Temporal Concept Dynamics in Diffusion Models via Prompt-Conditioned Interventions
Authors: Ada Görgün
Fawaz Sammani
Nikos Deligiannis
Bernt Schiele
Jonas Fischer
Venue: ICLR 2026 Poster (2026)
Keywords: temporal analysis
concept emergence
diffusion models
explainability
Primary area: interpretability and explainable AI
TL;DR: We propose Prompt-Conditioned Intervention (PCI), a framework that reveals how and when concepts can be inserted into diffusion trajectories, showing that timing critically shapes concept success across contexts and models.

Abstract: Diffusion models are usually evaluated by their final outputs, gradually denoising random noise into meaningful images. 
Yet, generation unfolds along a trajectory, and understanding this dynamic process is crucial for explaining how controllable, reliable, and predictable these models are in terms of their success/failure modes. In this work, we ask the question: *when* does noise turn into a specific concept (e.g., age) and lock in the denoising trajectory? We propose PCI Prompt-Conditioned Intervention) to study this question. PCI is a training-free and model-agnostic framework for analyzing concept dynamics through diffusion time. The central idea is the analysis of *Concept Insertion Success* (CIS), defined as the probability that a concept inserted at a given timestep is preserved and reflected in the final image, offering a way to characterize the temporal dynamics of concept formation. Applied to several state-of-the-art text-to-image diffusion models and a broad taxonomy of concepts, PCI reveals diverse temporal behaviors across diffusion models, in which certain phases of the trajectory are more favorable to specific concepts even within the same concept type. These findings also provide actionable
insights for text-driven image editing, highlighting *when* interventions are most effective without requiring access to model internals or training, and yielding quantitatively stronger edits that achieve a balance of semantic accuracy and content preservation than strong baselines.

Published as a conference paper at ICLR 2026
TEMPORAL CONCEPT DYNAMICS IN DIFFUSION MOD- ELS VIA PROMPT-CONDITIONED INTERVENTIONS
Ada Görgün' 1+ Fawaz Sammani Nikos Deligiannis Bernt Schiele'
Jonas Fischer!
Max Planck Institute for Informatics, Saarland Informatics Campus, Germany 2 ETRO Department, Vrije Universiteit Brussel and imec, Belgium lagcerguen, schiele, jonas. fischer) Empi-inf... mpg. de fawaz. , sammani, aikos.deligiann &eu....vvv...u.............u.e
@vub. be
Equal contribution.
ABSTRACT
Diffusion models are usually evaluated by their final outputs, gradually denoising random noise into meaningful images. Yet, generation unfolds along a trajectory, and analyzing this dynamic process is crucial for understanding how controllable, reliable, and predictable these models are in terms of their success/failure modes. In this work, we ask the question: when does noise turn into specific concept (e.g., age) and lock in the denoising trajectory? We propose PCI Crrompt-Conditiones Intervention) study this question. PCI training-free free and model- -agnosic framework for analyzing concept dynamics through diffusion time. The central idea the analysis Concept Insertion Success (CIS), defined as the probability that concept inserted at given timestep is preserved and reflected in the final image, offering a way to characterize the temporal dynamics of concept formation. Applied to several state-of-ther thetttttttatttt taxonomy concepts, PCI reveals diverse temporal behaviors across diffusion more favorable specific
sikttto-imaee diffusion models and broad
models, in which certain phases of the trajectory concepts even within the same concept type. These findings also provide actionable insights for text-driven image editing, highlighting when interventions are most effective without requiring access to model internals or training, and yielding quantitatively stronger edits that achieve a balance of semantic accuracy and content preservation than strong baselines. Code is available at: [PCI Framework
INTRODUCTION
Image diffusion models have emerged powerful class of generative frameworks Rombach ct al. (2022)); Podell et al." (2024); Esser et al. (2024a), redefining the landscape of text-driven image synthesis and editing. By progressively reducing random noise structured output, these models capture rich data distributions and enable realistic, creative, and novel image synthesis. Despite their success, the internal dynamics of how semantic concepts (e.g., gender, style, ethnicity) emerge and solidify during the denoising trajectory remain poorly understood. For more targeted steering and intervention of the generation, and better understanding of potential sources of systematic differences within the model, getting a better understanding how these dynamics look is essential. Without such understanding, attempts to debug, guide, or control generation remain largely heuristic.
Existing methods usually investigate concepts in diffusion models through the lens of attribution maps (Tang et al., 2022 Helbling et al., 2025), which localize concepts generated images and answer where but not when concepts manifest over time. Other works explore concepts in diffusion models using Concept Bottleneck Models [smail et al.) (2024): Kulkarni et al.) (2025) or Sparse Autoencoders Cywinssi CittenccoerrssCyiiskii & Dejal 2025), which map intermediate latent noise to human-interpretabbe concepts. These approaches, however, often overlook temporal dynamics, involve complex (and sometimes supervised) concept-naming procedures, not faithful to the original model with usually deteriorating performance, and require extra training. They are also largely confined to simple datasets and domains, limiting their scalability the typical roal-world settings.

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Deneising
Prompt: A realistic photo of person.
Concept Insertion
1.00
Success (CIS)
0.50
Diffusion
0.00
1.00
0.75
0.50
0.25
Time
Prompt Conditioned Intervention (PCT)
Prompt: realistic photo of an old person.
Figure I: Prompt-conditoned intervention (PCI) over diffusion timesteps. We suggest to study when noise turns into a specific concept through the lens of concept insertion success, i.c., the chance of inserting concept at a certain timestep successfully. By switching the base-prompt (top) at different time points the diffusion process with a prompt composed of the base prompt and the concept of interest (old), we can measure this success rate (CIS curve) across different seeds and base prompts to analyze temporal dependency and influence concepts in diffusion models.
In this work, we shed light on the temporal evolution of concepts in diffusion and flow matching models by introducing Prompt- compp--onddttooed Interventions (PCI), lightweight and model-agnostic mechanism in which we switch the text prompt at a chosen timestep and observe how the model reacts. quantify when concept can still influence the trajectory and
This probing procedure allows when the model has already committed to a semantic interpretation and investigate a wide range of temporal behaviors: concept insertion, by measuring when a new attribute can still be added; concept deletion, by testing when an existing attribute can be overridden; and multi-concept interactions, by examining how two or more attributes jointly affect one another when introduced at different times. Within this broader probing framework, our primary quantitative measure is the Concept Insertion Success (CIS) curve, which captures the probability that a concept remains insertable at each timestep.
Concretely, consider the example in Fig. [ Starting from the base prompt "a realistic photo of person", ", image is generated through the denoising process of diffusion models. Prompt- Conditioned Interventions (PCI) intervene (*) at different timesteps, augmenting the base prompt with target concept, here "a realistic photo of an old person". ". Generation then continues with this augmented prompt as prior. If the resulting image incorporates the target concept old, the intervention timestep marks a point at which the concept can still be flexibly and successfully inserted (high CIS). If intervening yields meaningful change and produces image nearly identical to the one from the base prompt, the concept effectively locked and no longer influences the trajectory (low CIS). PCT allows us derive CIS curves for any concept, in any context, and for any diffusion-
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flow-based model, offering a practical tool for understanding their temporal behavior. By analyzing 800 samples spanning diverse categories and fine- ame-grained concepts across five generative models, we observe the following trends: (1) Timing matters: global factors (time, weather, season, color) lock in early; human attrihutes (e.g., age, gender) stabilize in the middle; and aut-of-distributiin concepts (e.g., "horse in living room") insert unusually early. (2) Model choice matters: rectified-flor models insert concepts earlier, while diffusion models preserve editability deeper into the trajectory. (3) Context matters: alignment between concept and prompt context strongly shapes insertability, small context shifts (e.g.. outdoor- can flip concept from late-insertable to early-insertabe while seed noise largely suppressed by averaging.
Beyond analysis, PCI also enables practical, training-fiee free editing application. Because CIS identifies when a concept can be modified without disrupting unrelated content, provides a principled way to select effective editing windows. By switching the prompt at a CIS-informed timestep, we obtain edits that introduce the desired concept while staying close to the original trajectory-withee without relying on attention maps Epstein et al. (2023); Hertz et al. (2023) or segmentation modules Tewel et al. (2025). Quantitatively, this CIS-guided editing achieves a superior balance between preservation and semantic strength compared to strong baselines such as NTI+P2P Mokady et al. (2023) and Stable Flow Aowrhhraammi et al. (2025).
In summary, contributions are follows:
We introduce PCI, a unified temporal probing mechanism for text-to-image to-image models. Applied the insertion setting, it yields the Concept Insertion Success (CIS) curve, quantifying the probability that a concept is insertable at each timestep.
We analyze wide range of concepts, contexts, and models, including multi-concepp interactions and concept deletion, revealing consistent temporal patterns of concept emergence and stabilization.
We demonstrate a practical editing application enabled by CIS, showing that timing materially affects edit reliability and fidelity, and that CIS-guided intervention windows offer actionable guidance on when apply edits for high-quality, content-preserving results.
2 RELATED WORK
Diffusion Models (Ho et al. 2020 Dhariwal & Nichol 2021) form the foundation of modern
generative imaging, widely applied in both image (Rombach al. 2022b Saharia et al. 2022) and video generation (Esser et al. 2023). While early approaches rely on stochastic differential equations
(SDEs) (Song et al.| 2021] ). more recent formulations such as Rectified Flow QL.iu et al., 2023) and Flow Matching (Lipman et al., 2023) through ordinary differential equations (ODEs) (Albergo & Gunden-Eijndent 2023) provide faster and more stable training. At the same time, architectures have evolved from UNets (Ho et al. 2020) to Diffusion Transformers (DiT) (Labs. 2024 Esser al., 2024b Sauer et al. 2024), enabling greater scalability (Peebles & Xie 2023). Yet, this progress has outpaced our ability to understand their reasoning and generation dynamics, which is however essential for trust, reliability, and safe deployment (Shi et al. (025)..
Static Interpretability in text-to-image diffusion models examine how textual concepts manifest in the generated images. Recent attribution based approaches localize where prompt tokens appear in the image (Tang al.) 2022 Helbling et al. 2025). Such approaches provide spatial grounding that supports faithfulness checks, safety auditing, and region-level controllability. Other methods instead map internal latents to human-interprepretaale concepts. Here, concept-bottleneck layers or sparse autoencoders are learned either aligning latent directions with predefined attributes (Ismail et al. Cywinski & Deja 2025) or fitting to large-scale text embeddings (e.g., CLIP) (Kulkarni et al., Kim & Ghadiyaram 2025). An alternative approach to learn such mappings are pseudo-tokens in the text encoder that represent a concept as a weighted combination of vocabulary embeddings (Chefer et al.) 2024). These approaches clarify what the model represents and where in the image it appears. However, they are typically evaluated only at a single late stage of generation, treating the model as static mapping, providing only limited insight into the model's 's temporal dynamics. The questions when and how concepts are insertable or controllable remain unanswered.
2024
2025
Dynamic Interpretability approaches in contrast recognize that diffusion model's behavior changes throughout the denoising time. Wu et al. (2022) discovered that Stable Diffusion models naturally disentangle high-level content from stylistic attributes over time. Similarly, Chen et al. (2024b) show
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intervention
Owen-3B
Pi: base prompt
Pa concept promot
ezomcopt presern??
YES
Resampled Steed
probabiity af
1.0
intorvantion at
G5
0.5
YES
Averaging
0.C
NO
ciffusion trajectory 11: dean))
feessmp led Seed Bucket
Figure 2: Overview of the PCT framework. A base prompt Po is used as conditioning for generation, altered the concept prompt Po time t.. The generated images are evaluated through VQA to determine concept presence and aggregated across seeds to obtain CIS across the diffusion trajectory.
that diffusion--ern based editing is highly sensitive to the choice of timestep and noise initialization. By training multiple sparse autoencoders at different timesteps in the trajectory, researchers revealed early formation of scene layout and mid-trajectory fixation of object composition, yet without enforcing concept consistency across time (Tiaz et al., 2025). Huang et al. (2025) instead train single autoencoder across the entire trajectory to encourage temporal coherence of the learned representations, but do not aim for dynamic concept-level interpretability. Unlike timestep-sensitive 2022 Chen et al. (0024) autoencoder-basee probes (Tinaz et al. 20255
editing methods (Wu et al. Huang et al., (2225), PCI is training- and annotation-free approach to investigate when a diffusion model commits a concept directly from the model's generative behavior instead of surrogates,
3 METHODOL.OGY
In this section, we present our method for analyzing the temporal evolution of concepts in diffusion models. We refer to Appendix Sec. [ preliminaries on diffusion models. At a high level, our proposed method PCI (Sec. 3.1 begins with a base prompt that guides the generation up specific timestep. At that timestep, we replace the base prompt with an augmented version that combines the base prompt with the target concept we wish to analyze. The generation then continues with this augmented prompt and an image is synthesized. Finally, a Large Vision Language Model (LVLM) with the task of Visual Question Answering (VQA) used to detect the presence of the target concept in the synthesized image. By performing this intervention at every timestep, we obtain a Concept Insertion Success curve (Sec.(3.2 which quantifies, for each timestep, the probability that the concept is successfully inserted into the generation trajectory and expressed in the final image. A high-level overview of our framework is illustrated in Fig. 2 To complement CIS, we also explore Concept Deletion Success (CDS), which mirrors this procedure but switches from concept-bearing prompt back to the base prompt, quantifying when a concept can still be removed. While CDS provides a dual perspective
temporal concept behaviour indicating when concept becomes difficult erase, we do not adopt it as a primary metric as we found that it introduces confounding factors that make CDS less stable and interpretable across concepts and models. For this reason, CIS remains our main analytical tool and we refer to Appendix Sec. A.4 for further details regarding CDS.
3.1 PROMPT-CONDITIONED INTERVENTION (PCI)
Building on the iterative denoising framework of diffusion models, our method uses Prompt- Conditioned Intervention (PCI) to analyze the temporal evolution of semantic concepts, and the point where they become locked in the denoising trajectory and after which they do not influence the trajectory anymore. The key idea is to alter the denoising trajectory in an intermediate timestep i, by changing a part of the conditioning input (changing the text prompt to include a concept), and then continue the denoising process with this new condition to obtain reconstructed image. This modification serves to isolate the effect of introducing a specific concept, allowing its influence on the generative trajectory to be explicitly traced. By comparing reconstructions across different timesteps and conditioning variants, PCI reveals how individual concepts influence the generation trajectory and at which stages they become visually embedded.
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Formally, let Po denote the base prompt, neutral description without the target concept (e.g.. g.. realistic photo of a person). Let Pe denote the concept prompt, obtained by augmenting Pa with the explicit inclusion of the target concept (e.g.. realistic photo of an old person). We define Den y) as the denoising process of a diffusion model at timestep I, where zy is the noisy latent and y is the text condition. Over timesteps f. (7 ,..... 0), this process reconstructs clean latent 20. The synthesized image is obtained by decoding the clean (denoised) latent Xo through the decoder of the latent diffusion model. Given an initial noisy latent Xy and conditioning text input Po. we first ensure that the initial noisy latent Xy does not encode noise features that would lead to the generation of latent image Xy that contains the target concept c. To perform this, we apply a seed resampling operation. The details of this procedure are provided in Appendix Sec. A.2 We start the denoising process with:
X Denoise(xx;; Po),
(1)
where Xt, is the intermediate state reached under the text condition P& at timestep to. We then switch the conditioning from P. to Pa at a timestep ty:
x (n - P.) Denoise(xx,, Pa),
As illustrated in Fig. 1 this formulation provides a mechanism for probing the sensitivity of the generation process concepts and prompt alterations different timesteps when prompt switching is performed at tg: Po 1 Pa.
3.2 CONCEPT INSERTION SUCCESS (CIS)
central objective of PCT is to determine when a concept becomes locked in the denoising trajectory, and after which it does not influence the trajectory anymore. We formalize this through Concept Insertion Success (CIS), which is defined as the probability that a concept inserted through PCI at that timestep appears in the generated output. Intuitively, with CIS, we can investigate the earliest timestep where switching the conditioning from base prompt P, to a concept prompt P. no longer alters the base generation obtained solely from Po. By repeatedly applying the switch from P, to Pa at varying points i, along the trajectory, where varies from T - 1, we can measure when the introduced concept appears in the generated image. To efficiently quantify concept presence over time, we leverage Large Vision-Language Models (LVLMs) by posing the problem as Visual
Question Answering (VQA). For each reconstruction obtained by prompt switching x, (P, -4 Pc). the VQA model evaluates whether the concept is present by returning yes no. While perceptual similarity (e.g., CLIPScore Hessel et al.] (202T) or LPIPS Zhang et al. (2018)) would be an alternative, they proved less powerful and unspecific in preliminary experiments and having particular difficulties identifying the absence of concept. We provide details on prompts in Appendix Sec, A.3
4 EXPERIMENTS
We investigate when in diffusion trajectory, it becomes unlikely that the underlying model can still introduce a new target concept into the generation process. Using our PCI approach (Sec. 3.1 we investigate the temporal dynamics in taateoof-the-ar diffusion models including Stable Diffusion 2.1 (SD 2. Rombach et al. (2022a), Stable Diffusion XL (SDXL) Podell et al. (2024), Stable Diffu- sion 3.5 (SD 3.5) Esser et al. (2024b); Sauer al. (2024). PixArt-alpha XL Chen et al. (2024a) (a diffusion-based transformer (DiT) model) and FLUX.
1.U..--ddy dev Labs 2024) for a large set of concept categories (Sec. I) with our CIS scorer selected as Qwen- VL-3B (Bai et al. 2025) (see Appendix Sec. C.c.C..for. ablations on multiple VQA models). All CIS measurements reported in this work are aver- aged across multiple random seeds (see details in Appendix Sec. C.2 to ensure robustness and reduce variation robustness test in Appendix Sec. C.3 Through our CIS metric (Sec. (.2) we reveal differences in when certain types of concepts remain insertable into image generation well as differences across models (Sec. (4.2) and show that even for the same concept class, depending on the context, there can be stark differences in when concept can still be insertible (e.g., 'cat' versus "horse") in Sec. 4.2 We provide all details on setup, evaluation protocol across models, and analysis metrics in Appendix Secs. B and B.2 respectively.
variance. We further verify stability under prompt-wording changes through our
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Figure 3: CIS reveals cross-concept and cross-architecture differences. CIS for 750 and Tro across multiple concept categories and diffusion models.
4.1 CONCEPT GROUPS
We consider a large set of concepts organized into hierarchical structure of categories, subcategories, and individual concepts drawing inspiration from existing works in the literature [Bkrr et al.) (2023); Huang et al. (2023). It covers demographics (e.g., gender, ethnicity, age group), objects (animals, handmade objects, natural elements), human properties (clothing, accessories, physical traits), actions (common human activities), attributes (material, color), environmental factors (season, weather, time of day, setting/venue), and style (sketch, painting, realistic). Each subcategory contains diverse set of fine-grained concepts (e.g., for the age group, concepts are "baby", "child", "teenager", "adult", "old"), allowing systematic evaluation of models across wide spectrum of concepts. Each concept further represented across 8 differrnt contexts (e.g., river in landscape and river in street), resulting in approximately 800 fine-graine concept descriptions in total. We refer to Appendix Sec, A.3 regarding how we designed the questions for the VQA model for each subcategory.
4.2 TEMPORAL CONCEPT INSERTABILITY
We quantify when concepts are reliably insertable along the denoising trajectory using band-limited CIS statistics computed exclusively within the CIS probability band [0.50, 0.70). Our goal twofold: (i) characterize when insertion is possible (crossing times) at the specified CIS bands, which we refer to as (T60, T70), and how sensitive it is to the exact intervention point (steepness) through band-width W70-150 = 770 T50l that tracks the maximum slope the CIS curves; and (ii) compare these behaviors across models, subcategories, and for more fine- grainedinede intuition, across concept-prone pairs. We refer to more detailed definitions in Appendix Secs. and B.2 Beyond the quantitative CIS analysis, we include a complementary qualitative study of cross-attention maps (Appendix Sec. S.c. visualize how concept strength and spatial focus evolve with the intervention timestep. We

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additionally evaluate multi-concepl interactions (Appendix Sec. D.3) and extend our analysis to abstract concepts (Appendix Sec. D.4), examining how these settings modify insertion dynamics.
CATEGORY-LEVEL ANALYSIS
In Fig.
we report category-level findings over the CIS metrics 750 and T7o across models, grouped into subcategories defined in Sec. 4.1 Due space limitations, only include subset of categories in Fig. 3 and defer the rest to Appendix Sec. D
Cross-model Trends within Subcategories. Based on our analysis in Fig. 3 we observe stable timing hierarchy and alignment rule across SD 2.1, SDXL,, SD 3.5, PixArt-alpha, and FLUX.1-dev despite their architectural and scheduling differences:
Monotone CIS. For all subcategories and models, C(r) is empirically nondecreasing, yielding well-defined level-q crossing times Ty (see Fig. 4a for SDXL).
Global factors emerge earliest and most sharply. Style, time day, weather, season and color exhibit large Ty (they are inserted early) (Fig. 3 and narrow steep windows W70-550
Human properties sit in between. While gender and age group are inserted mid-time; ethnicity tends to be inserted later (smaller Tg) than other demographies in SD 2. 1/SDXI. Expressive cues (traits, accessories) follow the same ordering, with accessories typically having the latest insertion among them. (Fig.
observe model specific patterns that allows us to perform a distinct comparison regarding how they treat concepts within the diffusion trajectory.
Model-specific temporal patterns. Based analysis in Fig. 3
Rectified flow models. Compared to diffusion-based models such as SD 2.1 (U-Net architecture), SDXL (U-Net architecture), and Pit-rtaalpha (DiT architecture), petified-flow models such as SD 3.5 and FLUX.. 1-dev consistently sharpen the transition (smaller band W) across categories. In many cases this sharpening coincides with earlier crossings (larger Ty), reducing late-stage flexibility (Fig. 3). As result, concepts in rectified flow models are inserted much earlier than diffusion models.
Accessories as an exception. While SD 3.5 generally enforces early insertion for human prop- erties, accessories stand out: 750=0.53 0.05 and 7..=0..2+00.000 place the crossing window near the trjeecrjje,,rr, substantially later than SD 3.5 traits (7sw=0.69). Cross-model comparison shows accessories the most late-flexible human concept (SD 2.1: 0.46//0.58 SDXL: 0.41/0.53. SD 3.5: 0.53/0.00 (..6), though SD 3.5 still yields larger T, than SD 2. 1/SDXL at both levels. Unlike other human concepts in SD 3.5, accessories can often be adjusted mid-trajectory without being washed out, offering rare late-stage flexibility within this model family.
SD 2.1 and late-stage flexibility. SD 2.1 preserves more late-stage flexibility (smaller Ty in mul- tiple appearance categories) but at the cost of broader and more timing-sensitive windows (larger W) (Fig. 3.
Key findings. Our analysis isolates two drivers of when edits take effect and how reliably they succeed along the generation trajectory:
Timing of concept insertion. Global scene factors (e.g., style, time of day, weather, season and color) are inserted and locked in early; Human attributes (e.g., age, gender) typically lock in mid-trajectory, and fine-level details such as accessories typically lock in mid-to-late trajectory.
2. Model choice. Rectified- flow models insert concepts earlier than diffusion models, whereas diffusion models allow later insertions.
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Pomptt parson plogruud
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(a) age group under different con- texts (playground, bus)
(h) clothing under different contexts (hospital, street)
(c) role-concept alignment
Figure 4: Revealing context-dependent differences for the same concept. We show the difference in CIS curves of a concept provided with different context in the base prompt.
FINE-GRAINED ANALYSIS
While category-level summaries highlight broad timing patterns, they can mask the specific prompts (contexts) and concepts that underlie these patterns. Fine-grained analyses investigates these sources of variation by showing full CIS curves at the level of (i) a fixed concept across different prompts/con- texts (prompt-level), and (ii) different concepts within the same subcategory under matched prompts (concept-level), This reveals when the aggregated behavior of category is dominated by subset of contexts or a subset of its concepts and highlights meaningful concept-come context relationships that are obscured by averaging (c.g., how the same concept behaves across distinet settings, or how different concepts within category respond to the same context setting). For this, we carry out fine- fine-grained analyses for SDXL. Detailed prompt-level and concept-level comparisons are provided in Appendix Sec. [.I] where we vary either the base prompt (context) or the concept within a subcategory and re- port CIS curves with 95% Wilson CIs over seeds. Here, we highlight additional fine-grained findings at the concept-level that reveal nuanced differences within different concept-context interactions.
Additional case studies. In Fig. we present several examples illustrating how concept-context relationships determine when concept be successfully introduced during the diffusion trajectory. These cases demonstrate that the aligmment between a concept and its surrounding context plays central role in shaping CIS. For instance, within the subcategory "age group" in public settings (Fig. 4a). a baby is inserted later in the trajectory of "playground" than in that of a "bus stop" since the playground provides more natural and supportive context, allowing for later insertions. By contrast, a teenager shows the opposite pattern: it is locked earlier in the playground trajectory than in the bus stop trajectory. A similar dynamic emerges when comparing clothing (scrubs, suit) with workplace contexts (hospital, street) in Fig. 4b) In the less typical context of "street" scrubs lock in earlier in the diffusion trajectory than they do in a "hospital" whereas the pattern reverses for a suit. The same phenomenon is visible in Fig. 44 where in uncommon scenarios such as female football player or a male cashier, the gender category is inserted earlier in the diffusion trajectory, and the CIS window of persistence becomes narrower.
Finding: Dut-ro--iitrriuutinn (OOD) concepts relative to context (c.g.. horse in a living room, wearing scrubs in a street, baby at bus stop) become locked in earlier than common in- distribution concepts relative context (c.g., cat in living room, wearing scrubs in hospital, baby at playground). These OOD pairs inflate Ty early and W shows greater timing fragility.
TEXT-DRIVEN IMAGE EDITING
The CIS probability function derived from PCIs enables feature-preserviee preserving text-driven image editing, where generated image is modified according text prompt while maintaining the original
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Methods
CLIP img
CLIP txt
CLIP dir
NTI+P2P
0.8666
0.2215
0.0979
Stable Flow [CVPR 2025]
0.8324
0.2152
0.0631
PCI-730
0.9343
0.2125
0.1014
PCI-750
0.8885
0.2236
0.1387
PCI-T60
0,8625
0,2289
0.1531
PCI-770
0.8353
0.2341
0.1678
PCI-790
0.7679
0.2449
0.1963
Table 1:
Quantitative comparison of editing methods. We report CLIP img (content preservation), CLIP tat (semantic alignment), and CLIP dir (directional consistency). Higher CLIP indicates better preservation, while higher CLIP tut and CLIP dir indicate stronger concept insertion.
content as accurately as possible. Consider a concept category (e.g., age group) within a broader context, which we refer to as the base prompt (e.g., person at a city park). We first compute the CIS function for each fine-gres greingriidd concept of the concept category (e.g.. baby, child, young, adult and old) and then average across them obtain representative CIS function for the subcategory. This function encodes the probability that any fine-grained concept associated with the subcategory can be successfully inserted at given timestep.
To edit an image with a desired concept, we map user-defined probability to its nearest corresponding timestep ta on the CIS curve and run PCI. That is, perform prompt switching at t., replacing the base prompt augmented with the new concept for the remainder of the generation. Fig. Fig. iillustrates text-driven image editing for SDXL, and refer to Appendix Sec. D.5 (Fig. D6) for examples
SD 3.5. We show edits at different CIS probabilities (0.3, 0.5, 0.7, 0.9]. From the analysis in Sec. 4 find that edits High values at 790 achieve the intended modification but compromise content preservation, while low values 7so do affect the base image, as the concept no longer influences the trajectory.
reliable within the interval [T50, T70]. which is also reflected in Fig.
To rigorously assess the effectiveness of CIS-guided editing, we conducted quantitative comparison against two strong baselines: NTI+P2P Mokadd et al. (2023) and Stable-Flow Avrahami et al. (2025). For this purpose, we constructed dedicated evaluation dataset consisting of 88 concept pairs, each comprising a base prompt and a corresponding concept prompt, and generated random seeds per concept, resulting in a total of 1760 edited images across all methods. Following prior work Avrahami et al. (005)), we employ three complementary CLIP-derived metrics measure editing quality:
CLIPing img measures the perceptual similarity between the base image and the edited image in the CLIP image-embeddiee space. This captures overall content preservation, ensuring that changes do not unintentionally alter unrelated aspects of the scene.
CLIP lxt measures the aligmment between the edited image and the concept prompt the CLIP text-image embedding space. This captures the global semantic strength of the inserted concept.
CLIPair compares the image-edit direction (base - edited image) with the textual direction (base prompt - concept prompt). Rather than isolating the concept, this metric evaluates whether the semantic shift produced by the edit is consistent with the semantic shift implied by the prompts. High CLIPair indicates that the deviation introduced by editing is aligned with the intended conceptual change, rather than being arbitrary drift.
Together, these three metries provide a more complete quantification of the insertion pretiionntreeeaattionn trade-off, and the results are consistent with the temporal patterns reported in the main analysis. Specifically, across increasing CIS intervention bands, we observe a systematic decrease CLIP SinP indicating reduced content preservation as the edit is applied earlier in the denoising process. At the same time, both CLIP and CLIP dir increase with higher CIS bands, reflecting stronger alignment with the target concept and more consistent semantic shift relative to the intended prompt change. This temporal trend mirrors the insertion dynamics observed in our CIS curves: early interventions yield stronger concept insertion but the cost of greater deviation from the base image. Importantly,

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dog
cartoon
street
image a doctor
image person
walking
+
realieiic photo of person
Figure 5: Examples of text-driven image editing on SDXL. The edited images are shown at four different points with their respective CIS probabilities: T30. T50. T70, and 790- High probabilities until a certain point ensure the intended modification but reduce preservation of the original image. We observe that CIS probabilities above 0.7 start to noticeably compromise the original content, and probabilities between 0.5 to 0.7 as suggested by our analysis (red rectangle) are best for editing while preserving the original image.
the CIS-guided window (0.5, 0.7] consistently achieves the best balance between successful editing and fidelity across all metrics, and is therefore used as our main comparison setting. Tab, shows that our method outperforms both NTI+P2P, well as the recent state-of-the-art Stable Flow across all metrics. We report qualitative comparisons of text-driven image editing with respect to NTI+P2P and Stable Flow Appendix Sec. D.5 D7).
CONCLUSIONS
We introduced Prompt-Conditioned Intervention (PCI) simple, model-agnostic framework for probing when concepts can be injected during denoising under a fixed context. To complement this framework, we propose Concept Insertion Success (CIS) as the accompanying score that quantifies whether concept intervention at given timestep persists to the final image. For the interested reader we provide short discussion of limitations Appendix Sec. summary, PCI and CIS turn diffusion time into an interpretable axis for analysis and editing: they expose context dependence, reveal cross-model stabilization differences, and provide actionable guidance on when to intervene. Our method and results yield practical basis for temporally aware evaluation and more reliable, sensitive editing of generative models.
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REFERENCES
Michael S. Albergo and Eric Canden-Eijnden Building normalizing flows with stochastic interpolants. In Proceedings of the International Conference on Learning Representations (ICLR), 2023.
Kumail Alhamoud, Shaden Alshammari, Yonglong Tian, Guohao Li, Philip H. S. Torr, Yoon Kim. and Marzych Ghassemi. Vision-language models do not understand negation. In Proceedings of the JEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
Omri Avrahami,, Or Patashnik, Ohad Fried, Evgenii Nemchinov, Kfir Aberman, Dani Lischinski, and Daniel Cohen-Or. Stable flow: Vital layers for training-free image editing. In Proceedings of the IEEE/CVE Conference Computer Vision and Pattern Recognition (CVPR), 2025.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang. Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang. Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding. Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang. Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. arXiv preprint arXiv:2502.13923 2025.
Eslam Mohamed Bakr, Pengzhan Sun, Xiaoqian Shen, Faizan Farooq Khan, Li Erran Li, and Mohamed Elhoseiny. Hrs-bench: Holistic, reliable and scalable benchmark for text-to-image models. Proceedings of the IEFF/CVF International Conference on Computer Vision (ICCV), pp. 19984-19996, 2023.
Hila Chefer, Oran Lang, Mor Geva, Volodymyr Polosukhin, Assaf Shocher, michal Irani, Inbar Mosseri, and Lior Wolf. The hidden language of diffusion models. In Proceedings of the International Conference on Learning Representations (ICLR), 2024.
Dar-Yen Yen Chen, Hmrishav Bandyopadhyay, Kai Zou, and Yi-Zhe Song. Normalized attention guidance: Universal negative guidance for diffusion model. arXiv preprint arXiv:2505.21177 2025.
Junsong Chen, Jincheng YU, Chongjian GE, Lewei Yao, Enze Xie, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-Slalpha8: Fast training of diffusion transformer for photorealistic
synthesis. In Proceedings of the International Conference on Learning Representations (ICLR), 2024a.
Zhiheng Chen, Jiatao Gu, Jianmin Chen, Lizhuang Ma, and Ziwei Liu. Tino-edit: Timestep and noise optimization for robust diffusion-basee image editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024b.
Bartosz Cywiński and Kamil Deja. SAcuron: Interpretable concept unlearning in diffusion models with sparse autoencoders. In International Conference Machine Learning (ICMI), 2025.
Prafulla Dhariwal and Alex Nichol. Diffusion models beat GANs on image synthesis. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
Dave Epstein, Allan Jabri, Ben Poole, Alexei A Efros, and Aleksander Holynski. Diffusion self- guidance for controllable image generation. In Advances in Neural Information Processing Systems (NeurIPS), 2023.
Patrick Esser, Johnathan Chiu, Parmida Atighehchian, Jonathan Granskog, and Anastasis Germanidis. Structure and content-guided video synthesis with diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
Patrick Esser, Sumith Kulal, A. Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach. Scaling rectified flow transformers for high-resolution image synthesis. International Conference on Machine Learning (ICML), 2024a.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, al. Scaling rectified flow transformers for high-resolution image synthesis. In International Conference on Machine Learning (ICML), 2024b.
11

---

Published as a conference paper at ICLR 2026
Alec Helbling, Tuna Han Salih Meral, Benjamin Hoover, Pinar Yanardag, and Duen Horng Chau. Conceptattention: Diffusion transformers learn highly interpretable features. In International Conference Machine Learning (ICML), 2025.
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or -r. Prompt-to-prompy image editing with crss-attentioon control. In Proceedings of the International Conference on Learning Representations (ICIR), 2023.
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. CLIPSCore:
reference-free evaluation metric for image captioning. In EMNLP, 2021.
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems (NeurIPS), 2020.
Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2i-compbench: A comprehen- sive benchmark for open-world compositional text-to-image generation. Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2023.
Victor Shea-Jay Huang, Le Zhuo, Yi Xin, Zhaokai Wang, Peng Gao, and Hongsheng Li. Tide: Temporal-awer sparse autoencoders for interpretable diffusion transformers in image generation. arXiv preprint wrXiv:2503.07055 2025.
Aya Abdelsalam Ismail, Julius Adebayo, Hector Corrada Bravo, Stephen Ra, and Kyunghyun Cho. Concept bottleneck generative models. In Proceedings of the International Conference on Learning Representations (ICLR), 2024.
Dahye Kim and Deepti Ghadiyaram. Concept steerers: Leveraging k-sparse autoencoders for controllable generations. arXiv preprint arXiv:2501. 19066, 2025.
Akshay Kulkami,, Ge Yan, Chung-En Sun, Tuomas P. Oikarinen, and Tsui- Wei Weng. Interpretable generative models through post-hoc concept bottlenecks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 317711171, 2025.
Black Forest Labs. Flux. httpp:://gitthb..com//bb
flux 2024.
Bo Li, Yuanhan Zhang. Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. LLaVA--nevvision: Easy visual task transfer. Transactions Machine Learning Research (TMLRR), 2025. ISSN 2835-8856.
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. In Proceedings of the International Conference on Learning Representa- tions (ICLR), 2023.
Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. Proceedings of the International Conference on Learning Representations (ICLR), 2023.
Andrés Marafioti, Orr Zohar, Miquel Farré, Merve noyan, Elie Bakouch, Pedro Manuel Cuenca Jiménez, Cyril Zakka, Loubna Ben allal, Anton Lozhkov, Nouamane Tazi, Vaibhav Srivastav, Joshua Lochner, Hugo Larcher, Mathieu Morlon, Lewis Tunstall, Leandro Von Werra, and Thomas Wolf. SmolVLM: Redefining small and efficient multimodal models. In Second Conference Language Modeling. 2025.
Ron Mokady, Amir Hertz, Kfr Aberman, Yaron Pritch, and Daniel Cohen-Or. Null-text inversion for editing real images using guided diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEFF/CVF International Conference on Computer Vision (ICCV) 2023.
12

---

Published as a conference paper at ICLR 2026
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. In Proceedings of the International Conference on Learning Representations (ICLR), 2024.
Robin Rombach, A. Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10674-10685, 2022a.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVE Confer- ence on Computer Vision and Pattern Recognition (CVPR), 2022b.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lépes, Tim Salimans, Jonathan Ho. David J. Fleet, and Mohammad Norouzi. Photorealistic texi-to-image diffusion models with deep language understanding. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, and Robin Rombach. Fast high-resolution image synthesis with latent adversarial diffusion distillation. arXiv preprint arXiv:2403.12015. 2024.
Yingdong Shi, Changming Li, Yifan Wang. Yongxiang Zhao, Anqi Pang, Sibei Yang, Jingyi Yu, and Kan Ren. Dissecting and mitigating diffusion bias via mechanistic interpretability. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), 2025.
Yang Song, Jascha Sohl-Dickstein Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. Proceedings of the International Conference Learning Representations (ICLR), 2021.
Raphael Tang, Akshat Pandey, Zhiying Jiang, Gefei Yang, K. V. S. Manoj Kumar, Jimmy J. Lin, and Ferhan Ture. What the daam: Interpreting stable diffusion using cross attention. In Annual Meeting of the Association for Computational Linguistics, 2022.
Yoad Tewel, Rinon Gal, Dvir Samuel, Yuval Atzmon, Lior Wolf, and Gal Chechik. Add-it: Training- free object insertion in images with pretrained diffuusion models. In Proceedings of the International Conference on Learning Representations (ICLR), 2025.
Berk Tinaz, Zalan Fabian, and Mahdi Soltanolkotabi. Emergence and evolution of interpretable concepts in diffusion models, arXiv preprint wtwXXXv:200..54777 2025.
Qiucheng Wu, Yujian Liu, Handong Zhao, Ajinkya Kale, Trung M. Bui, Tong Yu, Zhe Lin, Yang Zhang, and Shiyu Chang. Uncovering the disentanglement capability in text-to-image diffusion models. Proceedings of the IEEF/CV Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1900-1910, 2022.
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018
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Temporal Concept Dynamics in Diffusion Models via Prompt-Conditionel Interventions
Appendix
This supplement provides the complete set of technical details, implementation components, and extended analyses supporting the main paper. Appendix Sec. A covers methodological foundations of PCI, including diffusion model preliminaries, seed resampling, VQA templates, and the full formulation of CDS. Appendix Sec. summarizes the experimental setup and evaluation protocol. Appendix Sec. epresents ablation studies assessing robustness across VQÅ models, seed counts, and prompt variations. Appendix Sec. Deroortss extended analyses such fine-grained concept behavior, cross-attention visualizations, multi-concept interactions, abstract concepts, and additional qualitative editing results, Appendix Sec. E outlines the limitations of our approach.
Implementation and Methodological Details
A.1
Diffusion Model Preliminaries..
A.2
Concept-Controlled Seed Resampling.
VQA Question Templates
A.4 4
Concept Deletion Success (CDS)
Experimental Setup and Evaluation Protocol.
B.1
Model Details and Cross-Model Timestep Normalization
2
Evaluation Metrics
Ablation Studies
Cross-Model VQA Consistency Analysis
Sample Size Sensitivity Analysis
20
Base Prompt Robustness Analysis
Extended Analyses and Additional Results
D.1
Fine-Grained Concept Analysis
D.2)
Cross-Attention Visualization Studies..
D.3
Multiple Concept Interaction Analysis
D.4
Abstract Concepts Analysis
26
D.5
Qualitative Results of Text-Driven Image Editing.
Limitations
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A IMPLEMENTATION AND METHODOLOGICAL DETAILS
Here, we provide supplemental details on our PCI framework, including diffusion- model preliminaries (Appendix Sec. A. our ceneppt-onntolled seed resampling procedure (Appendix Sec. A.2). VQA question templates (Appendix Sec. S.c. and the full definition of Concept Deletion Success (CDS), the complementary measure to CI (Appendix Sec. A.4
A.1 DIFFUSION MODEL. PRELIMINARIES
Diffusion models generate images by iteratively refining noisy samples through a learned reverse process (Chen et al. 2025 Ho et al. 2020). The forward (diffusion) process a fixed Markov chain that progressively adds Gaussian noise (O,, I) to clean latent input Xo. producing sequence {xt 17 At each step, the Gaussian variance schedule {B,)= determines the noise level, where
04 = 1- - St,
ō =
=I 0.-
(A.1)
Thus, Oe the cumulative product of noise-retention factors that controls the signal-to-noise ratio (SNR) across timesteps. The forward process admits the closed form
X
Ci X + V1 āi C,
t tE (1,.....
(A.2)
The generative model learns to reverse this chain by predicting the noise component from given Xt, using a neural network Eg (x,, t, parametrized by and optionally conditioned on auxiliary input (e.g., text embedding). reconstruction of the original data, denoted xi, can then be obtained by inverting the forward process:
X
(x+ vI 61 6o(x,tt,
(A.3)
Var
At inference time, the process begins by sampling a pure random Gaussian noise Xy and proceeds by iteratively denoising over timesteps until Xg, which forms synthesized image aligned with the conditioning input. For text-to-image generation, conditioning is injected via cross-attention layers, enabling semantic alignment between prompts and generated images.
To strengthen the alignment between conditioning inputs and generated content, diffusion models of- ten employ classifier- free guidance (CFG) (Ho & Salimans] (202)). In this approach, an unconditional prediction eø e (xe I, Ø) combined with the conditional prediction eo 5xx,, L, c) through a guidance scale w, yielding
ég (1 + w) (x w (x 4,
(A.4)
This extrapolation biases the denoising process toward the conditioning signal to further improve semantic fidelity.
A.2 CONCEPT- CONCEPT-CONTROLLED SEED RESAMPLING
concent
fractions leach
A key requirement of PCI is that generations ob- tained from the base prompt Ps remain neutral with respect to the target concept, ensuring that switching P. isolates the concept's influence on the trajectory. In practice, however, diffusion models may violate this requirement, sometimes rendering the concept even under Pd (c.g., glasses appearing in "neutral" face prompt). In addition to such prompt-level violations, models also ex- hibit structural priors that cause certain concepts to be overrepresented, regardless of whether they are specified in the prompt (e.g., mountain appear- ing in a "landscape" scene; Fig. AI). Both effects undermine the contrast between P/ and P. and lead unreliable CIS functions. To mitigate
VOS Thacterr
thacctom
0.8
0.6
0.80
0.77
0.2
0.20
0.0
mountals
dive
Figure A1: Per-concept VQA outcome fractions under the base prompt P. ="a realistic photo of landscape", using SD 2.1 over 1000 seeds.
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this issue, propose two-stage strategy prior our analysis. First, we apply seed filtering to discard cases where the target concept is already present under P. using the same VQA model. this fails to yield sufficient number of valid seeds, we incorporate negative guidance (Ho & Salimans 2021) as an additional constraint, where the concept itself is used as negative prompt to discourage its appearance during generation with Po. This approach goes beyond naïve prompt engineering with terms such as "no" or "not," which have been shown to be ineffective (Alhamoud et al. 2025). Instead, it follows the CFG formulation, but with the guidance term inverted steer the model away from the specified concept at each denoising step. While negative guidance has been shown to reduce undesired concepts, recent work such Normalized Attention Guidance (NAG) (Chen et al.) 2025) demonstrates that guidance alone is not sufficient to ensure stable outcomes. In our setting, we similarly find that negative guidance by itself does not provide reliable neutrality, but when combined with seed filtering it yields the most stable configuration. We therefore adopt this combined strategy in our main experiments.
A.3 VQA QUESTION TEMPLATES
We present the prompts we used for our VQA model for the different subcategories in Table A1 For each subcategory, the same VQA prompt applies all fine-grained concepts.
Category
Subcategory
Question
Demographies
Gender
Is the person in the image <concept>? the person in the image <concept>?
Ethnicity
Age group
the person the image a <concept>?
Objects
Animals
there <concept>> in the image?
Handmade objects
there <concept> in the image?
Nature objects
Is there a <concept> in the image?
luman properties
Clothing
the person the image wearing <concept>?
Accessories
Is the person in the image wearing <concept>?
Physical traits
Does the person in the image have <concept>?
Actions
Common actions
the person in the image <concept>?
Attributes
Material
the table the image made of <concept>?
Color
Is the table in the image <concept>?
Environment
Season
the season <concept>?
Weather
the weather <concept>? Is it <concept>?
Time
Setting/ / Venue
Is the photo taken in <concept>?
Stylc
Image style
Is the style of the image <concept>?
Table A1: Overview of dataset categories, subcategories, and associated questions.
A.4 CONCEPT DELETION SUCCESS (CDS)
While CIS measures the earliest timestep at which concept can be inserted into the denoising trajectory.an equally important question is: when does a concept become unavoidable? In other words, what point in the trajectory is concept already committed to the generation, such that later attempts to remove it no longer succeed? We formalize this complementary perspective through the Concept Deletion Success (CDS) score. Given concept prompt P. and base prompt P, that omits the target concept, CDS quantifies the probability that switching the conditioning from P. to Pt at timestep successfully removes the concept from the generated sample. Intuitively, CDS characterizes the latest timestep at which overriding the concept remains effective. Beyond this point, the concept becomes locked in: even reverting to P. cannot eliminate from the generation. Formally, for each timestep f,, we construct the reconstruction
from
(o fu, Pb),
(A.5)
which follows the concept prompt P. until timestep t., and subsequently denoises under the base prompt Ps. To evaluate whether the target concept e persists in the resulting image, we again employ
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Qwen-3B in VQA setup. For each reconstruction, the VQA model outputs a binary decision indicating whether the concept is present ("yes") or absent ("no"). We define the Concept Deletion Score for all timesteps computed across random seeds. Low CDS values indicate successful concept removal, whereas high CDS values indicate that the concept persists despite the attempted deletion. By evaluating CDS over i. T 0, we can identify the deletion locking point, i.e., the latest timestep at which a concept becomes resistant to removal. Together with CIS, CDS provides a comprehensive characterization of concept formation, stability, and irreversibility during the denoising process.
Empirically, we observe that deletion dynamies behave differently from insertion dynamics, but not in universally uniform manner. Figs. A2 and A3 show the Concept Deletion Success (CDS) trajectories for SD 2.1 and SDXL for a representative concept-base-promet
pair, averaged over 100 random seeds. While CIS curves exhibit substantial variability in when different concepts become insertable, CDS curves reveal distinct trend: concept deletion tends to fail considerably earlier in the denoising trajectory. However, there is still noticeable variation across concepts, categories, and prompt configurations, indicating that deletion is not governed by a single universal threshold. Two factors help explain this behaviour. First, when generation begins under the concept prompt Pe. the model is effectively primed toward that concept from the earliest steps, making subsequent removal more difficult and often shifting the effective deletion window to earlier timesteps. Second, certain concept pairs (e.g., binary attribute switches such as night vs. morning) behave differently from cases where a concept is simply present or absent, leading to sharper or more gradual transition behaviors depending on semantic structure.
denogaatics
atians carenan
Poudlr 2000
20-
BOWRE 21
Trawiig 1-
400
Figure A2: Concept Deletion Success (CDS) curves on SD21 for all concepts for a given concept-base prompt pair.
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drsppppr
10-
Trankiy 21 ---
80
Tivecte Abh
Figure A3: Concept Deletion Success (CDS) curves SDXL. for all concepts for given concept- base prompt pair.
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B EXPERIMENTAL SETUP AND EVALUATION PROTOCOL
Here, we summarize the experimental settings used throughout our study. We describe the models and inference configurations, including cross-model timestep normalization (Appendix Sec. Bec. [B11) and
outline the evaluation metrics applied to quantify temporal concept behavior (Appendix Sec. [..2.
B.1 MODEL DETAILS AND CROSS-MODDES TIMESTEP NORMALIZATION
For diffusion-based models, we evaluate Stable Diffusion 2.1 (SD 2.1) Rombach et al. (2022a) and Stable Diffusion XL (SDXL) Podell et al. (222)). For mot-mng models, evaluated Stable Diffusion 3.5 (SD 3.5) Esser et al. (2024b); Sauer et al. (2024). Unless otherwise mentioned, we use VLen-B Bai et al. (2025) the VQA scorer for CIS and report a model ablation in Appendix Sec. C.1 with other VQA models. We keep the original scheduler and CFG settings released for each used without modification. For all our experiments, we use 100 seeds. We Stable Diffusion model
refer to Appendix Sec. C.2 an ablation of the number of seeds required to establish a robust CIS. All models traverse the diffusion trajectory between timestep range / [0, 1000] (early t=1000 to late t=0) with model-specific step counts T. SD 2.1 and SD 3.5 use 7=50 inference steps, whereas SDXL employs 7-40 steps. For cross cross-model comparability, we normalize all timesteps to the range [0,1].
All models traverse the diffusion trajectory between timestep range t [0, 1000] (carly t=1000 to late t=0) with model-specific step counts T::
* 1000 1: At, 0....,,
At
1000 T
(B.1)
Concretely, SD 2.1 and SD 3.5 7550 inference steps, corresponding to step size of Al 20, whereas SDXL employs T-=0 steps with At = 25. For the rest of cross-model comparability we refer the timestep axis as 7 - 1000 E [0, 1].
B.2 EVALUATION METRICS
Let C(r) E [0. 1] be the CIS curve. For 9 E {0.50, 0.70} we define the crossing time as Ty minfr E [0, 1]: C(T) 2 g}. which is the smallest r that achieves CIS probability q. We also measure how quickly a concept locks within the analysis band via the bundwidlh-steepnees:: W70-50 770 750. where smaller W indicates sharper, less timing-fragile transition inside the 0.50-0.70 band. We chose q E {0.50, 0.70} because a CIS of 0.50 marks the indifference (coin-flip) point where concept becomes as likely not to insert successfully, and 0.70 serves high-confidence operating point for reliable insertions without requiring near-certainty.
Statistical aggregation and uncertainty. We evaluate CIS for each concept at each timestep under a fixed base prompt (the context) into which the concept introduced. For every concept-prompl pair, we run multiple random seeds and average the resulting binary VQA outcomes across seeds to obtain smooth CIS curve over timesteps. This removes seed-level noise and yields one representative curve per concept-prompt. To summarize when insertion starts to succeed, we analyze the timesteps which the averaged curve first reaches CIS target levels (e.g., 50% and 70%); if level never reached, the corresponding time is left undefined. For cross-model comparisons within a subcategory (e.g., all prompts and concepts in "animals" or "weather"), we report the mean of these crossing times across all concept-prompt pairs in S, together with their standard deviation across pairs.
For visualization, we also aggregate the seed-averaged CIS curves across all pairs in S show an overall trend, and overlay the individual curves to display variability across prompts/contexts. When focusing on single concept prompt pair, uncertainty primarily comes from finite seeds; we therefore display 95% Wilson confidence intervals around the evedaaeeegeed CIS at each timestep. In short, Wilson intervals quantify seed-level uncertainty for given pair, while the spread of curves (and the reported mean + std) captures prompt/context-lewe variability across the subcategory.
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Category. <objects- Aspect <animals>
8
KDA Mudles
VQA Muiide
V04 Huskeb
0.-
Gren38
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Josk Oroilee
Lufe Decu mn
0.0
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Cancupti <fra>
Concept
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VQA Mo
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VQA Modill
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VQA Hedes
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Dem
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Dare
62- LASS Dinoriaee
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SPeNuR220
0.0
0.0
0.0
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0.5 Timestpp (1
Timnstap (1- 5)
Timelep 11
Figure C1: Ablation studies across different LVLM models. We show the VQA evaluation of the CIS results for different concepts from animals subcategory, showing that CIS results remain consistent across different LVLM models: Qwen-3B, Qwen-7B, Vision---Vii and SmoIVL
C ABLATION STUDIES
Here, present ablation experiments that assess the robustness of our framework. We evaluate the consistency of CIS across different VQA scorers (Appendix Sec. C.I analyze sensitivity to the number of random seeds (Appendix Sec. C.2) and test stability under natural prompt rephrasings (Appendix Sec.
C.I CROSS-MODEL VQA CONSISTENCY ANALYSIS
To ensure our results
artifact of a single LVLM, we have expanded VQA evaluation include four models: Qwen-VL- VL-2.5 3B, Qwen-VL-2.55 7B, LLaVA-One Visionn-is Li et al. (2025), and SmoIVLM-225 Marafioti et al. (2025). Specifically, category using SD-2.1 with identical prompts in Fig. CI in which the responses of all models are reported. Across all concepts, the models produce closely matching CIS trajectories, demonstrating that PCI is agnostic and robust to the specific LVLM model in our setting, and that even small models (SmolVLMM-2 2B) achieve comparable results. Given the comparable outcomes and the lower computational cost, we adopt Qwen-V LL 3B for the main experiments.
conducted experiments on the animals
C.2 SAMPLE SIZE SENSITIVITY ANALYSIS
We evaluated how many seeds are required to estimate reliable CIS curves in a controlled experiment using SDD-2.1.
For the selected concepts, we first run PCI to ob- tain CIS trajectories for 1000 distinct seeds using SD 2.1. To quantify how many seeds needed, we repeatedly subsample E {l,..., 1000} seeds (100-bootstrap resampling) and compute the mean CIS trajectory and its variance across samples. Stability is then assessed by requiring low bootstrap variance and (ii) consistency with the mean curves obtained at larger k. For each concept, we record the smallest & that satisfies the stability criteria and aggregate these between con- cepts to obtain coverage-vs-k curve (fraction of concepts stable at each k). Based on this curve, set & 100 for all experiments, increasing the seed budget beyond does not appreciably raise coverage.
coverage
Baxtien concepto stalle (cweoogs)
100
700
220
400
Frumbe sAndO
Figure C2: Seed budget analysis for estimating stable CIS trajectories.
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cijects
actioro
1.0
10-
36-
04-
02-
0.
25
6.5
05
TIMETE 12
Drramap -00
Figure C3: Prompt robustness across semantic variations. We evaluate the stability of CIS trajectories under prompt variations for three representative subcategories: animals, handmade objects, and common actions. For each subcategory, we generate five semantically equivalent prompt variants and compute CIS curves averaged over all concepts and seeds. The resulting trajectories illustrate the robustness of our method to natural prompt rephrasings: while absolute CIS values may shift slightly across variants, the overall temporal patterns and locking behaviours remain consistent, demonstrating that our findings are not tied to a specific wording of the base or concept prompts.
C.3 BASE PROMPT ROBUSTNESS ANALYSIS
potential failure mode of coompt-based interpretability analyses is that they may capture prompt phrasing effects rather than genuine properties of the diffusion trajectory. To ensure that CIS reflects the intrinsic temporal behavior of the target concept and not artifacts of particular linguistic formulation, we perform a prompt robustness evaluation using SD 2.1. Given a base-concept prompt pair (Po. Pa) for a target concept c, we construct a small family of paraphrased variants obtained using GPT-5 (see Table CI). where each variant preserves the semantic context of the original pair while introducing minor syntactic or descriptive changes (c.g., "an image of person". "a photo of person"). These paraphrases are designed such that they neither introduce new visual attributes nor alter the underlying meaning of the target concept. For each variant, we recompute the CIS curve and compare those results with the original CIS curve.
As illustrated in Fig. the resulting CIS trajectories are highly consistent across the five paraphrased variants for representative subcategories such as animals, handmade objects, and common actions. While slight variations in absolute CIS values can occur due to wording-specific stylistic biases, the overall temporal profiles, including the onset, rise, and stabilization regions, remain stable. Crucially, all variants exhibit the same relative ordering of insertion windows across timesteps, indicating that the observed temporal dynamics stem from the model's generative behavior rather than from linguistic artifacts. These results confirm that CIS is robust to natural prompt rephrasings and reliably captures speccfc- temporal behavior independent of the exact phrasing of (Po, Pc).
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Subcategory
Base Prompts
Animals
1) image of park
2) photo of park
3) visual depicting park
4) detailed view of park
5) realistic photo of a park
Handmade Objects
1) image of empty scene
2) photograph of an empty scene
visual depicting an empty scene
4) detailed view of empty scene
5) realistic photo of an empty scene
Common actions
1) image of person
picture a person
3) illustration of a person
4) high-resolution image depicting a human fig-
ure without any explicit activity
5) depiction of person in neutral setting
Table C1: Overview the prompt variations. For the selected subcategories, we construct a total of five prompts: the original base prompt (shown in bold) and four semantically consistent paraphrased variants used to assess prompt robustness.
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Lovel unce tainty
Handmade Objects
[=1o]
Nature objects
SDM,
S03, 5
Animalss
aliph
FLUX
Venue-
Clothing
1.0
0.8
0.6
0.4
C.2
0.0
Diffusion Trajectory 11 eanyy late)
Figure DI: CIS reveals cross-concept and cross-architecture differences. CIS for 750 and 770 across multiple concept categories and diffusion models.
D EXTENDED ANALYSES AND ADDITIONAL RESULTS
Here, we report extended results complementing the main analysis, including additional global analyses results for the rest of the categories in Fig. DI. We provide fine-grained concept and context studies (Appendix Sec. [.I] cross-attention visualizations (Appendix Sec. [D.2), multi-concept interaction analyses (Appendix Sec. D.3) results on abstract concepts (Appendix Sec. D.4), and additional qualitative examples of text-driven image editing (Appendix Sec. D.5).
D.1 FINE-GRAINED CONCEPT ANALYSIS
While category-level summaries highlight broad timing patterns, they can mask the specific prompts (contexts) and concepts that underlie these patterns. Fine-grained analyses investigates these sources of variation by showing full CIS curves at the level of (i) fixed concept across different prompts (prompt-level), and (ii) different concepts within the same subcategory under matched prompts (concept-level), This reveals when the aggregated behavior of category is dominated by subset of contexts or a subset of its concepts and highlights meaningful concept-conee contexx relationships that are obscured by averaging (e.g., how the same concept behaves across distinct settings, or how different concepts within a category respond to the same context setting).
Here, we carry out this fine- ome-grained analysis for SDXL. For prompt-leve comparison, we fix a subcategory and vary the base prompt (context) for plotting CIS curve with 95% Wilson CIs seeds. For a concept-level comparison, we fix the prompt and plot the CIS curve for several concepts within the same subcategory with 95% Wilson Cls over seeds.
Finding: Scene-concept alignment governs timing: When the concept aligns with the context, both and W are smaller (later and more robust specification). Dut-of-distribution pairings inflate Ty (early-only window) and often W (greater timing fragility), unless model capacity compresses W.
Prompt-level analysis (Fig. D2a). In Fig. D2a we demonstrate representative prompt-level effects of the derived CIS function for "style". "animals" "age group" and "ethnicity" categories, under different two different contexts. Starting with a broad category of style, Fig. [D2al top-left, shows the CIS plots for the contexts kitchen interior (blue curve) and bowl of fruit (red curve). Under the context of bowl of fruit, the influence of style emerges earlier in the diffusion trajectory but rapidly diminishes to zero 0.5, whereas for the kitchen interior, style persists longer in the trajectory. For animals, Fig. D2al bottom-left reveals that under the context of outdoor environment (e.g., countryside). animals can be incorporated into the trajectory much later than those under the context of indoor environment (e.g.. living room) is generally harder to put wild animals within an indoor OOD context. In contrast, some categories, such as age, exhibit close dynamics across different contexts, resulting in closely aligned CIS plots shown in Fig. D2al bottom-right.
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Proppt: BY <etvory dref bockin
Greett:
eaay in wharis deme
Franpt: 21 iaage eathnoyy dador
(a) Category-level CIS dynamics for: style, ethnicity, animals, age group.
(b) Concept-level CIS dynamics for ethnicity.
Figure D2: CIS dynamics. (a) For each category, we plot the probability of Concept Insertion Success (CIS) across the diffusion trajectory (1 - O, noisy - clean) for multiple base prompts, averaged over all concepts in that category. Two representative prompts are highlighted (blue and red), showing that the context in the base prompt can affect the concept insertion. (b) For ethnicity, we decompose the CIS functions of the two prompts into their individual elements concepts) variations, with two different concepts highlighted (green and orange), demonstrating that impact of individual concepts the category-leve dynamics. Overall, CIS functions allow to understand the temporal evolution of broad categories and fine-graing mne-grained concepts in diffusion models.
Concept-level analysis (Fig. D2b). In Fig. D2b we further decompose the CIS functions for the category "ethnicity" under difterent contexts. into its different elements (the fine tne-ggrined concepts of the category "ethnicity").. We show two such concepts: "Asian" (orange curve) and "Latino" (green curve), under two different contexts: "chef in kitchen" and "doctor". This allows us to understand the impact of individual concepts on the "ethnicity" category. Certain ethnicities such as "Asian" appear very late in the diffusion trajectory compared to others, and are also affected by different contexts. Overall, the CIS function allows us to understand temporal evolution of category-level and concept-level dynamics in diffusion models.
D.2
VISUALIZATION STUDIES
To complement the binary Concept Insertion Success (CIS) metric. we additionally perform a qualita- rive analysis based cross-attention maps of SDXL. While CIS indicates whether concept appears in the final image, it does not provide information about concept strength or spatial manifestation. The cross-attention analysis offers complementary, spatially grounded perspective.
Given specific 7 value, we follow Tang al. (2022) extract cross-attention maps. Specifically, for given concept word c, we extract token-level cross-attention maps from all U-Net cross-attention layers during image generation. We perform this for all timesteps, including the timesteps before and after the intervention. We then average the cross-attention maps over all timesteps. This leads to a single heatmap representing intervention at r. We repeat this setup for several T values: To, T30. T50. T60. T70 and Tyo- We then take the global minimum and maximum across all the resulting cross-attention maps, and normalize each to the range of 0-1, using those minimum and maximum statistics.
We apply PCI at several representative CIS probabilities (7o, T30 T50. T70, 790) and visualize how the attention for token evolves as the intervention timestep changes. This procedure enables inspection of (i) which spatial regions are associated with the concept, (ii) how concentrated or diffuse the attention becomes, and (iii) how these patterns vary across the denoising trajectory.
As illustrative examples, Fig. D3 shows how the spatial activation of the concept token evolves across different CIS bands for several representative concepts. Although we do not provide quantitative measurements, we observe consistent qualitative tendencies. Interventions within higher-CIS regions often lead to clearer and more spatially focused cross-attentic associated with the concept, while interventions within lower-CIS regions show weaker or less coherent activations. These patterns offer
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thase promen Image)
tmage smpply scere with
with beard
Image!
Figure D3: Qualitative cross-attention maps for concept token at different intervention timesteps. Attention becomes stronger and more spatially coherent in high-CIS regimes and weaker or diffuse in low-CIS regimes, providing visual complement to the binary CIS metric.
intuitive insight into how concept strength and spatial coherence change insertion becomes more or less feasible.
D.3 MULTIPLE CONCEPT INTERACTION ANALYSIS
While our primary analysis focuses single-concept insertion through PCI, many real-world editing scenarios require simultaneously modifying or introducing multiple attributes. To study these interactions, extend PCI to the multi-concen setting using SD 2.1, where two concepts introduced simultaneously at a target timestep t,. For each concept pair (c1, C2). we construct CIS curves by switching from the base prompt P,, to the combined prompt P Popicg at different timesteps.
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03 Temmdy Di
Freedy Divc
(a) Pa an image of old Asian person
(b) Pe an image of a female Asian person
Metch
Traelo
Sestalt-
(c) Po an outdoor image of a person wearing coat snowy weather
(d) Pe a sketch image of female person
(e) P. an image of a bald person with mustache
Figure D4: Comparison of CIS curves under single- and multi-concept conditioning across five samples. Each subfigure contains two evaluations: (left) CIS of concept e₁ under its single-concept prompt vs. CIS of e1 under the joint (c1 + prompt: (right) the corresponding evaluation for concept c2. Although both subplots use the same multi-concept prompt, the target concept differs, resulting in different CIS trajectories. Most pairs exhibit near- coaposoopoiii behavior (a,b,c,e), whereas others (d) show interaction-dependeet shifts in insertion timing.
In this setting, CIS naturally remains concept-specifie measure: even if the conditioning prompt includes multiple concepts, insertion success is always evaluated with respect to one target concept at time.
CIS(C P. + Po.)
vs. CIS(c: P. + Per.oz):
(D.1)
which capture how each concept behaves when introduced alone versus jointly. Although both evaluations use the same multi-concept prompt, the target concept differs, and therefore the resulting CIS trajectories may also differ.
This formulation enables us to identify asymmetries: one concept may become temporally fixed earlier than another, or one may remain editable while the other is already locked in. Empirically, many concept pairs behave in near-compositional manner (e.g., coat + snowy and female Asian) where the joint CIS curves closely follow their single-concepl baselines. However, some combinations exhibit clear interaction effects. For instance, the pair female + sketch shows substantial shift of the insertion window toward later timesteps, allowing both attributes to remain editable deeper in the denoising trajectory than either concept alone. This demonstrates that certain concept pairs can meaningfully influence one another's 's temporal insertion dynamics.
D.4 ABSTRACT CONCEPTS ANALYSIS
Subjective or abstract concepts such as elegant, art, and creative can pose a challenge for LVLMs. These concepts are inherently subjective, and their interpretation may vary across cultures, contexts, or viewpoints. To better understand how the models behave in this setting, we computed CIS curves in Fig. D25 for these three concepts across all four LVLMSs used in our study.
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VOA Yoceb
VIL Mosel
VOA Hose
0.-
C2-
28
2.6
C4
26
Terestog 13
Terestop
Treesteg 21
Figure D5: CIS curves for single base coscept prompt pair involving the abstract concepts art, creative, and elegant.
Across the three abstract concepts, the three larger models (Qwen-3B, Qwen-7B, and LLaVA- One One display highly consistent behaviour: their CIS trajectories are smooth, monotonic, and closely aligned throughout the denoising process. In contrast, SmoIVLM-22 consistently produces lower CIS values and steeper decline, indicating weaker ability to validate these subjective attributes when inserted later in the trajectory. Nevertheless, its CIS curves still follow the same qualitative pattern. In the raw CIS curves with Qwen-3B, concepts such creative and art begin with higher initial CIS (around 0.8) but drop below 0.5 much earlier in the timestep sequence than elegant. This suggests that the LVLM has its own internal notion of what "elegant," "art," "creative" means, shaped by the data it was trained on. The multi-model comparison confirms that these interpretations broadly aligned across strong LVLMs, with only the smallest model deviating in magnitude but not in trend.
To account for subjectivity of individual models, we could perform multi-model agreement. Specifi- cally, we can aggregate CIS curves from multiple VLMs (which have been trained on different data) to calibrate the CIS curves. We have added a section on multi-model agreement of four VLMs in Sec. C.I If subjective ratings of an abstract concept are desired, we could instead extend the prompt to reflect the subjectiveness. For example, in the prompt "In Eastern Asia, would this image be considered elegant?", Eastern Asia provides the desired subjective context which a user might want evaluate CIS.
D.5 QUALITATIVE RESULTS OF TEXT-DRIVEN IMAGE EDITING
We show comparison of editing performance of method T80, compared to NTI-P2P Mokady et al. (2023) and Stable-Flow Avrahami et al. (2025) baselines in Fig, DD Our method (PCI) produces edits that are semantically stronger, more spatially localized, and better aligned with the target concept while preserving non-edited regions. Moreover, in Fig. D6 we provide additional qualitative results for the application of text-driven image editing on SI) 3.5.
E LIMITATIONS
Every study has limitations, and ours is no exception. We highlight two main ones. (1) Runtime: Computing CIS curve for single seed depends the number of interruption points at which we switch prompts. We used all native inference steps to achieve the highest resolution, but the process can be accelerated by sampling fewer points. Importantly, averaging across multiple seeds allows sparsely sampled curve to approximate a densely sampled one. (2) Editing sensitivity: There is no single CIS value that reliably works for text-driven editing. Instead, range, typically CIS probabilities between 0.5 and 0.7, performs best across categories and concepts. A practical workaround is to choose general value that works reasonably well overall, even if not optimal for every case.
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image of doctor
750
790
Asian
a portrait of dog
cartoon
+
mountain cabin
winter
passport style headshot
beard
Figure D6: Examples of text-driven image editing on SD 3.5.
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Stable
empty sceine withh
Ingut
NT1+
Stable Flow
PC
an image an Asian doctor
Stable Flow
old
Ingut
NT1+
Stable Flow
PCI-Tap Tsp
an image Of a person wearing a suit
Stable
an outdoor Image winter
Figure D7: Comparison of editing performance across NTI-P2P, Saabe--Flow and our PCI method at Teo- PCI produces edits that semantically stronger, spatially localized, and better aligned with the target concept while preserving non-edited regions.
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Acknowledgment
Fawaz Sammani is funded by the Fonds Wetenschappelijk Onderzoek (FWO) (PhD) fellowship strategic basic research 1SH7W24N). N. Deligiannis acknowledges the "Onderzoeksprogramna Artificiele Intelligentie (AI) Vlaanderen" programme and the ERC Consolidator Grant IONIAN (No. 101171240, DOI: 10.3030/101171240 Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency, Neither the European Union nor the granting authority can be held responsible for them.
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Reviews:
[{"review_id": "p50PieC5dX", "reviewer": "Reviewer_RMvL", "summary": "This paper investigates the denosing dynamics of concept formation in diffusion models, focusing on when specific concepts (e.g., age) emerge and stabilize during the denoising trajectory. It proposes PCI (Prompt-Conditioned Intervention), a training-free and model-agnostic framework that analyzes Concept Insertion Success (CIS) to study how concepts evolve over diffusion time.  Experiments on state-of-the-art text-to-image diffusion models reveal some insights about diverse temporal behaviors for concept formation.", "questions": "Please see the section of weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The presentation of figures is great and easy to understand.\n- The math notations in this paper are self-contained and well-defined.\n- The paper writing is easy to follow.\n- The idea of Prompt-Conditioned Intervention is cool.\n- The proposed method is straightforward.", "weaknesses": "- I am quite disappointed that, although this paper uncovers some interesting phenomena regarding denoising trajectories, the final method does not stand out significantly. This is my main concern.\n- PCI is also heavily based on the performance of the adopted MLLM (like Qwen-3B).\n- The evaluation is only based on SD-series models. How about FLUX and other SoTA models?\n- Some wrong citation formats are used in the paper.\n- I have to say that the performance of image editing is not so well in Fig. 5. It seems that the resultant methods didn't work well.", "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": "f5xXvsLgcf", "reviewer": "Reviewer_V3XN", "summary": "This paper studies how individual concepts in a text prompt influence the diffusion generation process over time. The proposed method, Prompt-Conditioned Intervention (PCI), provides an intuitive mechanism to probe when a specific concept meaningfully affects generation. In addition, the paper introduces the Concept Insertion Success (CIS) metric to quantitatively capture the temporal dynamics of concept influence during diffusion. Together, PCI and CIS offer a useful framework for analyzing how textual concepts guide the image generation trajectory.", "questions": "In addition to insertion, could PCI and CIS be extended to analyze the concept removal or replacement dynamics? For example, identifying the latest timestep at which removing a concept no longer changes the generated output.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The idea behind PCI and CIS are intuitive and well-motivated, offering a practical way to examine concept influence in diffusion models.\n- The manuscript is clearly written and the main claims are clearly communicated.", "weaknesses": "- The analysis is primarily focuses on isolated concept influence (while the interactions between concepts and contexts are mentioned). Further exploration of interactions between concepts and broader prompt context would enrich the contributions.\n- While PCI and CIS measure the latest timestep at which a concept can be successfully inserted, this does not directly indicate when the model begins to encode the concept. Studying the earliest insertion or concept disappearance behavior would strengthen the temporal interpretation of the findings.\n- Sensitivity analysis to variations in prompt phrasing and initial noise seeds is limited (as it is averaged over subcategory of concepts). Investigating robustness across seeds and prompt variations would help clarify the generality of PCI/CIS.", "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": "WBaNC6kb4g", "reviewer": "Reviewer_Qc2X", "summary": "This paper introduces PCI, a training-free framework for analyzing when concepts become locked into the generation trajectory of text-to-image diffusion models. The core method involves switching prompts at different timesteps during denoising: starting with a neutral base prompt and then introducing a concept-augmented prompt at various intervention points. The authors define CIS as the probability that a concept inserted at a given timestep appears in the final image, evaluated using a VLM.", "questions": "- if the inserted concept is very abstract and cannot be easily fit in the categories studied in this work, how can a user find the best inserting point without trying every possible timestep?\n- what if the VLM cannot evaluate the abstract concept, how do you solve this? Could you discuss failure modes where VQA might struggle (e.g., highly subjective concepts like \"elegance\" or \"tranquility\") and potential mitigation strategies?\n- The current analysis focuses on inserting one concept at a time. How would CIS behave when multiple concepts need to be inserted? Would their optimal insertion times interfere with each other?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- PCI is training-free, model-agnostic, and requires no access to model internals, making it broadly applicable and easy to implement across different diffusion architectures.\n- The seed resampling strategy with optional negative guidance ensures that base prompts remain neutral with respect to target concepts, addressing a potential confound that could undermine the analysis.\n- While the core idea of prompt switching is conceptually straightforward, the paper addresses a dimension that prior interpretability work has largely overlooked: the temporal evolution of concepts during generation. Through thorough experimentation across diverse concept taxonomies, the study reveals actionable patterns (e.g., global factors lock early, human properties mid-trajectory, accessories late) that provide valuable insights, filling an important gap in understanding diffusion model dynamics.", "weaknesses": "- The paper primarily focuses on CIS as the main metric for evaluating when concepts can be inserted. However, successful concept insertion does not necessarily mean the generated image maintains fidelity to the original intent or preserves other important content from the base prompt. The trade-off between concept insertion success and overall content preservation is not systematically quantified beyond qualitative observations in the editing examples. A more comprehensive analysis could include metrics that measure how much the image deviates from the base generation when concepts are inserted at different timesteps, helping users better understand the full cost-benefit landscape of intervention timing.\n- The prompts studied in this work are relatively simple and focused (e.g., \"a realistic photo of a person,\" \"a landscape\"). How well do the insights and CIS patterns generalize to more complex, compositional prompts with multiple objects, relationships, and attributes? For instance, if editing a complex scene like \"a young woman wearing sunglasses sitting on a red chair in a sunlit café,\" would the optimal insertion times for individual concepts (age, accessories, color, setting) still follow the patterns observed with simpler prompts, or would the interactions between multiple concepts shift the temporal dynamics?", "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": "mmJ1BSjqlH", "reviewer": "Reviewer_uBmP", "summary": "This paper proposes Prompt-Conditioned Intervention (PCI) to study when a semantic concept becomes established during the denoising process of diffusion and flow models. By switching prompts at different timesteps and using a vision-language model to check concept presence, the authors derive a Concept Insertion Success (CIS) curve that quantifies concept insertability over time. Experiments on Stable Diffusion 2.1, SDXL, and SD 3.5 show that global scene factors appear earlier, human attributes emerge mid-trajectory, and accessories lock in later. The study also demonstrates a simple text-driven image editing application, suggesting that edits are most effective when CIS is between 0.5 and 0.7.", "questions": "See weakness1.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "1. Exploring when concepts emerge along the diffusion timeline offers an interesting and novel temporal perspective on concept formation in generative models.\n\n2. The paper is clearly written and easy to follow, with well-structured figures and explanations that make the methodology and findings accessible even to non-experts.", "weaknesses": "1. Narrow task scope and unclear practical significance (main concern).\nThe paper leverages PCI and CIS to reveal that timing, model choice, and context influence concept insertion, but the exploration remains confined to a single downstream application—text-driven image editing—and primarily relies on qualitative demonstrations. Section 5 and Figure 5 (as well as Appendix Figure 11) merely show edits guided by CIS thresholds and report the empirical finding that the 0.5–0.7 probability range offers a “balanced trade-off.” However, the study does not substantiate why knowing that certain concepts lock in earlier or later yields quantifiable benefits in practice or theory—such as reducing trial-and-error, improving editing stability, strengthening concept binding, or informing training/scheduler design. Moreover, focusing exclusively on the [0.5, 0.7] CIS window without connecting it to broader temporal or theoretical frameworks limits the work’s contribution to understanding “when timing matters” beyond this heuristic range.\n\n2. Strong reliance on a single LVLM-based VQA judge with potential coupling bias.\nThe CIS measure depends entirely on a single VQA-style Large Vision–Language Model (Qwen-VL-3B) for concept detection. Although Appendix A.4.1 includes a brief comparison with a 7B variant, there is no human annotation calibration or multi-judge agreement analysis. The evaluation also remains binary (concept present/absent), which cannot capture concept strength, spatial accuracy, or attribute binding, potentially conflating weak or erroneous detections with genuine insertions. These factors introduce uncertainty into the CIS curve and may distort the estimated locking points.\n\n3. Editing experiments lack strong baselines and quantitative comparison.", "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)