Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models

Dongnam Byun; Jangwon Suh; Jungmin Ko; Jungwon Park; Wonjong Rhee

REVIEW 5 major objections 4 minor 35 references

Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models

T0 review · 5 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Cross-attention heads in text-to-image diffusion models encode human-specified visual concepts, and per-head relevance vectors built without fine-tuning can steer generation.

desk verdict A useful, training-free, per-head control signal for Stable Diffusion with decent empirical support; the load-bearing weakness is an unexamined coverage assumption in the HRV construction, not the headline claims. read the letter →

arxiv 2412.02237 v3 pith:HZTMZMWG submitted 2024-12-03 cs.CV cs.AI

Jungwon Park , Jungmin Ko , Dongnam Byun , Jangwon Suh , Wonjong Rhee This is my paper · ORCID

classification cs.CVcs.AI

keywords headrelevancevectorscross-attentionheadstext-to-imagediffusionmechanisticinterpretabilityconceptsteeringpolysemouswordsimageeditingmulti-conceptgeneration

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper's central claim is that the cross-attention heads—the connections that link each word in a prompt to image regions—carry stable, human-readable visual concepts, and that those concepts can be read out without changing the model. The authors build a Head Relevance Vector (HRV) for each of 34 visual concepts, with one entry per attention head, by counting which concept each head attends to most strongly while 2,100 random images are generated. Ordered weakening shows the counts are meaningful: removing the highest-scoring heads erases the target concept from generated images much faster than removing the lowest-scoring heads first. HRVs also work as steering controls, and rescaling head attention maps with them reduces misinterpretation of polysemous words, improves difficult image edits, and lessens catastrophic neglect in multi-concept generation. If the claim holds, attention heads give users a direct, fine-grained interface to what a text-to-image model draws.

What carries the argument

The central object is the Head Relevance Vector (HRV), an $H$-dimensional vector for each visual concept whose $h$-th component records how often the $h$-th cross-attention head was most responsible for that concept during construction. The construction feeds randomly sampled concept-words through the CLIP text encoder and each head's key-projection layer, concatenates the semantic-token embeddings into one key matrix, computes the softmax cross-attention map with the image query, averages over spatial and word-token dimensions, and applies argmax over the $N=34$ concepts so that every head-timestep contributes exactly one scale-free vote. The steering machinery is a rescaling vector: concept strengthening uses the desired concept's HRV as per-head multipliers on the semantic-token attention maps, and concept adjusting uses $2\cdot r_{\text{desired}} - r_{\text{undesired}}$ as the per-head multipliers; in both cases only the attention maps of semantic tokens are rescaled, leaving special tokens and the rest of the denoising process untouched.

What would settle it

Construct an HRV for a concept that is not among the 34, such as 'sky' or 'running', using the same pipeline, and perform ordered weakening on it. The claim would be falsified if the most-relevant-first and least-relevant-first weakening curves are indistinguishable, or if adding this distant concept substantially changes the original 34 HRVs; the paper's extension test uses Tableware, which is close to the existing set and therefore not a maximally informative check.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that cross-attention head positions in Stable Diffusion have concept-specific relevance patterns that align with human-specified visual concepts, and that these patterns are extractable from forward passes alone. For each concept, the procedure embeds one sampled concept-word per concept, concatenates the key projections, computes the cross-attention map against the image query, spatially averages it, and uses argmax to assign that head-timestep observation to the single most activated concept, incrementing that concept's HRV. The argmax is not a detail: cross-attention heads differ in scale by up to 8.1 times, so argmax converts each head into one scale-free vote. Validation by ordered weakening shows that weakening in most-relevant-first order removes the target concept from generated images markedly sooner than least-relevant-first order, in both Stable Diffusion v1.4 and SDXL. Applying the HRVs as rescaling vectors yields the paper's three application results: human-evaluated misinterpretation of polysemous words falls from 63.0% to 15.9%; image editing improves on five attributes relative to several baselines; and multi-concept generation improves over Attend-and-Excite by 2.3% to 6.3% across the reported metrics.

Load-bearing premise

The load-bearing assumption is that the 34 visual concepts, each expressed by 10 concept-words, are an exhaustive partition of what cross-attention heads can encode, because every head-timestep observation is assigned by argmax to exactly one concept and any head that truly encodes something outside the set would have its votes misattributed.

Editorial extensions

If this is right

A user can obtain concept control over a frozen text-to-image model by running the HRV construction once, then rescaling head attention maps at inference time.
Adding a new visual concept to the set does not materially disturb the existing HRVs, since the paper shows the 34-concept and 35-concept vectors are highly similar.
For ambiguous tokens, concept adjusting with a chosen desired and undesired concept lowers the human-perceived misinterpretation rate from 63.0% to 15.9% on the 10 prompts tested.
Coupling HRV rescaling with Prompt-to-Prompt improves image-text alignment and human preference for edits of color, material, geometric patterns, image style, and weather conditions.
Coupling HRV rescaling with Attend-and-Excite reduces catastrophic neglect in two-prompt-type benchmarks, improving over the baseline by 2.3% to 6.3% on full-prompt similarity, minimum object similarity, and BLIP score.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A stronger test than anything in the paper would build an HRV for a concept that lies far outside the 34, such as 'sky' or 'running'; the paper only demonstrates extension with Tableware, a concept close to the original set.
Because construction is driven by CLIP text embeddings and argmax competition, concepts with poor lexical separation—such as facial expressions, which the paper itself reports as a failure case—are the likely weak points of the method.
The finding that timesteps do not separate in the t-SNE of HRVs suggests head-level concept routing is settled globally rather than by a particular denoising phase; if so, HRV steering should transfer to shorter or distilled sampling schedules.
The authors note an LLM can choose the desired and undesired concepts automatically but do not build that pipeline; a natural next step is an end-to-end system that takes a prompt, detects ambiguous tokens, and applies concept adjusting without human selection.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The headline: this paper gives you a simple, training-free, per-head control signal for Stable Diffusion, and the evidence that it tracks human-specified visual concepts is decent. The HRV construction—tallying argmax concept attendance across heads and timesteps—is genuinely new relative to the layer-level or token-level manipulation in P2P, Attend-and-Excite, and Structured Diffusion. The downstream gains are modest but consistent across three tasks, and the human evaluation on polysemous words is a real plus.

What it does well: the ordered weakening analysis is the right sanity check. MoRHF removes the target concept noticeably faster than LeRHF, and the random-order baseline in Appendix C.3 clears away the worry that any ordering would do. The SDXL extension and the t-SNE timestep analysis are useful robustness checks. The paper is honest about failure cases—the Limitation section names concepts where HRVs do not separate MoRHF/LeRHF, which is more than most interpretability papers admit.

The soft spots, in proportion. The biggest is the coverage assumption: every head/timestep is forced to vote for one of the 34 concepts. The 2,100 construction prompts include ImageNet classes and PromptHero images that almost certainly contain content outside the list—coral reefs, glaciers, lighthouses, actions, textures. Those votes go to the nearest of the 34 by argmax, which contaminates the HRVs. The paper never measures how often this happens or how confident the argmax is. This is real, and it is load-bearing for HRV purity. That said, I would not call it fatal: the ordered weakening and the downstream tasks still work, which suggests the signal survives the noise. Appendix J adds a concept and shows the original HRVs barely move, but it does not test coverage against the actual prompt distribution.

Minor issues: no error bars or significance tests anywhere; the 10 polysemous prompts are hand-picked from known failures; the rescaling coefficients (2 and -1) are manual. These are worth a round of revision, not a rejection.

For whom: anyone working on T2I interpretability or training-free steering will find this useful to read and cite. It is a solid subfield advance, not a paradigm shift.

My recommendation: send it to peer review. The core claim holds up in its own empirical terms; the coverage question is a missing analysis, not a proof of error. Ask the authors to quantify coverage and add error bars, and I would accept.

Referee Report

5 major / 4 minor

Summary. The paper proposes Head Relevance Vectors (HRVs) as concept-specific importance scores for the 128 cross-attention heads of Stable Diffusion v1.4 (and SDXL). HRVs are built by generating 2,100 random images and, for each head and timestep, computing cross-attention maps against the concatenated CLIP key embeddings of one randomly sampled word per concept (34 concepts, 10 words each), argmaxing over concepts, and incrementing the winning concept's entry (Eqs. 2-5); vectors are L1-normalized. The authors validate HRVs with an ordered weakening analysis (MoRHF vs LeRHF), then apply HRV-based rescaling ('concept strengthening' and 'concept adjusting') to reduce polysemous-word misinterpretation, to improve P2P editing on five attributes, and to improve Attend-and-Excite in multi-concept generation.

Significance. If the central claim holds, HRVs provide a simple, training-free, head-level control mechanism for text-to-image diffusion models and a new interpretability probe. The paper's strengths are its clear methodology, released code, the random-order weakening baseline, human evaluations, three downstream task benchmarks, the SDXL extension, and the Appendix J concept-addition check. The main risks are that HRV construction assumes the 34 concepts cover all visual content encountered, the primary validation is partly self-referential, and the quantitative claims lack uncertainty estimates. These issues are fixable and do not, in my view, invalidate the approach, but they currently prevent the stronger conclusions ('effectively and reliably constructed') from being fully supported.

major comments (5)

[Section 3, Algorithm 1, Eq. (4)] The argmax in Eq. (4) assigns every head-timestep observation to exactly one of the 34 concepts, so any head that actually encodes an out-of-set concept (e.g., sky, water, motion, or an ImageNet/PromptHero category outside the list) has its votes absorbed by the nearest in-set concept, contaminating that HRV. The paper never reports the distribution of argmax margins or any coverage statistic over the 2,100 construction prompts. Appendix J's addition of Tableware shows that the original 34 HRVs are largely unchanged, but this checks robustness to adding one concept, not coverage of the prompt distribution. Please report top-1 vs top-2 margins, the fraction of low-confidence assignments, and a leave-one-concept-out or hold-out-prompt analysis.
[Section 4, Figures 3, Appendix C.3] The ordered weakening test uses the same cross-attention maps from which HRVs were estimated, so it is partly self-referential: a summary of which heads attended most to a concept will naturally identify heads whose weakening affects that concept. The random-order baseline in Appendix C.3 mitigates this, but it does not test whether the HRV construction generalizes across prompt distributions. A split-half validation (construct HRVs on one half of the 2,100 prompts, evaluate ordered weakening on the other half) and a random head-weighting baseline for the downstream applications (e.g., uniform or shuffled HRVs in A&E and P2P) would substantially strengthen the evidence that the vectors carry concept-specific information.
[Section 5 (Tables 1-2, Figure 7) and Appendix D.2] All quantitative comparisons are reported as point estimates without error bars, confidence intervals, or significance tests, despite the use of multiple seeds and prompts (e.g., 30 seeds, 500/250 edited images, 100 generated images per model in the polysemous evaluation). Improvements of 2.3-6.3% in Table 2 and 2.32-11.79% in Section 5.2 could be within seed or prompt variance. Please report bootstrap or paired tests over seeds and prompt sets for the main comparisons.
[Section 5.1, Table 7, Appendix D] The polysemous-word evaluation uses 10 prompts hand-picked from known failure cases, and for each prompt the desired and undesired concepts are manually chosen (e.g., Color vs Plants for 'lavender'). This makes the 63.0% to 15.9% misinterpretation reduction hard to interpret as a general claim. The AMT protocol is well designed, but it evaluates only this curated set. Please broaden the prompt set, pre-specify selection criteria, or run the LLM-based automatic selection pipeline described in Appendix D.1 and report its performance.
[Section 5 (Figure 4), Appendix C.4] The rescaling coefficients in the core methods are fixed without systematic justification: ordered weakening uses -2, and concept adjusting uses 2*HRV(desired) - 1*HRV(undesired). Appendix C.4 explores the weakening factor, but no sensitivity analysis is given for the concept-adjusting coefficients or for the L1 normalization in SDXL, which Appendix G.3 itself identifies as problematic. A small ablation over these coefficients would clarify how much of the reported gains depend on them.

minor comments (4)

[Section 5.2, Table 1] The method name 'MassaCtrl' should be 'MasaCtrl' to match the reference and the surrounding text.
[Appendix J] The sentence 'The two sets of HRVs for the original 34 visual concepts (Figure 48a and Figure 48b) are highly similar' is misworded; Figure 48b contains the 35-concept set, so the comparison is between the 34-concept and 35-concept constructions.
[Table 8] The 'Filtering process' column contains a stray 'O' rather than a description; the table should be cleaned up.
[References] The OpenAI reference entry uses the informal URL title 'Hello gpt-4o'; use a formal citation or consistent reference format.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the HRV construction is an empirical tally, and its validation and applications use held-out words, random-order controls, and external benchmarks that do not reduce to the construction inputs.

full rationale

The paper's chain is: (i) HRVs are built by tallying, per CA head and timestep, the argmax concept among 34 concept-word groups (Eqs. 3-5, Algorithm 1); (ii) ordered weakening tests whether MoRHF removes the concept faster than LeRHF; (iii) applications rescale CA maps with HRVs for polysemy, editing, and multi-concept generation. None of these steps equates a prediction to an input by construction. The ordered weakening is a causal intervention on the same model, but it is not forced: the construction uses attention statistics over 2,100 random prompts, while the reported validation uses different prompt templates and, for key concepts, held-out words (Appendix C.1 marks non-overlapping words with asterisks), and Appendix C.3 shows a random-order baseline with near-zero area. The downstream tasks are external benchmarks (P2P, A&E, human evaluation, CLIP/BLIP scores) using precomputed HRVs; success is not entailed by the argmax tally. The only self-citation (Kim et al. 2024, for the BG-DINO metric inspiration) is not load-bearing. The reader-flagged 34-concept coverage assumption is a robustness/validity concern about argmax misassignment, not a circularity, because it would make HRVs noisier rather than making the outputs equal to the inputs. Some validation and application words overlap the construction lists (e.g., Furniture and Weather in Table 5), which weakens those specific demonstrations, but the central results also include held-out concepts and external task benchmarks, so this overlap does not make the claim circular. No Eq. X = Eq. Y reduction or fitted-parameter-renamed-as-prediction was found.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The central method depends on a small number of hand-chosen components: the 34-concept/word list, the argmax tally rule, the -2 weakening factor, and the 2/-1 concept adjusting coefficients. None of these are derived from first principles; the paper validates them empirically. The biggest unexamined input is the assumption that the 34 concepts suffice to classify every head observation during HRV construction.

free parameters (5)

Concept adjusting coefficient for desired concept = 2
The rescaling vector is defined as 2 x HRV_desired - 1 x HRV_undesired (Figure 4). No derivation or ablation justifies these specific weights.
Ordered weakening rescaling factor = -2
Chosen for the MoRHF/LeRHF analysis (Section 4); inspired by P2P's rescaling range, but the specific value is a manual choice. Appendix C.4 shows the effect varies with the factor.
Self-attention replacement rates = 0.4 for P2P, 0.9 for P2P-HRV
Selected in Appendix E.4 as a balanced trade-off between CLIP and BG-DINO scores; these are evaluation hyperparameters tuned per method and affect the reported comparisons.
HRV L1 normalization constant = H (128 for SD v1.4, 1300 for SDXL)
HRVs are normalized to L1 norm H so that an all-ones vector leaves generation unchanged (Section 3). The authors note this normalization causes overly strong rescaling in SDXL, indicating the choice is not principled for large H.
Number of visual concepts and concept-words = 34 concepts x 10 words (Table 3)
The concept list was generated by GPT-4o and selected by the authors; the central claim of alignment with human visual concepts is tied to these specific words and categories.

assumptions (4)

domain assumption Attention between image queries and key-projected concept word embeddings is a valid measure of the visual concept present in the generated image.
Used in Section 3 Eq. 3 to define head-concept strength; if the CLIP key space does not separate concepts, the argmax tally is noise.
ad hoc to paper The 34 visual concepts and their 10 words cover all concepts relevant to the 2,100 random prompts, so argmax assignment is unbiased.
Required for Eq. 4-5; no coverage analysis is provided for ImageNet/PromptHero prompts.
domain assumption Multiplying a head's CA maps by a negative factor is a localized intervention that removes the head's concept contribution without breaking other generation structure.
Used in Section 4 for ordered weakening; the paper cautions that LeRHF weakening can affect other concepts, so the intervention is not perfectly selective.
domain assumption A head's relevance pattern measured on random prompts transfers to new prompts and tokens, such as applying the Color HRV to the unseen word 'lavender'.
Required for all three applications in Section 5; generalization across prompts is asserted but not systematically measured.

invented entities (1)

Head Relevance Vector (HRV) independent evidence
purpose: A 128-dimensional vector per visual concept scoring the importance of each cross-attention head for that concept.
HRV is a data-derived construct, not a physical entity, but it is the paper's central new object. It has falsifiable handles: ordered weakening predictions and downstream task improvements measured by human and CLIP evaluations.

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Cite this review

Pith. "Pith review of Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models." pith.science (2026). https://pith.science/paper/HZTMZMWG

@misc{pith2026241202237,
  author       = {Pith},
  title        = {Pith review of: Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HZTMZMWG}},
  note         = {Machine review of arXiv:2412.02237}
}

read the original abstract

Recent text-to-image diffusion models leverage cross-attention layers, which have been effectively utilized to enhance a range of visual generative tasks. However, our understanding of cross-attention layers remains somewhat limited. In this study, we introduce a mechanistic interpretability approach for diffusion models by constructing Head Relevance Vectors (HRVs) that align with human-specified visual concepts. An HRV for a given visual concept has a length equal to the total number of cross-attention heads, with each element representing the importance of the corresponding head for the given visual concept. To validate HRVs as interpretable features, we develop an ordered weakening analysis that demonstrates their effectiveness. Furthermore, we propose concept strengthening and concept adjusting methods and apply them to enhance three visual generative tasks. Our results show that HRVs can reduce misinterpretations of polysemous words in image generation, successfully modify five challenging attributes in image editing, and mitigate catastrophic neglect in multi-concept generation. Overall, our work provides an advancement in understanding cross-attention layers and introduces new approaches for fine-controlling these layers at the head level.

Figures

Figures reproduced from arXiv: 2412.02237 by the authors.

**Figure 2.** Overview of a single HRV update for a cross-attention (CA) head position [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Ordered weakening analysis for three visual concepts: The visual concept of interest [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (46 more)

**Figure 4.** Figure 4: Two rescaling vectors for visual concept steering. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Examples of image generations from Stable Diffusion (SD) and SD-HRV (ours) using prompts frequently misinterpreted by T2I models. SD-HRV effectively reduces misinterpretation compared to SD. The same word can have different meanings depending on the context. Stable …

**Figure 6.** Figure 6: Examples of image editing for the five challenging visual concepts. In these examples, all [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: Quantitative comparison of image editing methods for three object attributes using CLIP [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

**Figure 8.** Figure 8: Examples of multi-concept generation for Type 1 and Type 2 prompts. We compare [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]

**Figure 9.** Figure 9: Ordered weakening analysis for SDXL: generated images are shown as weakening pro [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]

**Figure 10.** Figure 10: t-SNE plot of 1700 head relevance vectors across 34 visual concepts and 50 generation timesteps. Diffusion models generate images by iteratively processing an image latent through the same U-Net network. A natural question is whether the patterns of head relevance v…

**Figure 11.** Figure 11: Ordered weakening analysis of six additional concepts: qualitative results using Stable [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]

**Figure 12.** Figure 12: Ordered weakening analysis of six additional concepts: quantitative results using Stable [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]

**Figure 13.** Figure 13: Ordered weakening analysis of nine concepts with additional examples: qualitative re [PITH_FULL_IMAGE:figures/full_fig_p023_13.png]

**Figure 14.** Figure 14: Ordered weakening analysis of nine concepts with additional examples: qualitative re [PITH_FULL_IMAGE:figures/full_fig_p024_14.png]

**Figure 15.** Figure 15: Ordered weakening analysis of nine concepts with additional examples: qualitative re [PITH_FULL_IMAGE:figures/full_fig_p025_15.png]

**Figure 16.** Figure 16: Ordered rescaling with varying rescaling factors, using HRV for the [PITH_FULL_IMAGE:figures/full_fig_p026_16.png]

**Figure 17.** Figure 17: Ordered rescaling with varying rescaling factors, using HRV for the [PITH_FULL_IMAGE:figures/full_fig_p026_17.png]

**Figure 18.** Figure 18: Complete set of generated images used for the human evaluation (Part 1 of 2). Images [PITH_FULL_IMAGE:figures/full_fig_p028_18.png]

**Figure 19.** Figure 19: Complete set of generated images used for the human evaluation (Part 2 of 2). Images [PITH_FULL_IMAGE:figures/full_fig_p029_19.png]

**Figure 20.** Figure 20: Comparison of concept strengthening and concept adjusting. Concept strengthening fails [PITH_FULL_IMAGE:figures/full_fig_p030_20.png]

**Figure 21.** Figure 21: Trade-off effect of self-attention replacement in P2P and P2P-HRV (Ours). Both methods [PITH_FULL_IMAGE:figures/full_fig_p033_21.png]

**Figure 22.** Figure 22: Qualitative results of image editing comparing P2P (Hertz et al., 2022) and ours, based [PITH_FULL_IMAGE:figures/full_fig_p034_22.png]

**Figure 23.** Figure 23: Qualitative results of image editing comparing P2P (Hertz et al., 2022) and ours, based [PITH_FULL_IMAGE:figures/full_fig_p035_23.png]

**Figure 24.** Figure 24: Qualitative results of image editing for three object attributes and two image at [PITH_FULL_IMAGE:figures/full_fig_p036_24.png]

**Figure 25.** Figure 25: Qualitative results of image editing for three object attributes and two image at [PITH_FULL_IMAGE:figures/full_fig_p036_25.png]

**Figure 26.** Figure 26: Qualitative results of image editing for the visual concept [PITH_FULL_IMAGE:figures/full_fig_p037_26.png]

**Figure 27.** Figure 27: Qualitative results of image editing for the visual concept [PITH_FULL_IMAGE:figures/full_fig_p038_27.png]

**Figure 28.** Figure 28: Qualitative results of image editing for the visual concept [PITH_FULL_IMAGE:figures/full_fig_p039_28.png]

**Figure 29.** Figure 29: Qualitative results of image editing for the visual concept [PITH_FULL_IMAGE:figures/full_fig_p040_29.png]

**Figure 30.** Figure 30: Qualitative results of image editing for the visual concept [PITH_FULL_IMAGE:figures/full_fig_p041_30.png]

**Figure 31.** Figure 31: Qualitative results of image editing for the visual concept [PITH_FULL_IMAGE:figures/full_fig_p042_31.png]

**Figure 32.** Figure 32: Qualitative results of image editing for the visual concept [PITH_FULL_IMAGE:figures/full_fig_p043_32.png]

**Figure 33.** Figure 33: Qualitative results of image editing for the visual concept [PITH_FULL_IMAGE:figures/full_fig_p044_33.png]

**Figure 34.** Figure 34: Qualitative results of image editing for the visual concept [PITH_FULL_IMAGE:figures/full_fig_p045_34.png]

**Figure 35.** Figure 35: Qualitative comparison of the results for Type 2 prompts between SynGen (Rassin et al., [PITH_FULL_IMAGE:figures/full_fig_p046_35.png]

**Figure 36.** Figure 36: Qualitative comparison of the results for Type 1 and Type 2 prompts. We compare [PITH_FULL_IMAGE:figures/full_fig_p047_36.png]

**Figure 37.** Figure 37: Ordered weakening analysis using SDXL: Change in CLIP image-text similarity score [PITH_FULL_IMAGE:figures/full_fig_p048_37.png]

**Figure 38.** Figure 38: Ordered weakening analysis using SDXL: Generated images as weakening progresses in [PITH_FULL_IMAGE:figures/full_fig_p049_38.png]

**Figure 39.** Figure 39: Ordered weakening analysis using SDXL: Generated images as weakening progresses in [PITH_FULL_IMAGE:figures/full_fig_p050_39.png]

**Figure 40.** Figure 40: Ordered weakening analysis using SDXL: Generated images as weakening progresses in [PITH_FULL_IMAGE:figures/full_fig_p051_40.png]

**Figure 41.** Figure 41: Ordered weakening analysis using SDXL: Generated images as weakening progresses in [PITH_FULL_IMAGE:figures/full_fig_p052_41.png]

**Figure 42.** Figure 42: Ordered weakening analysis using SDXL: Generated images as weakening progresses in [PITH_FULL_IMAGE:figures/full_fig_p053_42.png]

**Figure 43.** Figure 43: Ordered weakening analysis with more complex images: Generated images as weakening [PITH_FULL_IMAGE:figures/full_fig_p054_43.png]

**Figure 44.** Figure 44: Two examples on misinterpretation reduction in SDXL. Images showing misinterpreta [PITH_FULL_IMAGE:figures/full_fig_p055_44.png]

**Figure 45.** Figure 45: First type of failure cases: The baseline T2I model, SDXL, struggles to correctly un [PITH_FULL_IMAGE:figures/full_fig_p057_45.png]

**Figure 46.** Figure 46: Second type of failure cases: Our HRV fails to identify the relevant CA head order [PITH_FULL_IMAGE:figures/full_fig_p058_46.png]

**Figure 47.** Figure 47: Cosine similarity plots of (a) 34 head relevance vectors and (b) 50 timestep vectors. [PITH_FULL_IMAGE:figures/full_fig_p059_47.png]

**Figure 48.** Figure 48: Visualization of head relevance vectors (HRVs) for (a) 34 visual concepts used in this [PITH_FULL_IMAGE:figures/full_fig_p060_48.png]

**Figure 49.** Figure 49: Ordered weakening analysis for Tableware concept: Generated images as weakening progresses in either MoRHF or LeRHF order using Stable Diffusion v1.4. 60 [PITH_FULL_IMAGE:figures/full_fig_p060_49.png]

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Works this paper leans on

35 extracted references · 23 canonical work pages

[1]

Table 11 lists the 12 animals and 10 colors used to generate these prompts, with the full prompt list available in our core codebase

and (ii) ‘a {Color A} {Animal A} and a {Color B} {Animal B}’ (Type 2). Table 11 lists the 12 animals and 10 colors used to generate these prompts, with the full prompt list available in our core codebase. Table 11: Word list for multi-concept generation Visual Concept Words Animals dog, cat, squirrel, fox, lion, frog, deer, penguin, bird, horse, bear, fis...

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Table 3: 34 visual concepts and full list of concept-words. Visual Concept Concept-words Color red, blue, green, yellow, black, white, purple, gray, pink, brown Animals dog, cat, elephant, lion, bird, fish, butterfly, bear, horse, cow Plants tree, flower, grass, bush, cactus, vine, oak tree, moss, tulip, rose Fruits and Vegetables apple, banana, carrot, t...

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A larger (LeRHF − MoRHF) area indicates that the ordering of CA heads better reflects the relevance of the corresponding concept

For random order weak- ening, the H cross-attention heads are first ordered randomly, and then MoRHF is defined as the first-to-last order and LeRHF as the last-to-first order based on this random ordering. A larger (LeRHF − MoRHF) area indicates that the ordering of CA heads better reflects the relevance of the corresponding concept. Table 6 compares HRV...

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Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or

URL https://openreview.net/forum?id=NudBMY-tzDr. Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626,

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Sdedit: Guided image synthesis and editing with stochastic differential equations

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. arXiv preprint arXiv:2108.01073,

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Count how many of the following five images contain{intended meaning of the polysemous word} but no {unintended meaning of the polysemous word }

For each question, participants are shown 5 images and asked to count how many depict the intended meaning of the polysemous word without including the unintended meaning: “Count how many of the following five images contain{intended meaning of the polysemous word} but no {unintended meaning of the polysemous word }.” This count is then subtracted from 5 ...

work page 2025
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Maxime Oquab, Timoth ´ee Darcet, Th ´eo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al

Accessed: 2024-06-11. Maxime Oquab, Timoth ´ee Darcet, Th ´eo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193,

arXiv 2024
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Zero-shot image-to-image translation

Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. Zero-shot image-to-image translation. In ACM SIGGRAPH 2023 Conference Proceedings , pp. 1–11,

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14 Published as a conference paper at ICLR 2025 CONTENTS A 34 visual concepts and full list of concept-words 16 B Details of HRV construction 17 B.1 Pseudo-code for HRV construction . . . . . . . . . . . . . . . . . . . . . . . . . . 17 B.2 Role of the argmax operation in HRV ...

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We categorized the misinterpretation into three types: (i) containing the undesired meaning, (ii) missing the desired meaning, and (iii) both, and mark the images showing any of these misin- terpretations. For the last prompt, ‘A single rusted nut,’ where ‘nut’ was misinterpre...

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The MoRHF weakening rapidly removes concept-relevant content, whereas the LeRHF weakening either preserves the original image longer or removes irrelevant content first

Visual Concept Prompt Template Words Animals∗ photo of a{Animals} •{Animals}: rabbit, frog, sheep, pig, chicken, dolphin, goat, duck, deer, fox Color∗ a{Color} {Objects} •{Color}: coral, beige, violet, cyan, magenta, indigo, orange, turquoise, teal, khaki •{Objects}: car, benc...

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Both methods are evaluated on the Color editing benchmark with SA replacement rates varying from 0.0 to 1.0

Figure 21: Trade-off effect of self-attention replacement in P2P and P2P-HRV (Ours). Both methods are evaluated on the Color editing benchmark with SA replacement rates varying from 0.0 to 1.0. Red-highlighted SA replacement rates indicate points where P2P and P2P-HRV achieve ...

2025
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The words for Objects are sampled similarly. The same process is applied to the other editing tasks, except for Weather Conditions, which uses 5 attribute pairs (Weather Condition A, Weather Condition B), generating 250 images for each T2I model. The full list of prompts and a...

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The CLIP score measures the CLIP image-text similarity between the edited image and the target prompt, assessing how well the edited image aligns with the target prompt

and BG-DINO scores. The CLIP score measures the CLIP image-text similarity between the edited image and the target prompt, assessing how well the edited image aligns with the target prompt. Meanwhile, the BG-DINO score assesses structure preservation, focusing only on the non-...

2024
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While prior works (Parmar et al., 2023; Kim et al.,

model to obtain embeddings, and calculate cosine similarity between these two embeddings. While prior works (Parmar et al., 2023; Kim et al.,

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For human evaluation, we randomly select a seed previously used to measure CLIP image-text similari- ties. Images are then generated for each prompt pair using P2P-HRV and four other high-performing 32 Published as a conference paper at ICLR 2025 methods, resulting in 250 imag...

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34 Published as a conference paper at ICLR 2025 Figure 23: Qualitative results of image editing comparing P2P (Hertz et al.,

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and ours, based on the variation of self-attention replacement rate (Part 2 of 2). 35 Published as a conference paper at ICLR 2025 E.5 A DDITIONAL RESULTS ON IMAGE EDITING Figures 24–34 present additional qualitative results of image editing for three object attributes and two...

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and ours. 46 Published as a conference paper at ICLR 2025 F.2 A DDITIONAL RESULTS ON MULTI -CONCEPT GENERATION Figure 36 presents additional qualitative results of multi-concept generation for both Type 1 and Type 2 prompts. Figure 36: Qualitative comparison of the results for...

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with ours. 47 Published as a conference paper at ICLR 2025 G A DDITIONAL RESULTS USING SDXL G.1 A DDITIONAL RESULTS ON ORDERED WEAKENING ANALYSIS Figures 37–42 present additional results from the ordered weakening analysis on Stable Diffusion XL (SDXL) (Podell et al., 2024). F...

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We then construct HRVs individually for both the 34-concept and 35-concept sets and compare them through visualization

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In Figures 45-46, we generate images using SDXL with the same random seed for three prompts in each concept case. For the first type of failure, shown in Figure 45, the model often struggles to understand certain concepts, failing to distinguish between words like ‘three’ and ...

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Table 11 lists the 12 animals and 10 colors used to generate these prompts, with the full prompt list available in our core codebase

and (ii) ‘a {Color A} {Animal A} and a {Color B} {Animal B}’ (Type 2). Table 11 lists the 12 animals and 10 colors used to generate these prompts, with the full prompt list available in our core codebase. Table 11: Word list for multi-concept generation Visual Concept Words Animals dog, cat, squirrel, fox, lion, frog, deer, penguin, bird, horse, bear, fis...

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Table 3: 34 visual concepts and full list of concept-words. Visual Concept Concept-words Color red, blue, green, yellow, black, white, purple, gray, pink, brown Animals dog, cat, elephant, lion, bird, fish, butterfly, bear, horse, cow Plants tree, flower, grass, bush, cactus, vine, oak tree, moss, tulip, rose Fruits and Vegetables apple, banana, carrot, t...

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For each question, participants are shown 5 images and asked to count how many depict the intended meaning of the polysemous word without including the unintended meaning: “Count how many of the following five images contain{intended meaning of the polysemous word} but no {unintended meaning of the polysemous word }.” This count is then subtracted from 5 ...

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Accessed: 2024-06-11. Maxime Oquab, Timoth ´ee Darcet, Th ´eo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193,

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Zero-shot image-to-image translation

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work page 2023