0 and Juggernaut-X-V10.", "questions": "1. Maybe more math: what might be the (possibly) deepest mathematical reason of there being always a balance between ID and scene? Since this paper already contains a lot of math, digging into the reason can make this paper in a higher stage.\n2. Maybe more intuition: I feel visualizing more about the pull and restoration process and playing with $\\Omega$ might give some more insight.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper seems to strike a new balance between ID consistency and scene diversity.\n2. This paper derives the method from theory, which may inspire future researchers.", "weaknesses": "1. Does it really make sense to suppress $\\sigma_{\\cap}$? What if the scene has very strong information on the subject such as colorful lighting?\n2. Related to 1), the method seems sensitive to $\\Omega$ as shown in Figure 8. As both works on SVD eigenvalues, this paper seems an improvement on 1Prompt1Story. In this sense, sensitivity to  $\\Omega$ can significantly harm the contribution.", "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)