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Consistent Text-to-Image Generation via Scene De-Contextualization

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Text-to-Image generationIdentity-preservingPrompt embedding editingScene contextualization
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.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes Scene De-Contextualization (SDeC), a training-free prompt embedding editing method that addresses identity shift in text-to-image generation by suppressing latent scene-subject correlations. It resides in the Prompt Embedding Manipulation leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader Training-Free Personalization branch. This leaf sits alongside Encoder-Based Identity Preservation (four papers), suggesting that embedding manipulation approaches represent a smaller but distinct methodological cluster compared to encoder-driven techniques.

The taxonomy reveals that SDeC's parent branch, Training-Free Personalization, contrasts with Fine-Tuning Based Personalization (four papers including DreamBooth and StyleDrop), which optimizes model weights per subject. Neighboring leaves like Feature Decoupling and Fusion (three papers) explore identity-irrelevant feature separation, while Multi-Subject and Compositional Generation addresses simultaneous multi-identity synthesis. The scope notes clarify that Prompt Embedding Manipulation excludes encoder-based methods, positioning SDeC as a direct alternative to learned feature extractors. This structural context suggests the paper occupies a niche focused on lightweight, per-prompt interventions rather than holistic model adaptation.

Among thirty candidates examined, none clearly refute any of the three contributions: the scene contextualization perspective (ten candidates, zero refutable), the SDeC method itself (ten candidates, zero refutable), and the theoretical characterization (ten candidates, zero refutable). This limited search scope—top-K semantic matches plus citation expansion—means the analysis captures nearby work but cannot claim exhaustive coverage. The absence of refutable candidates across all contributions suggests that, within this examined set, the framing of identity shift as scene contextualization and the SVD-based embedding editing approach appear distinct from prior embedding manipulation strategies.

Given the sparse Prompt Embedding Manipulation leaf and the limited thirty-candidate search, the work appears to introduce a novel angle on training-free identity preservation. However, the small search scale and the presence of only two sibling papers leave open the possibility that related embedding editing techniques exist beyond the examined scope. The theoretical formalization of scene-subject correlation and the SVD directional stability mechanism seem to differentiate SDeC from existing prompt-level interventions, though broader literature coverage would strengthen confidence in this assessment.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: identity-preserving text-to-image generation across diverse scenes. The field centers on synthesizing images that maintain consistent subject identities—whether people, objects, or characters—while varying contexts, poses, and compositions according to textual prompts. The taxonomy reveals several major branches: Subject-Driven Personalization Methods explore how to adapt diffusion models to specific subjects, ranging from fine-tuning approaches like Dreambooth[2] and Styledrop[11] to training-free techniques that manipulate prompt embeddings or attention mechanisms without updating model weights. Multi-Subject and Compositional Generation addresses the challenge of placing multiple distinct identities in a single scene, as seen in works like Fastcomposer[1] and Multi-Concept Customization[8]. Sequential and Story Generation focuses on maintaining character consistency across narrative sequences, exemplified by Storynizor[3] and AutoStory[49]. Domain-Specific Identity Preservation targets specialized applications such as person-centric synthesis or animated characters, while Training Data and Evaluation and Auxiliary Generation Tasks provide foundational resources and complementary capabilities. A particularly active line of work contrasts optimization-based personalization, which fine-tunes model parameters for high fidelity, against training-free methods that prioritize efficiency and generalization. Within the training-free branch, approaches like Training-Free Consistent[12] and MagicNaming[29] manipulate embeddings or cross-attention maps to preserve identity without per-subject optimization. Scene DeContextualization[0] sits squarely in this training-free cluster under Prompt Embedding Manipulation, emphasizing how to disentangle subject features from their original contexts to enable flexible recontextualization. Compared to neighbors like MagicNaming[29], which focuses on naming strategies for identity tokens, Scene DeContextualization[0] appears to tackle the upstream problem of isolating identity-relevant information from scene-specific cues. This positioning highlights ongoing questions about the trade-off between computational cost and identity fidelity, and whether embedding-level interventions can rival the consistency achieved by fine-tuning methods while maintaining broader applicability across diverse prompts and subjects.

Claimed Contributions

Scene contextualization perspective for identity shift in text-to-image generation

The authors identify and formalize scene contextualization as the primary cause of identity shift in text-to-image models. They provide theoretical analysis showing this phenomenon is inevitable due to attention mechanisms and derive bounds on its strength.

10 retrieved papers
Scene De-Contextualization (SDeC) method

A training-free prompt embedding editing method that identifies and suppresses latent scene-ID correlation within ID prompt embeddings using SVD directional stability analysis and adaptive eigenvalue re-weighting. The method works per-scene without requiring prior knowledge of all target scenes.

10 retrieved papers
Theoretical characterization and quantification of scene contextualization

The authors formally prove the near-universality of scene-ID correlation through Theorem 1 and Corollary 1, and derive theoretical bounds on contextualization strength in Theorem 2 and Corollary 2, providing mathematical foundations for their proposed solution.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Scene contextualization perspective for identity shift in text-to-image generation

The authors identify and formalize scene contextualization as the primary cause of identity shift in text-to-image models. They provide theoretical analysis showing this phenomenon is inevitable due to attention mechanisms and derive bounds on its strength.

Contribution

Scene De-Contextualization (SDeC) method

A training-free prompt embedding editing method that identifies and suppresses latent scene-ID correlation within ID prompt embeddings using SVD directional stability analysis and adaptive eigenvalue re-weighting. The method works per-scene without requiring prior knowledge of all target scenes.

Contribution

Theoretical characterization and quantification of scene contextualization

The authors formally prove the near-universality of scene-ID correlation through Theorem 1 and Corollary 1, and derive theoretical bounds on contextualization strength in Theorem 2 and Corollary 2, providing mathematical foundations for their proposed solution.

Table of Contents