Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models

Kamil Deja; Katarzyna Zaleska; {\L}ukasz Popek; Monika Wysocza\'nska

arxiv: 2604.06052 · v2 · pith:5RQPEQ4Ynew · submitted 2026-03-29 · 💻 cs.CV

Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models

Katarzyna Zaleska , {\L}ukasz Popek , Monika Wysocza\'nska , Kamil Deja This is my paper

Pith reviewed 2026-05-14 21:06 UTC · model grok-4.3

classification 💻 cs.CV

keywords diffusion modelsself-attentionimplicit decisionstext-to-image generationmodel interpretabilityconcept localizationsteering methodsdebiasing

0 comments

The pith

Self-attention layers localize the implicit decisions that resolve ambiguous prompts in diffusion models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether diffusion models resolve unspecified prompt details through diffuse computation or through localized layers. It develops a probing technique that measures how well each layer separates different attribute concepts in its representations. The results point to self-attention layers as the primary site where these implicit choices occur. From that localization the authors derive a steering method, ICM, that edits only those layers and produces cleaner debiasing than prior broader interventions. A sympathetic reader would therefore expect more precise and less artifact-prone control over generative outputs once the right layers are targeted.

Core claim

Text-to-image diffusion models make implicit generative decisions for ambiguous prompts principally inside their self-attention layers. A probing-based localization method ranks layers by attribute separability and identifies self-attention blocks as the highest-ranking sites. Targeted edits applied only to this small subset of layers yield stronger debiasing performance and fewer unintended artifacts than existing state-of-the-art steering approaches.

What carries the argument

Probing-based localization that ranks layers according to their attribute separability for concepts, isolating self-attention layers as the dominant sites for resolving implicit generative choices.

If this is right

Interventions can be restricted to a small number of self-attention layers while still altering implicit choices.
ICM outperforms prior steering methods on debiasing tasks with reduced visual artifacts.
Explicit conditioning from the prompt can be kept separate from the implicit decision process during editing.
Fewer layers need modification, lowering the computational cost of precise generative control.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same localization approach could be applied to other generative architectures to find their implicit decision points.
Targeted layer edits might support fine-grained image editing tasks that current global methods cannot achieve cleanly.
Auditing self-attention layers could reveal where models systematically inject biases for particular ambiguous attributes.

Load-bearing premise

The probing method correctly isolates layers that handle implicit decisions separately from explicit prompt conditioning, and edits to those layers causally change the generated content without large side effects.

What would settle it

If applying the same magnitude of intervention to the identified self-attention layers produces no measurable shift in how ambiguous concepts are resolved, or yields the same level of artifacts as intervening on randomly chosen layers, the localization claim is falsified.

Figures

Figures reproduced from arXiv: 2604.06052 by Kamil Deja, Katarzyna Zaleska, {\L}ukasz Popek, Monika Wysocza\'nska.

**Figure 2.** Figure 2: Overview of ICM. We identify optimal layers for steering by measuring their discriminability using an external classifier. Layers are ranked by the classification accuracy of a linear probing (denoted here as LP) on their activations, and the top-performing layers (here in purple) are selected for targeted intervention. The selected layers can then be used for two applications: generation control via finet… view at source ↗

**Figure 3.** Figure 3: Comparison of mean accuracy across all timesteps for [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Accuracies of linear probes trained to predict gender [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Visual ablation of layer selection for activation steering. ICMselects the 3 best layers to preserve image quality while achieving desired modifications, compared to steering: 1) the 3 worst-performing layers, or 2) all layers without selection. comparison demonstrates that strategic layer selection is critical for effective steering—targeting optimal layers preserves image quality and achieves desired m… view at source ↗

**Figure 6.** Figure 6: Example generations from the SANA model with applied gender steering. We compare steering using only 6 best performing [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 7.** Figure 7: Example generations from SDXL model steered using probes trained on prompts describing a USA president, where positive [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 8.** Figure 8: Effect of increasing α values along the young–old direction. Larger α produces stronger age-related changes. B. Additional details on experimental Setup For the steering-based debiasing, we introduce a random component that selects the direction in which the entire batch of images is shifted. When there are n possible decisions, each direction is chosen with probability 1 n . For example, for race we con… view at source ↗

**Figure 9.** Figure 9: Example generations showing that applying the steering vector at later timesteps preserves the overall image structure while still [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗

**Figure 10.** Figure 10: Test accuracy across the selected layers and timesteps [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗

**Figure 11.** Figure 11: Comparison of generations from the original model [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗

**Figure 12.** Figure 12: Example generations showing how increasing the steering strength [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗

**Figure 13.** Figure 13: FLUX steering. Original Image Steer Top 10 Layers Steer 10 Random Layers Steer 10 Worst Layers P O S E S T E E RIN G [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗

**Figure 14.** Figure 14: SDXL steering. G. Scalability While our approach involves extensive linear probing, the process is computationally efficient (∼14 minutes on a 288-core CPU). We can achieve a 10× speedup by utilizing a single steering vector derived from five steps; this optimized workflow yields nearly identical performance, with an F D of 0.08 and a CLIP-I score of 0.89 for gender debiasing. We use average pooling prim… view at source ↗

**Figure 15.** Figure 15: Example SANA images generated after injecting a specific prompt into a chosen cross-attention layer. The general prompt is [PITH_FULL_IMAGE:figures/full_fig_p016_15.png] view at source ↗

read the original abstract

Text-to-image diffusion models exhibit remarkable generative capabilities, yet their internal operations remain opaque, particularly when handling prompts that are not fully descriptive. In such scenarios, models must make implicit decisions to generate details not explicitly specified in the text. This work investigates the hypothesis that this decision-making process is not diffuse but is computationally localized within the model's architecture. While existing localization techniques focus on prompt-related interventions, we notice that such explicit conditioning may differ from implicit decisions. Therefore, we introduce a probing-based localization technique to identify the layers with the highest attribute separability for concepts. Our findings indicate that the resolution of ambiguous concepts is governed principally by self-attention layers, identifying them as the most effective point for intervention. Based on this discovery, we propose ICM (Implicit Choice-Modification) - a precise steering method that applies targeted interventions to a small subset of layers. Extensive experiments confirm that intervening on these specific self-attention layers yields superior debiasing performance compared to existing state-of-the-art methods, minimizing artifacts common to less precise approaches. The code is available at https://github.com/kzaleskaa/icm.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper locates implicit decisions in diffusion models to self-attention layers via probing and builds a targeted ICM intervention on top, but the specificity of that localization rests on thin controls.

read the letter

The main thing to know is that this work uses a probing technique based on attribute separability to argue that self-attention layers handle the resolution of ambiguous concepts in text-to-image diffusion models, then introduces ICM as a steering method that intervenes only on those layers for tasks like debiasing. They claim this beats broader state-of-the-art approaches with fewer artifacts. The code is released, which is straightforward to check. What is new here is the explicit separation of implicit generative choices from explicit prompt conditioning, plus the idea that localization can be done by ranking layers on separability for underspecified concepts rather than relying on prompt-based edits alone. If the separability scores really isolate the implicit part, ICM could offer a lighter touch than full-model interventions. They do well by focusing on a small subset of layers and by putting the implementation out immediately for others to test. The soft spots are around whether the probing actually captures something specific to implicit decisions. Self-attention layers already do cross-token integration and long-range dependencies in these U-Nets, so high separability could just reflect that general role instead of a privileged spot for ambiguity resolution. The paper would be stronger with direct comparisons, such as running the same separability measure on fully specified prompts versus ambiguous ones, or checking whether the same layers dominate for explicit conditioning signals. The abstract also states superior debiasing performance without laying out the exact metrics, baselines, or controls, so the performance edge is hard to evaluate from what's here. This is for researchers working on interpretability and fine-grained control in diffusion models. Someone trying to reduce unintended biases or add targeted steering without retraining could get practical value from ICM if the localization holds up under scrutiny. It deserves peer review because the core hypothesis is testable, the method is implementable, and the code lets referees run their own checks on the controls and results.

Referee Report

2 major / 1 minor

Summary. The paper claims that implicit generative decisions in text-to-image diffusion models for ambiguous prompts are localized primarily in self-attention layers, identified via a probing technique that ranks layers by attribute separability. It introduces the ICM intervention method targeting a small subset of these layers and reports superior debiasing performance over existing methods with fewer artifacts.

Significance. If the localization holds, the work would offer a more precise mechanism for steering implicit choices in diffusion models, improving control over debiasing and reducing side effects from broad interventions. The public code release supports reproducibility and allows direct verification of the reported gains.

major comments (2)

[Abstract] The abstract asserts superior debiasing performance but supplies no quantitative metrics, baseline comparisons, or experimental controls; this absence prevents assessment of whether the central claim is supported by data.
[Method (probing technique)] The probing-based localization ranks self-attention layers highest for attribute separability on ambiguous concepts, yet the method does not include controls that would isolate this from the layers' known generic role in cross-token feature aggregation (e.g., separability scores on fully-specified versus underspecified prompts, or on non-ambiguous attributes). Without such controls the inference that these layers are the privileged site for implicit decisions remains under-supported.

minor comments (1)

[Method] Notation for the separability metric and the precise definition of 'attribute separability' should be formalized with an equation to allow replication.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights opportunities to strengthen the presentation of our results and the rigor of our localization analysis. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

Referee: [Abstract] The abstract asserts superior debiasing performance but supplies no quantitative metrics, baseline comparisons, or experimental controls; this absence prevents assessment of whether the central claim is supported by data.

Authors: We agree that the abstract's brevity limits immediate assessment of the quantitative claims. In the revised manuscript we will expand the abstract to include the primary performance metrics (e.g., the reported improvement in debiasing scores relative to baselines), a brief statement of the experimental controls used, and the key comparison against prior state-of-the-art methods. These additions will be drawn directly from the results already presented in the experimental section. revision: yes
Referee: [Method (probing technique)] The probing-based localization ranks self-attention layers highest for attribute separability on ambiguous concepts, yet the method does not include controls that would isolate this from the layers' known generic role in cross-token feature aggregation (e.g., separability scores on fully-specified versus underspecified prompts, or on non-ambiguous attributes). Without such controls the inference that these layers are the privileged site for implicit decisions remains under-supported.

Authors: The referee correctly identifies that additional controls would more convincingly isolate the role of self-attention layers in implicit generative choices from their general cross-token aggregation function. While our current probing focuses on attribute separability for ambiguous prompts, we acknowledge the value of the suggested comparisons. In the revised manuscript we will add new experiments reporting separability scores on fully-specified prompts and on non-ambiguous attributes, thereby providing the requested controls and strengthening the localization argument. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical probing and intervention results are independent of inputs

full rationale

The paper introduces a probing technique that ranks layers by attribute separability on ambiguous concepts, reports that self-attention layers score highest, and validates the finding by showing superior debiasing when intervening on those layers. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. The central claim rests on experimental measurements rather than reducing by construction to the probing inputs or prior self-references. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review based on abstract only; no specific free parameters, axioms, or invented entities can be extracted from the provided text.

pith-pipeline@v0.9.0 · 5508 in / 990 out tokens · 32646 ms · 2026-05-14T21:06:28.499707+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We introduce a probing-based localization technique to identify the layers with the highest attribute separability for concepts... self-attention layers... ICM (Implicit Choice-Modification)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.