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Interpreting Object-Dependent Concept Brittleness in Text-to-Image Diffusion Models

Yifan Yuan, Xiangyu Liu, Hongming Shan, Yu Han, Yu Jiang, Hao Tan, Junping Zhang, Linlin Shen

arXiv:2609.09909v1cs.CVcs.MM

TL;DR

The paper studies object-dependent concept brittleness, in which diffusion models repeatedly fail to realize the same concept for a small subset of objects. It diagnoses these failures with step-wise SAE representations and prototype-based inference-time correction, finding improved concept consistency and repair success across style and attribute settings and five diffusion backbones.

  • Problem

    Text-to-image diffusion models can repeatedly fail on a small subset of near-identical prompts differing only in object, revealing an underexplored form of uneven concept realization.

  • Method

    The framework compares successful and failed denoising trajectories in step-wise SAE space, constructs class-level sparse prototypes, and interpolates representations toward those prototypes without retraining.

  • Results

    Across style and attribute settings, the approach consistently improves concept consistency and repair success across five diffusion backbones.

  • Takeaways & Limitations

    Deeper denoising features provide clearer concept structure, while early-timestep correction offers the strongest repair leverage.

  • Takeaways & Limitations

    The framework assumes access to a reference set of samples for its analysis and prototype construction.

Abstract

from arXiv · show

Although text-to-image diffusion models generally exhibit strong prompt-following ability, we identify a persistent and previously underexplored failure pattern in which a small subset of prompts differing only in the object consistently fails to realize the same target concept under identical generation settings. We term this phenomenon object-dependent concept brittleness. Such cases suggest systematic internal blind spots rather than random sampling noise. In this paper, we present an interpretability-oriented framework to audit and minimally correct these failures. Our key idea is to analyze denoising trajectories in a step-wise sparse autoencoder (SAE) space, where abstract style and attribute concepts become more separable than in the raw denoising representation. This sparse space enables us to compare successful and failed generations, identify concept dimensions whose evidence is missing, weakened, or temporally delayed, and construct class-level concept prototypes from reliable class-consistent samples. Based on this audit process, we introduce a lightweight inference-time correction strategy that interpolates denoising features toward the corresponding prototype in SAE space. Rather than serving as a task-specific retraining method, this intervention acts as a validation of the diagnosed concept deficiency. We evaluate the proposed framework on style and attribute failure cases across multiple diffusion backbones, with significant improvements in concept consistency, text fidelity, and repair success. Further analyses show that deeper denoising representations provide clearer concept structure, while early-stage intervention offers the strongest correction leverage. Code is available at https://github.com/Metecade/Object-Dependent-Concept-Brittleness.

1 Introduction

The paper identifies object-dependent concept brittleness, where a target concept repeatedly fails for a small subset of semantically related objects despite succeeding elsewhere. It diagnoses these failures in step-wise SAE space and uses sparse concept prototypes for lightweight inference-time correction.

  • Motivation: Object-dependent concept brittleness occurs when the same target concept succeeds for most prompts but repeatedly fails on near-identical prompts differing only in object.The phenomenon is distinct from generic prompt-following failure and reflects uneven concept realization across objects.
  • Motivation: These sparse, process-dependent failures may reveal concepts that are not learned robustly or compositionally, while attention maps alone may not expose them.The relevant evidence evolves during denoising and can be global rather than local.
  • Framework: Step-wise SAEs make concept evidence more separable than raw denoising features, enabling comparisons between successful and failed generations through systematic activation differences.The method preserves denoising information while exposing concept-level structure that is difficult to observe in entangled raw features.
  • Framework: The framework constructs class-level concept prototypes from reliable samples and interpolates denoising representations toward them at inference time without retraining the diffusion model.The intervention both repairs outputs and probes whether diagnosed sparse discrepancies are relevant to failure outcomes.
  • Findings: Deeper denoising features provide clearer concept structure, while early-timestep intervention offers stronger correction leverage than later intervention.Prolonged strong intervention may damage unrelated content.
  • Findings: Across style and attribute settings, the approach improves concept consistency and repair success across five diffusion backbones.The evaluated backbones are SD 1.5, SD 3.5, SDXL, PixArt, and Flux.

2 Related Work

Prior diffusion interpretability work studies concept localization, internal mechanisms, and concept evolution, while SAE research extracts semantically meaningful sparse features from dense activations. This paper uses step-wise SAEs specifically to compare successful and failed concept realizations.

  • Prior interpretability work: Diffusion interpretability research spans attention-based localization, mechanism analysis of concept-relevant components, and studies of concept evolution or stage-dependent control.These directions respectively examine textual concept expression, internal module contributions, and temporal behavior during generation.
  • Sparse autoencoders: SAEs provide sparse, semantically meaningful features from dense activations and have increasingly been applied to diffusion-model analysis and intervention.Prior examples include concept-level unlearning and stage-dependent steering.
  • This work: In contrast, this work uses timestep-specific SAE spaces as a diagnostic representation for comparing successful and failed generations.The focus is selective object-dependent concept failure rather than general localization or steering.

3 Preliminaries

The paper formulates diffusion and flow-based generation as timestep-wise updates driven by backbone predictions, then analyzes those intermediate predictions with sparse autoencoders. SAEs reconstruct dense features while encouraging sparse, interpretable latent activations.

  • Unified denoising formulation: A unified denoising formulation represents generation through a sampler latent state, text prompt, timestep, sampler update, and backbone prediction.The backbone prediction is predicted noise for noise-prediction diffusion models and a vector field for flow- or velocity-prediction models.
  • Unified denoising formulation: The method analyzes intermediate backbone predictions at each timestep rather than directly analyzing the sampler latent state.This choice supplies the denoising features subsequently transformed into sparse representations.
  • Sparse autoencoders: SAEs map dense denoising activations into sparse latent representations that preserve generative information while making condition-related structure easier to analyze.The encoder produces latent activations from normalized token features using learned weights and biases.
  • Sparse autoencoders: SAE training combines reconstruction fidelity with sparsity regularization, including an auxiliary term that discourages inactive latent units.The Top-K variant retains only the K largest latent activations and zeros the rest.

4 Method

The method diagnoses object-dependent concept brittleness by comparing denoising trajectories in timestep-specific SAE spaces and uses class-consistent sparse prototypes for inference-time correction.

  • Problem Setup: Object-dependent concept brittleness occurs when a model realizes a target concept for most objects but repeatedly fails on a small subset of semantically close prompts.
  • Step-wise SAE Representation: Raw denoising features entangle content, style, texture, and attributes, whereas SAE embeddings make class structure more separable for concept-level comparison.
  • Step-wise SAE Representation: The method uses timestep-specific SAEs because denoising features change in distribution and semantic role across the trajectory, preserving stage-specific structure for diagnosis.
  • Diagnosing Concept Brittleness in SAE Space: Failed generations show larger deviations from class means, with dominant discrepancies concentrated on a small subset of sparse dimensions.
  • Diagnosing Concept Brittleness in SAE Space: After filtering prototype-inconsistent samples, distinct concept classes activate different sparse dimensions, supporting class-conditioned sparse prototypes.
  • Prototype-Guided Correction: The correction keeps the diffusion model frozen and interpolates denoising representations toward class-conditioned prototypes in the same SAE space used for diagnosis.

5 Experiments

Across style generation and attribute control, the proposed sparse-prior intervention improves prompt-level concept expression across five diffusion backbones with negligible runtime overhead, while largely preserving object identity and content.

  • 5.2 Main Results on Style Generation: Across five diffusion backbones, the method improves prompt-based style metrics and text fidelity while adding only negligible runtime overhead.The primary evidence is CLIP-Image, CLIP-Text, and Style Alignment because they directly measure prompt-conditioned improvement.
  • 5.3 Main Results on Attribute Control: The intervention improves most or all Color, Texture, and Shape dimensions while maintaining similarly small runtime overhead.The result extends the sparse prior beyond global style cues to fine-grained attributes.
  • 5.3 Main Results on Attribute Control: Improvement across style and attribute settings indicates that the intervention generalizes across concept families and remains lightweight and plug-and-play.Style cues are global, whereas attributes such as color, material, and shape are finer-grained.
  • 5.3 Main Results on Attribute Control: Qualitative corrections strengthen the target concept while largely preserving object identity and overall content.Figure 6 presents representative style and attribute failures generated by Flux.

6 Analysis

The analysis finds that deeper denoising representations make concept evidence more separable and actionable, while correction is most effective during a limited early timestep window.

  • 6.1 Where Is Concept Evidence Most Legible?: Noise Latent provides the best concept separability and prompt-conditioned correction, showing that later representations are both more legible and more actionable.Five-fold linear-probe accuracy and separation ratio improve toward the denoising output, and prompt-conditioned metrics favor Noise Latent.
  • 6.2 When Is Concept Evidence Correctable?: Early perturbations produce the largest LPIPS, PSNR, and SSIM deviations, indicating that early timesteps carry greater intervention leverage.Single-step mean ablation preserves overall feature scale while removing sample-specific information.
  • 6.2 When Is Concept Evidence Correctable?: “Early Only” performs best overall, whereas extending intervention into later steps weakens results and can cause overcorrection.“All Steps” performs worst, while “Early + Middle” raises Style Alignment but lowers CLIP-Text and worsens FID.
  • 6.2 When Is Concept Evidence Correctable?: The combined analyses identify a limited early correction window because early steps establish concept evidence and later steps mainly refine committed content.This temporal asymmetry motivates restricting intervention to an early subset of timesteps.

7 Conclusion

The paper frames object-dependent concept brittleness as a systematic failure affecting a small subset of closely related prompts and diagnoses it through step-wise SAE representations. Prototype-guided inference-time correction yields consistent gains across style and attribute settings on five diffusion backbones.

  • 7 Conclusion: Object-dependent concept brittleness occurs when the same target condition succeeds for most objects but fails on a small subset of closely related prompts.The conclusion characterizes this as the paper’s central failure phenomenon.
  • 7 Conclusion: A step-wise SAE framework makes structured mismatches in condition-related activations directly comparable between successful and failed denoising trajectories.The sparse representation makes the brittleness more interpretable than raw denoising features.
  • 7 Conclusion: Class-level sparse prototypes enable lightweight inference-time correction that also probes whether the diagnosed discrepancy is relevant to the failure outcome.Experiments cover style and attribute settings across five diffusion backbones.
  • 7 Conclusion: The framework shows consistent gains in condition consistency and repair success across the evaluated settings.The conclusion also reports clearer condition-related information in deeper features and correction concentrated in early denoising stages.

A Dataset Construction

The benchmark separates object content from style and attribute conditions to test object-dependent concept brittleness under controlled and compositional prompt variation. It uses distinct training and held-out test splits spanning diverse styles, attributes, entities, and prompt templates.

  • Concept Vocabulary: The training vocabulary includes abstract, medium-specific, and photographic styles alongside color, texture, and shape attributes.The 10 styles and 10 training attributes are expanded into richer held-out vocabularies for compositional testing.
  • Entity Coverage: Entity categories are deliberately clustered across related animals, furniture, transportation, cultural objects, and other semantic groups.This organization supports evaluation on semantically nearby objects, where object-dependent brittleness is most visible.
  • Prompt Protocol: Training prompts separate entity content from the target condition using [entity], [style] and attribute-specific controlled templates.This separation enables comparisons where the target concept remains fixed while object content varies.
  • Prompt Protocol: Held-out prompts compose color, texture, shape, entity, and style cues using the template [color] [texture] [shape] [entity], [style].The test split includes single- and multi-attribute prompts, including color + texture + shape combinations.

C Quantitative Metric Details

The evaluation combines style, attribute-control, and representation-layer metrics to measure visual style proximity, prompt and concept alignment, attribute binding, and class separability. The metrics are designed to distinguish target-concept realization from broader distributional similarity.

  • Style Generation Metrics: CLIP-Text evaluates alignment with the full prompt, whereas Style Alignment isolates the intended style term.This distinction separates overall prompt adherence from direct target-style expression.
  • Attribute-Control Metrics: Attribute evaluation uses BLIP-VQA for Color, Texture, and Shape binding accuracy and CLIPSim for continuous semantic similarity to attribute-focused text.BLIP-VQA asks whether the specified attribute is realized on the intended object.
  • Representation-Layer Metrics: Representation analysis measures class legibility with 5-fold logistic-regression accuracy and geometric separation with within-class versus between-class distances.Higher Acc indicates more linearly legible class information, while larger Sep indicates greater between-class separation and within-class compactness.
  • Style Generation Metrics: Style evaluation uses CLIP-Image, CLIP-Text, Style Alignment, and style-specific FID to measure style proximity, prompt adherence, target-style expression, and domain-level distributional similarity.Style-specific FID is treated as a secondary reference because it measures proximity to a style domain rather than prompt-level concept realization.

D More Qualitative Results

Additional qualitative examples show that sparse-prior intervention improves style and attribute realization across diverse prompts while largely preserving original content and layout. These results support the interpretation that the intervention restores missing or weakened concept evidence.

  • Failure Coverage: The intervention reduces missing-style and weak-attribute failures across animals, daily objects, vehicles, food items, and scene-related concepts.The effect is reported for both global style conditions and localized attribute conditions.
  • Qualitative Comparisons: Figures 8 and 9 show clearer target-style or target-attribute expression after intervention than in the original outputs.The comparisons provide qualitative support that sparse discrepancies identified in SAE space affect final generation outcomes.
  • Held-Out Qualitative Results: The corrected results improve concept realization across diverse object categories and prompt types while largely preserving the original content and layout.The held-out examples extend beyond isolated prompts and include varied semantic categories.
  • Interpretation: The examples support an internal sparse-activation mismatch, rather than purely random sampling variation, as an explanation for object-dependent brittleness.The conclusion is based on systematic restoration of missing or weakened concept evidence across prompts.

E Intervening in the Raw Feature Space versus in SAE Space

Intervention in SAE space produces stronger concept correction than raw-feature intervention because sparse representations separate concept evidence from entangled content structure. Sensitivity analyses further show that sparsity and filtering require balancing representational clarity against retained diversity.

  • Raw Space versus SAE Space: Raw-space intervention preserves coarse structure more conservatively but often produces only partial correction because content, style, and attribute information remain entangled.In sketch examples, it may move outputs toward grayscale without imposing a clear sketch-like appearance.
  • Raw Space versus SAE Space: SAE-space intervention more reliably restores intended styles and attributes while preserving object identity than raw-space intervention under matched settings.Both variants use intervention strength α=0.8, isolating the effect of the intervention space.
  • SAE Sparsity Sensitivity: Increasing SAE sparsity width k improves Acc and Sep from very small values before both metrics stabilize, motivating the main setting k=10.Beyond moderate width, larger values provide small and nonsystematic gains.
  • Filtering Threshold Sensitivity: Filtering becomes stricter as τ decreases, increasing failure capture while removing more training data: exclusions rise from 23/2080 at τ=2.0 to 1421/2080 at τ=0.75.The threshold therefore trades prototype purity against retained data and diversity.
  • Filtering Threshold Sensitivity: The authors select τ=1.0 because it captures most severe failures while retaining more intra-class diversity than the more destructive τ=0.75 setting.The default balances unreliable-sample removal with robustness of prototype construction.

F.3 Sensitivity to the Intervention Coefficient 𝛼

The intervention coefficient α trades off concept repair against preservation of object identity and scene content, with moderate values providing the most favorable balance. The analysis selects α=0.8 as the default because stronger guidance risks over-correction.

  • The qualitative comparison evaluates each α by both its ability to repair the concept failure and its preservation of object identity and overall content structure.
  • Small α values often leave failures only partially repaired because the intervention signal is too weak to establish the target concept.Examples include insufficiently sketch-like, painterly, or atmospheric results.
  • Moderate values around α=0.7–0.8 strengthen style and attribute realization across sketch, oil-painting, 3D-render, color, material, and texture examples.The target cues become progressively clearer as α increases from weak to moderate values.
  • Large α values can over-correct generations, altering scene layout, adding background structure, distorting object identity, or producing implausible structures.These effects are most pronounced at α=1.0.
  • α=0.8 is selected as the default because it provides strong concept guidance while reducing the content damage associated with more aggressive intervention.The paper uses α=0.8 in all main experiments.

G.1 Robustness Across Random Seeds

Multi-seed evaluation shows that the intervention consistently improves style-generation quality across different noise initializations. Step-wise SAE analyses further distinguish successful, stable trajectories from failed trajectories with persistent sparse mismatches.

  • Robustness Across Random Seeds: The intervention consistently outperforms the original model across tested seeds on CLIP-Image, CLIP-Text, and Style Alignment.The relative ranking and improvement margins remain stable across seeds.
  • Robustness Across Random Seeds: The multi-seed results indicate that style-realization gains are reproducible rather than dependent on a particular favorable sampling trajectory.The evaluation compares the original model and the proposed method under identical settings for seeds 2026, 512, 331, and 42.
  • Step-wise SAE Analysis: Successful generations maintain SAE activation peaks close to their class means across most early denoising steps, whereas failed samples show larger persistent deviations.The comparison covers successful dog and car prompts and failed orange and juice prompts over the first ten steps.
  • Step-wise SAE Analysis: Failed trajectories concentrate mismatches in a limited subset of highly activated sparse dimensions rather than exhibiting diffuse latent noise.
  • Step-wise SAE Analysis: The changing identity of top deviating dimensions across steps supports timestep-specific SAEs because shared latent spaces would blur temporal structure.
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