Deconstructing Guidance: A Semantic Hierarchy for Precise Diffusion Model Editing
Authors: Wootaek Jeong
Junghyo Sohn
Jee Seok Yoon
Heung-Il Suk
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion models
Image editing
Information Theory
Primary area: generative models

Abstract: Text-guided image editing requires more than prompt following—it demands a principled understanding of what to modify versus what to preserve. We investigate the internal guidance mechanism of diffusion models and reveal that the guidance signal follows a structured semantic hierarchy. We formalize this insight as the Semantic Scale Hypothesis: the magnitude of the guidance difference vector ($\Delta\boldsymbol{\epsilon}$) directly encodes the semantic scale of edits. Crucially, this phenomenon is theoretically grounded in Tweedie’s formula, which links score prediction to the variance of the underlying data distribution. Low-variance regions, such as objects, yield large-magnitude differences corresponding to structural edits, whereas high-variance regions, such as backgrounds, yield small-magnitude differences corresponding to stylistic adjustments. Building on this principle, we introduce Prism-Edit, a training-free, plug-and-play module that decomposes the guidance signal into semantic layers, enabling selective and interpretable control. Extensive experiments—spanning direct visualization of the semantic hierarchy, generalization across foundation models, and integration with state-of-the-art editors—demonstrate that Prism-Edit achieves precise, robust, and controllable editing. Our findings establish semantic scale as a foundational axis for understanding and advancing diffusion-based image editing.

Published as a conference paper at ICLR 2026
DECONSTRUCTING GUIDANCE: A SEMANTIC HIERAR- CHY FOR PRECISE DIFFUSION MODEL EDITING
Wootaek Jeong'
Junghyo Sohn!* Jee Seok Yoon?
Heung-Il Suk
'Department of Artificial Intelligence, Korea University
SK hynix
wt jeonnekkoee. kr jeeseok, yoon@sk..... com
jhschr0633@korea.se hisukekorea.ac.kk
ABSTRACT
Text-guided image editing requires more than prompt following- demands a principled understanding of what to modify versus what to preserve. We inves- tigate the internal guidance mechanism of diffusion models and reveal that the guidance signal follows structured semantic hierarchy. We formalize this insight as the Semantic Scale Hypothesis: the magnitude of the guidance difference vector (Ac) directly encodes the semantic scale of edits. Crucially, this phenomenon is theoretically grounded in Tweedie's s formula, which links score prediction to the variance of the underlying data distribution. Low-variance regions, such objects, yield sange-magnitude differences corresponding to structural edits, whereas high-variance regions, such as backgrounds, yield magniiuee dif- ferences corresponding stylistic adjustments. Building this principle, introduce Prism-Edit, training-fres free, plug-and-play module that decomposes the guidance signal into semantic layers, enabling selective and interpretable control. Extensive peprriments-spannine
direct visualization of the semantic hierarchy, generalization across foundation models, and integration with state-of-the-ar edi- tors demonstrate that Prism-Edi achieves precise, robust, and controllable editing. Our findings establish semantic scale foundational axis for understanding and advancing diffusion-based image editing.
Original Image
Stable Flow
Reflex
Ours
Original Image
Stable Flow
Reflex
Our
"A man jumping on the skatebourd" wears a white shirt"
TThree chairs"
Null
"Three statues"
(a) Attribute Editing
(b) Object Replacement (Global)
girl dog" "A girl
wild" [Nall]
(c) Object Replacement (Partial)
(d) Background Change
Figure 1: Prism-Ead achieves competitive precision across diverse editing tasks. By decomposing the guidance signal, our method prevents common failure modes like semantic leakage (a) and content degradation (b, c), while excelling the challenging task of background modification (d).
"Equal contribution "orresponniig author

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INTRODUCTION
The ability to sculpt our visual world through natural language central ambition of artificial intelligence. Recent advancements in text-to-image diffusion models (Rombach et al. 2022 Esser
al., 2024 Labs 2024) have brought this vision closer to reality, largely powered by Classifier- Free Guidance (CFG) (Ho & Salimans 2021). Yet despite their remarkable success, current editing methods suffer from a persistent weakness: background regions are notoriously difficult to modity, while object-centric edits succeed more reliably (Figure DT. For instance, a command to move owl from "the wild" to "a school" often fails to convincingly alter the scene or inadvertently degrades the
subject.
Prior approaches have mainly attacked this problem through heuristic spatial controls, asking where an edit should occur. These techniques often involve manipulating cross-attention maps (Hertz et al.) 2023
Cao et al.] 2023) Kim et al. (2225) or using the guidance difference vector to generate a spatial mask that separates the image into edit and preserve zones (Couairon al. 2023).
In contrast, we argue the true bottleneck lies in how the guidance signal itself is structured. We show that the guidance difference vector Ae, central to CFG, not random noise but the gradient of log- peelikklihhod ratio, whose expected magmitude is governed by local Fisher information density. This framing reveals fundamental statistical law: objects, being information-dense, naturally yield strong guidance, whereas backgrounds, being information-sparse, yield weak guidance. We call this principle the Semantic Scale Hypothesis, which reinterprets background editing failure as an information-theoretie inevitability rather than an incidental flaw of prior methods.
Building on this insight, we propose Prism-Edit, a training- free, model-agnostic technique that de- composes the guidance signal into semantic layers and selectively amplifies the weak, low-information components corresponding to backgrounds. As previewed in Figure this enables precise object edits while, for the first time, delivering robust and controllable background modifications. Our contributions are threefold:
1. Semantic Scale Hypothesis: We formalize a new principle that connects guidance mag- nitude Fisher information, providing the first theoretical explanation for the persistent difficulty of background editing.
2. Prism-Edit A simple, training- ffree and model-agnostic method that operationalizes this principle by amplifying wow-information signals.
3. Extensive Validation: We validate hypothesis and method across multiple foundation models, showing consistent gains over state-of-the-art editors, especially for challenging background edits.
RELATED WORK
Text-guided image editing with diffusion models (Ho et al. 2020 Song al. 2021; Ramesh et al.
2022 Esser et al., 2024 Labs] 2024). initiated by methods like SDEdit (Meng et al. 2022). has predominantly focused on spatial control deterniing "WHERE" apply edits. This paradigm
includes techniques like manipulating attention maps (Tumanyan et al., 2023 Hertz et al. 2023
Cao al. 2023) or refining sampling trajectories (Brack et al.] 2024) localize changes. Notably, DiffEdit (Couairon et al. 2023) pioneered using the guidance difference vector, Ae, to automatically generate spatial mask. However, this still interprets the signal spatially, partitioning the image into binary "edit" versus "preserve" zone. We provide detailed comparison highlighting the fundamental difference between DifiEdit's ss masking strategy and our gradient modulation approach in Appendix C.7
Our work poses complementary question: "HOW" should an edit be applied? We shift the focus from the signal's location to its intrinsic semantic nature. We posit that the magnitude of Ae is not merely a spatial indicator, but a rich signal encoding a semantic hierarchy. Instead of creating a binary mask, our method decomposes this signal into distinct semantic layers (e.g., object structure, btyle/background). This enables a more expressive, disentangled form of control by modulating the guidance signal's intrinsic semantic structure rather than just its spatial application.
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And our work builds upon the standard framework of rext-to-image diffusion models (Ho al. 2020) and Classifier-Fie Free Guidance (CFG) (Ho Salimans 2021]. During sampling, CFG steers the generation process by extrapolating from an unconditional noise prediction Eø (x,, 0) towards conditional prediction (x(x;: c). Our analysis focuses on the core of this mechanism: the Guidance Difference Vector, Ae 6 (x; Curget) co (x,, Csoucce, which represents the model's perceived direction to transform a source concept into target. We hypothesize that the magnitude of this vector, ||Ae|], structured semantic signal.
THEORETICAL FOUNDATION: GUIDANCE AS A GRADIENT FIELD
Our central claim, the Semantic Scale Hypothesis, is not merely empirical observation but appears to be a direct consequence of the statistical principles governing diffusion models. This section provides first-principles derivation, showing that the guidance difference vector Ae acts as gradient field of a log-likelihood ratio, whose magnitude is intrinsically linked to the local information density of the image. The temporal evolution Figure provides strong empirical support for this derived theory.
R 980 or 1000
925
700
600
300
Figure 2: Temporal evolution of the guidance difference vector, | [. during standard generation trajectory. migh-mmag signals, encoding object structure, dominate in the carly-to-mid timesteps before diminishing, while background regions remain consistently low-magni throughout. This provides strong empirical support for the Semantic Scale Hypothesis.
3.1 THE GUIDANCE DIFFERENCE AS LOG-LIKEL RATIO GRADIENT
The foundation of a diffusion model is its score function, Vx, (ogp(x; e), which points in the direction of maximal increase in data likelihood. With the standard e-parameterization, the predicted noise co (x,, c) is proportional to this score. Classifier- Free Guidance steers the generation by taking the difference between conditional and unconditional prediction. The core of any edit, however, is the difference between two conditional predictions (source ei and target (2). This guidance difference vector, Ae, is therefore proportional to the difference between scores:
AE(x C1, 02) Vx, log p(xp (o2) Vx, log ((x((())
(1)
By the properties of logarithms, this simplifies to the gradient of a single scalar field: the likeliieldoood ratio between the target and source conditions.
p( (c2) p( xx (c1)
Ae(x;;01 02) & Vx, log
This reframes the guidance vector: it is not just a directional hint, but vector field that points "uphill" on the surface of how much more likely the noisy image x, is under the target condition versus the source condition. The magnitude |Ae] thus reflects the steepness of this likelihood ratio landscape.
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3.2 INFORMATION DENSITY AND POSTERIOR CERTAINTY
The steepness of the landscape in Eq. 2 is determined by the model's "certainty" about the underlying clean image Xo. This certainty is directly related the local information density of the image content, a principle linked to the model's posterior via Tweedie's s formula (Efron 201T).
Structured Regions (e.g., objects): These areas are characterized by high information density (edges, textures, recognizable forms). Given a noisy patch from an object, the model has strong priors, leading to sharp posterior distribution p(xo Xg, with low variance. The model is "certain" " about what should be there.
Smooth Regions (e.g,, backgrounds): These areas have low information density (smooth gradients, skies, walls). The model's 's posterior is flat, with high variance, as many clean signals could have resulted in the same noisy patch. The model is "uncertain."
sharp, fow-variance posterior means that small change in the condition (from ej to 02) can cause a dramatic shift in the posterior mean Exxo Xx,c]. c]. Conversely, for flat, high-variance posterior, the same conditional change results much smaller shift.
3.3 SEMANTIC SCALE AS A CONSEQUENCE OF INFORMATION DENSITY
We can now connect these principles. The magnitude shift |Aull|:
|| | is proportional to the posterior mean
|ل dt
Ac(x;;),)))
Apt : x,, 02]
E[xo xx,c,] c1)-
This proportionality follows from the relationship between the c-parameterization and the posterior mean derived from Tweedie's formula (a brief proof sketch is provided in Appendix[A]] [or clarity). As established in Section 4.2, edits concerning high-information, low-variance regions (objects) induce large posterior shifts ([]A [D), resulting in a Varge-magnitude || Edits concerning low-information, high-variance regions (backgrounds, styles) induce small shifts, resulting in small- magnitude Ael|. Therefore, we interpret the Semantic Scale Hypothesis as a natural consequence of applying information-thhoretii principles to the score-matching objective. Large-magnitude guidance is not just correlated with objects; it appears to be the mathematical result of the model being more "certain" " and "opinionated" about these information-dent to leverage this insight, reframing editing not as masking problem, but as principled signal processing challenge: separating and amplifying semantically crucial components based on their cnormation-theorette signature.
regions. Prism-Edit is the first method
3.4 CLOSSED-ORRM BOUNDS UNDER GAUSSIAN POSTERIOR APPROXIMATION
The proportionality in Eq. 3 can be made quantitatively precise by adopting a local Gaussian approximation of the posterior:
p(xo Xx, x1)c,) R N(p, Eg)
(1,2).
Under this approximation, the mean shift is Ap Mez Me, and Eq 3 suggests ||a|| x [Apull// We now upper/lower bound || in terms closed-form divergences between Gaussians, which separates mean-shift and covariance-mismen
effects.
Theorem (KL-based bound for guidance magnitude). Let d be the dimensionality and define the Gaussian KL divergence
DRL (Mey Ee,) || (M Ec.)) = 3
(r(EE!EE.) {
(Aui) E-- Aus. dl log det Ene det Ee,
Let Xmax(2) (E) and Amin (2) denote the largest and smallest eigenvalues of symmetric positive definite matrix E, respectively. Then, for any t,
| Amax (Eez) {2 (N Eoi) IIN Ecs))
of
Ir tr(D-1 Eg,) ) d - loo det I- Ecy)
U(Eo, Ecs)
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and symmetricalty with (c1: 02) swapped:
|| Amax (Zer)
DRI Doz) || (1 Eo,) V(Zeog Eor)
(5)
of
Moreover, the following lower bounds hold:
| Amin (Fez) The term V ((Ee o0y)= tr( -0 the covariance mismatch (it vanishes iff Ee, Eoy). Hence the bound cleanly separates (i) the mean-shift captured by the KL divergence and (ii) the uncertainty gap captured by v.
{2 DKL Daj (1 Eoz)) V1(ee Ece)
(6)
of
and analogously with (c1, 02) swapped.
Interpretation.
log (et((et Ee 0 quantifies
Corollary (Equal-covariance simplification). De Eeg E, then v and
2 2mmnx (E) Ammin Σ) DKL(WWpe Σ) (pee Σ)) S | S
DRI(W Σ) (| E))
of
of
Thus larger guidance magnitude is driven either by a larger mean shift (object-level changes) or by smaller posterior variance (higher certainty), making the object/background gap visible in both mean and covariance channels.
Connection to Fisher divergence. From Eq. ((x,) gives
A.2 taking an expectation w.r.t. any reference density
E | of Eug [| log p(x (02) Vx, log pxxxx((x)))))))
of Ff(p(11o2)) p(-1c))),
the (generalized) Fisher divergence between the two conditionals under q. When g = c2) (or c1). F, reduces to the standard Fisher divergence; for Gaussian pairs, F, admits closed form, revealing the same mean/covariance decomposition as Theorem !
3.5 SEMANTIC SCALE AS INFORAATINNTTHEORETI NECESSITY
In summary,
|| oc local Fisher information density.
Objects, being information-dense, inevitably yield large guidance, while backgrounds, being information-sparss yield vanishing signals. Thus the Semantic Scale Hypothesis is not an empirical artifact but direct corollary score matching and Fisher information theory, explaining background editing failure statistical necessity.
METHOD: A PRINCIPLED FRAMEWORK FOR DISENTANGLED EDITING
Building on our theoretical foundation, introduce Prism-Edit, a framework designed opera- tionalize the Semantic Scale Hypothesis for precise, disentangled image editing. Unlike methods that rely on external parsers or attention manipulation, Fritt-Edit derives its control signals directly from the model's 's internal generation dynamics. As illustrated in Figure 3 our approach is a two-stage process: (1) principled extraction multi-layered Semantic Map, and (2) disentangled application of edits via one of two complementary modalities.
4.1 STAGE SEMANTIC MAP EXTRACTION
Section 3 establishhe that the guidance magnitude | scales with the posterior mean shift nor- malized by the posterior variance, i.e., with the local Fisher information density. This implies that absolute magnitudes not directly comparable across timesteps samples: regions with high variance (backgrounds) systematically appear weak, even when the underlying semantic change is substantial.
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Eguided
Deashing
6o Cocurce,
X4 Csource
Dynamic
Weights
MiPa
(botuato
aotwy timesttep
Dilference
"A plass of wine dish of potato pare" glass of wine a dish steak"
Normutization
Semantic Map
Ctarget
Static Mask based Blending
Denshing
Static
Opelnnal
cp Xe, Grangets
Mask
XF
X
[Null] - "A green saal"
Figure 3: Overall Prism-Edit framework. The Semantic Map is extracted from early denoising (left), and applied during sampling via dynamic guidance modulation (default) or static mask blending (optional). Region-specific scaling enables strong background edits without destabilizing objects.
To compensate for this Fisher information imbalance, we adopt or o-normalized thresholding scheme. Specifically, we probe a narrow, high-noise window (e.g., [900, 800) for 1000-step schedule). As detailed in Appendix C.6] this specific interval was selected based on empirical analysis showing maximizes semantic coverage while retaining structural plasticity, unlike later timesteps, which become overly rigid. We then compute averaged guidance difference:
Narcae
Ac
Ac
AC
M
Pmmbe
Ac
where Nprobe denote the number of probed timesteps in the chosen window. As predicted in Corol- lary [ the raw magnitude || varies significantly across different editing tasks and architectures due to posterior variance shifts (Information Imbalance). Therefore, absolute thresholding is infeasible. We employ this z-score normalization to transform the raw gradients into a vcale-invarian semantic signal. This allows us to use fixed relative thresholds (a (o-leveel) that generalize across prompts, seeds, and individual edits within given baseline model. a result, weak background signals are restored to comparable scale, while strong object signals are prevented from overwhelming the map.
Based on empirical analysis (see Figure C.2) , the extreme tails of this semantic map correspond to the cleanest semantic signals. Intermediate values often represent mixtures of object and background, making them unsuitable for disentangled edits. We therefore define two primary semantic layers using fixed thresholds that proved stable across models and prompts: buckground/style layer (Msem 0.6)
and an object-core core layer (Maem 2 3.0).
4.2 STAGE DISENTANGLED APPLICATION MODALITIES
The extracted semantic map Myem enables two distinct, training-free editing modalities.
Static Mask Blending for Maximum Fidelity (Optional). This static mask is optional and acts loose, permissive spatial constraint determined by the editing intent. Specifically, we define the active editing region using coarse threshold: targeting high-magnitude areas (M 2 0.6) for object edits, and low-magnitude areas (Myem 0.6) for background edits. Unlike methods relying on strict hard boundaries, this mask is designed to be broad, preventing edits from drifting into completely irrelevant regions while leaving the semantic boundaries flexible. Only for tasks requiring strict identity preservation do we impose a tighter constraint by explicitly excluding high-magnitude
object cores (Mam 2 3.0). At each step f, the edited latent xf"ed blended with the corresponding sre source latent x guaranteeing that unmasked regions remain unchanged:
pred 7-1
Minal
(1 Mina),
8
where Minal is obtained by thresholding Marm to get a coarse mask and refining it with morphological closing (see Appendix B

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Dynamic Guidance Modulation (Default). Our default modality offers greater flexibility by dynamically modulating guidance at each step. Although theoretically defined as a continuous map, in practice, we binarize W sem. based the z-score of the instantaneous || || (using < 0.60 for background edits and 2 3.0a for object edits) to ensure stability and prevent boundary artifacts. The guidance is then modulated element-wise:
( c) = ca( Carc) + 7 (Acr @ Wsem.).
This enables region-specific guidance scaling: background edits (low- now-information, high-variance, be amplified with large y (e.g., 20-40) without destabilizing object regions (already high-information).
Importantly, this dynamic modulation is a direct operationalization of the information-fiett per- spective from Section 3 by locally scaling weak, high-uncertainty regions while leaving strong, low-uncertainty regions untouched, we effectively re-balance the Fisher information disparity inherent in diffusion guidance.
Notes stability, scale, and hyperparameters. Because the binarized W sem. strictly isolates the target region, background edits remain stable even under large local scales, as the amplification is explicitly prevented from bleeding into the object core. Static masking serves as optional secondary safety filter, but dynamic modulation alone suffices for most edits and is our default. Regarding hyperparameters, while specifie thresholds vary per baseline architecture (e.g., account for distinet noise schedules), they remain invariant across diverse datasets and prompts. Once set for a baseline, no per-image tuning is required. The complete procedure is detailed in Algorithm in the Appendix.
5 EXPERIMENTS
We conduct a comprehensive evaluation of Prism-Edit to validate our core claims: (1) the Semantic Scale Hypothesis is a general principle, and (2) our method enables state-of-the-art disentangled editing. Our evaluation spans multiple foundational models, including Stable Diffusion v1.5, v3, and FLUX. to demonstrate model-agnosticism.
Implementation Details. Unless otherwise specified, all experiments are performed using the default schedulers and step counts for each model. Per our theoretical motivation in Section apply a large region-specific guidance scale (7 E [20, 40)) on low-magnitude regions for background edits, while conventional scales are used for object edits. Our ablation studies (see Appendix, Figures C.3 and C.4) confirm that this targeted amplification effectively modifies low-energy regions without introdueing artifacts destabilizing high-energy object structures. Hyperparameters for our static masking modalities are detailed in Table B.J. To ensure the reliability of our approach, further verified that our Semantic Scale Hypothesis remains robust across different sampling conditions, including varying inversion techniques (c.g.. DDIM vs. DPM- Lu et al. (2022);
Hong al. (0224) and target prompts, as detailed in Appendix C.9
5.1 QUANTITATIVE EVALUATION
We evaluate on the standard Wild-TI21 and ImageNet-R-TIII benchmarks. To specifically probe disentanglement, we partition Wild-TI2I into object-centric and background-centric subsets. We report standard metrics: DINOv2 (Oquab et al. 2024) for semantic alignment, SSIM (Wang et al. 2004) for structural preservation, and CLIP score (Radford et 202T) for text alignment.
On the Trade-off between Disentanglement and Global Alignment. While the CLIP score is a valuable metric for overall text-aligmment, we observed that it does not always capture the nuances of disentangled editing. Since CLIP is known to bias towards global image modifications, baseline methods that alter the entire scene often achieve higher scores even when they fail to preserve identity. In contrast, Prism-Fdit strictly preserves the unedited regions, which naturally limits this global drift. Consequently, while this may result in a slight CLIP decrease, it yiclds significantly higher semantic fidelity (DINO/SSIM), as intended. To provide more complete picture, we introduce supplementary metric:
DINOv2 (object similarity)
DINO/SSIM
SSIM (background preservation)
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Wild-background
(b) Wild-object
ImageNetkK e
075
A1 0.10
DINOVSSIW
poPM
0.43
717
CLT
0:5
050
1.2
DD
8300
Text CLIP
CLI
Text CL
Figure 4: Quantitative evaluation Prism-Edit. We report DINOv2, SSIM, and CLIP on Wild- TI2I (split into background/objeet subsets) and ImageNet-R- T121. Our method consistently improves DINOv2 similarity and maintains SSIM, validating disentangled editing performance.
DRIM BREM
Guldiace Scale:
DOIM Iav. Gabdanne Seales
sors
Griddenee
Gundancc Seals t0
Guidiass Seak:
[Nal] Cockey jenge??
DINOS2:
0.665
0.863
"coeereedery cheep"
SSMM:
0
4317
Sengground-sessittee metric (DINOv2/SSIM),
CLIP score w/ Prism Edit
Figure 5: Analysis of the Disentanglement Trade-off. (a) Our method improves background editing fidelity (DINOV2/SSIM), (b) This demonstrates that our edits prioritize disentanglement, which is not always captured by global text-alignment metrics like CLIP.
This ratio is designed to explicitly measure the success of preserving the primary object while altering the background. As shown Figure 4 our method consistently outperforms baselines on this metric. This trade-off is further illustrated in Figure [5) while CLIP scores may plateau (Fig. [5b). our method
maintains high DINO/SSIM ratio (Fig. Sap. highlighting its effectiveness in disentangled editing.
5.2 QUALITATIVE EVALUATION
To validate the universality our approach, evaluate Prism-Edit's performance plug-and-play enhancement for established editing methods on Stable Diffusion v1.5. Detailed descriptions of these baselines and the integration methodology are provided in Appendix B.3 As shown Figure 6 Prism-Edit consistently corrects common failure modes of baselines like DDIM/DDPM Inversion, PnP, and LEDITS++. For instance, when editing "an origami of hummingbird" "a sketch of parrot," Prism-Fdit successfully disentangles the object's 's identity ("parrot') from its style ( ('sketch'), a task where baselines often fail. This demonstrates the broad utility our principled guidance method's model-agnostic performance.
decomposition. Further results in Figure 7 confirm Furthermore, we demonstrate that our Semantic Scale Hypothesis generalizes beyond object-centrre images. Our analysis on object-scarce scenes (e.g., landscapes, textures) confirms that the guidance magnitude effectively disentangles implicit local structures from global atmosphere, as detailed in Appendix C.8 We demonstrate robust background and object edits modern architectures like Stable Diffusion v3, and showcase Prism-Edit's 's utility on FLUX. 1 by integrating it as plug-and-play enhancement for existing editors like RF- Inversiooo (Rout al. 2025) and Stable-flon (Avrahami
et al. 2025).
5.3
CAUSAL VALIDATION OF SEMANTIC DISENTANGLEMENT
key prediction of our hypothesis that distinct semantic layers can be edited independently. To provide causal evidence, design prompts that require simultaneous object and background changes.
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Original Image
DDIM
DDPM
Nuull "A photo golden the beach"
[Nall] image Paddington
origami hummninghind"
parrol"
"A Juy o'ajoy"" ombroidory minivax"
Figure 6: Prism-Eod Edit Universal Enhancement Module. Our method, integrated with various editing techniques on SD v1.5, consistently corrects common failure modes like semantic leakage (rows 3-4) and incomplete edits (rows 1-2).
Original
Stable Ditfusion
FLUX.1 [dei
Source Target Prompi [Null]
looking at bird on the 1) sea desert
3) forest"
Source & Target Prompt
"Birds standing the ground"
"1) birds,
2) Chess pieces,
3) Chickens standing the ground"
DDIM inv.
w/ Ours
RU-Inversion
w/ Ours
Stable-flow
w/ Ours
Figure 7: Model-agnostic editing, Results on SD v3 and FLUX. 1. Prism-Edii enables faithful background modifications (rows 1-2) and robust object edits (rows 3-4).
We then apply Prism-Edit in two controlled settings: (i) editing only high-magnitude (object) signals, and (ii) editing only low-magnitude (background) signals. As shown Figure g the results are cleanly disentangled. Modifying high-magnitude signals alters the object's identity while preserving

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Original Image
DDIM BaY
Global level
wet Durs
Lecal level
wi Oure
Original Imaage
DDIM Iay.
Glabal level
Mi Oure
wi Ours Local level
[Noll] - "A cal sittngg om the floor"
Noil photo of soldier New York
Dodiey
Dadaes
[ull] "A Dazz beings baz the restarrun"
[Null] "A poiiting of dog'
Figure 8: Semantic layer disentanglement. The results show a clear causal separation between Local level nigh-maggitude) edits that alter object identity, and Global level (low-magnitude) edits that alter background and style.
the background, and vice-versa. This experiment directly validates that guidance magnitude causally corresponds to the semantic scales we identified.
6 LIMITATIONS
Prism-Fdit has several limitations. Our theoretical analysis assumes a Gaussian posterior, which simplifies derivations but does not perfectly reflect the true diffusion process. The framework also requires manual specification of editing intent and relies on fixed z-score thresholds to separate semantic layers, introducing both user intervention and heuristic design choices. addition, the effectiveness of our method is influenced by the baseline diffusion model into which it is plugged, meaning that gains may vary depending on the underlying architecture.
CONCLUSION
introduced the Semantic Scale Hypothesis, framing guidance magnitude ([]el)) as an information- teeeoretct signal that reflects semantic hierarchy. Based on this principle, our training- free method Prism-Edit adaptively decomposes the guidance field to enable more disentangled edits, particularly in challenging background regions. Rather than relying solely spatial masks, this perspective highlights the role of signal-level structure within diffusion guidance. Future work may explore automatic detection of user intent and more adaptive layer selection, moving toward practical zero-shot editing pipeline.
ACKNOWLEDGMENTS
This work was supported in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grants funded by the Korea government (MSIT), including Artificial Intelligence Graduate School Program (No. R8-2019-11190079, Korea University), (Part 2) Few-Shot Learning Causal Inference in Vision and Language for Decision Making project (No. RS-2022- 11220959), and National AI Research Lab Project (No. RS-2022-00457888))
ETHICS STATEMENT
The authors adhere to the ICLR Code of Ethics. Our work introduces method for text-guided image editing. We acknowledge that, like all generative models, this technology could potentially be misused for creating misleading harmful content. However, the primary focus of our research is to provide a deeper understanding of the internal mechanisms of diffusion models and to offer controllable tools for creative and research purposes. We believe that by making the underlying principles of these models more transparent and controllable, our work contributes to a more responsible development path for generative AI.
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REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our research. To this end, our source code is included in the supplementary material and will be made publicly available upon publication. The appendix provides comprehensive details for replication: the full algorithm for Prism-Edit is presented in Algorithm [ all hyperparameters for our experiments and baselines are listed in Table B.I and the theoretical derivation of the Semantic Scale Hypothesis is available in Appendix A The appendix also contains further details on our experimental setup, including additional results.
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Omri Avrahami, Or Patashnik, Ohad Fried, Egor Nemchinov, Kfir Aberman, Dani Lischinski, and Daniel Cohen-Or. Stable flow: Vital layers for training-free image editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7877-7888, 2025.
Manuel Brack, Felix Friedrich, Katharia Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, and Apolinário Passos. Ledits++: Limitless image editing using text-to-image models. In Proceedings gs of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8861-8870 2024.
Mingdeng Cao, Xintao Wang. Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. Masactrl: Tuning-free free mutual self-attention control for consistent image synthesis and editing. In Proceedings of the IEEE/CVF International Conference Computer Vision pp. 22503-2255 2023.
Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. Diffedit: Diffusion- based semantic image editing with mask guidance. In International Conference on Learning Representations, 2023.
Bradley Efron. Tweedie's formula and selection bias. Journal of the American Statistical Association, 106(4966;1602-1611 2011.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, and Robin Rombach. Scaling rectified flow transformers for high-resolution image synthesis. In International Conference Machine Learning, pp. 18666-12633 PMLR, 2024.
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt- to to-prompt image editing with aooss-attention control. In International Conference on Learning Representations, 2023.
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurIPS 2021 Workshop Deep Generative Models and Downstream Applications, 2021.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6855 2020.
Seongmin Hong, Kyeonghyun Lee, Suh Yoon Jeon, Hyewon Bae, and Se Young Chun. On exact inversion of dpm-solvers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7069-7078, 2024.
Jimyeong Kim, Jungwon Park, Yeji Song, Nojun Kwak, and Wonjong Rhee. Reflex: Text-guie guxt-guided editing of real images rectified flow via mid-step feature extraction and attention adaptation. arXiv preprint wxXh:2200700196 2025.
Black Forest Labs. o
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Tsung- Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European Conference Computer Vision, pp. 740-755. Springer, 2014.
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in neural information processing systems, 35:5775-5787, 2022.

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Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. SDEdit: Guided image synthesis and editing with stochastic differential equations. In International Conference Learning Representations, 2022.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojeiech Galuba, Russell Howes, Po- Yao Huang, Shang- Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Ar- mand Joulin, and Piotr Bojanowski. DINOv2: Learning robust visual features without supervision. Transactions Machine Learning Research, 2024. ISSN 2835-8856.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretehen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pp. 8748-8763. PMLR, 2021.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text- conditional image generation with clip latents. arXiv preprint arXiv:2204.06/25, 1(2):3, 2022.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pp. 10674-10685, 2022.
Litu Rout, Yujia Chen, Nataniel Ruiz, Constantine Caramanis, Sanjay Shakkottai, and Wen-Sheng Chu. Semantic image inversion and editing using rectified stochastic differential equations. In International Conference on Learning Representations, 2025.
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Interna- tional Conference on Learning Representations, 2021.
Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel. Plug-and-play diffusion features for text-driven image-to-image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1921-1930, 2023.
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongic. The caltech-uesd birds-200-2011 dataset. 2011.
Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004.
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, pp. 586-595, 2018.
Jun-Ya Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.
2017.
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A DERIVATION OF THE SEMANTIC SCALE HYPOTHESIS
We provide here a more detailed derivation linking the magnitude of the guidance difference vector AE to posterior variance and information density, as introduced in Section 3
Step Connection between Posterior Mean and Score Function via Tweedie's Formula. For a noisy observation X₂ obtained by corrupting a clean image X₂ with Gaussian noise of variance of i... x, ~ (x, Xg), Tweedie's 's formula (Efron [011] connects the posterior mean of the clean image to the score of the noisy data distribution:
E(xo Xx, c) - + Vx logp(x: cc.
(A.1)
Step 2: Guidance Difference as a Difference of Scores. In standard diffusion models with c-parameterization, the noise predictor is trained to approximate the scaled score function: co(x,,)) x og xx, logp(x; c). Therefore, the guidance difference vector Åc between source condition 61 and target condition 02 is proportional to the difference in their respective score functions:
AE(x)C11,02 or (Vx, log,(x, |e) Vx, log (2)).
(A.2)
Step 3: Relating Guidance Magnitude to Posterior Mean Shift. By rearranging Eq. A. and combining it with Eq. A.2 we establish direct relationship between the magnitude of the guidance vector and the shift in the posterior mean estimate:
|| where
(1( x
Art == E(x X,, (2)
- Eixo
Xx, C1]-
(A.3)
This shows that large guidance magnitude corresponds to a large shift in the model's estimate the clean image when the condition changes from c2.
Step 4:
The Role of Posterior Variance and Information Density. The magnitude of the posterior shift Apt is determined by the "sharpness" or certainty of the posterior distribution p(xo x,, c).
Low- Variacee Posteriors (High Information Density): In image regions with rich struc- ture and detail (e.g., objects), the posterior distribution is sharply peaked (low variance). Here, the model is highly certain about the content. A change in condition (c: - o2) forces a significant shift in this sharp distribution, leading to a large Ajt and thus a large-magnitude Ac||.
High-Variance Posteriors (Low Information Density): In smooth, less structured regions (e.g., backgrounds), the posterior diffuse and spread out (high variance). The model is uncertain about the precise content. The same conditional change results in smaller adjust- ment to the broad distribution, yielding a small Aj4e and consequently a small-magnitude |
Conclusion. The Semantic Scale Hypothesis direct consequence of this relationship. The guidance magnitude || acts as proxy for the information-theonm -theoretc sharpness of the posterior. Structured objects correspond to high-information regions and yield large-magnitude guidance, while smooth backgrounds correspond to low-information regions and yield small-magnitude guidance. This theoretical relationship is formalized in Theorem 1, which provides closed-fore bounds on the guidance magnitude. A sketch of the proof follows:
Proof sketch of Theorem Start from Tweedie's 's relation (App. A implying |E x ||Auell/or Under the Gaussian approximation,
(Au)) Σn Aμt. = 2 DKL(WW Ec,) (IN Ec,)) v (EE,, Ecs), by rearranging the closed-form Gaussian KL. Using the Rayleigh quotient,
Amin A 11 < (A Ap, S Amax (2 A2,12
i.e.,
A S (Ap.) T Am
||
Xmax E
Xmin (Ea))
Combining with the KL identity and rescaling by o, yields the upper/lower bounds in Eqs. equa- tion equation 6 The symmetric versions follow by swapping (c1,
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B IMPLEMENTATION AND HYPERPARAMETERS
B.1 ALGORITHM DETAILS
The complete algorithm for the Prism-Edit framework is detailed in Algorithm | It outlines the two-stage process for semantic map extraction and disentangled application. Algorithm 2 specifies the morphological closing operation used to refine the binary mask, ensuring spatial contiguity.
Algorithm 1 Prism-Edit (Full Version with Optional Static Mask Refinement)
Require: Source prompt Care, target prompt Cigt. probe interval {1900s... ... [s00
1: Initialize latent Xr ~ (0,
2: // Stage 1: Semantic Map Extraction
3: for t, in probe interval
4: Dec, (xt, Cig) c0(xt, Care)
5: end for
6: Msem >-scoore /V E; ||
7: // Optional: Static Mask Generation
8: if target is object then
9: Mbsse & (M 2 0.6)
else
target is background
11:
Mhsse (Meem 0.6)
end if
13:
Milled f- Mask- (eai--eennieen(MMMann
See Algorithm
14:
if identity preservation mode then
15:
Mexchude (M 2 3.0)
16:
Minal t clamp (Mulled Mexclude, 0, 1)
17:
else
18:
Minal + Malled
end
if
20: for
= T..... do
D> Stage 2: Disentangled Application
Dynamic guidance modulation (always on)
22:
Ac, &- co(x;, Cigi) - 6o Car)
Binarize based on editing intent (e.g., 2 3.0d for object, < 0.6a for bg) meets r)
24:
Wsem, <- Binarize (z-score
25:
Eo < Cxc) + 7 (Ac, O W W)
26:
x -((x,: to,t) t)
27:
I/ Static blending (optional)
28:
if static mask mode then
29:
t pood @ Manal x @ (1 (1-Minal M
else
31:
X + XP
32:
end
33:
end for
34:
return Edited image X0
Algorithm 2 Mask Refinement (Morphological Closing)
Require: Base mask Mosse E {0, 1] HxW, number of iterations K
We apply morphological closing (dilation followed by erosion) to ensure the semantic mask is contiguous and free of small holes.
M t- Mussc
2: for k = to K do
3: M Dilate( M)
4: M < Erode M)
5: end for
6: return Milled +- M
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B.2 EXPERIMENTAL SETUP AND HYPERPARAMETERS
All experiments employ null-text inversion approach to prioritize content preservation. In Stable Diffusion v1.4/v1.5 (Rombach et al. 2022), we use 100 timesteps for DDIM inversion (Song et al.) 2021) with guidance scale of 1, and 50 steps for editing. Table Bists the specific hyperparameters used enhance various baseline methods.
Table B.1: Hyperparameter settings for applying Prism-Eae Edit to various baseline methods.
Baseline Method
Inv. Steps
Edit Steps
Base Str.
Tobj
7bg
Thresholds (Obj / Bg)
DDIM inv.
100
100
7.5
7.5
30
2 3.0/ < 0.4
P2P
100
100
15
30
2 3.0/ < 1.0
PnP
1000
50
10
25
40
2 3.0/ 2.0
DDPM inv.
100
100
25
40
2.0/
LEDITS++
50
50
10
20
30
2 3.0/ 0.6
B.3 BASELINE INTEGRATION DETAILS
To demonstrate the model-agnostic nature of Prism-Edit, we integrated it with several state-of-the-an editing methods. Since Prism-Edi operates directly the guidance vector Ae, it can be seamlessly combined with methods that manipulate internal network features or attention maps.
Soompt-to-Prompe (P2P) (Hertz et al., 2023) & Plug-and-Play (PnP) (Tumanyan et al., 2023): Both methods primarily control image structure by manipulating cross-attention maps or injecting spatial features during the denoising process.
Integration: We integrate Prism-Edit by strictly respecting the original pipeline for at- tention/feature injection. However, at the guidance computation stage of each sampling step, we replace the standard classifier- free guidance vector with our proposed Prism-Edit modulation (specifically, using the Dynamic Guidance Modulation mode only). This allows us to combine the structural stability of P2P/PnP with Prism-Edit's semantic disen- tanglement capability, enabling selective amplification preservation of specific regions without relying on hard masking.
LEDITS++ (Brack et al., 2024): The original LEDITS++ method employs native mechanism that identifies editing regions by thresholding the difference in guidance vectors via quantiles (similar to DifiEddi''s 's masking strategy).
Integration: We explicitly bypass this native quantile- based masking step. Instead, con- sistent with our integration for P2P and PnP, we apply Prism-Edit by directly modulating the classifier- free guidance term (using Dynamic Guidance Modulation only) during the sampling process. This demonstrates that our gradient-base based modulation offers a more robust alternative the hard-thresholding masks originally employed by LEDITS++.
In all cases, Prism-Edir does not require retraining or modifying the internal architecture of the baselines, confirming its aou--and-pll capability.
C ADDITIONAL EXPERIMENTAL RESULTS AND ANALYSES
C.1 GLOBAL VS. LOCAL CHANGES
To analyze the effectiveness our proposed method on both global and local image modifications, we consider two representative image-to-image translation scenarios: transitioning a Yosemite landscape from summer to winter (a global change) and transforming horse into a zebra (a local change). We utilize the same Yosemite (summer--winter) and Horse (horse+>zebra) datasets employed in the CycleGAN (hhu et al. 2017) benchmark, enabling direct comparison with existing methods. Experiments
conducted with both null-text and valid-text prompts. For valid-text prompts, used "a photo of Yosemite in summer" + "A photo of Yosemite winter" and "A photo of a horse" "A photo of zebra".. For null-text prompts, the source prompt null (t").
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Summer
Winter
Horse + Zebra
Inversion
Guidance
LPIPS (x100) 4
CLIP text
LPIPS (x100) 4
CLIP
Null
Valid
Null
Valid
Null
Valid
Null
Valid
CFG
58.99
62.76
23.15
25.77
62.30
72.50
20.43
30.00
DDIM Inv.
+ Prism-Edit (Ours)
36.75
28.39
21.53
22.18
30.94
56.51
21.26
30.65
CFG
33.50
39.56
21.26
22.03
29.77
29.65
28.54
28.55
DDPM Inv.
+ Prism-Edit (Ours)
33.42
28.52
21.59
21.29
25.92
27.66
28.44
28.54
Table C.I: Quantitative Comparison of Image-to-Image Translation Results. Comparison of method with existing methods using null-text and valid-text sampling approaches, evaluated by LPIPS and CLIP scores to assess perceptual similarity and alignment with text prompts.
Table C.I and Figure C.I present the quantitative and qualitative findings. In all cases, our proposed method preserved structural information significantly better than the baselines, as evidenced by the lower LPIPS (Zhang et al. 2018) scores. While CLIP (Radford et al., 202T) scores were sometimes similar or slightly lower, this typically occurred when the bascline methods failed to maintain the original structure, causing large deviation from the source image.
Orrigiaal
DDPM
DDIM
DDPM
Zobra
(a) Nall text inversion-based sampling
(b) Valid text inversion-hused sampling
Figure C.I: Qualitative comparison for global and local changes on CycleGAN datasets. Our method (w/ Ours) is applied to two I2I translation tasks: global style change (Summer-+Winiee and local object change (Horse-Zeerra) Compared to baselines, our method better preserves the structural integrity of the source image (e.g., mountain layout, horse's 's pose) while successfully applying the target transformation.
C.2 RATIONALE FOR SEMANTIC LAYER SELECTION
our framework is designed to operate on the tails of the semantic map's distribution. Figure C:2 provides the empirical validation for this design choice. We conduct an experiment to visualize which image content is affected by edits restricted different intervals of the Msm map. The results clearly indicate that the intermediate intervals (c.g., 0.5 < a I 2.0) contain mixture of object, background, and texture information. Edits applied to these regions often result undesirable artifacts and semantic leakage. In contrast, the extreme low- and high- magnitude regions (tails) correspond purer signals for background/style and object-core structure, respectively. Therefore, by selectively targeting these tails, Prism- Edit achieves cleaner, more disentangled manipulation.
As discussed in Section 4
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Original Image
Interval-1 (14)
Interval-2 (0La Sec 1)
Interval- ees
Interval-4
Original Image
Interval1 (o's (.4)
Interval-2 (0.6 sos
Interval-4
(LA sa
Null
pands
photo
[Nul]] valley waterfall swituming people"
[Noli] "An origumi of pink horse the desert'
Figure C.2: Impact of varying standard deviation thresholds for layer selection. Edits are applied only to pixels within the specified o-interval of the semantic map. The intermediate intervals (2 and 3) show a clear mixture of semantics, validating our design choice to operate on the tails (intervals and 4) for disentangled editing.
C.3 ABLATION STUDIES ON GUIDANCE SCALE
To validate our use of large, region- region-specifee guidance scales (7), we perform ablation studies on two distinet editing modes based on the targeted region of the semantic map (Mgem):
Outer-Interval Editing (Local Level):
This mode targets the tails of the distribution (e.g.. Mseml 2 kowee), corresponding the highest- and lowest-magnitude signals. As these signals cleanly map to object cores and uniform backgrounds, this mode primarily used for precise object editing.
Inner-Interval Editing (Global Level); This mode targets the central part of the distribution (e.g.. M < kinner), which contains the low-energy signals associated with overall style and texture. This mode is used for global background and stylistic changes.
Figures C.3 and C.4 show that Prism-Edit remains stable even with large values in both modes. Figure C.3 shows that increasing & in Outer-Interval mode correctly strengthens the target object concept without corrupting the background. Conversely, Figure C.4 demonstrates that large values (up 200) in Inner-Interval mode can achieve dramatic stylistic changes while the high-energy object regions remain structurally intact, protected by dynamic modulation.
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Source Prompt: [NULL]
Source Prompt: [NULL]
Target Prompt: "A dinosaur doll"
Target Prompt: "A laptop with a romantic comedy film on it"
Source Image
Source Image
Guidance Scales (n)
90
10
(-anillla)
5
(- Vanilla)
3
Outer-interval Thresholds (kouter)
Figure C.3: Ablation on guidance scale y for Outer-Interval (Object) editing. The left example demonstrates object replacement, while the right showcases object insertion.
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Souree Prompt: [NULL]
Source Prompt: [NULL]
Target Prompt: "A bridge on the moon"
Target Prompt: "A Van Gogh
painting woman"
Guidance Scales vanilla CRG
Source Image
Source Image
10
20
25
2
10
20
25
Guidance Scales (+)
000
100
50
200
Ini (-Vanilla)
0.8
0.6
0.4
(-Vanilla
Inner-interval Thresholds (Aquee))
Figure C.4: Ablation guidance scale for Inter-Interval (Background/Stylee editing. The left example demonstrates background replacement, while the right showcases texture replacement.
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C.4 THE ROLE OF NEGATIVE PROMPTS
Implementation Strategy. During the inversion phase, we set the negative prompt to an empty string (*")) maximize reconstruction fidelity. During the editing (sampling) phase, utilize the source prompt (Pace) as the negative prompt. This strategy effectively neutralizes the semantic features of the original concept, preventing them from leaking the edited result.
Our framework is fully compatible with negative prompts. As shown in Figure C.5 negative prompts crucial for overcoming the model's prior and achieving clean object replacement or attribute edits, negative
editing. However, as analyzed in Figure C.6] we found that for background-ong prompts can sometimes introduce subtle, undesirable changes to the foreground object. Therefore, we recommend using negative prompts primarily for object-focused manipulations.
KINDNESS MAGIC
Turget Prompt:
Target Prumpt: green light traffic light" Negative Prompt: "A red light at traftic light"
Negative Prompt: "KINDNESS IS MAGIC"
linage
Image
Visiion IS best
Visiion is best
Visiion the best
Vision
DDIM inv.
Vission Is best
Visiion Is best.
Visiion is best
Vision
Guidance Scale
20
20
20
Negative Prompt (Y/N)
Figure C.5: Effectiveness of negative prompts with Prism-Edit Negative prompts are essential for clean text replacement (left) and precise attribute editing (right).
Target Prompt: "a statue of cut newly mantedd couple the island"
Target Prompt: "a wamning sign the city" Negative Prompt: "a warning sign the forest"
Nogative Prompt nowly married couple"
Source Inaaee
Source Image
(plohal)
(lecal)
(glntel)
neg
neg.
neg
neg.
mag.
mag.
mag
Figure C.6: Interaction between negative prompts and background editing. When modifying only the background, adding a negative prompt can cause minor semantic leakage into the foreground object (rightmost two images).
C.5 ADDITIONAL QUALITATIVE AND QUANTITATIVE RESULTS
We provide further qualitative and quantitative results in Tables C.2 C.4 and Figures C.7
C.9
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Table C.2: Wild-background results.
Method
PnP
DDIM
DDPM Inv.
LEDITS+
CLIP
original
0.3151 0.3200
0.3275 0.3192
0.3051 0.3088
0,2120
0.2147
DINO/SSIM f
original
1.2240 1.7555
0.9796
1,0310
1.0317
ours
1.0731
1.1571
1,2328
Table C.3: Wild-object results.
Method
PnP
DDIM
DDPM
LEDITS+
CLIP
original 0.2995
0.3101
0.2972
0.2607
+ ours
0,2929
0.2997
0.2901
0.2625
SSIM
original
0,5554
0,4588
0.7721
0.6914
ours
0.6952
0.6942
0.7722
0.7499
Table C.4: Imagenet-R-e R-TI2I results.
Method
PnP
DDIM
DDPM inv.
LEDITS+
P2P
CLIP
original
0.3034
0.3242
0.3091
0.3027
0.3104
+ ours
0.2976
0.3199
0.3130
0.2980
0.3087
DINO/SSTM
original 0.9312
0.7604
0.8523
0.8426
0.7187
+ ours
1.0228
0.8979
0,8939
0,9982
0.8812
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Original Image
Nulli "A photo o bronze sculnture"
Nall "A phoco of wooden stattee
NNlli photo
Null "An image Kung Fw Panda"
(Null] "A photorcalisic image of bind near a lake moon"
[Nul]]
wooden couple dancing Ballrown"
Figure C.7: Further qualitative comparisons using null-text inversion.
Origiaad Imsge
DDIM
Our
DDPM Bay.
PuP
Ouera
LEDITS
Gurz
P2P
Our
*A cemoon of oat
" cedbriley ot"
Caleej" Jaay"
"As entraaddyy ceoll"
of
"Auvviner pupd
""Aandiili of penal"
"A e'a - skach of Epancase
goldfa"" geldfia?
Figure C.8: Further qualitative comparisons using valid-text inversion.
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Source Image
Image
Target Prompt: "A girl with yellow hair"
Target Prompt: woman smiling"
Negative Prompt: "A girl with real hair"
Negative Prompt:
Target Prompt: girl
Target Procspt: woman crying"
Target Proempe: "A girl with pink hair"
Target Prompt: "A woman closing
Target Prompt:
Target Prompt:
girl with rainhow hair"
"A woman winking eye"
Source Imagee
DDIM
w/ Ours
Source Image
Target Prompt:
Target Prompt:
chimpurrzee which is riding hurse"
" "Bananus the bowl"
Negative Prompt:
Negative Prompt:
"A man obscrving a woman who riding horse"
"Blucherries bow!"
Target Prompt: "..is riding lina"
Target Prompt:
"Watermelous the bow!"
Target Prompt: ".. Donald Trump who riding rkine"
Target Prompt
"Apples the bow?"
Target Prompt:
Target Prompt: empty
Biden observing Obama riding a
Figure C.9: Additional attribute and object editing results with negative prompts.
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C.6 ABLATION STUDY ON PROBE INTERVAL SELECTION
To determine the optimal timestep window for semantic map extraction, we conducted a quantitative ablation study using the COCO 2017 validation set (Lin et al. 2014). We focused the 'Person' class (50 randomly selected images, seed=0), as human subjects represent highly deformable objects that require both robust localization and structural flexibility during editing.
We evaluated the quality of the extracted semantic map Maem across different timestep intervals using two metries:
Coverage (Recall): Measures how well the semantic map covers the ground-truit object mask. High coverage indicates the map successfully captures the "semantic whole" of the object.
IoU (Intersection over Union): Measures the spatial tightness of the map against the ground truth.
Table C.5: Quantitative Analysis of Probe Intervals. The interval [900, 800] achieves the highest semantic coverage (0.9619), indicating best captures the global object structure. Later steps (e.g.. 500-400) show higher IoU but represent over-constraint to fine details.
Timestep Window
Coverage (Recall)
Avg. IoU
940-920
0,8839
0.2229
920-900
0,9206
0.2274
900-800 (Ours)
0.9619
0.2651
900-880
0.9109
0,2312
880-860
0.8818
0,2354
860-840
0,9254
0,2592
800-780
0,9026
0,2762
800-700
0,9302
0,2752
700-600
0.8768
0,2530
600-500
0,9021
0,2840
500-400
0,9123
0,2812
400-300
0.9478
0.2815
300-200
0.9568
0.2728
Analysis. As shown in Table C.5 the interval [900, 800] yields the highest coverage score (0.9619).
Early Phase (t E [900, 800)): The diffusion model establishes the global layout and existence of the object. The high coverage with moderate loU indicates "semantic blob" that robustly localizes the subject without being rigidly tied to pixel-perfect boundaries. This plasticity is crucial for editing, as allows the model to change the object's pose or shape.
Mid-to-Late Phase (e.g., 400): While IoU peaks around / 400, coverage drops (0.9123). At this stage, the model focuses on etc.), leading to fragmented maps. High IoU here implies rigid spatial constraints, which would limit the edit to simple texture swapping rather than structural manipulation.
textures (separating clothes, face,
Therefore, we select [900, 800) as the universal probe interval maximize semantic capture while retaining sufficient flexibility for structural edits.
C.7 COMPARISON WITH DIFFEDIT: MODULATION VS. FILTERING
Although both DiffEdit (Couairon et al. 2023) and Prism-Fdit leverage guidance differences to identify semantically meaningful regions, the two methods belong to fundamentally differrent classes of mechanisms.
DitfEdit (Latent Filtering). DiftEdit constructs binary spatial mask from the guidance magnitude and overwrites the latent representation inside the masked region. This latent replacement operation functions hard spatial filter: regions with weak guidance are entirely removed from the editing process, while strongly activated regions are preserved. Consequently, DiffEdit fails to edit back- ground regions where the guidance signal naturally weak. This limitation stems from theoretical
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Original Image
Dirtaditt
Prism-Fdis
Original Emage
Prism-Fale Fidit
horse
xebra"
room"
library"
Tobot
astrocaut
the screet" the snow"
Figure C.10: Qualitative Comparison with DiffEdit. We compare Prism-Edii against DiffEdit on both object and background editing tasks using Stable Diffusion v1.5. (Left Columns Object Edit): DiffEdit's hard latent masking introduces severe artifacts and unnatural boundaries (highlighted in red zooms), as it forcibly pastes the edited content. Prism-Edit, using guidance modulation, blends the zebra and astronaut naturally. (Right Columns- - Background Edit): DiffEdit fails to alter the background ("room" and "street" remain unchanged) because the weak background guidance signals are filtered out by its masking threshold. Prism-Edit successfully amplifies these signals to generate the "library" and "snow" scenes.
oversight: DiffEdit assumes low-magnitude regions are irrelevant. However, as discussed in Section 4 (Information Imbalance), these regions yield weak gradients not because they lack semantic meaning. but because they possess low Fisher information density. By filtering them out, DiffEdit inadvertently discards the valid semantic signals required for background editing.
Prism-Edit (Guidance Modulation). In contrast, Prism-Edit never masks or replaces latent variables. It operates exclusively in guidance space, applying a semantic weighting to the guidance update Ac. Weak but semantically relevant signals are not discarded; instead, they are selectively amplified through Z-score normalization. This effectively counteracts the Information Imbalance, allowing Prism-Edit forcefully edit low-information regions (backgrounds) that DiffTidit theoretically dis- cards as noise. This preserves structural continuity across the image and enables reliable background editing without introducing the hard spatial artifacts characteristic of latent filtering.
Quantitative Verification. We further validated this on a subset of 30 editing tasks using Stable Diffusion v1.5, covering both object and background modifications. Prism-Edit achieved higher mean CLIP score (0.2359) compared to DiffEdit (0.2298), confirming that our modulation approach aligns better with the target prompts while maintaining image naturalness. visual comparison is provided in Figure C.10
C.8 ANALYSIS ON OBJECT-SCARCE SCENES
To validate the universality of the Semantic Scale Hypothesis beyond object-centric images, we extend our analysis to object-scarce domains such as landscapes, fluid textures, and abstract gradients. We find that even in the absence of explicit foreground objects. the guidance magnitude () [e] effectively adapts to the local information density, separating "implicit structure" from "global atmosphere." As illustrated in Figure C.11 we categorize the behaviors into four distinct patterns:
Pattern 1: Localized Color Structures (e.g., Cloudy Sky). In scenes containing localized structures like cloud patterns or light reflections, these features act as high-information anchors. nighhmagn edits (Thigh) modify these local contrasts, while mowmaggnt edits (Now) shift the ambient tone or lighting.
Pattern 2: Structure vs, Atmosphere (e.g., Ocean). For scenes mixing smooth masses (e.g., water bodies) with sharp lines (e.g.. horizon), the guidance magnitude naturally disentangles them. High-magnitude guidance isolates sharp features for structural edits, whereas magnitude guidance alters the global sea state atmospheric mood.
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Implicit Object Hallucination (e.g., Marble). Strong texture features, such as marble veins, capture high guidance magnitudes. Editing these regions with object-centric prompts often "hallucinates" 3D-like structural changes, treating the veins pseudo-objects or anchors for new geometry.
Pattern 3:
Pattern 4: Frequency-Baae images, maggiiiee signals concentrate on high-freguncyy boundaries. causing sharp transitions edges. In contrast, low-magnitude signals induce smooth, global color drifts across the flat regions.
Scaling (e.g., Abstract Gradients). In edge-dominant
Failure Mode. extremely smooth, uniform images (e.g.. flat color fields) where high-frequency structure is virtually absent, the information density becomes uniform. In such cases, the semantic separation weakens, resulting in either negligible changes or global monotone shifts. This limitation consistent with our hypothesis that guidance magnitude relies on information disparity.
Change
Change
Change magnitade
Change magnitude
Source Image
high magnitude
magnitude
Souree Image
cloudy day in soft daylight" "a cloudyy day with dark heavy clouds" "a calm
with orange reflections"
"a marble texture in white and grey" marble texture green and black"
"un abstract watercolor grudient blue and green" "an abstract watercolor gradient red and orange"
Figure C.11: Analysis of Semantic Scale in Object-Scarce Scenes. Even without salient objects, the guidance magnitude separates local structures (e.g., veins, horizons) from the global atmosphere, supporting the universality of our hypothesis.
C.9 ROBUSTNESS ANALYSIS: INVERSION METHODS AND PROMPT VARIATIONS
To ensure the reliability of our proposed Semantic Scale Hypothesis, it is essential to verify whether the relationship between guidance magnitude and semantic scale holds consistently across different sampling conditions and prompts.
To address this, conducted comprehensive robustness analysis by varying two key factors the same source images:
1. Inversion Technique:
We compared the standard first-order DDIM Inversion with the high-order DPM-Solver Inversion (Lu et al.) 2022 Hong et al.) 2024), utilizing the exact inversion method for the latter.
2. Target Prompts: We applied distinct target prompts to the same source image (e.g., editing "wooden house" into "stone castle" vs. an "autumn forest") to test how the semantic map responds to different editing intents.
Figure C.12 presents the comparative results.
Robustness and Adaptivity. As shown in the histograms and difference maps, we observe two distinct behaviors that validate our hypothesis:
Solver Consistency (Nearly Invariant): Across different inversion solvers (DDIM vs. DPM-Solver), the magnitude distributions exhibit striking similarity. Although minor numerical fluctuations exist due to the solver order, the overall distributional shape and the heavy-tail characteristics remain nearly invariant, confirming that the guidance signal robust to the choice of inversion algorithm.
Prompt Adaptivity with Preserved Scaling: Across different prompts, the distribution adapts the nature of the edit (e.g., structural edits induce heavier tail than stylistic ones).
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Crucially, however, the spatial scaling principle remains intact. As visualized in the dif- ference maps, regardless the prompt, the relative hierarchy is preserved: mighimaa regions consistently localize the foreground object structure, while the background consis- tently falls into the low-magnitude range. This demonstrates that while the global energy may shift, the semantic separation logic (|bj > || is never broken.
This confirms that the Semantic Scale Hypothesis not only robust to solver variations but also correctly reflects the semantic intensity of the text prompt without violating the core information- theoretic scaling law.
wodern weeden house" mediaval stone castle
"A modera weeden busee"
"A medern wouden bruse is an sutionun forest with red leaver'
Difiraau pataao difieras latrrd
Haragan offecss sup per stimal
m00
Itteggam ot diferee mny bervel
origunl standing
origani
origani dragos standing
Infirrmns rups pidddan Effireacs vecir periatinal
DO0dt
Hidegese dfficnss istinaa
Figure C.12: Robustness Analysis of the Guidance Difference Vector. We visualize the histograms and spatial maps of the guidance difference vector magnitudes A|])))). (1) Across Inversion Methods, the distributions are nearly invariant. (2) Across Target Prompts, the distributions adapt the editing task magnitude. However, the spatial maps confirm that the semantic scaling principle holds: object regions consistently yield higher magnitudes than backgrounds, regardless of the prompt.
C.10 EFFECTIVENESS OF THE STATIC MASK MODULE
While our main experimental results demonstrate that the Dynamic Guidance Modulation alone achieves strong empirical effectiveness in general scenarios, included the Statie Mask module
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optional safety net for corner cases requiring strict preservation. To empirically validate this design choice, we conducted a controlled quantitative and qualitative ablation study.
Experimental Setup. We utilized the CUB-200-2011 dataset (Wah ct al. 2011). which contains images with high-frequency background textures (c.g.. dense foliage, branches) that are prone to semantic leakage. We randomly selected 100 images from the test set (seed=0) and performed the
editing task of transforming birds into "Wooden Carvings". This specific prompt was chosen because involves significant texture and geometric changes that often bleed into the background in standard diffusion editing.
We compared three settings: (1) Dynamic Only (Default), (2) Dynamic Static Mask (Optional), and (3) reference utilizing the Ground Truth (GT) bounding box mask. We measured CLIP Score (text alignment) and SSIM (background structural similarity).
Quantitative Analysis.
Table C.6: Quantitative Ablation on CUB-200. Comparison of editing modes on 100 samples (Bird + Wooden Carving). The Static Mask mode achieves the highest background preservation (SSIM), surpassing even the GT mask baseline, while the Dynamic mode offers higher editability (CLIP).
Method
CLIP (Editability)
SSIM (Preservation) f
Dynamic Only (Default)
0.2393
0,6792
Dynamic Static Mask (Optional)
0.2132
0.6896
(Ref) Dynamic GT Bbox Mask
0.2264
0.6859
As shown in Table C.6 a clear trade-ofi off exists. The Dynamic Mode yields a higher CLIP score, reflecting its flexibility in blending the target concept with the scene. Conversely, the Static Mask Mode achieves the highest SSIM, slightly outperforming even the Ground Truth mask baseline. This confirms that the static mask effectively acts as a "deterministic safety net" for pixel-perfect background preservation.
Qualitative Analysis. Figure C.13 visualizes the distinct behaviors of the two modes:
General Cases: In scenes with clear separation simple backgrounds, the Dynamic Mode achieves high-quality editing indistinguishable from the Static Mask mode.
Structural Degradation: In scenes with complex foliage, the Dynamic Mode occasionally struggles separate the object from the texture, causing the target style (e.g.. wood texture) to bleed into the surrounding leaves. The Static Mask fully prevents this leakage.
Geometric Hallucination: We observed that for prompts like "Wooden Carving," the Dy- namic Mode sometimes hallucinates contextual objects for instance, creating base under a seagull due to the model's 's prior that carvings typically sit on bases (see Figure C.13 bottom row). The Static Mask successfully suppresses this geometric hallucination, maintaining the original flat terrain.
Summary. Consequently, the Static Mask is not merely a redundant component but a strategic tool for handling corner cases. While the Dynamic Mode offers superior flexibility and text alignment for standard scenes, the Static Mask provides a deterministic guarantee for structural preservation. This makes it an essential optional module for users who need to strictly isolate the edit target from challenging, texture-rich backgrounds.
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Dynamic Only (Default)
Dynamic
(Ref) Dynamic
Dynamic Only (Default)
Dynamic
(Ref) Dynamic
Source Image
Statie Mask + GT Bbox Mask
Source Image
Static Mask GT Bbox Mask
General Case
Corner Case
Target Prompt: "a Wooden carving of bird"
Figure C.13: Qualitative Ablation the Static Mask. (Top) General Scenarios: In scenes with simple backgrounds or clear object separation, the Dynamic Mode (Default) achieves high-fidelity editing comparable to the Static Mask mode. (Bottom) Corner Cases: In complex scenarios, the Dynamic Mode exhibits specific failure modes: (1) Structural Degradation: In the examples with dense foliage (blue, black, and yellow birds), the bird's silhouette is lost the target texture bleeds into the complex background. (2) Geometric Hallucination: In the seagull example, the model hallucinates a new object (a base) under the bird. The Static Mask prevents these artifacts by enforcing a strict preservation constraint on non-edit regions.
D STATEMENT ON LLM USAGE
In line with the ICLR 2026 policy, disclose that used Large Language Models (LLMs) as an auxiliary tool during the preparation of this manuscript. LLM's role was primarily important in enhancing the clarity and readability of the manuseript. was utilized to refine sentence structure, correct grammatical errors, and improve the overall logical flow of paragraphs, with particular emphasis on the introduction, relevant work, and appendix sections.
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Reviews:
[{"review_id": "fkm657WdRi", "reviewer": "Reviewer_5VkF", "summary": "The paper proposes the Semantic Scale Hypothesis which motivates a training-free, plug-and-play module to improve image editing performances across models.\n\nThe paper could be a nice contribution on understanding the diffusion process. I'm willing to increase my score if the questions are clarified.", "questions": "- Why is the noise range $t \\in [900,880]$. Does a fixed $t=900$ or a wider range $t \\in [900,800]$ not work? How to justify the number $880$?\n- What is the motivation behind $W_{sem,t}$ as it's computed from $\\Delta_{\\epsilon_t}$ itself?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The paper is well-written\n- Strong empirical support and a nice theoretical connection for the Semantic Scale Hypothesis\n- The method is model-agnostic", "weaknesses": "- The thresholds used in Table 1 seem different for each baseline method, but \"fixed thresholds\" are claimed to be stable (286-288). The guidance scales seem to be different as well. How sensitive are these hyperparameters? How should a user pick the good ones?", "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": "I9RM1rr3hR", "reviewer": "Reviewer_jMi2", "summary": "This paper studies the guidance mechanism of the diffusion models, and proposes a hypothesis that the magnitude of the guidance difference vector directly encoded the semantic scale of the edits. Low-variance regions yield large magnitude differences with structural changes, while high-variance regions yield small magnitude differences with stylistic adjustments. By using this observation, it proposes to apply either a dynamic mask or a static mask computed from the guidance magnitude to the original guidance difference, then to perform edit. The results show that the proposed method can do the edit while preserving the layout better than the baselines.", "questions": "1. What is $M_{final}$ in Equation 8?\n\n2. What is $W_{sem,t}$ in Equation 9? Is it the absolute value of the guidance difference?\n\n3. Is the high-noise window in line 275 needed to be calculated per editing example, or is there a universal $t$ value that can work across every example?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. It’s interesting to mathematically show that the magnitude of the guidance difference itself contains useful information that can be useful to guide the edits.\n\n2. The proposed method can be applied to different editing methods in a plug-and-play way, which is flexible and universal.", "weaknesses": "While after applying the proposed method the results can be improved over the original editing method, the edited results are still not very satisfactory. I understand that the results are also greatly influenced by the base model / base method. Just to say in Figure 7, where the base model is advanced, the results after applying the proposed method are not good. With SD3, especially for the bear example, the background looks unrealistic. With Flux RF-Inversion, the background improves, but the overall style goes from realistic to more animation style. With Flux Stable-flow, the foreground objects either have a boundary shadow, or deteriorate. \n\nWhile I do agree that the proposed method is simple and can improve over the baselines, it seems that the proposed method itself is still not able to achieve very high quality results without any other modifications. This makes me doubt that either there is a better way to utilize the hypothesis rather than simply applying a static/soft mask, or the proposed hypothesis itself alone is not able to achieve a better quality.", "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": "aOXZ4J1KSI", "reviewer": "Reviewer_sjg1", "summary": "This paper suggests the Semantic Scale Hypothesis: the magnitude of the guidance vector correlates with the semantic scale of an edit. Specifically, low-variance regions, where the model is more certain and which typically correspond to foreground objects, exhibit higher guidance magnitudes, while high-variance regions, often background, show lower magnitudes. Building on this, the authors propose Prism-Edit, which adjusts edits by selecting ranges of guidance magnitudes, enabling object-only or background-only manipulation without requiring any object masks. The method is shown to work across various types of models and editing techniques.", "questions": "- Object-scarce scenes. The paper frames “semantic scale” mainly as object vs. background. How does the interpretation extend to images without salient objects (e.g., textures, landscapes, or abstract scenes)? What do high- vs. low-magnitude regions represent in such cases?\n\n- Magnitude variability. How much do guidance-magnitude distributions vary across images, prompts, and inversion methods? Including representative histograms for multiple settings would clarify variance and help practitioners choose thresholds.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clear conceptual novelty. To my knowledge, this is the first work to analyze editing behavior through the magnitude of the guidance vector itself. Furthermore, the analysis that higher-magnitude regions align with more certain, typically object-centric areas, while lower magnitudes map to uncertain, often background regions, is both intuitive and seems to have its own advantages\n\n- Mask-free, plug-and-play editing with broad model coverage. By selecting guidance-magnitude ranges, Prism-Edit enables foreground/background-targeted edits without segmentation masks. The procedure is training-free and plug-and-play, making it easy to drop into existing pipelines. Moreover, successful applications across models and editing methods indicate a model-invariant control axis, improving reliability and external validity.", "weaknesses": "- Condition sensitivity. Guidance vectors change with prompts, seeds, schedulers, and editing method choices. It is unclear whether the proposed magnitude, semantics relationship consistently holds under such variations. Is this tendency of the relation between guidance magnitude and the semantic scale of edit preserved regardless of the given condition?  Additional analysis would strengthen the claim.\n\n- Thresholding and disentanglement. Thresholds are manually tuned and depend on the editing method (Table 2, Appendix). Figure 10 also suggests continuous trade-offs as the threshold varies, implying object and background information remain partially entangled. While the paper achieves useful partial disentanglement, the dependence on thresholds should be better characterized.\n\n- Per-image statistics & efficiency. The method appears to compute per-image guidance-magnitude statistics. Even with z-score normalization, distributions may differ across images, raising concerns about the speed and stability of edited results depending on the given image and condition.", "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": "mkMo3vT6LC", "reviewer": "Reviewer_6BEq", "summary": "This paper introduces semantic scale hypothesis that frames guidance magnitude as an information-theoretic signal to reflect a semantic hierarchy and proposes Prism-Edit that decomposes the guidance signal into different semantic layers for selective control of diffusion model editing.", "questions": "1.\tIn Fig. 4, Prism-Edit has worse results w.r.t. the CLIP score. Can the authors give some explanation or analysis?\n2.\tIn Fig. 5b, it seems DDIM Inv. obtains better results than Prism-Edits. It would be better to include some analysis for this kind of results.\n3.\tHow does the proposed method utilize negative prompts?\n4.\tFor the different datasets, are the hyperparameter settings for Prism-Edit kept the same?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "1.\tExtensive experiments have been conducted to demonstrate the effectiveness of Prism-Edit, especially for challenging background edits. \n2.\tThe semantic scale hypothesis provides a theoretical perspective to reveal the semantic hierarchy in diffusion model guidance signal.", "weaknesses": "1.\tSince DiffEdit also leverages guidance differences to achieve selective control in diffusion models, this work’s contribution requires a detailed empirical comparison between DiffEdit’s hard spatial mask and Prism-Edit’s soft binary mask. Without such analysis, the novelty and advancements of the proposed method over previous studies are not sufficiently convincing. Moreover, because Algorithm 1 incorporates a hard-mask module, an ablation study on this component would also be necessary.\n2.\tA substantial portion of the paper is devoted to deriving closed-form bounds for the guidance difference; however, these results do not seem to be connected to the method’s technical design, nor do they provide deeper insights beyond the analyses already discussed in Sections 4.2 and 4.3.\n3.\tSome parts of the method are unclear. The choice of probe internal and how to calculate the soft binary mask $W$ lack explanation. Algorithm 2 is unclear.\n4.\tIt miss the introduction of baselines and the details about how to apply Prism-Edit to P2P, PnP and LEDITS++.", "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)